Moderating roles of green technologies and trade diversification in resource-driven ecological pressures: evidence from G-20 economies using a panel MMQR approach

The ecological consequences of natural resource dependence and energy exploitation are drawing increasing global concern, particularly under the accelerating pressures of climate change. This study explores the direct and moderating effects of trade openness, trade diversification, and environmental technologies on the relationship between natural resource rents and ecological sustainability in G-20 economies from 1990 to 2023. Employing the Method of Moments Quantile Regression approach, the analysis uncovers heterogeneous impacts across the distribution of the ecological footprint. The results indicate that natural resource rents significantly intensify ecological pressures, with stronger effects observed in highly polluted economies than in cleaner ones. Moreover, greater diversification in resource extraction is found to exacerbate environmental degradation, rather than mitigate it, by broadening the scale of resource use. In contrast, environmental technologies consistently and robustly reduce ecological footprints across all quantiles, underscoring their critical role in promoting sustainability transitions. Trade openness further supports ecological sustainability by amplifying the positive effects of environmental innovation while moderating the negative impacts of resource dependence. Robustness checks using Granger causality analysis confirm bidirectional links between ecological footprint, natural resource rents, diversification, and environmental technologies, while GDP and trade openness display unidirectional causal effects. These findings emphasize that reducing dependence on resource rents, expanding renewable energy adoption, and accelerating the diffusion of clean technologies are essential strategies for G-20 nations to align economic growth with long-term ecological sustainability.

1 Introduction

Over the past century, the global environment has deteriorated significantly alongside rapid industrialization and economic expansion. For the first time in recorded history, atmospheric CO₂ concentrations have surpassed 400 parts per million. Rising global temperatures have consequently led to higher sea levels and more frequent climate-related disruptions. Moreover, the number of recorded natural disasters has increased markedly compared with the previous 2 decades (United Nations Office for Disaster Risk Reduction, 2020).

In the aftermath of the COVID-19 pandemic—which severely impacted public health and the global economy ecological preservation and sustainable development have regained global prominence (Kaddachi and Benzina, 2025). Consequently, there has been a growing global commitment to adopting environmentally friendly production and consumption patterns. The G-20 Environment Ministerial Conference, held on March 30–31, 2022, brought together environmental ministers from G-20 member states, the European Union, and several non-member countries, along with representatives from international organizations. Participants reaffirmed their environmental commitments and expressed collective concern over the accelerating pace of climate change, emphasizing the shared objective of limiting global warming to below 1.5 °C.

To achieve net-zero emissions by 2050, these commitments encompass the formulation and implementation of effective environmental and climate policies. Additionally, countries have sought to align post-COVID-19 recovery strategies with ecological and climate objectives to ensure a just, sustainable, and resilient global transition. In pursuit of these goals, G-20 governments have adopted various policy measures, including the establishment of emissions trading schemes to facilitate carbon offset trading and incentivize greenhouse gas (GHG) reductions (Kumar et al., 2024).

To further encourage industries to align their production processes with ecological targets, several countries have enacted environmental tax legislation and introduced green financial mechanisms to fund conservation projects and promote sustainable practices. According to G-20 data from 2022, the proportion of GHG emissions covered by carbon pricing instruments such as taxes and emissions trading systems—has steadily increased across multiple sectors and nations. By 2021, approximately 25% of global GHG emissions in 71 countries were subject to carbon taxes, emissions trading schemes, or both. These strategies have produced positive environmental outcomes by incentivizing cleaner production methods. Previous studies (Ahmad and Zheng, 2021; Z. Wang et al., 2022) have demonstrated that environmental technologies enhance environmental quality. However, as noted by Dogan et al. (2022), the development and diffusion of environmental technologies may also have unintended adverse effects on natural ecosystems. Figure 1 illustrates the recent trends in environment-related technologies.

Figure 1

Figure 1. Environmental related technology trends in G-20 countries.

The effect of breakthroughs in the energy sector differs across different countries, demonstrated by the conflicting findings across numerous studies. This raises the issue about if environmental technology has a distinct effect on ecosystems in various nations. To tackle this problem, we utilize an innovative estimation methodology known as the approach of minutes for quantile regression (MMQR). With the help of this approach, we can assess how different environmental factors affect the geographic distribution of the statistic and successfully deal with endogeneity and variables that are missing. The results show that while the benefits of environmental technology are less noticeable but still beneficial in different nations, they become more noticeable in polluted nations. This indicates that improving an ecosystem’s quality in general requires implementing technological developments.

When it comes to environmental protection, the abuse of natural resources is just as important as technological problems. For it to promote economic growth, the process of production depends on the extraction of natural resources, both conventional and sustainable. The ecology suffers as a result of the exploitation of these natural resources (Dagar et al., 2022). The developing countries are more likely to engage in intense extraction of resources (Yu et al., 2023). This is a result of the fact that many countries still rely on selling of natural resources to generate income since they have not yet attained energy savings. Although it would be difficult to totally elude the abuse and misuse of natural resources, the main question is if nations can successfully reduce the adversarial environmental impacts connected with removal of resources. Here is Figure 2, showing the ecological footprints in G-20 countries.

Figure 2

Figure 2. Ecological Footprints in G-20 countries.

