Green Innovation at the Crossroads of Financial Development, Resource Depletion, and Urbanization: Paving the Way to a Sustainable Future from the Perspective of an MM-QR Approach

Abstract

Global warming has become a big problem around the world, and it is because of what people do. As a possible answer, countries are looking for ways to keep their economies growing and invest in technologies that use clean energy. Therefore, the notion of carbon neutrality has emerged as a crucial policy strategy for nations to attain sustainable development. This study expands the existing discussions on carbon neutrality by investigating the influence of key factors, including green innovation, financial development, natural resources depletion, trade openness, institutional quality, growth, and urbanization on the progress made towards attaining a carbon neutral state in the BRICS nations. This study considers the Method of Moment Quantile-Regression (MM-QR) and Prais–Winsten correlated panel corrected standard errors (PCSEs) estimators to investigate the study objectives over the period of 1990–2021. Under the investigated outcomes, this study validated the significant role of urbanization and growth in carbon neutrality. On the other hand, this study finds the positive role of openness, green innovation, resource depletion, institutional quality, and financial development on environmental deterioration. However, under a systematic analysis, this study utilizes different proxies of the financial sector, for instance, financial complexity, financial efficiency, financial stability, and domestic credit by financial sector, and provides interesting outcomes. Based on these outcomes, this study also provides suggestions to attain desired levels of sustainability.

1. Introduction

Over the past few decades, the global atmospheric system has undergone a prolonged period of instability as a result of a substantial rise in greenhouse gas (GHG) emissions. Concerns persist about the long-term sustainability of both human society and the natural environment [1]. Carbon dioxide (CO₂) emissions play a major role in the rise of greenhouse gas (GHG) emissions, causing concern among scholars, governments, and international organizations globally [2]. The ongoing and relentless rise in carbon emissions is a major contributing aspect in the occurrence of changes in the climate and global warming, which have had harmful consequences for the sustainability of property, life, and the natural environment [3]. Adverse repercussions of global warming encompass environmental challenges, such as the amplification of soaring temperatures, the occurrence of severe storms, the exacerbation of drought conditions, food scarcity, the extinction of species, and the deterioration of health conditions [4]. The creation of a global movement calling for carbon neutrality was a response to the terrible difficulties caused by climate change. The United Nations played a prominent role in this movement through the Conference of the Parties (COP). The current instance of this event was during COP27, which took place in November 2022 and was hosted by Egypt. The stakeholders at the 27th Conference of the Parties (COP27) underscored the pressing need to actively pursue the establishment of a low-carbon economy. It is predicted that achieving this goal will necessitate an annual expenditure of approximately USD 6 trillion [5]. According to the United Nations Climate Change [6], it is emphasized by the conference that the attainment of the objective to limit global warming to 1.5 °C may be jeopardized if proactive measures are not implemented. The carbon emission trend in the BRICS economies can be seen in Figure 1.

Figure 1 discloses that Brazil and Russia have much lower increasing trends in emissions, while China and South Africa are the big carbon emitters, followed by India. The analysis describes that all the countries have fluctuating trends, which highlights the dynamicity of carbon emissions from 1990 to 2021.

First, green innovation (GI) that reduces fossil fuel use and carbon emissions is the greatest way to clean up the environment and lessen the societal cost of pollution. GI—also known as “environment-associated innovation, environmental innovation, and eco-innovation”—refers to numerous forms of invention that improve ecological safety and protection [7]. New goods, services, and production methods are included. GI has been introduced to the expanding ecosystem framework as a determinant of global carbon emissions reduction. Thus, green technology research and development improves ecological sustainability and economic growth [8].

In addition, FD promotes the allocation of resources towards cleaner technology and the advancement of research and development, with the aim of replacing production methods that contribute to pollution. The primary aim of every nation is to attain sustainable economic development and ensure the protection of the environment. In this regard, fiscal discipline (FD) plays a pivotal role in facilitating the achievement of this purpose [9]. The reduction of carbon emissions is of the utmost importance as it facilitates the adoption of contemporary equipment within businesses, resulting in decreased pollution levels. Furthermore, the implementation of FD fosters enhanced research and development efforts in the domain of cleaner technology, thereby making a significant contribution to the promotion of environmental sustainability [10]. Developed financial systems have a significant role in fostering a sustainable environment through the promotion of eco-friendly measures. The promotion of a stable financial system has been found to have a positive impact on the adoption of cleaner technologies within energy-intensive industries, leading to a subsequent reduction in emissions [11]. The significance of research and development in fostering technological advancements in energy systems, promoting environmental sustainability, and addressing carbon emissions has been emphasized by Xu et al. [12]. In industrialized nations, governments and pertinent agencies allocate substantial financial resources towards the advancement of R&D in order to facilitate the progress of environmentally sustainable technological innovations [13]. The exponential growth of the global population has led to a corresponding surge in the demand for commodities. This surge, in turn, has significantly impacted several industries that rely heavily on non-renewable energy sources (NREs). Consequently, this reliance on NREs has not only contributed to economic development but has also resulted in a substantial increase in pollution levels. Consequently, the significance of research and development in this context is underscored by the findings of Sun et al. [14]. The importance of green innovation lies in its ability to improve environmental quality and mitigate pollution through the facilitation of new technological advancements. Moreover, the implementation of green innovations contributes to the mitigation of carbon emissions, the promotion of environmental sustainability, and the enhancement of energy efficiency [15].

Figure 2 depicts the financial development trend of all the BRICS member countries.

Figure 2 portrays precisely the financial development trend of all the countries included in BRICS. Brazilian financial development is at lowest point; in comparison, the Russian Federation has a better financial development system compared to Brazil, while India’s financial system is flourishing, which is very interesting. However, China, which has a strong hold on the financial and production sectors, is also flourishing, and the same is true with South Africa. Before moving to our key model, this study first of all applies some sensitivity tests to ensure data correctness and specification, which are listed in

Table 1. Sensitivity tests.

Pesaran, Yamagata. 2008
adj.	Delta	p-value
	−2.107	0.035
	−3.82	0.000
Blomquist, Westerlund. 2013
adj.	−2.449	0.014
	−4.44	0.000
Breusch Pagan test for heteroscedasticity H0: Constant variance
Chi2(1)	0.49
Prob > chi2	0.4843
Wooldridge test for autocorrelation in H0: no first order autocorrelation
F(1,1)	172.918
Prob > F	0.0483

.

Moreover, natural resource depletion is a prominent component contributing to the increasing levels of greenhouse gas (GHG) emissions for several key reasons. The majority of world production is heavily dependent on natural resources [16], which are frequently depleted, resulting in a subsequent increase in GHG emissions. Furthermore, empirical evidence has proved firmly the significance of natural resources in driving economic growth and development, as they serve as crucial inputs in various economic activities. Furthermore, in addition to the economic implications, the survival and well-being of human beings, animals, and plants are heavily reliant on the accessibility and abundance of naturally occurring resources [17]. Therefore, the act of preserving the environment necessitates the sustainable utilization of the existing reservoir of natural resources.

Furthermore, urbanization and industrialization are linked, and all BRICS countries face the dilemma of a growing urban population in polluting cities. Urbanization—economic, demographic, land, and social—affects carbon emissions [18]. Urbanization accelerates carbon emissions, and when it passes a threshold, it promotes emissions. Understanding the dynamic association between urbanization and CO₂ emissions helps BRICS countries meet carbon emission reduction objectives. On the other hand, trade openness boosts economic production, although carbon emission causes are still debated [19]. Trade is a major source of carbon emissions from production, and BRICS countries import demand. Globally, ensuring greener and more sustainable manufacturing is still a fundamental issue, and global trade and growth are the main determinants, despite countries shifting resources to focus on project efficiency and using many environmental technologies to balance carbon emissions and trade [20]. Early research on trade and carbon emissions often assumed that the cointegrating association between the nonstationary stochastic repressors was symmetrically linear, ignoring the possibility of a nonlinear link [21]. Thus, trade openness increases carbon emissions, while decreasing trade openness may lower them.

The present study sought to empirically examine the impact of green innovation, financial development, and ecological policy severity on the environmental deterioration of BRICS nations from 1990 to 2021. This study had two main objectives: the primary objective of this study was to examine the significant factors that contribute to the variances in carbon dioxide (CO₂) emissions within the BRICS states. Additionally, this study not only assessed the conditional mean effect of these factors but also analyzed their impact at different quantiles, providing a more comprehensive analysis.

The unique contribution of this study can be seen in a number of different ways. First, this research is the first of its kind to examine the key connection of trade openness on carbon emissions in BRICS economies, because these nations have a major share of trade activities across the globe. This study analyzes the economic impact of international commerce on environmental quality, taking into consideration the World Bank’s 2030 vision for sustainable development on climate change. Through this research, our aim was to contribute to the current body of knowledge by examining the efficacy of commerce and its enduring effects on natural surroundings. Furthermore, the progress of urbanization fosters the gathering of skilled individuals and financial resources so unquestionably infusing fresh energy into economic growth. Furthermore, urbanization stimulates the development and use of sophisticated technologies that can result in enhancements in energy efficiency. Simultaneously, urbanization is accompanied by increased energy and resource consumption, leading to higher emissions. The relationship between urbanization and carbon emission efficiency is intricate, and various stages of urbanization can lead to diverse effects. The relationship between urbanization and carbon emissions remains undetermined, as previously stated. How does urbanization affect the efficiency of carbon emissions? Will increased urbanization have a negative or positive impact on carbon efficiency? This research will assess carbon emission efficiency using panel data to address the aforementioned questions.

