Introduction

The detrimental effect of global warming is not only causing severe damage to the bionetwork and environment but also affecting every economic zone and aspect of society on a global level. The increasing utilization of fossil resources and ecological footprints is a significant factor contributing to ecological harm1. These challenges include storms, heavy rainfall, heat waves, health conditions, and wildfires2. A critical necessity exists for substantial alterations in the global use of energy resources. Altering energy resources would encourage the use of more sustainable, alternative, and renewable energy sources, rather than the wasteful combustion of fossil fuels. These developments can be ascribed to various sources, including minimal environmental impact, contributions to economic growth, enhanced energy availability and stability, and initiatives to address environmental changes and global warming3. Therefore, such findings have confirmed that carbon emissions play a significant role in promoting the use of alternative energy sources. On the other hand energy depletion can be obtained from both renewable sources, such as solar or wind power, and non-renewable sources, like fossil fuels4,5. Therefore, it is essential to consider that as an economy experiences an increase in economic growth and population growth, there will be a corresponding increase in the demand for energy over time. Consequently, this emphasizes the importance of motivating the present generation to adopt contemporary energy sources and formulate plans to fulfill upcoming energy requirements. Furthermore, the utilization of renewable energy sources6,7 emerges as a practical solution to fulfill the growing energy demands of nations undergoing substantial economic growth.

It is evident from the literature that the long-run impact of energy consumption, particularly from fossil fuels, leads to the release of anthropogenic greenhouse gases (GHGs), which have a negative impact on the environment8,9,10,11,12,13,14,15 The emissions of greenhouse gases have prompted scientists and policymakers to investigate a range of strategies aimed at reducing these emissions and addressing environmental degradation within the framework of global climate change. Additionally, the Kyoto Protocol was established in 1997, the Doha Agreement reached in 2012, the Paris Accord signed in 2015, and the adoption of the Sustainable Development Goals in 2016, underscore the reality that present energy consumption trends are unsustainable. The researchers like14,16,17 indicate that these renewable sources are associated with minimal carbon emissions.

The switch to renewable energy sources is motivated by various considerations, including worries about environmental deterioration, particularly the effects of global warming and climate change caused by excessive carbon emissions. Furthermore, the limited availability of fossil fuels and the unpredictable fluctuations in oil charges serve as driving factors for the shift towards renewable energy sources18,19. Substituting fossil fuel-dependent electricity production with renewable energy sources, environmentally friendly energy, and less carbon-intensive fuels is a substantial approach to diminish carbon emissions20,21. This requires discouraging the use of energy derived from fossil sources while simultaneously promoting and improving the use of energy from renewable sources to achieve green sustainability. Consequently, it is essential to increase the proportion of renewable energy sources within the energy mix to attain a carbon-neutral environment and foster a sustainable green economy. As a result, the share of renewable energy in the energy outline is anticipated to increase in the following 5 years, leading to a growing rate of 12.4% by 2023. This will constitute 30% of the entire energy utilization and contribute to around 70% of the global growth in power generation from renewable sources21,22,23.

The use of clean energy can ensure the reliability of electricity supply and address the issue of local environmental degradation. It is a well-established fact that renewable energy demonstrates minimal environmental impact and is supported by rigorous environmental standards and agreements aimed at reducing pollutants. The increased utilization of renewable energy sources holds the potential to generate employment opportunities, particularly within the sector of environmentally friendly technologies and associated economic activities that promote sustainable development13,24,25. This is especially pertinent inside the G-7 bloc, which serves as the primary focal point of our analysis. Based on the 2020 World Bank study, renewable energy made up 18% of the overall global energy consumption in 2015, while it stayed below 20% in most G7 countries.

This study is essential for G7 nations as they are major contributors to global emissions and ecological footprints due to their industrial scale and energy demands. As influential economic leaders, their policies significantly impact global sustainability efforts. By identifying key drivers of ecological impact, this study provides targeted insights to help G7 countries adopt greener practices, setting a model for worldwide environmental policy and action. More investment in renewable energy can promote environmentally sustainable economic growth. Moreover, the correlation between innovation and renewables can exert a significant impact on renewable energy, resulting in a noteworthy reduction in carbon emissions. Therefore, the G-7 nations can boost their uses of renewable energy by dedicating money towards R & D. As a result, the energy research advances provide both economic advantages as well as the benefit of reduced production costs and time. This will enhance the efficiency and efficacy of the implementation of renewable energy26,27,28. argue that this is achieved by advocating energy efficiency and enhancing environmental quality. Furthermore29,30, also discovered that technical innovation facilitates firms in acquiring knowledge more effortlessly, hence augmenting ecological sustainability. There is a need of substantial financial resources for energy research and innovation in order to facilitate the implementation of renewable energy and clean energy technologies22,31. As a result the global investment in the research and development of renewable energy has been inadequate, amounting to approximately $17 billion by 2014 (IEA, 2015). Based on the discussions for energy consumption Fig. 1 shows the energy consumption trends in G7 economies. France has shown the lowest trends among the G7 economies, while Japan has shown an increasing trend from 2011 onwards.

Fig. 1
figure 1

Energy consumptions trends in G7.

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However, the G7 economies have continuously prioritized renewable energy sources and have made substantial investments in R&D. This strongly suggests that the rise in these factors has the capacity to alleviate the levels of environmental deterioration in these nations. This study aims to deliver a comprehensive insight into the environmental advantages linked to renewable energy utilization and the distribution of resources for advancing energy research technologies. Policymakers can improve their decision-making on global warming and climate change by gaining a thorough understanding, backed by empirical evidence, of how investing in research and innovation for renewable energy greatly reduces environmental impact and improves its quality. This study seeks to examine the impact of renewable energy utilization, economic complexity, ITQ, and innovation on environmental degradation in G-7 countries. Technological advancements significantly influence the unique characteristics of renewable energy and the efforts to address global climate change through investments in cleaner energy alternatives. The study integrates the results of previous investigations conducted by15,30,32.

