Determinants of the Green Growth Index in Countries Around the World by Bayesian Belief Network Model

Determinants of the Green Growth Index in Countries Around the World by Bayesian Belief Network Model | 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 Determinants of the Green Growth Index in Countries Around the World by Bayesian Belief Network Model Hai Dinh Le, Thuy Phuong Nguyen, Thanh Thao Nguyen, Thi Hao Nguyen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7675836/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 Rising temperatures, increasing severity of natural disasters, and their simultaneous impacts on livelihoods have made green growth an essential priority. To assess the influence of factors on green growth of countries globally and to provide optimal scenarios for developed, developing countries and economies in transition in their efforts to promote sustainable economic growth, we employed Bayesian Belief Networks (BBNs) model to analyze data from 95 countries for period 2019–2021. Our research demonstrates that Country Classification, Global Innovation, Human Development Index, Forest Area, Education Expenditure, Trade Openness, and Worldwide Governance factors positively influence the green growth index of countries. Population Growth and Natural Resource Use negatively impact Green Growth. Moreover, it is clear from the results that countries at different levels of development will need tailored approaches to achieve their green growth goals. The authors have proposed tailored solutions for each scenario involving various groups of countries. Green Growth Index Determinants Bayesian Belief Network Vietnam Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The escalating temperatures, escalating severity of natural disasters, and their concurrent impact on livelihoods have elevated green growth to a paramount need. During the period from 2015 to 2022, unprecedented levels of heat were reported (The World Bank 2023 ). The most recent climate assessment report published by the International Plant Protection Convention (IPCC) affirms that the Earth's atmosphere, oceans, and ecosystems are experiencing extensive alterations that have never been experienced before (The World Bank 2023 ). The incidence of individuals impacted by natural catastrophes has increased compared to previous periods. The financial impact of climate change-related disasters has risen dramatically over the years, with global economic losses increasing by a factor of seven since the 1970s (The World Bank 2023 ). Low- and middle-income nations incur an estimated $ 18 billion annually in damages to power production and transportation infrastructure due to natural catastrophes. The overall expenses of these disasters amount to at least $ 390 billion (The World Bank 2023 ). Numerous nations are continuously seeking strategies to address the issue of climate change. In 1987, the World Commission on Environment and Development (WCED), led by Gro. Harlem Brundtland, as president, provided a comprehensive definition of sustainable development in the report "Our Common Future". They defined it as the development that fulfils the current needs without jeopardising the ability of future generations to meet their own needs. The concept aims to provide an improved standard of living for all individuals worldwide, both current and future generations, by maintaining a sustainable pace of economic growth and social advancement while preserving the planet's ecological equilibrium. Global Green Growth Week is an annual event that aims to collaborate and present optimal strategies for advancing the implementation of the Paris Agreement and achieving NetZero pledges. Furthermore, the 2030 Agenda has revitalised efforts to improve the environment, encompassing 17 objectives to harmonise the economy, society, and environment. We consider different factors that promote green growth, including Economic, Energy-related, Control, Institutional quality, and Internationalisation factors (Tawiah et al 2021 ). This study is being conducted in response to countries' efforts to achieve green growth objectives. Prior research has employed traditional econometric techniques, such as ordinary least squares (OLS), to examine the effects of various factors on green growth in different economies. However, no study has utilised a Bayesian Belief Networks (BBNs) network model to assess the influence of factors on the green growth of countries globally and offer optimal scenarios for developed and developing countries and economies in transition in their efforts to advance sustainable economic growth. This study utilises data from 95 countries spanning 2019–2021 to analyse the effects of various factors on green growth. The study combines adjustments and additions to influencing factors and the latest data to assess the impact of these factors on growth, thereby providing policy implications for Vietnam. 2. Literature review 2.1. Concept of Green Growth A novel strategy for the economic advancement of nations worldwide is known as "green growth". Green growth has been defined by numerous international organisations, including (UNESCAP 2005 ) and (The World Bank 2012 ), as economic growth that preserves the natural environment and its resources. Global Green Growth Institute ( 2014 ) popularised it as a model for economic development that focuses on reducing poverty, creating jobs, promoting social inclusion, protecting the environment, reducing the effects of climate change, preserving biodiversity, and facilitating access to clean water and energy. The notion of green growth Stéphane Hallegatte et al ( 2012 ) aims to make growth more efficient, cleaner, and flexible without harming resources and meeting growth needs to alleviate poverty (Sjak Smulders et al 2014 ). In Vietnam, the Green Growth Programme has been concretized through the “National Strategy for Green Growth 2011–2020 and Vision to 2050” (Vietnam Prime Minister 2012 ). Green growth refers to growth that is based on altering growth patterns, reorganising the economy to capitalise on comparative advantages, boosting economic efficiency and competitiveness through cutting-edge technology research and application, creating cutting-edge infrastructure systems to effectively use natural resources, lowering greenhouse gas emissions, addressing climate change, assisting in the eradication of poverty, and promoting sustainable economic growth. Overall, Green Growth has three main pillars: economic, environmental, and social: sustainable economic growth and development, improving economic quality; protecting and using natural resources efficiently, reducing pollution and environmental impacts; and linking to citizens' well-being, social justice, and meeting people's basic needs. Based on the concepts of world institutions and scholars, the (OECD 2024 ) definition of Green Growth is thought to be the most appropriate in our research, which promotesgrowth and economic development while ensuring that natural resources continue to provide the resources and environmental services necessary for human development Green growth is measured by green growth (Green Growth Index - GGI). This index is based on four aspects: (i) the efficient and sustainable use of natural resources (energy, water, land, etc.); (ii) the protection of natural resources (environment, biodiversity, and ecosystems, social and cultural); (iii) green economic opportunities (green investment, green trade, green jobs, green innovation); (iv) social inclusion (access to basic services and resources, social security, gender equality) (Global Green Growth Institute 2023 ). 2.2. Potential Factors Affecting Green Growth Index A wide range of potential factors influencing Green Growth Index has been identified through a literature review (Fig. 1 ). These factors are categorized into five groups: Economic factors; Institutional Quality; Internationalisation; Energy-related factors, and control factors. These five groups are presented below. 2.2.1. Economic factors Countries are allocating resources to encourage economic development to foster global economic growth. Nevertheless, the relationship between economic growth and the environment is sometimes complex. Prior research yields contradictory results regarding the influence of economic growth on the environment. Muhammad Shahbaz et al. ( 2013 ) assert that economic development is the primary catalyst for CO 2 emissions. The authors suggest mitigating the expenses associated with economic growth and funding the import of eco-friendly technology. According to Qichang Xie and Liu ( 2019 ) when economic growth is below the turning point, it leads to increased carbon emissions. However, once economic growth surpasses that turning point, there will be an improvement in environmental quality. According to Tawiah et al ( 2021 ), a positive relationship exists between high economic development and sustainable development. Moreover, nations with a substantial GDP per capita have sufficient resources to promote sustainable economic development actively. The authors suggest that implementing economic development strategies is necessary to achieve green growth and sustainable growth goals since it is crucial in increasing GDP. This finding aligns with the research conducted by (Enrico Maria de Angelis et al 2019 ). Hence, the influence of economic expansion on the environment can differ according to the degree of development in nations. Furthermore, Dimitar Eftimoski ( 2022 ) study examined the correlation between GDP growth and HDI. The findings indicate that the relationship between HDI and GDP is complex, as it can be positive and negative due to various economic development factors and contamination of the environment. At the onset of economic development, there is an adverse effect on the environment. Nevertheless, once a specific threshold of advancement is attained, particularly when there is a substantial level of money per person, economic progress will be accompanied by the preservation of the environment. Within an economy striving to decrease carbon emissions, certain unskilled labourers are being terminated by polluting manufacturing sectors in the market as part of the transition and upgrading procedure, owing to their incapacity to enhance their skills. The rapid acquisition of skills and qualifications leads to a significant increase in unemployment. Consequently, these unemployed individuals are more inclined to move to areas with greater pollution levels to find jobs in the manufacturing sector, which is experiencing a scarcity of workers. Investing in capital development leads to an increase in overall carbon emissions, as demonstrated by (Mirela Ionela Aceleanu et al 2015 ). Yiniu Cui et al ( 2022 ) also propose that a rise in unemployment will lead to a corresponding increase in carbon emissions. 