Environmental Green Innovation and Economic Performance of China: Mediating of Governance Quality Index

Environmental Green Innovation and Economic Performance of China: Mediating of Governance Quality Index | 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 Environmental Green Innovation and Economic Performance of China: Mediating of Governance Quality Index Lin Ying, Jawad Khan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7765055/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study aims to find the impact of environmental green innovation on the economic performance and also find the mediating role of the governance quality index. Utilizing annual time series data from 1996 to 2020 and find the long term impact through Cointegration analysis. Applied robust analysis method through canonical Cointegration regression (CCR), robust least squares (RLS) and fully modified least squares (FMOLS) to find the reliability of the results. The study evaluates the mediation effect to determine whether the governance quality index partially or fully explains the relationship between the dependent variable GDP per capita and independent variables green technological innovation indicators. Analysis find total effect through direct and indirect effect of green technological innovation on the economic performance after accounting the mediator effect of governance quality index. Findings reveal that the direct relationship between green innovation and economic performance is significantly mediated by governance quality. The results indicate that interventions aimed at improving governance quality could effectively enhance the positive impact of green innovation on economic performance. These findings have important implications for policymakers and stakeholders, suggesting that strengthening governance structures can amplify the benefits of green innovation for sustainable economic growth. Green Innovation Economic Performance Time-series Data and Mediating Role of Governance Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction China's impressive representation in the top scientific and technology clusters, as highlighted by the World Intellectual Property Organization (WIPO) and the Global Innovation Index (GII) annual innovation report, underscores its robust innovation ecosystem. According to the report, three of the top five scientific and technology clusters are located in China, and the country has the most clusters when the rankings are expanded to include the top 100. This achievement reflects the country’s significant investments in research and development, as well as its supportive policy environment for innovation 1 . China's entry into the "new normal" stage around 2014 marked a significant transition in its economic trajectory. After decades of rapid economic growth and substantial structural upgrades, this period saw a notable deceleration in GDP growth rates. Research by Dong, et, al., (2018) and Wang & Li ( 2017 ) highlights this shift, emphasizing the following key points: The era of double-digit GDP growth gave way to more moderate rates as the economy matured. This slowdown was not merely a cyclical phenomenon but a structural adjustment aimed at ensuring more sustainable long-term growth. The Chinese government focused on rebalancing the economy from being heavily investment and export-driven to one that prioritizes domestic consumption and services. This shift aimed to reduce vulnerabilities associated with external demand fluctuations and overinvestment in certain sectors. There was a concerted effort to upgrade the industrial base, moving from low-cost manufacturing to more advanced, high-tech industries. This involved significant investments in innovation, research, and development, aligning with the goals of initiatives like "Made in China 2025." Environmental sustainability became a key focus, with stricter environmental regulations and policies promoting green innovation and clean technologies. This was part of a broader strategy to address the environmental degradation that had accompanied rapid industrialization. Structural reforms in various sectors, including finance, state-owned enterprises (SOEs), and the labor market, were implemented to increase efficiency and competitiveness. These reforms were aimed at addressing deep-seated issues and fostering a more market-oriented economy. The government placed a strong emphasis on maintaining social and economic stability. This involved measures to reduce income inequality, improve social welfare systems, and ensure stable employment. And earlier attempts were expensive, including, among other things, unequal economic structures, severe overcapacity, low efficacy and vitality, environmental damage, and resource waste. In the emerging normal stage, resource constraints, ecological and environmental degradation, and major modifications to the development paradigm have made things worse than before. Rebalancing the economy, improving energy efficiency, and ensuring energy security are in high demand. 1.1. Environmental Technology China is the world's most significant emerging market for products related to environmental technology, where growth is expected to average 14.1% CAGR between 2016 and 2020. China environmental protection section operating income was around 2.18 trillion yuan in 2021 and which are increased 11.8% from 2020. With nearly 3.2 million employees, the environment protection industry's operational income represented 1.9% of GDP. The Chinese government main policy goal to addressing climate change and environmental preservation. The people’s republic China government announced to keep the environment clean and avoid the pollution by rid of heavy air pollution, cleaning up dirty, black water bodies in urban areas, stepping up efforts to prevent soil contamination, and enhancing environmental infrastructure development. Reducing particulate matter and ozone pollution, cleaning up sewage outflows into rivers and seas, treating wastewater from industrial parks, preventing agricultural contamination, and expanding the capacity of urban residential sewage collection are among the issues the PRC government is concerned with. Additionally, the PRC government is promoting environmentally friendly packaging, increasing the collection and handling of harmful and medical waste, and separating household rubbish 2 . China's energy consumption is rising, which specifies how rapidly manufacturing and urbanization are developing. Furthermore, an enormous amount of focus is being placed on how the building industry significantly contributes to environmental degradations, air pollution, water and land pollution, and the reduction of natural resources (Esen & Yuksel, 2013 ). According to Jin, et, al., (2018), China used the most energy worldwide in 2017. However, 90% of China's energy comes from coal and petroleum, the country's primary energy sources. Since environmental degradation has become a severe concern and China has enormous energy consumption and infrastructure dominated by coal, this has placed substantial pressure on the country's ability to expand economically sustainably (Li & Jiang, 2016 ). Throughout the current normal stage, China is under pressure to increase economic growth efficiency and quality while adopting an environmentally friendly development direction. The government is transforming the economy way from the investment driven growth model and resources towards the innovation driven development model, which are anticipated China primary driving in the future economy directions. Economic experts and policymakers emphasized the need for the energy industry to move towards innovation-driven development, especially about the more sustainable growth and development of renewable energy technologies. Energy innovation, particularly in the realm of renewable energy, has a profound impact on economic development. Studies by Garces & Daim ( 2012 ), Kander & Stern ( 2014 ), and Pahle, et, al., (2016) underscore the critical role that advancements in renewable energy play in boosting energy production and consumption, thereby laying a strong foundation for a highly industrialized economy. Technological innovations in solar, wind, hydro, and other renewable sources have significantly increased energy production capabilities, providing a reliable and sustainable energy supply. As renewable energy technologies become more efficient and cost-effective, they facilitate greater energy consumption, driving industrial activities and economic growth. Incorporating green technologies into construction and other industrial processes helps mitigate the negative environmental impacts traditionally associated with these activities. This includes reducing carbon emissions, lowering pollution levels, and conserving natural resources. Green technologies often result in long-term cost savings through improved energy efficiency, lower operational costs, and reduced waste. Studies by Esen, et, al., (2006 & 2007) and Chang, et, al., (2018) highlight the significant financial benefits of integrating green technologies in construction processes. Prioritizing green innovation and energy innovation transformation is crucial for achieving environmental sustainability. This involves developing and deploying technologies that reduce reliance on fossil fuels and minimize ecological footprints. Promoting the use of renewable energy sources is vital for long-term economic stability. This transition supports high-quality economic development by fostering a cleaner, healthier environment and creating new industries and job opportunities. Improvements in green technology are now having a significant influence on economic activities. It is gaining a lot of media coverage due to its usage as a means of coercion to assist the Chinese economy and its significance in defining the next phase of renewable energy growth. Therefore, additional study on green innovation is needed. Furthermore, China's now industrial and urban demand for energy, being an inflexible need, could sustain significant growth throughout the midterm (Jiang & Lin, 2012 ). Promoting cooperation on technological development is important for attempting global climate change or regional pollution. Advance research and technology promote the local enterprises use existing innovations 3 . Figure 2 graphical representation illustrates how historically trend going? the percentage of the overall energy supply that comes from renewable sources decreased from the 1990s to 2011 but then somewhat increased until 2019. From the 1990s until 2000, the development of environmental technologies (as a percentage of all technologies) decreased; however, it then increased for a year before fluctuating little until 2019. The GDP per capita growth direction, which serves as a proxy for the economy, is seen in Fig. 3 . However, this new normal stage requires not only the transition from an investment-driven approach to an innovation-driven mode of execution but also the protection of ecological and environmental resources. Therefore, conducting the comprehensive inquiry, the relation between China energy consumption, economic sustainability and green innovation transformation are vital rule, while keeping the following objectives to look at how China's economy is affected by green innovation. The Fig. 3 illustrates the complex dynamics between renewable energy supply and the development of environment-related technologies over three decades. While the renewable energy supply's share has declined and then stabilized, the development of green technologies has gained momentum, particularly in recent years. This shift towards green innovation is crucial for achieving long-term environmental sustainability and high-quality economic development. Since environmental issues have started to have a significant global impact, innovative green technology has grown in favor of scientific and political environments (Antonioli et, al., 2016; Mazzanti et, al., 2017). The word green innovation is an innovative approach for technological innovation and describing the novel concepts, products, services, conventions, and management approaches. And this will be minimizing the consumption of renewable resources and harmful emissions of chemical. It adheres to ecological values and prevents, eliminates, or minimizes environmental challenges (Kemp, 2010 ; Rennings, 2000 ). Green innovation is being suggested as a goal for national innovation programs to attain sustainable growth in the economy, society, and the environment. The economy of China improved significantly from last past 40 forty years and sustainability has currently drawn further attention. According to Ma et, al., (2019); Shaikh et, al., (2016); Wang et, al., (2016) previous research has shown a relationship between consistent economic development and an increase in the need for energy, namely the use of fossil fuels. The issues of resource reduction, economic growth, and ecological civilization downfall are all interrelated. In response to these concerns, China will unavoidably encourage green innovation in the region. Chinese government has pledged to pursue policies that support technologically sophisticated, ecologically conscious, market-oriented improvements. But moving ahead, should various disciplines focus on distinct facets of green innovation, or should they take a "one-size-fits-all" approach? There isn't a conclusive answer available from the studies underway. Examining the factors underlying regional innovations in sustainability as well as the spatial-temporal transitions of these features is of extreme importance right now. There are no medium-level examinations of countries within the literature currently under publication; rather, it focuses on comprehensive and in-depth studies of green innovation at the macro level of countries (Cho, et, al., 2018; Song, et, al., 2015) and at the micro-level of businesses (Cuerva, et, al., 2014; Stucki, 2019 ). An important metric for assessing the effectiveness and spread of environmental innovation across all areas is regional green innovation. It shows the overall competence of an area in terms of input, outcomes, process management, etc. related to green innovation. China is a sizable nation. Regional heterogeneity in emerging green technology is therefore inextricably impacted by disparities in the natural resource base, economic growth trajectory, and intellectual and technical capacities of its many areas. This diversity makes it difficult for the companies in the area to flourish since the environment and resources are coordinated to expand. This research uses a variety of theoretical studies and empirical data to methodically examine the important variables influencing green innovation and China's economic success. Using time series data, the study intends to evaluate the green innovation factors in China's economy while examining the good governance factors as a mediator. To look at how green innovation affects GDP per capita over the long run, using this as an economic proxy. In economics, the gross domestic product (GDP) per capita is used to examine the economic output per capita of a country's economy. Economists estimate a country's measure of individual person wealth using GDP per capita, which is dependent on economic growth. A country's GDP per capita is determined by dividing its total population. China is leading the way in the creation of a low-carbon, green, and sustainable economy. It uses eco-friendly production methods, promotes the energy revolution, resource economy and efficiency, cleaner production, and cooperative initiatives to reduce pollution and carbon emissions. 2. Literature Review Green innovation has become increasingly important as environmental issues have gained global significance, influencing both scientific research and political agendas (Antonioli et, al., 2016; Mazzanti et, al., 2017). The term "green innovation" describes the development and use of novel ideas, goods, services, and management strategies that reduce the consumption of renewable resources and the release of dangerous chemicals while upholding ecological principles to avoid or lessen environmental problems (Kemp, 2010 ; Rennings, 2000 ). This approach is crucial for achieving sustainable growth in the economy, society, and the environment. China's economy has seen significant growth over the past forty years, accompanied by increased attention to sustainability. Previous studies indicate a strong relationship between stable economic growth and an increased demand for energy, particularly fossil fuels (Ma, et, al., 2019; Shaikh, et, al., 2016; Wang, et, al., 2016). China's regional level must promote green innovation due to the interconnected issues of resource depletion, growth in the economy, and ecological degradation. Chinese government has committed to policies that support technologically advanced, ecologically conscious, market-oriented improvements. Moving forward, the debate remains whether different disciplines should focus on distinct aspects of green innovation or adopt a uniform approach. Current studies do not provide a conclusive answer. Therefore, it is crucial to examine the mechanisms driving regional innovations in sustainability and the spatial-temporal transitions of these traits. Most existing literature either focuses on comprehensive macro-level research on green innovation at the country level (Cho, et, al., 2018; Song, et, al., 2015) or detailed micro-level studies at the enterprise level (Cuerva, et, al., 2014; Stucki, 2019 ). There is a lack of medium-level assessments that examine green innovation across regions. Regional green innovation is an important metric for assessing the effectiveness and spread of environmental innovation, reflecting the overall competence of an area in terms of inputs, outcomes, and process management related to green innovation. This research employs various theoretical and empirical studies to systematically examine the key variables influencing green innovation and China's economic success. Using time series data, the study aims to evaluate the factors contributing to green innovation in China's economy while considering good governance factors as a mediator. The objective is to evaluate the long-term effects of green innovation on GDP per capita, a measure of a country's economic production per resident. China has made significant strides in developing a circular, green, and low-carbon economy. The country promotes eco-friendly production methods, an energy revolution, resource economy and efficiency, cleaner production, and collaborative initiatives to reduce pollution and carbon emissions. Emphasis on developing new technologies and processes that are environmentally friendly and sustainable. Support for green innovation through technologically advanced and ecologically conscious policies. Importance of understanding regional variations in green innovation due to differences in natural resources, economic growth trajectories, and technical capacities. Investigating the relationship between green innovation and economic success by combining theoretical and empirical data, with a particular emphasis on governance as a mediating component. Green innovation's long-term economic effects are assessed using GDP per capita. The goal of the research is to offer a thorough grasp of the elements influencing green innovation in China and how it affects the country's economic growth. By focusing on regional variations and the role of good governance, the research seeks to offer insights into how green innovation can be effectively promoted to achieve sustainable and high-quality economic growth. 