In addition, most established and rising economies have not attained a level of economic development that is conservational of natural resources and ecologically sustainable for the foreseeable future. More specifically, abundant resource endowments are often correlated with high pollution. In fact, many of the studies cited above claim that there is a trend from production- countries that rely on high emissions and low human capital education, into skill and knowledge-based developed economies. A structural change is increasingly recognized as the primary cause of ecological deterioration33,34. The economic complexity index (ECX) is an innovative metric for assessing economic progress that considers elements such as expertise, knowledge, prevalence, and product variety. It is regarded as a comprehensive factor for home economies. The ECX is a metric that quantifies the degree of intricacy, encompassing variations in the industrial domain and assessing the composition of output. In addition, ECX possesses the capacity to clarify and forecast both worldwide and local variations in economic advancement and greenhouse gas releases35,36. The product diversity has a significant impact on ecological factors and energy use37,38. Additionally, Fig. 2 shows the ecological footprint trends in G7 economies. The figure clearly shows that the USA and Canada have the highest ecological footprints compared to other G7 economies.

Fig. 2
figure 2

ECFP trends in G7 nations.

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Many studies of the growth, energy, and the environment have focused on measuring the degradation of the environment by evaluating carbon dioxide emissions (CO2). However, it is essential to recognize that CO2 only represents one aspect of the environmental state, namely air pollution. Such arguments are supported by14,39,40,41,42,43,44,45,46. To enhance the effectiveness of outcomes and assess the impact of environmental transformation policy, we employ a comprehensive metric called EFP, which quantifies the degree of environmental quality. The ecological indicator serves as a crucial tool for assessing the strain on key national resources and the overall sustainability of the environment40,47,48. Furthermore49,50, have provided a laconic explanation of the ecological footprint, describing it as a comprehensive measure. This index functions as a metric to evaluate the extent of human-caused modifications to the overall natural environment.

Previous research17,44,51,52,53,54,55 has examined the influence of renewable energy use on the lessening of CO2. In addition, certain studies have inspected the correlation between research novelty in overall energy and ecological quality26,56. Hence, this research provides unique and significant additions to the existing scholarly work. This research significantly contributes to the current body of literature in various ways: The objective of this study is to evaluate the influence of different elements, specifically globalization (GLB), economic complexity (ECX), exploitation of renewable energy (RNE), institutional quality (ITQ), and consumption of fossil fuels (FEC), on the ecological footprint (ECFP). This investigation focuses on analyzing how economic complexity influences the environmental performance of G7 nations. The concept of economic complexity refers to the depth and intricacy of a country’s economic activities57. Earlier studies have examined ecological impact by evaluating various factors including technological innovation. Although prior studies have thoroughly investigated the connections between environmental and economic elements like GDP, GHGs and CO2, there has been a lack of attention on the joint impact of these environmental and economic factors.

The objective of this study is to fill the gap in literature by utilizing influential indicators that are well acknowledged for their thorough assessment of both economic and environmental factors. In contrast to earlier studies that focused on CO2 emissions, our study uses the ecological footprint as a measure for assessing ecological deterioration. Several studies suggest the investigation of ECFP to see impact on environmental quality, for examples10,43,58,59,60,61,62. However, it is worth noting that these studies did not expressly give priority to innovation in energy research. Additionally, the United Nations has established a set of sustainable development goals, which include SDG-7, with a focus on guaranteeing accessible and clean energy; SDG-8, aiming to foster favorable employment and economic expansion; SDG-9, highlighting the significance of industry, innovation, and infrastructure; and SDG-13, addressing the urgent need to combat climate change. As a result, this research offers a valuable understanding of the complex interaction between economic, environmental, and energy concerns in G7 economies.

This paper contributes to empirical literature by analyzing how economic complexity, institutional quality, and globalization impact ecological footprints in G7 nations, a perspective often overlooked in studies of advanced economies. Using the PMG-ARDL method, it uniquely captures both short- and long-term effects while accounting for cross-sectional dependence, enhancing result robustness. The findings provide actionable policy insights specifically for high-emission economies, with implications for balancing growth and sustainability. Including recent literature will further situate this work within current global sustainability discussions, adding value for policymakers and researchers alike. The study is relevant as it offers actionable insights for policymakers in G7 countries, who must reconcile economic expansion with environmental stewardship. This research employs the PMG-ARDL technique, considering cross-sectional dependence and co-integration, to elucidate intricate relationships that guide sustainable policy solutions. Furthermore, using G7 economies as a sample underscores the study’s emphasis on significant global emitters, rendering the findings particularly relevant to high-emission nations while offering insights applicable to other regions with analogous economic characteristics.

The next sections of this study are prepared as follows: Sect. 2 presents literature review, whereas Sect. 3 explains the data and techniques used in methodologies. Section 4 expresses empirical data analysis and results discussions, while Sect. 5 closes the research by examining the policy implications.

