Towards Achieving Environmental Sustainability: Role of Energy, Green Innovation and Economic Growth in G-20 Countries

Towards Achieving Environmental Sustainability: Role of Energy, Green Innovation and Economic Growth in G-20 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 Towards Achieving Environmental Sustainability: Role of Energy, Green Innovation and Economic Growth in G-20 Countries Ipsa Khanna, Pooja Mehra, Saurabh Verma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6040627/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jul, 2025 Read the published version in Discover Sustainability → Version 1 posted 14 You are reading this latest preprint version Abstract This study examines the relationship between energy, innovation, economic growth and carbon emissions in the G20 countries, which substantially influence global economic and environmental policies. The study focuses on key variables such as GDP, carbon emissions, energy consumption (total energy utilised), and energy use (per capita metrics). Additionally, it considers the adoption of renewable energy sources and innovation indicators such as gross expenditure on research and developments and the number of patents in environmental-related technologies. The study investigates annual data from 2000 to 2020 using panel data analysis and econometric techniques. The findings reveal significant long-run relationships, signifying that a rise in innovation (measured by R&D expenditure and patents) is linked to reducing carbon emissions. Simultaneously, economic growth tends to correlate with increased energy consumption. Therefore, it is essential to strike a balance between growth and sustainability. Through panel data analysis, a long-run, bi-directional relationship is established between research and development (R&D) and CO2 emissions, as well as between patents in environmental-related technologies and CO2 emissions, indicating that increases in R&D and patents can lead to lower emissions. At the same time, changes in emissions also influence R&D investments. Additionally, bi-directional relationships are observed between R&D and GDP per capita and CO2 emissions and GDP per capita. The study indicates that G20 policymakers should prioritise innovation in clean technologies by increasing research and development funding, encouraging several companies to create and patent these technologies and promoting meaningful international collaboration to share best practices in sustainable energy. This study pinpoints potential pathways toward sustainable development within G-20 nations. These important findings should help policymakers and key stakeholders achieve innovation-driven economic growth that balances large economic progress with strong ecological protection. G-20 nations must implement policies encouraging renewable energy and energy efficiency and balancing economic growth with ecological protection to guarantee a sustainable future. Energy transition Innovation Clean technologies Carbon Emissions G20 nations Energy security Renewable energy SDG Figures Figure 1 Figure 2 Figure 3 1 Introduction Climate change and resource depletion have made environmental sustainability at the forefront of the global agenda. The United Nations Sustainable Development Goals Report 2024 emphasises the vital significance of sustainable consumption and production patterns, indicating that between 2019 and 2023, 62 Member States and the European Union implemented over 500 policy instruments that aimed at accelerating this transition [ 2 ]. However, achieving the Sustainable Development Goals (SDGs) remains a challenging task, with only 17 percent of targets on track and nearly half showing minimal or moderate progress due to the compounded impacts of the COVID-19 pandemic, escalating conflicts, and climate-related challenges [ 3 ]. This study highlights the critical need for innovative strategies and robust international cooperation to address these systemic deficiencies. Established in 2015, the Paris Agreement is the principal foundation for global climate action. To restrict global warming below 2°C while exerting efforts to limit the increase to 1.5°C, the agreement underlines the transition to renewable energy as a focus for climate change mitigation in the light of environmental protection. The OECD Global Corporate Sustainability Report 2024 indicates a growing awareness among companies of their responsibilities towards environmental stewardship. Corporations progressively integrate sustainability into their governance processes to improve their long-term economic viability by disclosing greenhouse gas emissions and pursuing plans aligning with climate resilience. [ 3 ]. This change in businesses highlights the connection between economic growth, environmental sustainability, and corporate governance. International conferences, particularly COP27, COP28, and COP29, have helped to achieve important milestones in global climate action. In a critical recognition of climate justice, the COP27 in Egypt placed a strong emphasis on the creation of a loss and damage fund to assist vulnerable countries [ 4 ]. The Mitigation Work Programme was introduced at COP28 in Dubai to strengthen national commitments and accelerate the shift away from fossil fuels, which still accounted for 80% of global energy consumption in 2022 [ 4 ]. This further broadened the global climate agenda. A collective commitment to renewable energy infrastructure was demonstrated at COP29 with the Global Energy Storage and Grids Pledge, which set a goal of deploying 1500 GW of energy storage by 2030 [ 5 ]. These gatherings demonstrate the growing acceptance of renewable energy as a vital component of sustainable growth. The United Nations Emissions Gap Report 2024 emphasises how urgent the switch to renewable energy is. To meet the targets of the Paris Agreement, global greenhouse gas emissions must be reduced by 42% by 2030 after reaching an astounding 57. 1 gigatons of carbon dioxide equivalent in 2023 [ 6 ]. The G20 countries have a crucial obligation because they are responsible for 77% of global emissions. To promote innovation and improve energy efficiency, these countries must prioritise funding for research and development of renewable energy sources and implement all-encompassing policies. The report also stresses the importance of a policy mix that includes regulatory actions and carbon pricing subsidies to promote the adoption of renewable energy technologies and energy efficiency standards [ 6 ]. The rise of renewable energy technologies has witnessed substantial advancements, such as considerable cost reductions and substantial growth in market accessibility. Although, systemic obstacles, such as governance issues and limited international financial support, especially for developing countries, hinder progress toward sustainability goals, overcoming these barriers is possible through the redesign of international economic architecture that would generate funds for a low-carbon transition [ 6 ]. The integration of national networks with renewable energy offers enhanced energy efficiency, which will help combat the detrimental effect of climate and promote economic resilience and energy security. The interaction between biodiversity, climate mitigation efforts, and sustainable development is another vital component of environmental sustainability. The Global Biodiversity Framework, Montreal 2022, aligns with the Paris Agreement's goals and integrates biodiversity conservation into climate strategies while calling for a holistic approach. This synergy is crucial for dealing with climate, ecosystem degradation, and social equity issues [ 4 ]. Such synergies are crucial to addressing the complex challenges associated with climate change, ecosystem degradation, and social equity. Additionally, initiatives such as the Energy Compacts launched at COP28, aim to fast-track investment in clean and affordable energy, especially for resource-limited regions, and to emphasise the importance of equity in the global energy transition [ 6 ]. Governmental and corporate efforts also highlight the possibility of attaining environmental sustainability. Businesses increasingly see sustainability as a strategic advantage, according to the OECD report, and are coordinating corporate governance with ecological objectives to mitigate climate risks and boost competitiveness [ 3 ]. Initiatives like the COP29 Breakthrough Agenda, in which 61 nations accounting for 80% of global emissions pledged to take priority measures to reduce their carbon footprints, also demonstrate the increasing momentum for coordinated global climate action [ 5 ]. However, achieving environmental sustainability is fraught with challenges. The World Bank's FY24 Climate-Related Financial Disclosures emphasize the urgent need for investments in renewable energy projects and innovation to fill in the gaps in energy infrastructure and lower emissions. According to the report, G20 countries should set an example by encouraging green innovation and making sure that economic expansion is consistent with environmental sustainability [ 7 ]. To close the emissions gap, it is also necessary to double global energy efficiency gains by 2030 and triple renewable energy capacity to meet the ambitious goals outlined in the Emissions Gap Report 2024 [ 6 ]. The rising frequency and severity of climate-related disasters like storms, heat waves, and wildfires further highlight the need for swift and coordinated action. These incidents provide a clear reminder of the disastrous results of inaction and the urgent need for preventative actions to lessen the effects of climate change. To effectively address the climate crisis, countries nationally determined contributions (NDCs) for COP30 in Brazil must demonstrate increased ambition and urgency [ 6 ]. For environmental sustainability, a cooperative, multifaceted strategy that incorporates international cooperation, backs sensible policies, and generates cutting-edge technologies will resolve climate change issues and preserve our planet for future generations by combining renewable energy biodiversity preservation and corporate sustainability. In the framework of economic growth that respects environmental conservation, investing in clean energy, fostering research and development, and formulating the appropriate policies will open the door for revolutionary change. The G20 ranks among the foremost economic, financial, and political forums and consists of 19 major economies across various continents: Asia, Europe, North and South America, the Middle East, and Oceania as represented in Fig. 1 . The G20 countries consist of developed and developing nations and represent a significant share of global GDP, population, and energy consumption [ 8 ]. The G20 is an international alliance of the world's 20 largest economies, including the United States, Canada, Australia, Germany, China, France, Russia, India, Saudi Arabia, Indonesia, Argentina, South Africa, Italy, Brazil, Turkey, United Kingdom, Japan, South Korea, Mexico and the European Union [ 9 ]. Sustainable development is impossible without its global expansion. Economic growth can support the 2030 Agenda across various sustainable development goals when combined with appropriate policies. The G20 shapes international economic and environmental policies, particularly in achieving sustainable and balanced growth. This study aims to explore the interplay between energy, innovation, and economic growth within G20 countries, highlighting how these factors contribute to the broader goals of sustainability and carbon emission reduction [ 10 ]. While global economic and population expansion significantly contributes to increased CO2 emissions from fossil fuel combustion, additional factors such as technological advancements, energy policies, and shifts in the energy mix must be considered. Recent literature highlights that innovations in energy efficiency and the transition to renewable energy sources can mitigate emissions, particularly in G20 countries, where diverse energy policies and economic structures influence emissions trajectories [ 11 ]. The literature claims that an increase in energy use accompanies increased economic activity. This increase in energy use damages the environment through the release of carbon dioxide [ 12 ]. The main causes include an increase in energy demand brought on by the rapid expansion of the economy and population, as well as a rise in the consumption of fossil fuels [ 11 ]. This study specifically intends to analyse the role of energy efficiency and the reduction of fugitive emissions within G20 countries to effectively reduce CO2 emissions from the energy sector while addressing the intertwined challenges of economic growth, energy security, and environmental sustainability. By examining how improvements in energy efficiency can lower carbon emissions and foster sustainable economic growth, the study will provide particular insights into the innovations and policies that can facilitate this shift. This focus highlights how urgently better energy practices are needed and advances the study's objective of figuring out how G20 nations can achieve their sustainability goals without compromising their economic performance. Moving away from fossil fuels and toward low-carbon technologies like renewable energy sources is also essential [ 13 ]. The G20 countries should prioritise research and development projects that concentrate on energy storage, renewable energy technologies, and sustainable practices because they understand how crucial innovation is to reaching sustainability goals. Through cultivating an innovative culture, G20 nations can tackle their environmental issues and support international initiatives to tackle climate change and advance sustainable development. Energy efficiency, which includes energy conservation, is a long-term G20 objective since it results in the best use of available energy sources. Enhancing energy efficiency collaboration can lead to better environmental outcomes increasing economic activity and productivity, according to the G20 countries [ 14 ]. The goal of reducing carbon emissions while maintaining or boosting economic growth is a challenge for policymakers in the G20 because each country has a different economic structure, degree of development, and environmental priorities. There are wide variations in the trade-offs between lowering emissions and improving economic performance because some nations may prioritise short-term economic expansion more than long-term sustainability. Others, however, could spend money on renewable energy sources and greener technologies. This study will examine these intricacies emphasizing how various G20 countries can manage their particular opportunities and difficulties in the pursuit of sustainable development. Innovation can be pivotal in reducing emissions[ 15 ] [ 16 ]. Although much research has been done on the connection between innovation and economic growth in G20 nations, there are still unanswered questions about how environmental technologies affect economic performance and carbon emissions. In light of the various economic structures and stages of development of G20 countries, this study seeks to expand on previous research by examining the relationship between energy innovation carbon emissions and economic growth. In the G20 context, the research will advance a more sophisticated understanding of how innovation can propel sustainable development by identifying these particular gaps [ 17 ]. The G20 should pledge to participate more actively in several multilateral initiatives, particularly through many cooperative venues. As part of the partnership on energy transitions, the G20 should create an energy innovation agenda [ 18 ]. The G20 nations can support the global energy transition with their governments' backing in sustainable economic development and cleaner energy. Energy transition goals include diversifying and modernising the economy, improving air quality, reducing climate change, and increasing energy security by securing energy access and reducing import dependency. The transitions in the G20 energy system are based on energy sources and technologies because the national resources of the G20 economies vary, as do GDP growth, per capita energy demand, and emissions [ 19 ]. The G20 countries' energy infrastructure must change to accommodate shifting energy consumption patterns, rising shares of variable renewable energy, new opportunities offered by EVs and increased digitalisation of the energy industry. In the energy transition system, energy security remains a top priority. The research study is highly relevant for G-20 countries as it comprises the world's major economies, representing a significant share of global GDP, population and energy consumption. G-20 countries are heavily dependent on energy resources to fuel their economies. Studying energy innovation helps diversify energy sources, reduce reliance on fossil fuels and enhance energy security. Research and development in energy technologies lead to breakthroughs in areas such as solar power, wind energy, energy storage and electric vehicles. These advancements have wide-ranging applications beyond the energy sector and can contribute to economic growth through technology transfer, patent creation and commercialisation. Researching the relationship between carbon emissions, economic growth, and innovation in G20 nations is critical to uncover trade-offs and synergies between these aspects and assist in guiding policy decisions. This study aims to analyse the relationship between carbon emissions, energy, innovation and economic growth. Lee (2005) [ 20 ] studied the relationship between energy use and GDP in 18 developing economies between 1975 and 2001. He discovered support for the growth hypothesis using panel-based cointegration error correction models. Energy consumption drives GDP growth over the long and short term. Therefore, restrictive energy policies may limit economic growth in developing nations. According to Canton et al. (2005) [ 21 ], human behaviour, such as educational attainment, and economic and technological factors, such as innovation and R&D intensity, impact the economic growth of nations. As stated by Cinnirella & Streb (2013), first, as skilled labour, human capital can directly increase total factor productivity. Second, it could encourage businesses to adopt new technologies through inventions, imitative practices or other technological activities [ 22 ] In France, Austria, Denmark, and Belgium, among other nations, Soytas and Sari (2006) found a unidirectional relationship between economic growth and energy consumption. This result aligns with conservation theory, which holds that rising economic activity increases energy consumption. Their study emphasises the vital role that energy consumption plays in propelling economic growth by concentrating mostly on the correlation between these two variables. Nevertheless, this method fails to consider the possible impact of innovation or technological developments on energy consumption, a crucial element that the proposed study seeks to investigate in greater detail [ 23 ]. R&D activities can result in innovation as a prerequisite for technical advancement, which will decide economic growth in a Schumpeterian creative destruction process, as demonstrated by Aghion & Howitt (2008) [ 24 ] in their model of Schumpeterian endogenous growth. Through his Schumpeterian analysis, Fagerberg (2010) also discovered that innovation becomes essential for sustained economic growth [ 25 ]. In order for policymakers to create effective energy policies that support sustainable economic growth, Costantini and Martini (2010) examined the complex relationship between economic growth and energy consumption, establishing the causal linkages at the sectoral level across different countries. The study's primary goals were to empirically analyse these causal relationships across sectors such as industry, services, transport, and residential while examining the impact of energy prices and public regulations on energy demand and economic performance, particularly highlighting differences between developed and developing countries. Using a multivariate econometric framework, the authors estimated energy demand functions and examined mutual causality relationships between variables by applying panel cointegration techniques to a dataset of 71 countries split into OECD and non-OECD groups. The research uncovered Significant insights suggesting that knowledge of these causal relationships could guide public policy, particularly concerning energy taxation and regulation. The study's extensive dataset was one of its distinctive features as it improved the analysis of energy-growth dynamics. Concurrently, incorporating sector-specific energy prices clarified how energy costs affected consumption trends. However, the study acknowledged certain shortcomings, including difficulty determining causal effects and reliance on historical data, which may not have adequately represented the dynamic character of energy markets. In conclusion, the research offered valuable insights into the energy consumption-economic growth nexus, emphasising the importance of considering energy prices and public regulations. As a basis for further study in sectoral energy models and the effects of energy policies on economic performance, it proposed that although increases in energy efficiency were necessary for sustainable growth, policymakers also needed to be aware of possible rebound effects and structural variations among nations [ 26 ]. An important study by Baek and Kim (2011) examined the connections between trade liberalisation, economic expansion, energy use, and CO2 emissions in G-20 economies. By incorporating these dimensions, the study sought to fill a gap in the literature by empirically evaluating the ways in which these interrelated factors affected environmental quality. The methodology employed was robust, utilising Johansen cointegration analysis to examine long-term relationships among non-stationary time series data. The authors analysed annual data from 1960 to 2006, focusing on per capita CO2 emissions as a proxy for environmental quality, real GDP per capita for income, energy consumption per capita for energy use, and trade openness defined as the ratio of total trade to GDP. By converting all variables to natural logarithms, the analysis facilitated a clearer understanding of the relationships. Key findings indicated that trade and income growth improved environmental quality in developed countries, while they had adverse effects in developing nations, where increased economic activity exacerbated environmental degradation. In every economy examined, energy use has deteriorated environmental quality. The study emphasized the necessity of customized policies that took into account the distinct circumstances of both developed and developing nations [ 27 ]. Similarly, Narayan and Popp (2012) also identified a unidirectional relationship between economic growth and energy use, reinforcing the conclusions drawn by Soytas and Sari. Their study advances our knowledge of this dynamic by looking at a larger number of nations and using reliable econometric methods to support their conclusions. Although their research sheds important light on the relationship between economic expansion and energy use, it mostly examines the historical relationship without considering innovation or improvements in energy efficiency. By combining metrics pertaining to research and development (R&D) and environmental technology patents, the proposed study aims to expand on these seminal studies and examine how technological developments can affect patterns of energy consumption and economic performance. By revealing the potential for innovation to disentangle economic growth from energy consumption, this methodological change seeks to provide a more thorough understanding of sustainable development and to inform policy choices that seek to maximize economic growth while minimizing environmental effects [ 28 ]. Yildirim & Aslan (2012) employed the Toda-Yamamoto technique and bootstrap-corrected causality tests to examine the relationship between energy use and economic growth across 17 OECD nations. Their findings revealed a reciprocal relationship indicating that economic growth affects energy use in addition to GDP. This insight emphasizes how complex the relationship is between energy and growth, suggesting that policies meant to improve energy efficiency should take this relationship's reciprocal nature into account [ 29 ]. Wang et al. (2012) made a distinction between carbon-free and fossil-fueled energy technologies and looked at the relationship between energy technology and CO2 emissions for 30 provinces in Mainland China from 1997 to 2008. Based on dynamic panel data, they discovered that, in contrast to energy technologies powered by fossil fuels, carbon-free ones assisted in lowering CO2 emissions, particularly in Eastern China [ 30 ]. Nasreen and Anwar (2014) investigated the relationship between economic performance, energy use, and trade openness for 15 Asian nations using panel unit root and panel cointegration tests. The findings show that trade openness, energy consumption, and economic growth are causally related in both directions [ 31 ]. Kasman & Duman (2015) used panel data from 15 new EU-member and candidate nations covering 1992–2010 to describe the inverted U-shaped EKC for GDP and CO2 emissions. In addition to using trade openness and energy consumption as explanatory variables, they also employ urbanisation, which measures the proportion of the population that lives in cities. They contend that nations with a larger proportion of urban residents generate more emissions than those with a smaller proportion [ 32 ]. Wang et al. (2016) examined the complex interrelationships between ASEAN nation's urbanisation energy consumption and carbon emissions, highlighting the urgent need to comprehend the effects of urban growth on environmental quality in the face of the region’s rapid urbanisation. The study's main goals were to evaluate the causal links between urbanization and carbon emissions, the contribution of energy consumption to this relationship, and the provision of empirical data to support policy decisions for sustainable urban development. The methodology employed included a series of panel unit root tests to examine the stationary properties of the variables, followed by panel cointegration tests to identify long-run equilibrium relationships and the use of Fully Modified Ordinary Least Squares (FMOLS) for estimating these relationships. The study found a significant positive relationship between urbanisation and carbon emissions, indicating that a 1% increase in urban population correlated with a 0.20% rise in carbon emissions, thus supporting the Environmental Kuznets Curve (EKC) hypothesis within the STIRPAT framework. The analysis emphasised the significance of patterns in energy consumption, indicating that carbon output was significantly influenced by the energy used in urban areas. In order to reduce carbon emissions, the study advised policymakers and urban planners to prioritize sensible urban development and effective energy use supporting mixed-use zoning, better public transit and energy-efficient technologies. Two distinctive features of the study were its emphasis on ASEAN nations, which had received little attention in previous research, and its all-encompassing methodology, which integrated several econometric approaches to analyse the data. However, The study recognised certain difficulties, including the possible drawbacks of short data periods in analyses of individual nations and the complexity of the relationship between urbanization and carbon emissions, which may differ depending on the context. In summary, the study revealed important information about the dynamics of urbanization and its effects on the environment, highlighting the necessity of focused policies to support sustainable urban growth and deal with the ASEAN region's carbon emission problems[ 33 ]. Saidi and Ben Mbarek (2016) investigate the complex connections between CO2 emissions, nuclear energy use, renewable energy, and economic growth in nine developed countries between 1990 and 2013. Examining the causal relationships between these variables is the study's main goal, which will advance knowledge of how energy use affects both environmental sustainability and economic performance. Unit root tests, panel cointegration tests, and Granger causality tests are used in the dynamic panel data analysis methodology to ascertain the variables' short- and long-term relationships. Important conclusions show that nuclear energy use and economic expansion are significantly correlated in some countries, which shows evidence of bidirectional causality. It further suggests that rising nuclear energy use may spur economic expansion, increasing energy consumption. The study emphasises how nuclear energy can be a good substitute for fossil fuels and shows how it can lower CO2 emissions while promoting economic growth. In order to accomplish sustainable economic growth and environmental objectives, the authors advise policymakers to consider nuclear energy as a strategic element in energy planning. The study's focus on developed countries, which frequently display distinct dynamics from developing ones, and its comprehensive panel approach, which captures broader relationships across various national contexts, are two of its unique features. However, the study also recognises certain difficulties, such as the possibility of omitted variable bias and the requirement for more detailed data to capture the subtleties of energy consumption patterns. Finally, highlighting the vital role that nuclear energy plays in accomplishing both economic and environmental goals, Saidi and Ben Mbarek (2016) offer insightful analysis of the intricate relationships between energy consumption and economic growth. The results emphasize how crucial it is to have well-informed energy policies that use nuclear power to promote sustainable development and lessen the effects of climate change. This research contributes significantly to the literature on energy economics and serves as a reference point for future studies exploring the nexus of energy consumption, economic growth, and environmental sustainability [ 34 ]. Employing the unit root test with structural breaks developed by Clemente et al. (1998) [ 35 ], Magazzino (2017) looked at the stationary properties of per capita energy use for the EU-19 countries during the years 1960–2013 [ 36 ]. The panel unit-root test results demonstrate that energy use is non-stationary in nearly all EU-19 nations. Using the Vector Auto Regression (VAR) method, Magazzino (2017)[ 37 ] examined the relationship between energy use, GDP and carbon emissions in the APEC region. The findings indicate no direct link between GDP and energy use. Hasanov et al. (2017)[ 38 ] examined the relationship between energy and economic growth in ten emerging Eurasian nations that export oil. The results showed that the main energy consumption-growth nexus supports the growth theory. The increase in domestic electricity usage and the results demonstrate the validity of the neutrality hypothesis. Fernández Fernández et al. (2017) addressed the critical objective of achieving sustainable economic growth while stabilising or reducing greenhouse gas emissions, particularly CO2 emissions. The primary goal of the study was to empirically verify the positive effects of innovation, specifically through research and development (R&D) spending, on reducing CO2 emissions across three distinct regions: the European Union (EU-15), the United States, and China, during the period from 1990 to 2013. An econometric model estimated using ordinary least squares (OLS) regression was part of the methodology used to examine the connection between R&D spending energy use and CO2 emissions. The need for a multifaceted approach was highlighted by key findings that showed that although public spending on R&D was crucial, it was insufficient to improve the innovation process or significantly reduce emissions. The study emphasised the importance of integrating public and private innovation efforts and suggested that future research should also consider the impact of patent activity as a measure of private innovation. The study was unique because it focused on the relationship between innovation and environmental sustainability, highlighting the potential for innovative processes to result in more ecologically friendly and energy-efficient products across various industries. However, the study recognised issues like data limitations that limited the analysis to a specific time period and three regions, indicating that larger datasets and longer time horizons could be advantageous for future research. In order to effectively combat environmental pollution and accomplish sustainable development goals, the study concluded that R&D spending must be promoted across all economic sectors. It also urged more investigation into public and private innovation dynamics in the fight against climate change [ 39 ]. Cheng & Co. (2019) examined the connection between CO2 emissions, renewable energy, and environmental patents in the BRICS nations (Brazil, Russia, India, Indonesia, China, and South Africa) between 2000 and 2013, emphasising the urgent need for successful climate change policies in developing nations. While addressing the shortcomings of conventional regression techniques, which frequently ignored individual and distributional heterogeneity, the study's main goals were to investigate the effects of renewable energy supply environmental patents and other economic factors on carbon emissions. The fixed-effect panel quantile regression approach was the methodology used, which captured the heterogeneous effects that traditional methods frequently overlooked and allowed for a nuanced analysis of how different factors affected CO2 emissions across various quantiles. Important conclusions showed that although the BRICS country's use of renewable energy and environmental patents had grown, CO2 emissions were still rising, indicating that these factors alone were not enough to reduce emissions. To successfully reduce emissions, the study emphasised the need for a multipronged strategy that includes encouraging renewable energy, creating environmental regulations, and modifying economic structures. The study's use of the fixed-effect panel quantile regression approach, which addressed the drawbacks of earlier research that mostly concentrated on mean effects, was one of its distinctive features. However, The study recognised some challenges, such as the lack of data on environmental patents and the challenge of estimating their direct impact on emissions. For the BRICS countries to achieve sustainable