3. Data and Methodology 3.1. Data Source This study is examined how the green innovation impact on the China economy and the mediation role of governance index, using secondary timeseries data of the national indicators from 1996 to 2020. The source of this data is Organization of Economic Cooperation and Development (OECD), World Development Indicators (WDI) and World Governance Indictors (WGI). Below Table 1 given are more detailed information. Table 1 Variables Measurement and Sources Variables Measurement Source Dependent Variable GDP Per Capita GDP per capita (current US $ ) WDI Independent Variables Green Technological Innovation Development of technologies related to the environment, % of all technologies OECD Research & Development Expenditure Research and Development expenditure (% of GDP) WDI Renewable Energy Supply of renewable energy as a percentage of overall supply WDI CO2 Production Based on CO2 Productivity and CO2 Production GDP per unit of energy-related CO2 emissions OECD Labor Force Labor force (% of population) WDI Trade Openness Sum of exports and imports, percentage of GDP WDI Mediating Variable Governance index Index of six indicators of Governance control of corruption, government effectiveness, political stability, regulatory quality, rule of law and voice of accountability WGI 3.2. Research Design 3.2.1. Empirical Model The following steps of Baron & Kenny ( 1986 ) for mediation analysis Its laid out to meet some requirements for the mediation relationship. Below are real-world example equations and see the Fig. 4 for the visual representation of the overall mediation relationship. In the mediation analysis the independent variables cause the mediator variable and mediator variable causes dependent variable. In the first step we regress the dependent variable GDP per capita as proxy variable of economic performance on the independent variables green technological innovation, research & development expenditure, renewable energy, CO2 production, labor force, and trade openness to confirm that independent variables are significant forecast of dependent variable. Independent variables =˃ dependent variable \(\:{Y\left(GDPCP\right)}_{t}=\:\beta\:+\:{\beta\:}_{1}{TI}_{t}+\:{\beta\:}_{2}{R\&DE}_{t}+\:{\beta\:}_{3}{RE}_{t}+\:{\beta\:}_{4}{CO2}_{t}+\:{\beta\:}_{5}{LF}_{t}+\:{\beta\:}_{6}{TO}_{t}+\:{\varepsilon\:}_{t}\:\) __ Eq. (1) In the second step we regress the mediator on the independent variables green technological innovation, research & development expenditure, renewable energy, CO2 production, labor force, and trade openness to confirm that independent variables are significant predictor to verify independent variables are the significant predictor for mediator. If mediator has not significant relation with the independent variables, then this means that this mediator variable could not possible. Independent variables =˃ mediator variable \(\:M({Gov)}_{t}\:=\:\beta\:+\:{\beta\:}_{1}{TI}_{t}+\:{\beta\:}_{2}{R\&DE}_{t}+\:{\beta\:}_{3}{RE}_{t}+\:{\beta\:}_{4}{CO2}_{t}+\:{\beta\:}_{5}{LF}_{t}+\:{\beta\:}_{6}{TO}_{t}+\:{\varepsilon\:}_{t}\:\) __ Eq. (2) In the third step we regress the dependent variable GDP per capita on the both independent variables green technological innovation, research & development expenditure, renewable energy, CO2 production, labor force, and trade openness and mediator variable governance index to confirm the first mediator variable is significant predict of the dependent variable and second it also check the strength of coefficient of previous significance independent variable in first step greatly reduced and if not rendered insignificant. \(\:{Y\left(GDPCP\right)}_{t}\:=\:\beta\:+\:{\beta\:}_{1}{TI}_{t}+\:{\beta\:}_{2}{R\&DE}_{t}+\:{\beta\:}_{3}{RE}_{t}+\:{\beta\:}_{4}{CO2}_{t}+\:{\beta\:}_{5}{LF}_{t}+\:{\beta\:}_{6}{TO}_{t}+{\beta\:}_{7}{M\left(Gov\right)}_{t}+\:{\varepsilon\:}_{t}\:\) ____ Eq. (3) Where t demotes time; and ɛ denotes error term; the dependent variable is as proxy variable of economic growth and it denotes GDP per capita; independent variable denotes the technological innovation as a proxy variable of green innovation; denotes the renewable energy; denotes the corban dioxide emissions; denotes labor force (% of population); denotes trade openness (sum of exports and imports) and is the mediating variable and it denotes the governance index of six world governance index indicators. The study further analyses national-level variables for robustness analysis. The research hypothesis was empirically investigated using canonical Cointegration, robust least square, fully modified ordinary least square, and other parametric and non-parametric Cointegration regression estimators. These estimators reduced the likelihood that the provided regression equations would have problems with endogeneity, serial correlation, and heteroscedasticity. These regressions allow for the inclusion of leads and lags in the given model, significantly alleviating the endogeneity issues. 3.2.2. Augmented dickey fuller test This test allows for higher-order autoregressive approach by involving for the econometric or statistical model. And this study has still this if \(\:{\varDelta\:y}_{t}=\:\alpha\:+\:\beta\:t+\:{{\rm\:Y}y}_{t-1}+\:{\delta\:}_{1}{\varDelta\:y}_{t-1}+\:+\:{\delta\:}_{2}{\varDelta\:y}_{t-2}\:\) ________________ Eq. (4) The null hypothesis of this data test is non-stationary, and we will reject null hypotheses; therefore, the p-value is probably less than 0.05 (Paparoditis & Politis 2018 ). 3.2.3. Serial Correlation The serial correlation normalized by the product of spread gives the serial correlation or autocorrelation of lags of the second-order stationary timeseries. That is, p 0 = C 0. It is worth noting that p 0 = C 0 2 = E[(xt - ) 2]. σ2 = σ2 σ2 = 1. A statistical term used to describe the relationship between variables is serial correlation (Li & Stengos 2003 ). 3.2.4. Structural Equation Model The structural equation model (SEM) applied here specifies governance as a mediator between innovation, energy, trade, labor, and economic performance, following a two-equation framework. In the first equation, governance is explained by green technological innovation, research and development (R&D) expenditure, renewable energy adoption, CO₂ productivity, labor force participation, and trade openness. This design reflects evidence that innovation and openness often strengthen institutional quality by promoting transparency, accountability, and efficiency, whereas environmental degradation and weak energy performance may undermine governance effectiveness (Acemoglu, & Robinson, 2013 ); OECD, 2017 ; World Bank, 2020 ). In the second equation, GDP per capita is explained by governance and the same predictors, thereby capturing both direct and indirect effects. The approach is consistent with institutional economics, which posits that effective governance determines how resources are allocated for growth (North, 1990 ; Kaufmann, et, al., 2011). Endogenous growth theory underscores the role of innovation and R&D in sustaining long-run growth through knowledge accumulation (Romer, 1990 ; OECD, 2019 ), while renewable energy adoption has been shown to foster sustainable growth and energy security (Apergis, & Payne, 2010 ). Trade openness contributes through technology transfer and productivity spillovers (Frankel, & Romer, 2017 ), though conditional on governance capacity. Labor force participation, as highlighted in cross-country growth studies, represents a fundamental input to economic growth through human capital and labor supply (Barro, 1991 ). Finally, the Porter Hypothesis suggests that environmental innovation such as green technologies can enhance both competitiveness and economic outcomes (Porter, & Linde, 1995 ). 4. Empirical Estimation and Discussion 4.1. Data Summary Descriptive statistics provide a summary of the data, which is crucial for decision-makers to understand and analyze populations effectively. For the time series data variables in this study, descriptive statistics will include measures such as the mean, median, maximum, minimum, and standard deviation. These measures help condense the information into a more comprehensible summary (Kaur, et, al., 2018). Table 2 Descriptive Statistics Variables Mean Median Maximum Minimum Standard Deviation GDP Per Capita 4129.064 3081.143 10143.86 709.415 3298.097 Green Technological Innovation 8.144 8.595 10.330 3.970 1.501 Research & Development Expenditure 1.461 1.409 2.244 0.563 0.547 Renewable Energy 12.529 9.710 20.830 7.460 4.998 CO2 Production 1.595 1.505 2.320 1.130 0.330 Labor Force 57.538 57.763 59.059 55.075 1.148 Trade Openness 45.282 43.826 64.478 32.424 10.121 Governance index 36.742 35.437 43.062 32.676 3.236 Table 2 provides the descriptive statistics, and the standard deviation shows the degree of data distribution concerning the mean. GDP per capita, renewable energy, trade openness and governance index standard deviation has more 2 and this means these variables data are more spread. The green technological innovation, research and development expenditure, CO2 production and labor force standard deviation value has less than 2, so this means these variables data has more spread. Variables with a standard deviation greater than 2 (GDP per capita, renewable energy, trade openness, and governance index) have data points that are more widely spread around the mean, indicating significant differences and fluctuations across observations. Variables with a standard deviation less than 2 (green technological innovation, R&D expenditure, CO2 production, and labor force) have data points that are less spread, indicating more consistent values across observations. Understanding the spread and variability of the data is crucial for decision-makers as it provides insights into the stability and consistency of the variables, helping to identify areas with significant disparities and potential targets for policy intervention and improvement. 4.2. Unit Root Test Below Table 3 presents the trend and intercept of time-series annual data from 1996 to 2020. The results are generated for each variable: GDP Per Capita, green Technological Innovation, Renewable Energy, CO2 Production, Labor Force, Trade Openness, Research and development and governance index. Time series models used for prediction. However, the most common methods for fitting these models require that the series remain stationary. Recently, several techniques have been proposed for locating unit roots in time series data (also known as nonstationary tests). Using autoregressive moving-average models with a linear chronological trend component, this study proposes a test for unit roots. The testing approach is founded on Fuller's (2009) estimate method, which is essentially a linear estimate. Table 3 Unit Root ADF Test Variables At Level At first difference Decision t-Statistic (Prob) t-Statistic (Prob) GDP Per Capita -1.570 (0.779) -4.580*** (0.007) I(1) Green Technological Innovation -2.386 (0.378) -6.536*** (0.000) I(1) Research & Development Expenditure -2.023 (0.559) -4.416*** (0.010) I(1) Renewable Energy -0.666 (0.966) -3.537** (0.053) I(1) CO2 Production -1.912 (0.622) -2.213** (0.028) I(1) Labor Force -1.308 (0.867) -6.567*** (0.000) I(1) Trade Openness -1.270 (0.877) -4.517*** (0.005) I(1) Governance index -2.796 (0.212) -4.905*** (0.004) I(1) Ho: Series contains Unit Root and Data is not stationary ***Rejection of Null hypothesis at 1% (P < 0.01) **Rejection of Null Hypothesis at 5% (P < 0.05) *Rejection of Null Hypothesis at 10% (P < 0.10) Table 3 presenting all variables dependent variable GDP per capita and independent variables green technological innovation, renewable energy, CO2 production, labor force, trade openness, research and development, and governance index are stationary at first difference through augmented dickey fuller test. This means that we need to use the Johansen Cointegration test for Cointegration connections between each variable that are non-stationary time series data at level but stationary at the first difference. Like the Engle-Granger test, the Johansen test permits more than one Cointegration connection (Poh & Tan, 1997 ). The results from the ADF test show that all variables are non-stationary at their levels but become stationary after taking the first difference. Thus, all variables are integrated of order 1 (I(1)). This finding indicates that further analysis, such as Cointegration tests, is necessary to determine if there is a long-term equilibrium relationship among the variables. The rejection of the null hypothesis at the first difference indicates that shocks to these variables are temporary, and they revert to their long-term mean over time. 4.3. Correlation Matrix The link between observations of the same variable over a specified period, encompassing 1990 to 2020, is described statistically by the phrase "serial correlation." If the serial correlation measurement for the variables in question is zero, then there is no link between those variables, and every observation is independent of the others. The data are sequentially correlated, meaning that if a serial correlation leans one way, it will affect subsequent observations. In other words, variables often associated have patterns and are not random (Anderson, 1942 ). Table 4 Serial Correlation Matrix Variables GDP Per Capita Green Technological Innovation Research & Development Renewable Energy CO2 Production Labor Force Trade Openness Gov GDP Per Capita 1.00 0.47 0.95 -0.76 0.93 -0.75 -0.18 0.88 Green Technological Innovation 0.47 1.00 0.38 -0.36 0.27 -0.49 -0.22 0.50 Research & Development Expenditure 0.95 0.38 1.00 -0.91 0.86 -0.50 0.10 0.74 Renewable Energy -0.76 -0.36 -0.91 1.00 -0.59 0.15 -0.45 -0.51 CO2 Production 0.93 0.27 0.86 -0.59 1.00 -0.77 -0.30 0.81 Labor Force -0.75 -0.49 -0.50 0.15 -0.77 1.00 0.73 -0.84 Trade Openness -0.18 -0.22 0.10 -0.45 -0.30 0.73 1.00 -0.36 Gov 0.88 0.50 0.74 -0.51 0.81 -0.84 -0.36 1.00 Table 4 presents GDP Per Capita has strong correlations with R&D Expenditure, CO2 Production, and Governance Index, indicating that these variables are likely important drivers of economic output per capita. Green Technological Innovation shows a moderate relationship with Governance Index, suggesting that good governance can positively influence green innovation. R&D Expenditure is strongly linked to economic output and environmental metrics, highlighting the importance of investment in research for economic and environmental progress. Renewable Energy has strong negative correlations with traditional economic measures such as GDP Per Capita and R&D Expenditure, indicating a possible trade-off between renewable energy adoption and conventional economic growth measures. CO2 Production is positively correlated with economic measures but negatively correlated with Labor Force, suggesting that higher emissions are associated with economic growth but not necessarily with employment growth. Labor Force is positively correlated with Trade Openness, indicating that open trade policies may support employment. Governance Index is crucially linked with multiple variables, emphasizing the importance of governance quality in economic and environmental outcomes. These quants' serial correlation is ascertained using the Durbin-Watson test. Either the link works properly, or it doesn't. Positive serial correlation indicates a positive trend in the variables. When security shows a negative serial correlation, it ultimately starts to damage itself (Savin & White, 1977 ). 4.3. Johansen Cointegration Test There are two kinds of Johansen tests: maximum eigenvalue tests and trace tests. Researchers set K0 to zero to see whether the null hypothesis is rejected when employing the trace test to look for Cointegration in a time-series data sample. Researchers might conclude that the time series sample of data has a Cointegration connection if it is rejected (Bernard & Durlauf, 1995 ). Table 5 Johansen Cointegration test for long term effect Unrestricted Cointegration rank test (trace) Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.991 362.650*** 125.615 0.000 At most 1 * 0.982 257.749*** 95.753 0.000 At most 2 * 0.964 168.604*** 69.818 0.000 At most 3 * 0.831 94.930*** 47.856 0.000 At most 4 * 0.785 55.817*** 29.797 0.000 At most 5 * 0.521 21.980*** 15.494 0.004 At most 6 * 0.229 5.743*** 3.841 0.016 Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.991 104.900*** 46.231 0.000 At most 1 * 0.982 89.145*** 40.077 0.000 At most 2 * 0.964 73.673*** 33.876 0.000 At most 3 * 0.831 39.113*** 27.584 0.001 At most 4 * 0.785 33.837*** 21.131 0.000 At most 5 * 0.521 16.236** 14.264 0.024 At most 6 * 0.229 5.743*** 3.841 0.016 The max-eigenvalue test indicates 7 cointegrating eqn(s) at the 0.05 level denotes rejection of the hypothesis at the 0.05 level. **MacKinnon-Haug-Michelis (1999) p-values Ho: No Cointegration between the variables. ***Rejection of null hypothesis at 1% (P < 0.01) **Rejection of null hypothesis at 5% (P < 0.05) *Rejection of null hypothesis at 10% (P < 0.10) Table 5 illustrates both the trace test and maximum eigenvalue test indicate multiple Cointegration relationships among the variables. This implies that there are several long-term equilibrium relationships in the dataset. The existence of these Cointegration relationships suggests that despite short-term fluctuations, the variables move together in the long run, maintaining a stable relationship over time. Policymakers should consider these long-term relationships when designing economic and environmental policies. Understanding these relationships can help in creating strategies that promote sustainable development and economic stability. Further studies could delve into the specific nature of these Cointegration relationships and explore the dynamics between different variables to develop a deeper understanding of the mechanisms at play. The Johansen Cointegration Test results indicate strong evidence of multiple Cointegration relationships among GDP per capita, green technological innovation, renewable energy, CO2 production, labor force, trade openness, research and development expenditure, and governance index. This highlights the interconnectedness of these variables and underscores the importance of considering these relationships in economic and environmental policymaking. This approach also demonstrates that all variables have a for a long time Cointegration relationship. There were two significant findings from this inquiry. First, theoretical results are connected to the market's effectiveness theory, followed by actual results (Nasseh & Strauss, 2000 ). These findings underline the importance of understanding the long-term interactions among key economic, environmental, and governance variables, providing a robust foundation for both theoretical insights and practical policy-making. 