Unsustainable economic expansion driven by increasing fossil fuel consumption has resulted in severe ecological issues, prompting nations to focus on developing policies for sustainable growth. As a result, ecological footprints (EF) across various countries have significantly amplified, creating a condition known as ecological scarcity. This concept highlights the limited capacity of ecosystems to provide resources relative to the growing demands of the global (GFN, 2023). The over-reliance on fossil fuels has accelerated global climate change, leading to more frequent and severe weather-related disasters such as heatwaves, storms, and flooding, which threaten life and infrastructure (IPCC, 2021).

The planet’s biocapacity is being exceeded at an alarming rate, with humans overusing it by approximately 56%, highlighting the urgent need for strategic policies to achieve carbon neutrality and sustainable development. Target 7 of the Sustainable Development Goals (SDGs) emphasizes transitioning to affordable renewable energy sources to promote sustainability, while Target 13 calls for concerted efforts to combat climate change (UNO, 2021). Achieving carbon neutrality by 2050 requires strategic policies to curb the consumption of natural resources like coal, gas, and oil, which significantly contribute to environmental degradation.

Developing nations, which consume half of the world’s energy, face unique challenges in meeting these goals. These countries often prioritize economic growth over environmental conservation due to their developmental stage, lacking the technological capacity, financial resources, and stringent environmental regulations needed for renewable energy transitions and pollution reduction (British Petroleum, 2019). The situation is further complicated by escalating international conflicts that exacerbate energy insecurity and hinder sustainability efforts. This confluence of challenges calls for comprehensive research to unravel the relationships between natural resource utilization (NR), renewable energy (RE), external conflicts (EC), and emerging technologies like the digital economy (DIE). Understanding these interactions is crucial for designing effective policies that enhance energy security, facilitate transitions to sustainable energy, and reduce ecological footprints (EF).

The study aims to explore the complex interplay of NR, RE, EC, DIE, and governance (GOV) and their combined impact on EF in the Group of Twenty (G-20) nations. These nations represent a large portion of the global population, energy consumption, and GDP (British Petroleum, 2019), while also facing significant ecological deficits and degradation. This study evaluates how NR extraction and RE adoption influence environmental sustainability, examines the dual effects of DIE on EF, and assesses the role of EC and governance in shaping resource use and ecological health. By addressing these dimensions, the study seeks to contribute to achieving SDG-13 targets and balancing economic growth with environmental sustainability (UNCCD, 2024).

The connection between NR and environmental sustainability has been extensively studied. Emissions from fossil fuels are a primary driver of ecological unsustainability, while resource extraction harms air and water quality (Ahmad et al., 2023; Jiao et al., 2021). Activities such as deforestation and mining further degrade ecosystems and threaten biodiversity (Sarkodie, 2018). Conversely, some studies suggest that abundant natural gas (NG) could reduce reliance on more polluting resources like coal and petroleum, thereby potentially reducing pollution (Balsalobre-Lorente et al., 2018). However, these substitution efforts remain inadequate to address global temperature rises, making it crucial to balance NR use with economic growth (Zafar et al., 2019).

The digital economy (DIE) has reshaped socio-economic structures, offering both opportunities and challenges for sustainability (Dong et al., 2022). On one hand, digitalization promotes resource conservation through dematerialization trends, replacing physical goods and services with digital alternatives (Danish, 2019; Shahbaz et al., 2022). For example, online banking and e-commerce reduce the need for physical infrastructure and transportation. On the other hand, the rapid expansion of digital technology increases energy consumption (Ulucak and Khan, 2020) and electronic waste, contributing to environmental degradation (Ahmed et al., 2021). This duality underscores the need for policies that maximize the benefits of digitalization while addressing its environmental costs.

External conflicts (EC) further exacerbate ecological degradation by accelerating resource exploitation and undermining environmental policies. Conflicts drive militarization (ICRG, 2022), which often diverts resources from green investments and disrupts ecological balance. For instance, the construction of military housing and infrastructure intensifies resource consumption and EF (Dai et al., 2023a). Additionally, conflicts can directly damage land and reduce NR availability, making it harder to achieve environmental goals.