development, the study’s conclusion emphasised the importance of integrating technological advancements like environmental patents into frameworks for climate policy and encouraging a switch to renewable energy sources. The results filled in knowledge gaps about how technology affects carbon emissions and offered practical policy suggestions for developing nations [ 40 ]. Sharif et al. (2019) investigated how carbon emissions and energy consumption types varied across 74 countries between 1990 and 2015. This study was essential for tackling the world's environmental problems because it offered factual data on the effects of various energy use patterns on carbon emissions, which helped shape policy choices to slow down environmental deterioration. The primary objectives included examining the long-run elasticity of renewable and non-renewable energy consumption concerning carbon emissions and assessing the role of financial development in this dynamic. The researchers used sophisticated econometric techniques to analyse cross-sectional independence and heterogeneity among nations, including the CIPS unit root test, Westerlund bootstrap cointegration, Pedroni cointegration, FMOLS, and heterogeneous panel causality methods. While renewable energy consumption significantly decreased carbon emissions, non-renewable energy consumption positively impacted environmental degradation, according to key findings. Financial development also affected environmental degradation, indicating that sustainability and economic growth could coexist. With a focus on encouraging renewable sources for sustainable economic growth, the study suggested incorporating renewable energy policies into national strategies to mitigate carbon emissions effectively. This study uniquely used heterogeneous panel analysis to account for cross-sectional dependence and provide a more nuanced understanding of the relationship between energy and the environment. However, the study acknowledged issues such as inconsistent data and the difficulty of capturing all relevant variables influencing carbon emissions that might have limited the generalizability of the findings. The study provided insightful information about the relationship between carbon emissions and energy consumption types, emphasising the vital role that renewable energy plays in advancing environmental sustainability. The findings underscored the need for targeted policies encouraging the transition to renewable energy sources, contributing to global efforts to combat climate change and foster sustainable development [ 41 ]. Munir et al. studied the EKC for the five major ASEAN countries (Indonesia, Malaysia, the Philippines, Singapore, and Thailand) between 1980 and 2016. Further, in 2020, the analysis of carbon dioxide emissions data demonstrates the presence of the EKC in the sample [ 42 ]Notably, the author's study indicates that three sample countries—Malaysia, the Philippines, and Thailand—are below the EKC's turning point. This suggests that further economic growth in these nations will increase emissions. Saidi and Omri (2020) investigated the intricate relationship between renewable energy consumption (REC), economic growth, and carbon emissions in 15 major renewable energy-consuming countries from 1990 to 2014, emphasising the dual role of renewable energy in fostering economic development while mitigating environmental degradation. The main goal was to close the gap in the literature by showing how renewable energy could successfully lower carbon emissions and promote economic growth. In earlier research, this relationship had not been thoroughly examined. A thorough examination of the variables' short- and long-term relationships was possible using fully modified ordinary least squares (FMOLS) and vector error correction model (VECM) estimation techniques. The key findings demonstrated that economic growth and the use of renewable energy were causally related in both directions, indicating that renewable energy promoted and facilitated economic growth. The study suggested that supporting renewable energy could have significant positive effects on the economy and the environment and underlined the importance of integrating economic and environmental factors into energy policy. Focusing on the main nations that use renewable energy and thoroughly examining the relationship between REC economic growth and carbon emissions within a single framework were two distinctive features of the study. However, the study also recognized some difficulties and constraints, like the possible unpredictability of data quality and the need for more investigation into policy thresholds that maximize the advantages of renewable energy sources without sacrificing environmental quality. The study’s conclusion emphasized how important renewable energy is to attain environmental sustainability and sustainable economic growth. It advocated for policy measures that supported the development and accessibility of renewable energy technologies to enhance both economic and ecological outcomes [ 43 ]. Wang and Zhang (2020) focused on the relationship between research and development (R&D) investment and its impact on economic growth and carbon emissions in BRICS countries (Brazil, Russia, India, China, and South Africa) from 1996 to 2014. The study was relevant for exploring sustainable development pathways, particularly in developing economies where economic growth often correlates with increased carbon emissions. The primary objectives were to analyse whether increased R&D investment contributed to economic growth decoupling from carbon emissions and to assess the role of R&D in promoting sustainable development. The methodology included the Tapio decoupling model to evaluate decoupling status alongside various econometric techniques such as unit root tests, cointegration tests, FMOLS regression estimation, and Granger causality tests used to examine the connection between carbon emissions and RandD investment. Important conclusions showed that the BRICS country's capacities for decoupling differed, with China, South Africa, and Russia exhibiting stronger capabilities than Brazil and India. The analysis demonstrated the critical role of research and development in attaining sustainable development, showing that a 1% increase in R&D investment resulted in an 8122 per cent reduction in carbon emissions. The study suggested that to promote economic growth and reduce carbon emissions, R&D investment should be increased, industrial structures should be optimised, and energy consumption should be shifted toward renewable sources. One of the study's distinctive features was its emphasis on the BRICS countries collectively, which shed light on how environmental sustainability and economic growth interact in developing economies. However, The study acknowledged certain difficulties that could compromise the accuracy of the findings, such as possible cross-sectional dependence among the nations. The study highlighted the value of R&D spending in promoting economic expansion independent of carbon emissions. In order to accomplish long-term environmental objectives, it promoted legislative actions that support sustainable industrial practices and renewable energy. The results provided policymakers in developing nations with a useful framework for creating strategies that effectively reduce carbon emissions and support global sustainable development [ 44 ]. Behera et al. (2024) looked into the intricate relationship between energy consumption and economic growth in developing nations like India, highlighting both renewable and non-renewable energy sources' role in attaining sustainable development. The study's main goals were to provide policy recommendations that matched energy choices with sustainable development goals and investigate the effect of disaggregated energy consumption on India's economic growth between 1985 and 2021. The authors employed an autoregressive distributed lag (ARDL) bound testing approach to analyse the short-run and long-run effects of various energy sources on economic growth, complemented by variance decomposition analysis (VDA) to assess the influence of energy consumption on economic variables. Key findings revealed that non-renewable energy consumption led to a more prominent role in driving economic growth than renewable energy sources, suggesting that while renewable energy was essential for sustainability, immediate economic growth was more closely tied to non-renewable energy. The report emphasised the need for more funding for clean energy technologies to slow environmental deterioration and promote economic expansion. A distinctive feature of the study was its emphasis on disaggregated energy sources, which addressed a gap in the literature by offering a sophisticated understanding of how various forms of energy influenced economic performance. However, The study recognised shortcomings, such as the absence of information on specific renewable energy sources, such as solar and wind, which would have allowed for a more thorough analysis. While ensuring that energy-saving measures do not impede economic growth, the authors advised policymakers to prioritise renewable energy integration in the energy mix. The study concluded that policies supporting the shift to renewable energy while acknowledging the current reliance on non-renewable sources for economic growth were necessary to highlight the significance of striking a balance between energy consumption and environmental sustainability. This study underlines the need for strategic energy policies that align with economic and environmental goals, providing insightful information about the relationship between energy and economic growth in India[ 45 ]. Degirmenci and colleagues. (2024) looked at the intricate relationships between energy intensity, energy depletion, the green energy transition, and the strictness of environmental policies in the G7 between 1990 and 2020. The main goal was to go beyond conventional analyses that frequently separated these factors and examine how they all affected environmental sustainability. To guarantee the reliability of the results, the methodology included a thorough empirical approach using panel data analysis, specifically looking at cross-sectional dependence and slope homogeneity. Sophistic econometric methods were used to analyse the relationships between the variables, including the CCEMG and AMG long-run estimators. Significant findings demonstrated that although stringent environmental regulations and the shift to green energy supported sustainability initiatives, energy intensity and depletion negatively impacted ecological quality. By considering the load capacity factor, which integrates the supply and demand aspects of natural resources, the analysis showed that although individual studies frequently concentrated on carbon emissions, this research emphasized the significance of a more comprehensive approach. The study suggested that policymakers could increase the efficacy of environmental policies by encouraging green technologies and enacting stronger laws to reduce pollution. A more nuanced understanding of environmental sustainability was provided by the research’s unique focus on the interrelatedness of the examined variables, which had been mainly ignored in earlier studies. However, the study's focus on the G7 countries may have limited the scope of data, making it difficult to represent global dynamics fully. Furthermore, if results rely too much on historical data, they might not be as applicable to rapidly changing environmental contexts. The study's findings encouraged a full understanding of how energy policies and practices interact to affect ecological outcomes and underlined the pressing need for integrated approaches in environmental research. In addition to encouraging green energy transitions and strict policies, it emphasized that tackling energy intensity and depletion is crucial to reaching long-term sustainability goals [ 46 ]. Behera et al. (2024) concentrate on the crucial nexus between environmental sustainability and economic growth in the BRICS countries (Brazil, Russia, India, China and South Africa). Given the growing ecological footprints of urbanisation and industrialisation, this document's significance stems from examining how these nations can meet sustainable development goals. The study's primary objectives were to assess the impact of renewable energy sources, specifically hydro and nuclear energy, on ecological footprints, evaluate the role of green technology innovation (GTI), and analyse the influence of political stability on environmental quality. The methodology employed a panel dataset from 1993 to 2022, utilising ecological footprint per capita as a key indicator alongside independent variables such as hydro energy consumption, nuclear energy consumption, GTI, and political stability. The study's key findings revealed that while hydro and nuclear energy consumption positively contributed to reducing ecological footprints, the effectiveness of GTI in mitigating ecological impacts was found to be insignificant. Furthermore, political stability was identified as a crucial factor for enacting coherent environmental policies, although its direct effect on ecological footprints was also deemed insignificant. The BRICS countries' governments should enact strict policies to support renewable energy sources and offer incentives for green technology research and development according to the applications of these findings. The study's thorough examination of the interactions between energy use, technological advancement, and political considerations is one of its distinctive features. It provides a sophisticated understanding of these economies' difficulties as they shift to sustainability. However, the study also identified some drawbacks, such as the exclusion of additional renewable energy sources like solar and wind because of limitations in data availability, which could have offered a more comprehensive picture of the energy landscape. The study argues for a well-rounded approach that considers economic expansion and environmental conservation, emphasising the necessity of coordinated efforts in formulating policies to encourage sustainable practices and mitigate ecological issues [ 47 ]. 2 Materials and Methods This study systematically examines the relationship between energy, innovation, carbon emissions, and the economic growth of G20 countries. The data sources utilised in this study include the World Bank, International Energy Agency (IEA), OECD (OECD Data Explorer), EnerData, and the BP Statistical Review of World Energy. These organisations are renowned for their reliability and extensive datasets, which are critical for analysing the energy and economic dynamics of the G20 nations. These sources were chosen because they employ strict data collection procedures and concentrate on global energy and economic statistics, guaranteeing that the information is accurate and pertinent to our research questions. These databases offer a wealth of historical data, enabling longitudinal analysis while upholding strict data consistency and accuracy standards. Annual data from G20 countries served as the basis for the study sample. Carbon Emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (RandD), Patents in Environmental Technologies (PAT), Per capita Energy Use (EU), and Per capita Renewables (REN) are the variables being used in this study. To capture various aspects of energy dynamics in G20 nations, the study includes both Per capita Energy Use (EU) and Energy Consumption per capita (EC). Regarding energy consumption and efficiency, Per capita Energy Use (EU) measures how much energy is used per person. On the other hand, Energy Consumption per capita (EC) captures general trends in energy consumption and offers a more comprehensive view of total energy demand in relation to population size. By integrating both variables, the research seeks to provide a more nuanced understanding of energy dynamics, enabling a thorough examination of the relationship between energy consumption, carbon emissions, and economic growth. Table 1 Description of Variables and Data Source S.No. Variable Purpose Data Source 1 GDP per capita ( GDP) (in USD) To measure economic growth World Bank [ 48 ] 2 Carbon Emissions per capita ( CO2 ) (in metric tonnes) To assess environmental damage OECD Data Explorer [ 49 ] 3 Energy Consumption per capita ( EC) (in gigajoules) To evaluate the overall energy burden on each individual EnerData [ 50 ] 4 Per capita Energy Use ( EU ) (in kWh) To assess energy efficiency and consumption patterns International Energy Agency [ 51 ] 5 Per capita Renewables ( REN ) (in kWh) To measure the adoption of renewable energy sources International Renewable Energy Agency [ 52 ] 6 Gross Expenditure on Research & Development ( R&D ) (% share of GDP) To capture the effect of innovation BP Statistical Review of World Energy [ 53 ] 7 No. of Patents ( PAT) (in numbers) To capture the effect of environmental-related technologies Our World in Data [ 54 ] This table provides a detailed overview of the variables used in the study, including Carbon Emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (R&D), Patents in Environmental Technologies (PAT), Per capita Energy Use (EU), and Per capita Renewables (REN). Each variable is accompanied by its measurement unit, a brief definition, and the corresponding data source. This table serves to clarify the key metrics employed in the analysis and their relevance to the study of economic growth, energy consumption, and environmental sustainability in G-20 countries. A total of 420 panel observations and 20 cross-sections are included in the dataset from 2000 to 2020, as shown in Table 1 . The time frame for this study, spanning from 2000 to 2020, was chosen to align with the availability of consistent and comprehensive data across all selected variables for G20 countries. This period captures significant global trends in energy consumption, innovation, and economic growth, particularly in response to climate change and technological advancements that have emerged in the 21st century. While earlier data may be available, focusing on this specific time frame effectively allows us to analyse the pre-pandemic economic landscape. Additionally, the decision to exclude data beyond 2020 was made to maintain a consistent dataset that reflects the dynamics of the period under study. The methodology employed in the analysis encompasses several econometric techniques, including Panel Unit Root tests, Panel Cointegration tests, ANOVA, Panel Causality tests, and Panel Quantile Regression. Each method was selected based on its specific strengths and relevance to the research objectives. Descriptive statistics were performed to provide a comprehensive overview of the G-20 panel data. This initial analysis revealed key trends and patterns, such as variations in energy consumption, GDP per capita, and carbon emissions across G-20 countries. For instance, the analysis may show that certain countries exhibit significantly higher carbon emissions per capita compared to others, which can inform subsequent econometric analyses by highlighting areas of concern or interest. This foundational understanding is crucial for contextualising the relationships explored in later tests. The selection of Panel Unit Root tests, such as the Levin, Lin & Chu (LLC) test and the Im, Pesaran, and Shin (IPS) test, is essential for determining the stationarity of the data series. Stationarity is a prerequisite for many econometric analyses, as non-stationary data can lead to spurious results. The LLC test is particularly effective for large panels with a common unit root process, while the IPS test accommodates heterogeneous panels, making it suitable for the diverse G-20 dataset. Establishing the presence of unit roots ensures that the subsequent analyses are based on reliable data. Following the unit root tests, Panel Cointegration tests are employed to examine the long-run relationships among the variables. This step is critical because it assesses whether a stable, long-term equilibrium exists between carbon emissions, GDP, energy consumption, and other variables. Cointegration suggests that the variables move together over time, which is vital for understanding the dynamics of economic growth and environmental impact in G-20 countries. The ANOVA test was specifically chosen to analyse variations among G-20 countries because it allows for comparing means across multiple groups. This method is particularly useful for identifying significant differences in energy use, carbon emissions, and economic performance among the countries. By highlighting these differences, ANOVA contributes to understanding how national policies or economic structures may influence environmental outcomes. For example, if the ANOVA results indicate significant differences in R&D expenditure among countries, this could suggest that investment in innovation plays a crucial role in shaping energy consumption patterns. Figure 2 below depicts the analytical tool's visual representation and the flow diagram of the sequential steps undertaken in the statistical analysis. Panel Causality tests are conducted to examine the causal relationships among the variables under study. This analysis is essential for determining the direction of influence between economic growth, energy consumption, and carbon emissions. Understanding these causal relationships is critical for policymakers, as it informs the design of interventions to reduce carbon emissions while promoting economic growth. Finally, Panel Quantile Regression analyses the heterogeneous effects across different distribution quantiles. This method allows for a more nuanced understanding of how the relationships among variables may differ across various levels of carbon emissions or economic performance. By capturing these variations, the study can provide insights into how different G-20 countries may respond to policy changes or economic shifts. The analysis transformed variables into logarithmic form to stabilise variance and interpret coefficients as elasticities, enhancing the findings' robustness. However, potential limitations of this transformation, particularly concerning variables that may contain zero or negative values, are acknowledged. To address these challenges, the dataset was carefully examined for such instances, and appropriate adjustments to zero values were applied to ensure that all variables were suitable for logarithmic transformation. This approach allows for the maintenance of data integrity while facilitating meaningful interpretations of the relationships among the variables. Descriptive statistics were performed to provide a comprehensive overview of the G-20 Panel data. Subsequently, Panel Unit root tests and assessments for cross-sectional dependence were conducted to ensure the robustness of the panel data. Panel cointegration tests followed these to ascertain the long-term relationships among variables. An ANOVA test was employed to analyse variations among G-20 countries, while Panel Causality tests were conducted to examine causal relationships among the variables under study. Finally, the study incorporates panel quantile regression to analyse the heterogeneous effects across distribution quantiles. The variables under investigation are transformed into logarithmic form to provide a more robust and meaningful approach to understanding and interpreting complex relationships among variables. i. Panel unit root test H0a: Panel Data has unit root (non-stationary series) H1a: Panel Data has no unit root (Stationary series) ii. Panel cointegration test H0b: There is no long-run relationship between panel data variables (No cointegration) H1b: There is a long-run relationship between panel data variables (cointegration) iii. Panel Granger causality test H0c: X does not homogenously cause Y (no causal relationship) H1c: X does homogeneously cause Y (causal relationship) The model for this study is defined as: CO2 = f (GDP, EC, R&D, PAT, EU, REN) ………………………………………….. (1) After conducting descriptive statistics, we performed various diagnostic tests to ensure the validity of our panel data analysis. Additionally, the study has conducted tests for heteroscedasticity and serial correlation to ensure the validity of the regression results. Although normality tests revealed that the data was not normally distributed, we transformed the variables using the log-log method, which ensured the robustness and suitability of our dataset for further analysis. A log-log regression model was deployed to estimate the long-run relationship between the variables under study. The log-log model estimates the long-run relationships among the variables, allowing for the interpretation of coefficients as elasticities, which is particularly useful in economic analyses. The functional form of the model is given below. The variables deployed in the analysis of the Panel OLS Regression Model are expressed in logarithmic scales: lnCO2 = β0 + β1lnGDP + β2lnEC + β3lnR&D + β4lnPAT + β5lnEU + β6lnREN + ε ………………….. (2) 2.1 Panel Unit Root The study conducts the panel unit root tests, which are crucial for ensuring the robustness of the panel data. Specific tests, including the Levin, Lin & Chu (LLC) test, the Im, Pesaran, and Shin (IPS) test, and Fisher-type tests, were employed, each selected for their advantages in addressing the characteristics of the data structure. The LLC test is particularly effective for large panels with a common unit root process, while the IPS test accommodates heterogeneous panels, making it suitable for the diverse G20 dataset. Additionally, cross-sectional dependence was assessed using the Pesaran CD test, which is relevant in G20 countries due to their interconnected economies and the likelihood of sharing common shocks and trends. The general equation for panel unit root tests for stationarity involves regressing the variable of interest on its lagged values and potentially additional covariates to test for the existence of a unit root. The study denotes the variable of interest as y it , where i represents the cross-sectional unit (country), and t represents time, i.e. year. The basic equation for a panel unit root test is typically specified as follows: $$\:\varDelta\:{\varvec{y}}_{\varvec{i}\varvec{t}}\:=\:{\varvec{\alpha\:}}_{\varvec{i}}+\:{\varvec{\beta\:}}_{{\varvec{y}}_{\varvec{i},\:\varvec{t}-1}}+\:\varvec{\gamma\:}{\varvec{X}}_{\varvec{i}\varvec{t}\:}+\:{\varvec{\epsilon\:}}_{\varvec{i}\varvec{t}}$$ 3 ……………………………………. where, \(\:\varDelta\:{y}_{it}\) is the first difference of the variable of the interest \(\:{\alpha\:}_{i\:}\:\) is an individual-specific intercept capturing any individual heterogeneity. \(\:\beta\:\) is the coefficient associated with the lagged dependent variable, representing the presence of a unit root. If \(\:\beta\:\) is close to 1, it suggests non-stationarity. \(\:{X}_{it\:}\:\:\) represents additional covariates that may be included in the model to control for potential determinants of the variable of interest. \(\:{\epsilon\:}_{it}\:\:\) is the error term. 2.2 Panel Cointegration After the panel unit root test verification, the study deploys Panel cointegration tests to check for evidence of a long-run relationship. The panel cointegration tests were used to examine the null hypothesis of no cointegration versus the existence of cointegration [ 55 ]. This study employs two-panel cointegration tests stated as Kao’s residual cointegration tests and Johansen Fisher Panel Cointegration tests. These tests were chosen due to their robustness in handling panel data with cross-sectional dependence and their ability to accommodate heterogeneous cointegration relationships among the variables. Kao’s test is particularly effective for small samples and is based on the residuals of the estimated long-run relationship, making it suitable for the characteristics of the G-20 dataset. On the other hand, the Johansen-Fisher test allows for the identification of multiple cointegration relationships, which is beneficial given the complexity of the interactions among carbon emissions, GDP, and energy consumption. In contrast, other tests, such as Pedroni’s or Westerlund’s, may impose different assumptions or may not be as effective in capturing the specific dynamics in the G-20 countries. Thus, the selected tests align well with the data characteristics and research objectives. For the Kao panel cointegration test, the basic equation can be written as follows: $$\:\varDelta\:{\varvec{y}}_{\varvec{i}\varvec{t}}\:=\:{\varvec{\alpha\:}}_{\varvec{i}}+\:{\varvec{\beta\:}\varvec{y}}_{\varvec{i},\varvec{t}-1}+\:\varvec{\gamma\:}{\varvec{X}}_{\varvec{i}\varvec{t}}+\:{\varvec{\epsilon\:}}_{\varvec{i}\varvec{t}}$$ 4 ……………………………………………… $$\:\varDelta\:{\varvec{X}}_{\varvec{i}\varvec{t}}\:=\:{\varvec{\delta\:}}_{\varvec{i}}+\:{\varvec{\phi\:}\varvec{X}}_{\varvec{i},\varvec{t}-1}+\:\varvec{\eta\:}{\varvec{X}}_{\varvec{i}\varvec{t}}+\:{\varvec{\mu\:}}_{\varvec{i}\varvec{t}}$$ 5 …………………………………..………… where, \(\:\varDelta\:{y}_{it}\) and \(\:\varDelta\:{X}_{it}\) are the first differences of the variables of interest and covariates, respectively. \(\:{\alpha\:}_{i}\) and \(\:{\delta\:}_{i}\) are individual-specific intercepts capturing any individual heterogeneity in the variables. \(\:\beta\:\) and \(\:\phi\:\) are coefficients on the lagged dependent variables in the respective equations, representing the presence of cointegration. If either \(\:\beta\:\) or \(\:\phi\:\) are significantly different from zero, it suggests cointegration. \(\:\gamma\:\) and \(\:\eta\:\) are coefficients on the covariates in the respective equations. \(\:{X}_{it}\) represents additional covariates that may be included in the model to control for potential determinants of the variables of interest. \(\:{\epsilon\:}_{it}\) and \(\:{\mu\:}_{it}\) are the error terms. 2.3 Panel Causality Test The Panel Granger Causality Test assesses whether one variable Granger causes another in a panel dataset. The Granger causality test assesses whether one variable's past values can predict another variable's current values, indicating a directional influence. In the context of this study, if GDP Granger causes CO2 emissions, it suggests that economic growth may lead to increased carbon emissions, which has significant implications for policy-making. This finding could indicate that as economies expand, they may need to implement stricter environmental regulations or invest in cleaner technologies to mitigate the adverse effects of growth on carbon emissions. The test is typically conducted using panel data, where observations are collected over multiple cross-sectional units (country) and periods (year). The basic equation for the Panel Granger Causality Test involves estimating two regression models: Consider two variables, \(\:{Y}_{it\:}\) and \(\:{X}_{it}\:\) where i represents the cross-sectional unit (country) and t represents time (year). We want to test whether \(\:{X}_{it}\) Granger-causes \(\:{Y}_{it\:}\) in a panel data set. The basic regression equations are: $$\:{\varvec{Y}}_{\varvec{i}\varvec{t}\:}=\:{\varvec{\alpha\:}}_{\varvec{i}}+\:{\varvec{\beta\:}}_{\varvec{Y}}{\varvec{Y}}_{\varvec{i},\varvec{t}-1}+\:\varvec{\gamma\:}{\varvec{X}}_{\varvec{i}\varvec{t}}+\:{\varvec{\delta\:}}_{1}{\varvec{X}}_{\varvec{i},\varvec{t}-1}+\:{\varvec{\epsilon\:}}_{\varvec{i}\varvec{t}}$$ 6 ………………………………….. $$\:{\varvec{X}}_{\varvec{i}\varvec{t}\:}=\:{\varvec{\alpha\:}\varvec{{\prime\:}}}_{\varvec{i}}+\:{\varvec{\beta\:}\varvec{{\prime\:}}}_{\varvec{X}}{\varvec{X}}_{\varvec{i},\varvec{t}-1}+\:{\varvec{\delta\:}}_{2}{\varvec{Y}}_{\varvec{i},\varvec{t}-1}+\:{\varvec{\epsilon\:}\varvec{{\prime\:}}}_{\varvec{i}\varvec{t}}$$ 7 ………………………………………… where, \(\:{Y}_{it\:}\:\) and \(\:{X}_{it}\) are the dependent and independent variables, respectively, for the i -th cross-sectional unit at time t \(\:{Y}_{i,t-1}\:\) and \(\:{X}_{i,t-1}\:\) are lagged values of the independent and dependent variables, respectively. \(\:{\alpha\:}_{i}\) and \(\:{\alpha\:{\prime\:}}_{i}\:\) are individual-specific intercepts. \(\:{\beta\:}_{Y}\:\:\) and \(\:{\beta\:{\prime\:}}_{X}\:\) are coefficients on the lagged values of \(\:{Y}_{it\:}\) and \(\:{X}_{it\:}\:\) , respectively, indicating the effect of their past values on their current values. \(\:\gamma\:\) and \(\:{\delta\:}_{1}\:\) are coefficients on the contemporaneous and lagged values of \(\:{X}_{it\:}\) in the equation for \(\:{Y}_{it\:}\:\) respectively. \(\:{\delta\:}_{2}\:\) is the coefficient on the lagged values of \(\:{Y}_{it\:}\) in the equation for \(\:{X}_{it\:}\) \(\:{\epsilon\:}_{it}\:\) and \(\:{\epsilon\:{\prime\:}}_{it}\) are error terms. 2.4 Panel Quantile Regression Panel quantile regression extends the idea of quantile regression to panel data, allowing for the estimation of conditional quantiles of the dependent variable given the independent variables in a panel dataset. The inclusion of panel quantile regression in the analysis is highlighted as a significant strength of the study, as it allows for examining heterogeneous effects across different distribution quantiles. This method is particularly suitable for analyzing the dynamics of energy and innovation among G-20 countries because it captures variations in relationships that may not be evident in traditional mean-based regression approaches. For instance, while mean regression provides an average effect, quantile regression can reveal how the impact of GDP on CO2 emissions may differ for countries at different emissions levels. This nuanced understanding aligns with the study's objectives of exploring the diverse responses of G-20 countries to energy consumption and innovation policies. By employing quantile regression, the study aims to provide insights that are more reflective of the varying contexts and challenges different nations face within the G-20 framework. The basic equation for panel quantile regression can be written as follows: $$\:{\varvec{Q}}_{\varvec{\tau\:}\:}\left({\varvec{Y}}_{\varvec{i}\varvec{t}\:}|{\varvec{X}}_{\varvec{i}\varvec{t}}\right)=\:{\varvec{\alpha\:}}_{\varvec{i}}\left(\varvec{\tau\:}\right)+\:{\varvec{X}}_{\varvec{i}\varvec{t}}\:\varvec{\beta\:}\left(\varvec{\tau\:}\right)+\:{\varvec{\epsilon\:}}_{\varvec{i}\varvec{t}}\left(\varvec{\tau\:}\right)$$ 8 ………………………….. where, \(\:{Q}_{\tau\:\:}\left({Y}_{it\:}|{X}_{it}\right)\) represents the conditional quantile of \(\:{Y}_{it\:}\) at the \(\:\tau\:\) th quantile given \(\:{X}_{it}\) \(\:{\alpha\:}_{i}\left(\tau\:\right)\) represents individual-specific effects at the \(\:\tau\:\) th quantile. \(\:\beta\:\left(\tau\:\right)\) represents the vector of coefficients for the independent variables \(\:{X}_{it}\) at the \(\:\tau\:\) th quantile. \(\:{\epsilon\:}_{it}\left(\tau\:\right)\:\) is the error term at the \(\:\tau\:\) th quantile. In panel quantile regression, the quantile-specific coefficients \(\:{\alpha\:}_{i}\left(\tau\:\right)\) and \(\:\beta\:\left(\tau\:\right)\:\) are allowed to vary across various quantiles of the conditional distribution of the dependent variable, thereby offering a more detailed understanding of how the influence of the independent variables varies across the distribution. 