Sustainable Transitions: Navigating Green Technologies, Clean Energy, Economic Growth, and Human Capital for a Greener Future

Abstract

Sustainable transitions are essential for balancing economic growth with environmental preservation. Ecological sustainability, measured through environmental footprints (ECV), serves as a strategic pathway for mitigating environmental degradation and fostering long-term ecological resilience. This study examines the role of industrialization (IDS), economic growth (EGR), human capital (HDV), green technologies (GNN), and renewable energy consumption (RNC) in shaping ECV across G5 countries from 2000 to 2022. Employing the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) estimator, alongside the augmented mean group (AMG) and common correlated effects mean group (CCEMG) for robustness, this study unveils a positive association between EGR and ECV, indicating its adverse impact on environmental sustainability. However, IDS, HDV, GNN, and RNC exhibit negative relationships with ECV, suggesting their contributions to improving ecological sustainability. These findings highlight the need for integrated policies that promote sustainable industrial practices, enhance technological advancements, and optimize human capital to counterbalance the environmental pressures of economic growth. Aligning economic expansion with ecological sustainability remains crucial for achieving long-term environmental balance in G5 nations.

1. Introduction

Ecological sustainability has gained significant attention in recent years, especially in emerging economies. Ecological civilization (ECV) highlights the harmonious cooccurrence of human activities on the natural environment. It is an all-encompassing and sustainable strategy for social and economic benefits. This concept motivates the actions to minimize ecological footprints to ameliorate environmental performance [1]. The study’s metric, ecological footprints, measures the stress that human activity places on Earth’s ecosystems and provides a thorough picture of environmental sustainability [2]. G5 countries, including Brazil, China, India, Mexico, and South Africa, collectively contribute to international ECV outputs. Figure 1 illustrates the ecological footprint trends for Brazil, China, India, Mexico, and South Africa from 2000 to 2022. The data reveal a significant increase in China’s ecological footprint, reflecting its rapid industrialization and economic expansion. In contrast, South Africa’s trend is marked by fluctuations rather than consistent growth, indicating periods of both environmental pressure and stabilization. Meanwhile, Mexico exhibits a general decline, suggesting improvements in resource efficiency or shifts in economic activities. Brazil and India show relatively stable trends, with minor variations over time. These findings highlight the diverse environmental trajectories among G5 economies, shaped by differing policy approaches, industrial developments, and sustainability commitments.

Industrial procedures often lead to ecological degradation despite that fact that it can drive economic growth (EGR). Industrialization (IDS) results in pollution, resource depletion, and habitat loss [3]. Manufacturing outcomes and energy consumption mark the process of IDS in G5 economies, playing a dynamic role in both economic development and environmental challenges. Figure 2 given below represents the patterns of IDS in G5. Brazil and India display a modest trend of IDS from 2000 to 2022, but China constantly leads with the greatest totals with an increase in the pattern of IDS, showing its considerable industrial output and economic activities. While still making significant contributions, South Africa and Mexico lag behind China. China is grappling with the most environmental concerns due to rapid IDS. The growth of industrial activities poses challenges to enhancing ecological conservation values, as it leads to a surge in ecological footprint levels [4].

Furthermore, human development (HDV) is another crucial element which can lessen environmental decline by curbing ecological footprints through the awareness and adoption of sustainable practices. HDV can foster ECV by contributing to a more productive population. HDV encapsulates health, academics, and income. HDV is generally measured by the human capital index (HDI) [5]. Varying levels of HDV in G5 nations influence the ability of authorities to implement and benefit from sustainable actions [6]. Figure 3 shows that Mexico from G5 leads the HDV with the highest ratio of 59.73, followed closely by Brazil (57.79), China (57.05), and South Africa (57.22). India has the lowest HDV sum at 45.34 between 2000 to 2022.

On the other hand, ref. [7] discovered that EGR can also be inhibited by the expansion of human capital through the educational process. This demonstrates the critical significance of HDV regarding negative and positive outcomes of HDV in terms of sustainability. The indicator of economic performance is gross domestic product (GDP). EGR provides fiscal resources required for environmental protection. The improper management of EGR can sometimes lead to overconsumption and waste generation, which results in deteriorating ecological quality [8]. Similarly, a balanced approach to guarantee environmental sustainability is necessary for G5 nations, as this group of countries has experienced substantial GDP expansion [9].

Figure 4 illustrates the EGR trends among G5 nations from 2000 to 2022. The significant surge in China’s EGR highlights its role as a global trade hub, with far-reaching economic, environmental, and climate implications. Additionally, India also seems to have significant growth, propelled by rising domestic demand, a booming service sector, and economic reforms. Due to external economic conditions, Brazil and Mexico have swings in their moderate growth, which could be influenced by their respective distinct economies. Comparatively speaking, South Africa’s EGR is slower due to variables including political shifts and fluctuations in the price of commodities globally. However, ref. [10] pointed out that in most developing countries, initiatives for environmental conservation have faced challenges due to economic and demographic shifts brought about by the steady rise in EGR.

In addition to this, renewable energy consumption (RNC) and green innovation (GNN) are another imperative factor in optimizing environmental consequences [11]. The shift toward RNC is indispensable for cutting emissions from greenhouse gases and mitigating climate change. Countries are making drastic strides in embracing clean energy and technologies [12,13]. Environment and RNC are intricately linked and have complicated relationships that are both direct and indirect [14]. The industrialized economies should put a lot of effort into GNN and RNC until the benefits of these technologies—such as fewer or carbon-neutral processes—start to trickle in. Since industrial output is a major factor in the expanding ecological footprint, it is equally imperative for industrial economies to switch to carbon-free technologies [15,16].

Figure 5 illustrates the trends in green innovations (GNN) and renewable energy consumption (RNC) for Brazil, China, India, Mexico, and South Africa from 2000 to 2022. The data reveal distinct patterns among these economies, reflecting their varying approaches to sustainable energy transitions [17]. China consistently outperforms other nations in both GNN and RNC, underscoring its strong investment in renewable technologies and energy infrastructure. India follows a fluctuating yet upward trajectory, indicating ongoing efforts to integrate green innovations. Brazil and Mexico exhibit moderate but steady growth in both indicators, while South Africa, despite gradual improvements in RNC, lags in GNN adoption. These differences highlight the influence of economic structures, policy frameworks, and technological capacities in shaping each country’s sustainability pathway. As noted in [18], increased financial support and policy incentives are crucial for accelerating advancements in GNN and RNC, ensuring a more balanced and effective global energy transition.