This work has added a number of fresh insights to the body of existing material. To the greatest extent of our information, this study is the innovative study to evaluate how differentiating natural resource rents affects the health of the environment. Previous studies by Adedoyin and Zakari (2020), Bekun et al. (2019) and Khan et al. (2020), only consider the effects of the total amount of assets utilized, ignoring the variety or makeup of these assets. In order to evaluate the indirect effect, we also take into account the structural influence of natural resource rents richness along with their spread.

Lastly, we apply (Machado and Santos Silva, 2019), MMQR approach to calculating minutes for quantile regression. We may evaluate the various and distributional impacts with this method, taking endogeneity and unobserved variable issues into consideration. By taking into account each country’s environmental technology breakthroughs and the condition of its natural resources, the aforementioned study methodologies allow us to make more relevant recommendations to governments extraction, as well as aspects related to water quality.

The structure of the paper is organized as follows. Section II provides a comprehensive review of the relevant literature. Section III outlines the methodology employed in the analysis, while Section IV presents the empirical results and discussion. Section V offers the main conclusions of the study, followed by a dedicated section on policy implications.

2 Literature review

2.1 NRR and environmental quality

Natural resources are defined as components of the natural world that are present in it absent direct human intervention and are distributed differently between nations (Amirova, 2022). There is conflicting evidence supporting the claim that natural resources have an effect on the state of the environment (Abbasi et al., 2021). The idea that natural resource rents may encourage sustainable development is supported by Adam Smith and David Ricardo’s “natural resource bless.” The money made from the exploitation of natural resources could go toward funding environmentally friendly initiatives. In addition, rents from natural resources may increase commerce and global investment, opening doors to new environmental technology and the improvement of the environment (Sinha and Sengupta, 2019).

On the other hand, (Gelb, 1988), “natural resource curse” could assist in clarifying the opposite connection among sustainable development and natural resources. The term “natural resource curse” describes the unfavorable effects that wealthy nations have within their politics, culture, or economy (Ross, 2015). According to (Ahmad et al., 2023), nations endowed with natural resources prioritize resource exports above using them to stimulate economic growth. Therefore, excessive use of natural resources causes serious environmental problems and high levels of greenhouse gases (Bölük and Mert, 2014; Cho et al., 2014). Notably, using antiquated technology to extract natural resources may increase their detrimental effects on the natural world (Adedoyin and Zakari, 2020).

The influence of natural resource rents on ecosystems is found to be diverse in empirical research. In 45 resource-rich Asian nations, (Shittu et al., 2021), establish a negative correlation between natural resource rents and pollution. Parallel to this, (Ahmad and Zheng, 2021), demonstrate how natural resource rents support ecological sustainability in the BRICS nations. Conversely, a great deal of research indicates that NRR has a positive impact on ecological footprint in a variety of situations, including five specific African countries (Akpa, 2023). The dependency on natural resources, the intuitive nature, and the commitment made by governments to promoting sustainable development may be the main causes of the ambiguous significance of natural resource rents (Alfalih and Hadj, 2022). Nonetheless, this research makes the assumption that the question of how NRR protects the planet needs a more thorough examination by looking at its variety.

2.2 NNR diversity and environmental quality

Although the variety of natural resources have been extensively recorded, the variation of natural resources rents is yet to be characterized in the available literature. Variety in natural resources is seen to be connected to a state of equilibrium leadership, and preservation (Baofu, 2014). First, conserving means protecting species with elevated rates of extinction and declining biotic interactions, as well as protecting their habitats and ecosystems. The leadership then tries to figure out who holds the right or not to use the natural resources. In conclusion, balance refers to the return of a state to its initial state after recurring disturbances. In addition, the range of natural resources refers to their possession or composition of various elements found in nature (Baofu, 2014; Soti et al., 2025). The extent to which a nation expands its natural resource rents is known in this study as natural resource rent variability.

The World Bank lists five primary categories of rents associated with natural resources substance, petroleum, coal, fuel, and forestry rents. Given the aforementioned research, the majority of earlier works test the association among natural resource rents and the state of the environment, while other papers only address the impact of a particular kind of natural resource rent. In oil-rich industries like Saudi Arabia, (Mahmood and Saqib, 2022; Mahmood et al., 2023), in 13 G-20 nations, and (Agboola et al., 2021), in MENA have all confirmed that oil rents are accountable for the release of pollutants. Furthermore, in the BRICS nations which are regarded as coal-dependent nations (Adedoyin and Zakari, 2020), discover a negative correlation among carbon dioxide production and fuel costs.

First, (Prebisch, 1962), created the Prebisch-Singer hypothesis, which suggests that over time, the cost of substances, petroleum, and natural gas may decline. Furthermore, world markets set the cost of natural resources, and nations with abundant natural resources are viewed as buyers of prices (Frankel, 2010). Therefore, relative with different states, those with a wider range in the rents from natural resources may be more negatively impacted by the downward trend in material costs.

Furthermore, the “Dutch disease” phenomenon (Wijnbergen, 1984), states that an over-reliance on nat ural resources results from the increase of natural resource extraction diminishing the rivalry of other sectors. Additionally, the characteristics of natural resources include their simplicity or the extraction process, which leads to earnings and later economic advantages (Ali et al., 2021). These financial benefits encourage rent-seeking behavior, which increases abuse and fraud. Environmental regulations may be compromised by fraud, which would lead to an increase in emissions of greenhouse gases and shade output (Chen et al., 2018). As a result, the investigation puts forth the two theories listed below.