3 Results and Discussions Table 2 includes measures of central tendency, such as the mean and median, and measures of dispersion, including standard deviation. Additionally, the discussion of the shape of the distributions has been clarified, and skewness and kurtosis have been explicitly mentioned as key indicators. The table summarizes key statistical measures for the variables under investigation, including Carbon Emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (R&D), Patents in Environmental Technologies (PAT), Per capita Energy Use (EU), and Per capita Renewables (REN). The mean values indicate that Energy Use (EU) has the highest average consumption at 10.379 kWh, reflecting significant energy utilization across the G20 nations. At the same time, Gross Expenditure on Research and Development (R&D) exhibits the lowest mean at 0.110% of GDP, suggesting limited investment in innovation relative to economic output. The standard deviation values reveal variability in the data. Carbon Emissions (CO2) and Patents (PAT) show considerable dispersion, as indicated by their respective standard deviations of 0.748 and 2.500, which may reflect diverse environmental policies and technological advancements across countries. Furthermore, the skewness statistics indicate that all variables are negatively skewed, suggesting that most observations are concentrated on the higher end of the distribution, with a few lower values pulling the mean down. All variables, including Carbon Emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (R&D), Patents in Environmental Technologies (PAT), Per capita Energy Use (EU), and Per capita Renewables (REN), exhibit negative skewness. This indicates that the distributions of these variables have longer left tails, suggesting that most of the data points are concentrated on the right side of the distribution. Table 2 Descriptive Statistics of the variables Variables Descriptive Statistics CO2 GDP EC R&D PAT EU REN Mean 1.924 9.558 4.754 0.110 7.399 10.379 7.235 Median 2.089 9.870 4.959 0.312 7.625 10.584 7.866 Maximum 3.058 11.129 6.060 1.567 11.121 11.688 10.47 Minimum -0.058 6.091 2.526 -3.162 1.797 8.151 -2.818 Std. Dev. 0.748 1.158 0.810 0.943 2.500 0.808 2.250 Skewness -0.629 -0.856 -0.684 -1.428 -0.204 -0.700 -2.552 Kurtosis 2.631 2.973 2.940 5.294 1.945 2.964 11.414 Jarque-Bera 30.128 51.340 32.826 234.940 22.395 34.332 1695.198 Probability 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Sum 808.413 4014.637 1997.020 46.387 3107.726 4359.314 3039.056 Sum Sq. Dev. 234.663 562.030 275.557 373.110 2620.320 274.101 2121.804 This table presents the summary statistics for the variables included in the study, providing insights into their distributions and central tendencies. It includes key metrics such as the mean, median, standard deviation, minimum, and maximum values for each variable, which encompasses Carbon Emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (R&D), Patents in Environmental Technologies (PAT), Per capita Energy Use (EU), and Per capita Renewables (REN). This summary aids in understanding the overall characteristics of the data and the variability of each variable across the G-20 countries during the study period. A normal distribution has a kurtosis of 3, where values above 3 indicate a leptokurtic distribution (peaked), and values below 3 indicate a platykurtic distribution (flatter than a normal distribution). The kurtosis values further elucidate the distribution shapes, with R&D and REN exhibiting leptokurtic characteristics, indicating a peaked distribution, while the other variables are classified as platykurtic, suggesting flatter distributions. Jarque-Bera is a test statistic to determine whether the series is normally distributed. The Jarque-Bera test assesses the null hypothesis that a series follows a normal distribution. A significant result, indicated by a low p-value, suggests that the series deviates from normality. In the context of this study, the Jarque-Bera statistic exceeds the critical value, and the associated low probability value provides strong evidence to reject the null hypothesis. For all the seven series (CO2, GDP, EC, R&D, PAT, EU, REN) utilised in the study, we reject the hypothesis of normal distribution at the 1% level. Based on the skewness and kurtosis statistics, none of the distributions of the variables satisfied the assumption of normality. These findings collaborated with the Jarque-Bera test's results, affirming the variables' distributions to be non-normal [ 56 ]. Collectively, these descriptive statistics provide a foundational understanding of the variables' central tendencies, variability, and distributional characteristics, which are critical for subsequent analyses in the study. A necessary condition before testing for the possible establishment of a long-run relationship between CO2 emissions per capita, GDP per capita, energy consumption per capita, gross expenditure on research and development, patents on environmental-related technologies, per capita energy use and per capita renewables is that all variables should be integrated in the first order. To examine this condition, panel unit root tests are performed, including the Levin, Lin and Chu (LLC), the Im, Pesaran and Shin (IPS), ADF-Fisher Chi-square and PP-Fisher Chi-square tests [ 57 ]These tests include cross-sectional dependence and cross-sectional independence cases. The findings of these tests are detailed in Tables 3 a and 3 b. Table 3 a: Panel Unit Root Tests at Level and First Difference Level Summary Methods CO2 GDP EC R&D PAT EU REN Stat. Prob. Stat. Prob. Stat. Prob. Stat. Prob. Stat. Prob. Stat. Prob. Stat. Prob. Levin, Lin & Chu -1.19 0.11 -7.42 0.00 -3.70 0.00 0.98 0.83 -5.39 0.00 -3.75 0.00 -0.54 0.29 Im, Pesaran & Shin 4.75 1.00 -5.35 0.00 3.39 0.99 4.28 1.00 -1.45 0.07 3.10 0.99 3.80 0.99 ADF-Fisher Chi-square 29.33 0.89 98.00 0.00 31.13 0.84 22.24 0.98 60.67 0.01 31.75 0.82 27.10 0.94 PP-Fisher Chi-square 20.24 0.99 77.89 0.00 28.66 0.90 34.98 0.69 98.28 0.00 29.72 0.88 35.49 0.67 First Difference Levin, Lin & Chu 0.78 0.78 -6.99 0.00 3.12 0.99 -1.81 0.03 0.36 0.64 3.01 0.99 -5.40 0.00 Im, Pesaran & Shin -3.15 0.00 -6.20 0.00 -3.73 0.00 -4.77 0.00 -5.96 0.00 -3.75 0.00 -9.22 0.00 ADF-Fisher Chi-square 80.30 0.00 110.6 4 0.00 81.97 0.00 102.09 0.00 109.58 0.00 81.74 0.00 159.84 0.00 PP-Fisher Chi-square 132.02 0.00 132.7 0.00 174.6 0.00 177.04 0.00 369.59 0.00 175.92 0.00 567.60 0.00 The findings of the panel unit root tests that were performed to evaluate the stationarity of the study variables are shown in this table. Each variable's test statistics and the corresponding p-values show whether or not the unit root null hypothesis can be rejected. The variables analysed include Carbon Emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (R&D), Patents in Environmental Technologies (PAT), Per capita Energy Use (EU), and Per capita Renewables (REN). The results provide essential information regarding the time series properties of the data, which is crucial for subsequent econometric analyses. Table 3 b: Cross-Sectional Dependence Test Test Statistic d.f. P-value Breusch-Pagan LM 1149.725 190 0.0000 Pesaran scaled LM 49.23285 0.0000 Pesaran CD 15.14461 0.0000 This table presents the findings from the cross-sectional dependence tests conducted on the panel data. It includes the test statistics and degrees of freedom for various tests used to evaluate the presence of cross-sectional dependence among the variables analysed. The findings reveal whether the null hypothesis of cross-sectional independence can be disproved, offering valuable information about the interdependencies between the G-20 nations concerning the variables under investigation. The unit root tests conducted in this study reveal that the null hypothesis of the unit root cannot be rejected at the 1% significance level for the seven-panel time series when analysed at a level. However, upon testing for the unit root in the first difference, all panel unit root tests reject the null hypothesis with a statistically significant level of 1%. This finding indicates the first-order integration of the variables, which is essential for further cointegration analysis. Since non-stationary series must be differentiated to achieve stationarity before examining long-term relationships, variables of the same order must be integrated. Non-stationarity has important ramifications since it can compromise the validity of any conclusions derived from the analysis and the robustness of the results. These results conclude that all panel time series are integrated with the first order [ 58 ]. To summarise, we note that irrespective of the type of tests employed, cross-sectional dependence or cross-sectional independence for the group of G-20 countries demonstrated strong evidence for non-stationarity at level and stationarity at first difference. At the level summary, p-value > 0.05 for all the variables, we did not find sufficient evidence to reject the null hypothesis, which means the series has a unit root and the data is non-stationary. At the first difference, the null hypothesis is rejected due to p-values being less than 0.05 for all variables. Therefore, the series has no unit root, and data is stationary for all the seven- series, i.e., CO2 emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (R&D), Patents in Environmental related Technologies (PAT), per capita Energy Use (EU) and per capita Renewables (REN) [ 59 ]. Empirical results propose strong evidence for panel cointegration between the CO2 emissions per capita, GDP per capita, energy consumption per capita, gross expenditure on research and development, patents in environmental-related technologies, per capita energy use and per capita renewables for G-20 countries. The panel cointegration tests favour the rejection of no cointegration besides the alternative of cointegration among the variables analysed. The Kao test results signify the presence of cointegration; hence, the rejection of the null hypothesis as p-value < 0.05 for the Kao test [ 60 ], as depicted in Table 4 a. However, the number of cointegration vectors is unknown. According to Table 4 b, Johansen Fisher test checks the probability of more than one cointegration vector among the variables analysed [ 61 ]. Johansen Fisher panel cointegration test results suggested, at most, six cointegration vectors. It implies that the presence of a single cointegration vector cannot be achieved among the variables analysed. Table 4 a: Kao Cointegration Test t-Statistic Probability ADF -3.7009 0.0001 Residual variance 0.00047 HAC variance 0.00043 This table summarises the results of the Kao panel cointegration test, which is employed to determine a long-run equilibrium relationship among the variables analysed in the study. It includes the test statistics, critical values, and p-values for the variables, such as Carbon Emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (R&D), Patents in Environmental Technologies (PAT), Per capita Energy Use (EU), and Per capita Renewables (REN). The findings indicate whether the null hypothesis of no cointegration can be rejected, thereby providing evidence of long-term relationships among the variables within the G-20 countries. Table 4 b. Johansen Fisher Panel Cointegration Test Trace Test Max-Eigen Test Hypothesised No. of CE(s) Stat. Prob. Stat. Prob. None 0.000 1.00 0.00 1.00 At most 1 811.7 0.00 442.4 0.00 At most 2 963.4 0.00 751.1 0.00 At most 3 647.3 0.00 439.0 0.00 At most 4 301.8 0.00 195.4 0.00 At most 5 161.2 0.00 121.4 0.00 At most 6 114.6 0.00 114.6 0.00 This table presents the results of the Johansen-Fisher panel cointegration test, which is utilised to identify the number of cointegration relationships among the variables analysed in the study. It includes the test statistics for both the Trace Test and Max-Eigen Test, along with their corresponding p-values for various hypothesised numbers of cointegration equations. The variables examined include Carbon Emissions per capita (CO2), per capita Energy Use (EU), per capita Renewables (REN), Gross Expenditure on Research and Development (RandD), Energy Consumption per capita (EC), Patents in Environmental Technologies (PAT) and GDP per capita (GDP). To comprehend the long-term relationships and dynamics among economic growth, energy consumption and environmental impacts in G-20 countries, it is essential to know that the results show the presence of multiple cointegration vectors, indicating that these variables move together over time. The main aim of performing the test of analysis of variance (ANOVA) is to examine whether or not the mean differences between the G-20 countries [ 62 ] are statistically significant based on the variables deployed in the study, i.e., CO2 emissions per capita, GDP per capita, energy consumption per capita, gross expenditure on research and development, patents in environmental related technologies, per capita energy use and per capita renewables. As shown in Table 5 , the ANOVA results show a statistically significant difference between the G-20 nations with a p-value less than 0.05. This importance implies notable differences between nations in the features of the research variables, including energy consumption innovation metrics and economic growth indicators. Since they may represent various policy environments, economic structures and degrees of technological advancement, it is essential to comprehend these distinctions when examining the relationship between energy innovation and economic growth. Significant ramifications result from these differences, underscoring the need for customised policy interventions considering each nation's situation. By recognising these disparities, the study can provide more nuanced insights into how energy and innovation strategies can be optimised to foster sustainable economic growth across the G-20 nations. Table 5 ANOVA F-test Test Type F-statistic p-value ANOVA F-test 2799.749 0.000 Welch F-test 7351.370 0.000 This table presents the results of the ANOVA F-test conducted to analyse the differences in means among the G-20 countries regarding the variables under study, such as Carbon Emissions, GDP, and Energy Consumption. It includes the F-statistic and p-value for the standard ANOVA F-test and the Welch F-test. The ANOVA F-test assesses whether there are statistically significant differences in the means of the groups. At the same time, the Welch F-test is used as an alternative when the assumption of equal variances is violated. The results indicate that the p-values are less than 0.05, leading to the rejection of the null hypothesis, which suggests significant differences in the means across the G-20 countries. This finding highlights the impact of national policies and economic structures on environmental outcomes and underscores the importance of tailored approaches to energy use and emissions reduction in different countries. The standard ANOVA consider that the errors (i.e., residuals) follow a normal distribution. However, if this normality assumption is violated, an alternative is to use a non-parametric test. To determine if there is a statistically significant difference between the medians of three or more independent groups, the Kruskal-Wallis test is the most widely used non-parametric test comparable to one-way ANOVA, i.e., G-20 nations in this instance. The data ranks are the foundation for the Kruskal-Wallis test [ 63 ]. The ranks of the standard normal distribution are converted to quantiles using the Van Der Waerden test. These are known as normal scores, and the computation of the tests is predicated on them. The Van Der Waerden test has the advantage of offering the robustness of the Kruskal-Wallis test in situations where the normality assumptions are not met, as well as the high efficiency of the conventional ANOVA analysis when they are. By addressing possible breaches of the data underlying normality assumptions, non-parametric tests like the Kruskal-Wallis and Van Der Waerden tests play a crucial part in the analysis. When normality is met, the Van Der Waerden test provides the efficiency of a standard ANOVA; however, when this assumption is not met, it retains robustness. This dual capability makes these tests superior to other options because they thoroughly examine the variations among the G-20 nations without being unduly impacted by the data distributional features. According to the results of the Kruskal-Wallis and Van der Waerden tests illustrated in Table 6 , due to the presence of different characteristics and variables of G- 20 countries over the period, the observed differences in medians among the countries are statistically significant as p-value < 0.0. Table 6 Tests for Equality of Medians Tests for Equality of Medians Method F-test value p-value Kruskal-Wallis 2456.51 0.000 Kruskal-Wallis (tie-adj.) 2456.51 0.000 Van der Waerden 2280.92 0.000 This table shows the findings of the Kruskal-Wallis and Van der Waerden tests for median equality across the examined variables in G-20 nations. A p-value less than 0. 05 indicates significant differences between the groups, while the F-test values and associated p-values show the statistical significance of differences in medians. The results demonstrate the diversity in the distribution of the variables under investigation, highlighting the necessity of specialised policy approaches to environmental sustainability. To test the equality of variances, Bartlett, Levene and Brown-Forsythe tests[ 64 ] are reported in Table 7 . Bartlett's test compares the logarithm of the weighted average variance with the weighted sum of the logarithms of the variances. In contrast, the Levene test relies on an analysis of variance (ANOVA) of the absolute deviation from the mean. The Brown-Forsythe test is a variation of the Levene test that substitutes the absolute median difference for the absolute mean difference. The results of the table depict that the distributional differences across the G-20 countries are statistically significant throughout the period in terms of the variables analysed as p-value < 0.05, therefore rejecting the null hypothesis and stating that there is a statistically significant difference in the distributional variances across G-20 countries for the variables deployed in the study. Table 7 Tests for Equality of Variances Tests for Equality of Variances Method F-test Value p-value Bartlett 1389.43 0.000 Levene 169.80 0.000 Brown-Forsythe 136.73 0.000 This table summarises the results of Bartlett, Levene, and Brown-Forsythe tests for equality of variances among the analysed variables in G-20 countries. The F-test values and associated p-values indicate the statistical significance of differences in variances, with p-values less than 0.05 suggesting significant disparities in the distributional variances across the groups. These results emphasise the heterogeneity in the variability of the examined variables, which is crucial for understanding the dynamics of environmental sustainability policies. Determining the direction of causality is critical for policy recommendations, and to examine the causal flow among the variables deployed in the study, the Dumitrescu-Hurlin Panel Granger Causality Test [ 34 ] is performed, and its results are given in Table 7 . Since the variables under study have a cointegration relationship, a change in one variable is expected to affect the other variable. Causality test results are reported in Table 8 . The diagrammatic representation of panel causality tests in Fig. 3 indicates that there is a bidirectional causal nexus between carbon emissions and economic growth, between carbon emissions and energy consumption, between carbon emissions and gross expenditure on research and development and between carbon emissions and energy use which ascertains that any policy changes and investments among G-20 countries in the variables will impact carbon emissions and vice versa. There is a bidirectional relationship between economic growth and energy consumption and between economic growth and energy use. However, unidirectional causality from economic growth to gross expenditure on research and development, as economic growth often creates a conducive environment for increased R&D spending. As a nation's economy expands, there is typically a higher capacity for investment in various sectors, including research and development and from economic growth to renewables exists as economic prosperity provides the financial means for developing and implementing renewable energy projects, such as installing solar panels, constructing wind farms or enhancing grid efficiency. Renewable energy, including solar, wind, and hydropower, has become attractive. Therefore, governments and businesses, recognising the long-term benefits of sustainable energy, may invest in renewable infrastructure to meet the growing energy demand while mitigating environmental impact in G-20 nations. Table 8 D-H Panel Granger Causality Test Results Hypothesis W- Stat. Zbar- Stat. Prob. Causality Conclusion GDP→CO 2 3.59 2.05 0.04 Yes Bi-directional causality between GDP and CO 2 CO 2 →GDP 4.54 3.58 0.00 Yes EC→CO 2 4.99 4.30 0.00 Yes Bi-directional causality between EC and CO 2 CO 2 →EC 5.58 5.26 0.00 Yes R&D→CO 2 5.46 5.06 0.00 Yes Bi-directional causality between R&D and CO 2 CO 2 →R&D 4.56 3.61 0.00 Yes PAT→CO 2 10.16 12.68 0.00 Yes Uni-directional causality from PAT to CO 2 CO 2 →PAT 2.32 -0.00 0.99 No EU→CO 2 4.42 3.38 0.00 Yes Bi-directional causality between EU and CO 2 CO 2 →EU 4.88 4.14 0.00 Yes REN→CO 2 2.87 0.88 0.37 No Uni-directional causality from REN to CO 2 CO 2 →REN 4.29 3.18 0.00 Yes EC→GDP 5.16 4.58 0.00 Yes Bi-directional relationship between EC and GDP GDP→EC 4.04 2.77 0.00 Yes R&D→GDP 3.52 1.9 0.05 No Uni-directional relationship from R&D to GDP GDP→R&D 6.15 6.18 0.00 Yes PAT→GDP 3.02 1.12 0.25 No There is no causality between PAT and GDP GDP→PAT 2.66 0.53 0.59 No EU→GDP 5.48 5.10 0.00 Yes Bi-directional causality between EU and GDP GDP→EU 3.97 2.65 0.00 Yes REN→GDP 2.55 0.35 0.72 No GDP→REN R&D→EC EC→R&D 4.75 5.89 5.54 3.92 5.76 5.20 0.00 0.00 0.00 Yes Yes Yes Uni-directional causality from GDP to REN Bi-directional causality between R&D and EC PAT→EC 10.87 13.84 0.00 Yes Uni-directional causality from PAT to EC EC→PAT 2.76 0.69 0.48 No EU→EC 2.97 1.03 0.30 No No causality between the EU and the EC EC→EU 3.07 1.19 0.23 No REN→EC 3.14 1.31 0.18 No Uni-directional causality from REN to EC EC→REN 4.86 4.09 0.00 Yes PAT→R&D 17.77 25.01 0.00 Yes Uni-directional causality from PAT to R&D R&D→PAT 2.18 -0.24 0.80 No EU→R&D 5.46 5.08 0.00 Yes Bi-directional causality between EU and R&D R&D→EU 6.01 5.96 0.00 Yes REN→R&D 3.38 1.70 0.08 No No causality between REN and R&D R&D→REN 3.34 1.64 0.09 No EU→PAT 2.82 0.79 0.42 No No causality between the EU and the PAT PAT→EU 10.97 14.00 0.00 Yes REN→PAT 1.89 -0.71 0.47 No Uni-directional causality from REN to PAT PAT→REN 3.7 2.28 0.02 Yes REN→EU 3.07 1.20 0.22 No Uni-directional causality from REN to EU EU→REN 4.74 3.89 0.00 Yes This table displays the results of the Dumitrescu-Hurlin (D-H) Panel Granger Causality Test, assessing the causal relationships among key variables related to carbon emissions, economic growth, energy consumption, and research and development in G-20 countries. The W-statistics and corresponding significance levels indicate whether a variable can predict another, with unidirectional and bidirectional causality identified. In analysing the intricate interplay between carbon emissions, economic growth, energy consumption, gross expenditure on research and development, patents in environmental-related technologies, energy use and renewables, deploying Panel Quantile Regression (PQR) presents a compelling rationale. The deployment of Panel Quantile Regression (PQR) in analysing the intricate interplay between carbon emissions, economic growth, energy consumption, gross expenditure on research and development, patents in environmental-related technologies, energy use, and renewables is particularly compelling due to its ability to capture heterogeneous effects across different quantiles of the dependent variable. While traditional models, such as Fixed Effects or Generalized Method of Moments (GMM), may provide average estimates, they often overlook the variability in relationships that can exist across different levels of carbon emissions. PQR addresses this limitation by allowing for a more nuanced exploration of how the impact of explanatory variables may differ at various points in the distribution of carbon emissions. Panel Quantile Regression (PQR) provides a methodological framework that allows for the examination of how relationships between variables vary across different quantiles of carbon emissions rather than relying solely on mean-based analysesThis method shows how the effects of economic growth energy consumption and innovation on carbon emissions can vary greatly at different emission levels revealing distributional changes that conventional approaches may hide. These revelations are essential to the study objectives because they allow for a more thorough comprehension of how economic activity affects the environment in each of the G-20 nations. The ability of PQR to capture distributional changes in the influence of variables on carbon emissions makes its adoption especially relevant [ 65 ]. This approach enables us to determine whether there are differences in the relationship between economic factors and environmental outcomes among the panel's entities or regions. These revelations are essential to the study objectives because they allow for a more thorough comprehension of how economic activity affects the environment in each of the G-20 nations. PQR's ability to capture distributional changes in the influence of variables on carbon emissions makes its adoption especially relevant [ 65 ]. Studies by Koenker and Hallock (2001)[ 68 ] and Yu et al.. (2003) [ 69 ] have shown that mean-based methods often fail to capture the full range of relationships between variables, particularly in heterogeneity. These studies emphasise that traditional regression models may overlook important distributional differences, leading to incorrect conclusions. On the other hand, Panel Quantile Regression (PQR) gives us a more thorough grasp of the relationship between economic factors and environmental outcomes by enabling us to identify differences in environmental impact across various distribution quantiles. This capability is particularly valuable for G20 countries, where the diversity in economic structures, energy consumption patterns, and innovation capabilities necessitates a nuanced analysis. Since it identifies how the relationship between economic variables and environmental outcomes varies across entities within the panel, PQR is useful in capturing regional variations among the G-20 countries. For example, the study might show that in contrast to nations that depend on fossil fuels, those that adopt more renewable energy have a different relationship between economic growth and carbon emissions. In sustainability research, where the goal is not merely to understand average effects but to uncover disparities in environmental impact, PQR becomes indispensable. By leveraging PQR, the study explores variations in the effectiveness of R&D investments, the significance of patents in environmental technologies and the adoption of renewable energy sources at different carbon intensity levels. Panel Quantile Regression (PQR) is particularly suitable for this study because it allows us to capture heterogeneous effects across different points of the conditional distribution of the dependent variable. According to Koenker and Bassett (1978) [ 70 ], quantile regression provides a comprehensive analysis by focusing on the conditional distribution of the dependent variable. Unlike Fixed Effects [ 71 ] or Generalized Method of Moments (GMM) [ 72 ], which focus on average effects, PQR provides a more nuanced analysis by revealing how the relationships between variables vary at different quantiles. This is crucial for understanding the diverse dynamics among G20 countries, which exhibit varying levels of economic growth, energy consumption, and innovation capabilities. Fixed Effects models, while useful for controlling for unobserved heterogeneity, assume homogeneity in the slope coefficients, which may not capture the varying impacts of the explanatory variables across different levels of the dependent variable. GMM, on the other hand, is designed to address endogeneity concerns but may not fully account for the distributional heterogeneity present in our data. By selecting PQR, we can better understand how distribution segments are impacted by variables such as economic growth innovation and carbon emissions, which helps us effectively customise policy recommendations. Policymakers looking to implement focused policies that target particular economic sectors or areas with different environmental issues will find this nuanced analysis essential. Given the diverse relationships examined in the study, an approach that recognises and analyses distributional disparities is necessary. By offering an adaptable framework for evaluating the conditional effects of explanatory variables across various quantiles of carbon emissions, PQR supports this requirement. Ultimately, the deployment of Panel Quantile Regression enhances the robustness of the research findings, offering policymakers tailored insights that can inform effective strategies for sustainable development, emission reduction and the promotion of green technologies across diverse sectors within the panel, as illustrated in Table 9 . Table 9 Panel Quantile Regression Variables Panel quantile regression estimates at 0.10th − 0.90th quantile (Dependent variable: CO2) 0.10th 0.25th 0.50th 0.75th 0.90 th GDP -0.05 -0.04 -0.04 -0.04 -0.03 EC -0.80 -0.54 -0.20 0.06 0.25 R&D 0.011 0.01 0.01 0.01 0.01 PAT 0.02 0.01 -0.00 -0.01 -0.01 EU 1.99 1.72 1.37 1.08 0.89 REN -0.03 -0.03 -0.02 -0.01 -0.01 This table presents the results of the panel quantile regression analysis conducted to examine the heterogeneous effects of various independent variables on carbon emissions (dependent variable) across different quantiles. The first column lists the independent variables, including GDP per capita (GDP), energy consumption per capita (EC), gross expenditure on research and development (R&D), number of patents in environmental technologies (PAT), energy use (EU), and renewable energy consumption (REN). Each subsequent row provides the estimated coefficients for these independent variables at different quantiles of carbon emissions, highlighting the varying impact of each factor depending on the level of emissions. The results aim to inform policymakers about the differential effects of economic and energy variables on environmental outcomes in G-20 countries. The relationship between economic growth and carbon emissions [ 66 ], in the PQR test results, where the effect coefficients of GDP on carbon emissions are consistently negative across the 0.10th to the 0.90th quantiles, implies a consistent pattern of decreasing carbon emissions associated with higher GDP levels across various segments of the distribution which means that on average, as GDP increases, carbon emissions decrease across different quantiles within the panel because of technological advancements as higher GDP levels may coincide with the adoption of cleaner and more efficient technologies, contributing to reduced carbon emissions. Also, due to environmental regulations, countries with higher GDPs may implement stricter environmental laws and policies, influencing industries to adopt cleaner practices and technologies. Another reason for this pattern could be transitioning to low-carbon economies, as economies with higher GDP might successfully transition towards low-carbon or green technologies, mitigating their environmental impact. The relationship between GDP and carbon emissions is intrinsically complex and impacted by several variables, such as a nation's energy mix, technological innovation potential and the particular regulatory frameworks that oversee economic operations. Sweden and Denmark, for example, have successfully lowered emissions while preserving strong economic performance, demonstrating how countries that prioritise investments in energy efficiency and renewable energy may see a decoupling of economic growth from carbon emissions. On the other hand, economic growth frequently corresponds with higher carbon emissions in economies that rely heavily on fossil fuels, underscoring the necessity of closely examining the circumstances surrounding growth. A more fair reading would highlight that attaining reduced emissions and economic expansion requires adopting all-encompassing policies that support sustainable practices like carbon pricing, clean technology investments and legislative frameworks that encourage the use of renewable energy. Considering these complexities, the study can offer more practical policy suggestions that help countries balance environmental sustainability and economic development, ultimately leading to a more sustainable future. The relationship between energy consumption and carbon emissions presents a complex dynamic that warrants careful interpretation [ 67 ]. According to the Panel Quantile Regression (PQR) results, the negative coefficients below the 50th quantile imply that a rise in energy use is generally linked to a fall in carbon emissions. This finding may seem counterintuitive initially because, according to conventional wisdom, higher energy consumption usually translates into higher carbon emissions, especially in the G-20 nations, distinguished by a heavy reliance on fossil fuels. The study investigates several plausible explanations to address this unexpected result. The type of energy used must be taken into account. First nations with rising energy consumption may switch to cleaner energy sources or put energy efficiency measures in place to reduce emissions. Furthermore, regional variations among the G-20 might be crucial since different energy portfolios and regulatory systems may result in different correlations between emissions and energy use. For example, the observed negative relationship in some quantiles may result from decoupling energy consumption and carbon emissions in countries investing in renewable energy technologies. The possible reasons for this pattern can be, first, efficiency gains, i.e. below the 50th quantile, G-20 countries may be achieving energy efficiency gains, leading to reduced emissions for a given level of energy consumption. Beyond this point, further