2.2.2. Energy-related factors Energy-related factors refer to energy criteria and energy use that impact economic development. According to the World Meteorological Organization, the United Nations Environment Programme, the Intergovernmental Panel on Climate Change, the energy sector is a major contributor to economic development and has a direct impact on greenhouse gas emissions (El-Fadel et al 2003 , Shahsavari and Akbari 2018 ). Energy consumption and economic growth have a positive correlation, and when consumption increases, output also increases. However, economic growth increases environmental degradation, affecting CO 2 emissions and the greenhouse effect (James B Ang 2007 , Ugur Korkut Pata 2018 ). High energy consumption impacts the decline in green growth (Tawiah et al 2021 ). For example, most types of energy production and use today are causing environmental problems on a local, regional and global scale, reducing the quality of life and endangering human health as well as the well-being of present and future generations (Shahsavari and Akbari 2018 ). So, improving energy efficiency can reduce total energy consumption and greenhouse gas (GHG) emissions and air pollutants while ensuring economic growth (Hancheng Dai et al 2016 ). Global climate change caused by greenhouse gases (GHGs), especially carbon dioxide (CO 2 ), poses unparalleled threats to the environment, development and sustainability (Asif Raihan and Almagul Tuspekova 2022). Discovery of a study in Malaysia, by Asif Raihan and Almagul Tuspekova (2022), shows that when economic growth increases by 1%, CO 2 emissions increase by 0.78%. On the other hand, according to Tawiah et al ( 2021 ), CO 2 emissions are less likely to be a significant determinant of green growth based on country classification. Natural resources, especially forests, are vital to sustainable development, provide many essential services to humans and are irreplaceable as a component of social service (Yu Hao et al 2019 ). Today, the world's forest resources tend to decline as a result of logging and logging for fuel and infrastructure materials (Hancheng Dai et al 2016 ). That has affected forest ecosystems and accelerated the rate of deforestation. Based on Kuznets curve theory: based on the relationship between economic growth and forest resources (Stern 2003 ). The hypothesis is that when economic growth is high, the intensity of the use of forest resources increases and leads to a decline in forest resources. However, when a certain degree of intensity creates a turning point, there will be a forest recovery due to fulfilling community duties after achieving high economic development. Yu Hao et al ( 2019 ) noted that countries with a high rate of economic growth will have a rapid rate of deforestation, unlike countries with high rates of forest conservation, economic growth is slower. 2.2.3. Control factors In addition to the four elements mentioned earlier, there are additional factors that influence green development, which we refer to as the set of control factors. An extensive population exerts a substantial influence on the environment. The exponential increase in population places an immense strain on the availability of natural resources, leading to a rapid depletion of forests. Furthermore, humans are progressively exerting higher pressure on natural resources, many of which have limited capacity for renewal. The depletion of resources is occurring as a result of over-exploitation, leading to heightened environmental deterioration (Rahul Mittal and Chandi Gupta Mittal 2013 ). Hence, we anticipate an inverse correlation between population expansion and green growth. The function of human capital, particularly educational qualifications, is crucial in facilitating the sustainable development of national economies in conjunction with environmental preservation. The study conducted by Ying Xin et al ( 2023 ) found that a rise in education has a substantial impact on the reduction of CO 2 emissions. Conversely, a decrease in educational attainment has led to increased CO 2 emissions in the BRICS economies. This finding aligns with the study conducted by Nan Liu et al ( 2022 ) which demonstrates a negative correlation between education and CO 2 emissions. Consequently, the authors anticipate that an increase in Government investment in education will be positively correlated with green growth. Furthermore, Mirela Ionela Aceleanu et al ( 2015 ) pointed out that within an economy striving to decrease carbon emissions, specific manufacturing industries contributing to pollution may lay off low-skilled workers during the transition and upgrading phase. This is because these workers cannot promptly enhance their skills and qualifications to enhance their skills and qualifications. Consequently, this leads to a significant rise in unemployment. These unemployed individuals are then more likely to relocate and join production sectors with higher pollution levels, especially when there is a shortage of job opportunities, which increases total carbon emissions. Natural resources play a crucial role in facilitating a country's economic growth. Excessive exploitation of natural resources will have a negative impact on a country's ability to regenerate those resources, resulting in an ecological deficit (Destek and Sarkodie 2019 ). Prior research has demonstrated that the exploitation of natural resources has adverse effects on the environment (Godfred A Bokpin 2017 , Ramon Lopez 2017 ). Tawiah et al ( 2021 ) employed the natural resource rent variable to reduce the influence of ongoing natural resource exploitation on the promotion of sustainable green growth. Previous studies confirm that increases in energy consumption have a positive impact on HDI. However, today this relationship is gradually changing due to the effects of environmental pollution and climate change. A study conducted by Qiaosheng Wu et al ( 2010 ) compared two groups of subjects and found that developed countries have low per capita energy consumption but high Human Development Index (HDI). In contrast, developing countries have a positive relationship between the Human Development Index (HDI) and per capita energy consumption. Besides, Steinberger and Roberts ( 2010 ) assert that a certain energy consumption level will lead to a continuous increase in HDI over time. As energy consumption rises, so does the Human Development Index (HDI). However, once a country reaches a certain level of HDI development, its energy consumption will decrease. Hence, the correlation between energy and human development is contingent upon the developmental stage of nations. 2.2.4. Institutional quality The efficacy of the system, which consists of the strength of the laws and the authority of the enforcement authorities, can aide or hinder green growth. Opeyemi Akinyemi et al ( 2021 ) discovered that political institutions significantly boost green growth, confirming the fundamental role that economic institutions play in Africa's green growth. This aligns with the theory put forth by Manamba and Kombe (2017), which holds that political stability and institutions are crucial to Africa's economic development. According to Ghulam Shabbir et al ( 2016 ), political stability promotes economic growth by lowering social unrest, political instability, and encouraging investment. Effective national institutions will have the right policies in place, including ownership, rules, and regulations, to lower carbon dioxide emissions, promote the use of renewable energy sources, and enhance the environment (Hamisu Sadi Ali et al 2019 ). Nonetheless, many institutional attributes, including democracy and the efficacy of a single nation, have the potential to degrade environmental quality (Mehdi Abid 2017 ). Furthermore, through rules on resource assurance and environmental quality management, the institutional environment can help or hurt an organisation's ability to thrive (Wang Feng and Ann Reisner 2011, Scott Victor Valentine 2012 ). Additionally, prior research has examined and highlighted the connection between corruption and economic expansion. Fabio Méndez and Facundo Sepúlveda (2006) demonstrate corruption and economic growth's strong and negative correlation. Noel D Johnson et al ( 2011 ), (Ghulam Shabbir et al 2016 ) contended that corruption has an impact on growth by way of investment in national development. A robust institutional framework with low levels of corruption, effective government officials, high credibility, efficient justice systems, rational policies, and strict regulation are the key factors driving green economic growth (Mita Bhattacharya et al 2017 , Samuel Asumadu Sarkodie and Samuel Adams 2018, Dado Fabrice Degbedji et al 2024 ). Some contend, however, that it is impossible to understand the connection between corruption and economic expansion without taking into account the function of national institutions, Fabio Méndez and Facundo Sepúlveda (2006), Jac C Heckelman and Benjamin Powell (2010) argued that political institutions were an important factor in determining the relationship between corruption and economic growth. Moreover, when referring to the factors affecting institutional quality, José Antonio Alonso and Carlos Garcimartín (2013) concluded that the level of national development determines the quality of institutions. This can be explained by the fact that undeveloped and emerging countries would come behind developing countries in building strong institutional frameworks and fostering a competitive environment in many industries. In order to support the research on the variables influencing institutional quality, this author subsequently added the elements of trade openness and education (José Antonio Alonso et al 2020 ). Scholars also agreed with this viewpoint (Omer Javed and Omer Javed 2016 ). Furthermore, according to Dawda Adams et al ( 2019 ), most research points to inadequate institutional quality as the root cause of the depletion of natural resources. 2.2.5. Internationalization factors Green growth is a new approach to economic development used by countries worldwide. In that context, internationalisation is considered one of the factors contributing to the promotion of green growth through its impact on the environment. Globalization include trade openness, foreign direct investment (FDI), and innovation, which do not always have a clear impact on the environment. The current research presents several perspectives on the impact of globalisation on the environment. The research findings of Mahwish Zafar et al ( 2019 ) indicate a definite correlation between foreign direct investment (FDI) and trade openness with both short-term and long-term economic growth through their positive and significant relationship with CO 2 emissions in the atmosphere. Similarly, Yongzhong Jiang et al ( 2023 ) indicates that trade openness positively impacts green economic growth in the group of emerging economies (E-7), including China, India, Brazil, Mexico, Russia, Indonesia, and Turkey. A study conducted by Muhammad Shahbaz et al ( 2015 ) demonstrates the contrary viewpoint that an increase in FDI inflows will lead to an increase in emissions, as FDI growth can stimulate production and consumption by exploiting the environment, resulting in the depletion of environmental resources. Similarly, the research findings of Dinkneh Gebre Borojo et al ( 2023 ) suggest that FDI has a negative impact on the environment, hence contributing to sustainable growth. Cong Wang and Yifan Lu (2020) demonstrate an inherent relationship between participation in international trade and its negative impact on the environment since trade openness leads to an increase in emissions from production. The issues related to promoting green growth extend beyond the impacts of trade and investment, including the innovation level in new energy science and technology (Xing Zhou et al 2017 ). According to Eckehard Rosenbaum ( 2017 ), cited in the OECD report: “Towards Green Growth”, innovation and investment are drivers of green technology development. Green technology will stimulate sustainable development, which means identifying environmentally friendly sources of growth, developing new environmentally friendly industries, and creating jobs and technology (Michael Toman 2012 ). To achieve green growth, it is necessary to enhance investment and foster innovation, which are the foundations of sustainable development and open up new economic opportunities (The World Bank 2012 ). 