Furthermore, the relationship between financial development and carbon emissions remains ambiguous and requires additional empirical research. Indeed, this type of work holds significance for BRICS in order to methodically devise a strategy for reducing carbon emissions and accurately assess the challenges in achieving the carbon emissions reduction goal by 2020. If there is a strong positive correlation between financial development and carbon emissions, then the continued growth of the BRICS nations’ financial sectors may lead to an increase in emissions that has not been taken into consideration. This will pose a greater challenge for BRICS in achieving their intended goals of reducing emissions. Interestingly, green innovation is also a significant factor that may reduce environmental deterioration across BRICS economies. However, the majority of studies are focused on conducting evaluations at the country, state, or sector level in response to green innovation. This is especially pertinent for developed economies, such as the United States. Specifically, there is a lack of empirical information about the impact of innovation on innovation among the BRICS countries. The reasons are twofold. Firstly, the safe trading system fosters innovation, positioning it as the largest-scale trading platform globally. Secondly, the majority of studies conducted on this topic have been focused on Europe. This paper presents actual evidence from the perspective of green innovation in BRICS countries, focusing on the world’s prominent merchant economies. This evidence will enhance research on the impact of market-based environmental regulatory mechanisms on innovation incentives in emerging countries.

This study also investigates the impact of natural resource depletion on carbon emissions. Resource depletion happens when resources are used up at a quicker rate than they can be replaced. Fossil fuels, water, fish, mining, logging, and fishing are all examples of resource depletion. The relationship between the growth of human beings and the availability of natural resources has been intertwined from ancient times, predating recorded human history. Presently, there is an insufficient focus directed towards this particular facet of the literature. This study focuses on an analysis of natural resources in relation to environmental quality and their indirect influence on human well-being. A nation endowed with abundant natural resources can experience accelerated development compared to a nation with limited resources. This study contributes to the existing information by examining the impact of institutional quality on carbon emissions. The term “institution” refers to a broad concept. This study will help researchers better understand which characteristics of institutions improve environmental quality the most. Moreover, the present research uses the cutting-edge econometric technique of Method of Moment Quantile Regression (MM-QR) to rely on the linear drift for the highly industrialized BRICS economies. Empirical research typically relies on symmetric mean centering findings to back its theoretical premise. Considering the existence of fixed symmetric effects, the current study applies the MM-QR approach proposed by Machado and Santos et al. [22] to determine the effect of the chosen sustainable measures on the distribution of CO₂ emissions across quantiles. This allows the BRICS nations to assess the effect of economic growth, trade liberalization, institutional strength, and urbanization on the state of the environment.

The study’s remaining portions are structured as follows: In the second section, we give a literature review that establishes a link between the selected variables of interest and the deterioration of the natural environment. Section 3 contains a description of the reported methodology for the statistical procedures. Section 4 provides an explanation of the variables studied and their meaning in the context of the empirical results. Finally, Section 5 draws conclusions and makes recommendations for future research and policy.

2. Literature Review

Humanity’s biggest issue is carbon pollution. Human carbon emissions create ecological problems, climate change, global warming, and pollution. COVID-19 reduced 2020 carbon emissions. BP’s 2021 statistical evaluation anticipated a 4.5% decrease in primary energy consumption and a 6.3% decrease in global carbon emissions in 2020 due to the COVID-19 pandemic [23]. Due to accumulating atmospheric carbon, mitigated warming will be mild. NASA1 announced that 2020 was the hottest year on record despite lower carbon emissions.

2.1. Green Innovation and Carbon Emissions

The ultimate goals of GI are social, economic, and environmental sustainability. It prevents or lessens environmental damage while guaranteeing the security of resources and energy [24]. The goals of energy efficiency, environmental protection, and the lessening of greenhouse gas emissions are all within reach. The energy sector, which contributes significantly to economic growth, can benefit from increased GI. The impact of GI on carbon neutrality in developing world economies was studied by Hailiang et al. [25], who looked at data from 1996 to 2012. They also provide estimates of the income ranges across which the GI for cutting carbon emissions varies. It is also predicted that there is a single impact threshold at which the GI begins to change. Additionally, for economies with lower income levels, GI does not considerably cut carbon emissions. However, the transition to the new regime takes place at a relatively high income level.

In addition, the study finds that carbon emissions per capita have an inverse U-shaped connection with GI [26]. In order to reduce the financial burden of GI dissemination in developing countries, the study suggests implementing changes in methods. Efficient carbon emission measures and climate policies are key to achieving sustainable development goals. The effect of GTI on reducing emissions through structural modifications and a linear framework is provided by the literature. Using panel data from 1990 to 2017, Luo et al. [27] analyze the connection between GI and CO₂ emissions in BRICS economies. The study’s empirical results highlight the cointegration between the variables at the indicated quantiles. Quantile Regression, Quantile Causality, the Unit Root Test, and Regression analysis are all nonlinear modeling techniques used to find the connection between the input variables. The outcomes reveal that in China, Brazil, Russia, and India, the benefits of GI in reducing carbon emissions are only applicable to the highest emission quantiles. In the case of lower quantiles of emissions, however, the correlation between GI and CO₂ is weaker. The results of the study show that increasing levels of GI lead to less CO₂ being released into the atmosphere.

Another major environmental concern that GI can help solve is the rise in economic growth that results in increased CO₂ emissions. Xu et al. [28] give a detailed account of how GI affects carbon dioxide output. Results reveal that GI works best for countries with higher per capita income. The evidence that GI reduces CO₂ emissions in developing nations is also weak. The results of this study can be relied upon, as validated by the study’s use of alternate model features. It suggests that GI be adopted and expanded in developing nations in order to improve living conditions and spur economic development. The research also recommends increasing green technology investment by using novel approaches. The research also emphasizes the need of policies like GT transfer in promoting the global adoption of GI. Carbon emission reductions due to GI are revealed by Li et al. [29]. The study uses data collected between the years 2000 and 2018 to assess the role trademarks and patents play in reducing CO₂ emissions. The research uses OLS methods and the nonlinear ARDL approach to determine if there is any link between the variables. Testing for a cause-and-effect connection between GI and CO₂ emissions also makes use of the Granger causality method. Both unidirectional and bidirectional causal links can be seen between the given factors; however, the outcomes vary significantly by country and economic circumstance.

The Paris Agreement consolidates international responses to environmental degradation into one coherent framework. Keeping the worldwide temperature rise below 2 °C is a long-term goal of this accord. One of the laws China plans to take to reach carbon neutrality by 2060 is a more thorough use of GI. Using panel data from 2001 to 2019, Wang et al. [30] examine GI prevalence among Chinese provinces. In order to examine the nonlinear effects of GI on CO₂ emissions in China, this research makes use of econometric and panel threshold models as well as the Global Malmquist Luenberger (GML) index. The study found that while GI is on the upswing, the effectiveness of innovation is poor in China’s western provinces. The spatial effects of GI are particularly important for cutting down carbon dioxide emissions. Reducing carbon emissions is expected to have a larger impact in less developed regions of China. GI is a key component of sustainable development and ultimately contributes to a more stable ecosystem.

Using data from 2006 to 2019, Zheng et al. [31] examine the correlation between GI and CO₂ emissions in the context of Chinese provinces and sub-regions. Space-panel econometrics, developed from the STIRPAT model, are used in this investigation. The spatial pattern and link between the variables are uncovered through an examination of the geographic data as well. The data demonstrates that GI and CO₂ levels are negatively cointegrated. Furthermore, regional data shows that GI is successfully cutting CO₂ emissions in Central and Eastern China. In contrast, higher GI values tend to boost Western regions’ CO₂ emissions. The results also show that the level of CO₂ emissions in China is positively influenced by factors such as the country’s industrial infrastructure, energy usage patterns, economic growth, and gross domestic product.

2.2. Financial Development and Carbon Emissions

Financial development is crucial for economic growth and technological advancement. Many scholars have suggested that financial development also has a significant impact on the environment, particularly in relation to the increase in carbon emissions [32]. Nevertheless, the evidence is inherently uncertain. In theory, financial development has the potential to improve the environment by decreasing carbon emissions through advancements in technology and research and development (R&D) [33]. Financial development enables enterprises and governments to use ecologically efficient technology that can effectively reduce carbon emissions, leading to enhanced environmental quality [34]. Furthermore, financial development plays a crucial role in promoting effective corporate governance and providing incentives for enterprises to engage in environmentally friendly projects, which in turn leads to a decrease in carbon emissions [35].

Although financial development is important, it often leads to increased energy consumption, economic expansion, and technical progress, which can have a negative impact on environmental quality and contribute to carbon emissions. An enhanced financial sector facilitates affordable credit access for both households and firms. This enables households to acquire energy-demanding equipment and allows firms to expand their operations and invest in energy-demanding machinery. Consequently, this may contribute to an increase in carbon emissions [32]. Financial development enhances economic growth by promoting risk diversification and technical progress. Consequently, this leads to increased energy consumption and carbon emissions [36]. While the current theoretical debate over the influence of financial development on carbon emissions is inconclusive, the empirical evidence continues to be inconsistent. Empirical studies have shown conflicting results regarding the relationship between financial development and carbon emissions. Some studies, such as [37,38], suggest that financial development reduces carbon emissions. On the other hand, other studies, including [39], indicate that financial development actually increases carbon emissions. Some claim that there is no correlation between financial development and carbon emissions, as suggested by [40].

