Introduction

The concept of green growth (GG) was emphasized in 2012 by the UNCSD in which focus was made for sustainable economic growth and ensuring the greener economy (Lorek and Spangenberg, 2014; Hickel and Kallis, 2020). In theoretical context, the concept of GG suggests to promote economic growth aligned with reducing environmental pollution through natural resource management to avoid depletion. The theory is considered crucial worldwide to promote environmental sustainability and economic development (Saleem et al., 2022; Zhao et al., 2023). Literature related to GG aims with creation of growth process based on resource efficiency and development of demand for output and green innovative methods (Haberl et al., 2020). Furthermore, GG is a framework to attain SDGs of United Nations presented in 2015. According to Imasiku et al. (2019), GG is closely related with SDG-8 of decent work and economic growth. Implications of GG are explored in numerous studies such as impact of GG on financial stability (Jadoon et al., 2021), corporate sustainability (Saufi et al., 2016), environmental quality (Saleem et al., 2022), carbon neutrality (Chien et al., 2021), and sustainable development (Zaman et al., 2016; Belmonte-Ureña et al., 2021). Conversely, few studies evaluated the responsible factors for green growth including financial inclusion (Dong et al., 2025), green behavior (Awan et el., 2025), energy efficiency (Liu and Chen, 2025), and renewable energy (Murshed, 2024). However, there is significant research gap in literature to analyze the potential influencing factors of GG particularly education, squared education, trade, and capital.

Education plays its role to boost economic growth (Agasisti and Bertoletti, 2022), sustainable economic development (Li et al., 2023), and environmental quality (Zhang et al., 2022; Qamruzzaman, 2025). Education at various levels plays a central role in developing awareness to the society about environmental challenges, such as climate change, global warming, and natural resource depletion (Zhang et al., 2023). It prepares communities for sustainable literacy, contributing positively to enhance sustainable economic growth. According to Yadav et al. (2024) education serves as the key factor for attaining SDGs, particularly in developing and highly populated economies. In such contexts, social literacy of sustainability can enhance the influence of green growth determinants. According to Liu et al. (2023), education contributes to green growth by improving technological advancement and public awareness. Eco-friendly behavior is promoted which causes rise in GG. Education also has favorable effect for reducing fossil fuel consumption and GHG emissions. Romer (1990) explained the education as a potential factor to boost growth of entire society by uplifting labor productivity and production efficiency. Wang et al. (2021) highlighted that GG outcomes of education take place by improving technological advancement. Furthermore, innovative industrial structure is outcome of education which helps in promoting resource efficiency and GG while reducing environmental degradation (Li et al., 2022).

Among economic factors, trade can also effect GG through different channels as highlighted in few studies (e.g., Khan et al., 2024; Ahmad et al., 2022; Liu et al., 2022; Huang and Zhao, 2022). Role of trade for GG is favorable when trade causes increase in economic growth without compromising environmental quality. Numerous studies analyzed the trade contribution for economic growth, environmental quality, and many other socioeconomic challenges. Global trade improves coordination among countries, reduce disputes, and ensure social sustainability which ultimately enhances economic growth (Ahmad et al., 2022). In environmental context, exports reduces carbon emissions while imports leads to high carbon emissions (Khan et al., 2022). Likewise, Majeed and Asghar (2021) explained that trade has mixed effects for environment and this require to develop and implement green growth policy to attain sustainable development. To enhance trade and economic growth, reliance on energy consumption increases which can effect environmental sustainability through scale effect, composition effect, and technological effect (Grossman and Krueger, 1995). Improving growth results in more output and enhanced energy use which cause more emissions (scale effect). Energy intensive production of primary goods also uplifts emissions (composition effect). While in the presence of trade, transition towards the advanced and green technological innovations improve environmental quality (technological effect).