2.3 Environmental technology and environmental quality

Environmentally conscious technology contributes to decreasing pollution and the use of energy, which improves the balance of nature and promotes long-term sustainable development (Obobisa et al., 2022; H. Wang et al., 2021). Environmentally-related science frequently makes it easier to produce and use renewable energy sources such as water, wind, or solar power, as well as fuel alternatives which have less of an effect on the planet than petroleum and coal (Appiah et al., 2023). Green technology may therefore promote ecological stability by increasing energy production and shifting into cleaner power supplies (Chen et al., 2022). But in certain circumstances, renewable energy could be a double-edged sword (Bentzen, 2004; Bessec and Fouquau, 2008). Environmental technology has the potential to degrade the natural world in two distinct manners. First, while micro-level increases in energy utilization are possible, they may lead to general rise in energy usage (Yang and Li, 2017).

“The rebound effect” is the term used to describe this phenomenon. The Utilizing greener power sources reduces energy prices, which in turn boosts output and usage. As a consequence, they hasten the depletion of natural resources and increase contamination (Ali et al., 2021). Secondly, countries possessing advanced technological capabilities could decide to transfer their outdated technologies to other countries. As a result, a significant quantity of pollutants are transferred to countries that import goods (Hussain et al., 2020). The crucial part that environmental technology plays in preventing ecological deterioration and promoting sustainable economic growth has been established in earlier research. Either single-country or multi-country data are used to examine the noteworthy and advantageous effect. For example (Álvarez-Herránz et al., 2017), has reduced the production of greenhouse gases greatly as a result of sophisticated technology.

The beneficial and substantial connection between green technology and the quality of the environment is also confirmed by additional research using national data, including (Abbasi et al., 2022) in China. However, the positive effects that technology advancements have on the natural world could be undermined by an excess of technological advancement (H. Wang and Wei, 2020). According to (Ahmad and Zheng, 2021), environmental technology negatively impacts the natural world in economies with low incomes. Nevertheless, a number of investigations also discover detrimental effects of renewable energy sources on environmental systems across industrialized nations, including the G-20 (Álvarez-Herránz et al., 2017).

These ambiguous results regarding the contribution of environmental technology also offer chances to investigate the nonlinear connection between climate privation and environment-related technology. For instance, evidence is found by Chien et al. (2021), to indicate an inverted U-shaped relationship among carbon dioxide emissions and sustainable development.

2.4 Literature gap

The conversation that was just had makes the case regarding the importance of researching how natural resource rents and environmental technology connect to one another. Several economic methodologies have been used in previous research to study variables affecting the sustainability of the environment in a variety of nations, nation groupings, and historical periods. Nonetheless, there is documentation of contradictory empirical results. Apart from this, the variety of natural resource rents, natural resource rents, and ecologically relevant technologies have not been examined in previous research to identify the ecological framework. In addition, there is a hole in the material discussed below that needs to be thoroughly investigated when it comes to the effect of variety in natural resource rents on the connection among natural resource rents and environmental integrity. In order to overcome the data gaps, the current study examines the effects of environmental technology, natural resource rents, and their richness and variety on the quality of the environment. We applied (Dietz and Rosa, 1997) “Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT)” paradigm.

3 Research methodology

3.1 Theoretical framework

This research builds an empirical model that connects the sustainability of the environment with its possible drivers using the benchmark methodology called STIRPAT. The impact of technical innovation and economic growth on the surroundings is evaluated by the STIRPAT model. The environmental patent acts as a barometer of technological advancement. Technological and scientific developments reduce pollution by using renewable energy sources and improving energy utilization. While there are usually positive consequences of this method on the surroundings, it is crucial to consider the “rebound effects” of technical improvements. Technological developments may lead to higher levels of production and use, both of that could be harmful to the planet. This research investigation measures the level of sustainability using ecological footprint per capita, and measures the amount of economic growth inside the country using GDP per capita and global trade parameters. The STIRPAT model is typically displayed as follows in Equation 1,

${\mathrm{I}}_{\text{iu}}={\mathrm{\alpha }}_{\text{it}}{\mathrm{P}}_{\text{iu}}^{{\mathrm{\beta }}_1}{\mathrm{A}}_{\text{iu}}^{{\mathrm{\beta }}_2}{\mathrm{T}}_{\text{it}}^{{\mathrm{\beta }}_3}{\mathrm{\epsilon }}_{\text{it}}(1)$

Where, I stands for the impact on the environment, P for people, A for economic growth, and T for technological advancement. Countries and dates are indicated by the subscripts i and t, respectively. The following is how equation-1 might be transformed into a natural logarithm form,

$\ln {\mathrm{I}}_{\dot{\mathrm{u}}}={\mathrm{\alpha }}_{\text{it}}+{\mathrm{\beta }}_1\ln {\mathrm{P}}_{\text{it}}+{\mathrm{\beta }}_2\ln {\mathrm{A}}_{\text{it}}+{\mathrm{\beta }}_3\ln {\mathrm{T}}_{\text{it}}+{\mathrm{\epsilon }}_{\text{it}}(2)$

Additionally, the evaluation of the impact of natural resource rents and diversifying them on the ecosystem can be done by extending the right-hand side of Equation 2. States that use a wide range of resources typically use a growing variety of tools and machinery since every kind of natural resource has particular specialized equipment for mining. These machines’ operational processes put stress on the environment through usage of fuel and pollutants.