Although the financial sector is essential to the growth and stability of any country’s economy, it also has serious negative consequences for the natural world that cannot be overlooked. There are unintended consequences for the environment from the high energy consumption of the financial sector in driving economic growth [41]. Carbon emissions might be reduced if firms were encouraged to explore eco-friendly activities through financial development’s contribution to encouraging good corporate governance and establishing reputational and financial incentives. There are major geographical variances in the connection between the growth of the financial system and environmental quality, as stated by [42]. There is a strong positive association between environmental degradation and the growth of the financial industry in developing countries. The results also indicated that this impact is less in industrialized nations. The association between economic growth and emissions of hazardous gases was also examined by Hai Ming et al. [43] using a panel data model. Carbon emissions are reported to be lower in regions with an established economic system, whereas they are found to be greater in locations with a less established financial system.

Tang et al. [44] found a similar effect, implying that broadening the scope of the financial system favors a minimalist setting. When using the GMM on data from 83 economies, including both developed and developing ones, FD and its subdivisions like depth and efficiency reduce CO₂ emission. Independent economies, as well as overall financial development and its subcategories, had no discernible effect on CO₂ emission intensity. The environment in West African countries is deteriorating, according to a pooled OLS regression conducted by Chen et al. [45]. Few studies have examined the link between economic progress and carbon emissions using the VAR panel method, indicating the necessity to do so in other parts of the world [46]. As a result, the following speculations evolved.

FD increases economic activity and degrades environmental quality, according to Zhou et al. [47]. Du et al. [48] suggest that simple access to financial instruments lowers financial costs, which increases energy-intensive output and reduces carbon emissions in the short and long term. Zhao et al. [49] confirm FD degrades the ecosystem. Nasir et al. [50] argue that ICT-mediated FD increases CO₂ emissions. Luo et al. [51] also argue that financing expensive products like air conditioners and autos increases carbon emissions.

Many scholars believe financial development improves environmental quality. Financial resources stimulate low-cost advanced technology and environmentally friendly project investments, according to Zhao et al. [52]. Ma et al. [53] note that financial services boost energy efficiency and innovation. FD removes financing limits, encouraging enterprises to engage in green manufacturing methods. Li et al. [54] found that FD reduces carbon emissions in developed nations, because leading financial organizations provide low-interest loans to R&D and renewable energy projects that increase energy efficiency and reduce carbon emissions.

The aforementioned theoretical and empirical contradictions can be traced to two primary factors. Prior empirical research has not taken into consideration the various stages of financial growth in different nations when analyzing the influence of financial development on the environment, specifically in terms of carbon emissions. This paper contends that neglecting to consider the varying levels of financial development among countries leads to a pessimistic assessment of the influence of financial development on carbon emissions. For example, countries that have a well-established financial system foster technological advancement and economic expansion while also minimizing information imbalances and offering loans to consumers and businesses at a more affordable rate compared to those with an underdeveloped financial system. Hence, the influence of financial development on carbon emissions cannot be presumed to have a uniform effect on carbon emissions in nations at varying stages of financial development. Nevertheless, it is still uncertain if the different phases of financial growth have any significance when analyzing the influence of financial development on carbon emissions. Consequently, it is crucial to thoroughly examine the influence of financial development on carbon emission intensity in order to understand the level of financial development in different countries. Furthermore, a significant portion of the empirical research has employed several individual indicators to measure financial development. More specifically, in the empirical research, proxies like domestic credit as a percentage of GDP, stock market capitalization, and stock market turnover, which are individual indicators of financial development, are the most prominent. Considering carbon emissions is crucial for this article, as reducing them is essential in addressing climate change.

2.3. Resource Depletion and Carbon Emissions

A multitude of empirical studies have been undertaken to examine the degree to which natural resources play an essential role in shaping the trajectory of efforts aimed at maintaining the global environment. The primary focus of the majority of these studies is to achieve a harmonious equilibrium between the negative environmental consequences linked with natural resource depletion and the positive economic benefits derived from their utilization [55]. This study aims to evaluate the impact of natural resource rent on CO₂ emissions within an empirical model that tests the Environmental Kuznets Curve (EKC) hypothesis. The analysis covers a period from 1990 to 2018 and includes data from 208 economies. This study surveys the association between human capital, renewable energy, and trade openness in the context of adopting GMM and FMOLS estimators [41]. The empirical evidence suggests that the EKC hypothesis can be supported for the economies included in the sample. Moreover, it has been stated that the presence of substantial natural resource rents contributes to the exacerbation of the rise in carbon dioxide emissions. Similarly, it can be observed that both economic growth and trade openness are contributing factors to carbon emissions; however, it is worth noting that economic growth follows a pattern known as the “inverted U-shaped” curve. Conversely, renewable energy serves to mitigate the rapid increase in global emissions. In a study conducted by Ntom et al. [56], it was shown that a panel of 56 advanced countries exhibited evidence supporting the presence of both inverted N-shaped and U-shaped Environmental Kuznets Curve patterns. The primary research objective of Huang et al. [57] is the examination of the selective results of obvious pointers of natural resources, specifically oil and natural gas, on the increasing trend in CO₂ emissions within a panel of 24 selected countries from the years 2001 to 2020. The empirical evidence presented demonstrates the relationship between nuclear energy, renewable energy, and economic growth. The feedback obtained from the calculated models indicates that the three constituents of natural resources exert a substantial influence on the levels of CO₂ emissions [58]. In contrast, the utilization of renewable energy and nuclear sources plays a crucial role in mitigating carbon dioxide emissions, rendering them suitable policy choices for the chosen sample economies in fostering sustainable growth.

In addition, a study conducted by Zhu et al. [59] studied the influence of economic performance, resource reliance, and price volatility on environmental quality in G7 countries throughout the period from 1990 to 2020. This study employs second-generation methodologies, such as cointegration testing, stationarity analysis, slope homogeneity examination, and cross-section dependence assessment. The statistical model employed in this study was validated through the use of panel quantile regression [60]. The findings of this analysis indicate that both economic performance and natural resource commodity prices have a detrimental impact on environmental quality. This is supported by the strong inducing effects observed on carbon emissions across an extensive range of quantiles. In contrast, the mitigation of CO₂ emissions, investment in research and development, the allocation of oil rents, and the promotion of RE sources contribute to the enhancement of environmental quality.

Interestingly, in another case study related to G20 economies, You et al. [61] also tried to investigate the role of NRs in environmental sustainability over the period of 1995–2018. However, outcomes under the PMG estimator describe the positive role of mineral, oil, and forest resources on carbon emissions. However, such outcomes describe the complex nature of natural resources for environmental sustainability in the G20 economies.

Hasan et al. [62] experimentally investigate the ecological impact of fluctuating pricing of natural resources in conjunction with technological advancements and the adoption of RE within the Chinese economy. The study utilizes annual time series data spanning from 1990 to 2017 in order to assess the proposed hypotheses. The evaluation is conducted through the application of various estimators, such as DOLS, FMOLS, and CCR. Feedback revealed that technological advancements and the exploitation of natural resources are significant factors contributing to the deprivation of the environment, as indicated by an increase in the ecological footprint.

2.4. Research Gap

Upon conducting a complete analysis of the aforementioned research, it becomes evident that none of them have specifically investigated the impact of green innovation, green energy, reliance on natural resources, trade openness, urbanization, institutional quality, or environmental pollution on the objective of attaining carbon neutrality within the BRICS countries. Hence, the present work serves to address this gap. Empirically, we have noticed the previous studies ignored the Method of Moment Quantile-Regression (MM-QR) while estimating these variables’ relationship. So, in this study, we have filled all these study gaps.

3. Data and Methodology

3.1. Data

This study examined BRICS economies from 1990 through 2021 using panel data. The data determine timeframes. This study examined CO₂ emissions and the effects of natural resource use, financial development, green innovation, resource depletion, urbanization, growth rates, and trade openness. WDI provides all data reported in

Table 2. Summary of variables.

Symbol	Description	Measurement Unit	Source
CO2	Carbon Emission	(Kt)	WDI
TO	Trade openness	(% of GDP)	WDI
URB	Urbanization	(% of total population)	WDI
FD	Financial Development	Domestic Credit to Private Sector % of GDP)	WDI
GI	Green innovation	Patent applications, total (residents and non-residents)	WDI
IQ	Institutional quality	Estimate	WDI
RD	Resource Depletion	(% of GDP)	WDI
GDP	Economic Growth	(annual %)	WDI

.

3.2. Theoretical Model

CO₂ emissions are used as a measure of environmental impacts. Low CO₂ emissions are related with environmental sustainability, while high emissions are linked to environmental degradation [63]. Green innovation, which can be tracked through patents on environmentally friendly technologies, is also often regarded as an important contributor to environmental sustainability [64]. In addition, the major predictors of CO₂ emissions include financial development, institutional quality, trade openness, GDP, and urbanization [65]. All of the aforementioned factors are already known in the literature to effect environmental sustainability. As a result, the environmental quality in the BRICS nations is inevitably affected by factors such as green innovation, financial development, trade openness, GDP, and urbanization. In light of these considerations, our model is described below.