Besides aforementioned factors, labor and capital are major determinants of economic growth as followed in Cobb-Douglas model. Existing literature intensively considered labor and capital role for growth and sustainability. Educated workforce is found highly supportive for economic growth (Amir et al., 2015; Karadaş, 2020), and sustainable green economy (Ahmed et al., 2022; Naseer et al., 2022). Labor force is further divided into educated and non-educated categories (Lewis and Pattanayak, 2012). Educated female workforce prefers the utilization of modern renewable energy source over biomass energy which can enhance green growth. On the other hand, labor force with low education have weak environmental awareness and relies on conventional energy source which decreases the green growth. Similarly impact of capital formation can also influence green growth in different ways. Use of capital supported by renewable energy source can improve green growth while traditional methods of capital formation can adversely affect sustainable growth (Tran et al., 2020).

This study followed Cobb-Douglas model to examine the labor and capital effects on green growth by including education and trade as GG determinants among G7 economies (USA, UK, Germany, France, Japan, Italy, and Canada). According to dataset published by World Bank (2023), share of G7 countries in world economy is about 50% and in global population 15%. These economies are committed to reduce their carbon emission by 80% by 2050 to attain sustainable development. Among G7 countries, 3 economies are included in the top 10 global carbon emitting nations, emitting carbon emissions (volumes in million tons) 5007 (USA), 1067 (Japan), and 675 (Germany) presented in global carbon emissions report (2021). Several studies focused G7 economies for economic and environmental perspectives (Ali et al., 2023; Rahman et al., 2016). However, there is significant research gap regarding determinants of green growth among G7 countries. Therefore, this research considered G7 nations to conduct empirical analysis and suggest policy measures for attainment of green growth and sustainable development.

The current study makes significant contributions to the existing literature. Previous studies mainly captured the determinants of economic growth and environmental quality. First, the existing literature is scant on the influencing factors of green growth in G7 nations. Thus, we fill this research void by exploring the drivers of GG in G7 countries. Secondly, the literature on the education-green growth nexus is sparse and concludes the vague results. Particularly, impact of squared education on green growth has not been examined in the previous literature. Therefore, we bridge this research lacuna by examining the non-linear role of education for green growth. Finally, the prior literature on green growth adopts an environmentally adjusted multi-factor productivity index or environmental cost-adjusted growth index as the proxy for green growth. These proxies have a limitation as these indices only cover economic and environmental factors. On the contrary, we adopt the novel green growth index put forward by Sarkodie et al. (2023). This index captures deep insights of the green economy by covering five dimensions such as technology, social inclusion, poverty, natural capital protection, and efficient resource use. Through exploring determinants of green growth, this study can assist policymakers from G7 economies to formulate policies that can attain SDG-4 (quality education), SDG-7 (affordable and clean energy), SDG-8 (decent work and economic growth), SDG-11 (sustainable cities and communities), and SDG-13 (climate action).

Literature review

Theoretical literature

Higher education and green growth nexus

Education can influence positively GG through various mechanisms. First mechanism is promotion of sustainable literacy. Increase in quality education at higher levels improves the awareness of people about circular economy, ecological challenges, and waste management and ultimately enables the individuals to deal with threats of climate change and global warming (Rahai et al., 2023). Secondly, higher education ensures sustainable development through increasing skills and training among individuals and making them as a green job work force that eventually causes green economy. For greening the production environment, development of green job workforce serves as supportive factor of green growth (Sulich et al., 2020). Moreover, Xie et al. (2020) explained that education is more appropriate way to develop green growth and green skills among labor. Spreading the advanced education of green economy in educational institutions could be a source for green growth as society is directly linked with institutes. Furthermore, making aware the household about renewable energy methods and to avoid usage of fossil fuels, education can improve green growth. Another channel is improvement of environmental technologies as a results of higher education which helps to promote green growth (Zhou et al., 2023). Moreover, role of financial development to boost environmental technology innovations, also varies with different levels of higher education. As a conclusion, higher education can improve green growth by improving public awareness about environmental issues, promoting skills and training to develop green labor force, transition towards renewable energy sources, energy efficiency, and green technology. Theoretical mechanism for education and GG nexus is given in Fig. 1.

Fig. 1
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Higher Education and Green Growth Linkage.