In addition, in contrast to a situation in which an extractive country focuses on a minor amount of asset kinds, the method of diversified utilization reduces the economic efficacy of scale, subsequent in greater total expenditures and pollution per unit of mining output. A further problem is that upgrading all of those technological devices to render them kinder to the environment will take longer and require more funds when a country uses a wide range of machinery and technologies. Because of this, the broadening of natural resources affects overall ecological health and technological advancement. Making use of existing frameworks of theory, we proposed the following study model in Equation 3,

$\begin{array}{c}\ln \text{EF}{\mathrm{C}}_{\text{it}}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1\ln \text{NR}{\mathrm{R}}_{\text{it}}+{\mathrm{\beta }}_2\ln \text{DRIVE}{\mathrm{R}}_{\text{it}}+{\mathrm{\beta }}_3\ln \text{ER}{\mathrm{T}}_{\text{it}}+{\mathrm{\beta }}_4\ln \text{TRAD}{\mathrm{E}}_{\text{it}}\\ +{\mathrm{\beta }}_6\ln \text{GD}{\mathrm{P}}_{\text{it}}+{\mathrm{\epsilon }}_{\text{it}}\end{array}(3)$

The potential mitigating influence of natural resource rent diversity on the link among natural resource rent diversity and overall value is a further area of interest. Spread of asset extraction may make the adverse environmental properties of natural resource rents worse because of the higher waste produced per unit of resource supply. Therefore, equation-3 is used to evaluate the moderate effect of diversification altered in the manner described below in Equation 4,

$\begin{array}{c}\ln \text{EF}{\mathrm{C}}_{\text{it}}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1\ln \text{NA}{\mathrm{T}}_{\text{it}}+{\mathrm{\beta }}_2\ln \text{DRIVE}{\mathrm{R}}_{\text{it}}+{\mathrm{\beta }}_3\ln \text{NA}{\mathrm{T}}_{\text{it}}*\ln \text{DRIVE}{\mathrm{R}}_{\text{it}}\\ +{\mathrm{\beta }}_4\ln \text{ER}{\mathrm{T}}_{\text{it}}+{\mathrm{\beta }}_5\ln \text{TRAD}{\mathrm{E}}_{\text{it}}+{\mathrm{\beta }}_6\ln \text{GD}{\mathrm{P}}_{\text{it}}+{\mathrm{\epsilon }}_{\text{it}}\end{array}(4)$

The following acronyms are utilized: ERT stands for environmental-related technologies; GDP stands for the national income per capita; NRR stands for natural resource rents richness; DIVER stands for natural resource rents diversification; and EFC stands for ecological footprint per capita.

3.2 Data description

The empirical framework of this study is tested on a balanced panel dataset covering the G-20 economies over the period 1990–2023. The choice of countries and time span is primarily determined by the availability and consistency of data across all variables. Data on ecological footprint per capita are obtained from Miller et al. (n.d., p. 202). The ecological footprint represents the biologically productive land and water area required to provide the resources consumed and to absorb the waste generated by human activities. It comprises six major components: built-up land, carbon emissions, grazing land, forest products, fishing grounds, and cropland. While most indicators are available up to 2021, data for the last 2 years are partially projected, and patent-related information on ecological technologies is consistently available until 2021. To maintain uniform representation across all G-20 countries, the analysis is limited to the 1990–2023 period. Table 1 provides the description, sources, and abbreviations of the variables used in the analysis, including ecological footprint (ECF), income, trade openness, natural resource rents (NRR), and green technologies.

Table 1

Table 1. Data description.

The focus and diversified approach was used in the valuation of natural resource rents diversification (DIVER). Specifically, the Herfindahl-Hirschman index (HHI) technique is used to evaluate how concentrated natural resource rents are in a country. The economics literature has made considerable use of the HHI to gauge specialization and assess the level of competitiveness in a variety of industries (Evren et al., 2021). An elevated HHI index denotes a noteworthy amount of focus, which is the same as having little diversity. The formula for calculating DIVER is shown below in Equation 5.

$\text{DIVE}{\mathrm{R}}_{\text{it}}^{\text{HHI}}=\frac{{\displaystyle {\sum }_{\mathrm{j}=1}^{{\mathrm{n}}_{\mathrm{i}\pi }}}{\left(\frac{\mathrm{n}{\mathrm{\pi }}_{\text{ij}}}{\text{NR}{\mathrm{R}}_{\text{it}}}\right)}^2-\frac{1}{{\mathrm{n}}_{\text{it}}}}{1-\frac{1}{{\mathrm{n}}_{\dot{\mathrm{i}}}}}(5)$

A sensitivity examination is carried out using an extra diversity index, the Shannon-Weiner index, in order to improve the dependability of the study’s results. Unlike the Herfindahl-Hirschman Index (HHI), the Shannon-Weiner Index shows that a higher value corresponds to a higher level of diversification in Equation 6.