increases in energy consumption may not yield the same efficiency level. The second reason is industrial processes, which are the turning point and indicate a stage where certain industries or processes become more energy-intensive, leading to higher emissions despite increased energy consumption. These results represent that the threshold effect is crucial for policymakers. Below the 50th quantile, policies may encourage energy efficiency measures to decouple energy consumption from emissions [ 67 ]. Beyond this point, policymakers should consider strategies that address the potential increase in emissions associated with higher energy consumption levels. For the association between gross expenditure on research and development and carbon emissions [ 44 ], the uniform positivity in the coefficients of R&D on CO2 suggests that, on average, as gross expenditure on R&D increases, carbon emissions also increase across different quantiles within the panel of G-20 countries. Even though the data shows a link between higher carbon emissions and more R&D spending, this relationship calls for a closer examination of the underlying causes of this paradox. One critical aspect to consider is the allocation of R&D investments. Emissions could increase despite technological advancements if a significant portion of R&D funding is directed towards carbon-intensive industries, such as fossil fuel extraction or traditional manufacturing processes. Conversely, suppose R&D expenditures are primarily focused on developing cleaner technologies. In that case, the long-term impact may ultimately result in reduced emissions, albeit with a time lag before these innovations are fully deployed and integrated into the market. The delay between R&D investment and the realisation of technological advancements must also be considered. Research and development (R&D) innovations frequently take years, if not decades, to leap from lab to market. Emissions could keep rising since outdated, inefficient technologies may still be used during this transitional phase. Therefore, it is essential to contextualise the findings within the broader framework of technological development and deployment timelines. Therefore, the only explanation for this pattern may be innovation dynamics since industries may go through transitional phases while pursuing innovation where newer technologies coexist with practices that may be carbon-intensive. According to this research, R&D investments may have unforeseen repercussions in the form of higher carbon emissions, even though they support economic expansion and technological advancements. Policymakers should consider incorporating environmental considerations and incentivising green innovation within R&D spending initiatives [ 39 ]. The link between patents related to environmental technologies and carbon emissions [ 40 ], the coefficients of PAT on CO2, up to the 25th quantile, is positive, indicating that, on average, an increase in patents in environmental technologies is associated with an increase in carbon emissions. This suggests that, in the lower range of emissions, having more patents in environmental technologies might not necessarily lead to emission reductions. After the 25th quantile, the negative coefficients suggest that, within the upper range of the distribution, an increase in patents in environmental technologies is associated with a decrease in carbon emissions. This implies that, in the higher emissions segment, having more patents in environmental technologies becomes associated with emission reductions. This pattern is due to the possibilities of technological adoption lag as it might take time for innovations in environmental technologies to be widely adopted and have a tangible impact on emission reduction, explaining the initially positive coefficients. This pattern can also be due to the economic threshold where industries with higher emissions might be more receptive to adopting environmental technologies once a certain threshold of emissions is reached. Therefore, understanding this threshold effect is crucial for global leaders and policy advisors of G-20 economies. Below the 25th quantile, policies may need to focus on promoting the practical implementation and adoption of environmental technologies. Beyond this point, policymakers might emphasise incentivising industries with higher emissions to adopt these technologies for effective emission reduction. In the relationship between energy use and carbon emissions [ 33 ], there is a positive pattern of coefficients of EU on CO2 from 0.10th quantile to 0.90th quantile, which implies that on average, as energy use increases, carbon emissions also increase across different quantiles within the panel of G-20 countries resulting from reliance on fossil fuels as higher energy use may be driven by a significant reliance on fossil fuels, contributing to increased carbon emissions. Industrial operations that use much energy and produce more emissions may cause a positive correlation. This finding highlights the difficulty of separating energy consumption from carbon emissions. To lessen the effect of rising energy consumption on emissions, policymakers may need to concentrate on measures that support energy efficiency, provide incentives for using renewable energy sources and stimulate the adoption of cleaner technologies. The association between renewables and carbon emissions [ 43 ], the panel quantile regression results where the effect coefficients of renewables on carbon emissions are consistently negative across the 0.10th to the 0.90th quantiles, suggests that there is a pervasive negative relationship between the use of carbon emissions and renewable energy across different quantiles within the G-20 panel because of clean energy adoption. Reducing dependency on carbon-intensive sources, implementing cleaner, low-carbon renewable energy sources and having active environmental policies could all hurt the relationship. Renewables could signify the impact of pro-environmental laws and a dedication to sustainable energy sources. This research bolsters the notion that boosting the share of renewable energy sources in the energy mix is a practical way to cut carbon emissions. Policymakers can think about investing in renewable infrastructure, encouraging renewable energy sources and offering incentives for moving away from carbon-intensive energy sources [ 41 ]. A sustainable future depends on understanding the intricate relationships between carbon emissions, economic growth, energy consumption, green technology patents, renewable energy sources, and gross R&D expenditure. Economic growth has historically been linked to rising energy and carbon emissions, particularly in developed nations that rely significantly on fossil fuels. As the economy expanded following the Industrial Revolution, more carbon-intensive activities were undertaken, resulting in environmental problems like climate change. However, as global environmental consciousness has developed, there is a growing acknowledgement that a more sustainable paradigm is required. In this context, gross R&D expenditure is critical; countries that devote significant resources to R&D witness technical advances in environmental issues. Innovation is stimulated by higher R&D spending, which results in longer-term solutions, cleaner technology and more energy-efficient procedures. Environmental technology patents demonstrate this innovation and a commitment to using innovative methods to address environmental issues. The link between R&D spending and environmental patents is crucial. Countries prioritising environmental research and development are more likely to create technology that cuts carbon emissions, improves energy efficiency and encourages sustainable behaviour. Patents demonstrate a country's scientific strength and catalyse additional breakthroughs and international collaboration in tackling environmental challenges. Energy consumption patterns are closely linked to carbon emissions. The carbon footprints of nations that rely heavily on fossil fuels for energy generation are larger. Decoupling economic growth from carbon-intensive activities requires a move to renewable energy sources. Carbon emissions can be decreased with the help of renewable energy sources like solar, hydropower, and geothermal. Energy policies must be significantly adjusted to transition to renewable energy. Governments everywhere quickly realise how important it is to include renewable energy sources in their energy mix. Renewable energy incentives, subsidies, and regulatory frameworks support the transition. Climate change is exacerbated as nations become less dependent on fossil fuels as the share of renewable energy sources in the energy mix increases. Conclusion The G20 nations comprise a significant portion of the world's energy consumers. The G20 nations made up over 80% of the world's energy consumption in 2019, according to the International Energy Agency (IEA). The G20 nations are large energy consumers who heavily rely on fossil fuels. Many countries recognise the benefits of moving to cleaner energy sources such as renewables, improving energy security and reducing greenhouse gas emissions. Large energy consumers in the G20 countries mainly depend on fossil fuels. A more thorough examination is necessary to enhance its relevance and specificity. The report acknowledges that several G-20 nations are leading the way in the switch to renewable energy, demonstrating noteworthy projects that demonstrate their dedication to sustainability. For example, Germany has adopted renewable energy through its Energy Transition Policy, which aims to switch to an environmentally friendly, low-carbon energy source. Similarly, Canada has invested significantly in wind and hydroelectric power, with British Columbia and Quebec setting the standard for clean energy production. By increasing the proportion of wind and solar energy in its energy mix, the UK has decreased its dependency on coal and achieved notable emissions reductions. By emphasising nuclear energy and investing in renewable technologies, France also helps reduce carbon emissions. Finally, China, the world's biggest manufacturer of solar panels, has greatly expanded its capacity for renewable energy by making impressive progress in deploying solar energy. Conversely, several G-20 nations still rely significantly on fossil fuels, making their sustainability objectives difficult to meet. For instance, the US remains a major consumer of fossil fuels, oil, and natural gas, which still account for most of the country's energy supply despite initiatives supporting renewable energy. Russia prioritises extracting and exporting fossil fuels due to its enormous oil and gas reserves, hindering its transition to cleaner energy sources. Australia is one of the top carbon emitters per capita despite its investments in renewable energy since it still primarily uses coal to generate electricity. Due to its economic dependence on the export of fossil fuels, Indonesia, a significant coal producer, faces obstacles in its transition to renewable energy. Lastly, despite its Vision 2030 initiative aimed at diversifying its economy, Saudi Arabia remains predominantly dependent on oil revenues, complicating its transition to renewable energy. Economic growth, environmental costs and excessive energy use must all be balanced by policymakers, even though energy conservation measures that reduce energy use may hinder growth. Policy Implications The challenge for policymakers is strengthening steps to promote economic development, provided green technologies and practices are pursued. It also means that we can grow without the assumption that economic growth equals greater carbon emissions, showcasing the potential to disassociate economic prosperity from environmental detriment. Policymakers across G20 countries focus on innovation and patents in R&D on carbon emissions and economic growth. G20 nations can lower greenhouse gas emissions, boost economic expansion and contribute to a future with sustainable energy by fostering innovation in the energy industry. Governments must consider energy technology development to advance clean energy and energy efficiency technologies. G20 nations should switch to renewable energy sources, boost energy efficiency in buildings, transportation and industry, encourage sustainable transportation options, implement carbon pricing mechanisms, and support climate finance for developing nations to support sustainable development and reduce emissions. Policy interventions are critical for navigating the tangled web of economic growth, carbon emissions and environmental sustainability. Governments can enact policies that support clean technologies, enforce carbon pricing mechanisms and set aggressive renewable energy targets. International cooperation is also necessary because environmental concerns transcend national borders, and joint actions must have a real global impact. Finally, the integrated and dynamic relationship between carbon emissions, economic growth, energy consumption, R&D expenditure, patents in environmental technology, energy use and renewables is demonstrated. A holistic approach incorporating technical innovation, policy assistance and ecological stewardship is required to achieve sustainable development. It is critical to balance economic prosperity and environmental sustainability to ensure a robust and peaceful future for the next generation. Based on our findings, we recommend a series of concrete actions that G20 policymakers can take to promote environmental sustainability. The first step is to create targeted funding mechanisms, which can be allocated to businesses engaging in product innovation and R&D in environmental-related technologies. Further tax benefits can be considered for companies that invest in green innovation or create grants to facilitate collaboration between universities and the industry. A one-stop-shop patent application process for environmental technologies would encourage more patents and future innovation. Furthermore, implementing policies to facilitate knowledge transfer and collaboration between the public and private sectors will ensure that innovative solutions are effectively integrated into practice. These specific and actionable recommendations aim to provide clear guidance for policymakers, helping to achieve the overarching goal of environmental sustainability. Limitations and Future Direction While this study provides valuable insights into the relationship between energy, innovation, economic growth, and carbon emissions within G20 countries, it is not without limitations. This study is limited by its use of historical data, and it cannot keep up with the pace of technological enhancement and policy evolution as they evolve. Moreover, the analysis is primarily concerned with aggregate G20 statistics, overlooking to acknowledge the unique contexts and needs that individual countries in the group confront. Future studies should take a more detailed approach, looking at case studies of particular G20 countries to better understand the various routes to sustainability. A more thorough grasp of the relationship between environmental sustainability and economic growth could also be obtained by adding qualitative analyses of stakeholder perspectives and policy frameworks. Declarations Clinical Trial Number: Not applicable . Funding Declaration: This research did not receive any funding. Ethics Declaration: This study did not involve any procedures requiring ethical approval. Therefore, the ethics declaration is not applicable. Consent to Publish Declaration: This study does not include identifiable information from participants, so the consent to publish declaration is not applicable. Consent to Participate Declaration: This study did not involve human participants. Therefore, the consent-to-participate declaration is not applicable. 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Retrieved from https://www.mapchart.net/ Additional Declarations No competing interests reported. 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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-6040627","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434888135,"identity":"8d9e73f8-ed9e-48a1-989d-20a6854b0a1e","order_by":0,"name":"Ipsa Khanna","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Ipsa","middleName":"","lastName":"Khanna","suffix":""},{"id":434888136,"identity":"98be8c21-36dd-491e-84fe-75bdcd0616bf","order_by":1,"name":"Pooja Mehra","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Pooja","middleName":"","lastName":"Mehra","suffix":""},{"id":434888137,"identity":"91d67c0a-1a4f-4c12-afaa-497c4e73ee14","order_by":2,"name":"Saurabh Verma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYPACGwY2IMkMZh8AI4IgjXQth8EkXAteID8j+dinGzXn8/nYjz/+XFDBIMd3I4HxcAEeLQY30pJn5xy7bdnGk2MmPeMMg7HkjQSGwzPwaeE5Y8ycw3bbgE2Ch42Zt40hcQNICw8+h/Wc/8yc8+8cUAv748+8/xjqCWphON7DzJzbdgCohcFAmreBIcGAkBaD423GzLl9yQZsYL8ckzCceeZhA36HNTM/Zs75Zmcg3w4KsRobeb7jyYc/43UYGpAAYsYGEjSMglEwCkbBKMAGAEdiSE/P2RCKAAAAAElFTkSuQmCC","orcid":"","institution":"Amity University","correspondingAuthor":true,"prefix":"","firstName":"Saurabh","middleName":"","lastName":"Verma","suffix":""}],"badges":[],"createdAt":"2025-02-16 10:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6040627/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6040627/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43621-025-01513-1","type":"published","date":"2025-07-21T15:56:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79600187,"identity":"b107c5e8-f850-4f62-9e45-cc6407ac197e","added_by":"auto","created_at":"2025-03-31 15:00:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":473620,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical representation of G-20 -Group of Twenty Countries\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSource: MapChart. (2025). Customized World Map [Online Image]. Retrieved from https://www.mapchart.net/ [73]\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6040627/v1/5ede1543495425d543244682.png"},{"id":79600923,"identity":"1183fc09-df6b-4ff9-a2bb-78538ec8d2ec","added_by":"auto","created_at":"2025-03-31 15:08:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40504,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFlow diagram of the Analytical Tools\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThis figure illustrates the sequential steps in the study's statistical analysis, detailing the analytical tools employed to examine the relationships among economic growth, energy consumption, and environmental sustainability in G-20 countries. The flow diagram outlines the progression from descriptive statistics and panel unit root tests to cross-sectional dependence assessments, panel cointegration tests, ANOVA tests, panel causality tests, and finally, panel quantile regression. This visual representation aids in understanding the methodological framework and the comprehensive approach taken in the analysis.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6040627/v1/3c969ea94d9878525d3b6f07.png"},{"id":79601599,"identity":"531378a5-0b64-4a2e-91f2-56e5124c9f5d","added_by":"auto","created_at":"2025-03-31 15:16:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":257119,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDiagrammatic Representation of D-H Panel Granger Causality Test Results\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThis figure graphically shows the Dumitrescu-Hurlin (D-H) Panel Granger Causality Test results, showing the causal relationships between the study’s crucial variables. The diagram features arrows indicating the direction of causality: unidirectional arrows represent a one-way influence from one variable to another, while bidirectional arrows indicate a mutual influence between variables.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6040627/v1/c08bc952b9ab852b9afde3b4.png"},{"id":87756530,"identity":"0cbb0dba-febe-4bd8-8f5f-64978b0a81a0","added_by":"auto","created_at":"2025-07-28 15:59:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2344302,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6040627/v1/25abec64-a3f7-4c89-9b86-c69e66e0f386.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Towards Achieving Environmental Sustainability: Role of Energy, Green Innovation and Economic Growth in G-20 Countries","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eClimate change and resource depletion have made environmental sustainability at the forefront of the global agenda. The United Nations Sustainable Development Goals Report 2024 emphasises the vital significance of sustainable consumption and production patterns, indicating that between 2019 and 2023, 62 Member States and the European Union implemented over 500 policy instruments that aimed at accelerating this transition [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, achieving the Sustainable Development Goals (SDGs) remains a challenging task, with only 17 percent of targets on track and nearly half showing minimal or moderate progress due to the compounded impacts of the COVID-19 pandemic, escalating conflicts, and climate-related challenges [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. This study highlights the critical need for innovative strategies and robust international cooperation to address these systemic deficiencies.\u003c/p\u003e\n\u003cp\u003eEstablished in 2015, the Paris Agreement is the principal foundation for global climate action. To restrict global warming below 2\u0026deg;C while exerting efforts to limit the increase to 1.5\u0026deg;C, the agreement underlines the transition to renewable energy as a focus for climate change mitigation in the light of environmental protection. The OECD Global Corporate Sustainability Report 2024 indicates a growing awareness among companies of their responsibilities towards environmental stewardship. Corporations progressively integrate sustainability into their governance processes to improve their long-term economic viability by disclosing greenhouse gas emissions and pursuing plans aligning with climate resilience. [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. This change in businesses highlights the connection between economic growth, environmental sustainability, and corporate governance.\u003c/p\u003e\n\u003cp\u003eInternational conferences, particularly COP27, COP28, and COP29, have helped to achieve important milestones in global climate action. In a critical recognition of climate justice, the COP27 in Egypt placed a strong emphasis on the creation of a loss and damage fund to assist vulnerable countries [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. The Mitigation Work Programme was introduced at COP28 in Dubai to strengthen national commitments and accelerate the shift away from fossil fuels, which still accounted for 80% of global energy consumption in 2022 [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. This further broadened the global climate agenda. A collective commitment to renewable energy infrastructure was demonstrated at COP29 with the Global Energy Storage and Grids Pledge, which set a goal of deploying 1500 GW of energy storage by 2030 [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. These gatherings demonstrate the growing acceptance of renewable energy as a vital component of sustainable growth.\u003c/p\u003e\n\u003cp\u003eThe United Nations Emissions Gap Report 2024 emphasises how urgent the switch to renewable energy is. To meet the targets of the Paris Agreement, global greenhouse gas emissions must be reduced by 42% by 2030 after reaching an astounding 57. 1 gigatons of carbon dioxide equivalent in 2023 [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. The G20 countries have a crucial obligation because they are responsible for 77% of global emissions. To promote innovation and improve energy efficiency, these countries must prioritise funding for research and development of renewable energy sources and implement all-encompassing policies. The report also stresses the importance of a policy mix that includes regulatory actions and carbon pricing subsidies to promote the adoption of renewable energy technologies and energy efficiency standards [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe rise of renewable energy technologies has witnessed substantial advancements, such as considerable cost reductions and substantial growth in market accessibility. Although, systemic obstacles, such as governance issues and limited international financial support, especially for developing countries, hinder progress toward sustainability goals, overcoming these barriers is possible through the redesign of international economic architecture that would generate funds for a low-carbon transition [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. The integration of national networks with renewable energy offers enhanced energy efficiency, which will help combat the detrimental effect of climate and promote economic resilience and energy security.\u003c/p\u003e\n\u003cp\u003eThe interaction between biodiversity, climate mitigation efforts, and sustainable development is another vital component of environmental sustainability. The Global Biodiversity Framework, Montreal 2022, aligns with the Paris Agreement\u0026apos;s goals and integrates biodiversity conservation into climate strategies while calling for a holistic approach. This synergy is crucial for dealing with climate, ecosystem degradation, and social equity issues [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. Such synergies are crucial to addressing the complex challenges associated with climate change, ecosystem degradation, and social equity. Additionally, initiatives such as the Energy Compacts launched at COP28, aim to fast-track investment in clean and affordable energy, especially for resource-limited regions, and to emphasise the importance of equity in the global energy transition [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eGovernmental and corporate efforts also highlight the possibility of attaining environmental sustainability. Businesses increasingly see sustainability as a strategic advantage, according to the OECD report, and are coordinating corporate governance with ecological objectives to mitigate climate risks and boost competitiveness [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. Initiatives like the COP29 Breakthrough Agenda, in which 61 nations accounting for 80% of global emissions pledged to take priority measures to reduce their carbon footprints, also demonstrate the increasing momentum for coordinated global climate action [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eHowever, achieving environmental sustainability is fraught with challenges. The World Bank\u0026apos;s FY24 Climate-Related Financial Disclosures emphasize the urgent need for investments in renewable energy projects and innovation to fill in the gaps in energy infrastructure and lower emissions. According to the report, G20 countries should set an example by encouraging green innovation and making sure that economic expansion is consistent with environmental sustainability [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. To close the emissions gap, it is also necessary to double global energy efficiency gains by 2030 and triple renewable energy capacity to meet the ambitious goals outlined in the Emissions Gap Report 2024 [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe rising frequency and severity of climate-related disasters like storms, heat waves, and wildfires further highlight the need for swift and coordinated action. These incidents provide a clear reminder of the disastrous results of inaction and the urgent need for preventative actions to lessen the effects of climate change. To effectively address the climate crisis, countries nationally determined contributions (NDCs) for COP30 in Brazil must demonstrate increased ambition and urgency [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eFor environmental sustainability, a cooperative, multifaceted strategy that incorporates international cooperation, backs sensible policies, and generates cutting-edge technologies will resolve climate change issues and preserve our planet for future generations by combining renewable energy biodiversity preservation and corporate sustainability. In the framework of economic growth that respects environmental conservation, investing in clean energy, fostering research and development, and formulating the appropriate policies will open the door for revolutionary change.\u003c/p\u003e\n\u003cp\u003eThe G20 ranks among the foremost economic, financial, and political forums and consists of 19 major economies across various continents: Asia, Europe, North and South America, the Middle East, and Oceania as represented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The G20 countries consist of developed and developing nations and represent a significant share of global GDP, population, and energy consumption [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. The G20 is an international alliance of the world\u0026apos;s 20 largest economies, including the United States, Canada, Australia, Germany, China, France, Russia, India, Saudi Arabia, Indonesia, Argentina, South Africa, Italy, Brazil, Turkey, United Kingdom, Japan, South Korea, Mexico and the European Union [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Sustainable development is impossible without its global expansion. Economic growth can support the 2030 Agenda across various sustainable development goals when combined with appropriate policies. The G20 shapes international economic and environmental policies, particularly in achieving sustainable and balanced growth. This study aims to explore the interplay between energy, innovation, and economic growth within G20 countries, highlighting how these factors contribute to the broader goals of sustainability and carbon emission reduction [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eWhile global economic and population expansion significantly contributes to increased CO2 emissions from fossil fuel combustion, additional factors such as technological advancements, energy policies, and shifts in the energy mix must be considered. Recent literature highlights that innovations in energy efficiency and the transition to renewable energy sources can mitigate emissions, particularly in G20 countries, where diverse energy policies and economic structures influence emissions trajectories [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. The literature claims that an increase in energy use accompanies increased economic activity. This increase in energy use damages the environment through the release of carbon dioxide [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. The main causes include an increase in energy demand brought on by the rapid expansion of the economy and population, as well as a rise in the consumption of fossil fuels [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThis study specifically intends to analyse the role of energy efficiency and the reduction of fugitive emissions within G20 countries to effectively reduce CO2 emissions from the energy sector while addressing the intertwined challenges of economic growth, energy security, and environmental sustainability. By examining how improvements in energy efficiency can lower carbon emissions and foster sustainable economic growth, the study will provide particular insights into the innovations and policies that can facilitate this shift. This focus highlights how urgently better energy practices are needed and advances the study\u0026apos;s objective of figuring out how G20 nations can achieve their sustainability goals without compromising their economic performance. Moving away from fossil fuels and toward low-carbon technologies like renewable energy sources is also essential [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. The G20 countries should prioritise research and development projects that concentrate on energy storage, renewable energy technologies, and sustainable practices because they understand how crucial innovation is to reaching sustainability goals. Through cultivating an innovative culture, G20 nations can tackle their environmental issues and support international initiatives to tackle climate change and advance sustainable development.\u003c/p\u003e\n\u003cp\u003eEnergy efficiency, which includes energy conservation, is a long-term G20 objective since it results in the best use of available energy sources. Enhancing energy efficiency collaboration can lead to better environmental outcomes increasing economic activity and productivity, according to the G20 countries [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. The goal of reducing carbon emissions while maintaining or boosting economic growth is a challenge for policymakers in the G20 because each country has a different economic structure, degree of development, and environmental priorities. There are wide variations in the trade-offs between lowering emissions and improving economic performance because some nations may prioritise short-term economic expansion more than long-term sustainability. Others, however, could spend money on renewable energy sources and greener technologies.\u003c/p\u003e\n\u003cp\u003eThis study will examine these intricacies emphasizing how various G20 countries can manage their particular opportunities and difficulties in the pursuit of sustainable development. Innovation can be pivotal in reducing emissions[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. Although much research has been done on the connection between innovation and economic growth in G20 nations, there are still unanswered questions about how environmental technologies affect economic performance and carbon emissions. In light of the various economic structures and stages of development of G20 countries, this study seeks to expand on previous research by examining the relationship between energy innovation carbon emissions and economic growth. In the G20 context, the research will advance a more sophisticated understanding of how innovation can propel sustainable development by identifying these particular gaps [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. The G20 should pledge to participate more actively in several multilateral initiatives, particularly through many cooperative venues. As part of the partnership on energy transitions, the G20 should create an energy innovation agenda [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe G20 nations can support the global energy transition with their governments\u0026apos; backing in sustainable economic development and cleaner energy. Energy transition goals include diversifying and modernising the economy, improving air quality, reducing climate change, and increasing energy security by securing energy access and reducing import dependency. The transitions in the G20 energy system are based on energy sources and technologies because the national resources of the G20 economies vary, as do GDP growth, per capita energy demand, and emissions [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. The G20 countries\u0026apos; energy infrastructure must change to accommodate shifting energy consumption patterns, rising shares of variable renewable energy, new opportunities offered by EVs and increased digitalisation of the energy industry. In the energy transition system, energy security remains a top priority.