International trade and green growth

Global trade can reveal both the adverse and supportive impact on green growth. Heckscher-Ohlin theory of trade explains that in the presence of free trade, developed economies converge towards the capital intensive production. While developing nations specialize in production of labor intensive products and rely on natural resource utilization. During trade process, goods are traded for consumption and production purposes. This highlights that environmental impact of trade occur during production or consumption of goods. In the case when an economy is producing highly energy-oriented commodities then pollution increases and vice versa (Suri and Chapman, 1998). Contradictory theoretical channels are present through which global trade have supportive and adverse impacts for green growth.

For instance, the Pollution Haven Hypothesis (PHH) states that enhanced trade openness may promote environmentally harmful industries in advanced nations to relocate their productive resources to economies with more lenient ecological regulations (Achuo and Ojong, 2025; Al Numan et al., 2025). This relocation is driven by cost-saving objectives, permitting firms to operate with lower compliance costs in host economies. While such trade may increase economic activity in developing countries, it can also lead to environmental degradation, undermining green growth efforts. PHH explains the potential downside of liberalized trade when not supported by uniform environmental standards, making it crucial for economies to balance trade policy with strong environmental governance. Furthermore, in the presence of rapid trade, economies adopt innovative technology to meet the global competitiveness which can reduce emissions and promote sustainable development (Shafik and Bandyopadhyay, 1992). Environmental regulations are also important to consider for nexus between trade and GG. Many studies have provided evidence for significant role environmental regulations for sustainable development. Economies that develop and implement environmental regulations, can get favorable outcomes of trade to boost green growth. While weak environmental policies cause natural resource depletion during trade and production process and reduce green growth. Theoretical link between trade and GG is presented in Fig. 2.

Fig. 2
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International Trade and Green Growth Linkage.

Labor, capital, and green growth

Labor and capital formation have socioeconomic implications. The study draws upon the Cobb-Douglas production function, which models production as a result of labor and capital. In its basic form, \(Y=A.\,{L}^{{\rm{\alpha }}}.{K}^{\beta }\) where Y shows output, while L and K represent labor and capital, respectively. Total factor productivity is symbolized by A, and output elasticities are shown by α and β. In the context of green growth, this framework is followed to evaluate how labor and capital impact the environmentally sustainable economic output, recognizing that increase in productivity can also stem from eco-friendly practices and technological innovation. High capital formation improve labor engagement in economic activities and leads to more economic growth (Aslan and Altinoz, 2021; Olufemi et al., 2022). Intensive labor force utilizations can effect green growth positive and negative both dimensions. For positive dimension, labor force having skills and expertise in renewable energy, green technologies, and sustainable production practices, can support in promoting low carbon economy. Resource conservation, and energy efficiency are also outcomes of well-trained labor force which help in growing GG. While dependence on traditional labor-intensive industries can lower GG. Similarly, rapid capital formation supported by conventional non-renewable energy source can harm GG but if increase in investment is accompanied with renewable energy methods, environment friendly technologies, and sustainable production practices, then ultimately GG improves. Theoretical linkages that how labor and capital can impact the green growth, is given in following Figs. 3 and 4.

Fig. 3
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Nexus between labor and green growth.

Fig. 4
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Nexus between capital and green growth.

FDI and green growth

FDI can impact green growth through both positive and negative channels. On one hand, FDI works as a channel for the transfer of innovative, eco-friendly technologies and managerial practices from advanced to host nations, improving energy efficiency, cleaner production, and innovation, thus supporting sustainable growth (Borensztein et al., 1998). It can also create green employment and stimulate investment in renewable sectors. In contrast, the Pollution Haven Hypothesis advocates that in the absence of strict environmental regulations, FDI may lead to the relocation of pollution-intensive industries to host economies, enhancing environmental degradation and declining green growth. Therefore, the impact of FDI on green growth relies on the environmental standards and regulatory frameworks of the host country. Theoretical linkages for FDI and green growth are shown in Figs. 5 and 6.

Fig. 5
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Foreign direct investment promoting green growth.

Fig. 6
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Foreign Direct Investment Hindering Green Growth.