While extensive research has been conducted on ECV, there remains a notable gap in understanding how diverse ecological, technological, and economic contexts shape its dynamics, particularly in the G5 nations from 2000 to 2022. Prior studies have largely examined these factors in isolation or within limited regional scopes, often overlooking their intricate interdependencies. For instance, research on industrialization and environmental degradation [19] or the role of renewable energy in reducing ecological footprints [20] has provided valuable insights but failed to offer a holistic perspective incorporating multiple drivers. Similarly, studies on human development and green innovations [21] highlight the transformative potential of these factors, yet their collective impact on ECV remains underexplored. Furthermore, a comprehensive empirical assessment of the interplay between IDS, EGR, HDV, GNN, and RNC in relation to ECV is still lacking.

The existing literature emphasizes the need for an integrated methodological approach that leverages advanced econometric techniques such as CS-ARDL, AMG, and CCEMG to capture both long- and short-term variations while addressing cross-sectional dependence and heterogeneous slopes [22]. By bridging these research gaps, this study aims to provide a holistic analysis of ECV drivers, offering critical insights for policymakers seeking to develop informed strategies aligned with the sustainable development goals (SDGs).

The primary objective of this study is to explore the intricate dynamics of ECV across G5 nations by considering the synergies between IDS, HDV, EGR, GNN, and RNC. This research contributes valuable insights that can support global efforts in achieving the sustainable development goals. Specifically, it aligns with SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation, and Infrastructure), SDG 13 (Climate Action), and SDG 15 (Life on Land). To achieve these objectives, the study seeks to address the following key research questions:

• How do HDV and IDS contribute to ECV in G5 economies?
• How do GNN and RNC influence the short-term and long-term environmental outputs of G5 nations?
• What are the long- and short-term interactions between EGR and ECV?

By addressing these questions, this study aims to provide a robust empirical foundation for policymakers and researchers, offering evidence-based strategies for enhancing environmental sustainability while fostering economic and technological progress.

To answer these questions, this research has incorporated the analysis of CS-ARDL to capture the short-term and long-term impacts of independent variables on the dependent variable (ECV). Furthermore, tests include matrix correlation, slope heterogeneity, CSD, the CIPS unit root test, and Westerlund cointegration analysis to verify the stability and dependability of the data. Simultaneously, AMG and CCEMG methods are employed to inspect the robustness of statistical findings [23]. The empirical aftermath of this study will provide valuable data. Likewise, conclusions of the investigation will be helpful in achieving ECV through the execution of relevant industrialized, ecological, and economic strategies in the G5 region.

In addition to this, Section 2 gives a detailed overview of the empirical literature, with particular attention to the theoretical framework (Section 2.1) and the research gaps (Section 2.2). Section 3 includes the data and methodology, while Section 4 summarizes the discussions and results. Lastly, Section 5 provides conclusions and policy recommendations.

2. Literature Review

The relationship between industrialization (IDS) and environmental footprints (ECV) is fundamental for achieving sustainable transitions. ECV promotes an integrated approach that balances economic, social, and environmental dimensions, ensuring that industrial growth aligns with sustainability goals. Similarly, refs. [24,25] clarifies ECV as a framework that integrates green technologies (GNN) with sustainable resource management, prioritizing ecological health. Principles of ECV serve as guiding strategies for international sustainability efforts, including climate change mitigation, biodiversity conservation, and resource preservation [26,27]. Furthermore, IDS has enhanced employment opportunities and lifestyles but has also significantly contributed to environmental stress, such as pollution, deforestation, and climate change. The environmental impacts of IDS in emerging economies have been examined in [28], the authors of which found that IDS has led to increased emissions of pollutants into air, water, and soil, intensifying ecological degradation.

Moreover, ref. [29] introduced the concept of “eco-efficiency”, arguing that IDS sectors can simultaneously achieve economic growth (EGR) and environmental benefits by enhancing resource efficiency and adopting cleaner production technologies. Integrating IDS with ECV within a sustainable transition framework requires policies that align industrial operations with ecological principles. The significance of embedding ecological strategies in IDS to support long-term sustainability has been emphasized in [30]. A high ECV resulting from unregulated IDS negatively impacts environmental quality [31]. Additionally, human capital (HDV), a crucial pillar of sustainability transitions, fosters socio-economic growth through improved education, healthcare, and technological skills. While ECV promotes environmental integrity, HDV ensures equitable development pathways, highlighting the interdependence between sustainability and well-being. Moreover, ref. [32] revealed that reducing environmental degradation strengthens HDV, especially in developing nations where sustainability challenges are more pronounced.

Likewise, studies suggest that socio-economic disparities influence ECV, with high-income groups leveraging their HDV to adopt sustainable practices, while low-income populations in densely populated regions exhibit higher ECV due to limited access to GNN and sustainable resources [33]. Additionally, ref. [34] analyzed the interaction between EGR, ECV, and HDV using the autoregressive distributive lag (ARDL) approach with a structural break from 1971 to 2014. Their findings indicate that unregulated EGR accelerates ECV, exacerbating environmental degradation. Ref. [35] explored the asymmetric effects of energy efficiency and EGR on GNN from 2000 to 2019, employing the quantile autoregressive distributed lag (QARDL) method. The study found that GNN demonstrates a positive correlation with energy efficiency and sustainable EGR, underscoring the role of energy transitions in sustainable development. Ref. [36] examined the link between GNN and ECV, suggesting that enhancing ECV strategies could facilitate GNN transformations for long-term sustainability.

Additionally, ref. [37] assessed the determinants of GNN efficiency in the Yangtze River Economic Belt (YREB) using the Super-SBM and Spatial Durbin Model. Their findings emphasized that GNN plays a crucial role in advancing ECV, particularly through the adoption of sustainable technologies and environmentally friendly IDS practices. Evidence suggests that excessive reliance on non-renewable energy sources hampers environmental progress, whereas GNN and renewable energy consumption (RNC) yield significant improvements in ecological outcomes [38,39]. Developing robust RNC systems remains central to fostering sustainable IDS transitions. Furthermore, ref. [40,41] highlighted how governments can enhance economic longevity by investing in clean energy policies, funding RNC infrastructure, and supporting low-carbon innovations. Moreover, ref. [42] employed the Method of Moments Quantile Regression (MMQR) to examine ASEAN countries, revealing that non-renewable energy consumption increases carbon emissions, degrading environmental performance, whereas RNC consistently reduces emissions across all quantiles.

