Triple Impact of Green Technology, Globalization, and Democracy on Ecological Footprint: A Method of Moment Quantile Regression Analysis in G7 Economies

Abstract

This study investigates the impact of energy consumption, economic growth, globalization, green technology, and democracy on ecological footprint in G7 countries from 1995 to 2020. Utilizing Fully Modified OLS (FMOLS), Dynamic OLS (DOLS), and Method of Moments Quantile Regression (MMQR), we estimated long-term relationships among variables. The Dumitrescu-Hurlin panel causality test was employed to assess causal directions, accounting for heterogeneity across G7 countries. The findings reveal that economic growth, energy consumption, and democracy increase the ecological footprint, degrading environmental quality, while globalization and green technology reduce it, enhancing sustainability. A unidirectional causal relationship exists between these factors and the ecological footprint. This study underscores the role of green technology and democratic governance in reducing ecological footprints and, offers G7-specific policy implications, including promoting green innovation and strengthening environmental regulations within democratic frameworks, to achieve sustainable outcomes.

1. Introduction

The consensus is growing stronger that anthropogenic environmental degradation will create increasingly greater problems for humanity in the future. According to the IPCC (2018) report, the world has experienced a temperature rise of 1 °C compared to before the Industrial Revolution. It is vital to stop and reverse environmental degradation for future generations. The target to limit global warming will be possible by reducing global emissions by 45% in 2030 compared to 2010 and reaching net-zero emissions, which means carbon neutrality in 2050 [1,2]. When efforts in this regard are considered holistically, the concept of a transition to sustainability emerges. Most of these efforts come from the G7 countries. G7 countries account for a significant share of global CO₂ emissions, driven by their robust linear economies, which rely heavily on resource-intensive production and consumption patterns. Given their significant environmental impact, G7 countries play a pivotal role in leading global efforts toward sustainable development. This leadership is evident in their organization of key international initiatives and conferences. To understand their role in global sustainability, it is essential to examine the specific contributions and responsibilities of G7 countries in driving the green transition.

For example, important conferences and meetings are mostly being organized by the leadership of G7 countries [1], which means these countries play a principal role in the green transition. The G7 countries are also among the world leaders in determining and implementing policies and making binding agreements for the future that will halt and reverse environmental degradation by transitioning to green energy and achieving sustainable development [3,4,5,6,7,8]. However, population growth and industrial activities initiate an increase in goods and energy demand. Considering the finite nature of fossil fuels, it becomes necessary to discover new sources of sustainable and unlimited energy supply for the global community [9,10]. Recently, green technologies have attracted the attention of researchers. In the broadest sense, green innovation technologies include producing new technologies and products that will reduce ecological pressure [11]. The difficulties in monitoring dispersed emission sources make the development of alternative technologies even more important [12]. The idea of green innovation or green growth is a comprehensive concept that promotes sustainable development and could become a key factor in shaping future production structures. However, the adoption of green growth models raises questions about their feasibility across different economic contexts. This is particularly relevant when considering the challenges faced by developing countries in this transition. These challenges highlight the need to explore how green growth strategies can be adapted across diverse economic and social contexts.

However, the transition to this green growth model may result in the emergence of different pathways for developed and developing countries. Green growth may present challenges, particularly for developing countries. For example, the focus on green energy sources in these countries may result in premature deindustrialization. The outcome could make these countries even poorer [9,13,14]. This effect may be due to the architecture of socio-political and international infrastructure. A holistic consideration of these factors in support of environmental well-being leads to the appearance of the concept of a transition to sustainable development. To achieve sustainable economic development and reduce CO₂ emissions, many countries have adopted many criteria, such as green energy conversion, resource efficiency, energy technologies, and emission control [15]. However, the transition to sustainability is not only a complex and long-term process but also involves multiple actors with conflicting interests. Therefore, its relationship with multiple phenomena such as democracy and globalization is important. Individuals’ motivation for the transition is limited, as the aim is to benefit the collective, resulting in the impasse of free riders and the prisoner’s dilemma. The free-rider problem occurs when individuals benefit from public goods without contributing, while the prisoner’s dilemma results in suboptimal outcomes when individual interests are prioritized over cooperation [16,17]. Given that the environment is a shared resource, individuals may endeavor to minimize consumption—or possibly incur no cost at all. This mentality would result in their unwillingness to contribute to sustainability efforts willingly. Public authorities and civil society are important to ensure the provision of public goods and address negative externalities [18]. These are some of the challenges associated with the transition to sustainability. Also, the shift to sustainability is unique because it is intentional and goal-oriented, unlike other historical transitions. In this sense, it can be considered artificial. One way to overcome these challenges and design the transition artificially involves the use of public authority. According to Söderholm (2020) [12], the public sector should design policies that will increase green innovations. Those policies may be domestic or internationally related. We take democracy and globalization as their representatives. The roles of democracy and globalization in shaping sustainability transitions are complex and multifaceted. Their influence on environmental outcomes, particularly in high-emission G7 countries, warrants further exploration. To address this, it is crucial to examine how democracy and globalization interact with environmental outcomes in the context of advanced economies.

Since democratic systems are defined with more public contribution and increased accountability [19], they can cause pressure for designing environmentally-friendly regulations [20]. However, if such regulations are lax, they may support polluting technology transfer, which expands polluting industries. So, the host countries may turn into net carbon importers [20,21,22,23]. Consequently, based on modernization theory, it can be concluded that democracy is expected to lead to ecological degradation [24,25,26]. However, there are contradictory results in the literature. Ahmed et al. (2021) [20] found that in G7 countries, democracy decreases EF, but Akalin and Erdoğan (2021) [25] observed the reverse effect for OECD countries. Like democracy, globalization also has two side effects: one side is about the diffusion of environmentally-friendly technologies and production techniques, which is helpful for sustainability transition [27,28], and the other is about FDI-trade and eco-political dimensions, which are expected to harm the transition [29]. Green technology, the other factor under discussion, is believed to decrease EF [30]. Despite advancements in green technology and democratic governance, the ecological footprint of G7 countries remains high, and the interplay between institutional factors, globalization, and technological innovation in driving sustainability transitions is underexplored. This study addresses this gap by empirically examining these relationships and proposing policy solutions to enhance sustainability. This empirical investigation builds on the complex interplay of these factors to provide actionable insights for G7 policymakers.

In this study, we investigate the role that green technology, globalization, and democracy—three elements that have garnered increasing attention in recent years but continue to be underexplored in the literature on sustainability transition—play in determining the ecological footprint of the G7 countries from 1995 to 2020. This study conducts an original empirical analysis of the impact of green technology, democracy, and globalization on the ecological footprint in G7 countries. The ecological footprint, which is measured in global hectares, is a measurement that accurately represents the environmental effect of human activity across all aspects of production, consumption, and waste absorption. This includes the dimensions of soil, water, and air. The gross domestic product (GDP) per capita and energy consumption are included as control variables to represent the dynamics of the economy and energy. The nations that make up the G7 were chosen because of their worldwide importance in the areas of economics, innovation, and policymaking, as well as their advanced stage in the management of emissions and their leadership in sustainability. The purpose of this research is to contribute to the existing body of knowledge by conducting an empirical investigation into whether or not the structural and institutional variables in advanced economies either help to alleviate environmental constraints or make them worse.

2. Theoretical and Conceptual Framework

Recently, the issue of transition to sustainability (TS) has become a popular topic in environmental literature. The topic is an area where opinions regarding a deeper understanding of transition policy and reaching more appropriate policies emerge. Policymakers and social scientists are increasingly focusing on methods for transitioning societies from the present state to a more sustainable model of consumption and production. This understanding necessitates fundamental alterations in basic systems, including significant changes in prevailing practices, institutions, technology, policies, lifestyles, and mindsets. Additionally, it fosters profound structural changes aimed at establishing innovative social dynamics and a more suitable and inclusive approach to economic growth and production systems [31]. Sustainable transition theory serves as the theoretical foundation for this transformation. The foundational works underpinning this theory are primarily [11,18,32,33,34].