3. Research methodology 3.1. Data collection and study scope Data selection started with 258 countries and territories listed in the World Bank and Our World databases. First, due to the limited time and research data of the Green Growth Index, we conducted the research data period from 2019 to 2021. Countries were excluded due to a lack of data, so the final set of data included 95 countries and 15 variables (Appendix 1). 3.2. Data Analysis Data collected is compiled and managed in Microsoft Excel. The data analysis process consists of four steps: First, the paper uses the Pearson correlation coefficient to examine the relevance of independent variables to pre-dependent variables and regression models and explore differences between independent variables by one-way ANOVA testing. The test was performed using IBM SPSS Statistic 23 (2015). Second, the study employs the Stata 17 software (2021) to do data analysis. Research using table data – is a combination of cross-data and time series. The Stata software enables authors to conduct regression models and assess hypotheses with a substantial quantity of observations (Federico Belotti et al 2017 ). Its user-friendly interface makes it convenient for authors during the data analysis process. The author employs quantitative analytical methods by utilising the FEM regression model (Fixed Effect Model) and REM (Random Effect Model). Conduct the Hausman testing to determine an appropriate research model. The study employed the Wald test to examine heteroskedasticity and the Wooldridge test to assess the presence of autocorrelation. If the model has defects, the study employs the Generalized Least Squares model (GLS). Third, Bayesian networks (BBNs) are designed to identify key factors that influence green growth in countries worldwide through the use of Norsys Software Corp Netica software version 6.06. The Bayes Network is a non-parametric statistical tool based on Bayes's inference of the influence of observed variables on a two-dimensional cause-effect and vice versa (Judea Pearl 1988 , Jens Frayer et al 2014 ). The Bayes network structure consists of two parts: The first part is the structure of a non-circular oriented graph, displayed through the corresponding nodes and arrows to describe the interdependence between variables; The second part is the parameters of the Bayes network, which are conditional probability tables (CPTs) to determine the probability distribution of nodes based on their root nodes (Huong and Hai Dinh 2024). We used heuristic techniques to transform the continuous variables into discrete variables because all random variables in Bayesian networks are expected to be discrete (Finn V Jensen and Thomas Dyhre Nielsen 2007) (e.g., equal interval/width, equal frequency, entropy minimisation, logical and expert knowledge) (Hai Dinh Le et al 2024 ). Fourth, we predict the scenario for the main factors that influence green growth. BBNs allow the evaluation of different scenarios on the resulting variable; the author deployed the scenarios by changing the probability distribution of existing variables. Then, the resulting change in the probability distribution of the target variable will correspond to the developments in the scenario conditions. At the same time, we adjust the probability distribution of individual variables to analyse how factors affecting green growth change. 4. Results and discussions 4.1. Descriptive statistics Results from independent samples t-tests in Appendix 2 show that there were no significant differences at the 5% level for GDP Growth, Primary Energy Consumption, Annual CO 2 Emissions, and Foreign Direct Investment between two groups of countries (developed countries vs. developing countries & transition economies). In contrast, there were significant differences at the 5% level for Unemployment; Forest Area; World Governance Index; Trade Openness; Global Innovation Index; Education Expenditure; Population Growth; Natural Resources Rent and HDI between two groups of countries (Appendix 2). 4.2. Linear regression model Based on the F-test findings of the FEM model presented in Table 1 , it is evident that the statistical significance of the Prob > F value is 0.000, which is less than the threshold of 0.05. Therefore, it can be inferred that the FEM model is more appropriate than the OLS model. The Hausman test is conducted to determine the suitable model for explaining the relationship between elements in the research model, namely between the FEM and the REM. The results indicate that the Pro > chi value is 0.000, leading to the conclusion that the FEM is better suitable. The study subsequently employed the Wald test to examine the presence of heteroskedasticity and the Wooldridge test to assess the existence of autocorrelation. If the model exhibits flaw or imperfections, it will be rectified or repaired using the Generalised Least Squares (GLS) method. Table 1 Summary of regression results using OLS, FEM, and REM models Independent variables Pool OLS FEM REM B P value VIF B P value B P value B P Value Ranking GDP Growth .371 .000 1.09 .297 .001 .402 .000 .336 .000 ** 3 Forest Area .163 .000 1.09 .075 .618 .172 .000 .146 .000 ** 5 Trade Openness .000 .969 1.30 .167 .037 .002 .840 − .007 .078 * 6 Global Innovation .269 .000 4.28 .273 .053 .295 .000 .264 .000 ** 4 Education Expenditure .581 .081 1.40 .175 .855 .490 .243 .228 .358 NS Population Growth -1.311 .012 2.21 .879 .391 -1.004 .095 − .750 .070 * 2 Natural Resources Rent − .0156 .070 1.32 .504 .004 − .035 .728 − .109 .160 NS HDI .190 .814 4.10 .240 .812 .260 .810 .557 .392 NS Country Classification -3.608 .000 2.94 -3.698 .000 -3.706 .000 -4.000 .000 ** 1 WGI − .072 .940 5.90 2.474 .664 − .257 .837 − .020 .979 NS _cons 47.558 .000 - 27.441 .008 45.851 .000 50.280 .000 Dependent variable : Green growth index Prob > F 0.000 0.000 0.000 Prob > chi2: 0.000 Model testing F-test 0.000 Hausman test 0.000 Heteroskedasticity 0.000 Autocorrelation 0.000 Note: * p < 0.1, ** p < 0.05, *** p < 0.01, NS: Not significant Table 1 reveals that the model exhibits violations of model assumptions such as heteroskedasticity and autocorrelation. Consequently, the study employs the Generalised Least Squares (GLS) method to address these shortcomings. The results of the linear regression model using the Generalised Least Squares (GLS) method show that there are six independent variables in the model that significantly influence the Green Growth Index (GGI): Country Classification, Population Growth, GDP growth, Global Innovation, Forest Area, and Trade Openness (Table 1 ). In addition, the significance of the GLS model was assessed using the Wald chi-square test, with a Chi-square value of 763.82 and a probability of less than 0.000. This leads to the conclusion that the GLS model is goodness fit for explaining the impact of factors on the Green Growth Index in countries around the World. Based on the regression coefficients from the GLS model (Table 1 ), we found that three variables, in descending order, have significantly positive impacts on the Green Growth Index: GDP growth (B = 0.336), Global Innovation (B = 0.264), and Forest Area (B = 0.146). Therefore, when GDP Growth increases by 1%, the Green Growth Index increases by 0.336 points, assuming all other factors remain constant. This applies similarly to the other two variables. On the contrary, Variables: Country Classification (B =-4.000), Population Growth (B=-0.750), and Trade Openness (B=-0.007) have significantly negative impacts on the Global Green Growth Index. To explain this, developed countries, lower population growth, and more trade openness tend to have higher levels of the Green Growth Index. 4.3. The resulting Bayesian Belief Network Figure 2 presents a network of 14 variables, illustrating the directional connections among these variables. The network was set at the initial probability distribution for all variables (Fig. 2 ). The dataset consists of 10 variables that are the primary determinants directly impacting Green Growth Index (GGI). These variables include HDI, Country Classification, Annual CO 2 emissions, Forest area, Education Expenditure, Population Growth, Natural Resources Rents, Trade Openness, Global Innovation, and World Government Index. This finding is in line with other previous research such as: Tawiah et al ( 2021 ); Nan Liu et al ( 2022 ); Rahul Mittal and Chandi Gupta Mittal ( 2013 ); Steinberger and Roberts ( 2010 ), and Asif Raihan and Almagul Tuspekova (2022). Variables that indirectly affect the green growth index (GGI) include 3 variables: GDP Growth, Primary Energy Consumptions, FDI. 4.4. Sensitivity analysis BBN model Sensitivity analysis was performed to measure the impact of each independent variable on the dependent variable. This involves calculating the sensitivity of the target variable of GGI to all other variables in the network model. The analysis employs variance reduction, a measure that gauges the extent to which one variable affects the belief in another variable. Figure 3 displays the outcomes of the sensitivity analysis for all influential factors, arranged in descending order based on their impact on the target variable (GGI). The findings indicate that countries' classification has the greatest significant impact on the green growth index (GGI) worldwide, with a variance reduction of 3.42%. The Global Innovation Index has a variance reduction of 2.99%, exerting a significant impact on the advancement of environmentally sustainable economic growth in nations. Other influence factors to GGI comprise WGI (World Governance Indicators), HDI (Human Development Index), Population Growth, Natural Resources Rentals, Trade Openness, and Education Expenditure. The remaining variables, such as CO 2 Emissions, Forest Area, Primary Energy Consumption, FDI, and GDP Growth, have a small impact on promoting green growth in countries, with a variance reduction of less than 0.5%. We further evaluate the strength of the interaction between the target variable and the remaining variables using a diagnostic analysis that takes advantage of the bidirectional inference capabilities of the BBNs model. 