Investigating the Role of Environmental Taxes, Green Finance, Natural Resources, Human Capital, and Economic Growth on Environmental Pollution Using Panel Quantile Regression

Abstract

Natural resources (NRs) are important for the operation of any economy and are crucial for preserving environmental quality. However, the persistent utilization of NRs has led to a severe deterioration of environmental quality. This presents a vulnerability to the steadiness of the ecosystem, emphasizing the urgent requirement to achieve a harmonious equilibrium concerning the utilization of NRs and the conservation of environmental quality. Environmental taxes (ETs), green finance (GF), and the cultivation of a proficient workforce dedicated to achieving sustainable development are essential for attaining equilibrium and advancing the Sustainable Development Goals (SDGs). We aim to investigate the impact of NR, ET, GF, and the human capital index (HCI) on environmental pollution (PM_2.5, CH₄, CO₂, and N₂O) in the G20 countries from 2000 to 2022. This study employs a novel and cutting-edge MMQR methodology, offering distinct perspectives that diverge from the conclusions of previous research. The study’s findings suggest that excessive use of NRs contributes to the degradation of environmental quality. ET, GF, and economic growth help to improve environmental quality, but HCI has a harmful impact. The paper proposes that the establishment and enforcement of environmental regulations are crucial for attaining ecological integrity and meeting SDGs 7, 12, and 13.

1. Introduction

Climate change, a highly debated worldwide issue, has adverse impacts on both the natural environment and humans [1]. The impacts of climate change include increasing temperatures and extreme weather that lead to more frequent instances of storms, droughts, and floods [2]. The excessive use of NRs significantly contributes to climate change. The economic sustainability of most nations depends on the usage of NRs. The burning of fossil fuels has had a major role in deteriorating air quality. Global net anthropogenic greenhouse gas (GHG) emissions reached 59 ± 6.6 GtCO₂-eq in 2019, a 12% increase from 2010 and a 54% increase from 1990 [3]. The average annual emissions during the decade of 2010–2019 were the highest recorded, with a growth rate of 1.3% per year, slower than the previous decade’s rate of 2.1%. The largest growth in absolute emissions was observed in CO₂ from fossil fuels and industry, followed by methane (CH₄), while the highest relative growth occurred in fluorinated gases [3]. The baseline scenarios of the Shared Socioeconomic Pathways project are global energy consumption between 400 and 1200 EJ and annual CO₂ emissions ranging from approximately 25 GtCO₂ to over 120 GtCO₂ by 2100, reflecting diverse socioeconomic developments, land-use dynamics, and mitigation challenges [4]. For 2021, total anthropogenic CO₂ emissions, including the cement carbonation sink, amounted to 10.9 ± 0.8 GtC yr⁻¹ (40.0  ±  2.9 GtCO₂), driven by fossil emissions (10.1 ± 0.5 GtC yr⁻¹) and land-use change emissions (1.1  ±  0.7 GtC yr⁻¹) [5]. According to the Intergovernmental Panel on Climate Change (IPCC), GHG emissions must be cut by 43% by 2030, compared to 2019 levels, and to limit global warming to 1.5 °C, the world must reach net zero emissions by 2050 [3]. In addition, predictions indicate that the world’s inhabitants will be over 9.7 billion in numbers by the year 2050 [6], which would further increase the need for NRs and exacerbate environmental problems [7]. The use of renewable energy sources, the application of environmentally friendly land and water management techniques, and the allocation of resources towards strong infrastructure can minimize the impact of climate change [8]. Global cooperation is apparent through the establishment of accords such as COP21 and COP28, as well as the implementation of the Sustainable Development Goals (SDGs). These projects strive to mitigate the impacts of climate change by promoting collaborative efforts [9]. However, even with the introduction of these policies, fossil fuels continue to make up over 80% of worldwide energy production, according to the United Nations’ report in 2023 [10]. In order to effectively achieve the SDGs and fulfill the goals set by international agreements like COP28, it is crucial to find a balanced and harmonious relationship between the usage of NRs and the preservation of the planet’s integrity.

Environmental quality refers to the overall state of air, water, and land in a person’s environment, encompassing its ability to support life, ensure public health, and maintain ecosystem balance [11]. Key indicators of environmental quality, particularly air quality and climate-related impacts, are the presence of pollutants such as PM_2.5, CO₂, CH₄, and N₂O. These pollutants are critical in assessing not only the immediate effects on public health, such as respiratory issues from air pollution, but also their long-term impact on global warming and climate change. Thus, the pollutants selected in this study serve as vital markers for evaluating the overall environmental health and sustainability of a given area.

PM_2.5 is a widely recognized indicator of air quality due to its significant health and environmental implications. Fine particulate matter contributes to respiratory and cardiovascular diseases and impacts visibility and ecosystem health. Ref. [12] emphasize the importance of PM_2.5 in air pollution studies, making it an essential component for analyses that link socioeconomic activities to environmental quality. CO₂ is the most prominent greenhouse gas associated with industrialization, energy use, and economic growth. As a standard measure of environmental impact, Ref. [13] highlight CO₂ emissions in evaluating the growth–environment nexus. Its inclusion captures the broader effects of human activities on global warming and resource consumption, aligning with the study’s aim to assess environmental degradation influenced by economic activities. Methane is a potent greenhouse gas with a global warming potential significantly higher than CO₂ over shorter timescales. Its emissions are primarily driven by agricultural practices, energy production, and industrial activities, particularly in emerging economies. Ref. [14] attributes the global increase in CH₄ emissions to rising consumption per capita and the reliance on agriculture in countries like China and India. Including CH₄ provides critical insights into the environmental effects of economic transitions. Nitrous oxide (N₂O) is another potent greenhouse gas with significant implications for climate change and ozone layer depletion. The authors of [15] emphasize its role in environmental assessments, particularly due to its emission sources, such as agriculture, industry, and energy utilization.