CO₂ = (GIit, FDit, TOit, GDPit, URBit, IQit, RDit)

(1)

In accordance with the below equation, all the variables have been converted to logarithmic form.

CO₂it = α0 + β1GIit + β2FDit + β3TOit + β4URBit + β5IQit + β6GDPit + β7RDit + μit

(2)

Here, t represents time (1990–2021), i represents BRICS countries, α₀ signifies a constant term, and μit represents an error term in the above equation. In addition, the coefficients β1, β2, β3, β4, β5, β6, and β7 are all weakened versions of themselves.

3.3. CSD and Homogeneity Test

As mentioned earlier, EU countries may have substantial interdependencies, mostly because they have implemented shared policies, like as the EST Act, to decrease carbon emissions in the region. It is crucial to manage the inter-dependencies that may exist in our panel dataset to avoid significant bias in the results, as highlighted by Pesaran, Ullah and Yamagata [66]. Consequently, we conducted an empirical examination to determine if there is cross-sectional dependence using the techniques established by Pesaran [67]. Subsequently, we focused more closely on the uniformity of the slope, as discussed by Baltagi, Feng and Kao [68] so as to convey the diverse nature of the member states.

The Lagrange multiplier (LM) statistics, first proposed by Faisal et al. [69], are used to test for cross-sectional dependence. The formula for these statistics can be expressed as

$${\mathrm{CD}}_{\mathrm{BP}}={\sum }_{i=1}^{N-1}{\sum }_{j=i+1}^N{\hat{\rho }}_{ij}^2$$

(3)

The term ${\hat{\rho }}_{ij}^2$ reflects the calculated value of the correlation coefficient between the residuals obtained from Ordinary Least Squares (OLS) calculations. The LM test is applicable when the null hypothesis assumes no cross-sectional dependence for panels with a fixed number of observations (N) and an infinitely large number of time periods (T). The distribution of CDBP approaches a chi-squared distribution with N(N − 1)/2 degrees of freedom as the sample size increases for panels of a significant size where T and N approach infinity.

Pesaran [67] devised the scaled version of the LM test (CDLM) in the following manner:

$${\sum }_{i=1}^{N-1}{\sum }_{j=i+1}^N(T{\hat{\rho }}_{ij}^2-1)~N(\mathrm{0,1})$$

(4)

Nevertheless, the rescaled version of the LM test in Equation (4) is expected to display significant deviations from the correct size when the sample size N is big and the time period T is small. Pesaran [67] devised a comprehensive test for cross-sectional dependence that is applicable to panel data with an infinite number of time periods (T→∞) and an infinite number of cross-sectional units (N→∞).

$$CD=\sqrt{\left({\displaystyle \frac{2T}{N(N-1)}}\right)}({\sum }_{i=1}^{N-1}{\sum }_{j=i+1}^N{\hat{\rho }}_{ij})~N(\mathrm{0,1})$$

(5)

Pesaran [67] demonstrates that the CD test is resilient when applied to dynamic models that exhibit heterogeneity, such as those with repeated breaks in slope coefficients and/or error variances. Safdar et al. [70] introduced a bias-adjusted version of the LM tests. This version utilizes the precise variance and mean of the LM statistic. It remains consistent even when the cross-section mean of the factor loading is close to zero. In contrast, the CD test does not exhibit this consistency. The calculation of the bias-corrected LM statistic is as follows:

$$L{M}_{adj}=\sqrt{\left({\displaystyle \frac{2}{N(N-1)}}\right)}{\sum }_{i=1}^{N-1}{\sum }_{j=i+1}^N{\hat{\rho }}_{ij}{\displaystyle \frac{(T-k){\hat{\rho }}_{\mathrm{i}j}^2-{\mu }_{Tij}}{\sqrt{v_{T\mathrm{i}j}^2}}}~N(\mathrm{0,1})$$

(6)

where k denotes the number of regressors, ${\mu }_{Tij}$ and $v_{T\mathrm{i}j}^2$ are, respectively, the exact mean and variance of $(T-k){\hat{\rho }}_{\mathrm{i}j}^2$.

The seminal contributions made by Koenker and Bassett [71] in the field of quantile regression applied to panel data have introduced an estimating approach that focuses on the conditional quantiles of the distribution rather than solely using the means, as was typically used in previous research [72]. Quantile Regression (QR) has semiparametric characteristics, allowing it to avoid reliance on distributional assumptions. Additionally, the method demonstrates robustness against data abnormalities. Zhang et al. [73] argue for the validity of Quantile Regression (QR) even when there is no conditional mean association between the variables. This study utilized the MM-QR methodology developed by Machado and Santos Silva [22] as the primary approach for estimating the conditional heterogeneity of the dependent variable. This study used Stata 18.0 software to accomplish this method. The methodology employed in this study offers advantages in mitigating the influence of individual effects and endogeneity issues inside the panel data model. According to the research conducted by Bashir et al. [74], the MM-QR method is an appropriate statistical approach for examining the impacts of heterogeneity across different quantiles. The definition of the conditional quantile model involves the estimation of updated location and scale parameters.

$$Y_{it}={\gamma }_i+X_{it}^{\prime }\alpha +({\beta }_i+Q_{it}^{\prime }\delta ){\in }_{it}0.75em$$

(7)

Estimating the parameters γ, α, β, and δ yields the probability that $({\beta }_i+Q_{it}^{\prime }\delta )$ > 0 is greater than or equal to 1. Both γ and β represent the model’s fixed effects. It is possible to define Q, the differentiable transformation of the m-vector of X, as

$$S_c=S_c\left(X\right),c=\mathrm{1,2},3,\dots ..,m$$

(8)

Both $X_{it}^{\prime }$ and ${\in }_{it}$ are IID in the sense that they do not depend on either time or units. This coincides with the orthogonality state given by Machado and Santos Silva (2019) and satisfies the moment criteria. The following estimation model will be used within the quantile framework:

$$Q_y\left(\tau \right|X_{it})=({\gamma }_i+{\beta }_i\left(q\right(\tau \left)\right)+X_{it}^{\prime }\alpha +Q_{it}^{\prime }{\delta }_q\left(\tau \right)$$

(9)

The explanatory variables—economic development, trade openness, eco-innovation, institutional quality, financial development and natural resources depletion—are represented by the vector $X_{it}^{\prime }$ in Equation (9). In this equation, the conditional quantile distribution of the endogenous variable is shown on the left-hand side. In contrast to previous LS-fixed effects methods, the intercept term will not be included in the individual effects. Since it is expected that the variables do not rely on time, the interunit variability should also shift. After obtaining the solution, the sample quantiles are produced by minimizing the following:

$$mi{n}_q{\sum }_i{\sum }_t{\vartheta }_{\tau }(Z_{it}-({\beta }_i+Q_{it}^{\prime }{\delta }_q)q)$$

(10)

where ${\vartheta }_{\tau }$ indicates the check function.

4. Estimations and Results

The technique of descriptive summary is a statistical method used to succinctly explain the primary characteristics of a given data set. The term is employed to delineate the measures of central tendency, variability, and dispersion of the data. The utilization of a descriptive summary in analyzing a data collection serves the purpose of enhancing comprehension of the data and detecting potential patterns or trends. Furthermore, it may be utilized to make comparisons between several collections of data. In general, the utilization of descriptive summary serves as a valuable instrument in condensing data and discerning prevalent patterns or trends. This tool facilitates the acquisition of a more comprehensive comprehension of a given data collection and enables the comparison of several data sets. The summary of key features is reported in

Table 3. Descriptive Summary.

Stats	CO2	FD	GI	GDP	TO	URB	RD	INQ
Mean	1.2996	3.6864	10.1432	1.4256	3.6342	3.9903	2.8277	−0.3081
Min	−0.4347	2.0794	8.8988	−1.6415	2.7184	3.2405	0.3812	−2.8713
Max	2.6825	4.1109	10.9096	2.6554	4.7057	4.4723	4.2095	0.8195
SD	0.9088	0.3749	0.6633	0.8425	0.3879	0.3978	1.3548	0.6761
Skewness	−0.1966	−2.4306	−0.6329	0.8425	−0.4853	−0.6067	−1.6847	−1.6412
Kurtosis	1.6267	4.4349	1.9340	−1.2305	2.5780	1.8710	1.4001	1.2720

.

Carbon exhibits a consistent upward trend over time, but with considerable heterogeneity in carbon levels seen across different nations. This observation implies that certain nations are releasing higher quantities of carbon dioxide compared to others. The average increase in the GDP over time is seen, accompanied with a decrease in the variability of GDP values across different nations. This observation indicates that a majority of nations are now seeing positive economic expansion. The average trend of the FD demonstrates a consistent increase over time. However, it is significant to notice that there exists significant heterogeneity in the values of FD among different nations. This implies that there exist disparities in the level of development of financial systems across different countries. TO has a consistent upward trend over time, and there is a reduced level of variability in TO values among different nations. This observation implies that a majority of nations are increasingly embracing a more liberal approach towards international commerce. The average trend of the URB demonstrates a consistent increase over time while exhibiting considerable variability across different nations. This observation indicates that certain nations are experiencing a faster pace of urbanization compared to others. The average trend of the GI exhibits a positive increase over time. However, there exists substantial heterogeneity in the GI values seen across different nations. This observation implies that there exists variation across countries in terms of their investments in research and development. The trend of natural RD indicates a consistent increase over time, while the variability of RSD values among nations has decreased. This observation indicates that a majority of nations are seeing an upward trend in their rates of enrolment in secondary education. The sensitivity tests are shown in Table 1.