Empirical literature

Education and green growth

Implications of education and ecological awareness have been widely analyzed for different global economies and regions. For instance, Ketlhoilwe (2019) examined the role of climatic knowledge for disaster risk management and showed that climatic awareness can play favorable role to attain sustainable development through ensuring disaster risk management. Ramírez et al. (2019) found significant positive influence of environmental education to boost environmental quality through increasing biodiversity conservation. Gao et al. (2019) analyzed the impact of higher education on conventional economic growth and green growth among 187 global economies by using dataset for time period of 1990–2015. Results depicted significant positive linkage of higher education with growth variables. However, impact on green growth was comparatively high as compared with traditional GDP. Likewise, Ngo et al. (2022) stated that education expenditure and improvement in human capital enhanced the green growth among panel of 36 global nations. Zhao et al. (2023) examined the influence of educational investment and digital economy on environmental quality among 287 cities of China. Regression outcomes showed that rise in educational investment and digital economy provide support to boost sustainable development through improving environmental quality. In contrast, Liu et al. (2022) revealed positive linkage between education and carbon emissions while role of green energy was found supportive to enhance environmental quality. Similarly, Zhang et al. (2022) investigated effect of education, ICT, and globalization on CO2 in developing nations for time duration of 1996–2019. Long run outcomes showed emission increasing impact of education and globalization while ICT was found favorable to enhance environmental quality.

International trade, FDI and green growth

Many scholars investigated the effects of global trade on economic growth, environmental quality, sustainable development, and green growth (e.g., Wei et al., 2023; Meng et al., 2022; Wu et al., 2024; Chen et al., 2022). Chu et al. (2025) revealed that digital trade has significant influence to improve inclusive green growth through uplifting industrial structure in China. Individual impact of exports and imports on environmental quality was analyzed among G7 countries (Khan et al., 2022). Results revealed that exports improved sustainable development by reducing CO2 and imports caused rise in CO2. Study advocated the promotion of exports, renewable energy, and institutional quality to ensure sustainable development. Using load capacity factor as proxy for environmental quality, Wu et al. (2024) evaluated the environmental role of trade openness among fast growing nations for time span of 1996–2019. Results showed that trade adversely affect the sustainable development by decreasing load capacity factor. Udeagha and Ngepah (2022) investigated short term and long term environmental influence of trade using ARDL method in South Africa for the time period of 1960–2020. Results revealed that trade improved environmental quality in short term but not in the long term. Zhang and Choi (2025) analyzed the linkage of digital trade and technological innovation with green productivity of 52 global nations for the time period of 2005–2001. Using panel ARDL, study showed that digital trade and technological innovation enhance the green growth but FDI has impeding impact for green growth. Derindag et al. (2023) concluded that effect of trade on CO2 emissions mainly depends on prevailing intensity of CO2 emissions in the economy. Their estimations showed that trade openness increases CO2 emissions when economy is facing weak CO2 intensity and decreases environmental pollution in the presence of strong CO2 emissions intensity. Omri and Saadaoui, (2023) found harming influence of trade on sustainable development by uplifting CO2 emissions in France. Findings of causality test depicted bidirectional causality linkage between trade and CO2 emissions.

Capital, labor, and green growth

Socioeconomic implications of capital and labor are analyzed in several studies depicting varying outcomes. Apinran et al. (2022) estimated the effects of capital, labor, and carbon emissions on economic growth in Nigeria for time period of 1981 to 2019. Using novel dynamic ARDL method, study revealed that capital and labor have favorable role for economic growth while carbon emissions have adverse impact. For sustainable growth, reduction of carbon emissions was suggested. Likewise, Soava et al. (2020) also explained that labor and capital formation had significant supportive influence on economic growth in economies of European Union. In case of Bangladesh, Khalid and Abdul (2025) found significant role of FDI and financial development on green growth and study concluded that human capital growth has favorable role to enhance green growth. Fedulova et al. (2019) stated that labor productivity, technological innovations, and industrial advancement are significant supportive factors of sustainable economic development. Role of capital formation for energy demand was analyzed by Rafindadi and Mika’Ilu (2019). Using ARDL method study depicted that rise in capital formation results in higher energy demand. To meet that energy demand through renewable sources can ultimately ensure green growth (Sohag et al., 2021). Explained that CO2 emissions varies more closely with changes in capital formation instead of GDP. Study also showed that investment structure was more favorable tool among developing economies as compared with developed nations to attain sustainable development.