Literature review

Numerous studies have observed the relationship between ITQ, the adoption of renewable energies, and environmental deterioration, specifically in relation to CO2, across different countries using various estimating strategies, for example13,63,64,65,66,67. These studies indicate that adoption of renewable energies has a substantial impact on mitigating environmental degradation. For an instance64 examined the link between R&D and CO2 in 15 European Union countries, as well as China and the USA, from 1990 to 2013. The findings clearly demonstrate that investing resources in R&D leads to a substantial lessening in CO229,68. use the newly established SOR unit root test and bootstrap bounds co-integration test to demonstrate the negative consequences of advancement in energy research on CO2 in France. Additionally65,69 employ a non-parametric panel methodology to examine the relationship between R&D and CO2 levels in G-7 countries throughout the time frame from 1870 to 2014. The relationship between energy, GDP, and the environment has been extensively investigated in several studies8,14,17,70,71,72. These studies have examined the relationship between economic expansion and environmental degradation, with an emphasis on the use of sustainable energy sources. This analysis is based on the EKC framework, and the results validate that the adoption of renewable energy sources leads to positive environmental outcomes. It is the proven fact that renewable energy sources are effective in addressing environmental degradation as suggested by26,44,54,73,74,75,76,77,78,79,80,81. Therefore, to fully comprehend the significant correlation between the variables being studied, it is crucial to analyze previous research and explore the topic in greater depth.

Globalization and environment

Number of research articles in the current literature establish a definite correlation between environmental degradation and GLB, resulting in sundry effects on GDP and ecological circumstances. For an instance82,83 explores the influence of GLB with ecological degradation in ASEAN countries. Additionally69,84, investigated the correlation among GLB, energy consumption, financial growth, and ECFP in GCC countries. Their study findings show that GLB and energy use exert a significant adverse influence on environmental quality. Furthermore, the comprehensive investigation demonstrates that economic progress, the phenomenon of worldwide integration, and the utilization of energy exert an adverse influence on the environmental balance. Furthermore85,86, has demonstrated similar outcomes that emphasize the noteworthy and advantageous effect of economic and GLB variables on CO2 in the economies of Central and Eastern European (CEE) countries. In divergence, multiple studies have discovered an inverse relationship between the phenomenon of GLB and the deterioration of the environment. Reference87,88 analyzes the impacts of GLB and environmental deterioration in China. Their observation authenticated the presence of a curvilinear link between environmental damage and long-term economic growth. Empirical data revealed the interplay between economic expansion, globalization, and the environment on the African continent. In summary, their research confirmed a negative association between CO2 and GLB. Nevertheless, the precise empirical findings differed when examined on a per-country level. A negative connection is observed in nine African economies out of a total of 19 countries, indicating that GLB has a detrimental effect on environmental degradation89,90. Conversely, in five economies, GLB is found to have a positive impact on environmental deterioration.

Economic complexity and environment

To enhance comprehension and tackle atmospheric issues, researchers have carried out numerous noteworthy national, regional, and global empirical investigations that have explored the correlation between the environment and economic indicators of growth37,91,92. These literature studies have found crucial economic aspects that determine ecological supremacy. Researchers93,94,95,96 analyzed the relationship between ECX and CO2. Their research revealed that sophisticated levels ECX are associated with a notable reduction in atmospheric pollution in the region, supporting the idea of the EKC. Moreover91,97, discovered that the expansion of the area and the rise in GDP are factors that contribute to the elevation of particulate matter (PM2.5) levels. In the meanwhile37,98 examined the association between economic hardships, utilization of RNE, and CO2 in the countries of Portugal, Italy, Ireland, Greece, and Spain using DOLS technique. In addition to this99,100 undertook an evaluation of the fluctuating relationship between CO2 and ECX in 118 varied economies. They determined that there is a strong correlation between ECX and environmental deterioration, providing evidence in favor of the EKC theory. Moreover93,101,102, , it has been found that there is an initial positive link between CO2 and income growth, which is then followed by a negative correlation. Furthermore, there exists an inverse relationship between CO2 and ECX.

Energy consumption and environment

Due to the strong correlation between social and economic activity, energy consumption plays a pivotal role. Therefore, it is imperative to examine the effect of energy usage on the condition of the ecosystem. Various studies have provided evidence indicating that the FEC is causing pollution in the climate. If this impact is not controlled, it can result in substantial alterations to the climate103,104,105,106,107,108,109. In contrast, RNE improves air quality107,108,110,111,112,113,114. Nevertheless, specific research has indicated a marginal impact of RNE on climate conditions115,116. Furthermore, the research carried out by117,118 revealed that the utilization of RNE is causing an increase in GHGs in India119,120. found that RNE is contributing to a deterioration in the climate conditions of European nations121,122. establish that the alleviation of economic and regulatory limitations substantially enhances the use of biogas facilities among farmers. The government needs an economic strategy, training for owners, daily management, and skilled technical assistance to construct biogas facilities. This paper explores the policy implications and potential avenues for upcoming research.

The efficient usage of energy resources is acknowledged as a crucial driver for attaining sustainable economic growth and ecological well-being in all economies. Nevertheless, it is also a substantial contributor to the release of greenhouse gases, which present a danger to the long-term viability of ecosystems123,124,125. Their findings indicate that the presence of plenty of natural resources and the exploitation of renewable energy sources effectively mitigate the burden on the environment. Furthermore, their data on causality demonstrated a one-way connection from RNE to ECFP. Experiential research126,127 suggests that FEC adversely affects the economic growth of developing nations, leading to a decline in their real income. Similarly, a study conducted by123,128 found that the use of renewable and alternative energy sources has the potential to improve environmental quality. The study, employing the panel quantile technique129,130, discovered that in the very polluted economies of Southeast Asia, renewable energy sources exhibited restricted efficacy in mitigating environmental pollution131,132. have validated comparable findings for GCC economies3,133, and 8 Arctic countries123,134 for 14 SSA nations. Remarkably, the adoption of renewable and alternative energy sources has resulted in a rise in EFP in the G-7 region134,135.