Panel data regression slope coefficient homogeneity is tested using Pesaran et al. [66]. The test’s null hypothesis is that all panel cross-sectional units have the same slope coefficients. The substitute hypothesis is that slope coefficients are heterogeneous, varying across panel cross-sectional units. The uncorrected Delta statistic’s p-value is 0.035/0.014, which is below 0.05. We may reject the null hypothesis of slope coefficient homogeneity at 5% significance. The modified Delta statistic has a p-value of 0, thus we may reject the null hypothesis of slope coefficient homogeneity at any significance level.

The Breusch–Pagan test for heteroscedasticity was run to see if the error term’s variance is the same for all possible settings of the independent variables. There is insufficient evidence to establish that the variance of the error term is not constant, as suggested by the test results. This provides support for the interpretation that the homoscedasticity assumption is not broken here. Further, to check for first-order autocorrelation in the regression’s error term, we used the Wooldridge test for autocorrelation. The test findings imply that the evidence for a correlation between the regression’s mistakes is insufficient. This provides support for the interpretation that the premise of independence is not broken here.

Next, partial and semi-partial correlations are valuable analytical techniques that facilitate the comprehension of interrelationships among variables within a multivariate context. Regression analysis is a valuable tool for identifying the key factors that contribute significantly to the variability observed in a dependent variable. Additionally, it aids in mitigating the influence of extraneous variables when drawing conclusions about the association between two variables. The outcomes of partial and semi-partial correlations are displayed in

Table 4. Partial and semi-partial correlations of carbon emission.

Variable	Partial Corr.	Semi Partial Corr.	Partial Corr^2	Semi Partial Corr^2	p-Value
TO	0.6774	0.1868	0.4589	0.0349	0.002
URB	−0.2822	−0.0597	0.0796	0.0036	0.2566
GI	0.0681	0.0139	0.0046	0.0002	0.7882
RD	0.1236	0.0253	0.0153	0.0006	0.6252
GDP	−0.209	−0.0434	0.0437	0.0019	0.4052
FD	0.1642	0.0338	0.0269	0.0011	0.5151

.

Table 4 discloses that even after correcting for other considerations, trade-open nations generate more carbon; open countries for trade have greater economic activity, which increases energy consumption and carbon dioxide emissions. Even after compensating for other considerations, urbanized nations release less carbon. This is likely because urban regions consume energy more efficiently than rural places. Urban regions have superior public transit, which reduces transportation-related carbon emissions. Carbon dioxide emissions appear unaffected by green innovation. This may be because green innovation is still in its infancy and has yet to significantly change energy production and use. Even after correcting for other considerations, nations with less resource depletion generate less carbon dioxide. Mining and industry, which deplete resources, generate a lot of carbon. Even after correcting for other considerations, slower-growing economies release less carbon. Economic expansion frequently increases energy use, which increases carbon emissions. Even after correcting for other reasons, industrialized financial systems release more carbon dioxide. Financial development increases investment in carbon-intensive sectors like energy and transportation.

However, to determine the cross-sectional independence among the factors, this study employed the CD test; the graphical representation of the CD test is portrayed in Figure 3.

Figure 3 discloses that all the factors are cross-sectionally dependent in the BRICS economies. When regression model errors are correlated, correlated panel corrected standard errors (PCSEs) are used to estimate regression coefficient standard errors in panel data. Over time, panel data is gathered for many units, such as nations or enterprises. The regression model’s error correlation determines PCSEs. The regression model’s mistakes are correlated using a variance–covariance matrix. Regression coefficient standard errors are calculated using the variance–covariance matrix. Further, this study employs the second-generation unit root, which assists in measuring the stationery and incorporated cross-sectional dependency in the dataset. For this objective, our study employed the CIPS and PSADF unit root tests, both of which have the capacity to deal with the cross-sectional dependency; the outcomes of the second-generation unit root are reported in

Table 5. Second generation unit root test.

	CIPS		PSADF
Variables	Level	Difference	Level	Difference
CO2	−1.620	−3.191 ***	−2.312	−3.380 ***
TO	−2.307 *	−5.028 ***	−2.712 **	−4.236 ***
URB	0.823	−3.003 ***	−2.619 **	0.115
GI	−1.187 *	−4.745 ***	−1.115	−3.970 ***
RD	−2.167 *	−4.237 ***	−1.654	−2.399 *
GDP	−3.442 ***	−5.958 ***	−2.630 **	−4.833 ***
FD	−2.036 *	−5.498 ***	−1.894	−4.449 ***
INQ	−1.998	−5.068 ***	−1.276	−3.773 ***

Note: ***, **, and * denote the 1%, 5%, and 10% significance level.

.

The analysis discloses that TO, GI, RD, GDP, and FD are stationery at level, while at first the difference of all the variables is stationary at a 1% significance level in the case of CIPS. In PSADF, the unit roots of TO, URB, and GDP are stationary at a 5% significance level. While at first RD has a unit root at 10%, the rest of the factors are stationary at a 1% significance level. Further, when regression model errors are correlated, PCSEs are more accurate than OLS standard errors. The findings of PCSEs are portrayed in

Table 6. Prais–Winsten regression, correlated panel corrected standard errors (PCSEs).

	Coefficient	Std. Errs.	z-Value	p-Value
TO	0.8690	0.0710	12.23	0.000
URB	−1.1883	0.1940	−6.13	0.000
GI	0.0274	0.0148	1.86	0.063
RD	0.0437	0.0219	1.99	0.046
GDP	−0.0228	0.0092	−2.47	0.014
FD	0.2360	0.0989	2.39	0.017
INQ	0.0426	0.0220	1.98	0.044
_cons	2.1322	1.3522	1.58	0.115

. As per given outcomes, this study shows TO, GI, RD, FD, and INQ positively correlated with the explained variable. On the other hand, urbanization and income describe a negative association with carbon emissions in the selected countries.

The PCSEs disclose that carbon emissions are strongly connected to TO, URB, RD, GDP, and FD. In econometrics, the estimation of a system of equations that is affected by endogeneity and simultaneity requires the application of a sophisticated statistical method known as Three-Stage Least Squares (3SLS) (Model 1). This has particular importance in simultaneous variable determination and feedback loop econometric models. It aids researchers in estimating the coefficients in a consistent and objective manner, taking into consideration the complications of endogeneity and simultaneity. Multivariate regression applies (Model 2) simple linear regression to more complicated scenarios with many factors. Multivariate regression estimates independent variable coefficients that best explain variance in the dependent variable. It lets researchers investigate the combined impacts of numerous factors, revealing how variables interact and contribute to data patterns. The results of Models 1 and 2 are stated in

Table 7. Direct impact.

Variables	Model 1	Model 2
TO		−0.869 ***
		(0.0710)
URB		−1.188 ***
		(0.194)
GI		−0.0274 *
		(0.0148)
RD	0.269 ***	0.0437 **
	(0.0623)	(0.0219)
GDP	0.00142	0.0228 **
	(0.0608)	(0.00924)
FD	−0.0776	0.236 **
	(0.135)	(0.0989)
TO	0.0373 ***
	(0.00354)
URB	0.0371 ***
	(0.00461)
GI	−7.8707 **
	(3.3207)
INQ	0.269 ***	0.0437 **
	(0.0623)	(0.0219)
Constant	−2.970 ***	2.132
	(0.551)	(1.352)
R2	0.953	0.933

Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

.

According to the results of the regression study (Models 1 and 2), TRO, URB, RSD, and FND are all statistically significant predictors of carbon emissions, whereas GRI brings down the amount of carbon emissions

Residual-based Kao cointegration test for panel data. Kao et al. [75] proposed that if two or more variables are cointegrated, their residuals will be serially uncorrelated. Pedroni et al. [76] devised the panel data cointegration test. It is stronger than the Kao cointegration test but more susceptible to model misspecification. Westerlund and Edgerton [77] created the panel data cointegration test. It may test panel data with a range of cross-sectional dependency patterns for cointegration. The results of these relationships are demonstrated in

Table 8. Tests for cointegration.

Kao
Cross-sectional means removed	Statistic	p-value
Modified Dickey–Fuller t	−3.6044	0.0002
Dickey–Fuller t	−2.9825	0.0014
Unadjusted modified Dickey–Fuller t	−5.6521	0.0000
Unadjusted Dickey–Fuller t	−3.5414	0.0002
Pedroni
Phillips–Perron t	−3.3558	0.0004
Augmented Dickey–Fuller t	−1.8598	0.0315
Westerlund
Variance ratio	−1.3179	0.0930

.