Research gap

The existing literature have examined the individual linkages of education, labor, capital, trade, and FDI on economic growth or environmental quality, limited focus has been made to their joint impact on green growth, particularly within developed nations. Most prior studies are either focused on developing economies or addresses isolated components of sustainable development. Additionally, studies rarely employ dynamic econometric techniques like AMG method to capture the long term equilibrium among these factors in the framework of green growth. This study fills this substantial gap by jointly analyzing these variables across G7 nations, offering comprehensive policy suggestions aimed at achieving sustainable and inclusive green growth.

Methods

Models

The present study considered the Cobb-Douglas production function and added trade as additional explanatory variables for panel of G7 nations. Education is included in model as key concerned factor of the study as given in Eqs. 1, 2, and 3 (model 1, 2, and 3). The study uses green growth as a comprehensive measure of sustainable development instead of using traditional GDP growth.

$${GGDP}={EDU}+{EDU}2+{CAP}+{EMP}$$
(1)
$${GGDP}={EDU}+{EDU}2+{CAP}+{EMP}+{FDI}$$
(2)
$${GGDP}={EDU}+{EDU}2+{CAP}+{EMP}+{TRD}$$
(3)

In above given Eqs. 1–3, GGDP is green growth. Furthermore, green growth index introduced by Sarkodie et al. (2023) is used as proxy for green growth.

Econometric form of three aforementioned models, is presented below in Eqs. 4, 5, and 6.

$${{GGDP}}_{{it}}=\alpha +{\beta }_{1}{{EDU}}_{{it}}+{\beta }_{2}{{EDU}2}_{{it}}+{\beta }_{3}{{CAP}}_{{it}}+{\beta }_{4}{{EMP}}_{{it}}+{\varepsilon }_{{it}}$$
(4)
$$\begin{array}{l}{{GGDP}}_{{it}}=\alpha +{\beta }_{5}{{EDU}}_{{it}}+{\beta }_{6}{{EDU}2}_{{it}}+{\beta }_{7}{{CAP}}_{{it}}\\\qquad\qquad\quad+\,{\beta }_{8}{{EMP}}_{{it}}+{\beta }_{9}{{FDI}}_{{it}}+{\varepsilon }_{{it}}\end{array}$$
(5)
$$\begin{array}{l}{{GGDP}}_{{it}}=\alpha +{\beta }_{10}{{EDU}}_{{it}}+{\beta }_{11}{{EDU}2}_{{it}}+{\beta }_{12}{{CAP}}_{{it}}\\\qquad\qquad\quad+\,{\beta }_{13}{{EMP}}_{{it}}+{\beta }_{14}{{TRD}}_{{it}}+{\varepsilon }_{{it}}\end{array}$$
(6)

Data

Specification of variables is given in Table 1. Green growth (GGDP) is dependent variable and measured as green growth index calculated by Sarkodie et al. (2023). Independent variables are education (EDU) measured as human capital index, based on year of schooling and return to education, capital (CAP) measured as gross fixed capital formation, labor (EMP) measured as employment ratio, trade (TRD) measured as total trade as percentage of GDP, and foreign direct investment (FDI) measured as net inflows as percentage share of GDP. The data Education collected from PWT 11.0 (Penn World Table Version 11), whereas the data for other independent variables is taken from World Bank (2023). Time period for the dataset is 1990–2020.