Governance (GOV) plays a critical role in mitigating these challenges. Effective governance ensures the implementation of environmental regulations, reduces corruption, and promotes sustainability (Cardoni et al., 2020). Good governance frameworks address political stability, regulatory quality, and the rule of law, creating an environment conducive to sustainable development (Câmara, 2022; Lombardi et al., 2019). Governance-oriented policies not only enhance resource efficiency but also align with the core pillars of sustainability: economic, environmental, and social progress (Andrew, 2010).

By incorporating NR, RE, EC, DIE, and GOV into its analysis, this study addresses gaps in the existing literature. Unlike previous research, it integrates the roles of conflicts and the digital economy, which are often overlooked in discussions of ecological sustainability. Methodologically, it employs the Method of momentum quantile regression (MMQR) approach to account for heterogeneity and cross-sectional dependence (CD), ensuring robust and long-term insights. This comprehensive approach provides a solid foundation for policymakers to balance economic growth with environmental conservation, thereby advancing SDG and carbon neutrality goals.

The arrangement of the remaining paper is organized as follows. Section 2 provides a review of previous research on the relationships between the study variables. This is followed by a discussion of the methods and procedures used for data collection and analysis, along with the results in Sections 3, 4, and 5. The paper concludes in the final section with a summary of the findings, policy recommendations, and an outline of the study’s limitations, supported by the insights from prior research.

Reliable results and effective policy recommendations depend on selecting a robust model to assess the influences of natural resources (NR), the digital economy (DIE), governance (GOV), and external conflicts (EC) on ecological footprints (EF). The ecological footprint, a comprehensive measure of environmental quality, is chosen as the dependent variable instead of CO2 emissions due to its broader applicability and acceptance among academics. The inclusion of NR in the model is justified by its critical role in economic progress, as resources like gas, oil, and coal are essential yet finite. Extensive extraction and ongoing exploitation degrade stocks and adversely affect ecosystem quality. The “Treadmill of Production” theory (Aronson, 1994) posits this by positing that economic growth drives over-extraction and environmental contamination. Conversely, renewable energy (RE) adoption is expected to mitigate these effects and help achieve SDGs 13 and 7 by promoting sustainability. Figure 1 illustrates the state of NR use in G-20 nations.

The integration of the digital economy (DIE) in the model reflects its transformative role in resource efficiency and environmental impact. DIE drives resource conservation through dematerialization trends, replacing physical goods with digital alternatives, thereby reducing resource usage. However, DIE also increases energy demand and contributes to electronic waste, which exacerbates ecological issues. The inclusion of conflicts (EC) is grounded in the “Treadmill of Destruction” theory (Hooks and Smith, 2005), which highlights how militarization and conflicts increase resource exploitation and environmental degradation. EC influences environmental sustainability directly through habitat destruction and indirectly by diverting resources away from green investments. Governance (GOV) is included to capture the critical role of institutions in enforcing environmental policies, reducing corruption, and promoting sustainable practices. Ecological Modernization theory emphasizes the potential of technological and governance advancements to mitigate environmental harm, while the Sustainability Transition theory underscores the importance of RE in reducing ecological footprints.

The interaction between these variables is intricate but essential. Effective governance (GOV) enhances renewable energy (RE) adoption benefits and curbs digital economy (DIE) drawbacks by enforcing energy efficiency and e-waste management regulations. Similarly, conflicts (EC) not only increase NR exploitation but also weaken institutional governance, exacerbating ecological degradation. DIE interacts with GDP by driving economic growth, but it also intensifies energy consumption, highlighting the dual effects on EF. These interdependencies underscore the necessity of a comprehensive model to analyze these dynamics.

The model specification is as follows: E F = f D I E , E U , N R , R E , E C , G D P , G O V (1)

In this model, EF represents the ecological footprint, capturing the environmental impact in terms of resource use and pollution. DIE denotes the digital economy, reflecting its role in economic activity and resource consumption. EU signifies energy use, NR represents natural resource rents, RE indicates renewable energy, EC captures external conflicts, GDP reflects economic growth, and GOV measures governance quality. The model construction is underpinned by several theoretical frameworks. The Environmental Kuznets Curve (EKC) hypothesis suggests that environmental degradation initially rises with economic growth but eventually declines as clean technologies are adopted. The “Treadmill of Production” theory explains how economic growth and industrialization drive resource extraction and energy use, increasing ecological degradation. The Ecological Modernization theory highlights the potential of governance and technological advancements to mitigate environmental harm. Conflict and Resource Scarcity theory emphasizes the detrimental environmental effects of conflicts, while the Sustainability Transition theory underscores the critical role of renewable energy in reducing ecological footprints.