Kao’s cointegration analysis was run after removing cross-sectional means. The findings demonstrate that at the 0.0002 level of significance, the adjusted Dickey–Fuller t statistic is −3.6044. Therefore, we can rule out the possibility that there is no cointegration. The modified Dickey–Fuller t statistic is more significant than the original Dickey–Fuller t statistic, but both are significant. We also performed a Pedroni cointegration test. The outcomes reveal that at the 0.0004 level of significance, the Phillips–Perron t statistic is −3.3558. As a result, we can also rule out the possibility that there is no cointegration. As is typically the case, the enhanced Dickey–Fuller t statistic fails to reach statistical significance. As with previous cointegration tests, the enhanced Dickey–Fuller test has been shown to have limited reliability. We also ran the Westerlund cointegration test. At the 0.05 level of significance, the data demonstrate that the variance ratio is −1.3179. That is why it is impossible to rule out the possibility that there is no cointegration. In conclusion, there appears to be cointegration between GDP, investment, and consumption according to the findings of the cointegration tests. This indicates a long-term association between the two variables. The Westerlund cointegration test suggests, however, that this may only be the case in the long run, and that the connection between these variables may be unstable in the short term.

Method of Moment Quantile Regression (MM-QR) uses moment conditions and quantile regression to estimate model parameters. It provides a flexible framework for recording variable interactions across response variable quantiles, revealing the conditional distribution beyond the mean. The outcomes of MM-QR as a robustness test are reported in

Table 9. Results from the MM-QR approach.

CO2	Coefficient	Std. Err.	z	p > z
location
FD	0.004	0.019	0.21	0.837
GI	0.000	0.000	−0.78	0.434
TO	0.168	0.017	9.94	0.000
URB	0.071	0.024	2.99	0.003
RD	−0.01	0.017	−0.58	0.56
GDP	−0.088	0.061	−1.46	0.144
INQ	−1.089	0.373	−2.92	0.004
_cons	−5.706	1.802	−3.17	0.002
Scale
FD	0.011	0.010	1.060	0.288
GI	0.000	0.000	−2.130	0.033
TO	0.007	0.009	0.720	0.469
URB	0.005	0.013	0.410	0.684
RD	−0.011	0.009	−1.210	0.225
GDP	−0.047	0.032	−1.460	0.143
INQ	−0.058	0.199	−0.290	0.771
_cons	1.236	0.961	1.250	0.212
qtile_10
FD	−0.157	0.073	−2.15	0.031
GI	0.000	0.000	0.650	0.518
TO	0.159	0.022	7.120	0.000
URB	0.064	0.031	2.040	0.042
RD	0.006	0.022	0.270	0.789
GDP	−0.020	0.080	−0.250	0.803
INQ	−1.005	0.491	−2.050	0.041
_cons	−7.441	2.378	−3.130	0.002
qtile_30
FD	−0.004	0.021	−0.19	0.85
GI	0.000	0.000	0.04	0.965
TO	0.164	0.019	8.73	0
URB	0.067	0.026	2.56	0.01
RD	−0.002	0.019	−0.1	0.921
GDP	−0.054	0.067	−0.8	0.422
INQ	−1.047	0.412	−2.54	0.011
_cons	−6.584	1.994	−3.3	0.001
qtile_60
FD	0.009	0.019	0.46	0.646
GI	0.000	0.000	−1.27	0.203
TO	0.171	0.017	10.01	0
URB	0.073	0.024	3.07	0.002
RD	−0.015	0.017	−0.87	0.386
GDP	−0.11	0.062	−1.79	0.074
INQ	−1.116	0.376	−2.96	0.003
_cons	−5.156	1.824	−2.83	0.005
qtile_90
FD	−0.157	0.073	−2.15	0.031
GI	0.000	0.000	−2.02	0.044
TO	0.178	0.02	8.68	0.000
URB	0.079	0.029	2.73	0.006
RD	−0.026	0.021	−1.26	0.208
GDP	−0.157	0.073	−2.15	0.031
INQ	−1.173	0.452	−2.6	0.009
_cons	−3.959	2.182	−1.81	0.07

.

The findings from the MM-QR analysis demonstrate the influence of variations in the independent factors on the dependent variable (CO₂ emissions) across several geographical areas. The coefficients in question exhibit varying economic implications, as certain factors demonstrate considerable impacts while others do not. The interpretation of coefficients and their respective directions can provide valuable insights into the interactions between variables and their economic consequences within different geographical contexts.

While the “Scale” coefficients have diverse economic implications, a majority of factors do not demonstrate statistically significant impacts on the dependent variable collectively. The coefficients of significance and their corresponding directions of effect provide valuable insights into the linkages between variables and their possible economic ramifications in different geographical locations.

The analysis describes that in the upper, middle, and lower quantiles, trade openness, urbanization, and institutional framework have a significant influence on carbon emissions, while financial development has an effect on carbon emissions in the upper and lower quantiles. Further, growth rate has a connection with carbon emissions in the middle and lower quantiles. Figure 4 describes the spread of the variables in their respective quantiles.

4.1. Systematic Analysis

The results indicate that a one-unit increase in trade openness will lead to a rise in the level of (CO₂) by 0.4256% in BRICS countries. Similarly, green innovation and resource depletion have a positive impact on carbon emissions (CO₂) in BRICS countries. This indicates that a one-unit increase in green innovation (GI) and resource depletion (RD) would cause an increase in the level of carbon emissions (CO₂) by 0.0790% and 0.1658% in BRICS countries. The outcomes show that urbanization and economic growth have a negative impact on carbon emissions (CO₂) in BRICS economies (See

Table 10. Systematic analysis.

Variables	FD	FE	FS	DBC	DBPC
TO	0.4256 ***	0.1782 **	0.5713 **	0.6278 ***	0.1029 **
URB	−0.6721 ***	−0.4256 ***	−0.4945 ***	−0.1240 ***	−0.0272 **
GI	0.0790 **	0.1290 **	0.0526 ***	0.0290 ***	0.1764 ***
RD	0.1658 **	0.1073 ***	0.0192 ***	0.1274 ***	0.1696 ***
GDP	−0.0645 **	−0.1452 ***	−0.1823 ***	−0.3986 ***	−0.1147 ***
INQ	0.1938 ***	0.5147 **	0.7165 **	0.1936 ***	0.5216 ***
FD	0.6243 **	- - - - - -	- - - - - -	- - - - - -	- - - - - -
FE	- - - - - -	−0.0257 ***	- - - - - -	- - - - - -	- - - - - -
FS	- - - - - -	- - - - - -	−0.0190 ***	- - - - - -	- - - - - -
DCF	- - - - - -	- - - - - -	- - - - - -	−0.2189 **	- - - - - -
DBPC	- - - - - -	- - - - - -	- - - - - -	- - - - - -	0.1045 *
Constan	3.9862 ***	3.4276 **	2.1093 ***	3.9014 ***	2.8472 **

Note: ***, **, and * show 1%, 5%, and 10% significance levels.

). This shows that with a one-unit increase in these factors, carbon emissions (CO₂) decrease by 0.6721% and 0.0645%. The outcomes also indicate that a one-unit increase in institutional quality, financial depth, and domestic bank private credit would cause a rise in the level of carbon emissions (CO₂) by 0.1938%, 0.6243%, and 0.1045% in BRICS countries. The DBPC (domestic credit to private sector) has a positive and insignificant effect on carbon emissions (CO₂) in BRICS countries. Lastly, the outcomes indicate that financial efficiency, financial stability, and domestic credit provided by the financial sector would cause a decrease in the level of carbon emissions (CO₂) in BRICS economies.

4.2. Discussion

The findings indicate that increased trade openness (TO) leads to a worsening of carbon emissions (CO₂) in BRICS countries. The results specify that a 1% rise in openness to trade cause to a corresponding increase in carbon emissions in the model. The findings confirm that increasing trade with a certain level of openness will have a substantial impact on CO₂ emissions. Additionally, a significant increase in CO₂ emissions can be attributed to the expansion of production capacity, which leads to a larger scale of international trade and worsens the negative effects in the import sector. These findings align with the studies conducted by [78,79]. This suggests that countries with high energy consumption in their industrial methods generate a substantial quantity of economic output. Consequently, the data demonstrate that a significant increase in production scale outweighs the advantages gained from technological advancements and changes in composition due to global commerce, leading to an increase in carbon emissions. Trade promotes the adoption of environmentally friendly and sophisticated creative technology in emerging nations, such as BRICS countries, leading to improvements in the standard of life and economic growth, as suggested by the theoretical literature. However, these trade activities consist of outdated technologies from industrialized nations that contribute to the use of polluting energy sources (non-renewable sources) and release significant amounts of carbon emissions.

From an elasticity standpoint, a long-term drop in CO₂ emissions is observed for each percentage point increase in the urbanization rate of BRICS countries. The consistent correlation between urbanization and negative consequences on CO₂ emissions demonstrates the strength and reliability of the findings. The long-term coefficients of urbanization rates show a significant negative impact on carbon emissions, suggesting that urbanization has a restraining effect on carbon emissions. However, this effect is relatively weak. This could be due to the “agglomeration effect” caused by urbanization slightly outweighing its “consumption effect”. Furthermore, it indicates that the scale effect of urbanization helps reduce resource consumption intensity and carbon emissions. Urbanization has been found to have an impact on economic growth and diminish its ability to promote carbon emissions. This could be attributed to the dissemination of information and the upgrading of industries that come with urbanization [80]. Consequently, this leads to the development of environmentally friendly economic growth and a reduction in carbon emissions [81]. Urbanization has been found to have an impact on energy intensity and diminish its positive influence on carbon emissions. This could be attributed to the concentrated utilization of energy and the technological advancements that come with urbanization. Consequently, this leads to improvements in energy efficiency and reductions in carbon emissions [82].