4.4. Scenario analysis for BBN model In contrast to sensitivity analysis, the diagnostic analysis gauges how the probabilities of influencing variables change given evidence in the target variable. We developed scenarios for a different set of variables. We build different optimal scenarios for all countries in the world and for two groups of countries: developed countries, developing countries and economies in transition. In order to determine how we can achieve a target level of GGI, the model needs to be interrogated inversely (diagnostic analysis). GGI is set to 100% for state “High”, and then we observe the changes in terminal nodes. The results are shown in Fig. 4 . Based on the linear regression model that analyses of influential factors combined with the sensitivity analysis and the BBNs model, we can give two policy groups for the four most important and influential factors in GGI of countries around the world: GDP Growth, Forest Area and Global Innovation (Fig. 5 ). The importance of factors influencing the process of promoting green growth in countries worldwide is shown in Fig. 3 , based on their order of significance. We propose scenarios and policy frameworks to promote green growth in developed countries worldwide as follows (Fig. 6 ). 5. Policy implications for Vietnam Vietnam has established a "National Strategy for Green Growth 2011–2020, Vision 2050" to promote ecologically sustainable economic progress. This strategy demonstrates the government's enduring commitment to sustainable economic development and its forward-thinking perspective since its beginning. Vietnam possesses a promising potential to enhance green growth due to its advantageous capacity for carbon storage in forest resources and substantial potential for the development of renewable energy (Ngoc Han 2023 ). The author presents some strategies to enhance green development in Vietnam: First, we need to focus on people in implementing green growth strategies, particularly in innovation endeavours aimed at developing initiatives about green cities and smart cities. Simultaneously, innovation must be integrated with sustainable development, which relies on achieving equilibrium among various forms of capital, including productive capital, human capital, and innovation capital. There is a requirement for policies that promote firms to engage in innovation regarding both the quantity and quality of their products. Secondly, to uphold Vietnam's pledge to achieve net zero emissions by 2050, it is imperative to adopt a discerning and enduring approach towards attracting foreign direct investment (FDI). To mitigate pollution, it is necessary to either decrease the presence of industries contributing to pollution or enforce strict environmental emission standards for foreign companies. It may be beneficial to focus on mining companies involved in sophisticated, eco-friendly research and technology, as well as those that contribute to the well-being and development of human capital, such as healthcare and education. Vietnam must exercise caution to avoid excessive dependence on foreign direct investment (FDI) capital, which could undermine the economy's resilience. Ultimately, it is crucial to continue conducting trade, collaborating, and interacting with emerging economies and trade organisations through participation in regional and global trade accords. Vietnam can utilise this opportunity to engage in the exchange, acquisition, and implementation of cutting-edge scientific and technological advancements and new ideas from around the globe. This will aid in addressing domestic issues, promoting economic growth, and enhancing social well-being. 6. Conclusion Sustainable development is the paramount objective pursued by nations worldwide. This study employed GLS regression and BBNs network model to investigate the elements that influence the green growth of countries worldwide. The results of our research reveal significant factors that contribute to the promotion of sustainable economic growth. The models indicate that Country Classification, Global Innovation, HDI, Forest Area, Education Expenditure, Trade Openness, and WGI positively impact the green growth index of countries. Population growth and the exploitation of natural resources have a detrimental effect on green growth. Furthermore, it is evident from the outcomes that nations at varying stages of development would necessitate distinct strategies to attain their green growth objectives. For instance, developing countries and economies in transition may contemplate loosening rules about resource exploitation shortly, given their inclination towards a resource-dependent approach in bolstering global economic influence. Nevertheless, in developed countries, it is imperative to construct suitable governance structures using both short-term and long-term plans to effectively promote national green growth and enhance the regulation of natural resource production and utilisation. The authors have suggested solutions appropriate for each scenario of distinct groups of countries. Declarations Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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1","display":"","copyAsset":false,"role":"figure","size":66667,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model for potential factors influencing the Green Growth Index\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7675836/v1/332c95e21d86aa4cce214e33.png"},{"id":95293149,"identity":"e38d3ca0-bf6e-4141-a58c-e7ebd39434dd","added_by":"auto","created_at":"2025-11-06 11:35:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":273211,"visible":true,"origin":"","legend":"\u003cp\u003eThe resulting Bayesian Network\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7675836/v1/bd806be9d39462c6246efd04.png"},{"id":95313726,"identity":"a12d44dc-a559-468e-bcb7-5d6b194e22ce","added_by":"auto","created_at":"2025-11-06 15:51:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":146644,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity to green growth index\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7675836/v1/7a3a9b955aa8a73b4c3bc72c.png"},{"id":95314658,"identity":"8ecd2dbd-d2ee-42c8-894f-e964e45e894a","added_by":"auto","created_at":"2025-11-06 15:53:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":241386,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic analysis for the target variable ‘green growth index’ is set to high 100% for the state “High”\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7675836/v1/19215ec4a56e728c3fc48913.png"},{"id":95293155,"identity":"87684ca5-3d54-4ad5-87cd-51a6629ce410","added_by":"auto","created_at":"2025-11-06 11:35:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":252950,"visible":true,"origin":"","legend":"\u003cp\u003eScenario for all countries around the world\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7675836/v1/4d8d9a140ceb2ace1661ccbf.png"},{"id":95293157,"identity":"c3bfc25c-cbcb-442f-a214-9a5d336a1ca2","added_by":"auto","created_at":"2025-11-06 11:35:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":227817,"visible":true,"origin":"","legend":"\u003cp\u003eA scenario shows the GGI reaching the highest score of 100 for developed countries.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7675836/v1/33d12adb329da0f8506d333c.png"},{"id":95653897,"identity":"378b2264-935e-4677-bb8c-4fda4a170594","added_by":"auto","created_at":"2025-11-11 16:04:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2227020,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7675836/v1/e0ec2af3-dc10-403a-963e-7604df34958a.pdf"},{"id":95315016,"identity":"bb8b4531-ceb6-4022-9352-e5a79b3e549a","added_by":"auto","created_at":"2025-11-06 15:53:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21607,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-7675836/v1/8d2bb528331816b2956f78d6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDeterminants of the Green Growth Index in Countries Around the World by Bayesian Belief Network Model\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe escalating temperatures, escalating severity of natural disasters, and their concurrent impact on livelihoods have elevated green growth to a paramount need. During the period from 2015 to 2022, unprecedented levels of heat were reported (The World Bank \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The most recent climate assessment report published by the International Plant Protection Convention (IPCC) affirms that the Earth's atmosphere, oceans, and ecosystems are experiencing extensive alterations that have never been experienced before (The World Bank \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The incidence of individuals impacted by natural catastrophes has increased compared to previous periods. The financial impact of climate change-related disasters has risen dramatically over the years, with global economic losses increasing by a factor of seven since the 1970s (The World Bank \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Low- and middle-income nations incur an estimated \u003cspan\u003e$\u003c/span\u003e18\u0026nbsp;billion annually in damages to power production and transportation infrastructure due to natural catastrophes. The overall expenses of these disasters amount to at least \u003cspan\u003e$\u003c/span\u003e390\u0026nbsp;billion (The World Bank \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNumerous nations are continuously seeking strategies to address the issue of climate change. In 1987, the World Commission on Environment and Development (WCED), led by Gro. Harlem Brundtland, as president, provided a comprehensive definition of sustainable development in the report \"Our Common Future\". They defined it as the development that fulfils the current needs without jeopardising the ability of future generations to meet their own needs. The concept aims to provide an improved standard of living for all individuals worldwide, both current and future generations, by maintaining a sustainable pace of economic growth and social advancement while preserving the planet's ecological equilibrium. Global Green Growth Week is an annual event that aims to collaborate and present optimal strategies for advancing the implementation of the Paris Agreement and achieving NetZero pledges. Furthermore, the 2030 Agenda has revitalised efforts to improve the environment, encompassing 17 objectives to harmonise the economy, society, and environment.