\u003c/p\u003e\n\u003cp\u003eThe research study is highly relevant for G-20 countries as it comprises the world\u0026apos;s major economies, representing a significant share of global GDP, population and energy consumption. G-20 countries are heavily dependent on energy resources to fuel their economies. Studying energy innovation helps diversify energy sources, reduce reliance on fossil fuels and enhance energy security. Research and development in energy technologies lead to breakthroughs in areas such as solar power, wind energy, energy storage and electric vehicles. These advancements have wide-ranging applications beyond the energy sector and can contribute to economic growth through technology transfer, patent creation and commercialisation. Researching the relationship between carbon emissions, economic growth, and innovation in G20 nations is critical to uncover trade-offs and synergies between these aspects and assist in guiding policy decisions. This study aims to analyse the relationship between carbon emissions, energy, innovation and economic growth.\u003c/p\u003e\n\u003cp\u003eLee (2005) [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] studied the relationship between energy use and GDP in 18 developing economies between 1975 and 2001. He discovered support for the growth hypothesis using panel-based cointegration error correction models. Energy consumption drives GDP growth over the long and short term. Therefore, restrictive energy policies may limit economic growth in developing nations.\u003c/p\u003e\n\u003cp\u003eAccording to Canton et al. (2005) [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e], human behaviour, such as educational attainment, and economic and technological factors, such as innovation and R\u0026amp;D intensity, impact the economic growth of nations. As stated by Cinnirella \u0026amp; Streb (2013), first, as skilled labour, human capital can directly increase total factor productivity. Second, it could encourage businesses to adopt new technologies through inventions, imitative practices or other technological activities [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eIn France, Austria, Denmark, and Belgium, among other nations, Soytas and Sari (2006) found a unidirectional relationship between economic growth and energy consumption. This result aligns with conservation theory, which holds that rising economic activity increases energy consumption. Their study emphasises the vital role that energy consumption plays in propelling economic growth by concentrating mostly on the correlation between these two variables. Nevertheless, this method fails to consider the possible impact of innovation or technological developments on energy consumption, a crucial element that the proposed study seeks to investigate in greater detail [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eR\u0026amp;D activities can result in innovation as a prerequisite for technical advancement, which will decide economic growth in a Schumpeterian creative destruction process, as demonstrated by Aghion \u0026amp; Howitt (2008) [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] in their model of Schumpeterian endogenous growth. Through his Schumpeterian analysis, Fagerberg (2010) also discovered that innovation becomes essential for sustained economic growth [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn order for policymakers to create effective energy policies that support sustainable economic growth, Costantini and Martini (2010) examined the complex relationship between economic growth and energy consumption, establishing the causal linkages at the sectoral level across different countries. The study\u0026apos;s primary goals were to empirically analyse these causal relationships across sectors such as industry, services, transport, and residential while examining the impact of energy prices and public regulations on energy demand and economic performance, particularly highlighting differences between developed and developing countries. Using a multivariate econometric framework, the authors estimated energy demand functions and examined mutual causality relationships between variables by applying panel cointegration techniques to a dataset of 71 countries split into OECD and non-OECD groups. The research uncovered Significant insights suggesting that knowledge of these causal relationships could guide public policy, particularly concerning energy taxation and regulation. The study\u0026apos;s extensive dataset was one of its distinctive features as it improved the analysis of energy-growth dynamics. Concurrently, incorporating sector-specific energy prices clarified how energy costs affected consumption trends. However, the study acknowledged certain shortcomings, including difficulty determining causal effects and reliance on historical data, which may not have adequately represented the dynamic character of energy markets. In conclusion, the research offered valuable insights into the energy consumption-economic growth nexus, emphasising the importance of considering energy prices and public regulations. As a basis for further study in sectoral energy models and the effects of energy policies on economic performance, it proposed that although increases in energy efficiency were necessary for sustainable growth, policymakers also needed to be aware of possible rebound effects and structural variations among nations [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eAn important study by Baek and Kim (2011) examined the connections between trade liberalisation, economic expansion, energy use, and CO2 emissions in G-20 economies. By incorporating these dimensions, the study sought to fill a gap in the literature by empirically evaluating the ways in which these interrelated factors affected environmental quality. The methodology employed was robust, utilising Johansen cointegration analysis to examine long-term relationships among non-stationary time series data. The authors analysed annual data from 1960 to 2006, focusing on per capita CO2 emissions as a proxy for environmental quality, real GDP per capita for income, energy consumption per capita for energy use, and trade openness defined as the ratio of total trade to GDP. By converting all variables to natural logarithms, the analysis facilitated a clearer understanding of the relationships. Key findings indicated that trade and income growth improved environmental quality in developed countries, while they had adverse effects in developing nations, where increased economic activity exacerbated environmental degradation. In every economy examined, energy use has deteriorated environmental quality. The study emphasized the necessity of customized policies that took into account the distinct circumstances of both developed and developing nations [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eSimilarly, Narayan and Popp (2012) also identified a unidirectional relationship between economic growth and energy use, reinforcing the conclusions drawn by Soytas and Sari. Their study advances our knowledge of this dynamic by looking at a larger number of nations and using reliable econometric methods to support their conclusions. Although their research sheds important light on the relationship between economic expansion and energy use, it mostly examines the historical relationship without considering innovation or improvements in energy efficiency. By combining metrics pertaining to research and development (R\u0026amp;D) and environmental technology patents, the proposed study aims to expand on these seminal studies and examine how technological developments can affect patterns of energy consumption and economic performance. By revealing the potential for innovation to disentangle economic growth from energy consumption, this methodological change seeks to provide a more thorough understanding of sustainable development and to inform policy choices that seek to maximize economic growth while minimizing environmental effects [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eYildirim \u0026amp; Aslan (2012) employed the Toda-Yamamoto technique and bootstrap-corrected causality tests to examine the relationship between energy use and economic growth across 17 OECD nations. Their findings revealed a reciprocal relationship indicating that economic growth affects energy use in addition to GDP. This insight emphasizes how complex the relationship is between energy and growth, suggesting that policies meant to improve energy efficiency should take this relationship\u0026apos;s reciprocal nature into account [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eWang et al. (2012) made a distinction between carbon-free and fossil-fueled energy technologies and looked at the relationship between energy technology and CO2 emissions for 30 provinces in Mainland China from 1997 to 2008. Based on dynamic panel data, they discovered that, in contrast to energy technologies powered by fossil fuels, carbon-free ones assisted in lowering CO2 emissions, particularly in Eastern China [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eNasreen and Anwar (2014) investigated the relationship between economic performance, energy use, and trade openness for 15 Asian nations using panel unit root and panel cointegration tests. The findings show that trade openness, energy consumption, and economic growth are causally related in both directions [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eKasman \u0026amp; Duman (2015) used panel data from 15 new EU-member and candidate nations covering 1992\u0026ndash;2010 to describe the inverted U-shaped EKC for GDP and CO2 emissions. In addition to using trade openness and energy consumption as explanatory variables, they also employ urbanisation, which measures the proportion of the population that lives in cities. They contend that nations with a larger proportion of urban residents generate more emissions than those with a smaller proportion [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eWang et al. (2016) examined the complex interrelationships between ASEAN nation\u0026apos;s urbanisation energy consumption and carbon emissions, highlighting the urgent need to comprehend the effects of urban growth on environmental quality in the face of the region\u0026rsquo;s rapid urbanisation. The study\u0026apos;s main goals were to evaluate the causal links between urbanization and carbon emissions, the contribution of energy consumption to this relationship, and the provision of empirical data to support policy decisions for sustainable urban development. The methodology employed included a series of panel unit root tests to examine the stationary properties of the variables, followed by panel cointegration tests to identify long-run equilibrium relationships and the use of Fully Modified Ordinary Least Squares (FMOLS) for estimating these relationships. The study found a significant positive relationship between urbanisation and carbon emissions, indicating that a 1% increase in urban population correlated with a 0.20% rise in carbon emissions, thus supporting the Environmental Kuznets Curve (EKC) hypothesis within the STIRPAT framework. The analysis emphasised the significance of patterns in energy consumption, indicating that carbon output was significantly influenced by the energy used in urban areas. In order to reduce carbon emissions, the study advised policymakers and urban planners to prioritize sensible urban development and effective energy use supporting mixed-use zoning, better public transit and energy-efficient technologies. Two distinctive features of the study were its emphasis on ASEAN nations, which had received little attention in previous research, and its all-encompassing methodology, which integrated several econometric approaches to analyse the data. However, The study recognised certain difficulties, including the possible drawbacks of short data periods in analyses of individual nations and the complexity of the relationship between urbanization and carbon emissions, which may differ depending on the context. In summary, the study revealed important information about the dynamics of urbanization and its effects on the environment, highlighting the necessity of focused policies to support sustainable urban growth and deal with the ASEAN region\u0026apos;s carbon emission problems[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eSaidi and Ben Mbarek (2016) investigate the complex connections between CO2 emissions, nuclear energy use, renewable energy, and economic growth in nine developed countries between 1990 and 2013. Examining the causal relationships between these variables is the study\u0026apos;s main goal, which will advance knowledge of how energy use affects both environmental sustainability and economic performance. Unit root tests, panel cointegration tests, and Granger causality tests are used in the dynamic panel data analysis methodology to ascertain the variables\u0026apos; short- and long-term relationships. Important conclusions show that nuclear energy use and economic expansion are significantly correlated in some countries, which shows evidence of bidirectional causality. It further suggests that rising nuclear energy use may spur economic expansion, increasing energy consumption. The study emphasises how nuclear energy can be a good substitute for fossil fuels and shows how it can lower CO2 emissions while promoting economic growth. In order to accomplish sustainable economic growth and environmental objectives, the authors advise policymakers to consider nuclear energy as a strategic element in energy planning. The study\u0026apos;s focus on developed countries, which frequently display distinct dynamics from developing ones, and its comprehensive panel approach, which captures broader relationships across various national contexts, are two of its unique features. However, the study also recognises certain difficulties, such as the possibility of omitted variable bias and the requirement for more detailed data to capture the subtleties of energy consumption patterns. Finally, highlighting the vital role that nuclear energy plays in accomplishing both economic and environmental goals, Saidi and Ben Mbarek (2016) offer insightful analysis of the intricate relationships between energy consumption and economic growth. The results emphasize how crucial it is to have well-informed energy policies that use nuclear power to promote sustainable development and lessen the effects of climate change. This research contributes significantly to the literature on energy economics and serves as a reference point for future studies exploring the nexus of energy consumption, economic growth, and environmental sustainability [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eEmploying the unit root test with structural breaks developed by Clemente et al. (1998) [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e], Magazzino (2017) looked at the stationary properties of per capita energy use for the EU-19 countries during the years 1960\u0026ndash;2013 [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. The panel unit-root test results demonstrate that energy use is non-stationary in nearly all EU-19 nations. Using the Vector Auto Regression (VAR) method, Magazzino (2017)[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] examined the relationship between energy use, GDP and carbon emissions in the APEC region. The findings indicate no direct link between GDP and energy use. Hasanov et al. (2017)[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e] examined the relationship between energy and economic growth in ten emerging Eurasian nations that export oil. The results showed that the main energy consumption-growth nexus supports the growth theory. The increase in domestic electricity usage and the results demonstrate the validity of the neutrality hypothesis.\u003c/p\u003e\n\u003cp\u003eFern\u0026aacute;ndez Fern\u0026aacute;ndez et al. (2017) addressed the critical objective of achieving sustainable economic growth while stabilising or reducing greenhouse gas emissions, particularly CO2 emissions. The primary goal of the study was to empirically verify the positive effects of innovation, specifically through research and development (R\u0026amp;D) spending, on reducing CO2 emissions across three distinct regions: the European Union (EU-15), the United States, and China, during the period from 1990 to 2013. An econometric model estimated using ordinary least squares (OLS) regression was part of the methodology used to examine the connection between R\u0026amp;D spending energy use and CO2 emissions. The need for a multifaceted approach was highlighted by key findings that showed that although public spending on R\u0026amp;D was crucial, it was insufficient to improve the innovation process or significantly reduce emissions. The study emphasised the importance of integrating public and private innovation efforts and suggested that future research should also consider the impact of patent activity as a measure of private innovation. The study was unique because it focused on the relationship between innovation and environmental sustainability, highlighting the potential for innovative processes to result in more ecologically friendly and energy-efficient products across various industries. However, the study recognised issues like data limitations that limited the analysis to a specific time period and three regions, indicating that larger datasets and longer time horizons could be advantageous for future research. In order to effectively combat environmental pollution and accomplish sustainable development goals, the study concluded that R\u0026amp;D spending must be promoted across all economic sectors. It also urged more investigation into public and private innovation dynamics in the fight against climate change [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eCheng \u0026amp; Co. (2019) examined the connection between CO2 emissions, renewable energy, and environmental patents in the BRICS nations (Brazil, Russia, India, Indonesia, China, and South Africa) between 2000 and 2013, emphasising the urgent need for successful climate change policies in developing nations. While addressing the shortcomings of conventional regression techniques, which frequently ignored individual and distributional heterogeneity, the study\u0026apos;s main goals were to investigate the effects of renewable energy supply environmental patents and other economic factors on carbon emissions. The fixed-effect panel quantile regression approach was the methodology used, which captured the heterogeneous effects that traditional methods frequently overlooked and allowed for a nuanced analysis of how different factors affected CO2 emissions across various quantiles. Important conclusions showed that although the BRICS country\u0026apos;s use of renewable energy and environmental patents had grown, CO2 emissions were still rising, indicating that these factors alone were not enough to reduce emissions. To successfully reduce emissions, the study emphasised the need for a multipronged strategy that includes encouraging renewable energy, creating environmental regulations, and modifying economic structures. The study\u0026apos;s use of the fixed-effect panel quantile regression approach, which addressed the drawbacks of earlier research that mostly concentrated on mean effects, was one of its distinctive features. However, The study recognised some challenges, such as the lack of data on environmental patents and the challenge of estimating their direct impact on emissions. For the BRICS countries to achieve sustainable development, the study\u0026rsquo;s conclusion emphasised the importance of integrating technological advancements like environmental patents into frameworks for climate policy and encouraging a switch to renewable energy sources. The results filled in knowledge gaps about how technology affects carbon emissions and offered practical policy suggestions for developing nations [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eSharif et al. (2019) investigated how carbon emissions and energy consumption types varied across 74 countries between 1990 and 2015. This study was essential for tackling the world\u0026apos;s environmental problems because it offered factual data on the effects of various energy use patterns on carbon emissions, which helped shape policy choices to slow down environmental deterioration. The primary objectives included examining the long-run elasticity of renewable and non-renewable energy consumption concerning carbon emissions and assessing the role of financial development in this dynamic. The researchers used sophisticated econometric techniques to analyse cross-sectional independence and heterogeneity among nations, including the CIPS unit root test, Westerlund bootstrap cointegration, Pedroni cointegration, FMOLS, and heterogeneous panel causality methods. While renewable energy consumption significantly decreased carbon emissions, non-renewable energy consumption positively impacted environmental degradation, according to key findings. Financial development also affected environmental degradation, indicating that sustainability and economic growth could coexist. With a focus on encouraging renewable sources for sustainable economic growth, the study suggested incorporating renewable energy policies into national strategies to mitigate carbon emissions effectively. This study uniquely used heterogeneous panel analysis to account for cross-sectional dependence and provide a more nuanced understanding of the relationship between energy and the environment. However, the study acknowledged issues such as inconsistent data and the difficulty of capturing all relevant variables influencing carbon emissions that might have limited the generalizability of the findings. The study provided insightful information about the relationship between carbon emissions and energy consumption types, emphasising the vital role that renewable energy plays in advancing environmental sustainability. The findings underscored the need for targeted policies encouraging the transition to renewable energy sources, contributing to global efforts to combat climate change and foster sustainable development [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eMunir et al. studied the EKC for the five major ASEAN countries (Indonesia, Malaysia, the Philippines, Singapore, and Thailand) between 1980 and 2016. Further, in 2020, the analysis of carbon dioxide emissions data demonstrates the presence of the EKC in the sample [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]Notably, the author\u0026apos;s study indicates that three sample countries\u0026mdash;Malaysia, the Philippines, and Thailand\u0026mdash;are below the EKC\u0026apos;s turning point. This suggests that further economic growth in these nations will increase emissions.\u003c/p\u003e\n\u003cp\u003eSaidi and Omri (2020) investigated the intricate relationship between renewable energy consumption (REC), economic growth, and carbon emissions in 15 major renewable energy-consuming countries from 1990 to 2014, emphasising the dual role of renewable energy in fostering economic development while mitigating environmental degradation. The main goal was to close the gap in the literature by showing how renewable energy could successfully lower carbon emissions and promote economic growth. In earlier research, this relationship had not been thoroughly examined. A thorough examination of the variables\u0026apos; short- and long-term relationships was possible using fully modified ordinary least squares (FMOLS) and vector error correction model (VECM) estimation techniques. The key findings demonstrated that economic growth and the use of renewable energy were causally related in both directions, indicating that renewable energy promoted and facilitated economic growth. The study suggested that supporting renewable energy could have significant positive effects on the economy and the environment and underlined the importance of integrating economic and environmental factors into energy policy. Focusing on the main nations that use renewable energy and thoroughly examining the relationship between REC economic growth and carbon emissions within a single framework were two distinctive features of the study. However, the study also recognized some difficulties and constraints, like the possible unpredictability of data quality and the need for more investigation into policy thresholds that maximize the advantages of renewable energy sources without sacrificing environmental quality. The study\u0026rsquo;s conclusion emphasized how important renewable energy is to attain environmental sustainability and sustainable economic growth. It advocated for policy measures that supported the development and accessibility of renewable energy technologies to enhance both economic and ecological outcomes [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eWang and Zhang (2020) focused on the relationship between research and development (R\u0026amp;D) investment and its impact on economic growth and carbon emissions in BRICS countries (Brazil, Russia, India, China, and South Africa) from 1996 to 2014. The study was relevant for exploring sustainable development pathways, particularly in developing economies where economic growth often correlates with increased carbon emissions. The primary objectives were to analyse whether increased R\u0026amp;D investment contributed to economic growth decoupling from carbon emissions and to assess the role of R\u0026amp;D in promoting sustainable development. The methodology included the Tapio decoupling model to evaluate decoupling status alongside various econometric techniques such as unit root tests, cointegration tests, FMOLS regression estimation, and Granger causality tests used to examine the connection between carbon emissions and RandD investment. Important conclusions showed that the BRICS country\u0026apos;s capacities for decoupling differed, with China, South Africa, and Russia exhibiting stronger capabilities than Brazil and India. The analysis demonstrated the critical role of research and development in attaining sustainable development, showing that a 1% increase in R\u0026amp;D investment resulted in an 8122 per cent reduction in carbon emissions. The study suggested that to promote economic growth and reduce carbon emissions, R\u0026amp;D investment should be increased, industrial structures should be optimised, and energy consumption should be shifted toward renewable sources. One of the study\u0026apos;s distinctive features was its emphasis on the BRICS countries collectively, which shed light on how environmental sustainability and economic growth interact in developing economies. However, The study acknowledged certain difficulties that could compromise the accuracy of the findings, such as possible cross-sectional dependence among the nations. The study highlighted the value of R\u0026amp;D spending in promoting economic expansion independent of carbon emissions. In order to accomplish long-term environmental objectives, it promoted legislative actions that support sustainable industrial practices and renewable energy. The results provided policymakers in developing nations with a useful framework for creating strategies that effectively reduce carbon emissions and support global sustainable development [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eBehera et al. (2024) looked into the intricate relationship between energy consumption and economic growth in developing nations like India, highlighting both renewable and non-renewable energy sources\u0026apos; role in attaining sustainable development. The study\u0026apos;s main goals were to provide policy recommendations that matched energy choices with sustainable development goals and investigate the effect of disaggregated energy consumption on India\u0026apos;s economic growth between 1985 and 2021. The authors employed an autoregressive distributed lag (ARDL) bound testing approach to analyse the short-run and long-run effects of various energy sources on economic growth, complemented by variance decomposition analysis (VDA) to assess the influence of energy consumption on economic variables. Key findings revealed that non-renewable energy consumption led to a more prominent role in driving economic growth than renewable energy sources, suggesting that while renewable energy was essential for sustainability, immediate economic growth was more closely tied to non-renewable energy. The report emphasised the need for more funding for clean energy technologies to slow environmental deterioration and promote economic expansion. A distinctive feature of the study was its emphasis on disaggregated energy sources, which addressed a gap in the literature by offering a sophisticated understanding of how various forms of energy influenced economic performance. However, The study recognised shortcomings, such as the absence of information on specific renewable energy sources, such as solar and wind, which would have allowed for a more thorough analysis. While ensuring that energy-saving measures do not impede economic growth, the authors advised policymakers to prioritise renewable energy integration in the energy mix. The study concluded that policies supporting the shift to renewable energy while acknowledging the current reliance on non-renewable sources for economic growth were necessary to highlight the significance of striking a balance between energy consumption and environmental sustainability. This study underlines the need for strategic energy policies that align with economic and environmental goals, providing insightful information about the relationship between energy and economic growth in India[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eDegirmenci and colleagues. (2024) looked at the intricate relationships between energy intensity, energy depletion, the green energy transition, and the strictness of environmental policies in the G7 between 1990 and 2020. The main goal was to go beyond conventional analyses that frequently separated these factors and examine how they all affected environmental sustainability. To guarantee the reliability of the results, the methodology included a thorough empirical approach using panel data analysis, specifically looking at cross-sectional dependence and slope homogeneity. Sophistic econometric methods were used to analyse the relationships between the variables, including the CCEMG and AMG long-run estimators. Significant findings demonstrated that although stringent environmental regulations and the shift to green energy supported sustainability initiatives, energy intensity and depletion negatively impacted ecological quality. By considering the load capacity factor, which integrates the supply and demand aspects of natural resources, the analysis showed that although individual studies frequently concentrated on carbon emissions, this research emphasized the significance of a more comprehensive approach. The study suggested that policymakers could increase the efficacy of environmental policies by encouraging green technologies and enacting stronger laws to reduce pollution. A more nuanced understanding of environmental sustainability was provided by the research\u0026rsquo;s unique focus on the interrelatedness of the examined variables, which had been mainly ignored in earlier studies. However, the study\u0026apos;s focus on the G7 countries may have limited the scope of data, making it difficult to represent global dynamics fully. Furthermore, if results rely too much on historical data, they might not be as applicable to rapidly changing environmental contexts. The study\u0026apos;s findings encouraged a full understanding of how energy policies and practices interact to affect ecological outcomes and underlined the pressing need for integrated approaches in environmental research. In addition to encouraging green energy transitions and strict policies, it emphasized that tackling energy intensity and depletion is crucial to reaching long-term sustainability goals [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eBehera et al. (2024) concentrate on the crucial nexus between environmental sustainability and economic growth in the BRICS countries (Brazil, Russia, India, China and South Africa). Given the growing ecological footprints of urbanisation and industrialisation, this document\u0026apos;s significance stems from examining how these nations can meet sustainable development goals. The study\u0026apos;s primary objectives were to assess the impact of renewable energy sources, specifically hydro and nuclear energy, on ecological footprints, evaluate the role of green technology innovation (GTI), and analyse the influence of political stability on environmental quality. The methodology employed a panel dataset from 1993 to 2022, utilising ecological footprint per capita as a key indicator alongside independent variables such as hydro energy consumption, nuclear energy consumption, GTI, and political stability. The study\u0026apos;s key findings revealed that while hydro and nuclear energy consumption positively contributed to reducing ecological footprints, the effectiveness of GTI in mitigating ecological impacts was found to be insignificant.