To comprehensively measure the cumulative environmental burden, this study constructs an environmental pollution index (EPI) using principal component analysis (PCA) on PM_2.5, CO₂, CH₄, and N₂O. Including CH₄ and N₂O in the EPI highlights their dual significance as critical contributors to climate change and as focal points for targeted policy interventions aimed at advancing sustainable development. The rationale for this approach lies in its ability to capture a broad spectrum of environmental impacts. The EPI integrates these pollutants into a single measure that reflects pollution intensity influenced by economic activities, policy initiatives, and human capital. The EPI offers a robust framework to comprehensively evaluate environmental quality and inform targeted policy interventions. Similar approaches have been utilized in previous studies to assess the environmental burden associated with economic activities [12,16,17]. While additional parameters could be considered to further refine the environmental pollution index (EPI), such as other air and water pollutants or biodiversity indicators, these are beyond the scope of the current study and could be explored in future research.

Prior research has examined the impact of NR on environmental quality, providing diverse outcomes. The “resource curse” hypothesis posits that nations endowed with abundant NRs tend to face increased levels of worse environmental quality due to excessive exploitation and dependence on resource-based productions. The extraction of fossil fuels causes environmental harm [18]. Various studies conducted on diverse geographic regions illustrate the dual impact of NRs on the environment. On the one hand, the excessive and unsustainable use of NRs contributes significantly to ecological degradation, as highlighted by Ref. [19]. On the other hand, Ref. [20] emphasized the potential of NR to sequester CO₂, thereby mitigating climate change. Similarly, Ref. [21] noted that the sustainable management of NRs can enhance environmental quality in the Middle East and North Africa (MENA) region. These findings underscore the importance of understanding NRs as both a positive and negative asset, depending on the context of their use and management. While unsustainable exploitation leads to ecological harm, sustainable practices can transform NRs into a vital tool for environmental improvement and climate mitigation. Countries with strong environmental laws and a focus on investing in environmentally aware human resources are better equipped to reduce the harmful effects of the excessive use of NRs while also improving the state of the environment.

Green finance encompasses financial services that facilitate economic activities aimed at enhancing the environment, mitigating climate change, and promoting resource preservation and efficient utilization [22]. These services include investment in projects and financing, project operation, and risk management in various sectors, such as protecting the environment, conserving energy, energy efficiency, green transportation, and green buildings [23,24,25]. China may establish environmental protection measures, improve environmental quality, limit pollutant emissions, encourage the growth of green industries, and advance production technology by prioritizing the development of green financing. Green finance not only imposes new demands on the manufacturing and research techniques of heavily polluting enterprises, thereby encouraging their transformation and upgrading but also fosters the growth of green projects and environmentally friendly technologies, facilitating simultaneous improvement in the economy and environment.

The human capital index (HCI) is a measure used to assess the productivity potential of a workforce based on factors such as education, skills, health, and the ability to adapt to changing economic and technological environments. The HCI used in this study is derived from the World Bank database [26], which measures the potential productivity of individuals based on health and education indicators. Specifically, the HCI includes metrics such as expected years of schooling, learning-adjusted years of education, and survival rates, which collectively reflect the workforce’s overall capability and productivity. However, it does not explicitly incorporate factors like proficiency in green technologies or environmental knowledge. This limitation highlights why the presence of human capital might not always lead to environmentally sustainable outcomes.

Environmental taxes (ET) can accelerate innovation by requiring firms to use clean energies. This will cause the emergence of green technology and attempts to mitigate environmental damage [27]. Nonetheless, the relationship between these policies and environmental quality has given uncertain results. Studies suggest that legislation can put substantial limitations on companies and worsen environmental degradation. On the other hand, opposing research argues that limitations can lead to long-lasting financial benefits by promoting innovation and lowering environmental decline. Impactful legislation can moderate harmful ecological influences on the exploitation and excessive mining of NRs by establishing clear directives.

The G20 has a wide variety of NRs, including gas from natural sources, gold, timber, agricultural products, and oil. The nation’s economy is strongly dependent on these resources. Nonetheless, the excessive mining of these reserves frequently leads to substantial environmental repercussions. Consequently, the G20 nations are experiencing a continual rise in environmental pollution. Figure 1 displays the spatial distribution of the average emissions of CO₂, CH₄, PM_2.5, and N₂O throughout the G-20 countries. Although there has been much research on the correlation between NR and environmental quality, there has been very little scholarly focus on the impact of ET and HCI in governing this relationship. It is crucial to acknowledge the significance of HCI and ET in lowering the harmful impacts of excessive mining of NR. The G20 encounters a substantial obstacle in achieving a balanced state of economic progress while safeguarding its environmental resources. This study primarily aims to investigate the role of HCI and ET in attaining a state of equilibrium or stability. The current research aligns with SDGs 7, 12, and 13.