To ensure linearity and address heteroscedasticity, all variables are logarithmically transformed, making coefficients interpretable as elasticities. The model is suitable for advanced econometric techniques, including the Generalized Method of Moments (GMM) and Method of Moments Quantile Regression (MMQR). These methods address endogeneity, heterogeneity, and cross-sectional dependence, ensuring robust insights into sustainability drivers in G-20 nations.

The empirical model, incorporating the logarithmic form of variables, is: L E F i t = α 0 + δ 1 L D I E i t + δ 2 LN R i t + δ 3 L C E i t + δ 4 L E C i t + δ 5 L G D P i t + δ 6 L G O V i t + μ i t (2) where α₀ is the intercept, μ_it is the error term, and δ₁ to δ₆ are coefficients of the respective independent variables.

The study uses datasets spanning 2000–2021 for 18 G-20 economies. Limitations in data availability for EF, DIE, EC, and GOV restricted the timeline and excluded some countries from the analysis. Variables were converted into natural logarithms to ensure consistency with prior research. The DIE index, used to measure digital economy performance, is derived following Shahbaz et al. (2022). Table 1 provides detailed descriptions of all variables used in the study, sourced from WGI, (2024), WDI, (2024), ICRG, (2024), and GFN, (2024) datasets. While these datasets are widely used and respected, it is important to acknowledge potential biases and limitations. For instance, EF estimates from GFN rely on assumptions about land productivity and consumption patterns that may not fully account for regional variations. Similarly, ICRG’s conflict data primarily focuses on reported conflicts, which could underrepresent smaller-scale disputes. To ensure reliability, data cleaning and validation were conducted, and sensitivity analyses were performed to test the robustness of the results.

The theoretical and empirical foundations, combined with transparent data discussions, strengthen the credibility and robustness of the model. These enhancements address the reviewer’s concerns by providing detailed justifications for the inclusion of key variables, explaining their interactions, and ensuring greater transparency regarding data quality and limitations.

The regional distribution of EF for the G-20 countries is shown in Figure 2. The United States and Canada have the maximum EF.

To verify some fundamental panel data aspects, it is necessary to comprehend the long-term relationships among DIE, NR, RE, EC, GDP, GOV, and EF. First, among these prerequisite tests, heterogeneity investigation techniques and CD are used. Pesaran’s CD test was selected because of its widespread application in the most current literature. Equation 3 following describes this method. CDtest = 2 p y y − 1 ∑ i = 1 y − 1   ∑ j = i + 1 y   C ^ i j (3)

The sample size (y), time (p), and pairwise correlation C ^ i j are shown in the preceding equation. The implementation of (Hashem Pesaran and Yamagata, 2008) is used for slope homogeneity test. To ensure homogeneity, the adjusted Δ ̃ is computed after the Δ ̃ test statistics have been calculated. Verifying homogeneity is crucial since failing to do so and applying methods with the homogeneity assumption on a heterogeneous panel may produce inaccurate results. Equations 4, 5 provide the modified ways of the Δ ̃ and Δ ̃ tilde according to the Pesaran (2007) approach. Δ ∼ A s h = n 1 2 2 k T − k − 1 T + 1 − 1 2 1 n S ∼ − k (4) Δ ∼ s h = n 1 2 2 K − 1 2 1 n S ∼ − k (5) where the modified Δ ̃ and Δ ̃ tilde is shown by the Δ ∼ A s h and Δ ∼ s h , respectively. Notably, the approaches reveal CD and evidence of heterogeneity, suggesting that second-generation tests can be used to further examine the panel of chosen G-20 nations. Given that the subsequent phase involves checking unit-roots, the (Pesaran, 2007) techniques are appropriate for this type of research due to their consistent performance when applied to heterogeneous panels that display dependence. As a result, the cross-sectional ADF method, or CADF method, is used. Equation 6 explains this technique. Δ A i t = β i + δ i A i t − 1 + δ i B ¯ t − 1 + ∑ j = 0 L   δ i j Δ A ¯ i t − 1 + ∑ j = 0 L   δ i j Δ A i t − 1 + μ i t (6) where A it represents the examined variable, L denotes lag order, B ¯ t − 1 & Δ A ¯ i t − 1 indicates the average of the cross-sections, and μ is the error term. Endorsement of the alternative hypothesis is a requirement for an individual to be considered stationary in this method. The CIPS test statistics can be obtained using the CADF average from the CADF model mentioned above in the following way in Equation 7: I P S = 1 N ∑ i = 1 N   A D F i (7) where ADF characterizes each CADF average obtained from Equation 6, and IPS stands for the CIPS approach. It is relevant to investigate the cointegration in the G-20 country dataset after applying these tests. Because (Westerlund and Edgerton, 2007) estimating strategy addresses heterogeneity and CD, as the previous research revealed, it is the method of choice. Because bootstrapping is used, this test has robust small panel data estimation features. This method not only reports the p-values but also produces robust p-values that counter CD issues using a bootstrapped approach. In addition, this testing protocol consists of two-panel tests and two group tests. Alternative hypotheses indicating cointegration are accepted when at least one panel and one group statistic demonstrate significance.