\u003c/p\u003e\u003cp\u003eWe consider different factors that promote green growth, including Economic, Energy-related, Control, Institutional quality, and Internationalisation factors (Tawiah et al \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study is being conducted in response to countries' efforts to achieve green growth objectives. Prior research has employed traditional econometric techniques, such as ordinary least squares (OLS), to examine the effects of various factors on green growth in different economies. However, no study has utilised a Bayesian Belief Networks (BBNs) network model to assess the influence of factors on the green growth of countries globally and offer optimal scenarios for developed and developing countries and economies in transition in their efforts to advance sustainable economic growth. This study utilises data from 95 countries spanning 2019\u0026ndash;2021 to analyse the effects of various factors on green growth. The study combines adjustments and additions to influencing factors and the latest data to assess the impact of these factors on growth, thereby providing policy implications for Vietnam.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Concept of Green Growth\u003c/h2\u003e\u003cp\u003eA novel strategy for the economic advancement of nations worldwide is known as \"green growth\". Green growth has been defined by numerous international organisations, including (UNESCAP \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and (The World Bank \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), as economic growth that preserves the natural environment and its resources. Global Green Growth Institute (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) popularised it as a model for economic development that focuses on reducing poverty, creating jobs, promoting social inclusion, protecting the environment, reducing the effects of climate change, preserving biodiversity, and facilitating access to clean water and energy.\u003c/p\u003e\u003cp\u003eThe notion of green growth St\u0026eacute;phane Hallegatte et al (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) aims to make growth more efficient, cleaner, and flexible without harming resources and meeting growth needs to alleviate poverty (Sjak Smulders et al \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In Vietnam, the Green Growth Programme has been concretized through the \u0026ldquo;National Strategy for Green Growth 2011\u0026ndash;2020 and Vision to 2050\u0026rdquo; (Vietnam Prime Minister \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Green growth refers to growth that is based on altering growth patterns, reorganising the economy to capitalise on comparative advantages, boosting economic efficiency and competitiveness through cutting-edge technology research and application, creating cutting-edge infrastructure systems to effectively use natural resources, lowering greenhouse gas emissions, addressing climate change, assisting in the eradication of poverty, and promoting sustainable economic growth.\u003c/p\u003e\u003cp\u003eOverall, Green Growth has three main pillars: economic, environmental, and social: sustainable economic growth and development, improving economic quality; protecting and using natural resources efficiently, reducing pollution and environmental impacts; and linking to citizens' well-being, social justice, and meeting people's basic needs.\u003c/p\u003e\u003cp\u003eBased on the concepts of world institutions and scholars, the (OECD \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) definition of Green Growth is thought to be the most appropriate in our research, which promotesgrowth and economic development while ensuring that natural resources continue to provide the resources and environmental services necessary for human development\u003c/p\u003e\u003cp\u003eGreen growth is measured by green growth (Green Growth Index - GGI). This index is based on four aspects: (i) the efficient and sustainable use of natural resources (energy, water, land, etc.); (ii) the protection of natural resources (environment, biodiversity, and ecosystems, social and cultural); (iii) green economic opportunities (green investment, green trade, green jobs, green innovation); (iv) social inclusion (access to basic services and resources, social security, gender equality) (Global Green Growth Institute \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Potential Factors Affecting Green Growth Index\u003c/h2\u003e\u003cp\u003eA wide range of potential factors influencing Green Growth Index has been identified through a literature review (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These factors are categorized into five groups: Economic factors; Institutional Quality; Internationalisation; Energy-related factors, and control factors. These five groups are presented below.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1. Economic factors\u003c/h2\u003e\u003cp\u003eCountries are allocating resources to encourage economic development to foster global economic growth. Nevertheless, the relationship between economic growth and the environment is sometimes complex. Prior research yields contradictory results regarding the influence of economic growth on the environment.\u003c/p\u003e\u003cp\u003eMuhammad Shahbaz et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) assert that economic development is the primary catalyst for CO\u003csub\u003e2\u003c/sub\u003e emissions. The authors suggest mitigating the expenses associated with economic growth and funding the import of eco-friendly technology. According to Qichang Xie and Liu (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) when economic growth is below the turning point, it leads to increased carbon emissions. However, once economic growth surpasses that turning point, there will be an improvement in environmental quality. According to Tawiah et al (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), a positive relationship exists between high economic development and sustainable development. Moreover, nations with a substantial GDP per capita have sufficient resources to promote sustainable economic development actively. The authors suggest that implementing economic development strategies is necessary to achieve green growth and sustainable growth goals since it is crucial in increasing GDP.\u003c/p\u003e\u003cp\u003eThis finding aligns with the research conducted by (Enrico Maria de Angelis et al \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hence, the influence of economic expansion on the environment can differ according to the degree of development in nations. Furthermore, Dimitar Eftimoski (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) study examined the correlation between GDP growth and HDI. The findings indicate that the relationship between HDI and GDP is complex, as it can be positive and negative due to various economic development factors and contamination of the environment. At the onset of economic development, there is an adverse effect on the environment. Nevertheless, once a specific threshold of advancement is attained, particularly when there is a substantial level of money per person, economic progress will be accompanied by the preservation of the environment.\u003c/p\u003e\u003cp\u003eWithin an economy striving to decrease carbon emissions, certain unskilled labourers are being terminated by polluting manufacturing sectors in the market as part of the transition and upgrading procedure, owing to their incapacity to enhance their skills. The rapid acquisition of skills and qualifications leads to a significant increase in unemployment. Consequently, these unemployed individuals are more inclined to move to areas with greater pollution levels to find jobs in the manufacturing sector, which is experiencing a scarcity of workers. Investing in capital development leads to an increase in overall carbon emissions, as demonstrated by (Mirela Ionela Aceleanu et al \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Yiniu Cui et al (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also propose that a rise in unemployment will lead to a corresponding increase in carbon emissions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Energy-related factors\u003c/h2\u003e\u003cp\u003eEnergy-related factors refer to energy criteria and energy use that impact economic development. According to the World Meteorological Organization, the United Nations Environment Programme, the Intergovernmental Panel on Climate Change, the energy sector is a major contributor to economic development and has a direct impact on greenhouse gas emissions (El-Fadel et al \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Shahsavari and Akbari \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Energy consumption and economic growth have a positive correlation, and when consumption increases, output also increases. However, economic growth increases environmental degradation, affecting CO\u003csub\u003e2\u003c/sub\u003e emissions and the greenhouse effect (James B Ang \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Ugur Korkut Pata \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). High energy consumption impacts the decline in green growth (Tawiah et al \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For example, most types of energy production and use today are causing environmental problems on a local, regional and global scale, reducing the quality of life and endangering human health as well as the well-being of present and future generations (Shahsavari and Akbari \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). So, improving energy efficiency can reduce total energy consumption and greenhouse gas (GHG) emissions and air pollutants while ensuring economic growth (Hancheng Dai et al \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGlobal climate change caused by greenhouse gases (GHGs), especially carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e), poses unparalleled threats to the environment, development and sustainability (Asif Raihan and Almagul Tuspekova 2022). Discovery of a study in Malaysia, by Asif Raihan and Almagul Tuspekova (2022), shows that when economic growth increases by 1%, CO\u003csub\u003e2\u003c/sub\u003e emissions increase by 0.78%. On the other hand, according to Tawiah et al (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), CO\u003csub\u003e2\u003c/sub\u003e emissions are less likely to be a significant determinant of green growth based on country classification.\u003c/p\u003e\u003cp\u003eNatural resources, especially forests, are vital to sustainable development, provide many essential services to humans and are irreplaceable as a component of social service (Yu Hao et al \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Today, the world's forest resources tend to decline as a result of logging and logging for fuel and infrastructure materials (Hancheng Dai et al \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). That has affected forest ecosystems and accelerated the rate of deforestation. Based on Kuznets curve theory: based on the relationship between economic growth and forest resources (Stern \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The hypothesis is that when economic growth is high, the intensity of the use of forest resources increases and leads to a decline in forest resources. However, when a certain degree of intensity creates a turning point, there will be a forest recovery due to fulfilling community duties after achieving high economic development. Yu Hao et al (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) noted that countries with a high rate of economic growth will have a rapid rate of deforestation, unlike countries with high rates of forest conservation, economic growth is slower.