Regarding green innovation, it has been observed that a positive impact from green innovation does not have a notable influence on CO₂ emissions. However, a decrease in the negative impact of green innovation leads to a considerable increase in CO₂ emissions. This indicates that a 1% decrease in the impact of green innovation leads to a long-term increase in CO₂ emissions. Green innovation facilitates the reduction of energy consumption, and so contributes to sustainable growth by minimizing energy use. Thus, the study conducted by [83] concludes that green innovation is not effective in lowering carbon emissions in BRICS countries. Green innovation can have a dual influence on carbon emission intensity. Green innovation facilitates the shift of production from low value-added and highly polluting industries to green industries with high value added. This promotes industrial upgrading and transfer, ultimately reducing the proportion of high-polluting industries in the overall economic output [84]. Green innovation, which prioritizes energy conservation, emission reduction, and cleaner production, directly and clearly contributes to the promotion of low-carbon development. Green innovation can facilitate the transformation of industries and enhance their capacity to absorb and implement large-scale changes. It also promotes the upgrading of industrial systems at a technological level. In addition, the BRICS countries have effectively managed carbon emissions resulting from consumption by implementing green innovations and improving energy efficiency. This has contributed to the achievement of sustainable growth goals, as highlighted by [85]. Moreover, energy efficiency and green innovation can play a substantial role in addressing environmental pollution. Moreover, green technologies are essential for ensuring the sustainable development of economic, social, and energy systems, as well as reducing carbon emissions in the economy.

Simultaneously, the opposing viewpoint argues that the evidence of resource depletion indicates a favorable and substantial influence on carbon emissions (CO₂) in BRICS countries. The findings pertain to resource depletion, which suggests that the utilization of natural resources leads to environmental degradation and an increase in carbon dioxide (CO₂) emissions in BRICS economies. The removal of natural resources is generally regarded as the main cause of environmental deformation. In their study, Ref. [86] investigated the influence of a human capital index and natural resource depletion on environmental deterioration. The results indicate that the depletion or overexploitation of natural resources leads to significant environmental damage. Natural resources are substances utilized to enable economic activity and direct consumption in order to fulfill various human requirements. The extensive utilization of natural resources not only impacts production efficiency but also exacerbates environmental degradation. In response to the depletion of resources, governments also provide subsidies for fuel usage, which cause a rise in CO₂ emissions [87]. This signifies the unviable utilization of natural resources in the BRICS nations. The findings of [88,89] are supported by our results. In addition, they confirm the negative impact of natural resource rent in BRICS countries. The environmental impact of natural resource extraction may be confirmed, as mining activities directly stimulate economic growth, resulting in a rise in CO₂ emissions. According to [90], the ongoing encouragement of fossil fuel exploration and production has led to a substantial rise in pollution. Throughout the years, the construction of coal power plants that release a significant number of pollutants has been a major factor in the increase of carbon emissions. As per [91], the overconsumption of natural resources during the process of industrialization will lead to a significant rise in pollution levels. In addition, Ullah et al. [92] advised that excessive utilization of natural resources can result in significant environmental issues, such as deforestation and global warming. Wang et al. [93] contended that the misuse of natural reserves can cause the countries being reliant on energy imports. The BRICS countries rely on energy imports instead of utilizing clean energy sources, and they depend on the unsustainable exploitation of natural resources to meet their economic objectives. This study focused on the correlation between natural resource rents and CO₂ emissions, considering the challenges presented by a change in climate and the diminution of natural reserves nationwide. Consequently, the discoveries from this research can be used as a basis for developing guidelines for the management of natural resources and the environment.

This study examined the adverse effects of the BRICS nations’ rapid economic growth on the their countries’ environmental degradation, specifically in terms of reducing CO₂ emissions. The initial sub-panel provides an account of the impact of specific variables on the emission levels within the relevant group. The findings suggest that a one-unit gain in GDP in BRISC countries would cause a decline in carbon emissions. As the economy grows, individuals’ expectations escalate, leading to a subsequent surge in pollution, waste generation, and environmental degradation [94]. The energy infrastructure of BRICS countries relies on economic development. Experts should substitute these outdated technologies with more advanced and environmentally friendly alternatives that effectively protect our natural resources, reduce our carbon emissions, and address environmental issues. BRICS sectors rely heavily on fossil fuels, which contribute to both environmental harm and economic growth. As a result, economic progress in these industries leads to an increase in CO₂ emissions [95]. According to a study by [96], Bangladesh’s emissions have started to rise due to the connection between growing oil consumption and the expansion of infrastructure, formation of commerce, and economic capitalization. These factors all contribute to the benefits of investment and firm output. Furthermore, this phenomenon is mostly attributed to fundamental economic changes, such as the move from agrarian to industrial pursuits. The economy of BRICS countries is undergoing a transition towards the industrial sector, which has high energy consumption. Elevated economic growth is correlated with heightened environmental degradation [39]. Consumption and development activities contribute to the satisfaction of growing societal requirements, but they also lead to higher levels of pollution, waste, and environmental deterioration. Consequently, economic activities seem to be compatible with both environmental conservation and development, rather than creating a persistent risk to environmental quality.

The data indicate that financial development has a considerable and favorable impact on environmental degradation in the long term. Over time, a 1% rise in financial development leads to a corresponding increase in CO₂ emissions, which aligns with the findings of [97]. Our findings suggest that in the developing regions of BRICS nations, the financial sector generates scale effects through the provision of loans, which in turn stimulate economic activities. The financial sectors in emerging nations are not focusing on energy-resourceful and green initiatives. As a result, the uncontrolled and unplanned capitalization in these sectors is having a negative impact on the environment. Various researchers have demonstrated that the quality of institutions positively impacts financial development [98]. Enhancing institutional quality facilitates the reorganization of the financial system to optimize its operational efficiency. Therefore, the poor quality of institutions is a contributing factor to the harmful effect of financial growth on the environment in developing economies of the BRICS nations. The findings corroborate the assertion by [99] that increased access to credit empowers customers to purchase energy-intensive machinery and automobiles. Domestic credit provided by the financial sector and broad money had a positive and statistically significant influence on carbon emissions. The finding aligns with the observation made by [58] that financial development leads to an increase in both the quantity and size of manufacturing activities in the country by providing more financial support to domestic companies. This, in turn, results in negative consequences, such as land degradation, pollution, and carbon emissions.

The results of the analysis on institutional quality indicate that a 2% rise in the INQ leads to a corresponding increase in CO₂ emissions in BRICS countries. The findings on institutional quality indicate that the laws and regulations pertaining to the environment have limited effectiveness in analyzing economies. This demonstrates that the rising regions are facing environmental degradation as a result of inadequate institutional performance and ineffective environmental protection practices. The efficiency of institutions has a significant impact on the economic activity of these nations, which can be measured on a scale. Increasing institutional quality has the potential to enhance economic production, attract additional trade and financial activities, and decrease inequality, hence amplifying the impact on CO₂ emissions. In addition, environmental sustainability is enhanced when national institutions are adequately enhanced to adhere to environmental laws and rules. Government may prioritize indicators such as the political and authorized framework, adequate financial resources, accessibility of feedback procedures, and proactive people who promote community contribution in order to maximize the value derived from open government data (OGD) for addressing societal challenges. This approach aims to improve institutional excellence and improve overall environmental quality, similar to the findings of [100] for the 47 Emerging Market and Developing Economies.

Those findings indicate that the various indicators of financial development, such as financial assets, financial efficiency, financial depth, domestic credit to banks, and domestic credit to private entities by banks, have both negative and positive effects on CO₂ emissions. These effects are consistent across all measures of financial development. This suggests that the coefficient estimates, as well as the financial efficiency measures used, yield significant and consistent results with a negative sign. This study establishes that the level of financial efficiency has a notable and adverse effect on the emissions of carbon dioxide (CO₂). Financial efficiency in BRICS countries has a favorable impact on improving environmental quality and promoting environmental cleaning. The outcome we obtained aligns with the conclusions established by Wang et al. [101] about Kenya and by Akram et al. [102] regarding other global areas. Still, our results do not align with the studies conducted by Khan et al. [103] on Saudi Arabia, Hu et al. [104] on Malaysia, Kalim et al. [105] on China, and Zhao et al. [106] on OBORI countries. Increased financial growth draws foreign direct investment (FDI), which might lead to a stimulation of research and development (R&D) activities in the respective regions. This scenario has the potential to stimulate investment endeavors, and so bolster economic expansion and perhaps influence the dynamics of environmental quality. Optimizing the administration of R&D in various places has the potential to improve the quality of the environment by minimizing CO₂ emissions. Furthermore, increased financial development might facilitate the distribution of financial resources towards enterprises aimed at environmental safety and reduce the burden of loan payments. Our findings align with the assertion by [107] that a robust financial sector enables all levels of government to secure loans for environmentally focused projects. This, in turn, promotes the adoption of advanced technologies in various regions, leading to a substantial decrease in emissions within the energy sector and a significant improvement in environmental quality. Therefore, it is imperative to prioritize financial growth, as it can have a substantial positive effect on the quality of the environment by decreasing CO₂ emissions in BRICS countries. According to Abdul et al. [108], bank savers find higher deposit rates particularly appealing, especially in developing and emerging nations. Banks can allocate a greater amount of capital towards business investments and lending to borrowers as a result of increasing savings. In addition to investing in financially lucrative enterprises, there is a growing interest from banks and other investors in supporting environmentally sustainable businesses [109]. Therefore, it is understandable that our research reveals the influence of banking and fiscal sector activities on overall carbon emissions. In general, providing greater interest rates to savers across all economic sectors helps decrease overall carbon emissions. Conversely, a rise in domestic credit to the private sector ultimately worsens total carbon emissions. Likewise, the impact of financial development, as measured by liquid liabilities and domestic loans to the private sector by the banking sector, on carbon emissions is found to be negligible. The minimal impact of financial development, as measured by domestic credit to the private sector by the banking sector, on carbon emissions aligns with the conclusions drawn by Ameer et al. [110]. Their research affirms that total credit has an inconsequential influence on carbon emissions. Moreover, previous research has demonstrated that financial development, as measured by liquid liabilities, is positively associated with energy consumption. The inadequate liberalization of the financial systems in BRICS nations is a significant obstacle to the ability of financial institutions to promote and facilitate the adoption of green technologies.