This work significantly enhances previous research in various significant ways. Prior studies mostly concentrated on utilizing CE as the only measure of environmental quality [21,28]. These investigations have neglected to account for other essential indicators of the environment, such as CH₄, N₂O, and PM_2.5, causing an inadequate evaluation of environmental quality. This research proposes an expanded environmental pollution index that encompasses the release of CO₂, CH₄, N₂O, and PM_2.5, providing a more thorough assessment. By embracing a wider viewpoint, one can attain a more all-encompassing comprehension of environmental quality, which can result in novel and distinctive findings in contrast to prior research. In addition, our comprehensive literature review has revealed that there is a lack of studies conducted on G20 nations to investigate the impact of NR, GF, ET, and HCI on the environmental pollution index. Thus, our study addresses the aforementioned gap. This work is among the initial investigations that examine the interactions among ET, GF, and HCI in influencing the correlation between NR and environmental quality. Considering the increasing abuse of resources, it is imperative to understand the significance of environmental laws and the concept of HCI to effectively tackle the worsening environmental degradation. These factors possess the capacity to diminish the environmental footprint and offer substantial guidance for the formulation of policies targeted at environmental preservation.

The study investigates five factors—ET, GF, NR, HCI, and EG—that influence environmental pollution. These factors were classified based on their theoretical and empirical relevance to pollution, reflecting diverse yet interconnected mechanisms. Environmental taxes and green finance represent policy instruments aimed at mitigating environmental pollution. Environmental taxes work by internalizing the external costs of pollution, incentivizing producers and consumers to adopt sustainable practices. Green finance, on the other hand, facilitates investments in cleaner technologies and renewable energy, fostering environmentally sustainable economic activities. Natural resources were included due to their critical role in economic activity and their significant environmental implications. Overexploitation of resources leads to deforestation, biodiversity loss, and greenhouse gas emissions, making it essential to examine their impact on pollution.

The human capital index was selected to capture the role of skilled labor and education in shaping environmental outcomes. While human capital can drive sustainability through innovation and technology, it may also contribute to environmental degradation due to increased industrialization and resource consumption. Finally, economic growth was included to evaluate the macroeconomic drivers of pollution, particularly in the context of industrialization, urbanization, and energy use. This dual role of economic growth aligns with the environmental Kuznets curve (EKC) hypothesis, which posits that environmental degradation initially increases with economic development but improves at later stages.

The inherent correlations among these factors are acknowledged. For example, green finance often supports economic growth through sustainable investments, while environmental taxes influence production costs and consumption patterns [29]. Human capital and economic growth are also closely linked, as a skilled workforce enhances productivity and innovation. These interconnections were addressed methodologically using the method of moments quantile regression (MMQR) technique. This approach ensures that the unique impact of each factor on environmental pollution is captured while minimizing multicollinearity. Robustness checks, including matrix correlation analysis, were conducted to validate the independence of each factor’s effects.

The classification and selection of these factors were guided by their distinct but interconnected contributions to environmental outcomes. By systematically addressing their correlations and employing robust econometric techniques, the study offers a comprehensive analysis of their impacts on environmental pollution. This framework provides policymakers with valuable insights into the multifaceted drivers of pollution and highlights pathways for achieving sustainable development goals.

This study is important since it furnishes legislators with strong empirical data and intuitive assessments regarding the efficiency of environmental laws. It emphasizes the pressing necessity to establish and enforce regulations that tackle the harmful impacts of excessive use of NR while also encouraging sustainable development plans. Policymakers can employ the research’s findings to enhance current rules, establish fresh policies that encourage environmentally conscious investments, and give priority to educational and training initiatives aimed at developing a proficient workforce in the green industry. The current research addresses a substantial gap in the literature, providing a basis for subsequent studies on the dynamic interplay between the variables.

The study is structured into multiple primary domains. Section 2 encompasses the evaluation of pre-existing content. Section 3 presents a summary of the materials and procedures employed in the study, whilst Section 4 encompasses the results and subsequent remarks. Section 5 provides a comprehensive summary of the ultimate conclusions and proposes recommendations for forthcoming policies.

2. Literature Review

Environmental quality and its determinants have been extensively studied in the academic literature. The factors influencing environmental pollution are multifaceted, encompassing policy instruments, economic conditions, resource management, and human capital. This study investigates the roles of environmental taxes (ET), green finance (GF), natural resources (NR), human capital index (HCI), and economic growth (EG) in shaping environmental outcomes. This section reviews the literature on these factors individually and highlights the gaps addressed by this study.

2.1. Policy and Economic Instruments: Environmental Taxes and Green Finance

Authorities have passed a variety of environmental laws to tackle environmental degradation. The effect of environmental regulations on environmental deterioration in the G-7 nations was examined by [30]. They employed the CS-ARDL to illustrate that environmental law exerts a significant influence in mitigating environmental degradation. Ref. [31] conducted a study to investigate the influence of environmental rules on the deterioration of the natural environment in China. Their findings suggest that implementing such restrictions has a substantial and beneficial impact on decreasing the CE. The introduction of higher environmental taxes effectively motivates businesses to adopt green transformation methods, thereby aiding in the attainment of environmental sustainability [32]. The enforcement of strict regulations on industrial waste in China has enhanced water quality for the inhabitants situated downstream [33]. The introduction of more rigorous ecological guidelines in China has resulted in a rise in the use of environmentally friendly technologies [34]. This, in turn, has led to the implementation of cleaner industrial practices and a reduction in pollution levels.

