The concept of the green economy appears to constitute a new ecological conservation and modernization terminology. This concept has been accepted worldwide as a requirement for global sustainable development (Lorek and Spangenberg, 2014). The idea of green economic growth is based on the work of Stavrakakis (1997), who developed the idea of the green economy to address environmental deterioration and provided the framework for a new environmental strategy. As a result, environmental law, policy, and management in the corporate sector all increased during the 1990s.

Then, GEG theory began with the straightforward observation that the natural environment is also a factor of production, but it has been largely disregarded in both classical growth theory and the historical patterns of economic expansion in practice (Solow, 1974). The theory was first proposed as a paradigm, theorizing that achieving GEG necessitates striking a balance between economic activity and the environment. Thus, GEG exemplifies decoupling the effects of economic expansion growth from environmental sustainability (Fletcher and Rammelt, 2017). According to eco-modernization theory, human initiatives can effectively balance economic progress with environmental development (Mol and Sonnenfeld, 2000). Further, this idea was revived after the financial crisis of 2008. Measures taken to improve the environment can help stimulate growth in economies that have been hit hard by the recession (Pollin et al., 2008).

Based on the abovementioned theoretical underpinning, the GEG concept can be understood as a resource-efficient, low-carbon-intensive approach to achieving economic development. Therefore, in the framework of a green economy, economic growth is fueled by public and private green investments that can increase the efficiency of resources and energy, safeguard ecosystems and biodiversity, and stimulate the economy by reducing CO₂ emissions.

The theoretical relationship between innovation and GEG is based on Solow’s (1956) model, which depicts the relationship between growth and innovation and implies that innovation plays a decisive role in fostering growth. In addition, Hart (1995) provided a concise definition of the term “ETs,” stating that it refers to the process of creating new tools for sustainable development and pollution control that significantly reduce the environmental cost of economic growth, thus promoting GEG. Besides, the association between ETs and GEG is based on endogenous growth theory that forecasts increasing returns to scale in innovation to promote long-term knowledge-based growth (Cortright, 2001). The central tenet of this theory is to explicitly model investment in technological progress and RD (Aghion and Howitt, 1997). Therefore, resource-saving innovations envisioned in most endogenous growth models are likely to be technologies to decrease pollution and conserve the use of raw materials and energy inputs. In this regard, endogenous growth theory supports the sustainability view (Burgess and Barbier, 2001). Besides, Maris and Holmes (2023) added an important dimension to endogenous growth models. By endogenizing technological change, this view of Maris and Holmes (2023) considered how investments in green innovation and RD could shift the growth path. This theory is consistent with a Green Solow model by Brock and Taylor (2010) that could be adapted to endogenize the abatement function.

Moreover, according to growth theories, a sustainable growth path can be established by fostering technical innovation through patents, innovations, and taxes (Acemoglu et al., 2016). However, promoting technological innovation can negatively impact environmental quality and GEG development. Improving energy efficiency, for instance, lowers carbon emissions, but when efficiency gains increase the level of resource and energy use, the rebound effect causes CO₂ emissions to rise as a result of technical advancement (Wang and Wei, 2019).

Furthermore, theoretically, the connection between RD and GEG can be explained by different mechanisms. For instance, increased RD investment leads to advancements in technology in the energy sector and promotes the production of green energy, which can significantly contribute to GEG (Lee and Min, 2015) and energy use efficiency (Ma et al., 2022). In addition, driving GEG through investment in RD can be considered critically important because it plays a vital role in abating CO₂ emissions.

Additionally, FG leads to greater financial openness and economic integration (Agénor, 2004). Through the exchange of commodities and services, information, technology, and foreign direct investment, globalization links nations worldwide (Grossman and Krueger, 1991). By linking nations, globalization encourages economic activity and raises living standards. There are two points of view regarding how globalization affects advancing economies and environmental quality. On the one hand, globalization negatively affects environmental quality by boosting the movement of goods and services, FDI, economic activity, and urbanization, as these activities require greater consumption of energy resources (Solarin et al., 2017; Haseeb et al., 2018; Shahbaz et al., 2018). On the other hand, globalization encourages the diffusion of information and the transition of economies from industry to service, positively impacting environmental quality. The impact of globalization on the environment is discussed in terms of scale, composition, and technology.