4.4. Robustness Models Estimation To robustly estimate the relationships between the dependent variable (GDP per capita) and the independent variables (Green Technological Innovation, Research & Development Expenditure, Renewable Energy, CO2 Production, Labor Force, Trade Openness, Governance Index), three advanced econometric techniques were employed Canonical Cointegration Regression (CCR) is designed to provide efficient and consistent estimators for Cointegrated systems by transforming the data to eliminate endogeneity and serial correlation. It corrects for potential biases and ensures robust long-term parameter estimates. Robust Least Squares (RLS) is used to handle potential outliers and heteroscedasticity in the data. It provides more reliable coefficient estimates by giving different weights to observations, reducing the influence of outliers. Fully Modified Least Squares (FMOLS) adjusts for serial correlation and endogeneity in the regressors that result from Cointegrated relationships. It produces asymptotically unbiased estimates by modifying the least squares estimator to account for the potential correlation between the regressors and the error term. Mediation analysis was employed to explore the underlying mechanisms through which green innovation influences GDP per capita, using governance quality as the mediating variable. This analysis helps to understand whether the impact of green innovation on economic performance is direct or occurs indirectly through the influence of good governance. Table 6 Step One for Green Technological Innovation and Economic Growth Variable FMOLS RLS CCR Coefficient (Prob Value) Coefficient (Prob Value) Coefficient (Prob Value) Green Technological Innovation 108.031** (0.050) 260.735*** (0.007) 109.975** (0.040) Research & Development Expenditure 2677.495*** (0.005) 6789.313*** (0.001) 2692.466*** (0.015) Renewable Energy 195.476** (0.023) 257.468 (0.107) 195.821** (0.058) CO2 Production 216.438 (0.699) 1058.851 (0.457) 122.378 (0.832) Labor Force -1344.502*** (0.000) -223.236*** (0.007) -1348.254*** (0.000) Trade Openness -5.291 (0.645) 0.261 (0.992) -6.153 (0.630) C 80796.170*** (0.000) 18.639* (0.082) 81194.910*** (0.000) R-squared 0.995 0.982 0.995 Adjusted R-squared 0.993 0.978 0.993 ***Significance level at 1% (P < 0.01) **Significance level at 5% (P < 0.05) *Significance level at 10% (P < 0.10) Table 6 presenting that green technological innovation, research & development expenditure, renewable energy and labor force has been statistically impact on the GDP per capita as proxy variable of economic growth. And this means that this study finds total effect (c path) between dependent and independent variables but without including mediator variable governance index. All one-unit increase in independent variables green technological innovation, research & development expenditure, renewable energy and labor force, dependent variable GDP per capita increases by independent variables green technological innovation, research & development expenditure, and renewable energy units, and mediator variable governance index is not included in the model. Table 7 Step two Green Technological Innovation and Mediator Governance Index Variables FMOLS RLS CCR Coefficient (Prob Value) Coefficient (Prob Value) Coefficient (Prob Value) Green Technological Innovation 0.093** (0.033) 0.115* (0.070) 0.116* (0.079) Research & Development Expenditure 0.576*** (0.002) 0.438** (0.048) 2.515** (0.054) Renewable Energy 0.574 (0.214) 0.647 (0.302) 0.593 (0.250) CO2 Production -6.535** (0.031) -5.659 (0.158) -6.320** (0.032) Labor Force 0.074 (0.963) 0.077 (0.969) 0.785 (0.745) Trade Openness 0.083 (0.161) 0.092 (0.261) 0.073 (0.261) C 22.734 (0.819) 18.639 (0.882) -17.679 (0.902) R-squared 0.888 0.768 0.887 Adjusted R-squared 0.836 0.899 0.834 ***Significance level at 1% (P < 0.01) **Significance level at 5% (P < 0.05) *Significance level at 10% (P < 0.10) Table 7 presenting the mediation analysis governance index variable is the mediator variable. Green technological innovation, research & development expenditure and CO2 production has had statistically positive significant impact on the governance index. And this means that this study finds the direct effect (c path). And the trend increasing in the green technological innovation, research & development expenditure and CO2 production, and this will also increase the governance index and quality of performance of governance. Table 8 Step three Green Technological Innovation and Economic Growth with Mediator Governance index Variables FMOLS RLS CCR Coefficient (Prob Value) Coefficient (Prob Value) Coefficient (Prob Value) Green Technological Innovation 96.418** (0.016) 92.116 (0.198) 95.726** (0.047) Research & Development Expenditure 2223.502** (0.036) 1907.923 (0.108) 2461.748* (0.085) Renewable Energy 209.315** (0.031) 228.447** (0.034) 193.473 (0.118) CO2 Production 668.927 (0.315) 862.636 (0.270) 475.998 (0.550) Labor Force -1131.546*** (0.000) -1153.132*** (0.000) -1194.676*** (0.001) Trade Openness 10.583** (0.031) 10.122** (0.028) 7.573 (0.683) Governance index 74.991* (0.063) 79.545* (0.087) 54.696 (0.512) C 66047.74*** (0.000) 67465.710*** (0.0000) 70047.190*** (0.0030) R-squared 0.996 0.913 0.995 Adjusted R-squared 0.994 0.996 0.994 ***Significance level at 1% (P < 0.01) **Significance level at 5% (P < 0.05) *Significance level at 10% (P < 0.10) Table 8 presents the indirect effect (a*b product) of the independent variables on GDP per capita through the mediator variable, Governance Index. This mediation model uses Canonical Cointegration Regression (CCR), Robust Least Squares (RLS), and Fully Modified Least Squares (FMOLS) regressions to understand how the mediator variable explains the relationship between the independent and dependent variables. Green technological innovation has a statistically significant positive impact on GDP per capita at a 5% significance level, indicating that innovations in green technology bolster and improve economic prosperity and Zhou and Luo ( 2018 ) suggest that economic growth and new technologies interact dynamically, enhancing economic performance through green technology advancements. Research & Development Expenditure shows a statistically significant positive impact on GDP per capita at a 5% significance level, implying that increased R&D expenditure drives economic growth and Bayarcelik & Taşel ( 2012 ), Huňady & Orviská ( 2014 ) found similar results, confirming the positive relationship between R&D and economic growth. Renewable energy has a negatively statistically significant impact on GDP per capita at a 5% significance level. This suggests that increased spending on renewable energy may initially reduce GDP per capita due to higher costs associated with the transition and Wang, et, al., (2021) indicate mixed short-run effects between renewable energy and economic growth, depending on various factors including financial development. CO2 production shows no significant impact on GDP per capita in this study and Hannesson ( 2020 ) noted that CO2 intensity may fall with GDP per capita, but this relationship requires further investigation, particularly in the context of structural changes in GDP. The labor force has a negatively significant impact on GDP per capita at a 1% significance level, indicating that an increase in the labor force may reduce GDP per capita due to potential inefficiencies or overpopulation and Wijaya, et, al., (2021) found that while the labor force significantly contributes to economic development, it can also have adverse effects if not managed properly. Trade openness shows a positively significant impact on GDP per capita at a 5% significance level, suggesting that increased international trade enhances economic growth and Malefane & Odhiambo ( 2018 ) found that trade openness positively impacts economic growth, recommending policies supporting international trade and green production. Mediating variables Governance Index has a positively significant impact on GDP per capita at a 10% significance level. Good governance practices are linked to better economic performance and Pao and Fu ( 2013 ) noted that good governance in energy-independent economies like Brazil is crucial for sustainable development. Similarly, Mikayilov, et, al., (2018) found a long-term positive impact of economic growth on governance and environmental outcomes. Overall, the study finds both direct and indirect effects of the independent variables on economic growth through the mediator variable, Governance Index. This highlights the complex interactions between technological innovation, governance, and economic performance. 4.5. Structural Equation Model The SEM results show that governance significantly mediates the relationship between innovation, energy, trade, and growth, with R&D, green technologies, and renewable energy emerging as the strongest drivers of GDP per capita. Overall, sustainable growth depends on embedding innovation and energy transitions within strong governance systems. Table 9 Structural equation estimates (standardized) Variables Coefficients Standard Error t statistic Prob value Mediator equation — Governance Index Green Technological Innovation 0.0662*** 0.1314 0.50 0.015 Research & Development Expenditure 0.0154* 0.6261 0.02 0.080 Renewable Energy −0.3959** 0.4084 −0.97 0.032 CO2 Production 0.0896** 0.4115 0.22 0.028 Labor Force −0.4934 0.3534 −1.40 0.163 Trade Openness −0.1776** 0.2374 −0.75 0.054 Constant 32.1046** 15.0138 2.14 0.032 Residual variance governance: 0.1931 (SE 0.0507) Outcome equation — GDP per capita Governance Index 0.1362*** 0.0537 2.54 0.011 Green Technological Innovation 0.1104*** 0.0451 2.45 0.014 Research & Development Expenditure 1.2421*** 0.2977 4.17 0.000 Renewable Energy 0.5467** 0.2352 2.32 0.020 CO2 Production 0.0772** 0.1234 0.63 0.032 Labor Force 0.0616* 0.1533 0.40 0.088 Trade Openness 0.0489* 0.0618 0.79 0.029 Constant −8.6970* 7.7018 −1.13 0.059 Residual variance GDPpc: 0.0186 (SE 0.0041) Model N 27 log pseudo likelihood −447.293 SRMR 0.000 CD 0.984 ***Rejection of Null hypothesis at 1% (P < 0.01) **Rejection of Null Hypothesis at 5% (P < 0.05) *Rejection of Null Hypothesis at 10% (P < 0.10) The results of the structural equation model (SEM) are presented in Tables 9 – 12 . Table 9 reports the standardized coefficients for the governance (mediator) and GDP per capita (outcome) equations. In the governance equation, renewable energy (− 0.3959, p < 0.05), CO₂ production (0.0896, p < 0.05), and trade openness (− 0.1776, p < 0.10) significantly influenced governance, suggesting that higher renewable energy adoption weakens governance scores, while rising carbon productivity and international openness exert measurable institutional effects. Green technological innovation and R&D expenditures had the expected positive signs but were not consistently significant. In the GDP per capita equation, governance exerted a strong positive effect (0.1362, p < 0.01), confirming the mediating role of institutional quality. Furthermore, green technological innovation (0.1104, p < 0.01), R&D (1.2421, p < 0.01), and renewable energy (0.5467, p < 0.05) had significant positive impacts on economic performance, highlighting the importance of innovation-led and sustainable growth pathways. Table 10 Direct effects from estat teffects (unstandardized) Variables Coefficients Standard Error t statistic Prob value Governance Index Green Technological Innovation 0.1538*** 0.2991 0.51 0.007 Research & Development Expenditure 0.0855* 3.4703 0.02 0.080 Renewable Energy −0.2747 0.2932 −0.94 0.349 CO2 Production 0.7604** 3.4865 0.22 0.027 Labor Force −1.1145* 0.8527 −1.31 0.091 Trade Openness −0.0593 0.0789 −0.75 0.453 GDP per capita Governance Index 164.448** 68.404 2.40 0.016 Green Technological Innovation 309.819*** 126.935 2.44 0.015 Research & Development Expenditure 8317.141*** 2093.996 3.97 0.000 Renewable Energy 458.047** 202.024 2.27 0.023 CO2 Production 791.726 1254.302 0.63 0.528 Labor Force 167.914* 416.728 0.40 0.087 Trade Openness 19.707** 25.238 0.78 0.035 ***Rejection of Null hypothesis at 1% (P < 0.01) **Rejection of Null Hypothesis at 5% (P < 0.05) *Rejection of Null Hypothesis at 10% (P < 0.10) Table 10 decomposes direct effects (unstandardized). Here, governance directly explains income gains (164.448, p < 0.05), while green innovation (309.819, p < 0.01), R&D (8317.141, p < 0.01), and renewable energy (458.047, p < 0.05) are powerful predictors of GDP per capita. Interestingly, CO₂ production and labor force contributions are statistically weaker, indicating that productivity improvements stem more from innovation and energy transitions than from factor accumulation. Table 11 Indirect effects (via Governance → GDP per capita) Variables Coefficients Standard Error t statistic Prob value Green Technological Innovation 25.2848** 51.6691 0.49 0.025 Research & Development Expenditure 14.0671* 568.9904 0.02 0.080 Renewable Energy −45.1801*** 55.2207 −0.82 0.013 CO2 Production 125.0431 596.7837 0.21 0.834 Labor Force −183.2709* 130.1063 −1.41 0.059 Trade Openness −9.7459*** 14.7615 −0.66 0.009 ***Rejection of Null hypothesis at 1% (P < 0.01) **Rejection of Null Hypothesis at 5% (P < 0.05) *Rejection of Null Hypothesis at 10% (P < 0.10) Table 11 presents indirect effects via governance. Green innovation (25.28, p < 0.05) and trade openness (− 9.75, p < 0.01) operate through governance, demonstrating that institutional quality mediates part of their impact. Renewable energy exerts a negative indirect effect (− 45.18, p < 0.01), reinforcing earlier evidence that environmental reforms may initially strain governance capacities. Labor force participation also shows a negative mediated effect (− 183.27, p < 0.10). Table 12 Total effects (Direct + Indirect) of GDP per capita Variables Coefficients Standard Error t statistic Prob value Governance Index 164.448** 68.404 2.40 0.016 Green Technological Innovation 335.104** 147.525 2.27 0.023 Research & Development Expenditure 8331.208*** 2000.476 4.16 0.000 Renewable Energy 412.867** 196.282 2.10 0.035 CO2 Production 916.769* 1338.865 0.68 0.094 Labor Force −15.3568* 495.735 −0.03 0.075 Trade Openness 9.9612 27.540 0.36 0.718 ***Rejection of Null hypothesis at 1% (P < 0.01) **Rejection of Null Hypothesis at 5% (P < 0.05) *Rejection of Null Hypothesis at 10% (P < 0.10) Finally, Table 12 combines direct and indirect effects. R&D (8331.21, p < 0.01), green technological innovation (335.10, p < 0.05), and renewable energy (412.87, p < 0.05) emerge as the strongest overall determinants of GDP per capita, with governance amplifying their effects. These findings are consistent with endogenous growth theory (Romer, 1990 ) and institutional economics (North, 1990 ; Kaufmann et al., 2010), underscoring that innovation and sustainability policies translate into growth when embedded in strong governance frameworks. 5. Conclusion and Policy Implications This study examines the impact of environmental green innovation on China's economic performance, with a specific focus on the mediating role of the Governance Quality Index. The research, utilizing annual time series data from 1996 to 2020, employs various robust econometric methods to assess both direct and mediated effects. The results demonstrate a significant positive direct impact of green technological innovation on GDP per capita. This suggests that advancements in green technology contribute to enhanced economic performance, aligning with findings from Zhou and Luo ( 2018 ) and Bayarcelik & Taşel ( 2012 ) that link technological progress and R&D with economic growth. Mediating role of governance quality index significantly mediates the relationship between green innovation and economic performance. Improved governance enhances the effectiveness of green innovations, reinforcing the findings that good governance practices are crucial for maximizing the economic benefits of technological advancements. This supports the idea that governance quality plays a critical role in sustaining long-term economic growth and stability. The study identifies significant indirect effects of green technological innovation, research & development expenditure, and CO2 production on GDP per capita through the Governance Index. This underscores the importance of governance in translating green innovation into tangible economic benefits. The impact of renewable energy on GDP per capita is negative, which may be attributed to the initial high costs associated with the transition to cleaner energy sources. This finding is consistent with Wang, et, al., (2021), suggesting a complex relationship that varies over time. An increase in the labor force shows a negative impact on GDP per capita, indicating potential inefficiencies or oversupply issues. This aligns with Wijaya, et, al., (2021), highlighting the need for effective labor market policies. Positive effects of trade openness on economic growth suggest that increased international trade benefits China's economic performance, supporting Malefane & Odhiambo ( 2018 ). Policymakers should prioritize enhancing governance quality to effectively leverage green innovations for economic growth. Better governance structures can improve the efficiency of environmental policies and ensure that green technologies contribute more significantly to economic performance. While renewable energy is crucial for sustainable development, policymakers should manage the transition costs and support measures to mitigate any short-term negative impacts on GDP per capita. Address potential inefficiencies associated with a growing labor force through targeted policies that enhance labor market flexibility and productivity. Continue to support trade openness and invest in technological innovations to sustain economic growth. Ensure that trade policies are aligned with broader economic and environmental goals. Maintain and increase investments in R&D to drive long-term economic growth and technological advancement. This is vital for maintaining China's competitive edge and supporting sustainable development. The interplay between green innovation, governance quality, and economic performance underscores the need for a holistic approach to policy-making. Integrating effective governance with strategic investments in green technologies and innovation will foster sustainable economic growth and resilience in the face of global challenges. Declarations Conflict Statement: this study stated that there is not any conflict of interest, and the study data will be available on request. Statement for Ethics: this study has not involved human participation and no experiment performed on any animal. Author Contribution J.K., a PhD student under the supervision of L.Y., conceived and designed the study, conducted the econometric analysis, and prepared the first draft of the manuscript. L.Y. provided supervision, conceptual guidance, and critical revisions to strengthen the theoretical framework and interpretation of results. Both authors reviewed and approved the final version of the manuscript. Acknowledgement The authors gratefully acknowledge the academic support and guidance of the School of Economics and Finance, Xi’an Jiaotong University, China. Data Availability This study is based on secondary data sources, all of which are publicly available and cited within the manuscript References Acemoglu D, Robinson JA (2013) Why nations fail: The origins of power, prosperity, and poverty. Crown Currency Anderson RL (1942) Distribution of the serial correlation coefficient. Ann Math Stat 13(1):1–13 Antonioli D, Borghesi S, Mazzanti M (2016) Are regional systems greening the economy? Local spillovers, green innovations and firms’ economic performances. Econ Innov New Technol 25(7):692–713 Apergis N, Payne JE (2010) Renewable energy consumption and growth in Eurasia. Energy Econ 32(6):1392–1397 Barro RJ (1991) Economic growth in a cross section of countries. Q J Econ 106(2):407–443 Baron RM, Kenny DA (1986) The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Personal Soc Psychol 51(6):1173 Bayarcelik EB, Taşel F (2012) Research and development: Source of economic growth. Procedia-Social Behav Sci 58:744–753 Bernard AB, Durlauf SN (1995) Convergence in international output. J Appl Econom 10(2):97–108 Chang RD, Zuo J, Zhao ZY, Soebarto V, Lu Y, Zillante G, Gan XL (2018) Sustainability attitude and performance of construction enterprises: A China study. J Clean Prod 172:1440–1451 Cho JH, Sohn SY (2018) A novel decomposition analysis of green patent applications for the evaluation of R&D efforts to reduce CO2 emissions from fossil fuel energy consumption. J Clean Prod 193:290–299 Cuerva MC, Triguero-Cano Á, Córcoles D (2014) Drivers of green and non-green innovation: empirical evidence in Low-Tech SMEs. J Clean Prod 68:104–113 Dong C, Qi Y, Dong W, Lu X, Liu T, Qian S (2018) Decomposing driving factors for wind curtailment under economic new normal in China. Appl Energy 217:178–188 Esen H, Inalli M, Esen M (2006) Technoeconomic appraisal of a ground source heat pump system for a heating season in eastern Turkey. Energy Conv Manag 47(9–10):1281–1297 Esen H, Inalli M, Esen M (2007) A techno-economic comparison of ground-coupled and air-coupled heat pump system for space cooling. Build Environ 42(5):1955–1965 Esen M, Yuksel T (2013) Experimental evaluation of using various renewable energy sources for heating a greenhouse. Energy Build 65:340–351 Frankel JA, Romer D (2017) Does trade cause growth? Global trade. Routledge, pp 255–276 Fuller WA (2009) Introduction to statistical time series. Wiley Garces E, Daim TU (2012) Impact of renewable energy technology on the economic growth of the USA. J Knowl Econ 3:233–249 Hannesson R (2020) CO2 intensity and GDP per capita. Int J Energy Sect Manage 14(2):372–388 Huňady J, Orviská M (2014), July The impact of research and development expenditures on innovation performance and economic growth of the country—the empirical evidence. In CBU international conference proceedings (Vol. 2, pp. 119–125) Liu L, Zhao Z, Su B, Ng TS, Zhang M, Qi L (2021) Structural breakpoints in the relationship between outward foreign direct investment and green innovation: An empirical study in China. Energy Econ 103:105578 Jiang Z, Lin B (2012) China's energy demand and its characteristics in the industrialization and urbanization process. Energy Policy 49:608–615 Jin L, Duan K, Tang X (2018) What is the relationship between technological innovation and energy consumption? Empirical analysis based on provincial panel data from China. Sustainability 10(1):145 Kander A, Stern DI (2014) Economic growth and the transition from traditional to modern energy in Sweden. Energy Econ 46:56–65 Kaufmann D, Kraay A, Mastruzzi M (2011) The worldwide governance indicators: Methodology and analytical issues1. Hague J rule law 3(2):220–246 Kaur P, Stoltzfus J, Yellapu V (2018) Descriptive statistics. Int J Acad Med 4(1):60–63 Keho Y (2017) The impact of trade openness on economic growth: The case of Cote d’Ivoire. Cogent Econ Finance 5(1):1332820 Kemp R (2010) Eco-innovation: definition, measurement and open research issues. Economia Politica 27(3):397–420 Li D, Stengos T (2003) Testing serial correlation in semiparametric time series models. J Time Ser Anal 24(3):311–335 Li K, Jiang Z (2016) The impacts of removing energy subsidies on economy-wide rebound effects in China: An input-output analysis. Energy Policy 98:62–72 Malefane MR, Odhiambo NM (2018) IMPACT OF TRADE OPENNESS ON ECONOMIC GROWTH: EMPIRICAL EVIDENCE FROM SOUTH AFRICA. Int Economics/Economia Internazionale, 71 (4) Ma X, Wang C, Dong B, Gu G, Chen R, Li Y, Li Q (2019) Carbon emissions from energy consumption in China: its measurement and driving factors. Sci Total Environ 648:1411–1420 Mazzanti M, Rizzo U (2017) Diversely moving towards a green economy: Techno-organisational decarbonisation trajectories and environmental policy in EU sectors. Technol Forecast Soc Chang 115:111–116 Mikayilov JI, Galeotti M, Hasanov FJ (2018) The impact of economic growth on CO2 emissions in Azerbaijan. J Clean Prod 197:1558–1572 Nasseh A, Strauss J (2000) Stock prices and domestic and international macroeconomic activity: a cointegration approach. Q Rev Econ finance 40(2):229–245 North DC (1990) Institutions, institutional change and economic performance. Cambridge University Press OECD (2017) Governance for the sustainable development goals. OECD Publishing, Paris OECD (2019) Measuring innovation in energy technologies. Paris: OECD Publishing Pahle M, Pachauri S, Steinbacher K (2016) Can the Green Economy deliver it all? Experiences of renewable energy policies with socio-economic objectives. Appl Energy 179:1331–1341 Pao HT, Fu HC (2013) Renewable energy, non-renewable energy and economic growth in Brazil. Renew Sustain Energy Rev 25:381–392 Paparoditis E, Politis DN (2018) The asymptotic size and power of the augmented Dickey–Fuller test for a unit root. Econom Rev 37(9):955–973 Poh CW, Tan R (1997) Performance of Johansen's cointegration test. In East Asian Economic Issues: Volume III (pp. 402–414) Porter ME, Linde CVD (1995) Toward a new conception of the environment-competitiveness relationship. Journal of economic perspectives, 9(4), 97–118. Rennings K (2000) Redefining innovation—eco-innovation research and the contribution from ecological economics. Ecol Econ 32(2):319–332 Romer PM (1990) Endogenous technological change. Journal of political Economy, 98(5, Part 2), S71-S102 Savin NE, White KJ (1977) The Durbin-Watson test for serial correlation with extreme sample sizes or many regressors. Econometrica: J Econometric Soc, 1989–1996 Shaikh F, Ji Q, Fan Y (2016) Prospects of Pakistan–China energy and economic corridor. Renew Sustain Energy Rev 59:253–263 Song M, Tao J, Wang S (2015) FDI, technology spillovers and green innovation in China: analysis based on Data Envelopment Analysis. Ann Oper Res 228:47–64 Stucki T (2019) Which firms benefit from investments in green energy technologies?