Green finance serves as a connection between the environment and finance. The extent of digitalization in digital finance plays a vital role in improving environmental performance and fostering green innovation [35,36]. Additionally, the government can utilize green finance measures to encourage the growth of the green industry in alignment with ecological concerns [37]. In 1997, the World Bank suggested that achieving a total investment in green finance equivalent to 1–1.5% of GDP would effectively control environmental pollution [38]. Furthermore, when the entire amount of eco-investing reaches 2–3% of GDP, it significantly affects environmental pollution and improves environmental quality [38].

Green financing yields environmental advantages and has specific governance impacts on environmental degradation. Ref. [39] argues that the process of financial liberalization and openness has the potential to contribute to the reduction in CO₂ emissions. According to Ref. [40], financial progress and trade openness promote technical innovation, leading to a decrease in environmental pollution. Conversely, the escalation of environmental pollution could likewise compel the advancement of green finance. In a study conducted by Ref. [41], it was discovered that green finance had the effect of decreasing the capital expenditures of highly polluting companies while simultaneously alleviating the financial limitations faced by environmentally friendly organizations. This resulted in a reduction in carbon emissions. The authors of [42] argue that the degradation of the environment will expedite the advancement and exploration of environmental protection technology, leading to enhanced environmental legislation and subsequently stimulating investments in green finance.

2.2. Natural and Human Capital: Resource Dependency and Workforce Dynamics

The inefficient extraction of NRs has a significant detrimental effect on the environment and wildlife. The literature has emphasized the considerable environmental damage instigated by actions, including mining in open pits, irresponsible logging, and excessive groundwater extraction, which subsequently results in environmental deterioration. Various research has analyzed the impact of NRs on the degradation of the environment to suggest green strategies, yielding a variety of results. A study by [43] examined the influence of NRs on environmental deterioration in developing countries. The study utilized the MMQR and found that the use of NRs is linked to an increase in ecological decline. While analyzing China’s province panel data, Ref. [44] discovered that relying on NRs results in a deterioration of environmental quality. In their examination of G7 countries from 1995 to 2022, ref. [19] gave an opposing viewpoint, concluding that NR use enhances environmental quality. In a study undertaken by [45], an analysis was carried out on Asian countries spanning the years 2010 to 2020. The researchers employed the pooled mean group regression technique and discovered compelling data that established a connection between the exploitation of NR and the improvement of environmental quality.

The rising alarm about ecological deterioration has created a spotlight on remedies created by human activities. The idea of HCI, encompassing the essential understanding, abilities, and mindsets required to achieve sustainable development, is widely identified as an important factor in lowering environmental harm [46]. The HCI quantifies the magnitude to which countries invest in the training, abilities, and health of their workers. The purpose of this investment is to facilitate the advancement of both sustainable economic development and conservation of the environment [47]. A study by [48] examined the effect of HCI on environmental quality in the most environmentally polluted countries globally from 1993 to 2020. The study employed the CS-ARDL approach to illustrate that HCI helps environmental sustainability by decreasing the CE. A study by [30] investigated the influence of government regulation and governance on environmental deterioration in the G7 nations from 1991 to 2017. Using CS-ARDL, their research has shown that HCI had a significant alleviating impact on environmental degradation. The authors of [49] conducted a study on the BRICS-T countries, focusing on the period from 1990 to 2018. Their research suggests that HCI has a role in promoting environmental sustainability. Nevertheless, the research conducted by [50] determined that depending exclusively on HCI is insufficient for effectively mitigating ecological degradation in the BRICS countries.

2.3. Economic Growth and the Environmental Kuznets Curve

Economic growth has long been studied for its complex relationship with environmental quality, often illustrated by the environmental Kuznets curve (EKC). According to the EKC hypothesis, environmental degradation initially worsens with economic growth but eventually improves as economies mature and adopt cleaner technologies [51]. However, the EKC has faced criticism for its oversimplification and lack of generalizability across different contexts. Recent studies suggest that the relationship between economic growth and environmental quality is highly context-dependent, influenced by factors such as governance, technological capacity, and resource endowment.

2.4. Integrative Perspectives: Bridging the Gaps

While the aforementioned factors have been studied extensively, there is a lack of integrative analyses that consider their combined effects on environmental pollution. Ref. [52] explored the intersection of economic growth and environmental policy but did not incorporate the roles of green finance or human capital. Similarly, ref. [53] focused on environmental taxes but did not address their interaction with natural resource management and economic growth.

This study bridges existing gaps by examining the collective impact of ET, GF, NR, HCI, and EG on environmental pollution in G20 countries from 2000 to 2022. Unlike previous research, this study employs a novel method of moments quantile regression (MMQR) methodology, providing nuanced insights into the varying effects of these factors across different pollution levels. By incorporating a long-term and global perspective, the study offers valuable contributions to the literature on sustainable development and environmental management.

The findings aim to inform policymakers about the synergies and trade-offs among these factors, emphasizing the importance of comprehensive strategies for achieving Sustainable Development Goals (SDGs) 7, 12, and 13.