Theoretically, FG impacts GEG through three channels such as scale effect, technique effect and composition effect (Ulucak et al., 2020; Chen et al., 2023b). The scale effect delineates how FG amplifies economic endeavours, resulting in heightened consumption of fossil fuels. This, in turn, deteriorates environmental quality, consequently impeding GEG. Conversely, the technique effect channel suggests that by fostering efficient and environmentally friendly green innovations, FG has the potential to mitigate pollution emissions, thereby fostering green growth. The literature also deliberates on another channel, the composition effect, which characterizes a debatable correlation between FG and GEG (Danish et al., 2018; Chen et al., 2023a; Huo et al., 2023). Also, ecological modernization theory aims to safeguard the quality of the environment through resource-efficient innovations (Jänicke, 2020), which can positively influence the progress toward GEG.

Furthermore, FG boosts the allure of RD and tends to improve GEG (Huang et al., 2021). FG is a factor of liberalization, financial openness, and digital financial inclusion. In this sense, the pollution haven and pollution halo hypotheses, which are both significant, explain the connection between globalization and the environment. According to the pollution haven hypothesis, environmentally hazardous enterprises shift their activities from industrialized economies to less-developed countries with lax environmental quality requirements and laws (Doytch and Uctum, 2016). On the other hand, the pollution halo hypothesis claims that FDI and commerce improve the environment by transferring effective management techniques and technologies (Sbia et al., 2014).

Most empirical literature on the connection between ETs and GEG progress primarily focuses on economically advanced countries. For example, Meiling et al. (2020), Fernandes et al. (2021), Ali et al. (2023), Nosreen et al. (2021), Herzer (2022), and Yang et al. (2022) focused their investigations on advanced economies, such as OECD, G7 and EU countries, while the remaining studies are focused on a single or only a few countries. In addition, most of these studies examine the effects of ETs and RD on environmental sustainability as proxied by CO₂ emissions. Hence, most previous research has concentrated on the impact of ETs and RD on environmental sustainability. In contrast, this study offers a distinctive examination of the role of ETs and RD in promoting green growth in EMEs. Therefore, this study bridges this gap by investigating the effects of ETs on GEG in EMEs using GEG, which proxies GTFP driven by the MLPI via the directional distance function.

Moreover, most existing studies focus on the relationship between FG and carbon emissions, primarily in developed countries. Nonetheless, the empirical findings are conflicting. Some researchers argue that globalization negatively influences environmental quality, and some other scholars argue that it has a positive effect on environmental quality. For example, Kirikkaleli et al. (2021), Akadiri et al. (2021), Xia et al. (2022), and Ramzan et al. (2023) contended that globalization has a destructive impact on environmental quality, which can negatively influence progress toward GEG. Conversely, Ulucak et al. (2020) and Chen et al. (2023b) revealed that enhancing FG improves environmental quality. Therefore, this research aims to address the current research gaps and bridge conflicting findings on the effects of FG on GEG in EMEs.

Besides, the existing research has a gap in bringing the role of FG on the effects of ETs and RD on GEG. Therefore, this study bridges this gap by examining the direct effect of FG on GEG and the moderating role of FG on the effects of ETs and RD on GEG in EMEs. Besides, globalization provides ecologically beneficial technology; the quality of the environment will improve as financial interaction increases (Ahmad et al., 2021). Besides, the concept is based on the idea that FG enhances the interaction between financial channels and international firms, potentially facilitating significant technology transfer for ETs and RD to EMEs (Fan and Hao, 2020). Furthermore, a high level of complementarity exists between environmental innovation and FG (Majeed et al., 2022). FG not only supports the growth of the financial sector but also facilitates the transfer of knowledge and technology to developing nations (Tesega, 2022), thereby contributing to enhanced innovation within these countries (Zheng et al., 2023). However, the existing literature is silent on how FG moderates the role of ETs and RD on GEG in the EMEs.