Sustainability transitions, according to Geels (2011) [18] and Smith et al. (2005) [34], happen when long-term big-picture pressures cause cracks in the current system (the main technologies, infrastructures, and players), allowing new and different ideas to come from smaller, innovative projects. While technology contributes to growth and increases energy consumption, it also has the potential to increase energy efficiency and facilitate the discovery of new, environmentally friendly methods. Technological innovations are divided into two categories: those that occur gradually and those that undergo sudden, radical transformations. Incremental innovations, i.e., increasing the energy and material efficiency of the existing production process, are key elements in the transition to a green economy. Fundamental transformations are essential for achieving deeper and even radical technical innovations. Therefore, people have gradually begun to adopt the view that sustainable technological developments can lead to Schumpeterian creative destruction [11].

On the other hand, Shove and Walker (2007) [33] argue that transition processes are composite, non-deterministic, and embedded in intricate social and political structures, rendering them challenging to control. Consequently, sustainability transitions inherently involve some uncertainty. This uncertainty pertains to channeling technological change toward appropriate objectives. Rogge and Reichardt (2016) [32] say that a key need for sustainability transitions is to guide and speed up technological change—usually involving invention, innovation, and diffusion (del Río González, 2009) [35]—toward sustainability goals. However, technological change faces numerous market, systemic, and institutional failures. Multifaceted policy interventions are therefore crucial [36].

Dincer and Rosen (2005) [37] establish sustainable development on four interconnected pillars: economic sustainability, environmental sustainability, social sustainability, and energy resource sustainability. It includes directing renewable energy resources to R&D expenditures and using renewable energy resources such as wind, solar, biomass, and hydroelectric energy [15]. Renewable energies are energies that reduce carbon emissions, such as photovoltaic, wind, hydroelectricity, biomass, and thermal, obtained from constantly renewed sources in nature and produced by solar or biological capacity [38]. Figure 1 illustrates the four pillars of sustainable development. In this study, we discuss the factors affecting the ecological footprint, which is a significant indicator of sustainable development.

This study is based on Sustainability Transition Theory (STT), which offers a comprehensive framework for comprehending how societies might transition from ecologically unsustainable systems to more sustainable pathways. STT underscores the necessity of systemic change, encompassing technical innovation, institutional reorganization, and extensive socio–economic reforms. Consistent with this framework, this study conceptualizes green technology as an innovation catalyst that fosters ecological efficiency, democracy as a promoter of inclusive governance and institutional reform, and globalization as a structural element that can either facilitate or obstruct sustainable practices based on its characteristics and trajectory. This research formulates three hypotheses: (1) the adoption of green technology diminishes ecological footprint, (2) democratic governance fosters sustainability by facilitating responsive environmental policies, and (3) globalization affects ecological footprint depending on its capacity to advance or hinder environmental objectives. By matching these variables with STT, this study establishes a coherent theoretical framework that directly connects the conceptual model to the empirical investigation.

3. Literature Review

Production processes based on fossil fuels have made major contributions to the increase in global energy consumption and simultaneously increased CO₂ emissions. These trends provide an immediate threat of global warming caused by industry, urbanization, population increase, and changes in lifestyle patterns. Rising carbon emissions cause climate change, harsh weather, resource depletion, scarcity, social unrest, and economic harm. Environmental devastation also endangers human health [39]. Consequently, there is a growing interest in green innovative technologies and the shift to green energy. This transition is important to curb carbon emissions [40]. Such developments aimed at reducing the ecological footprint are categorized as a transition to sustainability. Sustainable development and its mechanisms, which have been an important research area recently, will continue to maintain their importance in the future, as stated by [38].

In the 1990s, many studies and fields regarding the transition to sustainability began to emerge [31]. Numerous studies have revealed that parameters such as technology, innovation, green energy, and renewable energy closely relate to sustainable development and controlling environmental degradation. However, as stated by Abbasi et al. (2024) [38], despite the increasing volume of the literature, scientific research on some parameters and areas is still in its early stages. These parameters, energy transition, eco-innovation, and economic complexity, are integral to the transition process and serve as its foundation. Among them, economic complexity covers the relationships between social and economic units and the role of public authority in them. A frequent claim over the last decade has been that while traditional economic models deal with climate change, biodiversity decline, water scarcity, etc., they must also address significant social and economic problems. The global economic crisis of 2008–2009 sparked this debate, which subsequently evolved into the “green economy” vision [11]. The transition to sustainability requires not only the transition of energy consumption from fossil fuels to renewable energy sources but also a complete socio-economic transformation. Therefore, to achieve sustainable technological development, economic and social regulations, in addition to technological advancements, are necessary. It takes time to adapt all infrastructures, production techniques, and systems to new technology. Therefore, new technologies may take time to become efficient because of eco-social complexity [11].

When we take the complexity in question to the global level, we encounter the parameters of democracy and globalization, which are relatively less frequently addressed regarding environmental problems. In addition, there is a paucity of literature regarding the impact of energy investments and green technology on mitigating environmental degradation [15]. We discuss the effects of these two complex factors and green innovation technology on the ecological footprint, along with the control variables mentioned above.

Table 1. Summary of literature.

Title	Time Coverage	Cross-Section Coverage	Method	Finding
Abbasi et al. (2024) [38]	1980–2020	United States	ARDL Frequency Domain Causality	ET→ (+) EF Eco-complexity→ (+) EF
Adams and Acheampong (2019) [41]	1980–2015	Sub-Saharan Countries	GMM	DEMO→ (−) CO2 GDP→ (−) CO2
Agan (2024) [40]	2000–2021	25 EU Countries	Panel Quantile	GE↔ (−) CO2
Agbede et al. (2023) [42]	1971–2016	MINT	Quantile-GLS	GDP→ (+) CO2 Trade→ (+) CO2 DEMO→ (+) CO2
Ahmad et al. (2024) [43]	1990–2020	European Union	FMOLS	Fintech→ (+) GE Growth→ (−) GE Glob→ (+) GE
Ahmed (2024) [44]	1990–2019	G-7	Method of Moments Quantile Regression	Political Glob→ (+) SD Social Glob→ (−) SD DEMO, GDP→ (−) SD
Ahmed et al. (2022) [20]	1985–2017	G-7	DSUR, FMOLS, DOLS	GDP→ (+) EF DEMO→ (−) EF
Ahmed et al. (2024) [45]	1984–2017	Pakistan	ARDL	DEMO→ (−) EF
Akalın and Erdoğan (2021) [24]	1990–2015	26 OECD Countries	AMG	DEMO→ (−) EQ GDP→ (−) EQ
Chou et al. (2019) [46]	1990–2013	26 American Countries	Quantile Regression	DEMO→ (+) EE DEMO→ (−) CO2
Farooq et al. (2022) [27]	1980–2016	180 Countries	Quantile Regression	Political Glob→ (−) ED Economic Glob→ (+) ED
Geels (2011) [18]	-	-	Literature Review	MLP → (+) SD
Han et al. (2024) [47]	1995–2019	China	ARDL-FMOLS-DOLS	GT, RE→ (+) SD
Haseeb and Azam (2021) [48]	1995–2015	High Upper-Middle, Low, and Lower-Middle Income Countries	FMOLS	DEMO→ (−) CO2
Hossein et al. (2024) [9]	2003–2020	Developed-Developing 15 Countries	Quantile Regression Dynamic Panel	Digital Finance→ (+) GT Econ. Growth→ (−) GT
Islam et al. (2021) [49]	1972–2016	Bangladesh	ARDL	Glob→ (−) CO2 EU, GDP→ (+) CO2
Jahanger et al. (2020) [50]	1990–2016	74 Developing Countries	GMM-SYS	Glob→ (−) CO2 EU, GDP→ (+) CO2
Liu et al. (2024) [51]	1990–2020	China	Theoretical Model	GT→ (−) CO2
Lv (2017) [52]	1997–2010	19 Emerging Countries	Quantile Regression	DEMO→ (−) CO2
Ozkan et al. (2024) [15]	1974–2019	Germany	Wavelet Quantile on Quantile Regression	RE, R&D, GT→ (−) CI
Rogge and Reichardt (2016) [32]			Literature Review	Policy mix→ (+) TS
Rudolph and Figge (2017) [53]	1981–2009	146 Countries	Extreme Bounds Analysis	Glob→ (+) CO2
Sibt-E-Ali et al. (2024) [10]	1990–2021	11 Emerging Countries	FMOLS-DOLS	RE→ (+) SD
Song et al. (2024) [54]	1996–2020	10 Most Developed Countries	Quantile on Quantile	GIT→ (+) EE
Soto et al. (2024) [55]	1990–2022	Latin America	FMOLS	FDI→ (+) RE Income→ (+) TS
Söderholm (2020) [12]	-	-	Literature Review	Environmental challenges → (+) Creative destruction
Sultana et al. (2023) [56]	1990–2019	South Asian Countries	Panel Quantile	GDP→ (+) CO2 Trade→ (+) CO2 DEMO→ (−) CO2
Usman et al. (2020) [57]	1985–2014	USA	ARDL	Glob→ (+) EF EU, GDP→ (−) EF
Uzar (2024) [11]	1990–2018	Emerging Nations	AMG	Glob→ (−) EF Democracy→ (−) EF
Wang and Wang (2020) [58]	1970–2014	137 Countries	FE, Pooled OLS	Glob→ (−) EF for DC Glob→ (+) EF for GC
Zhang et al. (2023) [59]	1990–2020	South Asia countries	CEE, AMG, GMM	RE→ (−) EF DEMO→ (−) EF Glob→ (+) EF