3. Methodology

3.1. Data

This study investigates longitudinal data from the G20 countries, covering the period from 2000 to 2022. This study investigates the environmental quality as the outcome variable while taking into account the independent variables of NR, HCI, GF, GDP, and ET. This study uses principal component analysis (PCA) to construct indices for environmental quality, diverging from the conventional method used in previous research, which predominantly relies on CE as the sole indicator of environmental quality. This study utilizes various emissions, including carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), and fine particulate matter (PM_2.5), to construct an environmental pollution index. The data utilized in this analysis were sourced from the World Bank [54], with the exception of ET and HCI, for which data were collected from the Organization for Economic Cooperation and Development (OECD) [55] and Penn World Tables (PWT) [56], respectively.

3.2. Theoretical Underpinnings and Model Specification

With economic growth and excessive utilization of NR, the environment deteriorates. Nevertheless, after a specific level of affluence is attained, further economic progress can potentially improve environmental conditions by motivating nations to make investments in more environmentally friendly technologies and enforce stricter environmental laws [57]. However, in accordance with the resource curse idea, an over-dependence on resource mining might lead to a negative scenario known as the “paradox of plenty”. Consequently, countries that possess ample resources may have economic disparities, governance problems, and heightened environmental deterioration due to the negligence of other sectors and excessive utilization of their natural endowments. Unrestricted use of NRs, while potentially driving economic growth and prosperity in certain economies, can also lead to significant adverse consequences. This phenomenon, known as environmental degradation, arises when the exploitation and consumption of resources lead to detrimental effects on the ecosystem [43].

The notion of human capital posits that investments in educational opportunities, health care, and skill enhancement augment the efficiency of the labor force and stimulate economic growth. This progress is commonly facilitated by the sustainable utilization of NRs. The link functions through various mechanisms, including the promotion and adoption of eco-friendly technologies, the promotion of sustainable methods of consumption and manufacturing, and the establishment of rules to moderate ecological degradation. Empirical research has examined this association and found confirmation that investments in environmentally conscious human resources can lead to better environmental consequences.

Expanding on the prior conversations, the proposed model is clearly delineated in Equation (1).

$$EPI=f\left(NR,HCI,ET,GDP,GF\right)$$

(1)

where EPI, NR, HCI, ET, GDP, and GF refer to the environmental pollution index, natural resources, human capital index, environmental taxes, gross domestic product, and green finance, respectively,

3.3. Econometric Techniques

The empirical analysis is conducted through a series of five distinct steps: Firstly, we examine the cross-sectional dependence (CSD) in our data by employing the CSD test devised by [58]. The second phase involves checking the stationarity or unit root process of all variable series using the second-generation CIPS test established by [59]. The third stage entails utilizing the [60] cointegration test to identify the presence of cointegration in the study. The fourth phase involves conducting long-run parameter estimates using the MMQR approach. Lastly, in the fifth stage, we utilize FMOLS and DOLS regression to obtain a reliable estimation for quality control.

In order to estimate the parameters, we begin by employing three longitudinal panel techniques: DOLS, FMOLS, and MMQR estimations. This is done to determine the existence of cross-sectional dependency in the data. The FMOLS methodology, as presented by [61], is a non-parametric estimate method that accounts for individual specific intercepts and incorporates the diverse characteristics of serial correlation in the disturbance term. The FMOLS method is used to account for variations in the average or long-term cointegration equilibrium across different portions of a panel, which includes the intercept in each section. In addition, we utilized the DOLS method developed by [62]. DOLS (dynamic ordinary least squares) is a statistical technique that amplifies both past and future fluctuations in integrated data in order to address the issue of endogeneity. Due to its reliance on Monte Carlo simulations, DOLS is widely recognized as a superior estimation technique compared to FMOLS. DOLS has the capacity to address the problem of endogeneity by augmenting the disparities between leading and lagging data. FMOLS and DOLS are superior to basic OLS because they successfully address heteroscedasticity, serial correlation, and low finite sample bias convergence, resulting in more accurate estimates. Both FMOLS and DOLS methods address the presence of different types of correlation and endogeneity issues among different units in a cross-sectional dataset [63].

To account for the conditional distribution of data and provide estimates at different quantiles, we utilize the MMQR approach introduced by [64]. The MMQR method offers several key advantages, especially in its ability to handle multivariate conditional distributions. Unlike traditional methods such as ordinary least squares (OLSs), MMQR allows for the estimation of relationships between dependent and independent variables across different quantiles of the conditional distribution rather than just focusing on the mean. This capability is particularly useful when the data exhibit heterogeneity and when the relationship between variables varies at different points of the distribution (e.g., at the lower, middle, and upper quantiles). In a multivariate setting, where multiple independent variables influence the dependent variable, MMQR can model the conditional distribution of the dependent variable given the values of the predictors. This is crucial for capturing the complex, non-linear interactions that might not be adequately represented by traditional regression models. Furthermore, MMQR is non-parametric in nature, meaning that it does not rely on restrictive assumptions about the underlying data distribution, making it more flexible and robust for modeling data that may exhibit skewness, heteroscedasticity, or non-normality. The method of moments approach, MMQR, ensures that the model remains robust even in the presence of outliers or heavy-tailed distributions.