2.1. Theoretical Framework

There are profound implications of RNC, IDS, HDV, EGR, and GNN regarding ECV, particularly in G5 countries (Brazil, China, India, Mexico, and South Africa). This section of the paper seeks to understand the repercussions of these elements on ECV, providing insight into sustainability. Sustainable development is broadly defined as meeting present needs without compromising the ability of future generations to meet theirs [43]. However, its perception and implementation vary across countries due to differences in economic structures, institutional frameworks, and policy priorities [44]. For instance, in G5 countries, sustainable development entails balancing industrial growth with environmental preservation while addressing socio-economic disparities [45,46]. Societal infrastructure that integrates environmental maintenance with monetary and social development to balance human activity with the environment is known as ECV [47]. Ecological carrying capacity, a key component of sustainable development, integrates environmental maintenance with economic and social progress to harmonize human activity with nature [48]. Additionally, the ecological footprint can serve as a proxy for ECV, as it is a comprehensive measure of human demand on nature, quantifying the number of natural resources consumed and waste generated. Environmental influence from human activities is reflected by ecological footprints [49].

On the other hand, IDS is a critical driver of EGR but often comes at the expense of environmental sustainability. Moreover, ref. [50] highlights that IDS usually consists of modifications of the economy, from agrarian-based to industrial-based economies, leading to increased production, urbanization, and resource consumption. According to [51,52], ecological implications of IDS include increased levels of carbon emissions, the depletion of natural resources, and environmental pollution. IDS has the potential to contribute to increased ecological footprints due to intensified industrial activities, which makes the relationship between IDS and ECV more complicated. Empirical studies on sustainable development in emerging economies indicate that policy integration between economic and environmental strategies is vital for long-term sustainability [53].

Furthermore, awareness and aptitude for the adoption of sustainable practices are required to drive ECV, which can be achieved through HDV [54]. Human capital, productivity, academics, and health are the indicators by which HDV can be calculated. Education, more specifically, may foster innovation in green technologies and ecological consciousness [55]. Consequently, HDV may have an advantageous effect on ECV by promoting sustainable actions and technological advancements. Moreover, due to its transformation from a constituent component of cost to the primary productive and social factor of EGR, HDV now occupies a prime position in the national wealth of industrialized nations [56]. Gross domestic product (GDP) is imperative in optimizing living standards but can also exacerbate environmental degradation. GDP helps in determining economic expansion as well [57]. Similarly, the Environmental Kuznets Curve (EKC) hypothesis posits an inverted U-shaped relationship between EGR and environmental quality, suggesting that environmental degradation initially increases with EGR but eventually decreases as economies mature and adopt cleaner technologies [51,58]. Comprehensive acknowledgment of the links between EGR and ECV is quite significant in the context of G5 countries for formulating policies that balance economic, ecological, and sustainable development objectives.

In a similar vein, RNC is a key component in curbing ecological footprints and encouraging sustainability. RNC assists in lowering carbon emissions and its adverse effects on environmental performance. RNC, for instance, wind and hydro, provide sustainable alternatives to fossil fuels [59]. Ecological footprints are decreased, or environmental quality can be improved by RNC [60]. The transition to RNC is essential for achieving ECV by decoupling EGR from environmental degradation [61]. Likewise, GNN also plays a vital role in enhancing ecological quality. Patents of environmental technologies indicate the development and adoption of innovations aimed at reducing ecological imprints [62]. GNN can result in more efficient resource use, reduced emissions, and overall lower ecological footprints. Thus, the promotion and diffusion of GNN are critical to the progress of ECV.

In addition, CS-ARDL [63], AMG, and CCEMG models are suitable for evaluating the dynamic links and long-term equilibrium among ECV, HDV, EGR, RNC, IDS, and GNN. This study attempts to provide strong empirical evidence on how these factors together affect ecological footprints in G5 countries using these techniques, along with some preliminary tests (CIPS, CADF, and Westerlund). It is necessary to comprehend these dynamics in order to establish policies that effectively promote ECV and sustainable development, particularly through a structured approach that incorporates economic resilience, environmental governance, and institutional effectiveness [64,65].

2.2. Literature Gap

There is still a large knowledge vacuum on how diverse ecological, technological, or economic contexts affect ECV despite the substantial amount of study on the subject, especially when considering the G5 nations and the years 2000–2022. Most of the past research has examined each of the selected variables in isolation or within limited geographic regions, omitting the intricate relationships that may vary based on environmental, technological, and financial conditions. There has not been a full analytical investigation of how these elements (IDS, EGR, HDV, GNN, and RNC) interfere with ECV yet. Therefore, the current research in the scientific literature emphasizes the need for a comprehensive, multimodal evaluation that considers these elements and integrates cutting-edge techniques like CS-ARDL, AMG, and CCEMG. Additionally, with the ability to accommodate diverse slope coefficients and take into consideration cross-sectional dependence, the CS-ARDL approach offers reliable long- and short-term dynamics in panel data analysis. By accounting for unobserved common features, the AMG approach produces consistent estimates of heterogeneous slopes among cross-sectional units.

Furthermore, CCEMG considers common components to address cross-sectional dependence and provides accurate and impartial assessments in panels with variable slopes. These procedures can improve the accuracy and robustness of empirical findings in research requiring intricate panel data structures. Hence, the purpose of the current study is to fill these gaps by delivering a thorough analysis of the consequences of IDS, EGR, HDV, GNN, and RNC on ECV using CS-ARDL, AMG, and CCEMG, covering a 22-year period in G5 countries. Moreover, considering the interrelationships among SDGs, the research proposes to cater to useful insights for policymakers who wish to advance ECV through the formulation and implementation of relevant policies.

3. Data and Methodology

The environmental impacts of resource use and human activities have been taken into consideration while measuring ECV using environmental footprint data that are taken from the Global Footprint Network (GFN). This indicator contributes to the understanding of sustainability challenges and helps evaluate the progress of ECV. The scope of industrial activity and its possible impact on ecological deterioration are captured by IDS as a percentage of GDP from the World Development Indicators (WDIs). Additionally, data for EGR have also been extracted from the WDIs using constant dollars to measure economic expansion. This guarantees a clear picture of actual economic performance over time. Better policy insights can be made possible by being able to examine the effect of growth on ECV without being distorted by inflation. Similarly, the WDI metric of RNC reflects a country’s commitment to sustainable energy sources. This statistic is essential to comprehend how switching to renewable energy could mitigate ecological damage and speed up the shift to greener economies.