Thus, this research is innovative in further building on the literature by examining this intricate connection and shedding light on the moderating effects of FG on the impact of ETs and RD on GEG in EMEs using more advanced econometric strategies.

The DDF is an example of a production function that incorporates positive and negative outcomes into the model (Xia and Xu, 2020). This technique enables the alteration of numerous outputs while leaving multiple inputs unaltered, thus increasing the feasibility of the DDF (Chung et al., 1997). Shephard, (2006) introduced the input-output distance function. In addition, Chung et al. (1997) developed a DDF based on the Shephard function to address its shortcomings. Based on these methodologies, a production possibility set (PPS) was built to account for environmental parameters when incorporating elements such as energy and the environment.

We construct the GP using the output-oriented distance function of Chung et al. (1997), which is used to examine the output disparities among decision-making units (DMUs) under identical inputs. Assume that for each DMU across time t = 1 …   T , there are N inputs w = w 1 … , wn , M desirable outputs h = h 1 … , hm , and I undesirable outputs l = l 1 … , li . Thus, all units of a DMU exploit a vector of inputs w ∈ B + N , a vector of the desired output h ∈ B + N , and a set of unwanted outputs l ∈ B + I . Then, we can characterize technologies by their product sets. All production occurs within period t   1 , … . , T of the kth 1 , … , k decision-making unit. D 0 w , h , l = ⁡ inf ϕ : h , l / ϕ ∈ P w (1) P w = h , l : w  can provide  h , l (2)

We begin by estimating whether reducing undesired output is expensive, assuming in Eq. 3 that bad outputs are not easily disposed of. This means that decreasing undesired output is achievable only by concurrently decreasing the favourable outputs while maintaining a fixed level of inputs. h , l ∈ P w  and  0 ≤ ϕ ≤ 1  show  ϕ h , ϕ l ∈ P w (3)

Furthermore, when an acceptable output is believed to be freely reusable, the subsequent postulate in Eq. 4 can be stated. h , l ∈ P w  and  h ′ ≤ h  imply  h ′ , l ∈ P w (4)

Finally, as the desired and undesirable outputs are null-joint, we construct Eq. 5. The undesired output is an inevitable consequence of the intended outcome. Zero undesirable output equals nil desirable output. f   h , l ∈ P w  and  h = 0  then  l = 0 (5)

Therefore, the primary Malmquist index utilizes Shephard’s distance strategy, which is defined as: D 0 w , h , l = ⁡ inf ϕ : h , l / ϕ ∈ P w (6)

This function proportionally increases the desired and unwanted outputs (h,l). This strategy does not eliminate undesirables since both desired and undesired outputs increase concurrently, leading to the adjustment of the former Malmquist technique. Thus, we employ the DDF as stated in Eq. 7 to increase the desirable outputs while minimizing the unwanted outputs. D → O w , h , l ; d = ⁡ sup α : h , l + α d ∈ P w (7) where “d” is the vector of “directions” in which the outputs are scaled. In our case, d=(h,−l), i.e., the desirable outputs increase, and the undesirable outputs decrease.

The GP is calculated by applying the MLPI as established by Chung et al. (1997), which is subject to the DDF. Supposing t = 1 … , T periods, the MLPI with undesirable output is given as follows: M L P I = 1 + D o t w t , h t , l t , − l t   1 + D o t + 1 w t , h t , l t ; h t − l t 1 + D o t w t + 1 , h t + 1 , l t + 1 ; h t + 1 , − l t + 1 ( 1 + D o t + 1 w t + 1 , h t + 1 , l t + 1 ; h t + 1 , − l t + 1 1 / 2 (8)