Note: ET → (+) EF means that energy transition positively affects the ecological footprint. The arrow indicates the direction of the effect, while “+” or “−” represents the sign of the relationship. A double-headed arrow indicates a bidirectional effect. Abbreviations: ET: energy transition; RE: renewable energy; EF: ecological footprint; GT: green technology; SD: sustainable development; FDI: foreign direct investment; TS: transition to sustainability; GE: green energy; DEMO: democracy; Glob: globalization; ED: environmental degradation; GDP: gross domestic product; EQ: environmental quality; FinTech: financial technology; Social Glob: social globalization; Political Glob: political globalization; Economic Glob: economic globalization; MLP: multi-level perspective; EU: European Union; R&D: research and development; CI: clean innovation; Policy Mix: policy mixture; GIT: green innovation technologies.

shows a summary of some important studies in the literature on sustainability. It is designed to maintain a global scope by including studies from diverse regions, such as South Asia, Sub-Saharan Africa, and G7 countries, to comprehensively examine the relationships between democracy, globalization, economic growth, and environmental outcomes. Restricting the table to only G7 studies would limit the analysis to high-income economies, overlooking critical variations in these dynamics across developing nations, which are essential for understanding global environmental challenges. Table 1 focusing solely on G7 countries is added to address the reviewer’s request for regional delimitation while retaining the original table to preserve the study’s broader relevance and generalizability.

3.1. Ecological Footprint, Globalization, and Democracy

While research on environmental degradation mainly focuses on the connection between the environment and economic growth, the impact of technological and eco-political factors has not been examined to a similar extent [25]. These factors have attracted the attention of researchers in the last few years. These studies have emphasized the potential of eco-political factors and green technologies as a catalyst for sustainability. However, there have been limited empirical studies on the environmental consequences of these two factors [11]. Mitigating ecological degradation requires reducing energy consumption. To increase energy efficiency, it requires governments to lead the enactment of new laws to achieve this goal [43]. Globalization and democracy have two different effects on the environment, as explained above. Developed and developing countries yield diverse results. For example, Agbede et al. (2023) [42] found that democracy and trade in MINT (Mexico, Indonesia, Nigeria, and Turkey) increased CO₂ emissions, and Ahmad et al. (2024) [43] showed that globalization positively affects the green technology transition in EU countries. The G7 sample considered in our study has a higher level of democracy. Therefore, the educated people of these democratic nations often use their right to protest and express their demands for a clean environment [20].

Akalin and Erdogan (2021) [24] state that there is a harmonious relationship between economic growth and democracy, such that democracy increases economic growth and, therefore, resource use. So, we can conclude that democracy will not contribute to reducing environmental pollution and emissions. Akalin and Erdogan (2021) [24], Agbede et al. (2023) [42], and Ahmed (2024) [44] underline the negative role of democracy in the transition to a green economy. In contrast, numerous studies in the literature suggest that democracy enhances environmental quality. Studies on both developed and developing countries observe results that substantiate this conclusion; for example [11,20,41,46,48,52,56,59]. More detailed information can be found in Table 1.

3.2. Ecological Footprint, Green Technology, Energy Consumption, and GDP

The effort to draw attention to green sustainability goes parallel with the fight against global climate change and promotes sustainable development [40]. The use of green innovation technologies is important in sustainability [12]. Environmentally friendly technologies and green energy lead to the abandonment and modernization of methods and outdated practices that lead to environmental degradation and the depletion of natural resources [40]. Green technology can occur in different forms; for example, renewable energy technologies, energy-efficient technologies, and clean production technologies [54]. Research has shown that environmental innovations increase sustainability [10,15,60,61,62,63,64,65,66,67]. Using ARDL, DOLS, and FMOLS methods, Han et al. (2024) [47] showed that green technologies and RE positively affect sustainability, while increasing welfare raises pollution. Ozkan et al. (2024) [15], in their research on Germany, where they focus on the importance of resource efficiency, state that green technologies are an important factor for sustainability, providing ecological benefits by promoting the balance between environmental and economic welfare, leading to energy efficiency and reducing costs. Amin et al. (2023) [68] and Meng et al. (2022) [69] found that green technological innovations and eco-developments increase CO₂ emissions. According to Xu and Lin (2019) [70], Liu et al. (2024) [51] contended that green technological advancement aids in the diminution of carbon emissions through the following mechanisms: (1) promoting green transformation within the industrial sector and augmenting green technological research and development; (2) analyzing the impacts of energy production frameworks, industrial configurations, policies, and other mechanisms that alleviate carbon emissions; and (3) recognizing the geographical spillover effect as a significant issue. Nevertheless, the findings of the current studies indicate a lack of unanimity about the process by which green technology advancements influence carbon emissions [70]. Certain studies contend that advancements in green technology may not substantially affect carbon emissions and may exacerbate them [58,71,72,73]. Moreover, Ibrahim et al. (2022) [74] investigated how green technologies smoothed the change in CO₂ emissions by applying the ARDL model to data for the period 1996–2018. The study’s results indicate that technological innovations and renewable energy strongly reduce CO₂ emissions. Razzaq et al. (2023) [75] found a similar effect at low quantiles in their study using the panel quantile model. A similar result emerged in the study of [40]. The findings of this research show that green technological developments increase CO₂ emissions in low quantiles and decrease them in high quantiles. In the first phase of the economy, green transformation remains stationary; however, as economic growth attains a specific threshold, the degree of green technology becomes endogenous to economic growth, therefore enhancing green transformation and decreasing carbon emissions [51]. Therefore, governments should encourage investment in green technology [54]. However, Udeagha and Ngepah (2023) [76] advocate for the implementation of clean technologies to improve environmental quality without compromising economic growth.

Green energy is becoming more important in growth debates among the many energy categories. Energy, especially for sustainable development, became important in the 2000s [40]. Research indicates that green energy and technological advancements significantly reduce CO₂ emissions [71,77,78]. Thus, using green energy can prevent ecological deterioration and fossil fuel emissions [43]. Solar, wind, and hydro energy can reduce carbon emissions, making them appealing [40,73]. R&D for ecologically acceptable RE resources reduces carbon emissions according to Jiang et al. (2023) [79]. Jiangou et al. (2022) [80] proved renewable energy minimizes environmental impact. Research indicates a bidirectional or negative link between green energy and CO₂ emissions. Green energy has a bigger environmental impact at higher quantiles according to [40]. Abbasi et al. (2024) [38] found that renewable energy reduces CO₂ emissions. One factor is that low-income nations may struggle to create infrastructure for renewable energy [55]. We analyze the energy utilization variable. Energy usage degrades ecology, according to [50]. Islam et al. (2021) [49] and Jahanger et al. (2020) [50] found that energy usage, our control variable, harms the environment.