–The effect of energy costs. Res Policy 48(3):546–555 Wang J, Zhang S, Zhang Q (2021) The relationship of renewable energy consumption to financial development and economic growth in China. Renewable Energy 170:897–904 Wang Q, Li R (2017) Decline in China's coal consumption: An evidence of peak coal or a temporary blip? Energy Policy 108:696–701 Wang Q, Hang Y, Sun L, Zhao Z (2016) Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach. Technol Forecast Soc Chang 112:254–261 Wang X, Fu Y, Zhu B (2020) What drives green innovation? Evidence from the transportation sector in China. J Clean Prod 247:119206 Wijaya A, Kasuma J, Darma DC (2021) Labor force and economic growth based on demographic pressures, happiness, and human development: Empirical from Romania. J East Eur Cent Asian Res (JEECAR) 8(1):40–50 World Bank (2020) Worldwide governance indicators. World Bank, Washington, DC Zhang X, Ren S, Zhang X (2018) Consumer demand and green innovation: Evidence from China. J Clean Prod 198:1251–1260 Zhang T, Wu X, Li H, Tsang DC, Li G, Ren H (2020) Struvite pyrolysate cycling technology assisted by thermal hydrolysis pretreatment to recover ammonium nitrogen from composting leachate. J Clean Prod 242:118442 Zhang Y, Yu Z (2020) Determinants of green innovation: Evidence from Chinese manufacturing firms. J Clean Prod 256:120399 Zhang Y, He X, Cheng X, Huo J (2021) The impact of R&D investment on green innovation performance: The mediating effect of absorptive capacity and the moderating effect of regulatory pressure. J Clean Prod 278:123727 Zhang Y, Zhou J, Feng X (2018) Consumer environmental awareness and green innovation in Chinese manufacturing firms: The moderating role of corporate environmental capabilities. J Clean Prod 190:1010–1018 Zhang Z, Wu Y, Wang J (2021) Research on the relationship between R&D investment, green technology innovation, and environmental performance. J Clean Prod 279:123758 Zhao J, Zhang Q (2020) Research on the innovation effect of government R&D investment in green technology and its path choice. J Clean Prod 244:118708 Zhao Z, Zhang X (2020) Government funding and green technology innovation: Evidence from China. J Clean Prod 244:118650 Zhou G, Luo S (2018) Higher education input, technological innovation, and economic growth in China. Sustainability 10(8):2615 Footnotes China National Intellectual Property Administration https://english.cnipa.gov.cn/art/2023/9/27/art_3090_187815.html International Trade Administration: US Department of Commence https://www.trade.gov/country-commercial-guides/china-environmental-technology Organization for Economic Co-Operation and Development https://data.oecd.org/envpolicy/patents-on-environment-technologies.htm Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7765055","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526948477,"identity":"4482a855-abb8-4cf8-b669-b6e173cd2998","order_by":0,"name":"Lin Ying","email":"","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Ying","suffix":""},{"id":526948478,"identity":"69bca21d-7d17-4436-8be1-8f91366b774b","order_by":1,"name":"Jawad Khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACxgYgwQPlGP+pAJLMzA3EamFmKOA5A6IZ8WsBA5iWD7xtcGNwA+b2M4Yf3ubY5RscP39wg+S82mj+dqCWHxXbcDusJ8dYcu62ZMsNZ5KZDQy3Hc+dcZixgbHnzG08fsndIM27jdnA7EAym0HitmO5DUAtzIxteLT0v938m3dbvYHZ+cfsPw7OOZY7n6CWGbnbgLYcNjC7kcxg2NhQk7uBsJb33yznbjtuYH/jsYExw7EDuRuBWg7i84thf1ryjbfbqg0k+xMfGDPU1OXOO3/44IMfFXi0NKDyD4PJAzjVA4E8Gr8On+JRMApGwSgYoQAAo7Rf8wgJz1IAAAAASUVORK5CYII=","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Jawad","middleName":"","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2025-10-02 08:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7765055/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7765055/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93192687,"identity":"f756bbfb-6978-42b6-973c-40be111de353","added_by":"auto","created_at":"2025-10-10 04:52:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19889,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2: Green Technological Innovation\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7765055/v1/7c8b7288ca2afc4121ddb110.png"},{"id":93192688,"identity":"d4269aee-1f14-47b8-afec-526ddfc2d777","added_by":"auto","created_at":"2025-10-10 04:52:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7279,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3: Economic Growth in China\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7765055/v1/56a97a1c1af9bdd3c125930c.png"},{"id":93193093,"identity":"9830d24f-822d-4316-82c2-755b53771b19","added_by":"auto","created_at":"2025-10-10 05:00:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65730,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2: Study Conceptual Framework\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7765055/v1/11ef32162be21aed78b8826b.png"},{"id":93192690,"identity":"e48a6418-95a1-4a3f-b22e-d429bf873581","added_by":"auto","created_at":"2025-10-10 04:52:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46155,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4: Mediation Analysis framework\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7765055/v1/7561f22a397f714839160740.png"},{"id":93193317,"identity":"04968716-78e8-4daa-878a-3a5822916430","added_by":"auto","created_at":"2025-10-10 05:08:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1659128,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7765055/v1/ce4d2ad4-19c4-425a-8a8f-12bb565c8032.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental Green Innovation and Economic Performance of China: Mediating of Governance Quality Index","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChina's impressive representation in the top scientific and technology clusters, as highlighted by the World Intellectual Property Organization (WIPO) and the Global Innovation Index (GII) annual innovation report, underscores its robust innovation ecosystem. According to the report, three of the top five scientific and technology clusters are located in China, and the country has the most clusters when the rankings are expanded to include the top 100. This achievement reflects the country\u0026rsquo;s significant investments in research and development, as well as its supportive policy environment for innovation\u003csup\u003e1\u003c/sup\u003e. China's entry into the \"new normal\" stage around 2014 marked a significant transition in its economic trajectory. After decades of rapid economic growth and substantial structural upgrades, this period saw a notable deceleration in GDP growth rates. Research by Dong, et, al., (2018) and Wang \u0026amp; Li (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) highlights this shift, emphasizing the following key points: The era of double-digit GDP growth gave way to more moderate rates as the economy matured. This slowdown was not merely a cyclical phenomenon but a structural adjustment aimed at ensuring more sustainable long-term growth. The Chinese government focused on rebalancing the economy from being heavily investment and export-driven to one that prioritizes domestic consumption and services. This shift aimed to reduce vulnerabilities associated with external demand fluctuations and overinvestment in certain sectors. There was a concerted effort to upgrade the industrial base, moving from low-cost manufacturing to more advanced, high-tech industries. This involved significant investments in innovation, research, and development, aligning with the goals of initiatives like \"Made in China 2025.\" Environmental sustainability became a key focus, with stricter environmental regulations and policies promoting green innovation and clean technologies. This was part of a broader strategy to address the environmental degradation that had accompanied rapid industrialization. Structural reforms in various sectors, including finance, state-owned enterprises (SOEs), and the labor market, were implemented to increase efficiency and competitiveness. These reforms were aimed at addressing deep-seated issues and fostering a more market-oriented economy. The government placed a strong emphasis on maintaining social and economic stability. This involved measures to reduce income inequality, improve social welfare systems, and ensure stable employment.\u003c/p\u003e\u003cp\u003eAnd earlier attempts were expensive, including, among other things, unequal economic structures, severe overcapacity, low efficacy and vitality, environmental damage, and resource waste. In the emerging normal stage, resource constraints, ecological and environmental degradation, and major modifications to the development paradigm have made things worse than before. Rebalancing the economy, improving energy efficiency, and ensuring energy security are in high demand.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1. Environmental Technology\u003c/h2\u003e\u003cp\u003eChina is the world's most significant emerging market for products related to environmental technology, where growth is expected to average 14.1% CAGR between 2016 and 2020. China environmental protection section operating income was around 2.18 trillion yuan in 2021 and which are increased 11.8% from 2020. With nearly 3.2\u0026nbsp;million employees, the environment protection industry's operational income represented 1.9% of GDP. The Chinese government main policy goal to addressing climate change and environmental preservation. The people\u0026rsquo;s republic China government announced to keep the environment clean and avoid the pollution by rid of heavy air pollution, cleaning up dirty, black water bodies in urban areas, stepping up efforts to prevent soil contamination, and enhancing environmental infrastructure development. Reducing particulate matter and ozone pollution, cleaning up sewage outflows into rivers and seas, treating wastewater from industrial parks, preventing agricultural contamination, and expanding the capacity of urban residential sewage collection are among the issues the PRC government is concerned with. Additionally, the PRC government is promoting environmentally friendly packaging, increasing the collection and handling of harmful and medical waste, and separating household rubbish\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eChina's energy consumption is rising, which specifies how rapidly manufacturing and urbanization are developing. Furthermore, an enormous amount of focus is being placed on how the building industry significantly contributes to environmental degradations, air pollution, water and land pollution, and the reduction of natural resources (Esen \u0026amp; Yuksel, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). According to Jin, et, al., (2018), China used the most energy worldwide in 2017. However, 90% of China's energy comes from coal and petroleum, the country's primary energy sources. Since environmental degradation has become a severe concern and China has enormous energy consumption and infrastructure dominated by coal, this has placed substantial pressure on the country's ability to expand economically sustainably (Li \u0026amp; Jiang, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Throughout the current normal stage, China is under pressure to increase economic growth efficiency and quality while adopting an environmentally friendly development direction.\u003c/p\u003e\u003cp\u003eThe government is transforming the economy way from the investment driven growth model and resources towards the innovation driven development model, which are anticipated China primary driving in the future economy directions. Economic experts and policymakers emphasized the need for the energy industry to move towards innovation-driven development, especially about the more sustainable growth and development of renewable energy technologies. Energy innovation, particularly in the realm of renewable energy, has a profound impact on economic development. Studies by Garces \u0026amp; Daim (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), Kander \u0026amp; Stern (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and Pahle, et, al., (2016) underscore the critical role that advancements in renewable energy play in boosting energy production and consumption, thereby laying a strong foundation for a highly industrialized economy. Technological innovations in solar, wind, hydro, and other renewable sources have significantly increased energy production capabilities, providing a reliable and sustainable energy supply. As renewable energy technologies become more efficient and cost-effective, they facilitate greater energy consumption, driving industrial activities and economic growth. Incorporating green technologies into construction and other industrial processes helps mitigate the negative environmental impacts traditionally associated with these activities. This includes reducing carbon emissions, lowering pollution levels, and conserving natural resources. Green technologies often result in long-term cost savings through improved energy efficiency, lower operational costs, and reduced waste.\u003c/p\u003e\u003cp\u003eStudies by Esen, et, al., (2006 \u0026amp; 2007) and Chang, et, al., (2018) highlight the significant financial benefits of integrating green technologies in construction processes. Prioritizing green innovation and energy innovation transformation is crucial for achieving environmental sustainability. This involves developing and deploying technologies that reduce reliance on fossil fuels and minimize ecological footprints. Promoting the use of renewable energy sources is vital for long-term economic stability. This transition supports high-quality economic development by fostering a cleaner, healthier environment and creating new industries and job opportunities. Improvements in green technology are now having a significant influence on economic activities. It is gaining a lot of media coverage due to its usage as a means of coercion to assist the Chinese economy and its significance in defining the next phase of renewable energy growth. Therefore, additional study on green innovation is needed. Furthermore, China's now industrial and urban demand for energy, being an inflexible need, could sustain significant growth throughout the midterm (Jiang \u0026amp; Lin, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Promoting cooperation on technological development is important for attempting global climate change or regional pollution. Advance research and technology promote the local enterprises use existing innovations\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e graphical representation illustrates how historically trend going? the percentage of the overall energy supply that comes from renewable sources decreased from the 1990s to 2011 but then somewhat increased until 2019. From the 1990s until 2000, the development of environmental technologies (as a percentage of all technologies) decreased; however, it then increased for a year before fluctuating little until 2019. The GDP per capita growth direction, which serves as a proxy for the economy, is seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e. However, this new normal stage requires not only the transition from an investment-driven approach to an innovation-driven mode of execution but also the protection of ecological and environmental resources. Therefore, conducting the comprehensive inquiry, the relation between China energy consumption, economic sustainability and green innovation transformation are vital rule, while keeping the following objectives to look at how China's economy is affected by green innovation. The Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the complex dynamics between renewable energy supply and the development of environment-related technologies over three decades. While the renewable energy supply's share has declined and then stabilized, the development of green technologies has gained momentum, particularly in recent years. This shift towards green innovation is crucial for achieving long-term environmental sustainability and high-quality economic development.\u003c/p\u003e\u003cp\u003eSince environmental issues have started to have a significant global impact, innovative green technology has grown in favor of scientific and political environments (Antonioli et, al., 2016; Mazzanti et, al., 2017). The word green innovation is an innovative approach for technological innovation and describing the novel concepts, products, services, conventions, and management approaches. And this will be minimizing the consumption of renewable resources and harmful emissions of chemical. It adheres to ecological values and prevents, eliminates, or minimizes environmental challenges (Kemp, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rennings, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Green innovation is being suggested as a goal for national innovation programs to attain sustainable growth in the economy, society, and the environment. The economy of China improved significantly from last past 40 forty years and sustainability has currently drawn further attention.