Table 1 Variables specification.
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Descriptive specifications of variables are presented in Table 2 including mean, median, standard deviation, maximum, minimum, Kurtosis, Skewness, Jarque-Bera, and p-value. Mean values are 0.603 (GGDP), 1.763 (EDU), 48.824 (TRD), 21.807 (CAP), and 72.534 (EMP). Median values in same sequence of variables are 0.601, 1.779, 50.876, 21.348, and 73.98. Maximum values of variables are 0.896 (GGDP), 1.984 (EDU), 88.519 (TRD), 36.052 (CAP), and 80.343 (EMP). Minimum values are 0.298, 1.427, 15.81, 14.978, and 57.976. The GGDP, TRD, and CAP are positively skewed with 0.0178, 0.0199, and 1.295 respectively. While EDU and EMP are negatively skewed with −0.602 and −1.041. Values of Jarque-Bera (test for goodness of fit) are 0.142 (GGDP), 15.072 (EDU), 4.84 (TRD), 144.779 (CAP), and 40.556 (EMP).

Table 2 Descriptive statistics.
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The descriptive statistics show that green growth (GGDP) among G7 nations averaged around 0.60, depicting moderate performance, with limited variability (Std. Dev. = 0.11). Education (EDU), based on year of schooling and return to education, also highlights a low standard deviation (0.11), indicating relatively consistent education levels across nations. The trade variable (TRD) exhibits wide variation (Std. Dev. = 18.41), reflecting diverse trade openness across nations. Capital (CAP) has a positively skewed distribution with notable dispersion (Skewness = 1.29, Std. Dev. = 3.41), showing that in some cases, capital formation is significantly greater than the average. Employment (EMP) is negatively skewed (Skewness = −1.04), suggesting most values cluster at the higher end, but a few lower observations pull the mean down. The Jarque-Bera test assures that CAP, EDU, and EMP are not normally distributed (p < 0.05), which has implications for the choice of estimation methods.

Methodology

Augmented Mean Group (AMG)

Traditional panel regression methods may produce biased and inconsistent long-run results when cross-sectional dependence exists among units in the panel (Pesaran and Smith, 1995; Paramati et al., 2017; Dumitrescu and Hurlin, 2012). The Augmented Mean Group (AMG) estimator developed by Eberhardt and Teal (2010) is employed, as it effectively addresses cross-sectional dependence and parameter heterogeneity. The AMG method is appropriate technique for models with heterogeneous slope coefficients and remains consistent even in the existence of endogeneity, non-stationarity, and unobserved common factors. Its robustness under these conditions makes it a valuable tool for panel data estimations (Dumitrescu and Hurlin, 2012). AMG is suitable for panels with moderate time dimensions and a limited number of cross-sectional units, such as the G7 nations, making it more consistent than traditional estimators in such contexts. Additionally, AMG method operates under less restrictive assumptions as it does not require slope homogeneity or cross-sectional independence, confirming robustness even when these conditions are violated. These features make AMG a flexible and robust choice for long term panel estimations.

Equation (7) shows the common dynamic effect parameter, which represents the unobserved common factors (ft) captured by the AMG estimator. This is explained through the first-difference OLS equation given below, which forms a key step in the AMG estimation process.

$$\Delta {y}_{{it}}={\alpha }_{i}+{\beta }_{i}\Delta{X}_{{it}}+\mathop{\sum}\limits_{t=0}^{T}{\theta }_{t}{D}_{t}+{\varphi }_{i}{f}_{t}+{\varepsilon }_{{it}}$$
(7)

In Eq. (7), \({y}_{{it}}\) and \({X}_{{it}}\) represent the dependent and independent variables, respectively. \({\beta }_{i}\) shows the country-specific slope coefficients, while \({f}_{t}\) represents unobserved common factors with heterogeneous impacts. \({\alpha }_{i}\) and \({\varepsilon }_{{it}}\) are the intercept and error terms, respectively. The symbol Δ indicates the first-difference operator, \({\beta }_{i}\) reflects cross-sectional heterogeneity, and \({\theta }_{t}\) represents time-specific effects. The AMG estimator is then derived by averaging the group-specific parameters across all panel units.

$${AMG}=\frac{1}{N}{\alpha }_{i}+\mathop{\sum}\limits_{I=1}^{N}\widetilde{{\beta }_{i}}$$
(8)

In Eq. (8), \(\widetilde{{\beta }_{i}}\) represents the estimator of \({\beta }_{i}\) from Eq. (7). The AMG technique isused to estimate long-run parameters due to its demonstrated unbiasedness and efficiency across various sample sizes (N) and time dimensions (T) in Monte Carlo simulations.