Institutional quality and environment

The effectiveness of the government’s operations depends on the caliber of its institutions. It ensures strict compliance with the regulations of a specific community. The regulatory and institutional framework exerts a substantial impact on the environmental and economic progress of every nation136. There is a lack of research investigating this association. The work done by137,138 found that the law has a negligible effect on the environment, but political stability has a beneficial effect on the climate. Enforcing anti-corruption policies improves economic productivity and has a detrimental effect on the overall climate of South Asian nations. The research undertaken by139,140,141 has shown that applying anti-corruption measures can result in an improvement in environmental quality142,143. conducted a new study investigating the impact of various levels of institutional structures in different nations on environmental quality. Their research revealed that ECX exerts the most substantial positive influence on the ECFP in all countries. In contrast, the correlation between natural capital and ECX strengthens ecological integrity. The impact of study criteria on the ECFP is contingent upon the ITQ of the land.

Research gap

The primary research focus mostly revolves around analyzing the influence of energy consumption and carbon emissions on environmental deterioration, typically using CO2 as a metric, across different countries. However, previous research has not thoroughly investigated the impact of research and innovation on environmental deterioration using a comprehensive measure—the ecological footprint, chiefly among the G-7 states. Our work fills this void by employing data that is specifically pertinent to ECFP in the domain of RNE, diverging from the typically utilized statistics on overall research and development found in previous literature. In addition, our focus on the ECFP, rather than just CO2, provides a more nuanced view of ecological degradation in the G-7 countries. Our work notably diverges from existing research in terms of its objective, data exploitation, extent, and technique. Recent research highlights the importance of implementing ECX to achieve environmental sustainability, considering the changing economic and environmental circumstances. The interaction of ECFP, GLB, ECX, and RNE has gained great importance in the field of environmental studies. Nevertheless, there is a dearth of thorough assessments that concurrently consider the ECX, GLB, FEC, and ECFP, particularly utilizing the ARDL-PMG (Autoregressive Distributed Lag - Pool Mean Group) technique within the context of G7 economies. Moreover, the influence of ECX on ECFP differs depending on whether the research is conducted on individuals or groups, highlighting the need for a thorough assessment at the national level144. Several studies have examined the direct influence of ECX, GLB, RNE, and FEC on environmental deterioration, resulting in inconclusive results. Therefore, our research seeks to address this gap by offering a deeper understanding of complex economies as a new variable and emphasizing the connection between GLB and RNE as a strategy to alleviate environmental degradation by decreasing ECFP. By utilizing the ARDL-PMG technique, our analysis becomes more reliable, making our study a groundbreaking effort in thoroughly examining the influence of ECX, RNE, GLB, and ITQ on the ECFP in the G7 economies. This viewpoint has not undergone a thorough examination in prior academic literature. Given the scarcity of empirical research, it is imperative to undertake a thorough examination of the intricate connections among ECFP, ECX, GLB, and RNE and FEC. This is especially crucial when contemplating other developed economies.

Data, model specification, method

The aim of this study is to examine the effect of globalization, economic complexity, energy usage, institutional quality, and fossil energy consumption on the ecological footprint throughout the period from 2000 to 2021. The ARDL-PMG model will be utilized to do this. This study’s theoretical framework draws from Globalization Theory, Institutional Theory, and Ecological Modernization Theory to assess how globalization, economic complexity, energy usage, institutional quality, and fossil fuel consumption influence the ecological footprint in G7 nations from 2000 to 2021. Globalization Theory explores how increased trade, and connectivity can impact ecological outcomes, either intensifying resource use or facilitating access to green technologies. Institutional Theory emphasizes the role of governance in shaping environmental outcomes through policy enforcement and sustainability standards145. Ecological Modernization Theory suggests that advanced economies can achieve ecological goals by adopting innovative technologies and transitioning to renewable energy146. Together, these perspectives guide the study’s exploration of how these factors contribute to or mitigate environmental pressures, offering insights for sustainable development strategies in developed nations. The variables, together with their corresponding measurements and sources, are displayed in Table 1.

Table 1 Data, units and sources.
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The primary ecological objective of this study, serving as an empirical model, can be examined through the utilization of Eq. 1, as delineated in the following manner:

$$\:ECFP=f\left(GLB,\:ECX,\:RNE,\:FEC,\:ITQ\right)$$
(1)

The acronyms GLB, ECX, RNE, FEC, and ITQ represent globalization, economic complexity, renewable energy consumption, fossil fuel consumption, and institutional quality, respectively. Equation (1) explains the connection between environmental deterioration and the factors of GLB, ECX, RNE the use of FEC, and ITQ. In order to achieve precise and statistically reliable outcomes, we choose to display the data markers using a logarithmic scale. We utilized ARDL technique in the context of the PMG estimator architecture. The PMG estimator effectively integrates individual-specific coefficients into the data, leading to improved estimating efficiency. This estimator effectively combines the advantages of both the mean group and pooled OLS estimators. Utilizing this technique is crucial for examining the impacts of various time periods on separate entities within a dataset, encompassing both immediate and extended durations. The implementation of the error correction model significantly enhances the regression of the heterogeneous panel by effectively utilizing the PMG estimator.

$$\:{y}_{it}={\mu\:}_{i}+\sum\:_{j=1}^{p}\:{\lambda\:}_{ij}{y}_{i,t-j}+\sum\:_{j=0}^{q}\:{\delta\:}_{ij}^{{\prime\:}}{X}_{i,t-j}+{\epsilon\:}_{it}$$
(2)