Renewable energy investments decarbonize the energy industry, save resources, reduce pollution, and boost economic growth [15,77]. Growth is linked to energy usage. Some studies found a positive relationship between economic activity and the transition [42,58] while others found a negative one. Adams and Acheampong (2019) [41] found that GDP decreases CO₂ emissions in sub-Saharan nations. In a mixed panel of rich and developing nations, Hossain et al. (2024) [9] revealed that GDP per capita negatively influences green innovations in all quantiles. This study found that R&D and GDP positively impact green inventions. The study also indicated that economic expansion damages green advances. This happens because mainstream economic development models ignore resource reuse and recycling. High-income regions with steady-state economic growth can adapt to green growth better [81]. Our analysis focused on G7 countries for this reason.

Since moving towards sustainability takes a long time, as pointed out by Smith et al. (2005) [34], we think that democracy might increase environmental footprints because people acting on their own may not be enough for the transition, and support from public authorities might be necessary. Secondly, globalization may fix the environment by helping the diffusion of new technological innovations. Our third hypothesis is that green technology helps transition by reducing EF. We formulated the transition to sustainability, as shown in Equation (17).

4. Methodology

FMOLS and DOLS were selected for their ability to address endogeneity and serial correlation in cointegrated panel data, ensuring robust long-run estimates. MMQR complements these by capturing heterogeneous effects across EF quantiles, which is critical for G7 countries with varying environmental profiles. These methods are appropriate for our 1995–2020 panel dataset due to their ability to handle non-stationarity and cross-sectional dependence. The dataset displays non-stationary variables, including energy consumption and GDP per capita, which are cointegrated, as verified by panel unit root and cointegration tests. Moreover, the G7 nations exhibit considerable cross-sectional dependence attributable to common economic and environmental policies, rendering FMOLS and DOLS appropriate for rectifying biases in long-term estimations. MMQR is especially pertinent due to the variability in ecological footprints among G7 countries, influenced by disparities in economic structures, energy intensities, and regulatory frameworks.

First, FMOLS and DOLS are utilized to estimate the long-run equilibrium relationships among the variables, correcting for endogeneity and serial correlation typically observed in cointegrated panels. These methods are particularly appropriate for capturing average effects in the presence of non-stationary panel data. Our dataset, covering 1995–2020 and encompassing variables such as energy consumption, globalization, and democracy, employs FMOLS and DOLS to effectively mitigate potential endogeneity stemming from reverse causality (e.g., between ecological footprint and economic growth) and serial correlation due to enduring trends in environmental and economic data. However, relying solely on mean-based estimators may mask heterogeneous effects across the conditional distribution of the ecological footprint. To overcome this limitation, we employ the MMQR developed by Machado and Silva (2019) [82], which allows for a deeper understanding of how the explanatory variables affect the ecological footprint at different quantiles. MMQR is particularly advantageous because it accounts for both heteroskedasticity and nonlinearity, while also addressing potential endogeneity, thus yielding more robust inferences. Within our G7 dataset, MMQR delineates the varied environmental impacts among countries with differing ecological footprints, exemplified by Japan’s lower footprint relative to the United States’ higher footprint, facilitating a nuanced examination of the effects of green technology, globalization, and democracy on sustainability outcomes. By integrating these complementary methods, this study ensures that both the central tendency and distributional heterogeneity of the environmental impacts are captured, enhancing the reliability, robustness, and policy relevance of the empirical findings.

4.1. Slope Homogeneity and Cross-Section Dependence Tests

In panel data, cross-sectional dependency poses a substantial issue; if insufficiently addressed, it may result in skewed estimates and incorrect inferences. A multitude of statistical tests have been devised to identify and mitigate cross-sectional dependence, each with distinct traits and regions of applicability suitable for panel data formats. The Breusch-Pagan LM test, developed by Breusch and Pagan (1980), is the principal technique for identifying cross-sectional dependency in panels when N is less than T. The technique assesses the pairwise total correlation of the squared residuals across several cross-sections. The null hypothesis of the Breusch-Pagan LM test states that there is no cross-sectional dependency, meaning that the residuals from different units do not influence each other. The test findings indicate substantial evidence of cross-sectional dependency. The formula for this assessment is as follows:

$$LM=\sum _{i=1}^{N-1}\sum _{j=i+1}^N{\widehat{\rho }}_{ij}^2$$

(1)

Based on Breusch and Pagan’s research, Pesaran (2004) [83] introduced the Pesaran Scaled LM test to address scenarios where N significantly exceeds T. This alternative modifies the standard LM statistic by applying a scaling factor, making it suitable for large panels. Under the null hypothesis, there is no cross-sectional dependence among units. The scaled LM test statistic is expressed as follows:

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

(2)

This extension increases the power of the test in data sets with a large number of cross-sectional units.

Pesaran et al. (2008) [84] developed the Bias-Corrected Scaled LM test, which improves the accuracy of detecting cross-sectional dependence, especially in finite samples. This test improves the accuracy for smaller panels by adding a bias correction term. Therefore, the equation is derived as follows:

$${LM}_{bc}={\displaystyle \frac{1}{N\left(N-1\right)}}\sum _{i=1}^{N-1}\sum _{j=i+1}^{N}T{\widehat{\rho }}_{ij}^2-1-{\displaystyle \frac{{\widehat{\mu }}_T}{N}}$$

(3)

Pesaran’s CD test is another frequently used method that evaluates the average correlation between all pairs of error terms found from cross-sectional units. This test is extremely effective in large panels to detect cross-sectional dependence and provides a simple yet effective diagnostic tool for calculating. The procedure is conducted under the hypothesis that there is no cross-sectional dependence between units. The method for calculating its statistics is as follows:

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

(4)

One of the important statistics of cross-sectional dependence (CD) is that it shows potential dependencies among cross-sections in panel data. The test of slope homogeneity in the panel data model introduced by Pesaran and Yamagata (2008) [85] evaluates the consistency of slope coefficients among cross-sectional units. The test evaluates the null hypothesis of slope homogeneity against the alternative hypothesis of heterogeneity using the Delta statistic defined as follows:

$$∆=\sqrt{N}\left({\displaystyle \frac{\widehat{S}-\left(K+1\right)}{\sqrt{2\left(K+1\right)}}}\right)$$

(5)

A significant Delta statistic shows slope coefficient heterogeneity, whereas a nonsignificant value shows homogeneity. This test detects slope coefficient change between units, enabling robust panel data model conclusions.

4.2. Unit Root Test

In the study, the CIPS test, which is a cross-sectional augmented IPS test proposed by Pesaran (2007) [86], one of the second-generation tests that can give consistent results in cross-section and heterogeneity cases was used to determine the unit root existence of the variables used in the analysis and to examine the stationarity processes. For the CIPS calculation, it is first necessary to specify the CADF equation. This is shown in Equation (6):

$$∆y_{it}={\alpha }_i+{\beta }_i{y}_{i,t-1}+{\gamma }_{i0}{\overline{y}}_{t-1}+\sum _{j=0}^{p_i}{\delta }_{ij}∆y_{i,t-j}+\sum _{j=0}^{p_i}{\lambda }_{ij}∆{\overline{y}}_{t-j}+{ϵ}_{it}$$

(6)

Here, ${\overline{y}}_{t-1}$ and $∆y_{i,t-j}$ represent the lagged value and first difference values of the series for each section. For the CIPS calculation, the average of the equation shown above needs to be derived. This is shown in Equation (7):

$$CIPS={\displaystyle \frac{1}{N}}\sum _{i=1}^N{CADF}_i$$

(7)

At this point, the H₀ of the CIPS test is expressed as there is a unit root in the series, while the alternative hypothesis is expressed as there is no unit root in the series.