Data transformations and panel unit root test (CIPS) is conducted to ensure stationarity. Cross-sectional dependence and Westerlund (2007) Cointegration are used to assess the validity of the model’s assumptions.

Results and discussion

Correlation matrix of the variables of the study, is given in Table 3. The correlation matrix shows that green growth (GGDP) has low correlations with all independent factors, showing low multicollinearity. Employment (EMP) and education (EDU) are highly correlated (0.837), suggesting high multicollinearity. Trade (TRD) is negatively correlated with capital (CAP) and green growth (GGDP), while foreign direct investment (FDI) has a moderate positive correlation with trade (0.369) and employment (0.284) but a negative correlation with capital (−0.259). Overall, no extreme correlations suggest serious multicollinearity concerns among the variables. However, we drop EMP from models 1, 2, and 3 due to its high multicollinearity with EDU.

Table 3 Correlation matrix.
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Findings of CSD test are given in Table 4. To proceed for cointegration and regression estimations, this is crucial to assess the CSD because using conventional techniques in the existence of CSD reveal invalid conclusions and inconsistent estimators (Mao and Shen, 2019). The CSD test is significant for all the variables of the study. The CSD outcomes rejects the null hypothesis of non-existence of cross-section dependence and accepts the alternative hypothesis.

Table 4 Pesaran CSD test.
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The slope heterogeneity test (Table 5) shows p-values greater than 0.05, showing that cannot be rejected the null hypothesis of slope homogeneity. This reveals that the model assumes similar slope coefficients across panel units. Such homogeneity depicts that the influence of independent variables on green growth is consistent across the G7 nations, supporting the use of pooled estimators. To assess the stationarity of variables, study employs CIPS unit root test and outcomes are shown in Table 6. The CIPS unit root test is more suitable test in the presence of CSD among the variables. All variables are found stationary at first difference with 1% significance level.

Table 5 Slop heterogeneity test.
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Table 6 CIPS unit root.
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Cointegration among the variables is evaluated by applying Westerlund Cointegration method. Results are given in Table 7. This method presents four statistics (Gt, Ga, Pt, and Pa) with null hypothesis and alternative hypothesis for absence and existence of cointegration among the variables respectively. Estimations show two statistics (Gt and Pa) as significant which provide confirmation to reject the null hypothesis and confirm the presence of cointegration among the variables.

Table 7 Westerlund cointegration.
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Empirical outcomes of AMG method regarding impact of capital, education, squared education, and trade on green growth are given in Table 8. Findings show that capital has positive but insignificant link with green growth among all three models. Findings also reveal that education has positive but insignificant influence on green growth in all three AMG models. This depicts that, while education may usually contribute to environmentally sustainable development, its direct influence alone is not strong enough to bring significant improvement in green growth. While in squared education has favorable and significant impact on green growth with 0.15 coefficient value in all three models which reveals that 1% rise in squared education improves the green growth by 0.15%. This non-linear linkage shows that the impact of education becomes more impactful beyond a particular threshold level. In other words, initial or lower levels of education may not be sufficient, but as education levels rise, particularly in terms of higher education, research capacity, and environmental awareness, then its contribution to green growth becomes significant. This supports the argument that investments in advanced and targeted educational programs are essential to foster long-term sustainable development. Literature provides evidence positive impact of education on green growth (Ngo et al., 2022; Liu et al., 2022). Endogenous growth theory shows that boost in sustainable growth can be outcome of rising knowledge levels (Benos and Zotou, 2014; Zhou et al., 2023). Policymakers should prioritize education reforms that integrate sustainability into curricula and enhance innovation in clean technologies, as these measures can directly work for attaining long-term green growth.