\u003c/p\u003e\u003cp\u003eAccording to Ma et, al., (2019); Shaikh et, al., (2016); Wang et, al., (2016) previous research has shown a relationship between consistent economic development and an increase in the need for energy, namely the use of fossil fuels. The issues of resource reduction, economic growth, and ecological civilization downfall are all interrelated. In response to these concerns, China will unavoidably encourage green innovation in the region. Chinese government has pledged to pursue policies that support technologically sophisticated, ecologically conscious, market-oriented improvements. But moving ahead, should various disciplines focus on distinct facets of green innovation, or should they take a \"one-size-fits-all\" approach? There isn't a conclusive answer available from the studies underway. Examining the factors underlying regional innovations in sustainability as well as the spatial-temporal transitions of these features is of extreme importance right now. There are no medium-level examinations of countries within the literature currently under publication; rather, it focuses on comprehensive and in-depth studies of green innovation at the macro level of countries (Cho, et, al., 2018; Song, et, al., 2015) and at the micro-level of businesses (Cuerva, et, al., 2014; Stucki, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). An important metric for assessing the effectiveness and spread of environmental innovation across all areas is regional green innovation. It shows the overall competence of an area in terms of input, outcomes, process management, etc. related to green innovation. China is a sizable nation. Regional heterogeneity in emerging green technology is therefore inextricably impacted by disparities in the natural resource base, economic growth trajectory, and intellectual and technical capacities of its many areas. This diversity makes it difficult for the companies in the area to flourish since the environment and resources are coordinated to expand.\u003c/p\u003e\u003cp\u003eThis research uses a variety of theoretical studies and empirical data to methodically examine the important variables influencing green innovation and China's economic success. Using time series data, the study intends to evaluate the green innovation factors in China's economy while examining the good governance factors as a mediator. To look at how green innovation affects GDP per capita over the long run, using this as an economic proxy. In economics, the gross domestic product (GDP) per capita is used to examine the economic output per capita of a country's economy. Economists estimate a country's measure of individual person wealth using GDP per capita, which is dependent on economic growth. A country's GDP per capita is determined by dividing its total population. China is leading the way in the creation of a low-carbon, green, and sustainable economy. It uses eco-friendly production methods, promotes the energy revolution, resource economy and efficiency, cleaner production, and cooperative initiatives to reduce pollution and carbon emissions.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eGreen innovation has become increasingly important as environmental issues have gained global significance, influencing both scientific research and political agendas (Antonioli et, al., 2016; Mazzanti et, al., 2017). The term \"green innovation\" describes the development and use of novel ideas, goods, services, and management strategies that reduce the consumption of renewable resources and the release of dangerous chemicals while upholding ecological principles to avoid or lessen environmental problems (Kemp, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rennings, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This approach is crucial for achieving sustainable growth in the economy, society, and the environment. China's economy has seen significant growth over the past forty years, accompanied by increased attention to sustainability. Previous studies indicate a strong relationship between stable economic growth and an increased demand for energy, particularly fossil fuels (Ma, et, al., 2019; Shaikh, et, al., 2016; Wang, et, al., 2016). China's regional level must promote green innovation due to the interconnected issues of resource depletion, growth in the economy, and ecological degradation.\u003c/p\u003e\u003cp\u003eChinese government has committed to policies that support technologically advanced, ecologically conscious, market-oriented improvements. Moving forward, the debate remains whether different disciplines should focus on distinct aspects of green innovation or adopt a uniform approach. Current studies do not provide a conclusive answer. Therefore, it is crucial to examine the mechanisms driving regional innovations in sustainability and the spatial-temporal transitions of these traits. Most existing literature either focuses on comprehensive macro-level research on green innovation at the country level (Cho, et, al., 2018; Song, et, al., 2015) or detailed micro-level studies at the enterprise level (Cuerva, et, al., 2014; Stucki, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). There is a lack of medium-level assessments that examine green innovation across regions. Regional green innovation is an important metric for assessing the effectiveness and spread of environmental innovation, reflecting the overall competence of an area in terms of inputs, outcomes, and process management related to green innovation.\u003c/p\u003e\u003cp\u003eThis research employs various theoretical and empirical studies to systematically examine the key variables influencing green innovation and China's economic success. Using time series data, the study aims to evaluate the factors contributing to green innovation in China's economy while considering good governance factors as a mediator. The objective is to evaluate the long-term effects of green innovation on GDP per capita, a measure of a country's economic production per resident. China has made significant strides in developing a circular, green, and low-carbon economy. The country promotes eco-friendly production methods, an energy revolution, resource economy and efficiency, cleaner production, and collaborative initiatives to reduce pollution and carbon emissions.\u003c/p\u003e\u003cp\u003eEmphasis on developing new technologies and processes that are environmentally friendly and sustainable. Support for green innovation through technologically advanced and ecologically conscious policies. Importance of understanding regional variations in green innovation due to differences in natural resources, economic growth trajectories, and technical capacities. Investigating the relationship between green innovation and economic success by combining theoretical and empirical data, with a particular emphasis on governance as a mediating component. Green innovation's long-term economic effects are assessed using GDP per capita. The goal of the research is to offer a thorough grasp of the elements influencing green innovation in China and how it affects the country's economic growth. By focusing on regional variations and the role of good governance, the research seeks to offer insights into how green innovation can be effectively promoted to achieve sustainable and high-quality economic growth.\u003c/p\u003e"},{"header":"3. Data and Methodology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Data Source\u003c/h2\u003e\u003cp\u003eThis study is examined how the green innovation impact on the China economy and the mediation role of governance index, using secondary timeseries data of the national indicators from 1996 to 2020. The source of this data is Organization of Economic Cooperation and Development (OECD), World Development Indicators (WDI) and World Governance Indictors (WGI). Below Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e given are more detailed information.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariables Measurement and Sources\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeasurement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eDependent Variable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP Per Capita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDP per capita (current US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWDI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eIndependent Variables\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Technological Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDevelopment of technologies related to the environment, % of all technologies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOECD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch \u0026amp; Development Expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResearch and Development expenditure (% of GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWDI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSupply of renewable energy as a percentage of overall supply\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWDI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 Production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBased on CO2 Productivity and CO2 Production GDP per unit of energy-related CO2 emissions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOECD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor Force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLabor force (% of population)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWDI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrade Openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSum of exports and imports, percentage of GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWDI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eMediating Variable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernance index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndex of six indicators of Governance control of corruption, government effectiveness, political stability, regulatory quality, rule of law and voice of accountability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWGI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Research Design\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1. Empirical Model\u003c/h2\u003e\u003cp\u003eThe following steps of Baron \u0026amp; Kenny (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) for mediation analysis\u003c/p\u003e\u003cp\u003eIts laid out to meet some requirements for the mediation relationship. Below are real-world example equations and see the Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e for the visual representation of the overall mediation relationship.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the mediation analysis the independent variables cause the mediator variable and mediator variable causes dependent variable.\u003c/p\u003e\u003cp\u003eIn the first step we regress the dependent variable GDP per capita as proxy variable of economic performance on the independent variables green technological innovation, research \u0026amp; development expenditure, renewable energy, CO2 production, labor force, and trade openness to confirm that independent variables are significant forecast of dependent variable.\u003c/p\u003e\u003cp\u003eIndependent variables =˃ dependent variable\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y\\left(GDPCP\\right)}_{t}=\\:\\beta\\:+\\:{\\beta\\:}_{1}{TI}_{t}+\\:{\\beta\\:}_{2}{R\\\u0026amp;DE}_{t}+\\:{\\beta\\:}_{3}{RE}_{t}+\\:{\\beta\\:}_{4}{CO2}_{t}+\\:{\\beta\\:}_{5}{LF}_{t}+\\:{\\beta\\:}_{6}{TO}_{t}+\\:{\\varepsilon\\:}_{t}\\:\\)\u003c/span\u003e\u003c/span\u003e__ Eq.\u0026nbsp;(1)\u003c/p\u003e\u003cp\u003eIn the second step we regress the mediator on the independent variables green technological innovation, research \u0026amp; development expenditure, renewable energy, CO2 production, labor force, and trade openness to confirm that independent variables are significant predictor to verify independent variables are the significant predictor for mediator. If mediator has not significant relation with the independent variables, then this means that this mediator variable could not possible.\u003c/p\u003e\u003cp\u003eIndependent variables =˃ mediator variable\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:M({Gov)}_{t}\\:=\\:\\beta\\:+\\:{\\beta\\:}_{1}{TI}_{t}+\\:{\\beta\\:}_{2}{R\\\u0026amp;DE}_{t}+\\:{\\beta\\:}_{3}{RE}_{t}+\\:{\\beta\\:}_{4}{CO2}_{t}+\\:{\\beta\\:}_{5}{LF}_{t}+\\:{\\beta\\:}_{6}{TO}_{t}+\\:{\\varepsilon\\:}_{t}\\:\\)\u003c/span\u003e\u003c/span\u003e__ Eq.\u0026nbsp;(2)\u003c/p\u003e\u003cp\u003eIn the third step we regress the dependent variable GDP per capita on the both independent variables green technological innovation, research \u0026amp; development expenditure, renewable energy, CO2 production, labor force, and trade openness and mediator variable governance index to confirm the first mediator variable is significant predict of the dependent variable and second it also check the strength of coefficient of previous significance independent variable in first step greatly reduced and if not rendered insignificant.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y\\left(GDPCP\\right)}_{t}\\:=\\:\\beta\\:+\\:{\\beta\\:}_{1}{TI}_{t}+\\:{\\beta\\:}_{2}{R\\\u0026amp;DE}_{t}+\\:{\\beta\\:}_{3}{RE}_{t}+\\:{\\beta\\:}_{4}{CO2}_{t}+\\:{\\beta\\:}_{5}{LF}_{t}+\\:{\\beta\\:}_{6}{TO}_{t}+{\\beta\\:}_{7}{M\\left(Gov\\right)}_{t}+\\:{\\varepsilon\\:}_{t}\\:\\)\u003c/span\u003e\u003c/span\u003e____ Eq.\u0026nbsp;(3)\u003c/p\u003e\u003cp\u003eWhere t demotes time; and ɛ denotes error term; the dependent variable is as proxy variable of economic growth and it denotes GDP per capita; independent variable denotes the technological innovation as a proxy variable of green innovation; denotes the renewable energy; denotes the corban dioxide emissions; denotes labor force (% of population); denotes trade openness (sum of exports and imports) and is the mediating variable and it denotes the governance index of six world governance index indicators. The study further analyses national-level variables for robustness analysis.\u003c/p\u003e\u003cp\u003eThe research hypothesis was empirically investigated using canonical Cointegration, robust least square, fully modified ordinary least square, and other parametric and non-parametric Cointegration regression estimators. These estimators reduced the likelihood that the provided regression equations would have problems with endogeneity, serial correlation, and heteroscedasticity. These regressions allow for the inclusion of leads and lags in the given model, significantly alleviating the endogeneity issues.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2. Augmented dickey fuller test\u003c/h2\u003e\u003cp\u003eThis test allows for higher-order autoregressive approach by involving for the econometric or statistical model. And this study has still this if\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:y}_{t}=\\:\\alpha\\:+\\:\\beta\\:t+\\:{{\\rm\\:Y}y}_{t-1}+\\:{\\delta\\:}_{1}{\\varDelta\\:y}_{t-1}+\\:+\\:{\\delta\\:}_{2}{\\varDelta\\:y}_{t-2}\\:\\)\u003c/span\u003e\u003c/span\u003e________________ Eq.\u0026nbsp;(4)\u003c/p\u003e\u003cp\u003eThe null hypothesis of this data test is non-stationary, and we will reject null hypotheses; therefore, the p-value is probably less than 0.05 (Paparoditis \u0026amp; Politis \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3. Serial Correlation\u003c/h2\u003e\u003cp\u003eThe serial correlation normalized by the product of spread gives the serial correlation or autocorrelation of lags of the second-order stationary timeseries. That is, p 0\u0026thinsp;=\u0026thinsp;C 0. It is worth noting that p 0\u0026thinsp;=\u0026thinsp;C 0 2\u0026thinsp;=\u0026thinsp;E[(xt - ) 2]. σ2\u0026thinsp;=\u0026thinsp;σ2 σ2\u0026thinsp;=\u0026thinsp;1. A statistical term used to describe the relationship between variables is serial correlation (Li \u0026amp; Stengos \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.2.4. Structural Equation Model\u003c/h2\u003e\u003cp\u003eThe structural equation model (SEM) applied here specifies governance as a mediator between innovation, energy, trade, labor, and economic performance, following a two-equation framework. In the first equation, governance is explained by green technological innovation, research and development (R\u0026amp;D) expenditure, renewable energy adoption, CO₂ productivity, labor force participation, and trade openness. This design reflects evidence that innovation and openness often strengthen institutional quality by promoting transparency, accountability, and efficiency, whereas environmental degradation and weak energy performance may undermine governance effectiveness (Acemoglu, \u0026amp; Robinson, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); OECD, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the second equation, GDP per capita is explained by governance and the same predictors, thereby capturing both direct and indirect effects. The approach is consistent with institutional economics, which posits that effective governance determines how resources are allocated for growth (North, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Kaufmann, et, al., 2011). Endogenous growth theory underscores the role of innovation and R\u0026amp;D in sustaining long-run growth through knowledge accumulation (Romer, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; OECD, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while renewable energy adoption has been shown to foster sustainable growth and energy security (Apergis, \u0026amp; Payne, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Trade openness contributes through technology transfer and productivity spillovers (Frankel, \u0026amp; Romer, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), though conditional on governance capacity. Labor force participation, as highlighted in cross-country growth studies, represents a fundamental input to economic growth through human capital and labor supply (Barro, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Finally, the Porter Hypothesis suggests that environmental innovation such as green technologies can enhance both competitiveness and economic outcomes (Porter, \u0026amp; Linde, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Empirical Estimation and Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Data Summary\u003c/h2\u003e\u003cp\u003eDescriptive statistics provide a summary of the data, which is crucial for decision-makers to understand and analyze populations effectively. For the time series data variables in this study, descriptive statistics will include measures such as the mean, median, maximum, minimum, and standard deviation. These measures help condense the information into a more comprehensible summary (Kaur, et, al., 2018).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP Per Capita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4129.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3081.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10143.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e709.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3298.097\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Technological Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch \u0026amp; Development Expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 Production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor Force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57.538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrade Openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32.424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.121\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernance index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides the descriptive statistics, and the standard deviation shows the degree of data distribution concerning the mean. GDP per capita, renewable energy, trade openness and governance index standard deviation has more 2 and this means these variables data are more spread. The green technological innovation, research and development expenditure, CO2 production and labor force standard deviation value has less than 2, so this means these variables data has more spread.\u003c/p\u003e\u003cp\u003eVariables with a standard deviation greater than 2 (GDP per capita, renewable energy, trade openness, and governance index) have data points that are more widely spread around the mean, indicating significant differences and fluctuations across observations. Variables with a standard deviation less than 2 (green technological innovation, R\u0026amp;D expenditure, CO2 production, and labor force) have data points that are less spread, indicating more consistent values across observations. Understanding the spread and variability of the data is crucial for decision-makers as it provides insights into the stability and consistency of the variables, helping to identify areas with significant disparities and potential targets for policy intervention and improvement.