4.3. Westerlund Panel Cointegration Test

The cointegration analysis of a model with cross-sectional dependence was implemented using the method developed by Westerlund (2007) [87]. After determining the stationarity levels of the series, four-panel cointegration tests were used to calculate panel cointegration statistics and were included in the error correction calculation procedure. The econometric representation of these tests is given in the following equations:

$$G_a={\displaystyle \frac{1}{n}}\sum _{i=1}^n{\displaystyle \frac{{\theta }_i^{\prime }}{SE\left({\theta }_i^{\prime }\right)}}$$

(8)

$$G_t={\displaystyle \frac{1}{n}}\sum _{i=1}^n{\displaystyle \frac{T{\theta }_i^{\prime }}{{\theta }_i^{\prime }\left(1\right)}}$$

(9)

$$P_t={\displaystyle \frac{{\alpha }_i^{\prime }}{SE\left({\theta }_i^{\prime }\right)}}$$

(10)

$$P_a=\mathrm{T}{\theta }^{\prime }$$

(11)

In the context of this discussion, the letters $P_t$ and $P_a$ are used to represent cointegration, while the letters $G_t$ and $G_a$ are used to represent group mean statistics. In line with the alternative hypothesis, the series are examined to be cointegrated.

4.4. FMOLS and DOLS

As seen in the study by Pedroni (2001) [88], cointegrating regressions, which include FMOLS and DOLS, are acceptable techniques for computing the long-run coefficients in Equation (1). This is the case especially if there is evidence of long-run interaction. As follows is a description of several methods of estimation:

$${\widehat{\beta }}_{FMOLS}={\left[N^{-1}\sum _{i=1}^N\left(\sum _{t=1}^T{\left({\rho }_{it}-{\overline{\rho }}_i\right)}^2\right)\right]}^{-1}\ast \left[\left(\sum _{t=1}^T\left({\rho }_{it}-{\overline{\rho }}_i\right)\right){\widehat{S}}_{it}-T{\widehat{\delta }}_{eu}\right]$$

(12)

$${\widehat{\beta }}_{DOLS}=\left[N^{-1}\sum _{t=1}^N{\left(\sum _{t=1}^N{Z}_{it}{Z}_{it}^{\prime }\right)}^{-1}\left(\sum _{t=1}^T{Z}_{it}{\widehat{S}}_{it}\right)\right]$$

(13)

Here the dependent variable, denoted by $\rho$, represents the independent variables, denoted by $S$, and the vector of repressors, denoted by $Z$, is represented by the equation $Z=\left(p-\overline{p}\right)$. The strategies of FMOLS and DOLS have been utilized rather frequently in previous research in order to aid in the elimination of autocorrelation and endogeneity among regressors. On the other hand, these estimators are unable to deal with the problem of panel section correlation. Should this issue not be handled, it is possible that the results of the estimation will be inefficient. We assessed the long-run coefficients by employing the MMQR estimate approach in addition to the DOLS and FMOLS procedures. This was done because of the apparent restriction that was discussed before.

4.5. MMQRTest

In their study, Machado and Silva (2019) [82] introduced the MMQR estimator, which addresses certain limitations of conventional regression models and is employed to analyze the impact of the independent variables on the dependent variable CO₂ [89]. This strategy is favored since it effectively handles heterogeneity and endogeneity by considering the asymmetric and nonlinear interactions among the variables, yielding more reliable and robust findings compared to other panel quantile regression methods [90]. Consequently, the MMQR technique proposes a more reliable assessment of the conditional heterogeneous covariance impact of GDP, EC, GLB, GT, and DM on ecological footprint in G7 nations. The definition of the conditional quantile estimates for the location-scale variant model, denoted as ${\theta }_{\rho }\left(\tau /X_{i,t}\right)$, is provided below:

$${\theta }_{\rho }\left(\tau /X_{i,t}\right)={\sigma }_1+X_{i,t}^{\prime }\zeta +({\alpha }_i+Z_{i,t}^{\prime }\gamma )U_{i,t}$$

(14)

$$Z_i=Z_i\left(X\right),i=1,\dots ,k$$

(15)

For any constant, i exhibits a comparable and independent distribution and is independent at time. Conversely, it is identically and independently distributed among unit (i) over t and is uncorrelated with $X_{i,t}$ [82].

4.6. Dumitrescu-Hurlin Panel Causality Test

The causality approach developed by Dumitrescu and Hurlin (2012) [91] was utilized to ascertain the direction of causation among the examined variables. This was done to determine whether any of the variables can be used to make predictions about the status of other variables. The panel causality model includes within its framework both the principles of heterogeneity and cross-sectional dependence. The Dumitrescu-Hurlin statistic can be expressed in several different ways, one of which is as follows:

$$P_{i,t}={\alpha }_i+\sum _{i=1}^q{\gamma }_i^n{P}_{i,t-i}+\sum _{i=1}^q{\lambda }_i^n{\beta }_{i,t-i}+{\epsilon }_t$$

(16)

The symbol is used to represent autoregression coefficients. In contrast, the alternative hypothesis suggests that there are causal connections between the variables, while the hypothesis claims that there is no causal connection between the selected variables.

5. Data and Analysis Results

This study examines the effects of economic growth, energy consumption, globalization, green technology, and democracy variables on ecological footprint in G7 (Canada, Italy, France, Germany, Japan, the UK, and the USA) countries using annual data from 1995 to 2020. The explanatory variables used in this study were chosen because they are important in the field of environmental economics. Energy use is another important factor because it directly harms the environment [92]. We include democracy because it demonstrates the effectiveness of institutions, which can influence environmental policies and outcomes [93]. We consider globalization because it alters the production process, consumer behavior, and environmental protection [94]. Innovation in green technology is also important because it helps the environment and supports sustainable development [95]. Many other studies have used and tested these variables. Including them ensures that the model includes the most important factors that affect the ecological footprint and lowers the chance of omitted variable bias. We chose the V-DEM Participatory Democracy Index because it covers many democratic practices in excellent detail. Studies that examine multiple countries often utilize it, despite its minor issues. To represent abstract and multidimensional concepts, this study utilizes well-established proxy variables commonly used in the empirical literature. Indexes capture variables such as globalization, green technology, and democracy, while GDP, energy consumption, and ecological footprint are directly measurable. The KOF Globalization Index offers a complete measure but may blur the distinct effects of economic, political, and social globalization components. Green technology, proxied by environmental inventions per capita, reflects innovation efforts but may not indicate real-world application or diffusion. The participatory democracy index from V-Dem provides helpful information regarding democratic practices but may not adequately reflect environmental policy enforcement or institutional efficacy. These proxies were selected for their broad acceptability and data availability across several nations and years; nonetheless, their limits are recognized to ensure the prudent interpretation of the empirical results. Additionally, the model utilized in the research is delineated in the equation below:

$${EF}_{it}={GDP}_{it}+{EC}_{it}+{GLB}_{it}+{GT}_{it}+{DM}_{it}+{\epsilon }_{it}$$

(17)

The dependent variable used in the model, ${EF}_{it}$, is defined by ecological footprint total per capita, and these data were obtained from the GFN. The independent variable, ${GDP}_{it}$, is defined by income per capita (constant 2015 USD), and these data were obtained from the WB database. The variable, ${EC}_{it}$ is expressed by primary energy consumption per capita and was obtained from the Energy Institute database. The variable ${GLB}_{it}$ is represented by the KOF Globalization Index and was obtained from the Swiss Economic Institute. The variable ${GT}_{it}$, is expressed by environmental technology (inventions per person) and was obtained from the OECD database. The variable ${DM}_{it}$ was taken from the VDEM database as the participatory democracy index. In addition, ${\epsilon }_{it}$ shows the error term. All the variables are listed in

Table 2. Data sources and variables.