The MLPI is calculated by computing the difference between time t and time t + 1. The MLPI measurement suggests that productivity increases when the values are greater than one and decreases when the values are less than one. The Malmquist‒Luenberger efficiency change (ECH) and Malmquist‒Luenberger technical change (TCH) are two components of this index. E C H t t + 1 = 1 + D o t w t , h t , l t , − l t 1 + D o t + 1 w t + 1 , h t + 1 , l t + 1 ; h t + 1 , − l t + 1 (9) T C H t t + 1 = 1 + D o t + 1 w t , h t , l t , − l t   1 + D o t + 1 w t + 1 , h t + 1 , l t + 1 ; h t + 1 − l t + 1 1 + D o t w t , h t , l t , − l t + 1 ( 1 + D o t w t + 1 , h t + 1 , l t + 1 ; h t + 1 , − l t + 1 1 / 2 (10) T C H t t + 1 calculates the shift in the Frontier with the geometric mean of the technical change between t and t + 1 using input vectors from the two periods. E C H t t + 1 shows the variation in relative efficiency between t and t + 1. These indices show that productivity increases when the values are greater than one.

Finally, according to the current research, the labor force, capital stock, and energy consumption are frequently used as inputs, and real GDP is used as the desired output; CO₂ emissions, SO₂ emissions, and wastewater are used as proxies for undesirable outputs (Kumar, 2006; Li and Lin, 2016; Sun, 2022). Considering the availability of data, we use the labor force, capital stock, and energy consumption as the inputs, real GDP as the desirable output, and CO₂ emissions as the undesirable output. We used capital stock data from the Penn World Table database. In addition, total energy consumption is used to represent energy input.

Theoretically, ET aims to achieve sustainable development and pollution control to substantially reduce the environmental impact associated with economic growth (Hart, 1995). Consequently, ETs affect GEG by supporting green investments, which prioritize energy savings, assist in the transition from fossil fuels to renewable sources and reduce the level of gas emissions, thus positively contributing to the GEG of EMEs.

In addition, RD can impact GEG through various mechanisms. For example, increasing RD investments can facilitate technological advancements in the energy sector, fostering the generation of clean, green energy, which has the potential to make a substantial contribution to GEG (Lee and Min, 2015) and enhance the level of energy efficiency (Ma et al., 2022). Furthermore, RD investments can be crucial in mitigating CO₂ emissions, significantly contributing to GEG.

Following endogenous growth theory, which aimed to model investment in technological progress and RD to growth (Aghion and Howitt, 1997), the green Solow model, which incorporates abatement purpose (Brock and Taylor, 2010), modern growth theories that suggest technological innovation positively influences GEG (Acemoglu et al., 2016) and ecological modernization theory (Janicke, 2020), this study defines the expected effects of ETs and RD on GEG. Thus, based on these theoretical backgrounds, the GEG defined in Eq. 11 below is expected to be positively affected by ETs ( α = ∂ lnGEG / ∂ lnET > 0 ). Likewise, the GEG is projected to be positively affected by RD ( η = ∂ lnGEG / ∂ lnRD > 0 ). Furthermore, based on the technique effect channel of globalization, which suggests that by fostering efficient and environmentally friendly green innovations, FG has the potential to mitigate pollution emissions, thereby fostering green growth. Therefore, we propose that FG is assumed to positively affect GEG ( θ = ∂ lnGEG / ∂ lnFG > 0 ). However, the magnitude of ETs, RD and FG effects are heterogeneous across the GEG distribution. lnGEG i t = β n ′ X i t + α lnET i t + η ⁡ ln ⁡ R D i t + θ lnFG i t + ε i t (11)

GEG represents progress toward GEG as proxied by GP. X _it is the control variable and includes consumption, capital formation, energy consumption, the labor force and natural resources. RD _it is research and development spending, FG _it is financial globalization, and ET _it is environmental technology. βs, α, η and θ are parameters of coefficients, and ε denotes the stochastic term that is independently and identically distributed across individual country i at time t. t represents time in years (2000–2019), and i represents the panel of countries 1….25.