\u003c/p\u003e\n\u003cp\u003eFurthermore, political stability was identified as a crucial factor for enacting coherent environmental policies, although its direct effect on ecological footprints was also deemed insignificant. The BRICS countries\u0026apos; governments should enact strict policies to support renewable energy sources and offer incentives for green technology research and development according to the applications of these findings. The study\u0026apos;s thorough examination of the interactions between energy use, technological advancement, and political considerations is one of its distinctive features. It provides a sophisticated understanding of these economies\u0026apos; difficulties as they shift to sustainability. However, the study also identified some drawbacks, such as the exclusion of additional renewable energy sources like solar and wind because of limitations in data availability, which could have offered a more comprehensive picture of the energy landscape. The study argues for a well-rounded approach that considers economic expansion and environmental conservation, emphasising the necessity of coordinated efforts in formulating policies to encourage sustainable practices and mitigate ecological issues [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cp\u003eThis study systematically examines the relationship between energy, innovation, carbon emissions, and the economic growth of G20 countries. The data sources utilised in this study include the World Bank, International Energy Agency (IEA), OECD (OECD Data Explorer), EnerData, and the BP Statistical Review of World Energy. These organisations are renowned for their reliability and extensive datasets, which are critical for analysing the energy and economic dynamics of the G20 nations. These sources were chosen because they employ strict data collection procedures and concentrate on global energy and economic statistics, guaranteeing that the information is accurate and pertinent to our research questions. These databases offer a wealth of historical data, enabling longitudinal analysis while upholding strict data consistency and accuracy standards. Annual data from G20 countries served as the basis for the study sample. Carbon Emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (RandD), Patents in Environmental Technologies (PAT), Per capita Energy Use (EU), and Per capita Renewables (REN) are the variables being used in this study. To capture various aspects of energy dynamics in G20 nations, the study includes both Per capita Energy Use (EU) and Energy Consumption per capita (EC).\u003c/p\u003e\n\u003cp\u003eRegarding energy consumption and efficiency, Per capita Energy Use (EU) measures how much energy is used per person. On the other hand, Energy Consumption per capita (EC) captures general trends in energy consumption and offers a more comprehensive view of total energy demand in relation to population size. By integrating both variables, the research seeks to provide a more nuanced understanding of energy dynamics, enabling a thorough examination of the relationship between energy consumption, carbon emissions, and economic growth.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescription of Variables and Data Source\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePurpose\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData Source\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (\u003cem\u003eGDP)\u003c/em\u003e (in USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo measure economic growth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Bank [\u003cspan\u003e48\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbon Emissions per capita (\u003cem\u003eCO2\u003c/em\u003e) (in metric tonnes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo assess environmental damage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOECD Data Explorer [\u003cspan\u003e49\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy Consumption per capita (\u003cem\u003eEC)\u003c/em\u003e (in gigajoules)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo evaluate the overall energy burden on each individual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnerData [\u003cspan\u003e50\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer capita Energy Use (\u003cem\u003eEU\u003c/em\u003e)\u003c/p\u003e\n \u003cp\u003e(in kWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo assess energy efficiency and consumption patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternational Energy Agency [\u003cspan\u003e51\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer capita Renewables (\u003cem\u003eREN\u003c/em\u003e)\u003c/p\u003e\n \u003cp\u003e(in kWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo measure the adoption of renewable energy sources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternational Renewable Energy Agency [\u003cspan\u003e52\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGross Expenditure on Research \u0026amp; Development (\u003cem\u003eR\u0026amp;D\u003c/em\u003e) (% share of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo capture the effect of innovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBP Statistical Review of World Energy [\u003cspan\u003e53\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of Patents (\u003cem\u003ePAT)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(in numbers)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo capture the effect of environmental-related technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOur World in Data [\u003cspan\u003e54\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eThis table provides a detailed overview of the variables used in the study, including Carbon Emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (R\u0026amp;D), Patents in Environmental Technologies (PAT), Per capita Energy Use (EU), and Per capita Renewables (REN). Each variable is accompanied by its measurement unit, a brief definition, and the corresponding data source. This table serves to clarify the key metrics employed in the analysis and their relevance to the study of economic growth, energy consumption, and environmental sustainability in G-20 countries.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 420 panel observations and 20 cross-sections are included in the dataset from 2000 to 2020, as shown in Table \u003cspan\u003e1\u003c/span\u003e. The time frame for this study, spanning from 2000 to 2020, was chosen to align with the availability of consistent and comprehensive data across all selected variables for G20 countries. This period captures significant global trends in energy consumption, innovation, and economic growth, particularly in response to climate change and technological advancements that have emerged in the 21st century. While earlier data may be available, focusing on this specific time frame effectively allows us to analyse the pre-pandemic economic landscape. Additionally, the decision to exclude data beyond 2020 was made to maintain a consistent dataset that reflects the dynamics of the period under study.\u003c/p\u003e\n\u003cp\u003eThe methodology employed in the analysis encompasses several econometric techniques, including Panel Unit Root tests, Panel Cointegration tests, ANOVA, Panel Causality tests, and Panel Quantile Regression. Each method was selected based on its specific strengths and relevance to the research objectives.\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were performed to provide a comprehensive overview of the G-20 panel data. This initial analysis revealed key trends and patterns, such as variations in energy consumption, GDP per capita, and carbon emissions across G-20 countries. For instance, the analysis may show that certain countries exhibit significantly higher carbon emissions per capita compared to others, which can inform subsequent econometric analyses by highlighting areas of concern or interest. This foundational understanding is crucial for contextualising the relationships explored in later tests.\u003c/p\u003e\n\u003cp\u003eThe selection of Panel Unit Root tests, such as the Levin, Lin \u0026amp; Chu (LLC) test and the Im, Pesaran, and Shin (IPS) test, is essential for determining the stationarity of the data series. Stationarity is a prerequisite for many econometric analyses, as non-stationary data can lead to spurious results. The LLC test is particularly effective for large panels with a common unit root process, while the IPS test accommodates heterogeneous panels, making it suitable for the diverse G-20 dataset. Establishing the presence of unit roots ensures that the subsequent analyses are based on reliable data.\u003c/p\u003e\n\u003cp\u003eFollowing the unit root tests, Panel Cointegration tests are employed to examine the long-run relationships among the variables. This step is critical because it assesses whether a stable, long-term equilibrium exists between carbon emissions, GDP, energy consumption, and other variables. Cointegration suggests that the variables move together over time, which is vital for understanding the dynamics of economic growth and environmental impact in G-20 countries.\u003c/p\u003e\n\u003cp\u003eThe ANOVA test was specifically chosen to analyse variations among G-20 countries because it allows for comparing means across multiple groups. This method is particularly useful for identifying significant differences in energy use, carbon emissions, and economic performance among the countries. By highlighting these differences, ANOVA contributes to understanding how national policies or economic structures may influence environmental outcomes. For example, if the ANOVA results indicate significant differences in R\u0026amp;D expenditure among countries, this could suggest that investment in innovation plays a crucial role in shaping energy consumption patterns. Figure \u003cspan\u003e2\u003c/span\u003e below depicts the analytical tool\u0026apos;s visual representation and the flow diagram of the sequential steps undertaken in the statistical analysis.\u003c/p\u003e\n\u003cp\u003ePanel Causality tests are conducted to examine the causal relationships among the variables under study. This analysis is essential for determining the direction of influence between economic growth, energy consumption, and carbon emissions. Understanding these causal relationships is critical for policymakers, as it informs the design of interventions to reduce carbon emissions while promoting economic growth.\u003c/p\u003e\n\u003cp\u003eFinally, Panel Quantile Regression analyses the heterogeneous effects across different distribution quantiles. This method allows for a more nuanced understanding of how the relationships among variables may differ across various levels of carbon emissions or economic performance. By capturing these variations, the study can provide insights into how different G-20 countries may respond to policy changes or economic shifts.\u003c/p\u003e\n\u003cp\u003eThe analysis transformed variables into logarithmic form to stabilise variance and interpret coefficients as elasticities, enhancing the findings\u0026apos; robustness. However, potential limitations of this transformation, particularly concerning variables that may contain zero or negative values, are acknowledged. To address these challenges, the dataset was carefully examined for such instances, and appropriate adjustments to zero values were applied to ensure that all variables were suitable for logarithmic transformation. This approach allows for the maintenance of data integrity while facilitating meaningful interpretations of the relationships among the variables.\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were performed to provide a comprehensive overview of the G-20 Panel data. Subsequently, Panel Unit root tests and assessments for cross-sectional dependence were conducted to ensure the robustness of the panel data. Panel cointegration tests followed these to ascertain the long-term relationships among variables. An ANOVA test was employed to analyse variations among G-20 countries, while Panel Causality tests were conducted to examine causal relationships among the variables under study. Finally, the study incorporates panel quantile regression to analyse the heterogeneous effects across distribution quantiles.\u003c/p\u003e\n\u003cp\u003eThe variables under investigation are transformed into logarithmic form to provide a more robust and meaningful approach to understanding and interpreting complex relationships among variables.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei. Panel unit root test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH0a: Panel Data has unit root (non-stationary series)\u003c/p\u003e\n\u003cp\u003eH1a: Panel Data has no unit root (Stationary series)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eii. Panel cointegration test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH0b: There is no long-run relationship between panel data variables (No cointegration)\u003c/p\u003e\n\u003cp\u003eH1b: There is a long-run relationship between panel data variables (cointegration)\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eiii. Panel Granger causality test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH0c: X does not homogenously cause Y (no causal relationship)\u003c/p\u003e\n\u003cp\u003eH1c: X does homogeneously cause Y (causal relationship)\u003c/p\u003e\n\u003cp\u003eThe model for this study is defined as:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCO2\u0026thinsp;=\u0026thinsp;f (GDP, EC, R\u0026amp;D, PAT, EU, REN)\u003c/strong\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. (1)\u003c/p\u003e\n\u003cp\u003eAfter conducting descriptive statistics, we performed various diagnostic tests to ensure the validity of our panel data analysis. Additionally, the study has conducted tests for heteroscedasticity and serial correlation to ensure the validity of the regression results. Although normality tests revealed that the data was not normally distributed, we transformed the variables using the log-log method, which ensured the robustness and suitability of our dataset for further analysis. A log-log regression model was deployed to estimate the long-run relationship between the variables under study. The log-log model estimates the long-run relationships among the variables, allowing for the interpretation of coefficients as elasticities, which is particularly useful in economic analyses. The functional form of the model is given below. The variables deployed in the analysis of the Panel OLS Regression Model are expressed in logarithmic scales:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003elnCO2\u0026thinsp;=\u0026thinsp;\u0026beta;0\u0026thinsp;+\u0026thinsp;\u0026beta;1lnGDP\u0026thinsp;+\u0026thinsp;\u0026beta;2lnEC\u0026thinsp;+\u0026thinsp;\u0026beta;3lnR\u0026amp;D\u0026thinsp;+\u0026thinsp;\u0026beta;4lnPAT\u0026thinsp;+\u0026thinsp;\u0026beta;5lnEU\u0026thinsp;+\u0026thinsp;\u0026beta;6lnREN\u0026thinsp;+\u0026thinsp;\u0026epsilon;\u003c/strong\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. (2)\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Panel Unit Root\u003c/h2\u003e\n \u003cp\u003eThe study conducts the panel unit root tests, which are crucial for ensuring the robustness of the panel data. Specific tests, including the Levin, Lin \u0026amp; Chu (LLC) test, the Im, Pesaran, and Shin (IPS) test, and Fisher-type tests, were employed, each selected for their advantages in addressing the characteristics of the data structure. The LLC test is particularly effective for large panels with a common unit root process, while the IPS test accommodates heterogeneous panels, making it suitable for the diverse G20 dataset. Additionally, cross-sectional dependence was assessed using the Pesaran CD test, which is relevant in G20 countries due to their interconnected economies and the likelihood of sharing common shocks and trends.\u003c/p\u003e\n \u003cp\u003eThe general equation for panel unit root tests for stationarity involves regressing the variable of interest on its lagged values and potentially additional covariates to test for the existence of a unit root. The study denotes the variable of interest as \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e,\u003c/sub\u003e where \u003cem\u003ei\u003c/em\u003e represents the cross-sectional unit (country), and \u003cem\u003et\u003c/em\u003e represents time, i.e. year. The basic equation for a panel unit root test is typically specified as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ1\"\u003e\n \u003cdiv id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:\\varDelta\\:{\\varvec{y}}_{\\varvec{i}\\varvec{t}}\\:=\\:{\\varvec{\\alpha\\:}}_{\\varvec{i}}+\\:{\\varvec{\\beta\\:}}_{{\\varvec{y}}_{\\varvec{i},\\:\\varvec{t}-1}}+\\:\\varvec{\\gamma\\:}{\\varvec{X}}_{\\varvec{i}\\varvec{t}\\:}+\\:{\\varvec{\\epsilon\\:}}_{\\varvec{i}\\varvec{t}}$$\u003c/div\u003e\n \u003cdiv\u003e3\u003c/div\u003e\n \u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. \u003cp\u003ewhere,\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:\\varDelta\\:{y}_{it}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e is the first difference of the variable of the interest\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{\\alpha\\:}_{i\\:}\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003eis an individual-specific intercept capturing any individual heterogeneity.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e is the coefficient associated with the lagged dependent variable, representing the presence of a unit root. If \u003cspan\u003e\u003cspan\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e is close to 1, it suggests non-stationarity.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{X}_{it\\:}\\:\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003erepresents additional covariates that may be included in the model to control for potential determinants of the variable of interest.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{\\epsilon\\:}_{it}\\:\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003eis the error term.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Panel Cointegration\u003c/h2\u003e\n \u003cp\u003eAfter the panel unit root test verification, the study deploys Panel cointegration tests to check for evidence of a long-run relationship. The panel cointegration tests were used to examine the null hypothesis of no cointegration versus the existence of cointegration [\u003cspan\u003e55\u003c/span\u003e]. This study employs two-panel cointegration tests stated as Kao\u0026rsquo;s residual cointegration tests and Johansen Fisher Panel Cointegration tests.\u003c/p\u003e\n \u003cp\u003eThese tests were chosen due to their robustness in handling panel data with cross-sectional dependence and their ability to accommodate heterogeneous cointegration relationships among the variables. Kao\u0026rsquo;s test is particularly effective for small samples and is based on the residuals of the estimated long-run relationship, making it suitable for the characteristics of the G-20 dataset. On the other hand, the Johansen-Fisher test allows for the identification of multiple cointegration relationships, which is beneficial given the complexity of the interactions among carbon emissions, GDP, and energy consumption. In contrast, other tests, such as Pedroni\u0026rsquo;s or Westerlund\u0026rsquo;s, may impose different assumptions or may not be as effective in capturing the specific dynamics in the G-20 countries. Thus, the selected tests align well with the data characteristics and research objectives.\u003c/p\u003e\n \u003cp\u003eFor the Kao panel cointegration test, the basic equation can be written as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ2\"\u003e\n \u003cdiv id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:\\varDelta\\:{\\varvec{y}}_{\\varvec{i}\\varvec{t}}\\:=\\:{\\varvec{\\alpha\\:}}_{\\varvec{i}}+\\:{\\varvec{\\beta\\:}\\varvec{y}}_{\\varvec{i},\\varvec{t}-1}+\\:\\varvec{\\gamma\\:}{\\varvec{X}}_{\\varvec{i}\\varvec{t}}+\\:{\\varvec{\\epsilon\\:}}_{\\varvec{i}\\varvec{t}}$$\u003c/div\u003e\n \u003cdiv\u003e4\u003c/div\u003e\n \u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003cdiv id=\"Equ3\"\u003e\n \u003cdiv id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:\\varDelta\\:{\\varvec{X}}_{\\varvec{i}\\varvec{t}}\\:=\\:{\\varvec{\\delta\\:}}_{\\varvec{i}}+\\:{\\varvec{\\phi\\:}\\varvec{X}}_{\\varvec{i},\\varvec{t}-1}+\\:\\varvec{\\eta\\:}{\\varvec{X}}_{\\varvec{i}\\varvec{t}}+\\:{\\varvec{\\mu\\:}}_{\\varvec{i}\\varvec{t}}$$\u003c/div\u003e\n \u003cdiv\u003e5\u003c/div\u003e\n \u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; \u003cp\u003ewhere,\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:\\varDelta\\:{y}_{it}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e and \u003cspan\u003e\u003cspan\u003e\\(\\:\\varDelta\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003e are the first differences of the variables of interest and covariates, respectively.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{\\alpha\\:}_{i}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e and \u003cspan\u003e\u003cspan\u003e\\(\\:{\\delta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e are individual-specific intercepts capturing any individual heterogeneity in the variables.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e and \u003cspan\u003e\u003cspan\u003e\\(\\:\\phi\\:\\)\u003c/span\u003e\u003c/span\u003e are coefficients on the lagged dependent variables in the respective equations, representing the presence of cointegration. If either \u003cspan\u003e\u003cspan\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e or \u003cspan\u003e\u003cspan\u003e\\(\\:\\phi\\:\\)\u003c/span\u003e\u003c/span\u003e are significantly different from zero, it suggests cointegration.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:\\gamma\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e and \u003cspan\u003e\u003cspan\u003e\\(\\:\\eta\\:\\)\u003c/span\u003e\u003c/span\u003e are coefficients on the covariates in the respective equations.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e represents additional covariates that may be included in the model to control for potential determinants of the variables of interest.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{\\epsilon\\:}_{it}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e and \u003cspan\u003e\u003cspan\u003e\\(\\:{\\mu\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e are the error terms.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Panel Causality Test\u003c/h2\u003e\n \u003cp\u003eThe Panel Granger Causality Test assesses whether one variable Granger causes another in a panel dataset. The Granger causality test assesses whether one variable\u0026apos;s past values can predict another variable\u0026apos;s current values, indicating a directional influence. In the context of this study, if GDP Granger causes CO2 emissions, it suggests that economic growth may lead to increased carbon emissions, which has significant implications for policy-making. This finding could indicate that as economies expand, they may need to implement stricter environmental regulations or invest in cleaner technologies to mitigate the adverse effects of growth on carbon emissions. The test is typically conducted using panel data, where observations are collected over multiple cross-sectional units (country) and periods (year). The basic equation for the Panel Granger Causality Test involves estimating two regression models: Consider two variables, \u003cspan\u003e\u003cspan\u003e\\(\\:{Y}_{it\\:}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan\u003e\u003cspan\u003e\\(\\:{X}_{it}\\:\\)\u003c/span\u003e\u003c/span\u003ewhere \u003cem\u003ei\u003c/em\u003e represents the cross-sectional unit (country) and \u003cem\u003et\u003c/em\u003e represents time (year). We want to test whether \u003cspan\u003e\u003cspan\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003e Granger-causes \u003cspan\u003e\u003cspan\u003e\\(\\:{Y}_{it\\:}\\)\u003c/span\u003e\u003c/span\u003e in a panel data set.\u003c/p\u003e\n \u003cp\u003eThe basic regression equations are:\u003c/p\u003e\n \u003cdiv id=\"Equ4\"\u003e\n \u003cdiv id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$$\\:{\\varvec{Y}}_{\\varvec{i}\\varvec{t}\\:}=\\:{\\varvec{\\alpha\\:}}_{\\varvec{i}}+\\:{\\varvec{\\beta\\:}}_{\\varvec{Y}}{\\varvec{Y}}_{\\varvec{i},\\varvec{t}-1}+\\:\\varvec{\\gamma\\:}{\\varvec{X}}_{\\varvec{i}\\varvec{t}}+\\:{\\varvec{\\delta\\:}}_{1}{\\varvec{X}}_{\\varvec{i},\\varvec{t}-1}+\\:{\\varvec{\\epsilon\\:}}_{\\varvec{i}\\varvec{t}}$$\u003c/div\u003e\n \u003cdiv\u003e6\u003c/div\u003e\n \u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u003cdiv id=\"Equ5\"\u003e\n \u003cdiv id=\"FileID_Equ5\" name=\"EquationSource\"\u003e$$\\:{\\varvec{X}}_{\\varvec{i}\\varvec{t}\\:}=\\:{\\varvec{\\alpha\\:}\\varvec{{\\prime\\:}}}_{\\varvec{i}}+\\:{\\varvec{\\beta\\:}\\varvec{{\\prime\\:}}}_{\\varvec{X}}{\\varvec{X}}_{\\varvec{i},\\varvec{t}-1}+\\:{\\varvec{\\delta\\:}}_{2}{\\varvec{Y}}_{\\varvec{i},\\varvec{t}-1}+\\:{\\varvec{\\epsilon\\:}\\varvec{{\\prime\\:}}}_{\\varvec{i}\\varvec{t}}$$\u003c/div\u003e\n \u003cdiv\u003e7\u003c/div\u003e\n \u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; \u003cp\u003ewhere,\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{Y}_{it\\:}\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003eand \u003cspan\u003e\u003cspan\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003eare the dependent and independent variables, respectively, for the \u003cem\u003ei\u003c/em\u003e-th cross-sectional unit at time \u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{Y}_{i,t-1}\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003eand \u003cspan\u003e\u003cspan\u003e\\(\\:{X}_{i,t-1}\\:\\)\u003c/span\u003e\u003c/span\u003eare lagged values of the independent and dependent variables, respectively.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{\\alpha\\:}_{i}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e and \u003cspan\u003e\u003cspan\u003e\\(\\:{\\alpha\\:{\\prime\\:}}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003e are individual-specific intercepts.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{\\beta\\:}_{Y}\\:\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003eand \u003cspan\u003e\u003cspan\u003e\\(\\:{\\beta\\:{\\prime\\:}}_{X}\\:\\)\u003c/span\u003e\u003c/span\u003eare coefficients on the lagged values of \u003cspan\u003e\u003cspan\u003e\\(\\:{Y}_{it\\:}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan\u003e\u003cspan\u003e\\(\\:{X}_{it\\:}\\:\\)\u003c/span\u003e\u003c/span\u003e, respectively, indicating the effect of their past values on their current values.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:\\gamma\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e and \u003cspan\u003e\u003cspan\u003e\\(\\:{\\delta\\:}_{1}\\:\\)\u003c/span\u003e\u003c/span\u003eare coefficients on the contemporaneous and lagged values of \u003cspan\u003e\u003cspan\u003e\\(\\:{X}_{it\\:}\\)\u003c/span\u003e\u003c/span\u003e in the equation for \u003cspan\u003e\u003cspan\u003e\\(\\:{Y}_{it\\:}\\:\\)\u003c/span\u003e\u003c/span\u003erespectively.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{\\delta\\:}_{2}\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003eis the coefficient on the lagged values of \u003cspan\u003e\u003cspan\u003e\\(\\:{Y}_{it\\:}\\)\u003c/span\u003e\u003c/span\u003e in the equation for \u003cspan\u003e\u003cspan\u003e\\(\\:{X}_{it\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{\\epsilon\\:}_{it}\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003eand \u003cspan\u003e\u003cspan\u003e\\(\\:{\\epsilon\\:{\\prime\\:}}_{it}\\)\u003c/span\u003e\u003c/span\u003e are error terms.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 Panel Quantile Regression\u003c/h2\u003e\n \u003cp\u003ePanel quantile regression extends the idea of quantile regression to panel data, allowing for the estimation of conditional quantiles of the dependent variable given the independent variables in a panel dataset. The inclusion of panel quantile regression in the analysis is highlighted as a significant strength of the study, as it allows for examining heterogeneous effects across different distribution quantiles. This method is particularly suitable for analyzing the dynamics of energy and innovation among G-20 countries because it captures variations in relationships that may not be evident in traditional mean-based regression approaches.\u003c/p\u003e\n \u003cp\u003eFor instance, while mean regression provides an average effect, quantile regression can reveal how the impact of GDP on CO2 emissions may differ for countries at different emissions levels. This nuanced understanding aligns with the study\u0026apos;s objectives of exploring the diverse responses of G-20 countries to energy consumption and innovation policies. By employing quantile regression, the study aims to provide insights that are more reflective of the varying contexts and challenges different nations face within the G-20 framework.\u003c/p\u003e\n \u003cp\u003eThe basic equation for panel quantile regression can be written as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ6\"\u003e\n \u003cdiv id=\"FileID_Equ6\" name=\"EquationSource\"\u003e$$\\:{\\varvec{Q}}_{\\varvec{\\tau\\:}\\:}\\left({\\varvec{Y}}_{\\varvec{i}\\varvec{t}\\:}|{\\varvec{X}}_{\\varvec{i}\\varvec{t}}\\right)=\\:{\\varvec{\\alpha\\:}}_{\\varvec{i}}\\left(\\varvec{\\tau\\:}\\right)+\\:{\\varvec{X}}_{\\varvec{i}\\varvec{t}}\\:\\varvec{\\beta\\:}\\left(\\varvec{\\tau\\:}\\right)+\\:{\\varvec{\\epsilon\\:}}_{\\varvec{i}\\varvec{t}}\\left(\\varvec{\\tau\\:}\\right)$$\u003c/div\u003e\n \u003cdiv\u003e8\u003c/div\u003e\n \u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. \u003cp\u003ewhere,\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{Q}_{\\tau\\:\\:}\\left({Y}_{it\\:}|{X}_{it}\\right)\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e represents the conditional quantile of \u003cspan\u003e\u003cspan\u003e\\(\\:{Y}_{it\\:}\\)\u003c/span\u003e\u003c/span\u003e at the \u003cspan\u003e\u003cspan\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003eth quantile given \u003cspan\u003e\u003cspan\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{\\alpha\\:}_{i}\\left(\\tau\\:\\right)\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e represents individual-specific effects at the \u003cspan\u003e\u003cspan\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003eth quantile.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:\\beta\\:\\left(\\tau\\:\\right)\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e represents the vector of coefficients for the independent variables \u003cspan\u003e\u003cspan\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003e at the \u003cspan\u003e\u003cspan\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003eth quantile.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:{\\epsilon\\:}_{it}\\left(\\tau\\:\\right)\\:\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003eis the error term at the \u003cspan\u003e\u003cspan\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003eth quantile.\u003c/p\u003e\n \u003cp\u003eIn panel quantile regression, the quantile-specific coefficients \u003cspan\u003e\u003cspan\u003e\\(\\:{\\alpha\\:}_{i}\\left(\\tau\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan\u003e\u003cspan\u003e\\(\\:\\beta\\:\\left(\\tau\\:\\right)\\:\\)\u003c/span\u003e\u003c/span\u003eare allowed to vary across various quantiles of the conditional distribution of the dependent variable, thereby offering a more detailed understanding of how the influence of the independent variables varies across the distribution.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results and Discussions","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e includes measures of central tendency, such as the mean and median, and measures of dispersion, including standard deviation. Additionally, the discussion of the shape of the distributions has been clarified, and skewness and kurtosis have been explicitly mentioned as key indicators. The table summarizes key statistical measures for the variables under investigation, including Carbon Emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (R\u0026amp;D), Patents in Environmental Technologies (PAT), Per capita Energy Use (EU), and Per capita Renewables (REN). The mean values indicate that Energy Use (EU) has the highest average consumption at 10.379 kWh, reflecting significant energy utilization across the G20 nations. At the same time, Gross Expenditure on Research and Development (R\u0026amp;D) exhibits the lowest mean at 0.110% of GDP, suggesting limited investment in innovation relative to economic output.\u003c/p\u003e \u003cp\u003eThe standard deviation values reveal variability in the data. Carbon Emissions (CO2) and Patents (PAT) show considerable dispersion, as indicated by their respective standard deviations of 0.748 and 2.500, which may reflect diverse environmental policies and technological advancements across countries.\u003c/p\u003e \u003cp\u003eFurthermore, the skewness statistics indicate that all variables are negatively skewed, suggesting that most observations are concentrated on the higher end of the distribution, with a few lower values pulling the mean down. All variables, including Carbon Emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (R\u0026amp;D), Patents in Environmental Technologies (PAT), Per capita Energy Use (EU), and Per capita Renewables (REN), exhibit negative skewness. This indicates that the distributions of these variables have longer left tails, suggesting that most of the data points are concentrated on the right side of the distribution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\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\u003eDescriptive Statistics of the variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCO2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEC\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eR\u0026amp;D\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePAT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eEU\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eREN\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.924\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.558\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.754\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.399\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.379\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.235\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMedian\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.089\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.870\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.959\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.625\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.584\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.866\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMaximum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.058\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.129\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.060\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.567\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.121\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.688\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.47\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMinimum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.091\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.526\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.162\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.797\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.151\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.818\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eStd. Dev.