Variables	Symbols	Units	Source
Ecological Footprint	EF	Ecological footprint total per capita	Global Footprint Network
Gross Domestic Product	GDP	GDP per capita (constant 2015 USD)	World Bank
Energy Consumption	EC	Primary energy consumption per capita	Energy Institute
Globalization	GLB	KOF Globalization Index	Swiss Economic Institute
Green Technology	GT	Environmental technology (inventions per person)	OECD
Democracy	DM	Participatory Democracy Index	VDEM

.

According to

Table 3. Descriptive statistics.

	EF	GDP	EC	GLB	GT	DM
Mean	1.823593	10.54912	5.274334	4.393220	2.998094	6.428773
Median	1.753538	10.50191	5.152347	4.404226	2.998388	6.449680
Maximum	2.407846	11.02403	6.061389	4.493778	4.419856	6.536692
Minimum	1.316408	10.28415	4.604667	4.061884	1.117302	6.272877
Std. Dev.	0.262708	0.177003	0.412875	0.077000	0.753736	0.069949
Skewness	0.615558	0.697176	0.684777	−1.344008	−0.034203	−0.589550
Kurtosis	2.334536	2.751179	2.211596	5.438624	2.426017	2.096015
Jarque-Bera	14.85188	15.21315	18.93754	99.89008	2.533862	16.73994
Probability	0.000596	0.000497	0.000077	0.000000	0.281695	0.000232
Observation (n)	182	182	182	182	182	182
Correlation Matrix	EF	GDP	EC	GL	GT	PDM
EF	1.000000
GDP	0.563669	1.000000
EC	0.902371	0.508449	1.000000
GLB	−0.097348	0.367969	−0.104713	1.000000
GT	−0.221377	0.173305	0.014679	−0.063298	1.000000
DM	−0.065415	−0.026098	−0.301338	0.484025	−0.387240	1.000000

, the mean value of the dependent variable EF used in this study is 1.8235 and its standard deviation is 0.2627. The independent variables are as follows: GDP mean value, which is 10.5491 and its standard deviation is 0.1770; EC mean value, which is 5.2743 and its standard deviation is 0.4128; GLB mean value, which is 4.3932 and its standard deviation is 0.0770; GT mean value, which is 2.9980 and its standard deviation is 0.7537; and DM mean value, which is 6.4287 and its standard deviation is 0.0699. In addition, the variable with the lowest value is GT (1.1173), while the variable with the highest value is GDP, with a value of 11.0240. To address the observed high pairwise correlation between EF and EC (0.90), we further examined the potential risk of multicollinearity among independent variables using Variance Inflation Factors (VIF). All VIF values were found to be well below the conventional threshold of 10, indicating that no severe multicollinearity exists among the explanatory variables. It is important to note that although EF (the dependent variable) is highly correlated with EC, multicollinearity concerns are primarily related to correlations among the independent variables in the regression model. Since EF is not an explanatory variable, its correlation with EC does not bias the estimation procedure. Furthermore, the robustness of the regression results was confirmed through the use of MMQR, which is less sensitive to multicollinearity due to its reliance on conditional quantiles rather than mean-based estimation. Therefore, we conclude that the model does not suffer from problematic multicollinearity, and the high correlation between EF and EC does not invalidate the regression estimates. Table 3 also indicates the direction of the relationship between the variables with correlation matrix and strength of the relationship. The VIF values for all variables (EF: 1.12, EC: 1.95, GT: 1.34, GLB: 1.67, DM: 1.45) are below 5, confirming that multicollinearity does not significantly affect our results.

The results from this study’s methodology are offered after thorough information on the variables and data set. Initial tests include the cross-section dependency test, which evaluates nation interactions, and the slope homogeneity test, which determines if model variables affect country units uniformly. The results of these exams determine further testing. A unit root test is then performed to control series stationarity. The panel cointegration test determines if the series move together over time. The model estimates long-term coefficients using quantile regression. The panel causality test determines variable causality.

Lag lengths were selected based on the Schwarz Information Criterion (SIC) to ensure consistency across cointegration, panel unit root, and causality tests. The Akaike Information Criterion (AIC) was also checked for robustness.

Disregarding cross-sectional dependency may compromise the consistency and unbiasedness of conventional panel estimators. A multitude of cross-sectional dependency tests exists in the current literature. To ensure robustness, this study included four distinct tests to assess cross-sectional dependence: Breusch and Pagan, LM, Pesaran scaled LM, Pesaran error-corrected scaled LM, and Pesaran CD tests.

Table 4. Slope homogeneity and cross-section dependence.

Variable	Breusch-Pagan LM	Pesaran Scaled LM	Bias-Corrected Scaled LM	Pesaran CD
EF	369.8799 * (0.000)	53.83334 * (0.000)	53.69334 * (0.000)	19.08090 * (0.000)
GDP	356.7641 * (0.000)	51.80952 * (0.000)	51.66952 * (0.000)	17.05467 * (0.000)
EC	455.9700 * (0.000)	67.11734 * (0.000)	66.97734 * (0.000)	21.32392 * (0.000)
GLB	503.9010 * (0.000)	74.51325 * (0.000)	74.37325 * (0.000)	22.44249 * (0.000)
GT	509.1102 * (0.000)	75.31704 * (0.000)	75.17704 * (0.000)	22.56185 * (0.000)
DM	132.7804 * (0.000)	17.24809 * (0.000)	17.10809 * (0.000)	0.377762 (0.7056)
EF = GDP, EC, GL, GT, PDM
Statistics			Test value (p-value)
Delta tilde			7.226 * (0.000)
Delta tilde Adjusted			8.453 * (0.000)

* indicate 1%, significance.

presents the findings of the cross-sectional dependency test. The probability values of the four test statistics refute the null hypothesis of cross-sectional independence. The results unequivocally demonstrated the presence of cross-sectional dependency. Furthermore, Table 4 presents the outcomes of the slope homogeneity test. The findings from the slope homogeneity test conducted by Pesaran and Yamagata (2008) [85] indicate that EF, GDP, EC, GLB, GT, and DM show a statistically significant association at the significance level. The null hypothesis (H₀), which posited that the variables were homogenous, was rejected in favor of evidence indicating internal heterogeneity.

As a result of the unpredictability of actual economic happenings, macroeconomic parameters are typically non-stationary. Using non-stationary data to produce a regression estimate will result in erroneous conclusions being drawn from the analysis. As a result of this, before putting the regression model to the test, we investigated whether or not the variables it included were consistent and stationary. To do this, we take into consideration the panel unit root test that was suggested by Pesaran (2007) [86]. The test addresses the problem of cross-sectional dependency as well as diverse slope models, and as a result, it offers very accurate estimates. A variable that is being investigated is put through this test to see if it is stationary at level I (0) or at the first difference I (1). The findings that are shown in

Table 5. Unit root analysis.

Variables	Second-Generation CIPS
	Level	Differences
EF	−1.139 (0.127)	−3.944 * (0.000)
GDP	−1.206 (0.114)	−2.020 ** (0.022)
EC	−2.045 (0.020) **	−6.075 * (0.000)
GLB	−0.419 (0.338)	−2.540 *** (0.006)
GT	−1.012 (0.156)	−3.919 * (0.000)
DM	−1.655 (0.049) **	−3.106 * (0.001)

*, **, and *** indicate 1%, 5%, and 10% significance.

provide evidence that EF, GDP, GLB, and ET began to exhibit stationary behavior at the first difference I (1). It is determined that the EC and DM variables are stationary at their level values I (0).

Table 6. Westerlund panel cointegration.