Unlike other approaches that only evaluate the linear association between series based on their averages, the MMQR approach takes into consideration the entire conditional distribution of the data. Conventional quantile estimation methods lack the ability to handle outliers due to their inability to account for the unobserved differences among all cross-sections of panel data. The MMQR enables the generation and impact of particular individual effects through the allowance of “conditional heterogeneity of variable effects”. In addition, MMQR offers reliable and effective results when dealing with problems of multicollinearity and endogeneity in the data. This approach also offers an impartial explanation of the non-linear relationship between variables. MMQR is capable of accommodating the nonlinearities and asymmetries in dependent variables while adhering to moment restrictions. It also effectively addresses the challenges posed by heterogeneity and endogeneity in panel data. The conditional quantile Q_y ($\delta !\ddot{\mathrm{X}}\mathrm{\prime }\mathrm{i}\mathrm{t}$) of the dependent variable for various quantiles (depending on location) is expressed in MMQR, as given in Equation (2).

$${\dot{Y}}_{it}={\dot{\alpha }}_i+X_{it}\varnothing +\left({\lambda }_i+Z_{it}^{\mathrm{\prime }}\psi \right){\ddot{U}}_{it}$$

(2)

The expression (${\lambda }_i+Z_{it}^{\mathrm{\prime }}\psi$ > 0) represents a probability of 1. The symbols ($\dot{\alpha }$, $\varnothing$, λ, and ψ) represent parameters that are used for estimation. The variables (${\dot{\alpha }}_i$ and ${\lambda }_i$), where i ranges from 1 to n, indicate individual fixed effects. Z is a k-vector consisting of known differentiable transformations of the components of X. Element u of Z is given by Equation (3).

$$Z_u=Z_u\left(\ddot{X}\right),u=1,\dots \dots ,k$$

(3)

In Equation (3), X_it is distributed in an equivalent and independent manner for a fixed individual (i) regardless of time (t). The error term (μ_it) is distributed independently among individuals and over time. It is also orthogonal to the dependent variable and has been normalized sufficiently to satisfy the moment criteria that do not involve rigorous heterogeneity.

$$Q_y\left(\delta !\ddot{\mathrm{X}}\mathrm{\prime }\mathrm{i}\mathrm{t}\right)={\ddot{X}}_{it}\phi +Z_{it}^{\mathrm{\prime }}\mathsf{\Psi }q\left(\delta \right)+{\dot{\alpha }}_i+{\lambda }_{i}q\left(\delta \right)$$

(4)

In Equation (4), X′_it denotes the vectors of independent variables, namely NR, HCI, GF, GDP, and ET in the present study. The distribution Q_y (τ|X_it) represents the quantiles of the explanatory variables Y_it, given the position of the explanatory variable. The scalar coefficient X′_it represents the product of X_it and X_it′. The expression −α_i(τ) ≡ Տip(τ) + α_i represents the scalar coefficient. The individual effects do not accurately reflect the changes in the intercept contrast compared to the standard OLS fixed effects. The parameters remain constant throughout time, whereas heterogeneous effects are permitted to fluctuate within the constrained probability distribution of quantiles for the dependent variable. The symbol q(τ) denotes the τth quantile, which can be found in Equation (5).

$${\mathrm{M}\mathrm{i}\mathrm{n}}_q={\sum }_i \sum \mathrm{t}\eta \delta \left(R_{it}-\left({\lambda }_i+Z_{it}^{\mathrm{\prime }}\gamma \right)q\right)$$

(5)

4. Empirical Findings and Discussion

The CSD test findings in

Table 1. CSD test results.

Variable	Test Stat/Prob
EQI	−1.63 ***
NR	19.27 ***
HCI	26.29 ***
ET	8.37 ***
GF	25.75 ***
GDP	33.29 ***

*** represent significance at 1%.

first show the existence of CSD in the data. Any interruption in a specific sequence inside one country has a cascading impact on other economies. The presence of CSD in various data sets is directly attributed to the spillover effect. Stationarity challenges are a frequent obstacle encountered while working with longitudinal data, and they are usually tackled as the first step in the analytical process. Ignoring unit roots can potentially yield inaccurate and misleading outcomes. The MMQR requires the dependent variable to have an integrated order of 1 (I(1)), whereas the independent variables might have either an integrated order of 0 (I(0)) or an integrated order of 1 (I(1)). This study employed the widely recognized CIPS unit root test to analyze the stationary properties of the variables. The outcomes of CIPS unit root testing are presented in

Table 2. Unit root test (CIPS) results at level and difference.

Series	I(0)	I(1)
EQI	−2.448	−4.445 ***
NR	−2.845 ***	−5.317 ***
HCI	−1.208	−2.639 ***
ET	−2.754	−6.323 ***
GF	−3.884 ***	−5.218 ***
GDP	−1.925	−3.548 ***

*** represent significance at 1%.

. The test indicates that the series demonstrates first-order integration when the first difference is taken. The [60] cointegration test was employed to estimate the long-term cointegration relationship between variables. The findings, as shown in

Table 3. Panel cointegration test results.

Statistic	Value	Z-Value	p-Value
Gt	−5.242	−10.549	0.000
Ga	−13.920	0.496	0.690
Pt	−20.436	−10.403	0.000
Pa	−14.288	−1.382	0.084

, demonstrate a substantial and enduring cointegration relation among the variables. The null hypothesis of no cointegration is disproven with a 1% level of significance.

The outcomes of the first MMQR simulation are presented in

Table 4. MMQR estimation findings.