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3. Control factors\u003c/h2\u003e\u003cp\u003eIn addition to the four elements mentioned earlier, there are additional factors that influence green development, which we refer to as the set of control factors.\u003c/p\u003e\u003cp\u003eAn extensive population exerts a substantial influence on the environment. The exponential increase in population places an immense strain on the availability of natural resources, leading to a rapid depletion of forests. Furthermore, humans are progressively exerting higher pressure on natural resources, many of which have limited capacity for renewal. The depletion of resources is occurring as a result of over-exploitation, leading to heightened environmental deterioration (Rahul Mittal and Chandi Gupta Mittal \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Hence, we anticipate an inverse correlation between population expansion and green growth.\u003c/p\u003e\u003cp\u003eThe function of human capital, particularly educational qualifications, is crucial in facilitating the sustainable development of national economies in conjunction with environmental preservation. The study conducted by Ying Xin et al (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that a rise in education has a substantial impact on the reduction of CO\u003csub\u003e2\u003c/sub\u003e emissions. Conversely, a decrease in educational attainment has led to increased CO\u003csub\u003e2\u003c/sub\u003e emissions in the BRICS economies. This finding aligns with the study conducted by Nan Liu et al (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) which demonstrates a negative correlation between education and CO\u003csub\u003e2\u003c/sub\u003e emissions. Consequently, the authors anticipate that an increase in Government investment in education will be positively correlated with green growth.\u003c/p\u003e\u003cp\u003eFurthermore, Mirela Ionela Aceleanu et al (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) pointed out that within an economy striving to decrease carbon emissions, specific manufacturing industries contributing to pollution may lay off low-skilled workers during the transition and upgrading phase. This is because these workers cannot promptly enhance their skills and qualifications to enhance their skills and qualifications. Consequently, this leads to a significant rise in unemployment. These unemployed individuals are then more likely to relocate and join production sectors with higher pollution levels, especially when there is a shortage of job opportunities, which increases total carbon emissions.\u003c/p\u003e\u003cp\u003eNatural resources play a crucial role in facilitating a country's economic growth. Excessive exploitation of natural resources will have a negative impact on a country's ability to regenerate those resources, resulting in an ecological deficit (Destek and Sarkodie \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Prior research has demonstrated that the exploitation of natural resources has adverse effects on the environment (Godfred A Bokpin \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Ramon Lopez \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Tawiah et al (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) employed the natural resource rent variable to reduce the influence of ongoing natural resource exploitation on the promotion of sustainable green growth.\u003c/p\u003e\u003cp\u003ePrevious studies confirm that increases in energy consumption have a positive impact on HDI. However, today this relationship is gradually changing due to the effects of environmental pollution and climate change. A study conducted by Qiaosheng Wu et al (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) compared two groups of subjects and found that developed countries have low per capita energy consumption but high Human Development Index (HDI). In contrast, developing countries have a positive relationship between the Human Development Index (HDI) and per capita energy consumption. Besides, Steinberger and Roberts (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) assert that a certain energy consumption level will lead to a continuous increase in HDI over time. As energy consumption rises, so does the Human Development Index (HDI). However, once a country reaches a certain level of HDI development, its energy consumption will decrease. Hence, the correlation between energy and human development is contingent upon the developmental stage of nations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4. Institutional quality\u003c/h2\u003e\u003cp\u003eThe efficacy of the system, which consists of the strength of the laws and the authority of the enforcement authorities, can aide or hinder green growth.\u003c/p\u003e\u003cp\u003eOpeyemi Akinyemi et al (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) discovered that political institutions significantly boost green growth, confirming the fundamental role that economic institutions play in Africa's green growth. This aligns with the theory put forth by Manamba and Kombe (2017), which holds that political stability and institutions are crucial to Africa's economic development. According to Ghulam Shabbir et al (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), political stability promotes economic growth by lowering social unrest, political instability, and encouraging investment.\u003c/p\u003e\u003cp\u003eEffective national institutions will have the right policies in place, including ownership, rules, and regulations, to lower carbon dioxide emissions, promote the use of renewable energy sources, and enhance the environment (Hamisu Sadi Ali et al \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nonetheless, many institutional attributes, including democracy and the efficacy of a single nation, have the potential to degrade environmental quality (Mehdi Abid \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, through rules on resource assurance and environmental quality management, the institutional environment can help or hurt an organisation's ability to thrive (Wang Feng and Ann Reisner 2011, Scott Victor Valentine \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, prior research has examined and highlighted the connection between corruption and economic expansion. Fabio M\u0026eacute;ndez and Facundo Sep\u0026uacute;lveda (2006) demonstrate corruption and economic growth's strong and negative correlation. Noel D Johnson et al (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), (Ghulam Shabbir et al \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) contended that corruption has an impact on growth by way of investment in national development. A robust institutional framework with low levels of corruption, effective government officials, high credibility, efficient justice systems, rational policies, and strict regulation are the key factors driving green economic growth (Mita Bhattacharya et al \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Samuel Asumadu Sarkodie and Samuel Adams 2018, Dado Fabrice Degbedji et al \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Some contend, however, that it is impossible to understand the connection between corruption and economic expansion without taking into account the function of national institutions, Fabio M\u0026eacute;ndez and Facundo Sep\u0026uacute;lveda (2006), Jac C Heckelman and Benjamin Powell (2010) argued that political institutions were an important factor in determining the relationship between corruption and economic growth.\u003c/p\u003e\u003cp\u003eMoreover, when referring to the factors affecting institutional quality, Jos\u0026eacute; Antonio Alonso and Carlos Garcimart\u0026iacute;n (2013) concluded that the level of national development determines the quality of institutions. This can be explained by the fact that undeveloped and emerging countries would come behind developing countries in building strong institutional frameworks and fostering a competitive environment in many industries. In order to support the research on the variables influencing institutional quality, this author subsequently added the elements of trade openness and education (Jos\u0026eacute; Antonio Alonso et al \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Scholars also agreed with this viewpoint (Omer Javed and Omer Javed \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, according to Dawda Adams et al (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), most research points to inadequate institutional quality as the root cause of the depletion of natural resources.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.5. Internationalization factors\u003c/h2\u003e\u003cp\u003eGreen growth is a new approach to economic development used by countries worldwide. In that context, internationalisation is considered one of the factors contributing to the promotion of green growth through its impact on the environment. Globalization include trade openness, foreign direct investment (FDI), and innovation, which do not always have a clear impact on the environment. The current research presents several perspectives on the impact of globalisation on the environment.\u003c/p\u003e\u003cp\u003eThe research findings of Mahwish Zafar et al (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) indicate a definite correlation between foreign direct investment (FDI) and trade openness with both short-term and long-term economic growth through their positive and significant relationship with CO\u003csub\u003e2\u003c/sub\u003e emissions in the atmosphere. Similarly, Yongzhong Jiang et al (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicates that trade openness positively impacts green economic growth in the group of emerging economies (E-7), including China, India, Brazil, Mexico, Russia, Indonesia, and Turkey.\u003c/p\u003e\u003cp\u003eA study conducted by Muhammad Shahbaz et al (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) demonstrates the contrary viewpoint that an increase in FDI inflows will lead to an increase in emissions, as FDI growth can stimulate production and consumption by exploiting the environment, resulting in the depletion of environmental resources. Similarly, the research findings of Dinkneh Gebre Borojo et al (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) suggest that FDI has a negative impact on the environment, hence contributing to sustainable growth. Cong Wang and Yifan Lu (2020) demonstrate an inherent relationship between participation in international trade and its negative impact on the environment since trade openness leads to an increase in emissions from production.