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.158\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.500\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.250\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSkewness\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.629\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.856\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.684\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.428\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.204\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.552\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKurtosis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.631\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.973\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.940\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.294\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.945\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.964\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.414\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJarque-Bera\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.128\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.340\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.826\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e234.940\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.395\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34.332\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1695.198\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eProbability\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e808.413\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4014.637\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1997.020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.387\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3107.726\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4359.314\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3039.056\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSum Sq. Dev.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234.663\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e562.030\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e275.557\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e373.110\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2620.320\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e274.101\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2121.804\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eThis table presents the summary statistics for the variables included in the study, providing insights into their distributions and central tendencies. It includes key metrics such as the mean, median, standard deviation, minimum, and maximum values for each variable, which encompasses Carbon Emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (R\u0026amp;D), Patents in Environmental Technologies (PAT), Per capita Energy Use (EU), and Per capita Renewables (REN). This summary aids in understanding the overall characteristics of the data and the variability of each variable across the G-20 countries during the study period.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eA normal distribution has a kurtosis of 3, where values above 3 indicate a leptokurtic distribution (peaked), and values below 3 indicate a platykurtic distribution (flatter than a normal distribution). The kurtosis values further elucidate the distribution shapes, with R\u0026amp;D and REN exhibiting leptokurtic characteristics, indicating a peaked distribution, while the other variables are classified as platykurtic, suggesting flatter distributions.\u003c/p\u003e \u003cp\u003eJarque-Bera is a test statistic to determine whether the series is normally distributed. The Jarque-Bera test assesses the null hypothesis that a series follows a normal distribution. A significant result, indicated by a low p-value, suggests that the series deviates from normality. In the context of this study, the Jarque-Bera statistic exceeds the critical value, and the associated low probability value provides strong evidence to reject the null hypothesis. For all the seven series (CO2, GDP, EC, R\u0026amp;D, PAT, EU, REN) utilised in the study, we reject the hypothesis of normal distribution at the 1% level. Based on the skewness and kurtosis statistics, none of the distributions of the variables satisfied the assumption of normality. These findings collaborated with the Jarque-Bera test's results, affirming the variables' distributions to be non-normal [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Collectively, these descriptive statistics provide a foundational understanding of the variables' central tendencies, variability, and distributional characteristics, which are critical for subsequent analyses in the study.\u003c/p\u003e \u003cp\u003eA necessary condition before testing for the possible establishment of a long-run relationship between CO2 emissions per capita, GDP per capita, energy consumption per capita, gross expenditure on research and development, patents on environmental-related technologies, per capita energy use and per capita renewables is that all variables should be integrated in the first order. To examine this condition, panel unit root tests are performed, including the Levin, Lin and Chu (LLC), the Im, Pesaran and Shin (IPS), ADF-Fisher Chi-square and PP-Fisher Chi-square tests [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]These tests include cross-sectional dependence and cross-sectional independence cases. The findings of these tests are detailed in Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\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\u003ea: Panel Unit Root Tests at Level and First Difference\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"15\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003eLevel Summary\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMethods\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCO2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eGDP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eEC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eR\u0026amp;D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ePAT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eEU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e\u003cb\u003eREN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStat.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStat.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStat.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStat.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eStat.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eStat.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eStat.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevin, Lin \u0026amp; Chu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-5.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-3.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIm, Pesaran \u0026amp; Shin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eADF-Fisher Chi-square\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e60.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e31.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e27.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePP-Fisher Chi-square\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.89\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e98.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e29.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e35.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFirst Difference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevin, Lin \u0026amp; Chu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-5.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIm, Pesaran \u0026amp; Shin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.73\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-5.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-3.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-9.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eADF-Fisher Chi-square\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110.6\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e102.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e109.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e81.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e159.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePP-Fisher Chi-square\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e174.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e177.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e369.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e175.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e567.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe findings of the panel unit root tests that were performed to evaluate the stationarity of the study variables are shown in this table. Each variable's test statistics and the corresponding p-values show whether or not the unit root null hypothesis can be rejected. The variables analysed include Carbon Emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (R\u0026amp;D), Patents in Environmental Technologies (PAT), Per capita Energy Use (EU), and Per capita Renewables (REN). The results provide essential information regarding the time series properties of the data, which is crucial for subsequent econometric analyses.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb: Cross-Sectional Dependence Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTest\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStatistic\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ed.f.\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\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\u003eBreusch-Pagan LM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1149.725\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePesaran scaled LM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.23285\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePesaran CD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.14461\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eThis table presents the findings from the cross-sectional dependence tests conducted on the panel data. It includes the test statistics and degrees of freedom for various tests used to evaluate the presence of cross-sectional dependence among the variables analysed. The findings reveal whether the null hypothesis of cross-sectional independence can be disproved, offering valuable information about the interdependencies between the G-20 nations concerning the variables under investigation.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe unit root tests conducted in this study reveal that the null hypothesis of the unit root cannot be rejected at the 1% significance level for the seven-panel time series when analysed at a level. However, upon testing for the unit root in the first difference, all panel unit root tests reject the null hypothesis with a statistically significant level of 1%. This finding indicates the first-order integration of the variables, which is essential for further cointegration analysis. Since non-stationary series must be differentiated to achieve stationarity before examining long-term relationships, variables of the same order must be integrated. Non-stationarity has important ramifications since it can compromise the validity of any conclusions derived from the analysis and the robustness of the results. These results conclude that all panel time series are integrated with the first order [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo summarise, we note that irrespective of the type of tests employed, cross-sectional dependence or cross-sectional independence for the group of G-20 countries demonstrated strong evidence for non-stationarity at level and stationarity at first difference. At the level summary, p-value \u0026gt; 0.05 for all the variables, we did not find sufficient evidence to reject the null hypothesis, which means the series has a unit root and the data is non-stationary. At the first difference, the null hypothesis is rejected due to p-values being less than 0.05 for all variables. Therefore, the series has no unit root, and data is stationary for all the seven- series, i.e., CO2 emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (R\u0026amp;D), Patents in Environmental related Technologies (PAT), per capita Energy Use (EU) and per capita Renewables (REN) [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmpirical results propose strong evidence for panel cointegration between the CO2 emissions per capita, GDP per capita, energy consumption per capita, gross expenditure on research and development, patents in environmental-related technologies, per capita energy use and per capita renewables for G-20 countries. The panel cointegration tests favour the rejection of no cointegration besides the alternative of cointegration among the variables analysed. The Kao test results signify the presence of cointegration; hence, the rejection of the null hypothesis as p-value \u0026lt; 0.05 for the Kao test [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], as depicted in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003ea. However, the number of cointegration vectors is unknown. According to Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, Johansen Fisher test checks the probability of more than one cointegration vector among the variables analysed [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Johansen Fisher panel cointegration test results suggested, at most, six cointegration vectors. It implies that the presence of a single cointegration vector cannot be achieved among the variables analysed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ea: Kao Cointegration Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003et-Statistic\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eProbability\u003c/em\u003e\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\u003eADF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.7009\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidual variance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.00047\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHAC variance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00043\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eThis table summarises the results of the Kao panel cointegration test, which is employed to determine a long-run equilibrium relationship among the variables analysed in the study. It includes the test statistics, critical values, and p-values for the variables, such as Carbon Emissions per capita (CO2), GDP per capita (GDP), Energy Consumption per capita (EC), Gross Expenditure on Research and Development (R\u0026amp;D), Patents in Environmental Technologies (PAT), Per capita Energy Use (EU), and Per capita Renewables (REN). The findings indicate whether the null hypothesis of no cointegration can be rejected, thereby providing evidence of long-term relationships among the variables within the G-20 countries.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb. Johansen Fisher Panel Cointegration Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTrace Test\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMax-Eigen Test\u003c/em\u003e\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\u003eHypothesised\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eNo. of CE(s)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStat.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStat.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNone\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAt most 1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e811.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e442.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAt most 2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e963.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e751.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAt most 3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e647.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e439.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAt most 4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e301.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAt most 5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAt most 6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eThis table presents the results of the Johansen-Fisher panel cointegration test, which is utilised to identify the number of cointegration relationships among the variables analysed in the study. It includes the test statistics for both the Trace Test and Max-Eigen Test, along with their corresponding p-values for various hypothesised numbers of cointegration equations. The variables examined include Carbon Emissions per capita (CO2), per capita Energy Use (EU), per capita Renewables (REN), Gross Expenditure on Research and Development (RandD), Energy Consumption per capita (EC), Patents in Environmental Technologies (PAT) and GDP per capita (GDP). To comprehend the long-term relationships and dynamics among economic growth, energy consumption and environmental impacts in G-20 countries, it is essential to know that the results show the presence of multiple cointegration vectors, indicating that these variables move together over time.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe main aim of performing the test of analysis of variance (ANOVA) is to examine whether or not the mean differences between the G-20 countries [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] are statistically significant based on the variables deployed in the study, i.e., CO2 emissions per capita, GDP per capita, energy consumption per capita, gross expenditure on research and development, patents in environmental related technologies, per capita energy use and per capita renewables. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the ANOVA results show a statistically significant difference between the G-20 nations with a p-value less than 0.05. This importance implies notable differences between nations in the features of the research variables, including energy consumption innovation metrics and economic growth indicators. Since they may represent various policy environments, economic structures and degrees of technological advancement, it is essential to comprehend these distinctions when examining the relationship between energy innovation and economic growth. Significant ramifications result from these differences, underscoring the need for customised policy interventions considering each nation's situation. By recognising these disparities, the study can provide more nuanced insights into how energy and innovation strategies can be optimised to foster sustainable economic growth across the G-20 nations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA F-test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTest Type\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eF-statistic\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eANOVA F-test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2799.749\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWelch F-test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7351.370\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eThis table presents the results of the ANOVA F-test conducted to analyse the differences in means among the G-20 countries regarding the variables under study, such as Carbon Emissions, GDP, and Energy Consumption. It includes the F-statistic and p-value for the standard ANOVA F-test and the Welch F-test. The ANOVA F-test assesses whether there are statistically significant differences in the means of the groups. At the same time, the Welch F-test is used as an alternative when the assumption of equal variances is violated. The results indicate that the p-values are less than 0.05, leading to the rejection of the null hypothesis, which suggests significant differences in the means across the G-20 countries. This finding highlights the impact of national policies and economic structures on environmental outcomes and underscores the importance of tailored approaches to energy use and emissions reduction in different countries.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe standard ANOVA consider that the errors (i.e., residuals) follow a normal distribution. However, if this normality assumption is violated, an alternative is to use a non-parametric test. To determine if there is a statistically significant difference between the medians of three or more independent groups, the Kruskal-Wallis test is the most widely used non-parametric test comparable to one-way ANOVA, i.e., G-20 nations in this instance. The data ranks are the foundation for the Kruskal-Wallis test [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The ranks of the standard normal distribution are converted to quantiles using the Van Der Waerden test. These are known as normal scores, and the computation of the tests is predicated on them. The Van Der Waerden test has the advantage of offering the robustness of the Kruskal-Wallis test in situations where the normality assumptions are not met, as well as the high efficiency of the conventional ANOVA analysis when they are. By addressing possible breaches of the data underlying normality assumptions, non-parametric tests like the Kruskal-Wallis and Van Der Waerden tests play a crucial part in the analysis. When normality is met, the Van Der Waerden test provides the efficiency of a standard ANOVA; however, when this assumption is not met, it retains robustness. This dual capability makes these tests superior to other options because they thoroughly examine the variations among the G-20 nations without being unduly impacted by the data distributional features. According to the results of the Kruskal-Wallis and Van der Waerden tests illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e6\u003c/span\u003e, due to the presence of different characteristics and variables of G- 20 countries over the period, the observed differences in medians among the countries are statistically significant as p-value \u0026lt; 0.0.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTests for Equality of Medians\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTests for Equality of Medians\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eF-test value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\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\u003eKruskal-Wallis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2456.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKruskal-Wallis\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(tie-adj.)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2456.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVan der Waerden\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2280.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eThis table shows the findings of the Kruskal-Wallis and Van der Waerden tests for median equality across the examined variables in G-20 nations. A p-value less than 0. 05 indicates significant differences between the groups, while the F-test values and associated p-values show the statistical significance of differences in medians. The results demonstrate the diversity in the distribution of the variables under investigation, highlighting the necessity of specialised policy approaches to environmental sustainability.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTo test the equality of variances, Bartlett, Levene and Brown-Forsythe tests[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] are reported in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Bartlett's test compares the logarithm of the weighted average variance with the weighted sum of the logarithms of the variances. In contrast, the Levene test relies on an analysis of variance (ANOVA) of the absolute deviation from the mean. The Brown-Forsythe test is a variation of the Levene test that substitutes the absolute median difference for the absolute mean difference. The results of the table depict that the distributional differences across the G-20 countries are statistically significant throughout the period in terms of the variables analysed as p-value \u0026lt; 0.05, therefore rejecting the null hypothesis and stating that there is a statistically significant difference in the distributional variances across G-20 countries for the variables deployed in the study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTests for Equality of Variances\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTests for Equality of Variances\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMethod\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eF-test Value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\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\u003eBartlett\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1389.43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevene\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBrown-Forsythe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136.73\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eThis table summarises the results of Bartlett, Levene, and Brown-Forsythe tests for equality of variances among the analysed variables in G-20 countries. The F-test values and associated p-values indicate the statistical significance of differences in variances, with p-values less than 0.05 suggesting significant disparities in the distributional variances across the groups. These results emphasise the heterogeneity in the variability of the examined variables, which is crucial for understanding the dynamics of environmental sustainability policies.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eDetermining the direction of causality is critical for policy recommendations, and to examine the causal flow among the variables deployed in the study, the Dumitrescu-Hurlin Panel Granger Causality Test [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] is performed, and its results are given in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Since the variables under study have a cointegration relationship, a change in one variable is expected to affect the other variable.\u003c/p\u003e \u003cp\u003eCausality test results are reported in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The diagrammatic representation of panel causality tests in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicates that there is a bidirectional causal nexus between carbon emissions and economic growth, between carbon emissions and energy consumption, between carbon emissions and gross expenditure on research and development and between carbon emissions and energy use which ascertains that any policy changes and investments among G-20 countries in the variables will impact carbon emissions and vice versa. There is a bidirectional relationship between economic growth and energy consumption and between economic growth and energy use. However, unidirectional causality from economic growth to gross expenditure on research and development, as economic growth often creates a conducive environment for increased R\u0026amp;D spending. As a nation's economy expands, there is typically a higher capacity for investment in various sectors, including research and development and from economic growth to renewables exists as economic prosperity provides the financial means for developing and implementing renewable energy projects, such as installing solar panels, constructing wind farms or enhancing grid efficiency.\u003c/p\u003e \u003cp\u003eRenewable energy, including solar, wind, and hydropower, has become attractive. Therefore, governments and businesses, recognising the long-term benefits of sustainable energy, may invest in renewable infrastructure to meet the growing energy demand while mitigating environmental impact in G-20 nations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eD-H Panel Granger Causality Test Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHypothesis\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eW-\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStat.\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eZbar-\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStat.\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eProb.\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCausality\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eConclusion\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGDP→CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBi-directional causality between GDP and CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e→GDP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEC→CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBi-directional causality between EC and CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e→EC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u0026amp;D→CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBi-directional causality between R\u0026amp;D and CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e→R\u0026amp;D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePAT→CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUni-directional causality from PAT to CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e→PAT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEU→CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBi-directional causality between EU and CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e→EU\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eREN→CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUni-directional causality from REN to CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e→REN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEC→GDP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBi-directional relationship between EC and GDP\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGDP→EC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u0026amp;D→GDP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUni-directional relationship from R\u0026amp;D to GDP\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGDP→R\u0026amp;D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePAT→GDP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThere is no causality between PAT and GDP\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGDP→PAT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEU→GDP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBi-directional causality between EU and GDP\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGDP→EU\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eREN→GDP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGDP→REN\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eR\u0026amp;D→EC EC→R\u0026amp;D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003cp\u003e5.89\u003c/p\u003e \u003cp\u003e5.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003cp\u003e5.76\u003c/p\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003cp\u003e0.00\u003c/p\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUni-directional causality from GDP to REN\u003c/p\u003e \u003cp\u003eBi-directional causality between R\u0026amp;D and EC\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePAT→EC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUni-directional causality from PAT to EC\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEC→PAT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEU→EC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo causality between the EU and the EC\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEC→EU\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eREN→EC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUni-directional causality from REN to EC\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEC→REN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePAT→R\u0026amp;D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUni-directional causality from PAT to R\u0026amp;D\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u0026amp;D→PAT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEU→R\u0026amp;D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBi-directional causality between EU and R\u0026amp;D\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u0026amp;D→EU\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eREN→R\u0026amp;D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo causality between REN and R\u0026amp;D\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u0026amp;D→REN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEU→PAT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo causality between the EU and the PAT\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePAT→EU\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eREN→PAT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUni-directional causality from REN to PAT\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePAT→REN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eREN→EU\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUni-directional causality from REN to EU\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEU→REN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eThis table displays the results of the Dumitrescu-Hurlin (D-H) Panel Granger Causality Test, assessing the causal relationships among key variables related to carbon emissions, economic growth, energy consumption, and research and development in G-20 countries. The W-statistics and corresponding significance levels indicate whether a variable can predict another, with unidirectional and bidirectional causality identified.