Quantile	NR	HCI	ET	GDP	GF
Location	0.1154 ***	0.1323 ***	−0.9482 ***	0.4749 ***	−0.109 ***
Scale	0.0878 ***	0.0126 ***	−0.0631 ***	−0.0315 ***	−0.056 ***
0.1	−0.0306	0.1113 **	−0.8432 ***	−0.4224 *	−0.108 ***
0.2	0.0269	0.1196 ***	−0.8846 ***	−0.4431 **	−0.124 ***
0.3	0.0507	0.1230 ***	−0.9017 ***	−0.4517 ***	−0.145 ***
0.4	0.0777 **	0.1269 ***	−0.9211 ***	−0.4614 ***	−0.152 ***
0.5	0.1070 ***	0.1311 ***	−0.9421 ***	−0.4719 ***	−0.160 *
0.6	0.1495 ***	0.1372 ***	−0.9727 ***	−0.4872 ***	−0.168 *
0.7	0.1891 ***	0.1429 ***	−1.0012 ***	−0.5014 ***	−0.180 *
0.8	0.2120 ***	0.1462 ***	−1.0176 ***	−0.5096 ***	−0.195 *
0.9	0.2535 ***	0.1522 ***	−1.0475 ***	−0.5246 ***	−0.204

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

. The findings indicate that, over time, the use of NR leads to an increase in environmental contamination. The statistical significance of this association is shown within the quantile range of 0.4–0.9. The continuous rise in pollution can be linked to the exploitation and consumption of resources that are not renewable, which significantly worsens environmental degradation. The discovery supports the conclusions of [65] for BRICS states.

The findings indicate a strong and statistically significant correlation between HCI and environmental pollution across all quantiles. This unforeseen outcome can be partially explained by the rebound effect, where advancements in environmentally friendly technologies lead to improved energy efficiency but also lower costs, encouraging higher consumption and increased resource use. Another explanation lies in the role of human capital in driving industrialization and economic growth. Higher levels of skilled labor can accelerate the expansion of industries, such as manufacturing, energy, and transportation, that are often associated with greater emissions and pollution. As societies develop and accumulate human capital, their production capacity increases, leading to higher energy consumption, greater industrial output, and more waste generation. This phenomenon aligns with the environmental Kuznets curve (EKC) hypothesis, which posits an inverse U-shaped relationship between economic development and environmental pollution. In the early stages of development, environmental pollution increases due to industrialization and weak regulatory frameworks. However, at later stages, as economies mature, societies adopt cleaner technologies and implement stricter environmental regulations, leading to improved environmental outcomes. The results are consistent with the research conducted by [66] on European Union countries and [67] on BRICS nations. The findings suggest that an elevation in HCI levels is a contributing factor to environmental degradation.

Environmental rules successfully reduce environmental contamination, as ET has a consistently adverse and statistically significant effect on environmental pollution across all quantiles. ET has been successfully implemented in various countries, showing positive impacts on reducing pollution and encouraging sustainable practices. For instance, Sweden was one of the first countries to implement a carbon tax, which has played a significant role in reducing carbon emissions. The tax has been levied on fossil fuels used for heating and transportation, and it has incentivized businesses and individuals to reduce their carbon footprints. Since its introduction, Sweden has managed to decouple economic growth from carbon emissions, demonstrating how environmental taxes can contribute to both environmental sustainability and economic growth. The UK implemented a landfill tax to reduce the environmental harm caused by landfills. This tax has significantly decreased the volume of waste sent to landfills, as businesses and households have been incentivized to reduce, reuse, and recycle. The tax has also generated substantial revenue, which has been reinvested into environmental initiatives, thus benefiting both environmental quality and waste management systems. France introduced an ecotax on road transportation, targeting the environmental impacts of heavy freight vehicles. The revenue generated is intended to fund sustainable transport initiatives and reduce the environmental footprint of the transportation sector. While controversial at first, this policy has contributed to reducing road congestion and encouraging the use of more sustainable transport alternatives. Environmental regulations in the G20 have a positive impact on environmental well-being by setting standards and rules for corporations, industries, and individuals, which define the maximum permissible levels of pollution. Consequently, these limitations efficiently decrease the total levels of pollution, resulting in enhanced water and air quality, increased preservation of ecosystems, and declined health risks related to ecological contaminants. The findings are in line with the study conducted by [42] on OECD nations, which illustrates that environmental regulations successfully reduce environmental pollution. However, the results of [68]’s study contradict the notion that environmental regulation is effective in reducing environmental degradation.

Economic expansion exerts a detrimental influence on environmental pollution, as the GDP exhibits a statistically significant negative effect on environmental pollution. This indicates that when the economy grows and attains larger levels of money, it becomes more feasible to make investments in cleaner technologies, resulting in a change in focus towards environmental preservation. As a society attains greater levels of economic prosperity, it gains the ability to fund and give priority to environmentally friendly activities and technology, leading to a decrease in the amount of environmental pollution. This persistent trend corresponds to the research conducted by [69] in Africa. However, the findings of [70] oppose the conclusions made in this study, as they questioned the existence of a curved relationship between economic growth and environmental degradation. Environmental contamination is found to have a strong and statistically significant negative correlation with GF within the quantile range of 0.1–0.8. GF plays a crucial role in tackling environmental pollution by providing essential financial resources to assist projects and activities aimed at promoting environmental sustainability.