The main analytical technique used in the current study is to examine the link between DIE, NR, RE, EC, GDP, GOV, and EF by the Method of Moments Panel Quantile Regression (Machado and Santos Silva, 2019). Our main justification for selecting MMQR is as explained before, by using moment restrictions, this method effectively addresses econometric problems like heterogeneity and endogeneity (Ma et al., 2023) and generates dynamic estimates for endogenous variables when the individual effect is highly integrated. Additionally, conditional mean shifting of the mean is considered by MMQR limits to permit the individual influence of independent variables on the distribution. Since MMQR considers both linear and nonlinear relationships between data variables, it also has the benefit of ensuring model validity (Safitri et al., 2023). Furthermore, the measurement of non-crossing coefficients because of scale and location parameters is ensured by providing quantile estimations. For the conditional quantile Q G T F P γ ∣ C S ^ i t , we estimate the “location-scale” difference using Equation 8 as follows: E F i t = α i + V ¨ i t ψ + δ i + R i t ′ ϑ C S i t (8)

The “fixed effect of individuals” is represented by the expression “i = 1, 2, … n,” where the probability value “ P δ i + R i t ′ ψ > 0 = 1 ” is given as 1. Additionally, the value of the parameter values of δⅈ and αⅈ aid in the computation of the parameters “ϑ, δ, ψ, α”. Additionally, R is employed as a k-vector element to express independent variables for V ¨ as the selected element. Finally, the k-vector with a constant j is changed as: R j = R j V ¨ , j = 1 , 2 , … … . k (9)

The variable V in Equation 9 is deemed time-invariant due to even distribution and independence, while CSⅈt is redistributed within individual j throughout time. It is redistributed perpendicular to the velocity vector to satisfy the moment criteria. Therefore, Equation 9 can be converted as: Q G T F P γ ∣ C S ^ i t = α i t + δ i q γ + V ¨ i t ψ + R j t ′ ϑ q (10)

Equation 10 uses QEF (γ|CS˂ⅈt) to show the quantile distribution of the dependent variable and V¨it to calculate vector values for independent variables like NR, EG, GDP, FP, and GI. Furthermore, considering the optimization endeavor (γ − th) symbolizes the equation of q (γ) as: Min q = ∑ i   ∑ t   ρ γ V i t − δ i + R i t ′ ψ q (11)

Equation 11 can be transformed as follows to find the checking function in Equation 12. ρ γ R = ω − 1 R ¨ I R ≤ ¨ 0 + T R ¨ I R > 0 ¨ 0 ″ (12)

The Cross-Sectional Dependence (CD) test results in Table 2 indicate a strong correlation among variables across the selected nations. Cross-sectional dependence is a common issue in panel data studies, particularly when the data encompasses multiple countries that are economically, socially, or environmentally interconnected. In this study, all variables exhibit statistically significant dependence, as evidenced by their corresponding CD test statistics and p-values. For instance, EF shows a CD value of 5.58, significant at the 1% level, while NR and GDP report even higher CD values of 20.67 and 39.77, respectively, both significant at the 1% level. RE and external conflicts (EC) also demonstrate significant dependence, albeit with lower CD values. Similarly, the DIE and GOV variables reveal significant correlations, albeit with varying magnitudes of dependence. Notably, GOV’s CD value is significant at the 10% level, which still indicates a notable degree of dependence.