$\text{DIVE}{\mathrm{R}}_{\text{it}}^{\text{SWI}}={\displaystyle \sum _{\mathrm{j}=1}^{{\mathrm{n}}_{\dot{\mathrm{i}}}}}\left(-\left(\frac{\text{nr}{\mathrm{r}}_{\text{it}}}{\text{NR}{\mathrm{R}}_{\text{it}}}\right)\times \ln \left(\frac{\text{nr}{\mathrm{r}}_{\text{it}}}{\text{NR}{\mathrm{R}}_{\text{it}}}\right)\right)(6)$

The ratio for every Natural Resources Rents (NRR) compared to the aggregate rents of all natural resources is used to compute the HHI and Shannon-Weiner indices. In particular, readily available resources include oil, coal, minerals, woodlands, and gas from the earth. The World Development Indicator dataset is where the data were gathered. Utilizing the result technique, environmental technology is evaluated. A country’s ability to innovate in environmental matters is measured by the percentage of environmental patents it holds. By evaluating the percentage of total exports and imports in relation to GDP, the degree of trade openness can be measured. However, the GDP per capita can be used to determine the degree of economic growth. We use data in the form of per capita for each of the independent metric (GDP per capita) and the factor that is dependent (Ecological footprint per capita) in the research framework to ensure information consistency.

3.3 Estimation method

To choose the best estimating methods, we examine the characteristics of the information being analyzed. Cross-sectional dependency may result from the interactions among the financial, technological, and environmental characteristics of G-20 countries. The absence of sloping homogeneity that is related to the diversity in the variables being studied within the investigation the nation’s sampling people is another cause for caution. If these problems are present, using conventional first-generation estimating methodologies could lead to inconsistent and unreliable results from research. Therefore, we use what follows approaches to investigate issues with information characteristics, the (Westerlund, 2007) test for cross-sectional dependence, the (Hashem Pesaran and Yamagata, 2008), test for homogeneous slope, the (Pesaran, 2007), investigate for stationarity, and the (Pesaran, 2021) test for cross-sectional reliance.

In empirical research, linear regression approaches are frequently used to assess the diverse effects on environmental condition in G-20 countries of natural resource rents, natural resource rent growth, along with other economic and technological factors. For a number of causes, panel quantitative regression is used in this work. First, data from panels with non-additive fixed impacts can be used with the quantile regression approach to estimate parameters for different variable dependence conditional ranges. Consequently, quantile regression makes it possible to develop suggestions for policies that are more focused.

Additional, quantile regression does not depend on specific presumptions about the goal variable’s distributions. Once the distribution’s normality is broken, statistically speaking, contradicting estimation outcomes may arise if conventional techniques for determining the average are applied. Thirdly, the quantile method offers more benefits whenever because it uses a median for estimate instead of the median alone, it is less suitable for handling dense distributions and extremes (Koenker, 2005). Lastly, This method addresses the problem of unnoticed different heterogeneity by allowing the estimation of conditionally heterogeneous covariance impacts across factors inside a particular nation (Canay, 2011).

The study uses (Machado and Santos Silva, 2019) approach to minutes for quantile regression (MMQR) and Table 8 presents its results. It is possible to estimate panel information with specific impacts using this method. The examination of the heterogeneous and distributional effects of separate variables within various quantiles of the variable in question is made possible by this method. It also includes the current technique, which addresses endogeneity and the impact of unknown variables by using the lag variables of the separate variables. The following is the equation for MMQR estimation in Equation 7,

${\mathrm{Q}}_{\mathrm{Y}}\left(\left(\mathrm{\tau }\mid {\mathrm{X}}_{\text{it}}\right)\right)=\left({\mathrm{\alpha }}_{\mathrm{i}}+{\mathrm{\delta }}_{\mathrm{i}}\mathrm{q}\left(\mathrm{\tau }\right)\right)+{\mathrm{X}}_{\text{iu}}^{\prime }\mathrm{\beta }+{\mathrm{Z}}_{\text{iu}}^{\prime }\gamma \mathrm{q}\left(\mathrm{\tau }\right)(7)$

Where the external factor X′_it, the known differentiable (with probability 1) transformations of the components of X (Z′_it), the scalar coefficient (αi + δiq (τ), and the conditional quantile QY (τ |X_it) for a panel of n individuals i over t time intervals are given as functions.

4 Results and discussion

The descriptive statistics for all dependent and independent variables are presented in Table 2. From 1990 to 2023, the average ecological footprint per capita among G-20 economies exhibits considerable heterogeneity across countries. The mean value of natural resource rents (0.541), coupled with a relatively high standard deviation, indicates substantial disparities in resource dependence among member states. Owing to the structural and economic similarities of G-20 economies such as their high degree of global integration and policy interlinkages cross-sectional dependence and long-term interrelationships among the panel variables are likely to exist. Accordingly, this study investigates these statistical characteristics using panel unit root tests, cointegration analysis, and cross-sectional dependence diagnostics. The corresponding results are reported in Tables 4–6, respectively. As shown in Table 3, significant correlations at the 10% level or higher confirm the presence of cross-sectional dependence among the examined economies. In order to allow the panel unit root test to determine whether cross-sectional dependence is present, Table 4 uses the second version of the figure. The results showed that the factors in the study’s design produced a single root once moved to their initial distinct value. The results of the cointegration test in Table 5 show that, across and within the panel nations, natural resource rents, trade transparency, environmental technology, and GDP per capita are all correlated over the long run.

Table 2

Table 2. Descriptive statistics.

Table 3

Table 3. CD test.

Table 4

Table 4. Unit Root test results.

Table 5

Table 5. Panel Conitegration test results.