In the above equation, the letter j signifies lags’ count, p shows the lag of the dependent variable y and q indicates the lag of the independent variables. The range of letter i is from 1 to N, which represents the cross-sectional units, whereas the letter t varies from 1 to T, signifying annual intervals. However, the fixed effect has been shown by the symbol “i,” the dependent variable is shown by the term “y,” and the independent variables are represented by the symbol “X.“

$$\:{\Delta\:}{y}_{it}={\mu\:}_{i}+{\varphi\:}_{i}{y}_{it}+{\beta\:}_{i}^{{\prime\:}}{X}_{it}+\sum\:_{j=1}^{p-1}\:{\lambda\:}_{ij}^{\text{*}}{\Delta\:}{y}_{i,t-j}+\sum\:_{j=0}^{q-1}\:{\delta\:}_{ij}^{{\text{*}}^{{\prime\:}}}{\Delta\:}{X}_{i,t-j}+{\epsilon\:}_{it}\:\:$$
(3)

where \(\:{{\varphi\:}_{i}=-\left(1-{\sum\:}_{j=1}^{p-1}\:{\lambda\:}_{ij}\right)},\:\:{\beta\:}_{i}^{{\prime\:}}={\sum\:}_{j=0}^{q-1}\:{\delta\:}_{ij}{\lambda\:}_{ij}^{\text{*}}=-{\sum\:}_{m=j+1}^{p}\:{\lambda\:}_{im},\:\:j\:=\text{1,2},\dots\:,p-1,\), \({\delta\:}_{ij}^{\text{*}}=-{\sum\:}_{m=j+1}^{q}\:{\delta\:}_{im},\text{j}=\text{1,2},\dots\:,q-1.\)

Equation (3) has been modified to address the additional variables within the error correction framework.

$$\:{\Delta\:}{y}_{it}={\mu\:}_{i}+{\varphi\:}_{i}\left({y}_{it}+{\theta\:}_{i}{X}_{it}\right)+\sum\:_{j=1}^{p-1}\:{\lambda\:}_{ij}^{\text{*}}{\Delta\:}{y}_{i,t-j}+\sum\:_{j=0}^{q=1}\:{\delta\:}_{ij}^{\text{*}{\prime\:}}{\Delta\:}{X}_{i,t-j}+{\epsilon\:}_{it}\:$$
(4)

The significance of a stable equilibrium between variables \(\:{y}_{it}\) and \(\:{X}_{it}\) has been determined by their correlation with variables \(\:{\theta\:}_{i}=-\left({\beta\:}_{i}^{{\prime\:}}/{\varphi\:}_{i}\right).{\delta\:}_{ij}^{\text{*}}\) and\(\:\:{\lambda\:}_{ij}^{\text{*}}\). These variables link growth with the past values of other parameters, as described in Eq. (4). In addition, the value of \(\:{\varphi\:}_{i}\) which represents the correction coefficient, indicates the speed at which a transition to the long run happens after a change in\(\:\:t{X}_{it}\). Moreover, the value of \(\:{\varphi\:}_{i}\) must be negative and fall within the range of 0 to 1. Therefore, the equation be written as follows:

$$\:{\stackrel{\prime }{\theta\:}}_{PMG}=\frac{\sum\:_{i=1}^{N}\:\:{\stackrel{\prime }{\theta\:}}_{i}}{N},{\stackrel{\prime }{\beta\:}}_{PMG}=\frac{\sum\:_{i=1}^{N}\:\:{\stackrel{\prime }{\beta\:}}_{i}}{N};{\stackrel{\prime }{\lambda\:}}_{jPMG}=\frac{\sum\:_{i=1}^{N}\:\:{\stackrel{\prime }{\lambda\:}}_{i}}{N},\text{\:and\:}{\stackrel{\prime }{\gamma\:}}_{jPMG}=\frac{\sum\:_{i=1}^{N}\:\:{\stackrel{\prime }{\gamma\:}}_{i}}{N}$$
(5)

where \(\:j=0,\dots\:,\text{q}\:{\stackrel{\prime }{\theta\:}}_{\text{P}\text{M}\text{G}}=\stackrel{\prime }{\theta\:}\)

The panel ARDL method deals well with issues of endogeneity and serial correlation by using a suitable lag structure for both independent and dependent variables. According to147, the PMG estimator ensures short-term fluctuations and long-term stability. Meanwhile, the MG estimator, a key part of the ARDL approach, runs separate regressions for each country, considering both short-term and long-term changes based on the data148. The PMG estimator combines pooling and averaging of coefficients, as described by149.

The analysis of panel statistics recognizes that different units in the study can influence each other due to both unique and common factors. This CSD is influenced by elements like trade advancements, globalization, monetary integration, and harmonized economic policies. Considering CSD is crucial because ignoring it can lead to inaccurate conclusions and less effective estimations. The study used the CSD test from150 to measure the level of CSD among entities. This test works well for lists of any length and is especially useful when there are more cross-sections than time periods (N > T), as noted by151. The protocol for conducting the CSD test has been shown in equation form as follows:

$$\:CSD_{{TN}} = \left[ {\frac{{TN(N - 1)}}{2}} \right]^{{1/2}} \hat{\rho }_{N}^{{\prime}} \:$$
(6)

Equation (6) specifies \(\hat{\rho }_{N}^{{\prime}}\) as the correlation coefficient, where T represents time and N represents the CD units.