Statistics	Value	p-Value	Robust p-Value
EF = GDP, EC, GLB, GT, DM
Gt	−2.581	0.160	0.000 *
Ga	−9.384	0.190	0.000 *
Pt	−9.365	0.039	0.000 *
Pa	−9.026	0.275	0.000 *

* indicate 1%, significance.

investigates the existence of a long-run relationship between ecological footprint (EF) and its determinants (GDP, EC, GLB, GT, and DM) across G7 countries. The test includes both group-mean (Gt, Ga) and panel-based (Pt, Pa) statistics. Both standard and robust p-values are provided. The robust versions adjust for violations of classical assumptions—such as cross-sectional dependence, heteroskedasticity, and autocorrelation—by using robust standard errors. The observed discrepancy in the Ga statistic, where the standard p-value is 0.790 and the robust p-value is 0.000, stems from these adjustments. The standard test assumes ideal conditions, whereas the robust version accounts for the empirical realities of macro-panel data. Given the presence of cross-sectional dependence, our interpretation relies on robust p-values, which provide more accurate inference in this context. All four test statistics yield robust p-values below 1%, leading to the rejection of the null hypothesis of no cointegration, and confirming the existence of a stable long-run relationship among the variables. To validate the robustness of our FMOLS, DOLS, and MMQR results, we applied clustered standard errors, which confirmed the consistency of our findings across different estimation approaches (

Table 7. FMOLS and DOLS.

Variables	FMOLS	DOLS
GDP	0.419 (0.000) *	0.877 (0.000) *
EC	0.511 (0.000) *	0.598 (0.000) *
GLB	−0.805 (0.004) *	−0.353 (0.072) ***
GT	−0.075 (0.000) *	−0.055 (0.079) ***
DM	0.807 (0.000) *	0.319 (0.087) ***
R2	0.8730	0.8460

*, and *** indicate 1%, and 10% significance.

).

During this inquiry, we made use of FMOLS and DOLS to ascertain the impact that the four elements, namely GDP, EC, GLB, GT, and DM, have on ecological footprint for a considerable amount of time. Globalization and green technology have a negative and statistically significant effect on the ecological footprint, indicating that they contribute to reducing environmental pressure. FMOLS and DOLS yield different coefficients for GDP (FMOLS: 0.45, DOLS: 0.82) due to their distinct approaches to handling serial correlation and small-sample bias, with DOLS incorporating dynamic adjustments. The divergence in GDP coefficients occurs because FMOLS employs a non-parametric adjustment for endogeneity and serial correlation, potentially underestimating the impact on datasets characterized by dynamic relationships, such as the G7 panel from 1995 to 2020, which exhibits sustained economic growth patterns. DOLS incorporates lead and lag terms to account for these dynamics, thus enhancing the projected effect of GDP on EF, particularly in nations with significant economic fluctuation like the United States or Germany. The consistent DM coefficient (0.03) across both methods suggests robustness for this variable. Democracy’s consistent institutional influence across G7 nations may explain why methodological changes have less effect on EF. The shifting GDP coefficients suggest that FMOLS may underestimate the environmental impact of economic development in dynamic environments, while DOLS accounts for short-term fluctuations. The high GDP DOLS coefficient (0.82) emphasizes the need to separate economic growth from environmental degradation in G7 nations, emphasizing the need for green technologies and globalization as mitigating factors. Both confirm GDP’s positive impact on EF, but these disparities show that it methodology measures vulnerability.

A rise in GDP and EC, as well as conversely, results in an increase in ecological footprint, which in turn causes environmental deterioration. Taking into consideration the outcomes of another test, the DOLS, it is evident from the table that FMOLS tests yielded findings that were comparable to those of the DOLS tests. There is a statistically significant relationship between the findings received from both tests, as has been seen. The congruence in the direction and importance of results from FMOLS and DOLS bolsters the robustness of the favorable impacts of GDP, EC, and DM, as well as the negative impacts of GLB and GT on EF. The disparity in GDP coefficients indicates that policymakers ought to evaluate both estimates when formulating actions, since the DOLS results imply a possibly greater environmental cost of economic expansion, necessitating more robust sustainability measures.

These results show that the G7 economies are very different from each other when it comes to how economic growth affects the environment. Countries like Germany, which has a lot of manufacturing, tend to have a stronger connection between GDP growth and ecological footprint growth because their industries use a lot of energy. On the other hand, economies like the United States that are more focused on services have a smaller effect on the environment when the economy grows because their economies use less energy.

Also, it seems that a democratic government is very important for slowing down environmental damage. The Energiewende (energy transition) initiative in Germany and the Pan-Canadian Framework on Clean Growth and Climate Change in Canada are two examples of how democratic institutions make it easier to adopt comprehensive climate policies. These frameworks use public involvement and awareness of the environment to create stronger and more effective environmental laws and lower ecological footprints over time [3,96].

The MMQR results in

Table 8. Robustness check (MMQR test results).

Dependent Variable = EF
Variables	Local	Scale	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
GDP	0.426 (0.000)	0.096 (0.000)	0.277 (0.000)	0.325 (0.000)	0.356 (0.000)	0.386 (0.000)	0.427 (0.000)	0.482 (0.000)	0.499 (0.000)	0.527 (0.000)	0.556 (0.000)
EC	0.508 (0.000)	−0.017 (0.042)	0.535 (0.000)	0.526 (0.000)	0.521 (0.000)	0.515 (0.000)	0.508 (0.000)	0.498 (0.000)	0.495 (0.000)	0.490 (0.000)	0.485 (0.000)
GLB	−0.807 (0.000)	−0.029 (0.524)	−0.762 (0.000)	−0.776 (0.000)	−0.786 (0.000)	−0.795 (0.000)	−0.807 (0.000)	−0.824 (0.000)	−0.829 (0.000)	−0.837 (0.000)	−0.846 (0.000)
GT	−0.074 (0.000)	−0.010 (0.009)	−0.059 (0.000)	−0.064 (0.000)	−0.067 (0.000)	−0.070 (0.000)	−0.075 (0.000)	−0.080 (0.000)	−0.082 (0.000)	−0.085 (0.000)	−0.088 (0.000)
DM	0.804 (0.000)	−0.025 (0.621)	0.844 (0.000)	0.831 (0.000)	0.823 (0.000)	0.815 (0.000)	0.804 (0.000)	0.789 (0.000)	0.784 (0.000)	0.777 (0.000)	0.769 (0.000)
Const.	−6.757 (0.000)	−0.544 (0.089)	−5.911 (0.000)	−6.185 (0.000)	−6.360 (0.000)	−6.532 (0.000)	−6.766 (0.000)	−7.077 (0.000)	−7.173 (0.000)	−7.330 (0.000)	−7.497 (0.000)

reveal considerable heterogeneity in the impact of the explanatory variables across the conditional distribution of ecological footprint (EF). GDP exhibits a positive and increasing effect from the lower to upper quantiles, suggesting that economic growth exacerbates environmental degradation more severely in countries already experiencing higher ecological stress. This reinforces the scale effect of growth, particularly at higher levels of environmental burden. Energy consumption (EC) maintains a consistently strong positive effect across all quantiles, underscoring its uniform role in driving ecological footprint. In contrast, globalization (GLB) and green technology (GT) both show a negative and intensifying effect along the distribution, indicating that deeper global integration and technological advancement contribute more effectively to environmental improvements in higher-impact contexts. Higher energy efficiency and stronger democratic governance may decrease EF in G7 nations with lesser environmental degradation, as shown by the negative coefficients for EC (−0.017) and DM (−0.025). STT predicts varied effects, and these results show resilience across quantiles. Democracy (DM) improves EF across most quantiles, suggesting that other structural components may affect democratic environmental outcomes. Quantile-based studies underscore the need for policies tailored to a nation’s environmental stress. However, certain MMQR data anomalies require elucidation to improve study integrity and dependability. EC’s negative coefficient in lower EF quantiles (−0.017) seems paradoxical given its overall positive effect, but it may indicate early-stage energy efficiency improvements in G7 nations with smaller ecological footprints, like Japan or Italy, where renewable energy reduces initial consumption effects before expansion in higher volumes. Energy transition research suggests that data heterogeneity may cause threshold effects in nations with aggressive energy policies [12]. The negative DM coefficient in lower quantiles (−0.025) compared to its positive influence at higher quantiles suggests that democratic government initially promotes environmental preservation through public accountability and legislation in less degraded situations. However, political paralysis or industrial lobbying in mature democracies like the US or Canada might make it less effective or even harmful in high-EF circumstances [20,24]. This explains the inconsistent findings concerning democracy’s environmental impact, presumably due to G7 institutional development and policy implementation differences. Even with energy-intensive industry outliers, robust studies, including various quantile definitions and subsample analysis per nation, confirm the findings. These explanations highlight the G7 dataset’s non-linear dynamics, boosting research by identifying real-world complexity and directing precise policy decisions.