Moreover, FG fosters connections between financial systems, potentially through the introduction of green manufacturing methods and RD-driven green manufacturing endeavours in host countries (Sbia et al., 2017). Therefore, FG facilitates the development of environmentally friendly and efficient innovative technologies through its technique effect, thereby amplifying GEG. According to the technique impact pathway, FG has the potential to curtail pollutant emissions by bolstering the adoption of efficient and eco-friendly innovative practices, consequently expediting the progress toward GEG (Chen et al., 2023a). Additionally, FG prompts the emergence of ETs, which in turn catalyzes the green revolution of EMEs (Li et al., 2019). Therefore, FG positively moderates and catalyzes the positive effects of ETs and RD on GEG in EMEs (for details, see Section 4.4). GEG i t = β n ′ X i t + ϑ lnET i t + η ⁡ ln ⁡ R D i t + α lnFG i t + ψ lnFG i t × lnET i t + κ lnFG i t × ⁡ ln ⁡ R D i t + ε i t (12) where ψ and κ are the coefficients of the interaction terms.

In panel data investigations, capturing the individual effects on the entire distribution, as well as outliers, to regulate and classify the conditional heterogeneous covariance effects is crucial, particularly when the error term is not normally distributed (Flores et al., 2014; Musibau et al., 2021). These issues can be captured through the use of quantile regression methods because quantile regression strategies correct the sample size bias caused by endogenous regressors (Canay, 2011) and eliminate the bias caused by distributional heterogeneity (Zhu et al., 2016). However, despite being resilient to outliers, traditional quantile regression does not take into account any possible unobserved heterogeneity across individuals in a panel (Ike et al., 2020).

The MMQR model with fixed effects proposed by Machado and Silva (2019) represents a robust and alternative method capable of solving individual heterogeneity concerns. It produces accurate and resilient results when the dataset’s distribution lacks a clear shape, contains outliers, demonstrates minimal or no correlation, and deviates from normality. It effectively discerns the unique characteristics of different quantile values, tackling challenges stemming from uneven distribution (Alhassan et al., 2020). This method is used to identify the conditional heterogeneous covariance effects of RD, ETs and FG on GEG by permitting the individual effects to influence the entire distribution, as opposed to merely altering means, as in Canay (2011). This strategy implies that the covariate variables influence the distribution of the variables of interest only through the location and scale functions and not through location shifters alone. Consequently, the MMQR estimation method is most applicable when the panel data framework contains individual effects and endogenous explanatory variables (Ike et al., 2020). It additionally offers noncrossing regression quantile estimates. Moreover, it accommodates asymmetry based on location, as the parameters may vary based on the position of the predicted variable and income inequality, and delivers reliable estimates across diverse conditions, even in non-linear models (Halidu et al., 2023).

Hence, applying the MMQR estimation technique in our study is justifiable because the progression of GEG in EMEs is quite heterogeneous (Figure 1). Additionally, the economic status of each country is reflected in its emission levels (Figure 2). Hence, conducting studies incorporating individual and distributional heterogeneity across conditional quantiles is worthwhile. We, therefore, applied the MMQR with fixed effects. The econometric model for investigating the effects of RD, ETs, and FG is defined in the following Equation. QGEG i t τ / Z i t = β n ′ X i t + α lnET i t + η ⁡ ln ⁡ R D i t + θ lnFG i t + ε i t (13) where QGEG i t τ / Z i t denotes the τth conditional quantile function. Z represents all explanatory variables. The residuals are orthogonal to Z_it and normalized to satisfy the moment conditions described in Machado and Silva (2019). All variables are transformed to their natural logarithms, thus enabling the estimation coefficients to be interpreted in terms of their elasticities.

Equation 13 implies the following: Q G E G i t ( τ / Z i t ) = δ i + α i q τ + Z i t ′ β + X i t ′ χ q τ (14) δ i τ ≡ δ i + α i q τ is the scalar parameter, which is indicative of the quantile-τ fixed effect for individual i. The α across individual i and the sample quantile is depicted by q(α). X is a k-vector of identified components of Z that are differentiable transformations with element l given by X_l = X_l(Z), L = 1,2, … k. The individual effects in this method do not represent intercept shifts.

This is evaluated by addressing optimization in the manner described below: min q ⁡ ∑ i ∑ t ρ τ R ^ i t − α i + X i t ′ χ q (15) where ρ τ Α = τ − 1 Α Ι Α ≤ 0 + Τ Α Ι Α > 0 (16)

Due to the marginal change in i, the parameter for a dependent variable (GEG) i might represent the marginal change in the rth conditional quantile of QGEG i t τ / Z i t .