However, HDV is a reliable measure of a nation’s degree of education and competence. The source of HDV material is the human capital index from the Penn World Table (PWT). Given that it may motivate more sustainable behavior, this evaluation is necessary for comprehending how ECV can be altered by human growth. Moreover, The OECD’s tracking of environment-related technologies emphasizes the crucial role of GNN to reach sustainability objectives. This gauge makes it easier to figure out whether technological advancements support sustainable development and decrease ecological footprints. Thus, the study effectively conveys the complexity of ECV and its forces by leveraging these multiple factors, which further help in providing a solid foundation for recommendations for policy and plans for sustainable development. All the variables are presented in

Table 1. Description, measurements, and sources of variables.

Variables	Measurements	Data and Source
ECV	Environmental footprints	GFN
IDS	Industrialization % of GDP	WDI
EGR	GDP constant $	WDI
HDV	Human capital index	PWT
GNN	Environmental related technologies	OECD
RNC	Renewable energy consumption	WDI

, together with their measurement and sources.

This study used a range of approaches for a thorough analysis, including descriptive statistics, AMG, CCEMG, CSD, the CIPS unit root, matrix correlation, slope heterogeneity, and CADF. Robust and complex understandings of the dynamic interactions between ECV and its drivers are produced through the integration of various methodologies. They also aid in the procedures of relationship identification, stationarity and cointegration testing, data summarizing, and cross-sectional dependence accounting. Descriptive statistics summarize the main features of the data, providing an overview of the central tendency, dispersion, and distribution of each variable. Descriptive statistics include the mean, median, standard deviation, and range (as shown in

Table 2. Descriptive statistics.

	ECV	IDS	EGR	HDV	GNN	RNC
Mean	2.571234	30.26487	2.52 × 1012	2.409923	19.71438	24.03818
Median	2.844094	28.40490	1.26 × 1012	2.473362	20.71904	14.90000
Maximum	3.867813	47.55740	1.79 × 1013	3.091373	34.91573	50.00000
Minimum	0.745011	18.18850	1.29 × 1011	1.782071	4.672897	7.600000
Std. Dev.	0.919460	7.864959	3.70 × 1012	0.293879	7.861371	15.45770
Skewness	−0.890313	0.768831	2.648805	−0.308310	−0.071636	0.410384
Kurtosis	2.456484	2.645159	9.393080	2.488320	1.848298	1.409472
Jarque–Bera	16.60810	11.93277	330.3190	3.076432	6.454110	15.34982
Probability	0.000248	0.002563	0.000000	0.214764	0.039674	0.000464
Sum	295.6919	3480.460	2.90 × 1014	277.1412	2267.153	2764.391
Sum Sq. Dev.	96.37633	7051.764	1.56 × 1027	9.845579	7045.331	27,239.20

), which help in understanding the basic characteristics of the dataset [66]. Moreover, the degree of association between pairs of variables can be identified by matrix correlation [67]. However, Equation (1) given below is the computation of the CSD model.

$${\displaystyle \frac{\sum _{i=1}^{N-1}\sum _{j=i+1}^N{\widehat{\rho }}_{ij}}{N(N-1)/2}}$$

(1)

By calculating the average pairwise correlation coefficients of residuals, the CSD test statistic determines whether cross-sectional units in panel data are correlated. $N$ denotes the total number of cross-sectional units in Equation (1) (e.g., countries, firms) and $d{\widehat{\rho }}_{ij}$ showcases the estimated pairwise correlation coefficient of the residuals between cross-sectional units $i$ and $j$. Furthermore, the numerator $\left(\sum _{i=1}^{N-1}\sum _{j=i+1}^N{\widehat{\rho }}_{ij}\right)r$ represents the sum of all pairwise correlations between the cross-sectional units. The denominator $\left(N\right(N-1)/2$) is the number of unique pairs of cross-sectional units. Findings from CSD analysis can provide a measure of the average correlation between units in the panel data [68]. A crucial step disregarding cross-sectional dependency might result in inaccurate results. Also, the CIPS unit root test permits cross-sectional dependence while searching for stationarity in panel data. By using cross-sectional averages of the lag levels and beginning differences in the separate series, the CIPS test expands upon the traditional IPS unit root test [69]. The CIPS unit root model can be expressed as follows:

$$\mathsf{\Delta }{y}_{it}={\alpha }_i+{\beta }_{i}t+{\rho }_i{y}_{i,t-1}+\sum _{j=0}^{p-1}{\varphi }_{ij}\mathsf{\Delta }{y}_{i,t-j}+{\gamma }_i{\overline{y}}_{t-1}+\sum _{j=0}^{p-1}{\theta }_{ij}\mathsf{\Delta }{\overline{y}}_{t-j}+{ϵ}_{it}$$

(2)

where $y_{it}$ is the variable of interest, ${\alpha }_i$and ${\beta }_i$ are parameters, ${\rho }_i$is the coefficient of the lagged variable, ${\varphi }_{ij}$and ${\theta }_{ij}$ are coefficients of the differenced terms, ${\gamma }_i$is the coefficient of the cross-sectional average, and ${ϵ}_{it}$ is the error term. Furthermore, the CADF test is a broadened Dickey–Fuller test, like the CIPS test, which considers CSD by incorporating cross-sectional averages into the test equation [69]. This facilitates the process of figuring out whether a particular series is non-stationary. The CADF equation can be written as

$$\mathsf{\Delta }{y}_{it}={\alpha }_i+{\beta }_{i}t+{\rho }_i{y}_{i,t-1}+\sum _{j=1}^p{\varphi }_{ij}\mathsf{\Delta }{y}_{i,t-j}+\sum _{j=0}^q{\theta }_{ij}\mathsf{\Delta }{\overline{y}}_{t-j}+{ϵ}_{it}$$

(3)

where $y_{it}$ is the variable of interest, ${\alpha }_i$and ${\beta }_i$ are parameters, ${\rho }_i$is the coefficient of the lagged variable, ${\varphi }_{ij}$and ${\theta }_{ij}$ are coefficients of the differenced terms, ${\overline{y}}_t$is the cross-sectional average, and ${ϵ}_{it}$ is the error term.

$$\mathsf{\Delta }{y}_{it}={\alpha }_i+{\beta }_{i}t+{\rho }_i{y}_{i,t-1}+\sum _{j=1}^p{\varphi }_{ij}\mathsf{\Delta }{y}_{i,t-j}+{\gamma }_i\left(y_{i,t-1}-{\delta }_i{x}_{i,t-1}\right)+{ϵ}_{it}$$

(4)