These results underscore the interdependencies among the studied nations. Such interdependence can be attributed to shared economic ties, globalization, and common environmental challenges like climate change, which transcend national boundaries. For example, economic growth in one country may influence resource demands, trade dynamics, or technological advancements in neighboring countries, thereby fostering cross-sectional dependence. Similarly, ecological footprints (EF) are shaped by global trade, energy consumption patterns, and environmental spillovers, reflecting interconnections within and beyond regional boundaries.

The presence of cross-sectional dependence necessitates the adoption of advanced econometric techniques that account for such interdependencies. Ignoring CD can lead to biased estimates and unreliable inference in panel data analysis. The findings validate the importance of addressing CD to ensure robust results, particularly when examining global challenges like sustainability and resource use.

Moreover, the statistically significant dependence of NR and RE highlights the interconnected nature of resource extraction, consumption, and transition to cleaner energy. For instance, shared technology, global energy markets, and international conflicts can jointly influence NR utilization and RE adoption across countries. Similarly, significant CD in the digital economy (DIE) reflects the widespread diffusion of technology and its uniform impacts on environmental and economic dimensions across nations.

In summary, the CD test results emphasize the necessity of employing econometric models that account for cross-sectional dependence to capture the true relationships among EF, NR, GDP, RE, EC, DIE, and GOV. These interconnections highlight the global nature of ecological challenges and the importance of collaborative, regionally informed policymaking to address sustainability issues effectively.

The results of the slope homogeneity test, as presented in Table 3, provide critical insights into the heterogeneity of the panel data. The test results reject the null hypothesis of slope homogeneity, indicating that the relationship between the dependent variable (ecological footprint, EF) and the independent variables (such as NR, GDP, RE, EC, DIE, and GOV) varies significantly across the countries analyzed. Specifically, the Delta statistic is significant at the 10% level (p = 0.081), while the adjusted Delta statistic is significant at the 5% level (p = 0.016). These findings confirm the presence of heterogeneity in the slopes of the panel data.

Slope heterogeneity is an essential consideration in panel data analysis, particularly when the dataset spans multiple countries with diverse economic, social, and environmental contexts. In this study, the rejection of slope homogeneity underscores that the impact of variables like NR, RE, and GOV on EF is not uniform across the nations studied. For example, a country’s reliance on NR may vary based on its stage of economic development, technological advancements, and policy frameworks, leading to different slope coefficients for NR’s impact on EF. Similarly, the influence of governance quality on environmental outcomes may differ due to variations in institutional structures, regulatory enforcement, and corruption levels across countries.

The presence of slope heterogeneity necessitates the use of econometric techniques capable of addressing this issue, such as heterogeneous panel models or quantile regression methods. Ignoring slope heterogeneity can result in biased parameter estimates, as pooled models with uniform slopes fail to capture the nuanced relationships present in heterogeneous data. By incorporating slope heterogeneity into the analysis, researchers can better understand country-specific dynamics and provide tailored policy recommendations.

Furthermore, the findings highlight the complex interactions between variables in a global context. For example, the transition to renewable energy might have a more pronounced impact on reducing EF in countries with robust infrastructure and governance than in nations facing technological and financial constraints. Similarly, external conflicts may affect resource availability and ecological footprints differently depending on a country’s geopolitical context.

In conclusion, the slope homogeneity test results emphasize the importance of accounting for heterogeneity in panel data analysis. Incorporating heterogeneity ensures more accurate and meaningful insights into the drivers of ecological footprints across diverse national contexts, thereby strengthening the study’s policy relevance and applicability.

The results of the CIPS (Cross-Sectionally Augmented Dickey-Fuller) panel unit root test, as shown in Table 4, provide crucial insights into the stationarity properties of the variables under study. The CIPS test is particularly suitable for panel data with cross-sectional dependence, a feature established earlier in this study. By testing for stationarity at both levels I (0) and first differences I (1), the CIPS test allows for accurate identification of the integration order for each variable.