China has made substantial investments in green finance, particularly in renewable energy and green infrastructure. In 2015, the country’s central bank, the People’s Bank of China, launched the Green Finance Task Force, which led to the development of green bonds and the promotion of green finance policies. By 2020, China had become the world’s largest green bond market, using these funds to finance projects aimed at reducing pollution, improving energy efficiency, and supporting the transition to a low-carbon economy. Germany has long been a leader in promoting green finance through policies such as the Energiewende (Energy Transition), which has encouraged significant investments in renewable energy projects. The German government has supported green bonds and other financing mechanisms to fund energy efficiency programs and renewable energy infrastructure, leading to a reduction in greenhouse gas emissions and fostering green jobs. The European Investment Bank has issued green bonds to raise capital for financing projects that support climate action. These bonds fund projects in renewable energy, energy efficiency, and low-carbon transport, among other sectors. This initiative has been a key driver in funding the EU’s green transition and has set a standard for other financial institutions to follow in funding environmentally sustainable projects.

To ensure the reliability of our analysis, we employed the FMOLS and DOLS techniques. Both regression approaches produced similar outcomes.

Table 5. FMOLS and DOLS estimation results.

Series	FMOLS	DOLS
NR	0.0082 ***	0.0436 ***
HCI	0.1532 ***	2.4521 ***
ET	−0.0283 ***	−0.7618 ***
GF	−0.0365 ***	−0.6521 ***
GDP	−0.1687 ***	−0.8246 ***

*** represent significance at 1%.

demonstrates that NR and HCI have a favorable influence on the EPI, but GDP, ET, and GF have a negative influence on the EPI. Figure 2 displays graphical representations depicting the link between independent factors and the dependent variable.

5. Conclusions

The G20 countries hold a wide array of rich NRs. While considerable research has been conducted on G20 countries, gaps remain in understanding certain aspects of their economic and environmental dynamics, which could benefit from further exploration. Moreover, the combined effect of environmental rules and the cultivation of environmentally aware personnel on the relationship between NR and environmental quality has not been well studied. The research employed the distinctive MMQR approach to examine the advantageous and disadvantageous connections among the factors, using data from G20 nations from 2000 to 2022. The findings suggest that the excessive use of NRs plays a role in causing environmental pollution. Paradoxically, the existence of environmentally aware humans has an adverse effect on environmental sustainability. Energy consumption, greenhouse gas emissions, and GDP have a detrimental effect on environmental quality.

5.1. Policy Implications

To reduce the long-term environmental impacts of resource exploitation, it is a must to apply programs that are aligned with SDGs 7, 12, and 13. One of the responsibilities involves devising and implementing strategies for the long-term conservation and utilization of resources. These rules should encourage the careful use of resources, making sure that the rate at which resources are used does not surpass the rate at which they are replaced. Possible courses of action encompass the enforcement of limitations on the extraction of resources, guaranteeing thorough evaluations of the environmental impact, and promoting the utilization of renewable resources. It is imperative for decision-makers to give utmost importance to environmental factors during all phases of development and devote resources towards the advancement and execution of sustainable technology. This includes technology that improves the efficiency of resources, reduces waste, and mitigates the negative environmental effects of mining and consumption.

Financial institutions of all types should fully utilize their initiative and driving force in green finance, incorporating green development into the business’s strategic planning. When engaging in green finance development, financial institutions must prioritize risk control and build and enhance risk management and evaluation systems. At the societal level, it is imperative to enhance the promotion and acknowledgment of environmentally friendly businesses and financial institutions that demonstrate exceptional performance. We should actively encourage people to engage in green consumerism and foster a positive climate for collaborative efforts in constructing an ecological and civilized society.

Moreover, it is crucial to carry out a thorough evaluation of the existing HCI strategy, as it has harmful consequences for environmental integrity. This entails acknowledging the adverse effect on environmental integrity resulting from the production and exploitation of HCI. The evaluation should cover all facets of the development of HCI, ranging from the gaining of expertise and instruction to the practical use of these skills in the job market. Moreover, this form of education should not alone provide knowledge but also foster inventive methods to address environmental challenges.

Moreover, environmental policies significantly improve the overall condition of the natural environment. Authorities must prioritize the continual enhancement of existing ecological laws. This refers to the action of maintaining and modernizing the current order of affairs in light of new discoveries in science and progress in technology. Strategy outlines should offer incentives to encourage firms and sectors to embrace environmentally sustainable practices. These measures would promote innovation in eco-friendly activities and improve agreement with ecological regulations. Achieving this goal necessitates unwavering commitment, and any deviations from the current strategy must be averted as they have the potential to harm forthcoming economic and environmental advantages. Encouraging the development of innovative environmental technologies in the extraction of NR can help to overcome immediate obstacles and achieve long-term advantages.

5.2. Limitations of the Study

This research focuses on the G20, but its findings may be relevant to other growing nations that are dealing with similar difficulties. However, it is wise to be cautious when applying these results to other countries. Performing supplementary inquiries in other global regions could aid in generalizing these conclusions. Moreover, the distinctive MMQR methodology is employed to assess the relationship among the research variables. Nevertheless, employing alternate robust time-series data analysis approaches may produce divergent outcomes. In addition, the analysis incorporates carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), and fine particulate matter (PM_2.5) emissions in order to create an environmental performance index. Different environmental indicators may have unique responses to the variables analyzed in the study. While the selected pollutants provide critical insights, we acknowledge that environmental quality encompasses a broader spectrum of pollutants, such as SO₂, NOx, and O₃. Future research should consider incorporating these additional pollutants to offer a more comprehensive evaluation of environmental impacts. This expansion would further enhance the accuracy and applicability of the environmental pollution index in diverse socioeconomic and environmental contexts.