Table 8 Models estimated through Augmented Mean Group (AMG).
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The findings also show that trade has a direct and insignificant impact on green growth across all three AMG models, as the p values are greater than 5% significance level. This shows that while trade openness may be associated with improvements in green growth, the linkage is not strong or consistent enough to be considered statistically impactful in this study. This finding could be due to the nature of trade in the G7 nations, which may not be sufficiently oriented towards environmentally sustainable goods and technologies (Wu et al., 2024). It also suggests that without complementary ecological policies or green trade practices, enhanced trade alone may not significantly improve green growth. Moreover, FDI has negative association with green growth. Numerous studies confirmed the validity of pollution haven hypothesis which shows that in the presence of weak environmental regulations, rise in FDI increases environmental degradation in the host economy (Esmaeili et al., 2023; Bashir, 2022; Huang et al., 2023) which ultimately reduces the green and sustainable growth. Policymakers should emphasize on attracting green FDI by offering incentives for sustainable projects and ensuring environmental compliance, which can improve domestic capacity for low carbon development. The adverse influence of FDI on green growth aligns with the pollution majhaven hypothesis, highlighting that environmentally harmful investments may be flowing into sectors with weaker environmental oversight.

Conclusion and policy suggestions

Conclusion

Green growth has become very pressing phenomenon for researchers and policymakers since 20th century. This study investigates the empirical influence of education, squared education, capital, and trade on green growth among G7 nations using annual data for time period of 1990–2020. The study utilizes AMG method to long run coefficients. Findings of Westerlund cointegration test ensure the existence of long run cointegration among all the variables in the model. Findings of the AMG method reveal that education has positive and insignificant impact on green growth while squared education has positive significant impact on green growth, that environmental awareness improves with rise in advanced levels of education which leads to green and sustainable growth. Outcomes show positive association of trade with GGDP. While capital has positive but insignificant association with GGDP. Moreover, study shows that FDI proves adverse factor for green growth among G7 nations which supports the pollution haven hypothesis.

Policy implications

Based on findings of the study following policy suggestions are advocated to the governments of G7 nation for boosting the quality education (SDG-4), decent work and economic growth (SDG-8), and climate action (SDG-13).

  1. 1.

    Given the positive but insignificant impact of capital on green growth, G7 nations should focus on redirecting capital investment toward ecologically sustainable sectors. This can be attained by promoting incentives for clean energy, green infrastructure, and eco-innovation, confirming that capital contributes more efficiently to green growth. For example, economies like the U.S. and Germany could boost capital flow into sustainable manufacturing and energy-efficient technologies through green bonds, tax benefits, and public-private partnerships. Such measures would improve the productivity of capital in uplifting environmental sustainability.

  2. 2.

    G7 nations should invest in advanced and higher-level education programs that integrate sustainability-focused curricula and promote green skills development. Collaboration with research institutions and universities should be supported to grow green innovation hubs and support public-private R&D initiatives. Economies like UK and japan can further scale up their efforts by improving university-led research clusters dedicated to sustainable technologies and environmental solutions.

  3. 3.

    To strengthen the positive contribution of trade to enhance green growth, G7 nations should adopt proactive eco-friendly trade measures. These may consist of incentivizing the export of green technologies, integrating environmental standards into trade agreements, and facilitating low-carbon supply chains. Italy and Japan, as leading trading economies, can further support sustainability by promoting green exports, encouraging environmentally responsible imports, and aligning trade policies with global climate goals.

  4. 4.

    Governments of G7 economies should provide green investment incentives to avoid adverse ecological impact of FDI. A transparent reporting framework for monitoring the environmental impact of FDI can ensures that global investments contribute positively to the host country’s SDGs. Economies like the U.S. and Canada, which receive high volumes of FDI, can improve ecological screening criteria to attract only sustainability-oriented investments.

Limitations of the study

Following are some limitations of the present study.

  1. 1.

    The study emphasizes solely on G7 nations, limiting the applicability of results to other regions with different economic and environmental conditions.

  2. 2.

    The analysis excludes other potentially significant factors such as environmental policies, renewable energy adoption, and technological innovation beyond trade and FDI.

  3. 3.

    The adverse linkage of capital and trade on green growth may be context-specific, requiring further investigation into sectoral or industry-level differences.