The amount and diversity of assets that are used up have a negative impact on the ecosystem between countries. Likewise, free trade and economic growth are factors that lower environmental quality in the nations. The structure of the study in model 2 can be protracted to include the hidden effects of diversifying natural resource rents. Based on this approach, countries can reduce the negative environmental impacts caused by resource abuse by focusing on a small number of asset groups.

Although the empirical results provided above are compelling, they only provide a partial view of the relationship among resource extraction with ecological footprint. In order to provide a more thorough representation of the environmental influence of the explanatory factors through the variable of interest’s shipping, they therefore adopt the MMQR technique.

We continue by analyzing the residuals’ normality (Table 6) and slope homogeneity (Table 7). The results show that both Models 1 and 2 have non-normal leftovers and gradient variation. This finding suggests that using conventional mean estimations to describe the characteristics of the investigation’s link may be deceptive. The 10th quantile represents the highest standard of living, the 50th quantile represents average level, and the 90th quantile represents lower grade. The projected outcomes shown in Panel A are discussed first, followed by those in Panel B.

Table 6

Table 6. Normality test results.

Table 7

Table 7. Slope homogeneity test.

Initial research show that the amount from natural resources rents has a variety of effects on environmental deterioration. As the ecological condition declines across various dispersion levels, the impact’s value rises. Once environmental contamination surpasses the 30th quantile, the impact coefficient which was previously statistical insignificant—becomes statistically significant. This indicates that nations with low levels of environmental quality will experience the negative effects of NRR richness more severely. This study’s conclusion is consistent with earlier studies and supports the theory that countries ought to mine fewer natural resources to lessen their negative environmental effects (Abbasi et al., 2021; Ahmad et al., 2023). Previous studies have also demonstrated the importance of laws controlling the removal of natural resources in states with high levels of harmful emissions and the alterations in the impact of natural resource rents between countries.

Second, with the exception of the 90th quantile, natural resource rent diversity shows an adverse calculated factor across almost all environmental quality criteria. It is assumed that countries with more natural resource rent diversity will also have less quality of life than countries with lower variation. Additionally, we noticed that the effect intensity is not uniform and instead drops down progressively. Thus, the impact of natural resource rent dispersion shall be greatest in nations which have attained a high level of sustainability integrity.

Second, natural resource rent variability exhibits an adverse computed effect across nearly all of the environmental quality parameters, with an exception of the 90th quantile. It is expected that the standard of living in nations with greater diversity in their natural resource rents will be poorer compared to that of nations that have fewer variability. Furthermore, we observed that the impact’s strength is not constant, but rather regularly declines from the 10th to the 90th quantile.

Consequently, the nations that have reached a high degree of sustainability purity will be most affected by the dispersion of natural resource rent. In addition, the lack of concentration in the use of natural resources can effect in the dispersal of technical and financial assets, obstructing the development of advances in technology towards a more environmentally friendly path, when contrasted with nations that concentrate on improving the extraction technique of a particular resource. It is also important to keep in mind that the concurrent mining of several closely linked kinds of resources may increase their cumulative impact, which could have more profound effects than the effects of every asset alone.

There are three main ways that the application of advanced technology contributes to the development of the natural world. First, by reducing total energy consumption while preserving sufficient production levels, current technology improves the use of energy (Gyamfi et al., 2022). Second, by introducing the state-of-art waste disposal technologies, modern technology also enables to minimize the environmental pollutants. Modern technology in the environment hence, aids the evolution of the environment in three main ways. The study’s conclusions also show that the degree of impact tends to rise as the ambient quality quantile rises. The degree of economic expansion and trade openness both lead to higher output, which raises productivity and requires maintenance of environmental standards. The study’s conclusions are consistent with other research examining the factors influencing the condition of the environment (Hussain et al., 2020).

Once the moderating impact of diversification of natural resource rents is taken into account, we give the estimated findings. At every quantile level, the computed interactions factor coefficient is statistically significant and has an adverse value. When the level of ecological footprint increases, the amount of its effect shows a considerable rise, ranges from to greater than double that the quartiles of 10–90. According to this research, increasing the scope of natural resource utilization will increase the detrimental impacts of resource extraction on the state of the ecosystem. In other words, nations that heavily utilize a variety of resources typically have lower overall quality levels than nations that similarly utilize assets but focus on one or a small number of distinct resource categories.

The ecological effects caused by resource development can be effectively controlled by focusing only on the misuse of one resource (referred to as high DIVER). For example, the effect of NRR is measured as multiplied by DIVER for countries that are in the 90th percentile. This implies that the negative effects of extraction can be lessened if the level of DIVER is high enough, indicating that the nation depends on a limited amount of taken assets. Furthermore, the study’s conclusions show a significant relationship among the total amount and diversity of assets utilized through the effects such resources have on the ecosystem in nations placed among 50th and 90th. As a result, especially in nations with high levels of pollution, it is critical to consider both the decline in the total amount of resources used and the individual kinds of resources being exploited. This collection of nations frequently relies on antiquated technology and aggressively plunders their assets in an effort to boost economic expansion. For those nations, the method of converting machines and technology to be more ecologically friendly will subsequently take longer and cost more money. Because so many different resources are extracted, more advanced technology and machinery must be used because different mining techniques are frequently needed for every item. As a result, in this specific group of nations, the effects of natural resource rent diversification become more noticeable.