In addition, Eq. 13 is modified to include the interaction between ETs and globalization to illustrate the moderating role of FG on the impacts of RD and ETs on GEG. QGEG i t τ / Z i t = β n ′ X i t + α lnET i t + η ⁡ ln ⁡ R D i t + θ lnFG i t + ψ lnFG i t × lnET i t + κ lnFG i t × ⁡ ln ⁡ R D i t + ε i t (17)

In this approach, we divide the distribution into 25th, 50th, 75th and 90th quantiles. The 25th quantile is regarded as the lower quantile, the 50th and 75th quantiles are considered the middle quantile, and the 90th quantile is considered the upper quantile.

Finally, we used the two-step system GMM, FMOLS and Tobit models for robustness purposes. The MMQR model may not solve endogeneity concerns. The two-step system GMM method recommended by Arellano and Bond, (1991) and Blundell and Bond (1998) is applied to solve potential endogeneity issues. Endogeneity issues could occur through reverse causality between GEG and any of the covariates, including our target variable. The two-step system GMM model employs lagged differences of variables as instruments for equations in levels to solve the concerns. This approach exhibits better efficiency in mitigating issues related to heteroscedasticity and autocorrelation. It employs optimal weighting matrices and characterizes each observation by its deviation from the future observations’ average within the same individual while simultaneously standardizing the variance by assigning appropriate weights to each deviation (Blundell and Bond, 1998). Furthermore, it provides asymptotically precise inference with minimal statistical assumptions and employs internal instruments (Blundell and Bond, 1998). It is particularly well-suited for scenarios with a smaller time dimension than panels. Additionally, it incorporates the lagged levels of the dependent variable as instruments for equations in first differences. This approach effectively addresses the issue of endogeneity, specifically the challenge of reverse causality, thereby yielding consistent results. Moreover, the FMOLS model can provide long-term efficient estimates because it controls for the issues of cross-sectional dependency, heterogeneity and serial correlation (Özcan, 2013). These methods correct for serial correlation concerns (Özcan, 2013), provide trustworthy estimates and address concerns regarding cross-sectional dependency and heterogeneity issues (Zafar et al., 2020). In addition, the FMOLS approach has become popular because it accounts for bias and endogeneity concerns in small samples (Zafar et al., 2020). Therefore, following Zafar et al., 2020, we use the FMOLS estimator to examine the long-term role of globalization, ETs and RD on GEG in EMEs. The compelling evidence allows us to employ FMOLS to validate the long-term association among the suggested variables. Panel FMOLS offers several advantages, accommodating serial correlation, addressing endogeneity and accounting for cross-sectional heterogeneity. Additionally, it provides insights from both within and between dimensions (Erdal and Erdal, 2020). Research design, including methods of analysis and findings framework, is reported in Supplementary Figure SA2.

In light of the data, we utilize 25 EMEs covering the 2000–2019 period, depending on IMF classifications ¹ . This study period is delimited to 2000–2019 due to the lack of updated data available on the stock of capital since 2019 in the Penn World Table Database. The rationale for variables is based on the existing theoretical and empirical literature. The data for all variables have been collected from the most commonly used and reliable secondary sources such as the WDI of the World Bank, OECD, UNESCO, KOF Globalization Index and Our World in Data. These sources are widely used to utilize secondary data. Details of each variable have been discussed in the following sections and Supplementary Table SA1.

The cross-sectionally augmented IPS method of Pesaran (2007) is used to test the unit root. The results show that all the other variables are stationary at the first difference. In addition, the dependent variable, GEG, is stationary at I (1). Moreover, we determined if there was cross-sectional dependence in the dataset using the cross-sectional dependence test. The result demonstrates that EMEs have a strong economic connection, as cross-sectional independence is not accepted (Table 2).