In panel data analysis, different slopes can mess with the accuracy of conclusions, risking the precision of the findings. This issue is important because economic and demographic systems vary widely. In this context, heterogeneity means there are differences in the key parameter among the various cross-sectional units. To explore this, the study used a slope heterogeneity test to check for differences in slopes among these units. The exact phrasing for this test of heterogeneity is as follows:

$$\tilde{\Delta } = (N)^{{\frac{1}{2}}} (2{\text{~}}K)^{{ - \frac{1}{2}}} \left( {\frac{1}{N}\tilde{S} - k} \right)\:\:$$
(7)
$$\:{\text{adj}}\tilde{\Delta } = \left( N \right)^{{\frac{1}{2}}} \left( {\frac{{2k(T - k - 1}}{{T + 1}}} \right)^{{ - \frac{1}{2}}} \left( {\frac{1}{N}\tilde{S} - k} \right)\:$$
(8)

In the above equation, the symbol Δ ˜ represents delta tilde, while adj Δ ˜ signifies adjusted delta tilde. Moreover, the flowchart of the study has been shown in the Fig. 3.

Fig. 3
figure 3

Study flowchart.

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Results and discussions

Descriptive statistics are crucial for characterizing and assessing data by offering essential information regarding measures of central tendency, variability, and the shape of the distribution. This facilitates researchers in understanding the attributes of the data. Statistical tools are essential for conducting initial analyses of datasets, enabling further investigations, and improving the efficient sharing of research findings. Table 2 shows the descriptive statistics of the variables. This study exhibit discernible patterns and distinct traits, as revealed by the descriptive analysis. The average impact of ECFP is quite low, with a somewhat positively skewed distribution (skewness: 0.5177) showing a slight tendency towards bigger ecological footprints. The kurtosis statistic of 2.2219 suggests that the distribution is platykurtic, indicating a lower concentration around the tails compared to a normal distribution. ECX exhibits a broader spectrum, characterized by an average value of 0.8823. The distribution has a modest negative skewness (-0.594), indicating a slight preference for higher ECX. The kurtosis value, which is less than 3, indicates a platykurtic distribution. The FEC has a mean of 1.8854, showing an average level of consumption. The distribution is negatively skewed with a skewness value of -1.6184, and it has a higher degree of kurtosis with a value of 4.5079. This suggests that the distribution has heavy tails and is leptokurtic. Whereas GLB and ITQ have mean values of 1.9149 and 1.9447, respectively and both distributions can be classified as leptokurtic. Finally, the RNE has an average of 0.9269. The distribution is considerably skewed to the left and has a kurtosis value of 4.0376, indicating heavy tails and the possibility of outliers. This suggests a leptokurtic distribution.

Table 2 Descriptive statistics.
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Unit root is an important econometrics test as it helps determine the stationarity of variables. Stationarity of data is essential for research findings because non-stationary variables might produce misleading regression results and compromise the soundness of statistical judgments. Therefore, it is crucial to consider the presence of unit roots and promote the use of stationary variables to guarantee the precision and reliability of research results in the field of time series analysis. The most commonly used test is Hadri, Breitung, Levin-LinChu (LLC), Im-Pesaran-Shin (IPS), and Fisher. Prior to evaluating the stationarity of the data, it is imperative to conduct CSD tests. In order to guarantee the dependability of the unit root test, it is imperative that all series complete the CSD test152,153. Table 3 shows the findings for CSD. The150 test is utilized to examine cross-sectional interdependence. The null hypothesis, which posits that CSD does not exist, is rejected for all values. This implies that any alteration in one of the variables inside one nation will be observable in the other research countries. Consequently, these countries are interconnected152. The data shown in Table 3 unambiguously demonstrates that all three tests—Pagan LM, Scaled LM, and Pesaran CD—exhibit a statistically significant value at the 1% significance level. This demonstrates the presence of CD dependency. The cross-sectional data for ECFP, GLB, ECX, RNE, FEC, and ITQ demonstrate statistical significance at a significance level of 1%. These findings indicate that alterations to the ECFP of one nation will impact the other member countries of G7.

Table 3 Cross Sectional dependence (CSD).
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After analyzing the CSD, it is essential to forecast the degree of stationarity for the data using methods that consider the CSD. Stationarity is a fundamental concept in the analysis of time series data, indicating that the statistical characteristics remain consistent throughout the whole period. A stationary time series is defined as having a consistent and unchanging average, variability, and autocorrelation pattern over time154. The variables’ stationarity was assessed using the Levin, LinChu unit-root tests by the researchers. The results of the panel-unit-root test are displayed in Table 4. The findings clearly indicate that all variables, except ECFP and FEC, are non-stationary at I(0). To address this issue, we calculated the first difference and assessed the stationarity of the variables at I(1). All the variables were determined to be stationary at the first order of integration (I(1)) with a p-value of 0.0000. Furthermore, when the variables become stationary after the first difference, interpreting regression results is easier because the coefficients show the direct effect of the independent variables on the dependent variable. This improves the accuracy of the statistical results and makes predictions in empirical analyses more precise. Therefore, using the ARDL model is considered a good approach.

Table 4 Unit Root test (Levin Lin Chu).
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Co-integration tests are essential in PMG-ARDL models. It shows a long-term balance between variables, meaning they move together over time despite short-term fluctuations155. In PMG-ARDL models, checking for co-integration is crucial because it confirms a stable and lasting connection among the variables. The importance of co-integration lies in its ability to prevent misleading regression results when dealing with non-stationary variables. The confirmation of co-integration indicates a legitimate and non-false correlation between the variables. Although not mandatory, confirming the existence of a co-integration connection improves the accuracy of the model’s results. The study utilizes the Westerlund co-integration test, a widely preferred method, to study the existence of co-integration among the variables. The results, presented in Table 5, confirm the existence of a long-term association between the variables, reinforcing this notion.