This research shows that for nations with intermediate and high levels of emissions, natural resource rent has a negative impact on the natural world. Further resource extraction will exacerbate environmental problems in nations that already experience them. Our findings are consistent with (Ullah et al., 2021) concluded for the highest 15 green energy usage countries. Furthermore, using Cross-Sectional Auto Regressive Distributive Lag (CS-ARDL) (Faisal et al., 2023), come to a comparable conclusion. Notably, we also highlight the little and adverse impact that natural resource rents has on the ecological footprint of nations with low levels of pollution. Thus, in order to strengthen their economies, nations with higher quality of life may be encouraged to capitalize on natural resource rents. This conclusion raises the issue whether or not there can be a point at which the association among ecological sustainability and the sign of natural resource rentals changes.

Furthermore, the level of natural resource rents mitigates the detrimental effects of natural resource rents on the environment. Natural resource rents have a favorable effect on the environmental footprint if diversity is significant. However, the effect of natural resource rents on environmental footprint grows negative as diversification decreases. We think that switching to alternative forms of energy will help avoid the curse of resources. In opposition to the influence at small percentiles, environmental technology at central and higher numbers has important adverse effect on ecological footprints.

In Table 8, the results of Panel A present the direct influence of natural resource rents (NRR), trade diversification (DIVER), environmental technologies (ERT), trade openness (TRADE), and economic growth (GDP) on the ecological footprint (ECF) across different quantiles of the conditional distribution. By employing the Method of Moments Quantile Regression (MMQR), the analysis accounts for heterogeneous responses at various levels of environmental sustainability, which offers a richer understanding compared to mean-based estimators. The findings yield several noteworthy insights.

Table 8

Table 8. Moment quantile regression results.

First, natural resource rents (NRR) exert a consistently positive and statistically significant effect on ecological footprint, particularly at lower and median quantiles. This indicates that in countries with smaller or moderate ecological burdens, resource dependence translates into higher ecological pressures. The marginal effect of NRR, however, diminishes at higher quantiles, becoming weaker and statistically insignificant at the 90th quantile. This pattern suggests that nations already facing high environmental pressure may experience saturation effects, where additional resource rents add less marginal strain. The overall implication is that continued reliance on resource rents undermines ecological sustainability, especially in countries transitioning from lower to middle levels of environmental stress.

Second, trade diversification (DIVER) shows a negative association with ecological footprint at lower quantiles, significant at the 10th and 30th quantiles, but its effect tapers off at higher quantiles. This outcome implies that diversified trade structures help mitigate ecological pressures, particularly in relatively sustainable contexts where economies are less burdened by ecological stress. In highly resource-intensive settings, however, diversification alone may not be sufficient to counteract environmental pressures. Thus, diversification appears to be more effective in prevention rather than in correction of ecological damage. Third, environmental technologies (ERT) emerge as a robust determinant of sustainability. Across all quantiles, ERT significantly reduces ecological footprint, with the strongest effects observed at lower and middle quantiles. This indicates that countries adopting greater levels of ecological patents and green innovations experience broad reductions in ecological stress. The diminishing but still significant impact at the upper quantiles suggests that technological progress consistently contributes to sustainability transitions, even in contexts of high environmental pressure, though the marginal gains may decline over time.

Fourth, the role of trade openness (TRADE) is mixed but generally favorable. While insignificant at the lowest quantile, TRADE becomes increasingly significant and negative at higher quantiles, indicating that openness to international markets contributes to reducing ecological pressures in more environmentally intensive contexts. This finding may reflect the diffusion of cleaner technologies, knowledge transfer, and stricter global standards embedded in international trade agreements. It also signals that global integration can act as a corrective force in countries facing elevated ecological stress.

Finally, GDP per capita consistently exerts a strong and highly significant positive effect on ecological footprint across all quantiles. Economic expansion increases ecological demand, reflecting the classical tension between growth and environmental sustainability. While the magnitude of this effect declines slightly at higher quantiles, the results highlight that growth without corresponding sustainability measures intensifies ecological pressures, reinforcing concerns about the environmental Kuznets curve debate. Taken together, the direct effects suggest a dual narrative. On one hand, reliance on natural resource rents and unchecked economic expansion exacerbate ecological pressures. On the other hand, diversification, green technologies, and trade openness provide important mitigating channels. However, their effectiveness is uneven across quantiles, indicating that policy prescriptions must be tailored to a country’s ecological burden level. For countries at lower levels of ecological stress, diversification and innovation offer preventive strategies, whereas for countries at higher levels, the focus should shift to enhancing technological adoption and leveraging global trade mechanisms to offset entrenched ecological challenges.

To confirm that the research theory is stable, we run additional experiments. The reliability of research findings might be significantly impacted by an endogenous issue, which can emerge due to inverse correlations or measurement mistakes (Hill et al., 2021). We use analysis using instruments (Panel-1) to solve this issue. Second, we replace ecological quality with other measurements such as emission of carbon (Panel-4), ecological deficit (Panel-3), and ecological footprint consumption (Panel-2). Additionally, we perform estimates using alternative specimens, calculating for the G-20, leaving out three nations with remarkably varied natural resource rents. To evaluate its direct effect, we apply the Granger causality test for robustness check and it verified our results. The study results illustrate the dual connection involving environmental technology and natural resource rents (both in terms of diversity and size) and environmental quality in G-20 countries. Likewise, there is a unidirectional relationship between the rate of financial growth and freedom of trade and the state of the environment.