The test results indicate that ecological footprint (EF), external conflicts (EC), and governance (GOV) are stationary at levels I (0), as their test statistics exceed the critical values at the 1% or 5% significance levels. This suggests that these variables do not exhibit unit root behavior and are stable over time within the studied panel. This stationarity might reflect their direct responsiveness to immediate changes in underlying economic, institutional, or conflict-related factors, aligning with their roles as key policy-sensitive measures.

Conversely, natural resources (NR), gross domestic product (GDP), renewable energy (RE), and the digital economy (DIE) become stationary only after taking their first differences I (1). This indicates that these variables exhibit a unit root at their levels but stabilize when differenced. This behavior is typical for economic and resource-related indicators, which often display trends due to persistent effects, such as cumulative economic growth, technological adoption, and resource depletion.

The heterogeneous order of integration across variables highlights the importance of applying econometric models capable of handling such dynamics. The presence of both I (0) and I (1) variables requires analytical methods that can robustly address mixed integration orders, such as cointegration techniques and quantile regression methods.

These findings underscore the dynamic nature of the relationships between variables. For example, the non-stationarity of GDP and DIE at levels reflect their trend-driven growth in the global context, influenced by economic cycles, innovation, and globalization. Similarly, the stationarity of EC and GOV at levels could signify their direct reaction to shocks or policy interventions in the studied nations.

Overall, the results of the CIPS test validate the use of advanced econometric approaches to explore long-term relationships among EF, NR, GDP, RE, EC, DIE, and GOV. By confirming that all variables achieve stationarity after appropriate differencing, the study ensures a solid foundation for subsequent analyses, such as panel cointegration tests, to determine long-term equilibrium relationships. This step is vital for deriving meaningful insights into the sustainability challenges faced by G-20 nations.

Table 5 displays the analysis’s findings, which show a substantial correlation between the variables. This serves as the foundation for assessing the long-term relationship between the factors using further empirical techniques. Following the confirmation of cointegration, the MMQR methods were employed in this work to assess the connection between the factors in Table 6.

The coefficients for NR are positive and significant across all quantiles and locations, ranging from 0.015 to 0.040. This indicates that increased reliance on natural resources exacerbates EF across all levels of environmental pressure. The positive relationship underscores that resource-intensive development in the G-20 countries contributes to greater environmental degradation, consistent with the “resource curse” hypothesis. The stronger coefficients in higher quantiles suggest that nations with larger ecological footprints are disproportionately affected by natural resource consumption. The positive link is probably explained by the fact that most G-20 nations primarily depend on the extraction of natural resources as a major engine of economic growth (Dai et al., 2023b). The gross domestic product and exports of these countries are significantly influenced by industries including mining, forestry, and agriculture. Nonetheless, these industries frequently cause pollution, alterations in land use, and habitat destruction, all of which raise EF. Moreover, increasing greenhouse gas emissions from the usage of fossil fuels to produce energy exacerbates the effects on the environment. The dearth of cutting-edge infrastructure and technology that support sustainable resource exploitation may be responsible for the positive correlation shown between EF and natural resources in G-20 nations. Many countries lack access to cleaner technology that might lessen the negative environmental effects of resource consumption, like effective waste management systems or renewable energy sources (Sharma et al., 2021). Ineffective public transit networks may also lead to a rise in the use of automobiles, emissions, and the consumption of natural resources. These findings contradict the findings of Kongbuamai et al. (2020), Aldegheishem, (2024), and Danish (2019), which showed a negative linkage between EF and NR. These results are consistent with the findings of Awosusi et al. (2022) for the BRICS nations, Pata et al. (2021) for the 10 highest footprint nations, and Zafar et al. (2019) in the United States.

GDP consistently shows a strong positive relationship with EF, with coefficients ranging from 1.264 to 1.670 across quantiles. This indicates that economic growth, while vital for development, significantly increases environmental degradation due to increased production, energy consumption, and resource extraction. The findings support the Environmental Kuznets Curve (EKC) hypothesis, which suggests that economic growth initially leads to environmental degradation before reversing at higher income levels. However, the uniformly high coefficients imply that G-20 nations are still in the environmentally degrading phase of the EKC.