\u003c/p\u003e\u003cp\u003eThe issues related to promoting green growth extend beyond the impacts of trade and investment, including the innovation level in new energy science and technology (Xing Zhou et al \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to Eckehard Rosenbaum (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), cited in the OECD report: \u0026ldquo;Towards Green Growth\u0026rdquo;, innovation and investment are drivers of green technology development. Green technology will stimulate sustainable development, which means identifying environmentally friendly sources of growth, developing new environmentally friendly industries, and creating jobs and technology (Michael Toman \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). To achieve green growth, it is necessary to enhance investment and foster innovation, which are the foundations of sustainable development and open up new economic opportunities (The World Bank \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Research methodology","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Data collection and study scope\u003c/h2\u003e\u003cp\u003eData selection started with 258 countries and territories listed in the World Bank and Our World databases. First, due to the limited time and research data of the Green Growth Index, we conducted the research data period from 2019 to 2021. Countries were excluded due to a lack of data, so the final set of data included 95 countries and 15 variables (Appendix 1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Data Analysis\u003c/h2\u003e\u003cp\u003eData collected is compiled and managed in Microsoft Excel. The data analysis process consists of four steps:\u003c/p\u003e\u003cp\u003eFirst, the paper uses the Pearson correlation coefficient to examine the relevance of independent variables to pre-dependent variables and regression models and explore differences between independent variables by one-way ANOVA testing. The test was performed using IBM SPSS Statistic 23 (2015).\u003c/p\u003e\u003cp\u003eSecond, the study employs the Stata 17 software (2021) to do data analysis. Research using table data \u0026ndash; is a combination of cross-data and time series. The Stata software enables authors to conduct regression models and assess hypotheses with a substantial quantity of observations (Federico Belotti et al \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Its user-friendly interface makes it convenient for authors during the data analysis process. The author employs quantitative analytical methods by utilising the FEM regression model (Fixed Effect Model) and REM (Random Effect Model). Conduct the Hausman testing to determine an appropriate research model. The study employed the Wald test to examine heteroskedasticity and the Wooldridge test to assess the presence of autocorrelation. If the model has defects, the study employs the Generalized Least Squares model (GLS).\u003c/p\u003e\u003cp\u003eThird, Bayesian networks (BBNs) are designed to identify key factors that influence green growth in countries worldwide through the use of Norsys Software Corp Netica software version 6.06. The Bayes Network is a non-parametric statistical tool based on Bayes's inference of the influence of observed variables on a two-dimensional cause-effect and vice versa (Judea Pearl \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1988\u003c/span\u003e, Jens Frayer et al \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The Bayes network structure consists of two parts: The first part is the structure of a non-circular oriented graph, displayed through the corresponding nodes and arrows to describe the interdependence between variables; The second part is the parameters of the Bayes network, which are conditional probability tables (CPTs) to determine the probability distribution of nodes based on their root nodes (Huong and Hai Dinh 2024). We used heuristic techniques to transform the continuous variables into discrete variables because all random variables in Bayesian networks are expected to be discrete (Finn V Jensen and Thomas Dyhre Nielsen 2007) (e.g., equal interval/width, equal frequency, entropy minimisation, logical and expert knowledge) (Hai Dinh Le et al \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFourth, we predict the scenario for the main factors that influence green growth. BBNs allow the evaluation of different scenarios on the resulting variable; the author deployed the scenarios by changing the probability distribution of existing variables. Then, the resulting change in the probability distribution of the target variable will correspond to the developments in the scenario conditions. At the same time, we adjust the probability distribution of individual variables to analyse how factors affecting green growth change.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and discussions","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Descriptive statistics\u003c/h2\u003e\u003cp\u003eResults from independent samples t-tests in Appendix 2 show that there were no significant differences at the 5% level for GDP Growth, Primary Energy Consumption, Annual CO\u003csub\u003e2\u003c/sub\u003e Emissions, and Foreign Direct Investment between two groups of countries (developed countries vs. developing countries \u0026amp; transition economies). In contrast, there were significant differences at the 5% level for Unemployment; Forest Area; World Governance Index; Trade Openness; Global Innovation Index; Education Expenditure; Population Growth; Natural Resources Rent and HDI between two groups of countries (Appendix 2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Linear regression model\u003c/h2\u003e\u003cp\u003eBased on the F-test findings of the FEM model presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, it is evident that the statistical significance of the Prob\u0026thinsp;\u0026gt;\u0026thinsp;F value is 0.000, which is less than the threshold of 0.05. Therefore, it can be inferred that the FEM model is more appropriate than the OLS model. The Hausman test is conducted to determine the suitable model for explaining the relationship between elements in the research model, namely between the FEM and the REM. The results indicate that the Pro\u0026thinsp;\u0026gt;\u0026thinsp;chi value is 0.000, leading to the conclusion that the FEM is better suitable. The study subsequently employed the Wald test to examine the presence of heteroskedasticity and the Wooldridge test to assess the existence of autocorrelation. If the model exhibits flaw or imperfections, it will be rectified or repaired using the Generalised Least Squares (GLS) method.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of regression results using OLS, FEM, and REM models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIndependent variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePool OLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eFEM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eREM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003evalue\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eVIF\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003evalue\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003evalue\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003eValue\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eRanking\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP Growth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForest Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5\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.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.078\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlobal Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.358\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Growth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.070\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNatural Resources Rent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.0156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.160\u003csup\u003e\u003cb\u003eNS\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.392\u003csup\u003e\u003cb\u003eNS\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry Classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3.706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-4.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWGI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.979\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e45.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e50.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDependent variable\u003c/b\u003e: Green growth index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel testing\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF-test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHausman test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeteroskedasticity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutocorrelation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eNote: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, NS: Not significant\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=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reveals that the model exhibits violations of model assumptions such as heteroskedasticity and autocorrelation. Consequently, the study employs the Generalised Least Squares (GLS) method to address these shortcomings.\u003c/p\u003e\u003cp\u003eThe results of the linear regression model using the Generalised Least Squares (GLS) method show that there are six independent variables in the model that significantly influence the Green Growth Index (GGI): Country Classification, Population Growth, GDP growth, Global Innovation, Forest Area, and Trade Openness (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, the significance of the GLS model was assessed using the Wald chi-square test, with a Chi-square value of 763.82 and a probability of less than 0.000. This leads to the conclusion that the GLS model is goodness fit for explaining the impact of factors on the Green Growth Index in countries around the World.\u003c/p\u003e\u003cp\u003eBased on the regression coefficients from the GLS model (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), we found that three variables, in descending order, have significantly positive impacts on the Green Growth Index: GDP growth (B\u0026thinsp;=\u0026thinsp;0.336), Global Innovation (B\u0026thinsp;=\u0026thinsp;0.264), and Forest Area (B\u0026thinsp;=\u0026thinsp;0.146). Therefore, when GDP Growth increases by 1%, the Green Growth Index increases by 0.336 points, assuming all other factors remain constant. This applies similarly to the other two variables. On the contrary, Variables: Country Classification (B =-4.000), Population Growth (B=-0.750), and Trade Openness (B=-0.007) have significantly negative impacts on the Global Green Growth Index. To explain this, developed countries, lower population growth, and more trade openness tend to have higher levels of the Green Growth Index.