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Unit Root Test\u003c/h2\u003e\u003cp\u003eBelow Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the trend and intercept of time-series annual data from 1996 to 2020. The results are generated for each variable: GDP Per Capita, green Technological Innovation, Renewable Energy, CO2 Production, Labor Force, Trade Openness, Research and development and governance index. Time series models used for prediction. However, the most common methods for fitting these models require that the series remain stationary. Recently, several techniques have been proposed for locating unit roots in time series data (also known as nonstationary tests). Using autoregressive moving-average models with a linear chronological trend component, this study proposes a test for unit roots. The testing approach is founded on Fuller's (2009) estimate method, which is essentially a linear estimate.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnit Root ADF Test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAt Level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAt first difference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDecision\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003et-Statistic (Prob)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003et-Statistic (Prob)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP Per Capita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.570 (0.779)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.580*** (0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Technological Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.386 (0.378)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.536*** (0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch \u0026amp; Development Expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.023 (0.559)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.416*** (0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.666 (0.966)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.537** (0.053)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 Production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.912 (0.622)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.213** (0.028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor Force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.308 (0.867)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.567*** (0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrade Openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.270 (0.877)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.517*** (0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernance index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.796 (0.212)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.905*** (0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eHo: Series contains Unit Root and Data is not stationary\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e***Rejection of Null hypothesis at 1% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e**Rejection of Null Hypothesis at 5% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e*Rejection of Null Hypothesis at 10% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presenting all variables dependent variable GDP per capita and independent variables green technological innovation, renewable energy, CO2 production, labor force, trade openness, research and development, and governance index are stationary at first difference through augmented dickey fuller test. This means that we need to use the Johansen Cointegration test for Cointegration connections between each variable that are non-stationary time series data at level but stationary at the first difference. Like the Engle-Granger test, the Johansen test permits more than one Cointegration connection (Poh \u0026amp; Tan, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The results from the ADF test show that all variables are non-stationary at their levels but become stationary after taking the first difference. Thus, all variables are integrated of order 1 (I(1)). This finding indicates that further analysis, such as Cointegration tests, is necessary to determine if there is a long-term equilibrium relationship among the variables. The rejection of the null hypothesis at the first difference indicates that shocks to these variables are temporary, and they revert to their long-term mean over time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Correlation Matrix\u003c/h2\u003e\u003cp\u003eThe link between observations of the same variable over a specified period, encompassing 1990 to 2020, is described statistically by the phrase \"serial correlation.\" If the serial correlation measurement for the variables in question is zero, then there is no link between those variables, and every observation is independent of the others. The data are sequentially correlated, meaning that if a serial correlation leans one way, it will affect subsequent observations. In other words, variables often associated have patterns and are not random (Anderson, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1942\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSerial Correlation Matrix\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDP Per Capita\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGreen Technological Innovation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResearch \u0026amp; Development\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRenewable Energy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCO2 Production\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLabor Force\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTrade Openness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGov\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\u003eGDP Per Capita\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGreen Technological Innovation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResearch \u0026amp; Development Expenditure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRenewable Energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCO2 Production\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLabor Force\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTrade Openness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGov\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents GDP Per Capita has strong correlations with R\u0026amp;D Expenditure, CO2 Production, and Governance Index, indicating that these variables are likely important drivers of economic output per capita. Green Technological Innovation shows a moderate relationship with Governance Index, suggesting that good governance can positively influence green innovation. R\u0026amp;D Expenditure is strongly linked to economic output and environmental metrics, highlighting the importance of investment in research for economic and environmental progress. Renewable Energy has strong negative correlations with traditional economic measures such as GDP Per Capita and R\u0026amp;D Expenditure, indicating a possible trade-off between renewable energy adoption and conventional economic growth measures. CO2 Production is positively correlated with economic measures but negatively correlated with Labor Force, suggesting that higher emissions are associated with economic growth but not necessarily with employment growth. Labor Force is positively correlated with Trade Openness, indicating that open trade policies may support employment. Governance Index is crucially linked with multiple variables, emphasizing the importance of governance quality in economic and environmental outcomes. These quants' serial correlation is ascertained using the Durbin-Watson test. Either the link works properly, or it doesn't. Positive serial correlation indicates a positive trend in the variables. When security shows a negative serial correlation, it ultimately starts to damage itself (Savin \u0026amp; White, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1977\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Johansen Cointegration Test\u003c/h2\u003e\u003cp\u003eThere are two kinds of Johansen tests: maximum eigenvalue tests and trace tests. Researchers set K0 to zero to see whether the null hypothesis is rejected when employing the trace test to look for Cointegration in a time-series data sample. Researchers might conclude that the time series sample of data has a Cointegration connection if it is rejected (Bernard \u0026amp; Durlauf, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eJohansen Cointegration test for long term effect\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eUnrestricted Cointegration rank test (trace)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHypothesized\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTrace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo. of CE(s)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eEigenvalue\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eStatistic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eCritical Value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eProb.**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362.650***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 1 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e257.749***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95.753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 2 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168.604***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 3 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94.930***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 4 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.817***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 5 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.980***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 6 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.743***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eUnrestricted Cointegration Rank Test (Maximum Eigenvalue)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHypothesized\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMax-Eigen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of CE(s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEigenvalue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCritical Value\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\u003eNone *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104.900***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 1 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.145***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 2 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.673***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 3 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.113***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 4 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.837***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 5 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.236**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 6 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.743***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eThe max-eigenvalue test indicates 7 cointegrating eqn(s) at the 0.05 level denotes rejection of the hypothesis at the 0.05 level.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e**MacKinnon-Haug-Michelis (1999) p-values\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eHo: No Cointegration between the variables.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e***Rejection of null hypothesis at 1% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e**Rejection of null hypothesis at 5% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e*Rejection of null hypothesis at 10% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates both the trace test and maximum eigenvalue test indicate multiple Cointegration relationships among the variables. This implies that there are several long-term equilibrium relationships in the dataset. The existence of these Cointegration relationships suggests that despite short-term fluctuations, the variables move together in the long run, maintaining a stable relationship over time. Policymakers should consider these long-term relationships when designing economic and environmental policies. Understanding these relationships can help in creating strategies that promote sustainable development and economic stability. Further studies could delve into the specific nature of these Cointegration relationships and explore the dynamics between different variables to develop a deeper understanding of the mechanisms at play.\u003c/p\u003e\u003cp\u003eThe Johansen Cointegration Test results indicate strong evidence of multiple Cointegration relationships among GDP per capita, green technological innovation, renewable energy, CO2 production, labor force, trade openness, research and development expenditure, and governance index. This highlights the interconnectedness of these variables and underscores the importance of considering these relationships in economic and environmental policymaking. This approach also demonstrates that all variables have a for a long time Cointegration relationship. There were two significant findings from this inquiry. First, theoretical results are connected to the market's effectiveness theory, followed by actual results (Nasseh \u0026amp; Strauss, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). These findings underline the importance of understanding the long-term interactions among key economic, environmental, and governance variables, providing a robust foundation for both theoretical insights and practical policy-making.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Robustness Models Estimation\u003c/h2\u003e\u003cp\u003eTo robustly estimate the relationships between the dependent variable (GDP per capita) and the independent variables (Green Technological Innovation, Research \u0026amp; Development Expenditure, Renewable Energy, CO2 Production, Labor Force, Trade Openness, Governance Index), three advanced econometric techniques were employed Canonical Cointegration Regression (CCR) is designed to provide efficient and consistent estimators for Cointegrated systems by transforming the data to eliminate endogeneity and serial correlation. It corrects for potential biases and ensures robust long-term parameter estimates. Robust Least Squares (RLS) is used to handle potential outliers and heteroscedasticity in the data. It provides more reliable coefficient estimates by giving different weights to observations, reducing the influence of outliers. Fully Modified Least Squares (FMOLS) adjusts for serial correlation and endogeneity in the regressors that result from Cointegrated relationships. It produces asymptotically unbiased estimates by modifying the least squares estimator to account for the potential correlation between the regressors and the error term.\u003c/p\u003e\u003cp\u003eMediation analysis was employed to explore the underlying mechanisms through which green innovation influences GDP per capita, using governance quality as the mediating variable. This analysis helps to understand whether the impact of green innovation on economic performance is direct or occurs indirectly through the influence of good governance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStep One for Green Technological Innovation and Economic Growth\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMOLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCCR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient (Prob Value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient (Prob Value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoefficient (Prob Value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Technological Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e108.031** (0.050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e260.735*** (0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e109.975** (0.040)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch \u0026amp; Development Expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2677.495*** (0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6789.313*** (0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2692.466*** (0.015)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e195.476** (0.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e257.468 (0.107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e195.821** (0.058)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 Production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e216.438 (0.699)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1058.851 (0.457)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e122.378 (0.832)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor Force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1344.502*** (0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-223.236*** (0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1348.254*** (0.000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrade Openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-5.291 (0.645)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.261 (0.992)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.153 (0.630)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80796.170*** (0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.639* (0.082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81194.910*** (0.000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted R-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e***Significance level at 1% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e**Significance level at 5% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e*Significance level at 10% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presenting that green technological innovation, research \u0026amp; development expenditure, renewable energy and labor force has been statistically impact on the GDP per capita as proxy variable of economic growth. And this means that this study finds total effect (c path) between dependent and independent variables but without including mediator variable governance index. All one-unit increase in independent variables green technological innovation, research \u0026amp; development expenditure, renewable energy and labor force, dependent variable GDP per capita increases by independent variables green technological innovation, research \u0026amp; development expenditure, and renewable energy units, and mediator variable governance index is not included in the model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStep two Green Technological Innovation and Mediator Governance Index\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMOLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCCR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient (Prob Value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient (Prob Value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoefficient (Prob Value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Technological Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.093** (0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.115* (0.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.116* (0.079)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch \u0026amp; Development Expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.576*** (0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.438** (0.