Table 5 Westerlund co-integration test:
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Before applying the PMG-ARDL method to see the long-run and short-run relationships between the variables, we applied the Hausman test to check whether we should use PMG, MG, or DFE. Table 6 presents the Hausman test results. The outcome demonstrates that we should go for PMG.

Table 6 Hausman test.
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The error correction term (ECT) quantifies the rate at which a system adjusts itself from a temporary imbalance to a stable, long-term state. Recent academic research suggests that the ECT parameter is expected to have a highly negative value. The PMG calculation validates this expectation, demonstrating a statistically significant adverse valuation at the 1% significance level. The ECT coefficient is crucial as it determines whether the system will return to its long-term equilibrium level after a departure. A coefficient value between 0 and − 1 indicates long-term convergence to the equilibrium level156. The current study reveals that the PMG data indicates an ECT coefficient of -0.824355, which satisfies the required requirement and exhibits significance at the 1% level. This indicates that the shift of variables from a temporary imbalance to a lasting equilibrium is a significant process, with an annual adjustment rate of 0.824355% for the G7 countries. The graphical form of relationships among the variables has been shown in the Fig. 4.

Fig. 4
figure 4

Graphical representation of relationships.

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Table 7 shows the results for ECT and the long- and short-run PMG-ARDL results. It is evident from Table 7 that ECX has a significant negative relationship with ECFP at the 1% level in the long run with a coefficient value of -0.156776. It means 1% in ECX would bring down the ECFP at the rate of 0.156%. ECX is the only variable in the long-run association that shows a negative relationship with ECFP. All the other variables, like FEC, GLB, ITQ and RNE, show a positive association with ECFP. The significant negative relationship between ECX and ECFP suggests that as economies become more complex—typically by advancing towards knowledge-based industries and high-value production—resource use becomes more efficient, thereby reducing environmental impact. The significant positive relationship observed between FEC and ECFP, denoted by a coefficient value of 1.368088, and underscores a noteworthy connection in the context of the G7 countries. This coefficient signifies that a 1% increase in FEC corresponds to a substantial 1.36% rise in ECFP. This connection highlights the impact of FEC on ECFP in advanced economies. Burning fossil fuels increase greenhouse gas emissions and harms environmental sustainability. The complex relationship between FEC and ECFP suggests that reducing fossil fuel use could lower the ecological footprint, helping to protect the environment and promote sustainability in the G7 nations. This finding stresses the need to explore alternative energy sources and adopt eco-friendly practices to reduce the negative environmental effects of fossil fuel use. The results of our findings are similar to the findings of157.

Furthermore, the findings show a significant positive relationship between GLB and ECFP in the G7 economies as indicated by the coefficient value of 2.715571 at the 1% level of significance. This shows that a 1% change in GLB is linked to a significant 2.71% rise in ECFP. This shows that globalization plays a substantial role in the increase of ECFP in G7 countries. The result indicates that increased globalization may be driving up environmental impact. This can be attributed to higher levels of production, trade, and consumption associated with globalized economies, which often lead to increased resource use, waste, and emissions. In G7 countries, globalization can intensify ecological footprints as the demand for imported goods, energy, and industrial outputs grows, often outpacing gains in environmental efficiency. As a result, the strong association highlights the crucial impact of GLB on environmental sustainability, emphasizing the necessity of implementing strategic measures to manage and oversee globalization tendencies within the G7. However, effectively addressing and managing the consequences of globalization is a vital component of ecological conservation endeavors, as it guarantees that worldwide economic activities are in harmony with sustainable practices. The findings highlight the need for global strategies and policies that balance economic growth and environmental preservation to reduce ECFP caused by increased GLB in G7 nations. These outcomes are similar to the findings of158,159. A strong positive correlation was found between ITQ and ECFP, with a coefficient value of 1.081271. This means a 1% improvement in ITQ leads to a 1.081% increase in both economic competitiveness and ECFP in the G7 countries over the long term. The positive association between ITQ and EFP in G7 economies could stem from the role that strong institutions play in fostering economic growth and industrialization, which often increases energy and resource demand. While higher institutional quality typically supports regulatory frameworks and efficient governance, these same structures can also stimulate economic activities that contribute to environmental impact. This shows that while better ITQ boosts economic performance, it can also negatively impact environmental sustainability. Therefore, it is crucial to carefully evaluate and manage institutional quality to minimize potential environmental harm. Environmental laws and regulations that align with sustainability goals are necessary. Balancing improved institutional standards with environmental conservation is essential for a sustainable relationship between institutional quality and ecological health in the G7 nations. These findings stress the importance of integrating environmental considerations into institutional development for a more sustainable future. The results are similar to the findings of160.

Table 7 PMG-ARDL.
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The study on the relationship between RNE and ECFP in G7 economies has revealed interesting findings. Over time, there’s been a strong and statistically significant positive link, shown by a coefficient value of 0.183155. This means a 1% increase in renewable energy is connected to a small but noticeable 0.183% increase in ECFP across G7 countries in the long run. This suggests that while renewable energy is usually seen as environmentally friendly, its use might lead to a rise in ecological footprints over time. This finding points to the need for further research into the long-term effects of renewable energy on ECFP. However, in the short term, there’s a statistically significant negative correlation between renewable energy and ECFP, with a significant level of 5%. This might indicate initial challenges or adjustments in transitioning to renewable energy sources, which could temporarily affect ECFP before long-term benefits are realized. The study also found that other factors like ECX, FEC, GLB, and ITQ have insignificant relationships with ECFP in the short run in G7 countries.