Figure 6 demonstrates the order of techniques which have been deployed in this research. However, the Westerlund test, which is carried out prior to conducting CS-ARDL analysis, is another method advised to determine whether panel data display cointegration. Cointegration indicates a long-term equilibrium relationship between the dependent variable and one or more independent variables [70]. It is based on the error correction model depicted in Equation (6), where $y_{it}$ and $x_{it}$ are the variables of interest, ${\alpha }_i$and ${\beta }_i$ are parameters, ${\rho }_i$is the coefficient of the lagged variable, ${\varphi }_{ij}$is the coefficient of the differenced terms, ${\gamma }_i$is the adjustment coefficient, ${\delta }_i$is the cointegration coefficient, and ${ϵ}_{it}$ is the error term. Similarly, the CS-ARDL model takes cross-sectional dependence into account when determining the long- and short-term relationships between variables. This method helps to understand how fluid linkages appear in panel data [63]. Equation (5) can be applied to CS-ARDL analysis. $y_{it}$ represents the dependent variable (ECV), $x_{i,t-j}$ are the independent variables (IDS, HDV, EGR, GNN, and RNC), ${\alpha }_i$is the constant term, ${\varphi }_{ij}$and ${\theta }_{ij}$ are coefficients, ${\gamma }_i$and ${\delta }_i$ are coefficients of the cross-sectional averages, and ${ϵ}_{it}$ is the error term.

$$y_{it}={\alpha }_i+\sum _{j=1}^p{\varphi }_{ij}{y}_{i,t-j}+\sum _{j=0}^q{\theta }_{ij}{x}_{i,t-j}+{\gamma }_i{\overline{y}}_t+{\delta }_i{\overline{x}}_t+{ϵ}_{it}$$

(5)

This study uses CS-ARDL to examine the long- and short-term dynamics in panel data while accounting for cross-sectional dependence and heterogeneity among G5 countries. The CS-ARDL model is particularly valuable for panel data analysis, as it effectively captures both immediate and long-term effects of explanatory variables on the dependent variable. Unlike conventional panel ARDL models, CS-ARDL integrates cross-sectional averages, ensuring robust estimates even in the presence of common shocks and interdependencies among cross-sectional units. This feature makes it highly applicable to studies involving macroeconomic, environmental, and financial variables, where cross-country interactions are inevitable. Additionally, CS-ARDL provides error correction mechanisms, allowing researchers to assess adjustment speeds toward equilibrium while accommodating heterogeneous slope coefficients. Its flexibility and robustness make it an essential tool for generating reliable empirical insights, aiding policymakers in devising sustainable and data-driven strategies. To enhance the validity of the results obtained from CS-ARDL, AMG, and CCEMG, the methodologies recommended in [69,71] are employed. CSD is considered in both the AMG and CCEMG approaches. Panel data often suffer from this problem when multiple cross-sections are affected simultaneously by similar causes or unobserved shocks. Ignoring this dependence could result in erroneous and skewed projections. Equation for the AMG model can be illustrated as shown in Equation (6), where ${ECV}_{it}$ is the ecological civilization for country $i$ at time $t$. ${\alpha }_i$is the country-specific intercept. ${\beta }_{1i},{\beta }_{2i},{\beta }_{3i},{\beta }_{4i},{\beta }_{5i }$are the coefficients for the independent factors. Additionally, unobserved common factors with heterogeneous factor loadings are represented by ${\lambda }_{i}t$ while ${ϵ}_{it}$ is the error term.

$$EC{V}_{it}={\alpha }_i+{\beta }_{1i}ID{S}_{it}+{\beta }_{2i}EG{R}_{it}+{\beta }_{3i}HD{V}_{it}+{\beta }_{4i}GN{N}_{it}+{\beta }_{5i}RN{C}_{it}+{\lambda }_{i}t+{ϵ}_{it}$$

(6)

AMG and CCEMG can take notice of differing slope coefficients amongst the G5 nations, acknowledging that variations in legal frameworks, economic systems, and other nation-specific elements may influence the way independent variables modify ECV in various economies. These approaches increase the accuracy and efficiency of inference by taking into consideration both common and diverse traits, which results in reliable policy suggestions.

4. Results and Discussion

Based on 115 observations, Table 1 presents descriptive statistics for six variables, namely HDV, IDS, EGR, GNN, RNC, and ECV. Measures of shape (skewness, kurtosis), dispersion (maximum, minimum, standard deviation), and central tendency (mean, median) are all included. The results of the Jarque–Bera test show that most variables have non-normal distributions, with notable skewness and kurtosis, especially for EGR. The dataset appears to have significant variability and outliers, as indicated by the high standard deviations and skewness scores. Furthermore, maximum and minimum values highlight the range of data, showing wide variability, especially in EGR. Standard deviation values underscore this variability, particularly for EGR and RNC, suggesting diverse levels of economic expansion and RNC across observations.

Table 3. Matrix correlation.

	ECV	IDS	EGR	HDV	GNN	RNC
ECV	1.0000
IDS	0.0466 ***	1.0000
EGR	0.2309 ***	0.4970 ***	1.0000
HDV	0.6111 **	−0.0130 **	0.1735 ***	1.0000
GNN	−0.2509 ***	0.6488 ***	0.4686 ***	−0.3157 ***	1.0000
RNC	−0.5862 ***	−0.4466 ***	−0.1523 ***	−0.4325 ***	−0.2072 ***	1.0000

*** and ** show the significance levels at 1% and 5%.

presents the correlation matrix between ECV, IDS, EGR, HDV, GNN, and RNC, highlighting key relationships. ECV has a strong positive correlation with HDV (0.6111 **) and a moderate positive correlation with EGR (0.2309 ***), suggesting that human development and economic growth contribute to environmental sustainability, aligning with the EKC hypothesis. Conversely, ECV negatively correlates with RNC (−0.5862 ***) and GNN (−0.2509 ***), indicating that greater use of renewable energy and green innovation reduces ecological footprints. IDS is significantly correlated with GNN (0.6488 ***), EGR (0.4970 ***), and ECV (0.0466 ***), showing its role in fostering economic growth and technological innovation. However, its weak link with ECV suggests that industrialization alone does not ensure sustainability without supportive policies. These findings highlight the need for balanced policies integrating industrial expansion, human capital development, and renewable energy adoption to achieve sustainable growth in G5 countries.

The findings of the CSD assessment for the variables are shown in

Table 4. Cross-sectional dependence analysis.

CSD	Test Stat/Prob
ECV	1.94 *** (0.000)
IDS	9.51 *** (0.000)
EGR	12.35 *** (0.000)
HDV	14.71 *** (0.000)
GNN	0.01 *** (0.000)
RNC	4.87 *** (0.000)

*** represents the level of significance at 1%.

. If cross-sectional dependence is evident, it can be detected by test statistics and related p-values. The cross-sectional dependence of ECV, IDS, EGR, HDV, and RNC is significant. This points out that the G5 countries have similar shocks or shared factors that contribute to such factors. With a test statistic of 0.01 and a p-value of 0.000, on the other hand, GNN demonstrates no cross-sectional dependence, confirming that green innovations are basically independent of one another across nations. Apart from this,

Table 5. Slope heterogeneity analysis.