Several methods have been devised for testing the cointegration of a set of variables. The cointegration connection is studied through a variety of methodologies, such as Westerlund (2007), Pedroni (1999), and Kao (1999). Therefore, we applied these methods in our research. Kao’s (1999) cointegration test considers the intercepts of cross-sections and homogenous coefficients on the first step of regression. This method requires the intercept to be heterogeneous across cross-sections and the slope coefficients to be homogenous. Kao’s cointegration test results indicate that the null hypothesis is rejected (Table 3).

The results of the MMQR estimator are presented in Table 4.

These findings imply that capital formation and the labor force have statistically robust positive effects on GEG in all quantiles aside from the nonsignificant effect of the labor force that occurs in the 90th quantile. Similarly, the coefficients of consumption expenditure that are used to proxy for fiscal policy measures are robustly positive in the lower and middle quantiles (Q_.25 and Q_.5), inferring that an increase in consumption expenditure improves the GEG in countries with lower levels of GEG progress. In addition, the effects of natural resources are weakly positive. However, the estimates of total energy consumption show that total energy consumption has a significant negative effect on GEG in all quantiles aside from 90th quantile, denoting that an intensification in energy consumption (nonrenewable) negatively affects the GEG of EMEs. These results are not surprising given the extremely low diversity of energy sources in EMEs, where fossil fuels generally dominate (Capozza and Samson, 2019). Moreover, these findings support existing empirical studies, such as those of Adebayo et al. (2022), Wu et al. (2022), Hussain et al. (2022) and Wang et al. (2023b), that have revealed mitigating emission effects of renewable energy consumption.

Concerning the target, the coefficients of ETs are positive and statistically significant across all quantiles. Therefore, ETs have a statistically significant beneficial effect on GEG, suggesting that ETs encourage development toward GEG in EME countries. However, the effect of ETs decreases from lower quantiles to higher quantiles, indicating that improvements in ETs in countries with lower GP levels have greater effects on GEG. Therefore, ETs serve as an important determining factor in promoting the GEG of EMEs. This may be because technological innovations can spur green technological progress, enhance resource allocation, and positively affect GEG. In other words, by developing eco-friendly green technologies, firms in EMEs can reduce energy consumption and pollution emissions, enabling green production to enhance green growth. Additionally, green innovation and technologies help in the reutilization of production waste and recycling (Zhang et al., 2018), which can positively contribute to GEG in EMEs. These outcomes are comparable with the findings of Sohag et al. (2019), Jason and Giorgos (2020), Meiling et al. (2020), Wang et al. (2018), Liu and Xin (2019) and Obobisa et al. (2022), confirming that technological progress and ETs positively impact GEG and sustainable development. Furthermore, by developing eco-friendly green technologies, EMEs can reduce their energy consumption and pollution emissions, thus enabling green production to enhance GEG. Additionally, ETs help in the reutilization of production waste and recycling (Zhao et al., 2021).

Moreover, the results of this study support the use of ecological modernization theory frameworks, the Green Solow model and extended endogenous growth theory, which argues that promoting environmental-related innovation promotes GEG by advocating for investments that not only yield economic benefits but also enhance the broader goal of environmental sustainability (Aghion and Howitt, 1997; Brock and Taylor, 2010; Jänicke, 2020).

Furthermore, the effects of RD on GEG are positive and statistically significant across all quantiles (Q_.25 to Q_.9). The magnitude of the coefficients of RD decreases from lower quantiles to higher quantiles, implying that RD investment is more important for EMEs exhibiting less GEG progress. Thus, the results verify that increasing the share of RD spending promotes GEG in EMEs. This finding is driven by the notion that investing in RD is a crucial means of shifting the economic structure from a predominance of fossilized energy towards renewable energy sources, thereby assisting EMEs in achieving their carbon neutrality goals and GEG targets. In other words, RD contributes to the improved utilization of resources, slows the growth of pollutants associated with energy and other resource consumption, and constrains economic expansion while driving GEG in EMEs. Similarly, RD spending is anticipated to decrease the reliance of EMEs on fossil fuels, encourage the adoption of renewable energy, and increase investment in ETs. Our findings are in line with the justifications of the studies by Kihombo et al. (2021), Petrović and Lobanov (2020b), Han et al. (2023) and Herzer (2022), who argued that RD investment positively contributes to CO2 mitigation and thus contributes to GEG.