Finally, in Table 8, robustness tests using the AMG and CCEMG methods reveal the relationships between EFP and independent variables. ECX shows a significant negative relationship with EFP across both models indicating that higher economic complexity may reduce the ecological footprint by promoting less resource-intensive industries. Conversely, FEC, GLB, ITQ, and RNE all exhibit significant positive associations with EFP, suggesting that despite potential sustainability gains, these factors currently contribute to higher ecological footprints in G7 countries. This underscores the need for targeted policies to mitigate the ecological impacts of these drivers.

Table 8 Robustness tests.
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Conclusion and policy implication

The rapid increase in fossil fuel use and large ecological footprints is a major cause of environmental damage. To address this issue, we need to shift globally towards cleaner, sustainable, and renewable energy sources, reducing our reliance on fossil fuels and non-renewable resources. The growing acceptance of cleaner energy is driven by several factors: reducing environmental impact, supporting economic growth, improving energy access and security, and combating climate change and global warming. Therefore, the shift toward cleaner, renewable energy is essential due to its potential to significantly reduce environmental damage, curb emissions, and alleviate the ecological footprint associated with fossil fuels. Cleaner energy supports sustainable economic growth, enhances energy security by diversifying sources, and improves access to reliable power, especially in underserved regions. Additionally, it plays a crucial role in combating climate change and global warming, providing a path to mitigate the adverse effects of non-renewable resources and build resilience against environmental degradation. Moreover, a successful approach to address the increasing energy needs of economically growing countries entails the extensive adoption of renewable energy sources. To enhance the incorporation of renewable energy sources into the broader energy system and promote environmentally conscious economic expansion, it is crucial to make significant and enduring investments in research and development for renewable energy. The G-7 nations may increase their use of renewable energy by investing money in research and development of renewable energy technology.

The key goal of this work is to assess the impact of GLB, ECX, use of RNE and FEC, and ITQ on the ECFP. The research specifically examines how ECX affects the environmental performance of G7 countries. Nonetheless, a considerable gap exists in comprehending the ramifications of ITQ, ECX, and emissions on environmental degradation using a more holistic metric known as the ECFP, particularly among the G-7 nations. Our analysis addresses this gap by utilizing data specifically associated with research and innovation in renewable energy, diverging from the more common statistics on general research and development present in prior work. Employing the ARDL-PMG methodology enhances the reliability of our analysis, positioning our work as a pioneering endeavor in thoroughly examining the impact of ECX, RNE, GLB, and ITQ on the ECFP among G7 economies from 2000 to 2021. The study’s empirical findings demonstrate a substantial and positive correlation between variables such as FEC, GLB, ITQ, and RNE with ECFP in the long run. In the long term, there is a notable and adverse correlation between ECX and ECFP. Only the variable RNE has a statistically significant negative correlation with ECFP in the short term, at a significant level of 5%. Conversely, all other parameters display a statistically significant positive correlation with ECFP, with a significant level of 1%.

To tackle the pressing environmental damage driven by rising fossil fuel consumption and substantial ecological footprints, policymakers need a comprehensive, multi-pronged strategy. Prioritizing and incentivizing significant investments in renewable energy research and development is crucial to fostering a large-scale transition to cleaner energy sources. This transition requires G-7 nations, as global leaders, to cooperate closely, sharing technological advancements and supporting an efficient, coordinated shift to sustainable energy. Moreover, integrating environmental considerations into economic policy is also essential, with a focus on reducing ecological footprints through economic incentives that promote sustainable practices and discourage resource-intensive activities.

As a conclusion, the demonstrated link between institutional quality and ecological outcomes underscores the importance of strengthening institutions to foster environmentally sustainable behaviors. Policymakers must consider potential short-term economic and social trade-offs in the renewable transition and should develop policies that cushion these impacts, such as workforce retraining programs and subsidies for affected industries, while prioritizing long-term ecological benefits. Public awareness and education initiatives are equally vital; these can help citizens understand the intricate connections between energy use, economic development, globalization, and environmental health, cultivating a collective responsibility for conservation.

Furthermore, ongoing policy assessment and adaptation are necessary, with an emphasis on data-driven decision-making to refine environmental strategies continually. This comprehensive, adaptive policy approach enables G-7 nations to more effectively address the interconnected challenges of environmental sustainability, economic growth, and globalized energy demands, setting an example for other nations in the transition to a sustainable future.

This work, while providing valuable insights into the effects of globalization, economic complexity, energy usage, institutional quality, and fossil fuel consumption on the ecological footprint, has several limitations. First, the analysis is restricted to G7 nations, which, while economically influential, may not fully represent the dynamics of emerging or lower-income economies where different environmental challenges and energy dependencies exist. Additionally, the study’s timeframe, from 2000 to 2021, limits our understanding of more recent developments in environmental policies and technological advancements that could affect ecological footprints. The reliance on aggregated national data may overlook regional variations within countries that could offer more nuanced insights into ecological outcomes. Furthermore, this study does not fully explore potential interactions among variables, such as the effect of combined economic complexity and renewable energy investment on ecological impacts, which could provide a deeper understanding of sustainability dynamics.

Future studies could expand this analysis by including a broader range of countries, particularly from emerging economies, to provide a more globally representative view. Extending the timeframe to capture post-2021 data would also allow for the assessment of recent policy impacts and technological shifts. Future studies could explore regional analyses within nations and investigate interaction effects among variables, which could illuminate complex relationships within ecological systems. Research focusing on sector-specific impacts of energy consumption and institutional quality could further refine our understanding of sustainability pathways, offering policymakers more targeted strategies for reducing ecological footprints.