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.515** (0.054)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.574 (0.214)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.647 (0.302)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.593 (0.250)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 Production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.535** (0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.659 (0.158)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.320** (0.032)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor Force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.074 (0.963)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.077 (0.969)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.785 (0.745)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrade Openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.083 (0.161)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.092 (0.261)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.073 (0.261)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.734 (0.819)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.639 (0.882)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-17.679 (0.902)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted R-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e***Significance level at 1% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e**Significance level at 5% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e*Significance level at 10% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presenting the mediation analysis governance index variable is the mediator variable. Green technological innovation, research \u0026amp; development expenditure and CO2 production has had statistically positive significant impact on the governance index. And this means that this study finds the direct effect (c path). And the trend increasing in the green technological innovation, research \u0026amp; development expenditure and CO2 production, and this will also increase the governance index and quality of performance of governance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStep three Green Technological Innovation and Economic Growth with Mediator Governance index\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMOLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCCR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient (Prob Value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient (Prob Value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoefficient (Prob Value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Technological Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96.418** (0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92.116 (0.198)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.726** (0.047)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch \u0026amp; Development Expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2223.502** (0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1907.923 (0.108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2461.748* (0.085)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e209.315** (0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e228.447** (0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e193.473 (0.118)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 Production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e668.927 (0.315)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e862.636 (0.270)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e475.998 (0.550)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor Force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1131.546*** (0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1153.132*** (0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1194.676*** (0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrade Openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.583** (0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.122** (0.028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.573 (0.683)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernance index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.991* (0.063)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79.545* (0.087)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.696 (0.512)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66047.74*** (0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67465.710*** (0.0000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70047.190*** (0.0030)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted R-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e***Significance level at 1% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e**Significance level at 5% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e*Significance level at 10% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the indirect effect (a*b product) of the independent variables on GDP per capita through the mediator variable, Governance Index. This mediation model uses Canonical Cointegration Regression (CCR), Robust Least Squares (RLS), and Fully Modified Least Squares (FMOLS) regressions to understand how the mediator variable explains the relationship between the independent and dependent variables.\u003c/p\u003e\u003cp\u003eGreen technological innovation has a statistically significant positive impact on GDP per capita at a 5% significance level, indicating that innovations in green technology bolster and improve economic prosperity and Zhou and Luo (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggest that economic growth and new technologies interact dynamically, enhancing economic performance through green technology advancements. Research \u0026amp; Development Expenditure shows a statistically significant positive impact on GDP per capita at a 5% significance level, implying that increased R\u0026amp;D expenditure drives economic growth and Bayarcelik \u0026amp; Taşel (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), Huňady \u0026amp; Orvisk\u0026aacute; (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found similar results, confirming the positive relationship between R\u0026amp;D and economic growth. Renewable energy has a negatively statistically significant impact on GDP per capita at a 5% significance level. This suggests that increased spending on renewable energy may initially reduce GDP per capita due to higher costs associated with the transition and Wang, et, al., (2021) indicate mixed short-run effects between renewable energy and economic growth, depending on various factors including financial development. CO2 production shows no significant impact on GDP per capita in this study and Hannesson (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) noted that CO2 intensity may fall with GDP per capita, but this relationship requires further investigation, particularly in the context of structural changes in GDP.\u003c/p\u003e\u003cp\u003eThe labor force has a negatively significant impact on GDP per capita at a 1% significance level, indicating that an increase in the labor force may reduce GDP per capita due to potential inefficiencies or overpopulation and Wijaya, et, al., (2021) found that while the labor force significantly contributes to economic development, it can also have adverse effects if not managed properly. Trade openness shows a positively significant impact on GDP per capita at a 5% significance level, suggesting that increased international trade enhances economic growth and Malefane \u0026amp; Odhiambo (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that trade openness positively impacts economic growth, recommending policies supporting international trade and green production. Mediating variables Governance Index has a positively significant impact on GDP per capita at a 10% significance level. Good governance practices are linked to better economic performance and Pao and Fu (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) noted that good governance in energy-independent economies like Brazil is crucial for sustainable development. Similarly, Mikayilov, et, al., (2018) found a long-term positive impact of economic growth on governance and environmental outcomes. Overall, the study finds both direct and indirect effects of the independent variables on economic growth through the mediator variable, Governance Index. This highlights the complex interactions between technological innovation, governance, and economic performance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Structural Equation Model\u003c/h2\u003e\u003cp\u003eThe SEM results show that governance significantly mediates the relationship between innovation, energy, trade, and growth, with R\u0026amp;D, green technologies, and renewable energy emerging as the strongest drivers of GDP per capita. Overall, sustainable growth depends on embedding innovation and energy transitions within strong governance systems.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStructural equation estimates (standardized)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProb value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMediator equation \u0026mdash; Governance Index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Technological Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0662***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch \u0026amp; Development Expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0154*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.3959**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 Production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0896**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor Force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.4934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrade Openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.1776**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.1046**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.0138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual variance governance:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1931 (SE 0.0507)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOutcome equation \u0026mdash; GDP per capita\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernance Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1362***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Technological Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1104***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch \u0026amp; Development Expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2421***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5467**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 Production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0772**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor Force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0616*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrade Openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0489*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;8.6970*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.7018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual variance GDPpc:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0186 (SE 0.0041)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elog pseudo likelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;447.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSRMR\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e***Rejection of Null hypothesis at 1% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e**Rejection of Null Hypothesis at 5% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*Rejection of Null Hypothesis at 10% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results of the structural equation model (SEM) are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e reports the standardized coefficients for the governance (mediator) and GDP per capita (outcome) equations. In the governance equation, renewable energy (\u0026minus;\u0026thinsp;0.3959, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), CO₂ production (0.0896, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and trade openness (\u0026minus;\u0026thinsp;0.1776, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10) significantly influenced governance, suggesting that higher renewable energy adoption weakens governance scores, while rising carbon productivity and international openness exert measurable institutional effects. Green technological innovation and R\u0026amp;D expenditures had the expected positive signs but were not consistently significant. In the GDP per capita equation, governance exerted a strong positive effect (0.1362, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming the mediating role of institutional quality. Furthermore, green technological innovation (0.1104, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), R\u0026amp;D (1.2421, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and renewable energy (0.5467, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) had significant positive impacts on economic performance, highlighting the importance of innovation-led and sustainable growth pathways.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDirect effects from estat teffects (unstandardized)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProb value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eGovernance Index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Technological Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1538***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch \u0026amp; Development Expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0855*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.4703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.2747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 Production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7604**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.4865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor Force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;1.1145*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrade Openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.0593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGDP per capita\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernance Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164.448**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Technological Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e309.819***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch \u0026amp; Development Expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8317.141***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2093.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e458.047**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e202.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 Production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e791.726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1254.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.528\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor Force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e167.914*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e416.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrade Openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.707**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e***Rejection of Null hypothesis at 1% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e**Rejection of Null Hypothesis at 5% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*Rejection of Null Hypothesis at 10% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e decomposes direct effects (unstandardized). Here, governance directly explains income gains (164.448, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while green innovation (309.819, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), R\u0026amp;D (8317.141, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and renewable energy (458.047, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are powerful predictors of GDP per capita. Interestingly, CO₂ production and labor force contributions are statistically weaker, indicating that productivity improvements stem more from innovation and energy transitions than from factor accumulation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndirect effects (via Governance \u0026rarr; GDP per capita)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProb value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Technological Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.2848**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.6691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch \u0026amp; Development Expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.0671*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e568.9904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;45.1801***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.2207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 Production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e125.0431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e596.7837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor Force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;183.2709*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e130.1063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrade Openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;9.7459***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.7615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e***Rejection of Null hypothesis at 1% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e**Rejection of Null Hypothesis at 5% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*Rejection of Null Hypothesis at 10% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e presents indirect effects via governance. Green innovation (25.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and trade openness (\u0026minus;\u0026thinsp;9.75, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) operate through governance, demonstrating that institutional quality mediates part of their impact. Renewable energy exerts a negative indirect effect (\u0026minus;\u0026thinsp;45.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), reinforcing earlier evidence that environmental reforms may initially strain governance capacities. Labor force participation also shows a negative mediated effect (\u0026minus;\u0026thinsp;183.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTotal effects (Direct\u0026thinsp;+\u0026thinsp;Indirect) of GDP per capita\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProb value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernance Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e164.448**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Technological Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e335.104**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e147.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch \u0026amp; Development Expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8331.208***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2000.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenewable Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e412.867**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e196.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 Production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e916.769*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1338.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor Force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;15.3568*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e495.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrade Openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.9612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e***Rejection of Null hypothesis at 1% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e**Rejection of Null Hypothesis at 5% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*Rejection of Null Hypothesis at 10% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFinally, Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e combines direct and indirect effects. R\u0026amp;D (8331.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), green technological innovation (335.10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and renewable energy (412.87, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) emerge as the strongest overall determinants of GDP per capita, with governance amplifying their effects. These findings are consistent with endogenous growth theory (Romer, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) and institutional economics (North, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Kaufmann et al., 2010), underscoring that innovation and sustainability policies translate into growth when embedded in strong governance frameworks.