Theoretically, the results corroborate the endogenous growth theory, which posits that RD is a pivotal catalyst for economic expansion, with investments in RD yielding enduring enhancements in productivity (Brock and Taylor, 2010). Thus, within the framework of GEG, directing RD investments toward creating eco-friendly technologies can ignite innovation, enhance resource utilization, and mitigate environmental repercussions, thereby nurturing GEG’s progress.

Furthermore, FG positively affects GEG in all quantiles, indicating that FG robustly enhances the progression toward GEG in the EMEs. Additionally, the magnitude of the estimates of FG increases from the lower quartile to the higher quantile (Q_.25-Q_.9), implying that FG has a greater positive impact on the GEG of countries that already exhibit a high level of GEG. These results can be used to explain why FGs encourage firms to use environmentally favourable manufacturing practices, which results in environmental quality and contributes to GEG. The positive link between FG and GEG is also based on the pollution halo hypothesis and technical effect, which provides evidence that FG improves the environment through the transfer of effective management techniques and through financial and technological spillover and transfer (Sbia et al., 2014). These findings are congruent with those of Huang et al. (2021), Sbia et al. (2014), Xia et al. (2022), Chen et al. (2023a) and Dauvergne and Lister (2012), who all reported positive associations among environmental quality, green growth and FG.

Table 5 provides the moderating effects of FG on the effects of ETs and RD on GEG. The coefficients of the interaction terms are positive and statistically significant in all quantiles.

To determine the marginal effects of ETs and RD on GEG under FG in greater detail, the partial derivative of GEG is conditional on FG. The marginal effect of the improvement in FG to the average level in the sample can be calculated using the partial derivative Formula below: ∂ logGG i , t / ∂ F G i , t = β ^ + α ^ logFG i , t (18) where β ^ denotes the effect of a percent increase in ETs and RD on GEG when FG is equivalent to zero and α ^ represents the coefficient of the interaction term when FG is greater than zero.

For example, the marginal effect is calculated using the first quantile (Q.25) of the MMQR estimates displayed in Table 5 and the average of the natural logarithm of FG (Table 1). Thus, at the minimum FG in the natural logarithm (3.220), the marginal effect of ETs on GEG as conditioned on FG is 0.126+ (0.066∗3.220) = 0.338. Nevertheless, the conditional marginal impact of ETs on GEG subject to an approximately average FG according to the natural logarithm (3.993) is 0.126+ (0.066∗ 3.993) = 0.390. These results, therefore, indicate that the positive effect of ETs is increasing with respect to FG, implying that the openness of countries to FG promotes the positive effect of ETs on GEG. Similarly, at the minimum FG in the natural logarithm, the marginal effect of RD on GEG as conditioned on FG is 0.149+ (0.058∗3.220) = 0.336. Nevertheless, the conditional marginal impact of ETs on GEG as subject to the approximately average natural logarithm of FG is 0.149+ (0.058∗ 3.993) = 0.381, implying that the beneficial effect of RD on GEG increases under FG.

Based on the theoretical framework, our findings align with the technique effect mechanism of globalization. This suggests that by promoting efficient and environmentally friendly green innovation, FG can potentially decrease pollution emissions, thus enhancing GEG.

In addition, our findings are congruent with those of Chen et al. (2023b), Li et al. (2019) and Sbia et al. (2017), who argued that FG stimulates connections between financial systems that could bring about green manufacturing techniques and RD that may encourage clean production activities and easy access to a vast array of financial goods and services that can help encourage RD operations, improve technological innovations, and improve GEG.

Therefore, FG enables the movement of capital across international borders, providing EMEs access to funds from global markets, implying that with greater financial resources available, firms in EMEs may allocate more funding towards projects focused on ETs and RD, driven by the desire to seize opportunities for innovation and competitiveness in the global marketplace. Also, FG, marked by liberalization and digital financial inclusion, enhances the allure of RD, bolstering green growth (Chen et al., 2023b).