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3. The resulting Bayesian Belief Network\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a network of 14 variables, illustrating the directional connections among these variables. The network was set at the initial probability distribution for all variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The dataset consists of 10 variables that are the primary determinants directly impacting Green Growth Index (GGI). These variables include HDI, Country Classification, Annual CO\u003csub\u003e2\u003c/sub\u003e emissions, Forest area, Education Expenditure, Population Growth, Natural Resources Rents, Trade Openness, Global Innovation, and World Government Index. This finding is in line with other previous research such as: Tawiah et al (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Nan Liu et al (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Rahul Mittal and Chandi Gupta Mittal (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); Steinberger and Roberts (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and Asif Raihan and Almagul Tuspekova (2022). Variables that indirectly affect the green growth index (GGI) include 3 variables: GDP Growth, Primary Energy Consumptions, FDI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Sensitivity analysis BBN model\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSensitivity analysis was performed to measure the impact of each independent variable on the dependent variable. This involves calculating the sensitivity of the target variable of GGI to all other variables in the network model. The analysis employs variance reduction, a measure that gauges the extent to which one variable affects the belief in another variable. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the outcomes of the sensitivity analysis for all influential factors, arranged in descending order based on their impact on the target variable (GGI). The findings indicate that countries' classification has the greatest significant impact on the green growth index (GGI) worldwide, with a variance reduction of 3.42%. The Global Innovation Index has a variance reduction of 2.99%, exerting a significant impact on the advancement of environmentally sustainable economic growth in nations. Other influence factors to GGI comprise WGI (World Governance Indicators), HDI (Human Development Index), Population Growth, Natural Resources Rentals, Trade Openness, and Education Expenditure.\u003c/p\u003e\u003cp\u003eThe remaining variables, such as CO\u003csub\u003e2\u003c/sub\u003e Emissions, Forest Area, Primary Energy Consumption, FDI, and GDP Growth, have a small impact on promoting green growth in countries, with a variance reduction of less than 0.5%.\u003c/p\u003e\u003cp\u003eWe further evaluate the strength of the interaction between the target variable and the remaining variables using a diagnostic analysis that takes advantage of the bidirectional inference capabilities of the BBNs model.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Scenario analysis for BBN model\u003c/h2\u003e\u003cp\u003eIn contrast to sensitivity analysis, the diagnostic analysis gauges how the probabilities of influencing variables change given evidence in the target variable. We developed scenarios for a different set of variables. We build different optimal scenarios for all countries in the world and for two groups of countries: developed countries, developing countries and economies in transition. In order to determine how we can achieve a target level of GGI, the model needs to be interrogated inversely (diagnostic analysis). GGI is set to 100% for state \u0026ldquo;High\u0026rdquo;, and then we observe the changes in terminal nodes. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on the linear regression model that analyses of influential factors combined with the sensitivity analysis and the BBNs model, we can give two policy groups for the four most important and influential factors in GGI of countries around the world: GDP Growth, Forest Area and Global Innovation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe importance of factors influencing the process of promoting green growth in countries worldwide is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, based on their order of significance. We propose scenarios and policy frameworks to promote green growth in developed countries worldwide as follows (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Policy implications for Vietnam","content":"\u003cp\u003eVietnam has established a \"National Strategy for Green Growth 2011\u0026ndash;2020, Vision 2050\" to promote ecologically sustainable economic progress. This strategy demonstrates the government's enduring commitment to sustainable economic development and its forward-thinking perspective since its beginning. Vietnam possesses a promising potential to enhance green growth due to its advantageous capacity for carbon storage in forest resources and substantial potential for the development of renewable energy (Ngoc Han \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The author presents some strategies to enhance green development in Vietnam:\u003c/p\u003e\u003cp\u003eFirst, we need to focus on people in implementing green growth strategies, particularly in innovation endeavours aimed at developing initiatives about green cities and smart cities. Simultaneously, innovation must be integrated with sustainable development, which relies on achieving equilibrium among various forms of capital, including productive capital, human capital, and innovation capital. There is a requirement for policies that promote firms to engage in innovation regarding both the quantity and quality of their products.\u003c/p\u003e\u003cp\u003eSecondly, to uphold Vietnam's pledge to achieve net zero emissions by 2050, it is imperative to adopt a discerning and enduring approach towards attracting foreign direct investment (FDI). To mitigate pollution, it is necessary to either decrease the presence of industries contributing to pollution or enforce strict environmental emission standards for foreign companies. It may be beneficial to focus on mining companies involved in sophisticated, eco-friendly research and technology, as well as those that contribute to the well-being and development of human capital, such as healthcare and education. Vietnam must exercise caution to avoid excessive dependence on foreign direct investment (FDI) capital, which could undermine the economy's resilience.\u003c/p\u003e\u003cp\u003eUltimately, it is crucial to continue conducting trade, collaborating, and interacting with emerging economies and trade organisations through participation in regional and global trade accords. Vietnam can utilise this opportunity to engage in the exchange, acquisition, and implementation of cutting-edge scientific and technological advancements and new ideas from around the globe. This will aid in addressing domestic issues, promoting economic growth, and enhancing social well-being.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eSustainable development is the paramount objective pursued by nations worldwide. This study employed GLS regression and BBNs network model to investigate the elements that influence the green growth of countries worldwide. The results of our research reveal significant factors that contribute to the promotion of sustainable economic growth. The models indicate that Country Classification, Global Innovation, HDI, Forest Area, Education Expenditure, Trade Openness, and WGI positively impact the green growth index of countries. Population growth and the exploitation of natural resources have a detrimental effect on green growth. Furthermore, it is evident from the outcomes that nations at varying stages of development would necessitate distinct strategies to attain their green growth objectives. For instance, developing countries and economies in transition may contemplate loosening rules about resource exploitation shortly, given their inclination towards a resource-dependent approach in bolstering global economic influence. Nevertheless, in developed countries, it is imperative to construct suitable governance structures using both short-term and long-term plans to effectively promote national green growth and enhance the regulation of natural resource production and utilisation. The authors have suggested solutions appropriate for each scenario of distinct groups of countries.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics Declaration\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003edeclaration\u003c/p\u003e\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthors contribute equally to the manuscript\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWESP: World Economic Situation and Prospects. 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Prod. \u003cb\u003e142\u003c/b\u003e, 783\u0026ndash;800 (2017)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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 Growth Index, Determinants, Bayesian Belief Network, Vietnam","lastPublishedDoi":"10.21203/rs.3.rs-7675836/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7675836/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRising temperatures, increasing severity of natural disasters, and their simultaneous impacts on livelihoods have made green growth an essential priority. To assess the influence of factors on green growth of countries globally and to provide optimal scenarios for developed, developing countries and economies in transition in their efforts to promote sustainable economic growth, we employed Bayesian Belief Networks (BBNs) model to analyze data from 95 countries for period 2019\u0026ndash;2021. Our research demonstrates that Country Classification, Global Innovation, Human Development Index, Forest Area, Education Expenditure, Trade Openness, and Worldwide Governance factors positively influence the green growth index of countries. Population Growth and Natural Resource Use negatively impact Green Growth. Moreover, it is clear from the results that countries at different levels of development will need tailored approaches to achieve their green growth goals. The authors have proposed tailored solutions for each scenario involving various groups of countries.\u003c/p\u003e","manuscriptTitle":"Determinants of the Green Growth Index in Countries Around the World by Bayesian Belief Network Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 11:35:44","doi":"10.21203/rs.3.rs-7675836/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":"fd9bdf6e-b130-4104-9c85-3c8696a5b595","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T16:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 11:35:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7675836","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7675836","identity":"rs-7675836","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}