Test	Test Stat	-Prob
Delta	8.214 ***	0.000
Adj.	9.848 ***	0.000

*** represents the level of significance at 1%.

given below presents the observations obtained from slope heterogeneity analysis. The slope heterogeneity analysis results utilizing the Delta and adjusted tests are reported. These tests determine whether there are differences in the correlations between the independent variables and the dependent variable (ECV) among the G5 nations. Test statistics for the Delta test and the adjusted test are 8.214 and 9.848, respectively, with a p-value of 0.000 and 0.000. The strong evidence of slope heterogeneity indicated by these significant p-values implies that there are notable differences in the effects of IDS, EGR, RNC, and GNN on ECV among the G5 countries.

The CIPS unit root test conclusions are summarized in

Table 6. CIPS unit root.

Variable	I(0)	I(1)
ECV	−2.915	−4.642 ***
IDS	−1.541	−4.385 ***
EGR	−2.033	−4.189 ***
HDV	−3.733	−6.037 ***
GNN	−3.389	−5.194 ***
RNC	−1.837	−3.385 ***

*** represents the level of significance at 1%.

for the variables at both levels (I(0)) and first differences (I(1)). Whether every component is steady at these levels is determined by the test statistics. The test statistic for ECV is −2.915 at I(0), exhibiting limited evidence of non-stationarity, and −4.642 at I(1), indicating significant stationarity. IDS becomes stationary at I(1) (−4.385), but it is not stationary at I(0) (−1.541). Comparably, EGR showcases significant stationarity at I(1) (−4.189) but non-stationarity at I(0) (−2.033). At both levels, HDV (−3.733 ***, −6.037 ***), GNN (−3.389, −5.194), and RNC (−1.837 at I(0) and −3.385 at I(1)) are all stationary. Values marked with ** or *** are considered statistically significant at their respective levels, indicating strong evidence of stationarity or non-stationarity in the context of the unit root test. Overall, the outcomes show that all the variables acquire stationarity at initial differences, making them eligible for additional study even though some of them are non-stationary at levels.

The results of the CADF test, which evaluates the stationarity of the variables at both levels and first differences, are displayed in

Table 7. CADF results.

Variables	T-Bar at Level	T-Bar at Difference
ECV	−3.318	−4.165 ***
IDS	−1.930	−3.450 ***
EGR	−2.079	−3.761 ***
HDV	−2.056	−2.277 ***
GNN	−3.082	−4.628 ***
RNC	−2.447	−3.283 ***

*** represents the level of significance at 1%.

. The t-bar values show the presence of unit roots, with significant results denoted by asterisks. For ECV, the t-bar at level is −3.318 and at difference is −4.165, demonstrating stationarity at first differences. IDS and EGR show non-stationarity at levels (−1.930 and −2.079, respectively) but acquire stationarity at first differences (−3.450 and −3.761). HDV and GNN reveal noteworthy outcomes at first differences; GNN is stationary at both levels and differences (−3.082 and −4.628). RNC displays minimal significance at levels (−2.447) but is stationary at first differences (−3.283).

Additionally,

Table 8. Westerlund cointegration analysis.

Statistic	Value	Z-Value	p-Value
Gt	−3.959 ***	−3.079	0.001
Ga	−11.953 ***	0.820	0.004
Pt	−10.902 ***	−5.177	0.000
Pa	−14.475 ***	−0.848	0.008

*** represents the level of significance at 1%.

outlines the Westerlund analysis, which assesses if there are any long-term correlations between the variables. The results from all four Westerlund cointegration tests (Gt, Ga, Pt, and Pa) strongly suggest the presence of cointegration among the variables in the study. This indicates a long-term equilibrium relationship among the examined variables.

The CS-ARDL results shown in

Table 9. CS-ARDL analysis (ECV: dependent variable).

Variables	Coefficients	Standard Errors
Short-term estimations
ΔIDS	−0.0685 **	0.0379
ΔEGR	0.3289 **	0.1617
ΔHDV	−0.9154 *	1.5403
ΔGNN	−0.0472 **	0.00591
ΔRNC ΔIDS X GNN	−0.0681 *** −0.0238 ***	0.0239 0.00497
Long-term estimations
IDS	−0.0672 ***	0.0375
EGR	0.3493 **	0.1759
HDV	−0.7982 **	1.3754
GNN	−0.0436 ***	0.00547
RNC IDS X GNN	−0.0699 *** 0.0179 ***	0.0258 0.00465
R-squared	0.515

***, **, and * represent the level of significance at 1%, 5%, and 10%, respectively.

, provide critical insights into the short-term and long-term determinants of ECV in G5 countries. In the short run, IDS negatively influences ECV, which is beneficial for environmental quality. This suggests that industrialization, when coupled with sustainability measures, leads to efficiency gains, cleaner production techniques, and pollution control mechanisms, reducing environmental degradation [3,72]. From an economic standpoint, this underscores the role of technological advancements and regulatory frameworks in transforming industrial activities into more sustainable practices. Additionally, the interaction term IDS × GNN further strengthens this beneficial impact, highlighting that investments in green innovation enhance the environmental efficiency of industrial processes [73]. This aligns with economic theories suggesting that technological progress can offset the negative externalities of industrialization through innovation-led resource efficiency improvements. Conversely, EGR exhibits a positive and significant impact on ECV, implying that unchecked economic expansion exacerbates ecological degradation due to increased energy consumption, emissions, and resource depletion [74]. This finding aligns with the Environmental Kuznets Curve (EKC) hypothesis, which suggests that economic growth initially worsens environmental quality before improvements occur at higher income levels. However, the results indicate that without explicit sustainability policies, economic expansion remains a driver of ecological degradation, reinforcing the need for green growth strategies [75].

Furthermore, HDV, GNN, and RNC all exhibit negative effects on ECV, indicating their positive role in environmental sustainability. The negative impact of HDV suggests that more human development, through increased awareness, education, and technological adaptation, fosters environmentally responsible behaviors [76,77]. This supports economic arguments that a more educated and skilled workforce accelerates the transition toward green technologies and sustainable consumption patterns. Similarly, the significant negative effects of GNN and RNC reinforce the role of green innovation and renewable energy in reducing ecological footprints, highlighting the economic feasibility of transitioning to low-carbon energy systems [78,79]. From a policy perspective, this suggests that financial and institutional support for renewable energy and green technologies can yield long-term environmental and economic benefits, aligning with theories of sustainable development.