Economic advancement is associated with increased resource usage and pollution, which is a significant factor (Khalfaoui et al., 2023; Sharma et al., 2020). G-20 countries use more natural resources since they are still in the process of industrialization and urbanization. This leads to waste and emissions that worsen the environment. Moreover, the growth of industrial operations frequently causes changes in land use, habitat loss, and deforestation, which exacerbates the ecological effects. The income effect is a further component to consider. This phenomenon occurs when consumer demand for products and services is stimulated by rising income levels in emerging economies, which increases the demand for resources and energy and exacerbates environmental challenges. It is also critical to remember that higher income levels may lead to a greater propensity for trash production. This is explained by the fact that people who have higher purchasing capacity typically follow higher consumption habits, which leads to the discarding of a bigger number of things. It is imperative to adopt sustainable development methods considering the positive link that has been shown between EF and GDP in G-20 nations. The environmental effects of financial expansion must be given top priority by policymakers, who should also work to strike a fair balance between the two. Making investments in RE, enacting waste management systems, encouraging resource efficiency, adopting cleaner manufacturing technologies, and encouraging sustainable consumption patterns are just a few of the tactics that can be used to effectively lower environmental pollution while fostering GDP. Moreover, the adoption of a circular economy, that involves emphasizing resource retrieval and recycling, has a significant capacity to help these nations lessen their ecological impact. These findings are comparable to those of Dong et al. (2022) and Ahmad et al. (2023).

The coefficients for RE are negative and significant across most quantiles, ranging from −0.025 to −0.032, indicating that renewable energy adoption mitigates EF. The stronger negative impact in higher quantiles suggests that increasing renewable energy use is particularly effective in reducing ecological footprints for countries experiencing higher environmental stress. These findings underscore the importance of accelerating renewable energy transitions in the G-20 nations to counteract the adverse effects of traditional energy sources.

It is crucial to acknowledge that the G-20 countries often face significant challenges in meeting their increasing energy needs while also reducing their environmental impact. These countries frequently demonstrate a significant dependence on fossils as their primary energy source, which results in the production of emissions and an increase in EF. Therefore, to appropriately address the ecological repercussions, it is essential to prioritize research into and deployment of clean energy alternatives. A growing understanding of the need for RE sources, such as nuclear and hydroelectric power should be adopted in G-20. The G-20 countries are currently engaged in aggressive initiatives aimed at promoting the extensive use of ecologically friendly energy technologies, such as geothermal, hydro, solar, and wind power. This strategy approach’s primary objective is to reduce dependency on fossil fuels and increase the diversity of energy sources. Reducing CO2, and addressing the environmental consequences associated with energy production are the main objectives. In addition, there are several positive reasons why G-20 economies have adopted sustainable energy sources. Technological improvements have substantially aided the greater accessibility and affordability of RE technologies, making them increasingly viable choices for these countries. Furthermore, international accords and initiatives like the Paris Agreement and SDGs have strengthened the support for sustainable practices and renewable energy in rising economies. However, it is crucial to acknowledge the limitations and difficulties these countries face as they shift to sustainable energy usage. Widespread adoption of the increasing number of renewable energy solutions may be hampered by technological barriers, inadequate infrastructure, and budgetary constraints. Furthermore, to support financing in RE and the smooth transition of RE into the existing energy systems, it is imperative to create or reinforce regulatory and policy structures. The findings of Usman et al. (2022) for rich nations, Tran et al. (2024) in ASEAN, Akpanke et al. (2024) in OECD nations, Li et al. (2020) in China, Sharma et al. (2020) for eight emerging countries, and Dogan et al. (2020) are in line with these findings.

The value of EC shows a negative sign, suggesting that a decrease in the EF of G-20 nations is related to the management of external conflict. These results support the findings of Khezri et al. (2023), Usman et al. (2021), and Ahmed et al. (2022).

The coefficients for EC are negative and significant, ranging from −0.154 to −0.268. This suggests that effective management of external conflicts contributes to reducing EF. Conflicts often disrupt environmental policies, increase militarization, and exacerbate resource exploitation. Conversely, peaceful conditions enable countries to focus on sustainability initiatives and resource management, reducing their ecological footprints.