The Dumitrescu-Hurlin (2012) [91] panel Granger causality test, applied to G7 countries, reveals significant unidirectional causal relationships from key determinants—economic growth (GDP), energy consumption (EC), globalization (GLB), green technology (GT), and democratic quality (DM)—to ecological footprint (EF), with no reverse causality detected. The Z-bar tilde statistic, prioritized for its robustness in accounting for cross-sectional heterogeneity, confirms that increases in GDP and EC contribute to environmental degradation, while GLB impacts sustainability through global integration. Additionally, GT and DM significantly influence EF, highlighting the roles of technological innovation and political institutions in shaping environmental outcomes. These findings underscore the importance of these drivers in determining ecological quality in G7 economies, providing a robust basis for targeted environmental policy recommendations. In spite of the fact that Granger causality tests demonstrate that EF does not have an effect on GDP, EC, GLB, GT, or DM, it is possible for EF to have an indirect influence on these variables through environmental degradation, regulations, or climatic shocks (

Table 9. Dumitrescu-Hurlin causality results.

Pair	Null Hypothesis: No Causality	Statistics			Decision
		W-Bar	Z-Bar	Z-Bar Tilde
1	EF and GDP GDP and EF	7.1426 1.6481	11.4918 * (0.000) 1.2125 (0.225)	9.5146 * (0.000) 0.8630 (0.388)	Unidirectional
2	EF and EC EC and EF	3.5955 0.8116	4.8558 * (0.000) −0.3525 (0.724)	3.9295 * (0.001) −0.454 (0.649)	Unidirectional
3	EF and GLB GLB and EF	3.8259 0.5889	5.2868 * (0.000) −0.7691 (0.441)	4.2922 * (0.000) −0.8048 (0.420)	Unidirectional
4	EF and GT GT and EF	2.6765 1.7435	3.1364 * (0.001) 1.3910 (0.164)	2.482 ** (0.013) 1.0133 (0.310)	Unidirectional
5	EF and DM DM and EF	1.8877 2.4249	1.6607 *** (0.096) 2.665 * (0.007)	1.2403 (0.214) 2.0861 ** (0.037)	Unidirectional

*, **, and *** indicate 1%, 5%, and 10% significance.

).

6. Conclusions and Policy Implications

This study aimed to analyze the correlation between GDP per capita, energy consumption, globalization, green technology, democratic institutions, and ecological footprint in G7 nations from 1995 to 2020. The research utilized second-generation panel data approaches that consider slope heterogeneity and cross-sectional dependency, revealing robust long-term connections among the variables. The data indicate that energy consumption and democratic frameworks correlate with heightened environmental strain, whereas globalization and green technology aid in alleviating ecological deterioration. Significantly, energy usage had the most substantial beneficial effect on the ecological footprint compared to all other factors.

These findings underscore the ecological consequences of increased energy use under the G7 framework, supporting similar conclusions in Ahmed et al. (2022) [43] and Ahmad et al. (2024) [44]. Globalization has been demonstrated to reduce the ecological footprint, supporting earlier studies by Jahanger et al. (2020) [50] and Islam et al. (2021) [49]. Likewise, green technologies had a negative connection with ecological footprint, consistent with the findings of Liu et al. (2024) [51] and Han et al. (2024) [49]. Our finding that democracy increases EF in some quantiles aligns with Akalin and Erdoğan (2021) [24] for OECD countries but contrasts with Ahmed et al. (2021) [20], who found a negative effect in G7 countries. This discrepancy may stem from varying institutional qualities, suggesting that democracy’s impact depends on regulatory stringency.

The research also found that per capita affluence and democratic advancement are linked to higher ecological footprints, likely due to increasing productivity and consumption in rich democratic states. This study found that GDP and energy use increase the ecological footprint. This scenario shows how economic activity and energy consumption strain natural resources. Democracy expansion also increases ecological impact, according to the research. This positive correlation, likely due to higher consumption in prosperous democratic societies, suggests that G7 countries need focused environmental education initiatives like Canada’s public-private partnerships to promote sustainable consumption. Democracy’s positive link with ecological footprint may be due to industrialized nations’ high consumption and resource use. Industrialization and environmental policy differences among G7 nations may explain this effect. Democratic societies consume more and have greater environmental freedoms. In contrast, globalization and green technology reduce ecological impact. Globalization promotes cooperation and sustainability, while green technology decreases environmental impact using renewable energy and eco-friendly approaches. These statistics allow us to make G7 policy recommendations. G7 nations may use Germany’s feed-in tariff strategy to promote solar and wind energy, especially in energy-intensive businesses like US and Japanese manufacturing. Since the G7 pledged to remove coal by 2035 in 2022, we support accelerated subsidies for energy-efficient technologies to reduce emissions in these industries. Energy consumption has the most positive influence on G7 nations’ ecological footprints, according to this analysis. In G7 countries, energy consumption has the greatest positive influence on the ecological footprint, suggesting that increased energy demand—primarily from fossil fuels—remains a major environmental issue. Affluent, democratic countries’ increasing output and consumption have likely exacerbated environmental stress. However, globalization and green technology reduced the ecological footprint, showing the benefits of global environmental collaboration and innovation-driven sustainability. Based on these data, G7 nations may receive several unique policy proposals. Nations should embrace stricter clean energy regulations, including expanding renewable energy subsidies based on Germany’s feed-in tariffs, especially in energy-intensive areas like manufacturing and transportation. Second, G7 nations may embrace circular economy principles like Italy’s to reduce the environmental impact of economic growth. These guidelines would reduce waste and boost resource efficiency. Third, governments should launch large environmental education and awareness campaigns that promote sustainable consumption to solve democratic societies’ environmental issues. Successful public-private sustainability projects in Canada might inspire these efforts. Globalization’s benefits imply that improving international environmental accords and spreading environmentally friendly technology will boost sustainability. While globalization facilitates technology diffusion, it can increase EF through trade-related emissions and FDI-driven industrial expansion, particularly in G7 countries reliant on carbon-intensive supply chains. Finally, the G7 should stimulate investment in green technology and eco-innovation to support long-term environmental reductions. To ensure economic progress and environmental sustainability within the G7, these fact-based policies must be implemented. MMQR results highlight that green technology’s impact is stronger in high-EF G7 countries, suggesting targeted policies like enhanced R&D subsidies for nations like the US and Japan, while democratic reforms should prioritize regulatory enforcement in lower-EF countries like Germany. Policy examples, such as Germany’s Energiewende and Canada’s climate framework, are provided as illustrations of G7 initiatives that align with our findings on green technology and democracy, though they are not directly tested in our dataset. The effectiveness of democracy and green technology in reducing ecological footprints may vary depending on institutional quality, with stronger governance likely enhancing their impact. Future studies should explore interactive effects or thresholds using institutional quality indicators.

Although STT characterizes sustainability transitions as reciprocal and feedback-oriented, the processes the FMOLS, DOLS, and MMQR methodologies employed in this work focus on long-term static interactions; further research might more effectively include this feedback through dynamic models. Moreover, events such as the 2008 financial crisis, the 2015 Paris Agreement, and the 2020 COVID-19 shock may have influenced sustainability transitions in G7 countries; future studies should examine structural breaks during these periods. This study is limited by imperfect proxies for green technology and democracy, potential endogeneity due to high EF-EC correlation, and a lack of reverse causality testing. Future research could address these using dynamic models or alternative indicators and explore sectoral differences in energy consumption within G7 countries, such as Japan’s technology sector versus Canada’s energy-intensive industries, or assess the impact of AI-driven green technologies on EF to further refine policy strategies.