In the long run, the findings remain consistent. IDS continues to exhibit a negative effect on ECV, reinforcing that industrialization, when guided by sustainability-oriented policies, contributes to environmental improvement. The interaction term IDS × GNN further confirms that industrialization becomes more environmentally sustainable when green innovations are effectively integrated [80]. This highlights the importance of strategic investments in green technology to enhance industrial efficiency while minimizing environmental harm [81]. The persistent negative relationship between GNN, RNC, and ECV confirms that technological advancements and renewable energy adoption are essential drivers of long-term environmental benefits. This supports economic arguments favoring clean energy transitions, as they improve resource efficiency and lower emissions over time [82]. Additionally, the negative effect of RNC on ECV underscores the role of renewable energy in mitigating environmental degradation by reducing dependence on fossil fuels [83]. However, the extent to which green technologies and renewable energy achieve their full potential depends on proper policy implementation and institutional support [84]. Economic factors such as relative energy pricing, subsidies, and financial incentives play a critical role in determining the effectiveness of these sustainable energy transitions.

Moreover, the inclusion of the interaction term IDS × GNN directly addresses concerns about model robustness and omitted variables. By capturing the conditional effect of industrialization on ECV, the model accounts for variations in energy trade balances and sectoral differences across countries, demonstrating that industrialization can support environmental quality when complemented by strong green innovation efforts. Additionally, the improved R-squared value (0.515) suggests a stronger explanatory power of the model, reinforcing the validity of the approach. From an economic policy perspective, these findings emphasize that a shift toward industrial sustainability requires integrating innovation, regulatory policies, and economic incentives to ensure long-term environmental benefits.

Comparing these findings with previous research, this study reaffirms the environmental benefits of renewable energy and technological advancements [40]. However, it challenges the traditional notion that industrialization is inherently harmful to ecological quality [3], demonstrating that when coupled with green innovation, industrial growth can contribute to environmental sustainability. Furthermore, while earlier studies suggest that economic growth may eventually decouple from environmental degradation [84], our results indicate that without targeted sustainability measures, economic expansion continues to exert pressure on ecological systems. This nuanced perspective contributes to the ongoing discourse on sustainable industrial policies by providing empirical evidence that industrialization’s environmental effects depend on its alignment with green innovation and policy support. From an economic standpoint, these findings highlight the need for market-driven mechanisms, fiscal incentives, and regulatory frameworks to ensure that industrialization, economic growth, and environmental sustainability evolve in tandem.

To validate the findings from the CS-ARDL analysis, an augmented mean group (AMG) robustness check was conducted, as summarized in

Table 10. AMG robustness analysis.

Variables	Coefficients
IDS	−0.01038 *
EGR	0.2193
HDV	−0.14902
GNN	−0.00266 **
RNC	−0.02609 **
_cons	−3.152 ***

***, **, and * represent the level of significance at 1%, 5%, and 10%, respectively.

. The results further reinforce the relationships between industrialization (IDS), economic growth (EGR), human development (HDV), green innovations (GNN), renewable energy consumption (RNC), and ecological civilization (ECV).

The analysis reveals that industrialization negatively impacts ECV (coefficient: −0.01038), significant at the 10% level, aligning with the earlier findings and highlighting the environmental challenges posed by industrial expansion. Similarly, green innovations (coefficient: −0.00266, p < 0.05) and renewable energy consumption (coefficient: −0.02609, p < 0.05) negatively affect ECV, indicating potential inefficiencies or unintended ecological trade-offs associated with these factors.

Conversely, economic growth demonstrates a positive relationship with ECV (coefficient: 0.2193), reinforcing its role as a critical driver of ecological advancements. However, human development shows a negative but statistically insignificant association with ECV (coefficient: −0.14902), suggesting that further exploration of its nuanced impacts is necessary.

The constant (_cons: −3.152, p < 0.01) highlights the significant baseline challenges in achieving ECV in the G5 countries. These AMG results complement the CS-ARDL findings and underscore the importance of designing holistic policy interventions to address the adverse impacts of industrialization, renewable energy practices, and green innovations while leveraging economic growth for sustainable ecological outcomes.

To further validate the robustness of the results, the common correlated effects mean group (CCEMG) estimator was employed, as detailed in

Table 11. CCEMG robustness analysis.

Variables	Coefficients
IDS	−0.02862
EGR	0.2371
HDV	−0.5571 ***
GNN	−0.00309 **
RNC	−0.39389 **
_cons	7.1648

*** and ** represent the level of significance at 1% and 5%, respectively.

. The findings align with the previous analyses and provide additional support for the relationships between industrialization (IDS), economic growth (EGR), human development (HDV), green innovations (GNN), renewable energy consumption (RNC), and ecological civilization (ECV).

The results indicate that industrialization negatively impacts ECV (coefficient: −0.02862), consistent with the AMG and CS-ARDL analyses, although the relationship is statistically insignificant. Economic growth exhibits a positive and significant association with ECV (coefficient: 0.2371), further emphasizing its constructive role in fostering ecological sustainability. However, human development has a pronounced negative effect on ECV (coefficient: −0.5571, p < 0.01), highlighting potential trade-offs that require careful policy consideration.

Both green innovations (coefficient: −0.00309, p < 0.05) and renewable energy consumption (coefficient: −0.39389, p < 0.05) demonstrate significant negative impacts on ECV, suggesting that these strategies may not yet be fully optimized or implemented effectively in the G5 context. The constant term (_cons: 7.1648) underscores the baseline challenges and potential for improvement in achieving sustainable ecological outcomes.

These CCEMG results corroborate the robustness of the findings from the CS-ARDL and AMG analyses. They emphasize the need for comprehensive and integrated policies to mitigate the adverse effects of industrialization, renewable energy consumption, and underperforming green innovations while leveraging economic growth and human development for sustainable ecological progress. The relationship of independent variables with the dependent variable is shown in Figure 7.