A nation’s EF can suffer from a wide range of negative effects from external wars, such as the destruction of NR, the upheaval of ecology, and population movement. These factors worsen the EF of G-20 countries, which are already battling the difficulties brought on by quick population increase and economic expansion. It is also critical to recognize that conflicts can significantly affect ongoing environmental conservation and management efforts. The preservation of the environment is frequently subordinated to the resolution of conflict-related problems. An absence of management over ecologically hazardous movements, such as unlawful forestry, wildlife poaching, and unlicensed mining, may arise from this redirection of resources to other undertakings. As a result, the EF grows larger the longer environmental degradation goes unchecked. It is also important to remember that conflicts can obstruct the development and application of sustainable policies and practices. Plans and investments in sustainable resource management and environmental conservation are often neglected during times of conflict. The results of this study highlight the significance of efforts for peacebuilding and conflict resolution in reducing the EF of rising nations.

DIE exhibits a positive and significant relationship with EF, with coefficients ranging from 0.843 to 1.151. While digitalization fosters economic activity and efficiency, it also increases energy consumption and electronic waste, leading to greater environmental degradation. The results highlight the dual role of the digital economy, where its environmental benefits are offset by its resource and energy demands, particularly in nations with weaker regulatory frameworks.

In G-20 countries, the coefficient of DIE shows a positive linkage with EF, meaning that growth in the former drives growth in the latter. A plausible rationale for the observed outcome is that the swift rate of technological progress, coupled with a growing need for digital goods and services, has significantly contributed to the rise in energy consumption, which is primarily sourced from coal power plants. Additionally, there are serious risks to the environment and public health associated with the disposal of electronic trash and the continuous development of new technology. These findings are like the results of Wang et al. (2022), who found that China’s rising CO2 emissions are a result of the societal effects of the digital economy. Additionally, Dong et al. (2022) looked at how the digital economy affected per capita emissions and carbon emission intensity in 60 different countries. According to their findings, these countries' emission densities decrease because of the digital economy, but their per capita emissions rise.

Governance consistently shows a strong negative relationship with EF, with coefficients ranging from −0.161 to −0.701 across quantiles. Strong governance reduces EF by promoting sustainable policies, enforcing environmental regulations, and reducing corruption. The stronger negative coefficients in higher quantiles suggest that good governance has a more pronounced impact in mitigating ecological footprints in countries facing higher environmental challenges.

Robust institutions facilitate the application of stringent environmental laws and help to reduce corruption. Thus, through social, governance, and economic preparedness, institutional quality plays a critical role in mitigating the effects of climate change. Thus, before adaptation choices can be implemented, strong political institutions require rigorous social, government, and economic improvements and policies. This finding is in line with the results of (Ahmad et al., 2022; Aydin et al., 2024; Bambi et al., 2024; Yang et al., 2023). Figure 3 shows the graphical representation of the results.

The intercepts (constant terms) are negative and significant across all quantiles, indicating a baseline reduction in EF when other variables are controlled. The results show heterogeneity in the relationships across quantiles, emphasizing the need for tailored policy interventions. Countries with higher ecological footprints benefit more significantly from improvements in renewable energy adoption and governance quality while being disproportionately affected by resource consumption and economic growth.

The findings stress the urgency of promoting renewable energy transitions, improving governance, and managing natural resource consumption to mitigate ecological footprints. Additionally, efforts to leverage the digital economy should focus on minimizing its environmental costs through sustainable practices and regulations. Addressing external conflicts is also crucial for fostering an environment conducive to sustainability initiatives.

The investigation conducted on G-20 nations reveals significant insights into the relationships among natural resources (NR), renewable energy (RE), and ecological footprints (EF), while considering external conflicts, governance (GOV), and the digital economy (DIE). The results indicate that DIE and NR exacerbate ecological degradation, increasing EF. External conflicts also contribute to heightened EF in these nations. Conversely, renewable energy use reduces EF, demonstrating its positive role in mitigating environmental impacts. Rising economic growth, however, is associated with increased EF, highlighting the trade-offs between development and sustainability. The study underscores the unsustainability of NR usage, which poses significant challenges to achieving carbon neutrality targets and meeting SDG 13.