The results of the Granger causality analysis reported in Table 9 provide important robustness checks on the dynamic relationships between ecological footprint (EFP) and its key determinants: trade diversification (DIVER), natural resource rents (NRR), environmental technologies (ERT), trade openness (TRADE), and economic growth (GDP). Unlike regression estimates that capture long-run and distributional effects, the causality test reveals the direction of short-run predictive linkages, thereby complementing the main empirical findings.

Table 9

Table 9. Robustness check, granger causality analysis.

The analysis shows a two-way causality between DIVER and EFP, indicating that trade diversification not only influences ecological sustainability but is also shaped by ecological pressures. Countries under higher ecological stress may be compelled to diversify trade structures, while more diversified trade patterns can mitigate ecological pressures. Similarly, a bi-directional relationship is observed between NRR and EFP: dependence on natural resource rents significantly worsens ecological outcomes, and worsening ecological conditions, in turn, affect the pace and structure of resource exploitation. For environmental technologies (ERT), the results again demonstrate two-way causality. Green innovations help reduce ecological footprints, while rising ecological pressures create stronger incentives for the development and adoption of such technologies. This finding underscores the feedback loop between sustainability challenges and technological progress.

In contrast, trade openness (TRADE) and economic growth (GDP) exhibit one-way causal relationships. TRADE Granger-causes EFP, but EFP does not predict TRADE, suggesting that international integration drives ecological outcomes without reciprocal influence. Likewise, GDP causes EFP but not vice versa, reaffirming the growth-driven increase in ecological stress. Overall, the causality tests confirm the robustness of the study’s central results: natural resource dependence and GDP expansion drive ecological pressures, while trade diversification and green technologies play dual roles as both drivers and responses. These findings highlight the importance of reciprocal dynamics in sustainability transitions.

5 Conclusion

This study investigates the dynamic relationship between natural resource rents, resource diversification, environmental technologies, and ecological footprints across G-20 economies from 1990 to 2023. The findings reveal that dependence on natural resource rents significantly exacerbates ecological pressures, with the most pronounced effects observed in highly polluted economies. Moreover, diversification in resource extraction—although often pursued to stabilize economic performance appears to intensify environmental degradation by expanding the overall scale of resource utilization. In contrast, the diffusion of environmental technologies consistently mitigates ecological footprints across all quantiles, emphasizing their pivotal role in facilitating sustainability transitions.

These findings offer several important policy insights. G-20 countries, endowed with abundant natural resources yet committed to environmental preservation, must strike a balance between the economic benefits of resource exploitation and the ecological costs it imposes. Expanding renewable energy deployment and accelerating the adoption of green technologies are crucial pathways to mitigating ecological footprints. Such measures align with the G-20’s broader environmental commitments and provide a framework for achieving long-term ecological sustainability. Ultimately, the evidence highlights that curbing excessive reliance on resource rents while fostering innovation-driven growth will be critical for ensuring sustainable development in the world’s leading economies.

5.1 Policy implications

The findings highlight several important directions for policymakers in G-20 economies. First, governments should reduce their excessive reliance on natural resource rents, as both abundance and diversification of exploitation have been shown to intensify ecological pressures. Instead of overexploiting a broad set of resources, countries should concentrate on those assets where they hold a relative advantage, while adopting sustainable management practices. Policymakers are encouraged to revise existing resource governance frameworks, strengthen monitoring mechanisms, and design policies that discourage overuse. In parallel, price incentives and subsidy reforms can promote a shift toward renewable energy adoption among businesses and households. Regulatory measures should further encourage firms to integrate low-carbon technologies and eco-friendly practices into their operations.

Second, governments must actively promote the diffusion of environmental technologies. Legislative support, targeted subsidies, and research and development incentives are vital to accelerate the transition toward greener production systems. Environmental taxes can act as both deterrents to non-green practices and catalysts for innovation by stimulating demand for low-carbon alternatives. Finally, maintaining strong stocks of human and financial capital is essential for supporting the shift from energy-intensive to cleaner technological paradigms.

5.2 Future research directions

Although this study provides robust insights, several avenues remain for future investigation. First, the present analysis adopts a largely linear framework, which may overlook the potential non-linear effects of resource rent diversification on ecological footprints. Exploring threshold effects or asymmetric relationships could provide a deeper understanding of these dynamics. Second, while the variables employed were carefully selected from existing literature, the exclusion of some important factors limits the scope of inference. Future research should integrate additional dimensions such as governance quality, financial complexity, innovation systems, and green finance to capture the broader context of sustainability transitions.

Finally, while this study focuses on G-20 economies, the findings may not be unique to this group. Extending the analysis to other blocs such as BRICS, E7, G7, or MENA could provide valuable comparative evidence and reveal region-specific patterns. Such comparative studies would further enrich the global understanding of how resource dependence, diversification, and technological progress interact to shape ecological sustainability.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

SL: Investigation, Project administration, Writing – review and editing. ZD: Formal Analysis, Writing – original draft.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

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Abbreviations

ECF, Ecological Footprint; NRR, Natural Recourse Rent; TO, Trade Openness; TD, Trade Diversification; ET, Environmental Technologies; GDP, Gross Domestic Product; MMQR, Method of Moments Quantile Regression; BRICS, Brazil, Russia, India, China, South A; DRIVER, Natural resources Rents Diversification.

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