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion and Policy Implications","content":"\u003cp\u003eThis study examines the impact of environmental green innovation on China's economic performance, with a specific focus on the mediating role of the Governance Quality Index. The research, utilizing annual time series data from 1996 to 2020, employs various robust econometric methods to assess both direct and mediated effects. The results demonstrate a significant positive direct impact of green technological innovation on GDP per capita. This suggests that advancements in green technology contribute to enhanced economic performance, aligning with findings from Zhou and Luo (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Bayarcelik \u0026amp; Taşel (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) that link technological progress and R\u0026amp;D with economic growth. Mediating role of governance quality index significantly mediates the relationship between green innovation and economic performance. Improved governance enhances the effectiveness of green innovations, reinforcing the findings that good governance practices are crucial for maximizing the economic benefits of technological advancements. This supports the idea that governance quality plays a critical role in sustaining long-term economic growth and stability. The study identifies significant indirect effects of green technological innovation, research \u0026amp; development expenditure, and CO2 production on GDP per capita through the Governance Index. This underscores the importance of governance in translating green innovation into tangible economic benefits.\u003c/p\u003e\u003cp\u003eThe impact of renewable energy on GDP per capita is negative, which may be attributed to the initial high costs associated with the transition to cleaner energy sources. This finding is consistent with Wang, et, al., (2021), suggesting a complex relationship that varies over time. An increase in the labor force shows a negative impact on GDP per capita, indicating potential inefficiencies or oversupply issues. This aligns with Wijaya, et, al., (2021), highlighting the need for effective labor market policies. Positive effects of trade openness on economic growth suggest that increased international trade benefits China's economic performance, supporting Malefane \u0026amp; Odhiambo (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePolicymakers should prioritize enhancing governance quality to effectively leverage green innovations for economic growth. Better governance structures can improve the efficiency of environmental policies and ensure that green technologies contribute more significantly to economic performance. While renewable energy is crucial for sustainable development, policymakers should manage the transition costs and support measures to mitigate any short-term negative impacts on GDP per capita. Address potential inefficiencies associated with a growing labor force through targeted policies that enhance labor market flexibility and productivity. Continue to support trade openness and invest in technological innovations to sustain economic growth. Ensure that trade policies are aligned with broader economic and environmental goals. Maintain and increase investments in R\u0026amp;D to drive long-term economic growth and technological advancement. This is vital for maintaining China's competitive edge and supporting sustainable development.\u003c/p\u003e\u003cp\u003eThe interplay between green innovation, governance quality, and economic performance underscores the need for a holistic approach to policy-making. Integrating effective governance with strategic investments in green technologies and innovation will foster sustainable economic growth and resilience in the face of global challenges.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict Statement: \u003c/h2\u003e\n\u003cp\u003ethis study stated that there is not any conflict of interest, and the study data will be available on request.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatement for Ethics: \u003c/h2\u003e\n\u003cp\u003ethis study has not involved human participation and no experiment performed on any animal.\u0026nbsp;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.K., a PhD student under the supervision of L.Y., conceived and designed the study, conducted the econometric analysis, and prepared the first draft of the manuscript. L.Y. provided supervision, conceptual guidance, and critical revisions to strengthen the theoretical framework and interpretation of results. Both authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge the academic support and guidance of the School of Economics and Finance, Xi\u0026rsquo;an Jiaotong University, China.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study is based on secondary data sources, all of which are publicly available and cited within the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcemoglu D, Robinson JA (2013) Why nations fail: The origins of power, prosperity, and poverty. Crown Currency\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnderson RL (1942) Distribution of the serial correlation coefficient. Ann Math Stat 13(1):1\u0026ndash;13\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAntonioli D, Borghesi S, Mazzanti M (2016) Are regional systems greening the economy? Local spillovers, green innovations and firms\u0026rsquo; economic performances. Econ Innov New Technol 25(7):692\u0026ndash;713\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eApergis N, Payne JE (2010) Renewable energy consumption and growth in Eurasia. Energy Econ 32(6):1392\u0026ndash;1397\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarro RJ (1991) Economic growth in a cross section of countries. Q J Econ 106(2):407\u0026ndash;443\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaron RM, Kenny DA (1986) The moderator\u0026ndash;mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Personal Soc Psychol 51(6):1173\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBayarcelik EB, Taşel F (2012) Research and development: Source of economic growth. Procedia-Social Behav Sci 58:744\u0026ndash;753\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBernard AB, Durlauf SN (1995) Convergence in international output. J Appl Econom 10(2):97\u0026ndash;108\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang RD, Zuo J, Zhao ZY, Soebarto V, Lu Y, Zillante G, Gan XL (2018) Sustainability attitude and performance of construction enterprises: A China study. J Clean Prod 172:1440\u0026ndash;1451\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCho JH, Sohn SY (2018) A novel decomposition analysis of green patent applications for the evaluation of R\u0026amp;D efforts to reduce CO2 emissions from fossil fuel energy consumption. J Clean Prod 193:290\u0026ndash;299\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCuerva MC, Triguero-Cano \u0026Aacute;, C\u0026oacute;rcoles D (2014) Drivers of green and non-green innovation: empirical evidence in Low-Tech SMEs. J Clean Prod 68:104\u0026ndash;113\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong C, Qi Y, Dong W, Lu X, Liu T, Qian S (2018) Decomposing driving factors for wind curtailment under economic new normal in China. Appl Energy 217:178\u0026ndash;188\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEsen H, Inalli M, Esen M (2006) Technoeconomic appraisal of a ground source heat pump system for a heating season in eastern Turkey. Energy Conv Manag 47(9\u0026ndash;10):1281\u0026ndash;1297\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEsen H, Inalli M, Esen M (2007) A techno-economic comparison of ground-coupled and air-coupled heat pump system for space cooling. Build Environ 42(5):1955\u0026ndash;1965\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEsen M, Yuksel T (2013) Experimental evaluation of using various renewable energy sources for heating a greenhouse. Energy Build 65:340\u0026ndash;351\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrankel JA, Romer D (2017) Does trade cause growth? Global trade. Routledge, pp 255\u0026ndash;276\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFuller WA (2009) Introduction to statistical time series. Wiley\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarces E, Daim TU (2012) Impact of renewable energy technology on the economic growth of the USA. J Knowl Econ 3:233\u0026ndash;249\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHannesson R (2020) CO2 intensity and GDP per capita. Int J Energy Sect Manage 14(2):372\u0026ndash;388\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuňady J, Orvisk\u0026aacute; M (2014), July The impact of research and development expenditures on innovation performance and economic growth of the country\u0026mdash;the empirical evidence. In \u003cem\u003eCBU international conference proceedings\u003c/em\u003e (Vol. 2, pp. 119\u0026ndash;125)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu L, Zhao Z, Su B, Ng TS, Zhang M, Qi L (2021) Structural breakpoints in the relationship between outward foreign direct investment and green innovation: An empirical study in China. Energy Econ 103:105578\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang Z, Lin B (2012) China's energy demand and its characteristics in the industrialization and urbanization process. Energy Policy 49:608\u0026ndash;615\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJin L, Duan K, Tang X (2018) What is the relationship between technological innovation and energy consumption? Empirical analysis based on provincial panel data from China. Sustainability 10(1):145\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKander A, Stern DI (2014) Economic growth and the transition from traditional to modern energy in Sweden. Energy Econ 46:56\u0026ndash;65\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaufmann D, Kraay A, Mastruzzi M (2011) The worldwide governance indicators: Methodology and analytical issues1. Hague J rule law 3(2):220\u0026ndash;246\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaur P, Stoltzfus J, Yellapu V (2018) Descriptive statistics. Int J Acad Med 4(1):60\u0026ndash;63\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeho Y (2017) The impact of trade openness on economic growth: The case of Cote d\u0026rsquo;Ivoire. Cogent Econ Finance 5(1):1332820\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKemp R (2010) Eco-innovation: definition, measurement and open research issues. Economia Politica 27(3):397\u0026ndash;420\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi D, Stengos T (2003) Testing serial correlation in semiparametric time series models. J Time Ser Anal 24(3):311\u0026ndash;335\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi K, Jiang Z (2016) The impacts of removing energy subsidies on economy-wide rebound effects in China: An input-output analysis. Energy Policy 98:62\u0026ndash;72\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalefane MR, Odhiambo NM (2018) IMPACT OF TRADE OPENNESS ON ECONOMIC GROWTH: EMPIRICAL EVIDENCE FROM SOUTH AFRICA. Int Economics/Economia Internazionale, \u003cem\u003e71\u003c/em\u003e(4)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa X, Wang C, Dong B, Gu G, Chen R, Li Y, Li Q (2019) Carbon emissions from energy consumption in China: its measurement and driving factors. Sci Total Environ 648:1411\u0026ndash;1420\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMazzanti M, Rizzo U (2017) Diversely moving towards a green economy: Techno-organisational decarbonisation trajectories and environmental policy in EU sectors. Technol Forecast Soc Chang 115:111\u0026ndash;116\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMikayilov JI, Galeotti M, Hasanov FJ (2018) The impact of economic growth on CO2 emissions in Azerbaijan. J Clean Prod 197:1558\u0026ndash;1572\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNasseh A, Strauss J (2000) Stock prices and domestic and international macroeconomic activity: a cointegration approach. Q Rev Econ finance 40(2):229\u0026ndash;245\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNorth DC (1990) Institutions, institutional change and economic performance. Cambridge University Press\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOECD (2017) Governance for the sustainable development goals. OECD Publishing, Paris\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOECD (2019) Measuring innovation in energy technologies. Paris: OECD Publishing\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePahle M, Pachauri S, Steinbacher K (2016) Can the Green Economy deliver it all? Experiences of renewable energy policies with socio-economic objectives. Appl Energy 179:1331\u0026ndash;1341\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePao HT, Fu HC (2013) Renewable energy, non-renewable energy and economic growth in Brazil. Renew Sustain Energy Rev 25:381\u0026ndash;392\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaparoditis E, Politis DN (2018) The asymptotic size and power of the augmented Dickey\u0026ndash;Fuller test for a unit root. Econom Rev 37(9):955\u0026ndash;973\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePoh CW, Tan R (1997) Performance of Johansen's cointegration test. In \u003cem\u003eEast Asian Economic Issues: Volume III\u003c/em\u003e (pp. 402\u0026ndash;414)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePorter ME, Linde CVD (1995) Toward a new conception of the environment-competitiveness relationship. \u003cem\u003eJournal of economic perspectives, 9(4), 97\u0026ndash;118.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRennings K (2000) Redefining innovation\u0026mdash;eco-innovation research and the contribution from ecological economics. Ecol Econ 32(2):319\u0026ndash;332\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRomer PM (1990) Endogenous technological change. \u003cem\u003eJournal of political Economy, 98(5, Part 2), S71-S102\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSavin NE, White KJ (1977) The Durbin-Watson test for serial correlation with extreme sample sizes or many regressors. Econometrica: J Econometric Soc, 1989\u0026ndash;1996\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShaikh F, Ji Q, Fan Y (2016) Prospects of Pakistan\u0026ndash;China energy and economic corridor. Renew Sustain Energy Rev 59:253\u0026ndash;263\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong M, Tao J, Wang S (2015) FDI, technology spillovers and green innovation in China: analysis based on Data Envelopment Analysis. Ann Oper Res 228:47\u0026ndash;64\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStucki T (2019) Which firms benefit from investments in green energy technologies?\u0026ndash;The effect of energy costs. Res Policy 48(3):546\u0026ndash;555\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, Zhang S, Zhang Q (2021) The relationship of renewable energy consumption to financial development and economic growth in China. Renewable Energy 170:897\u0026ndash;904\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Q, Li R (2017) Decline in China's coal consumption: An evidence of peak coal or a temporary blip? Energy Policy 108:696\u0026ndash;701\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Q, Hang Y, Sun L, Zhao Z (2016) Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach. Technol Forecast Soc Chang 112:254\u0026ndash;261\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang X, Fu Y, Zhu B (2020) What drives green innovation? Evidence from the transportation sector in China. J Clean Prod 247:119206\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWijaya A, Kasuma J, Darma DC (2021) Labor force and economic growth based on demographic pressures, happiness, and human development: Empirical from Romania. J East Eur Cent Asian Res (JEECAR) 8(1):40\u0026ndash;50\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Bank (2020) Worldwide governance indicators. World Bank, Washington, DC\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang X, Ren S, Zhang X (2018) Consumer demand and green innovation: Evidence from China. J Clean Prod 198:1251\u0026ndash;1260\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang T, Wu X, Li H, Tsang DC, Li G, Ren H (2020) Struvite pyrolysate cycling technology assisted by thermal hydrolysis pretreatment to recover ammonium nitrogen from composting leachate. J Clean Prod 242:118442\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Yu Z (2020) Determinants of green innovation: Evidence from Chinese manufacturing firms. J Clean Prod 256:120399\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, He X, Cheng X, Huo J (2021) The impact of R\u0026amp;D investment on green innovation performance: The mediating effect of absorptive capacity and the moderating effect of regulatory pressure. J Clean Prod 278:123727\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Zhou J, Feng X (2018) Consumer environmental awareness and green innovation in Chinese manufacturing firms: The moderating role of corporate environmental capabilities. J Clean Prod 190:1010\u0026ndash;1018\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Z, Wu Y, Wang J (2021) Research on the relationship between R\u0026amp;D investment, green technology innovation, and environmental performance. J Clean Prod 279:123758\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao J, Zhang Q (2020) Research on the innovation effect of government R\u0026amp;D investment in green technology and its path choice. J Clean Prod 244:118708\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao Z, Zhang X (2020) Government funding and green technology innovation: Evidence from China. J Clean Prod 244:118650\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou G, Luo S (2018) Higher education input, technological innovation, and economic growth in China. Sustainability 10(8):2615\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e China National Intellectual Property Administration\u003c/span\u003e\u003cdiv id=\"Par10\" class=\"Para\"\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://english.cnipa.gov.cn/art/2023/9/27/art_3090_187815.html\u003c/span\u003e\u003cspan address=\"https://english.cnipa.gov.cn/art/2023/9/27/art_3090_187815.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e International Trade Administration: US Department of Commence \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.trade.gov/country-commercial-guides/china-environmental-technology\u003c/span\u003e\u003cspan address=\"https://www.trade.gov/country-commercial-guides/china-environmental-technology\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Organization for Economic Co-Operation and Development \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.oecd.org/envpolicy/patents-on-environment-technologies.htm\u003c/span\u003e\u003cspan address=\"https://data.oecd.org/envpolicy/patents-on-environment-technologies.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Green Innovation, Economic Performance, Time-series Data and Mediating Role of Governance","lastPublishedDoi":"10.21203/rs.3.rs-7765055/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7765055/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study aims to find the impact of environmental green innovation on the economic performance and also find the mediating role of the governance quality index. Utilizing annual time series data from 1996 to 2020 and find the long term impact through Cointegration analysis. Applied robust analysis method through canonical Cointegration regression (CCR), robust least squares (RLS) and fully modified least squares (FMOLS) to find the reliability of the results. The study evaluates the mediation effect to determine whether the governance quality index partially or fully explains the relationship between the dependent variable GDP per capita and independent variables green technological innovation indicators. Analysis find total effect through direct and indirect effect of green technological innovation on the economic performance after accounting the mediator effect of governance quality index. Findings reveal that the direct relationship between green innovation and economic performance is significantly mediated by governance quality. The results indicate that interventions aimed at improving governance quality could effectively enhance the positive impact of green innovation on economic performance. These findings have important implications for policymakers and stakeholders, suggesting that strengthening governance structures can amplify the benefits of green innovation for sustainable economic growth.\u003c/p\u003e","manuscriptTitle":"Environmental Green Innovation and Economic Performance of China: Mediating of Governance Quality Index","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 04:52:22","doi":"10.21203/rs.3.rs-7765055/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df8b8aaf-f7dc-4158-9304-435408e72dbd","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-10T04:52:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 04:52:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7765055","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7765055","identity":"rs-7765055","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}