5. Conclusions and Policy Recommendations

5.1. Conclusions

This study investigates the relationship between IDS, EGR, HDV, GNN, and RNC on ECV in G5 countries from 2000 to 2022. Using robust econometric methods such as CS-ARDL, AMG, and CCEMG, the findings provide nuanced insights into the dynamics shaping ecological outcomes. The results reveal that EGR significantly contributes to ECV in both the short and long term, underscoring its detrimental impact on environmental quality. However, IDS and RNC exhibit consistent negative impacts on ECV, which is beneficial for environmental sustainability. This suggests that industrialization, coupled with sustainability measures, and renewable energy adoption play a vital role in reducing ecological degradation. Similarly, GNN and HDV also demonstrate negative effects on ECV, reinforcing the importance of green innovation and human development in enhancing environmental sustainability. The robustness checks using AMG and CCEMG further validate these findings, confirming the need for integrated policy interventions. The inclusion of the interaction term (IDS × GNN) highlights how industrialization, when complemented by green innovation, can align with ecological sustainability goals. This addresses concerns about optimizing industrial practices and renewable energy systems within the economic framework [85]. Furthermore, by employing CS-ARDL, the study effectively accounts for country heterogeneity and long-term dynamics, strengthening the validity of the results.

Optimizing renewable energy systems remains crucial for sustainability transitions. Current inefficiencies in renewable energy deployment may be attributed to outdated infrastructure, lack of energy storage solutions, and misaligned policy incentives. Similarly, GNN may not yet fully align with ecological objectives due to barriers in research funding, technology diffusion, and regulatory support [86]. Addressing these issues requires targeted financial support for clean energy, investment in energy infrastructure, and enhanced regulatory frameworks to facilitate green innovation. Moreover, the negative relationship between HDV and ECV highlights the importance of integrating human development with environmental consciousness. Strengthening education and training programs focused on sustainability, expanding green job opportunities, and fostering environmentally friendly urban planning can help mitigate potential trade-offs. Additionally, recognizing the role of economic structures, this study acknowledges that in market economies, renewable and non-renewable energy demands are influenced by relative prices and policy interventions, while planned economies have more direct mechanisms for optimizing sustainability transitions.

5.2. Policy Recommendations

To achieve ecological sustainability and align with sustainable development goals (SDGs), G5 policymakers must adopt a holistic strategy that integrates environmental, economic, and social priorities. Given the alarming rate of ecological degradation and the exceeding of multiple planetary boundaries, policy efforts should focus not only on mitigating environmental harm but also on actively reversing damage [87,88,89,90]. A comprehensive approach, including regulatory reforms, technological advancements, and international collaboration, will be crucial in steering G5 nations toward a greener future [91,92]. The following policy recommendations provide a structured pathway to enhance ecological sustainability while ensuring long-term economic resilience and social well-being:

• Expand financial incentives for environmental technology R&D, ensuring targeted funding for clean energy, carbon capture, and industrial efficiency innovations. This contributes to SDG 9 (Industry, Innovation, and Infrastructure) by promoting sustainable industries and innovation.
• Increase subsidies and financial support for renewable energy projects, thereby enhancing their scalability and affordability while reducing reliance on fossil fuels. This aligns with SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), fostering the transition to renewable energy sources.
• Enhance the efficiency of renewable energy consumption (RNC) by addressing grid instability, intermittency issues, and energy storage limitations to maximize its contribution to reducing ecological footprints. This directly supports SDG 13 (Climate Action), promoting energy efficiency and reducing greenhouse gas emissions.
• Promote smart grids and advanced energy storage solutions to ensure a seamless integration of renewable energy into national power systems. This action contributes to SDG 9 (Industry, Innovation, and Infrastructure) by enhancing infrastructure and fostering innovation in energy systems, as well as SDG 13 (Climate Action).
• Invest in sustainability education and public awareness campaigns to encourage eco-conscious behavior and responsible consumption patterns, which directly contribute to SDG 12 (Responsible Consumption and Production) and SDG 4 (Quality Education).
• Strengthen workforce training programs to equip industries with green skills, facilitating a smooth transition toward a low-carbon economy. This supports SDG 8 (Decent Work and Economic Growth) by promoting sustainable economic growth and creating green jobs.
• Enforce stricter environmental regulations on industrialization (IDS) to minimize its ecological burden, particularly in energy-intensive sectors. This supports SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action) by reducing pollution and promoting more sustainable production practices.
• Encourage sustainable sourcing and circular economy models to reduce industrial waste and promote resource efficiency. This contributes to SDG 12 (Responsible Consumption and Production) and SDG 15 (Life on Land), supporting sustainable resource management and biodiversity conservation.
• Implement robust legal frameworks with clear penalties for environmental violations, ensuring greater accountability and compliance with sustainability goals. This supports SDG 16 (Peace, Justice, and Strong Institutions) by promoting effective legal frameworks and governance systems.
• Establish real-time monitoring systems to track industrial emissions, carbon intensity, and renewable energy adoption, ensuring transparency in meeting environmental commitments. This aligns with SDG 13 (Climate Action) by providing the necessary tools to track and reduce emissions effectively.
• Facilitate cross-border technology-sharing agreements among G5 nations to promote equitable access to advanced clean energy solutions, supporting SDG 17 (Partnerships for the Goals) by fostering global cooperation on climate action and technology transfer.
• Develop joint sustainability projects that align with international environmental commitments, fostering coordinated efforts in climate action. This directly contributes to SDG 13 (Climate Action) and SDG 17 (Partnerships for the Goals).

By integrating these measures, G5 nations can effectively balance economic growth, environmental preservation, and social stability, ensuring a structured and sustainable transition toward a resilient, low-carbon future.

This study offers valuable insights into the determinants of ecological sustainability in G5 countries; however, some limitations should be acknowledged. While the model includes key environmental and economic variables, it does not explicitly account for institutional quality factors like legal certainty, governance effectiveness, and the rule of law, which significantly shape environmental policies and market efficiency. Future research could integrate these aspects to better understand their role in sustainability transitions. Additionally, despite employing robust econometric techniques, data constraints and measurement issues in environmental indicators may affect estimation precision. Exploring alternative sustainability metrics or high-frequency data could refine the findings. Moreover, the study does not consider each country’s technological level or its proximity to the technological frontier, both of which influence dependence on polluting energy sources. Countries with limited access to advanced technologies may face structural barriers to cleaner energy transitions, impacting ecological sustainability. Future studies should incorporate these factors to assess their role in shaping environmental policies. Furthermore, while the aggregate HDV captures the combined effects of education, health, and income on ecological sustainability, it may obscure the individual influence of these dimensions. Recognizing this, we include it as a limitation and suggest that future research disaggregates the index to explore the distinct impacts of life expectancy, education, and income. Similarly, although GDP growth is a widely used economic measure, it does not account for population differences across countries. Expressing economic growth in per capita terms or through labor and total factor productivity could provide deeper insights into how economic expansion affects ecological sustainability. Lastly, sectoral-level analyses could reveal how industry-specific policies and technological innovations shape ecological outcomes, enabling more targeted policy recommendations for sustainable development.