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn analysing the intricate interplay between carbon emissions, economic growth, energy consumption, gross expenditure on research and development, patents in environmental-related technologies, energy use and renewables, deploying Panel Quantile Regression (PQR) presents a compelling rationale. The deployment of Panel Quantile Regression (PQR) in analysing the intricate interplay between carbon emissions, economic growth, energy consumption, gross expenditure on research and development, patents in environmental-related technologies, energy use, and renewables is particularly compelling due to its ability to capture heterogeneous effects across different quantiles of the dependent variable. While traditional models, such as Fixed Effects or Generalized Method of Moments (GMM), may provide average estimates, they often overlook the variability in relationships that can exist across different levels of carbon emissions. PQR addresses this limitation by allowing for a more nuanced exploration of how the impact of explanatory variables may differ at various points in the distribution of carbon emissions. Panel Quantile Regression (PQR) provides a methodological framework that allows for the examination of how relationships between variables vary across different quantiles of carbon emissions rather than relying solely on mean-based analysesThis method shows how the effects of economic growth energy consumption and innovation on carbon emissions can vary greatly at different emission levels revealing distributional changes that conventional approaches may hide. These revelations are essential to the study objectives because they allow for a more thorough comprehension of how economic activity affects the environment in each of the G-20 nations. The ability of PQR to capture distributional changes in the influence of variables on carbon emissions makes its adoption especially relevant [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. This approach enables us to determine whether there are differences in the relationship between economic factors and environmental outcomes among the panel's entities or regions.\u003c/p\u003e \u003cp\u003eThese revelations are essential to the study objectives because they allow for a more thorough comprehension of how economic activity affects the environment in each of the G-20 nations. PQR's ability to capture distributional changes in the influence of variables on carbon emissions makes its adoption especially relevant [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Studies by Koenker and Hallock (2001)[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] and Yu et al.. (2003) [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] have shown that mean-based methods often fail to capture the full range of relationships between variables, particularly in heterogeneity. These studies emphasise that traditional regression models may overlook important distributional differences, leading to incorrect conclusions. On the other hand, Panel Quantile Regression (PQR) gives us a more thorough grasp of the relationship between economic factors and environmental outcomes by enabling us to identify differences in environmental impact across various distribution quantiles. This capability is particularly valuable for G20 countries, where the diversity in economic structures, energy consumption patterns, and innovation capabilities necessitates a nuanced analysis.\u003c/p\u003e \u003cp\u003eSince it identifies how the relationship between economic variables and environmental outcomes varies across entities within the panel, PQR is useful in capturing regional variations among the G-20 countries. For example, the study might show that in contrast to nations that depend on fossil fuels, those that adopt more renewable energy have a different relationship between economic growth and carbon emissions. In sustainability research, where the goal is not merely to understand average effects but to uncover disparities in environmental impact, PQR becomes indispensable. By leveraging PQR, the study explores variations in the effectiveness of R\u0026amp;D investments, the significance of patents in environmental technologies and the adoption of renewable energy sources at different carbon intensity levels.\u003c/p\u003e \u003cp\u003ePanel Quantile Regression (PQR) is particularly suitable for this study because it allows us to capture heterogeneous effects across different points of the conditional distribution of the dependent variable. According to Koenker and Bassett (1978) [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], quantile regression provides a comprehensive analysis by focusing on the conditional distribution of the dependent variable. Unlike Fixed Effects [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] or Generalized Method of Moments (GMM) [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], which focus on average effects, PQR provides a more nuanced analysis by revealing how the relationships between variables vary at different quantiles. This is crucial for understanding the diverse dynamics among G20 countries, which exhibit varying levels of economic growth, energy consumption, and innovation capabilities. Fixed Effects models, while useful for controlling for unobserved heterogeneity, assume homogeneity in the slope coefficients, which may not capture the varying impacts of the explanatory variables across different levels of the dependent variable. GMM, on the other hand, is designed to address endogeneity concerns but may not fully account for the distributional heterogeneity present in our data. By selecting PQR, we can better understand how distribution segments are impacted by variables such as economic growth innovation and carbon emissions, which helps us effectively customise policy recommendations.\u003c/p\u003e \u003cp\u003ePolicymakers looking to implement focused policies that target particular economic sectors or areas with different environmental issues will find this nuanced analysis essential. Given the diverse relationships examined in the study, an approach that recognises and analyses distributional disparities is necessary. By offering an adaptable framework for evaluating the conditional effects of explanatory variables across various quantiles of carbon emissions, PQR supports this requirement. Ultimately, the deployment of Panel Quantile Regression enhances the robustness of the research findings, offering policymakers tailored insights that can inform effective strategies for sustainable development, emission reduction and the promotion of green technologies across diverse sectors within the panel, as illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePanel Quantile Regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePanel quantile regression estimates at 0.10th − 0.90th quantile (Dependent variable: CO2)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.10th\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.25th\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.50th\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.75th\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.90 th\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e-0.05\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e-0.04\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e-0.04\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-0.04\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-0.03\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e-0.80\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e-0.54\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e-0.20\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.06\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u0026amp;D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.011\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePAT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e-0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEU\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1.99\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.72\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.37\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e1.08\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.89\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eREN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e-0.03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e-0.03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e-0.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eThis table presents the results of the panel quantile regression analysis conducted to examine the heterogeneous effects of various independent variables on carbon emissions (dependent variable) across different quantiles. The first column lists the independent variables, including GDP per capita (GDP), energy consumption per capita (EC), gross expenditure on research and development (R\u0026amp;D), number of patents in environmental technologies (PAT), energy use (EU), and renewable energy consumption (REN). Each subsequent row provides the estimated coefficients for these independent variables at different quantiles of carbon emissions, highlighting the varying impact of each factor depending on the level of emissions. The results aim to inform policymakers about the differential effects of economic and energy variables on environmental outcomes in G-20 countries.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe relationship between economic growth and carbon emissions [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], in the PQR test results, where the effect coefficients of GDP on carbon emissions are consistently negative across the 0.10th to the 0.90th quantiles, implies a consistent pattern of decreasing carbon emissions associated with higher GDP levels across various segments of the distribution which means that on average, as GDP increases, carbon emissions decrease across different quantiles within the panel because of technological advancements as higher GDP levels may coincide with the adoption of cleaner and more efficient technologies, contributing to reduced carbon emissions. Also, due to environmental regulations, countries with higher GDPs may implement stricter environmental laws and policies, influencing industries to adopt cleaner practices and technologies. Another reason for this pattern could be transitioning to low-carbon economies, as economies with higher GDP might successfully transition towards low-carbon or green technologies, mitigating their environmental impact. The relationship between GDP and carbon emissions is intrinsically complex and impacted by several variables, such as a nation's energy mix, technological innovation potential and the particular regulatory frameworks that oversee economic operations. Sweden and Denmark, for example, have successfully lowered emissions while preserving strong economic performance, demonstrating how countries that prioritise investments in energy efficiency and renewable energy may see a decoupling of economic growth from carbon emissions.\u003c/p\u003e \u003cp\u003eOn the other hand, economic growth frequently corresponds with higher carbon emissions in economies that rely heavily on fossil fuels, underscoring the necessity of closely examining the circumstances surrounding growth. A more fair reading would highlight that attaining reduced emissions and economic expansion requires adopting all-encompassing policies that support sustainable practices like carbon pricing, clean technology investments and legislative frameworks that encourage the use of renewable energy. Considering these complexities, the study can offer more practical policy suggestions that help countries balance environmental sustainability and economic development, ultimately leading to a more sustainable future.\u003c/p\u003e \u003cp\u003eThe relationship between energy consumption and carbon emissions presents a complex dynamic that warrants careful interpretation [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. According to the Panel Quantile Regression (PQR) results, the negative coefficients below the 50th quantile imply that a rise in energy use is generally linked to a fall in carbon emissions. This finding may seem counterintuitive initially because, according to conventional wisdom, higher energy consumption usually translates into higher carbon emissions, especially in the G-20 nations, distinguished by a heavy reliance on fossil fuels. The study investigates several plausible explanations to address this unexpected result. The type of energy used must be taken into account. First nations with rising energy consumption may switch to cleaner energy sources or put energy efficiency measures in place to reduce emissions. Furthermore, regional variations among the G-20 might be crucial since different energy portfolios and regulatory systems may result in different correlations between emissions and energy use. For example, the observed negative relationship in some quantiles may result from decoupling energy consumption and carbon emissions in countries investing in renewable energy technologies.\u003c/p\u003e \u003cp\u003eThe possible reasons for this pattern can be, first, efficiency gains, i.e. below the 50th quantile, G-20 countries may be achieving energy efficiency gains, leading to reduced emissions for a given level of energy consumption. Beyond this point, further increases in energy consumption may not yield the same efficiency level. The second reason is industrial processes, which are the turning point and indicate a stage where certain industries or processes become more energy-intensive, leading to higher emissions despite increased energy consumption. These results represent that the threshold effect is crucial for policymakers. Below the 50th quantile, policies may encourage energy efficiency measures to decouple energy consumption from emissions [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Beyond this point, policymakers should consider strategies that address the potential increase in emissions associated with higher energy consumption levels.\u003c/p\u003e \u003cp\u003eFor the association between gross expenditure on research and development and carbon emissions [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], the uniform positivity in the coefficients of R\u0026amp;D on CO2 suggests that, on average, as gross expenditure on R\u0026amp;D increases, carbon emissions also increase across different quantiles within the panel of G-20 countries. Even though the data shows a link between higher carbon emissions and more R\u0026amp;D spending, this relationship calls for a closer examination of the underlying causes of this paradox. One critical aspect to consider is the allocation of R\u0026amp;D investments. Emissions could increase despite technological advancements if a significant portion of R\u0026amp;D funding is directed towards carbon-intensive industries, such as fossil fuel extraction or traditional manufacturing processes. Conversely, suppose R\u0026amp;D expenditures are primarily focused on developing cleaner technologies. In that case, the long-term impact may ultimately result in reduced emissions, albeit with a time lag before these innovations are fully deployed and integrated into the market. The delay between R\u0026amp;D investment and the realisation of technological advancements must also be considered. Research and development (R\u0026amp;D) innovations frequently take years, if not decades, to leap from lab to market. Emissions could keep rising since outdated, inefficient technologies may still be used during this transitional phase. Therefore, it is essential to contextualise the findings within the broader framework of technological development and deployment timelines. Therefore, the only explanation for this pattern may be innovation dynamics since industries may go through transitional phases while pursuing innovation where newer technologies coexist with practices that may be carbon-intensive. According to this research, R\u0026amp;D investments may have unforeseen repercussions in the form of higher carbon emissions, even though they support economic expansion and technological advancements. Policymakers should consider incorporating environmental considerations and incentivising green innovation within R\u0026amp;D spending initiatives [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe link between patents related to environmental technologies and carbon emissions [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], the coefficients of PAT on CO2, up to the 25th quantile, is positive, indicating that, on average, an increase in patents in environmental technologies is associated with an increase in carbon emissions. This suggests that, in the lower range of emissions, having more patents in environmental technologies might not necessarily lead to emission reductions. After the 25th quantile, the negative coefficients suggest that, within the upper range of the distribution, an increase in patents in environmental technologies is associated with a decrease in carbon emissions. This implies that, in the higher emissions segment, having more patents in environmental technologies becomes associated with emission reductions. This pattern is due to the possibilities of technological adoption lag as it might take time for innovations in environmental technologies to be widely adopted and have a tangible impact on emission reduction, explaining the initially positive coefficients. This pattern can also be due to the economic threshold where industries with higher emissions might be more receptive to adopting environmental technologies once a certain threshold of emissions is reached. Therefore, understanding this threshold effect is crucial for global leaders and policy advisors of G-20 economies. Below the 25th quantile, policies may need to focus on promoting the practical implementation and adoption of environmental technologies. Beyond this point, policymakers might emphasise incentivising industries with higher emissions to adopt these technologies for effective emission reduction.\u003c/p\u003e \u003cp\u003eIn the relationship between energy use and carbon emissions [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], there is a positive pattern of coefficients of EU on CO2 from 0.10th quantile to 0.90th quantile, which implies that on average, as energy use increases, carbon emissions also increase across different quantiles within the panel of G-20 countries resulting from reliance on fossil fuels as higher energy use may be driven by a significant reliance on fossil fuels, contributing to increased carbon emissions. Industrial operations that use much energy and produce more emissions may cause a positive correlation. This finding highlights the difficulty of separating energy consumption from carbon emissions. To lessen the effect of rising energy consumption on emissions, policymakers may need to concentrate on measures that support energy efficiency, provide incentives for using renewable energy sources and stimulate the adoption of cleaner technologies.\u003c/p\u003e \u003cp\u003eThe association between renewables and carbon emissions [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], the panel quantile regression results where the effect coefficients of renewables on carbon emissions are consistently negative across the 0.10th to the 0.90th quantiles, suggests that there is a pervasive negative relationship between the use of carbon emissions and renewable energy across different quantiles within the G-20 panel because of clean energy adoption. Reducing dependency on carbon-intensive sources, implementing cleaner, low-carbon renewable energy sources and having active environmental policies could all hurt the relationship. Renewables could signify the impact of pro-environmental laws and a dedication to sustainable energy sources. This research bolsters the notion that boosting the share of renewable energy sources in the energy mix is a practical way to cut carbon emissions. Policymakers can think about investing in renewable infrastructure, encouraging renewable energy sources and offering incentives for moving away from carbon-intensive energy sources [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA sustainable future depends on understanding the intricate relationships between carbon emissions, economic growth, energy consumption, green technology patents, renewable energy sources, and gross R\u0026amp;D expenditure. Economic growth has historically been linked to rising energy and carbon emissions, particularly in developed nations that rely significantly on fossil fuels. As the economy expanded following the Industrial Revolution, more carbon-intensive activities were undertaken, resulting in environmental problems like climate change. However, as global environmental consciousness has developed, there is a growing acknowledgement that a more sustainable paradigm is required. In this context, gross R\u0026amp;D expenditure is critical; countries that devote significant resources to R\u0026amp;D witness technical advances in environmental issues. Innovation is stimulated by higher R\u0026amp;D spending, which results in longer-term solutions, cleaner technology and more energy-efficient procedures. Environmental technology patents demonstrate this innovation and a commitment to using innovative methods to address environmental issues. The link between R\u0026amp;D spending and environmental patents is crucial. Countries prioritising environmental research and development are more likely to create technology that cuts carbon emissions, improves energy efficiency and encourages sustainable behaviour. Patents demonstrate a country's scientific strength and catalyse additional breakthroughs and international collaboration in tackling environmental challenges. Energy consumption patterns are closely linked to carbon emissions. The carbon footprints of nations that rely heavily on fossil fuels for energy generation are larger. Decoupling economic growth from carbon-intensive activities requires a move to renewable energy sources. Carbon emissions can be decreased with the help of renewable energy sources like solar, hydropower, and geothermal. Energy policies must be significantly adjusted to transition to renewable energy. Governments everywhere quickly realise how important it is to include renewable energy sources in their energy mix. Renewable energy incentives, subsidies, and regulatory frameworks support the transition. Climate change is exacerbated as nations become less dependent on fossil fuels as the share of renewable energy sources in the energy mix increases.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThe G20 nations comprise a significant portion of the world's energy consumers. The G20 nations made up over 80% of the world's energy consumption in 2019, according to the International Energy Agency (IEA). The G20 nations are large energy consumers who heavily rely on fossil fuels. Many countries recognise the benefits of moving to cleaner energy sources such as renewables, improving energy security and reducing greenhouse gas emissions. Large energy consumers in the G20 countries mainly depend on fossil fuels. A more thorough examination is necessary to enhance its relevance and specificity. The report acknowledges that several G-20 nations are leading the way in the switch to renewable energy, demonstrating noteworthy projects that demonstrate their dedication to sustainability. For example, Germany has adopted renewable energy through its Energy Transition Policy, which aims to switch to an environmentally friendly, low-carbon energy source. Similarly, Canada has invested significantly in wind and hydroelectric power, with British Columbia and Quebec setting the standard for clean energy production. By increasing the proportion of wind and solar energy in its energy mix, the UK has decreased its dependency on coal and achieved notable emissions reductions. By emphasising nuclear energy and investing in renewable technologies, France also helps reduce carbon emissions. Finally, China, the world's biggest manufacturer of solar panels, has greatly expanded its capacity for renewable energy by making impressive progress in deploying solar energy.\u003c/p\u003e\u003cp\u003eConversely, several G-20 nations still rely significantly on fossil fuels, making their sustainability objectives difficult to meet. For instance, the US remains a major consumer of fossil fuels, oil, and natural gas, which still account for most of the country's energy supply despite initiatives supporting renewable energy. Russia prioritises extracting and exporting fossil fuels due to its enormous oil and gas reserves, hindering its transition to cleaner energy sources. Australia is one of the top carbon emitters per capita despite its investments in renewable energy since it still primarily uses coal to generate electricity. Due to its economic dependence on the export of fossil fuels, Indonesia, a significant coal producer, faces obstacles in its transition to renewable energy. Lastly, despite its Vision 2030 initiative aimed at diversifying its economy, Saudi Arabia remains predominantly dependent on oil revenues, complicating its transition to renewable energy. Economic growth, environmental costs and excessive energy use must all be balanced by policymakers, even though energy conservation measures that reduce energy use may hinder growth.\u003c/p\u003e\u003cp\u003e \u003cb\u003ePolicy Implications\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe challenge for policymakers is strengthening steps to promote economic development, provided green technologies and practices are pursued. It also means that we can grow without the assumption that economic growth equals greater carbon emissions, showcasing the potential to disassociate economic prosperity from environmental detriment. Policymakers across G20 countries focus on innovation and patents in R\u0026amp;D on carbon emissions and economic growth. G20 nations can lower greenhouse gas emissions, boost economic expansion and contribute to a future with sustainable energy by fostering innovation in the energy industry. Governments must consider energy technology development to advance clean energy and energy efficiency technologies. G20 nations should switch to renewable energy sources, boost energy efficiency in buildings, transportation and industry, encourage sustainable transportation options, implement carbon pricing mechanisms, and support climate finance for developing nations to support sustainable development and reduce emissions.\u003c/p\u003e\u003cp\u003ePolicy interventions are critical for navigating the tangled web of economic growth, carbon emissions and environmental sustainability. Governments can enact policies that support clean technologies, enforce carbon pricing mechanisms and set aggressive renewable energy targets. International cooperation is also necessary because environmental concerns transcend national borders, and joint actions must have a real global impact. Finally, the integrated and dynamic relationship between carbon emissions, economic growth, energy consumption, R\u0026amp;D expenditure, patents in environmental technology, energy use and renewables is demonstrated. A holistic approach incorporating technical innovation, policy assistance and ecological stewardship is required to achieve sustainable development. It is critical to balance economic prosperity and environmental sustainability to ensure a robust and peaceful future for the next generation.\u003c/p\u003e\u003cp\u003eBased on our findings, we recommend a series of concrete actions that G20 policymakers can take to promote environmental sustainability. The first step is to create targeted funding mechanisms, which can be allocated to businesses engaging in product innovation and R\u0026amp;D in environmental-related technologies. Further tax benefits can be considered for companies that invest in green innovation or create grants to facilitate collaboration between universities and the industry. A one-stop-shop patent application process for environmental technologies would encourage more patents and future innovation. Furthermore, implementing policies to facilitate knowledge transfer and collaboration between the public and private sectors will ensure that innovative solutions are effectively integrated into practice. These specific and actionable recommendations aim to provide clear guidance for policymakers, helping to achieve the overarching goal of environmental sustainability.\u003c/p\u003e\u003cp\u003e \u003cb\u003eLimitations and Future Direction\u003c/b\u003e \u003c/p\u003e\u003cp\u003eWhile this study provides valuable insights into the relationship between energy, innovation, economic growth, and carbon emissions within G20 countries, it is not without limitations. This study is limited by its use of historical data, and it cannot keep up with the pace of technological enhancement and policy evolution as they evolve. Moreover, the analysis is primarily concerned with aggregate G20 statistics, overlooking to acknowledge the unique contexts and needs that individual countries in the group confront. Future studies should take a more detailed approach, looking at case studies of particular G20 countries to better understand the various routes to sustainability. A more thorough grasp of the relationship between environmental sustainability and economic growth could also be obtained by adding qualitative analyses of stakeholder perspectives and policy frameworks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u0026nbsp;\u003c/strong\u003eNot applicable\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u0026nbsp;\u003c/strong\u003eThis research did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration:\u0026nbsp;\u003c/strong\u003eThis study did not involve any procedures requiring ethical approval. Therefore, the ethics declaration is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish Declaration:\u0026nbsp;\u003c/strong\u003eThis study does not include identifiable information from participants, so the consent to publish declaration is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate Declaration:\u0026nbsp;\u003c/strong\u003eThis study did not involve human participants. Therefore, the consent-to-participate declaration is not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUnited Nations, \u0026quot;The Sustainable Development Goals Report 2024.\u0026quot; United Nations Statistics Division, 2024. [Online]. Available: https://unstats.un.org/sdgs/report/2024/.\u003c/li\u003e\n\u003cli\u003eOECD, \u0026quot;Global Corporate Sustainability Report 2024.\u0026quot; OECD Publishing, 2024. [Online]. 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(2025). \u003cem\u003eCustomised World Map\u003c/em\u003e [Online Image]. Retrieved from https://www.mapchart.net/\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Energy transition, Innovation, Clean technologies, Carbon Emissions, G20 nations, Energy security, Renewable energy, SDG","lastPublishedDoi":"10.21203/rs.3.rs-6040627/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6040627/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the relationship between energy, innovation, economic growth and carbon emissions in the G20 countries, which substantially influence global economic and environmental policies. The study focuses on key variables such as GDP, carbon emissions, energy consumption (total energy utilised), and energy use (per capita metrics). Additionally, it considers the adoption of renewable energy sources and innovation indicators such as gross expenditure on research and developments and the number of patents in environmental-related technologies. The study investigates annual data from 2000 to 2020 using panel data analysis and econometric techniques. The findings reveal significant long-run relationships, signifying that a rise in innovation (measured by R\u0026amp;D expenditure and patents) is linked to reducing carbon emissions. Simultaneously, economic growth tends to correlate with increased energy consumption. Therefore, it is essential to strike a balance between growth and sustainability. Through panel data analysis, a long-run, bi-directional relationship is established between research and development (R\u0026amp;D) and CO2 emissions, as well as between patents in environmental-related technologies and CO2 emissions, indicating that increases in R\u0026amp;D and patents can lead to lower emissions. At the same time, changes in emissions also influence R\u0026amp;D investments. Additionally, bi-directional relationships are observed between R\u0026amp;D and GDP per capita and CO2 emissions and GDP per capita. The study indicates that G20 policymakers should prioritise innovation in clean technologies by increasing research and development funding, encouraging several companies to create and patent these technologies and promoting meaningful international collaboration to share best practices in sustainable energy. This study pinpoints potential pathways toward sustainable development within G-20 nations. These important findings should help policymakers and key stakeholders achieve innovation-driven economic growth that balances large economic progress with strong ecological protection. G-20 nations must implement policies encouraging renewable energy and energy efficiency and balancing economic growth with ecological protection to guarantee a sustainable future.\u003c/p\u003e","manuscriptTitle":"Towards Achieving Environmental Sustainability: Role of Energy, Green Innovation and Economic Growth in G-20 Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 15:00:14","doi":"10.21203/rs.3.rs-6040627/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-27T14:10:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-26T10:26:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101451773234639020144420233590461404676","date":"2025-03-24T15:41:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-24T07:22:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-23T10:42:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301359650285850438481898446173496895214","date":"2025-03-22T15:19:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299236485647146014262079279256138281153","date":"2025-03-21T03:22:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168613289793001681386868120769747225071","date":"2025-03-20T23:39:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46668535053178214334624746548915367327","date":"2025-03-20T23:28:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18758643827711091783609741241430714109","date":"2025-03-20T18:40:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-20T18:06:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-18T12:08:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-18T12:07:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-02-16T10:35:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a18f1c57-56f3-4d93-93b7-7a48f8e62be0","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T15:58:34+00:00","versionOfRecord":{"articleIdentity":"rs-6040627","link":"https://doi.org/10.1007/s43621-025-01513-1","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2025-07-21 15:56:51","publishedOnDateReadable":"July 21st, 2025"},"versionCreatedAt":"2025-03-31 15:00:14","video":"","vorDoi":"10.1007/s43621-025-01513-1","vorDoiUrl":"https://doi.org/10.1007/s43621-025-01513-1","workflowStages":[]},"version":"v1","identity":"rs-6040627","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6040627","identity":"rs-6040627","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}