5. Conclusions and Policy Implications

The present research is carried out to measure the fluctuating role of financial development, resource depletion, green innovation, trade openness, and institutional frameworks on carbon emission in BRICS economies over the period from 1990 to 2022. This study employs the Q-GMM to investigate the study’s objective. However, the given outcomes describe the positive role of trade, green innovation, and resources depletion with carbon emissions. Similarly, financial development and institutional quality also significantly contribute to environmental deterioration. Finally, GDP and urbanization significantly reduce environmental stress across the BRICS nations. Moreover, under a systematic analysis, financial efficiency and financial stability try to secure environmental sustainability. Finally, financial depth describes a positive role in environmental degradation. However, this study also suggests some interesting policy implications in order to attain the desired levels of sustainability.

5.1. Additional Policy Recommendations

Policymakers should enhance and revise environmental policies to enhance the production and commerce of green products. A policy involving taxation or subsidies could be implemented to discourage the sale of environmentally harmful items and promote the trade of environmentally benign ones. In addition to mitigating the adverse environmental impacts of trade, implementing this approach can expedite the dissemination of environmentally friendly technologies to BRICS nations. Moreover, it is imperative for the BRICS countries to establish more robust regulations to ensure that trade liberalization contributes to the enhancement of their environmental conditions, as there are numerous benefits associated with embracing international trade. Hence, it is imperative for the governments of BRICS nations to intensify their endeavors in promoting the use of contemporary, eco-friendly, and more sustainable technology by international investors. Ultimately, this will enable the region to transition from non-renewable energy sources to renewable or low-carbon alternatives while also upholding industrial process excellence. Replacing non-renewable energy sources with solar electricity will greatly enhance the environmental state of the area. Specifically, the government should apply tax incentives to promote the exchange of low-carbon goods and restrict the exchange of high-carbon goods. Simultaneously, the correlation between positive economic production and CO₂ emissions suggests that in order to attain environmentally friendly and sustainable economic development, the country must incorporate energy-saving and emission-reducing policies while supporting economic growth. Furthermore, while economic openness continues to have a positive influence on CO₂ emissions, the overall impact has diminished as a result of the Free Economic Agreement (FTA) being implemented. This suggests that regimes must encourage technology communication and advance technology commerce in order to enhance the impact of technology spillover between the three countries. More precisely, they have the ability to offer targeted financial assistance to businesses to support their efforts in conducting technical innovation and engaging in technological commerce. This would also facilitate the advancement of indigenous industries that manufacture high value-added and low-carbon products. Furthermore, by examining the impact mechanism, it is evident that there is a positive technical effect between trade openness and CO₂ emissions. This suggests that the three nations should modify their trade structures to focus on high-tech industries and enhance the volume of technology trade. This will enhance the notable and substantial impact of trade openness on reducing technical emissions.

Individuals relocate to places that offer a reasonably abundant array of amenities in order to improve their quality of life. Providing social services to rural populations will decrease the migration rate to urban centers, cutting emissions. Furthermore, it is imperative for authorities in BRICS countries to prioritize the generation of employment opportunities and the development of the quality of life for urban residents. This will hinder their migration to metropolitan areas. In addition, it is imperative for the BRICS countries to establish a well-coordinated and structured Urban Resilience Building (URB) initiative. In order to do this, countries must establish a specific threshold of urbanization (URB) at which emissions related to urbanization begin to decline. In addition, rising urbanization places a heavier strain on the current physical and social infrastructure. The increase in urban population requires the renovation of infrastructure in cities. This could provide exciting new opportunities for investors. Ultimately, the establishment of intelligent urban areas can effectively mitigate URB-related issues in BRICS nations. Smart cities enhance the quality and efficiency of urban services, such as energy and transportation, among others. This can contribute to the attainment of efficiency, innovation, and sustainability in the sub-region.

Sustainable innovation plays a significant role in influencing carbon emissions. The governments of BRICS countries should enhance financial support for sustainable technologies, expand the market size of environmentally friendly resources, and enhance the market presence of sustainable products in order to create a mutually beneficial outcome of economic development and environmental conservation. Policy tools, such as grants, rebates, feed-in tariffs, and incentives, can be employed to reassure and stimulate environmentally friendly investments while ensuring the preservation of the environment and fostering economic growth. Authorities need to modify their strategic approaches to address the increasing demands for sustainable energy. The BRICS countries should revise and execute environmentally sustainable policies and initiatives in order to attain carbon neutrality. The government ought to dedicate a significant portion of its green public expenditure towards promoting green environmental innovation. Smart technologies can be utilized by BRICS economies to advance environmental consciousness. The BRICS countries should increase their investments in environmental technology in order to effectively clean their environment. The study’s findings emphasize the role of GI in reducing CO₂ emissions. The study recommends a higher allocation of resources towards research and development, as well as patent applications for innovative green technology, in order to reduce CO₂ emissions. Additionally, it proposes that the government should actively promote the monetization of patents and the advancement of clean and renewable energy technology. The policies of BRICS countries should give top priority to the advancement of green technologies that foster sustainable energy use, characterized by reduced resource utilization and minimized waste and pollution, thereby contributing to a cleaner environment. Energy-efficient hybrid vehicles and air conditioning systems can offer equivalent service and comfort while conserving energy and reducing expenses. The utilization of corncob and sugarcane bagasse to produce biodegradable plastics has the potential to decrease pollution and surpass the performance of conventional petroleum-based polymers. The government should provide subsidies to incentivize enterprises to participate in GI research focused on energy savings, emission reductions, and the development of low-carbon technologies, since this can provide favorable social, economic, and environmental outcomes. The BRICS countries should prioritize the identification of specific sources of emissions and the use of innovative technology to mitigate environmental degradation. The nations should leverage the advantages of globalization and collaborate with technologically advanced countries to efficiently adopt and study state-of-the-art green technology.

Governments should promote the use of clean technologies, such as renewable energy and plastic recycling, among their citizens. Moreover, BRICS countries utilize natural resources such as fossil fuels for the purpose of power generation. In order to reduce the exhaustion of natural resources caused by fossil fuels, it is recommended to encourage the generation of power from renewable sources such as wind and sunshine. Furthermore, it is important to acknowledge that woods possess a significant abundance of natural resources. Hence, the most effective approach to reduce the exhaustion of natural resources is to advocate for sustainable forest management techniques. Examples include the development of strategies for logging forests and the creation of designated zones for conservation. The second discovery derived from this research indicates that the profits generated from natural resource rentals have a detrimental impact on ecology. Therefore, in light of this discovery, it is imperative for the government to implement policies aimed at curbing the overconsumption of natural resources. To accomplish this aim, it is important to enhance and improve the existing legislation on natural resource taxation. Furthermore, it is imperative to formulate green taxation principles that are not only ecologically advantageous but also enduring, with the aim of promoting green investment. Based on the experiential results, this paper recommends the following policy recommendations: The countries identified as having the highest levels of pollution should address the issue by enhancing their current legislation on taxing natural resources in order to reduce excessive exploitation. Furthermore, it is imperative to implement tax legislation that promotes the sustainable and eco-friendly utilization of natural resources, with the aim of fostering increased investment in environmentally conscious initiatives. This study offers policy implications derived from the reported findings. (1) The findings emphasize that the exploitation and use of natural resources in BRICS nations has reached an unprecedented level that cannot be sustained. Hence, regulations that align with the conservation of natural resource extraction should be promptly enacted whenever feasible. (2) BRICS economies should transfer resources from resource-rich sectors to the manufacturing sector in order to harness the benefits of the natural resources’ rent. This will not only enhance economic growth but also ensure the efficient utilization of these resources for a green environment. Technological innovation can greatly benefit society when it is efficiently and appropriately implemented to adjust the use of natural resources. (3) Policymakers should implement strategies to ensure the green utilization of natural resources and levy taxes on coal and other fossil fuels to discourage their consumption. Policymakers should formulate strategies in highly polluted countries to attract foreign investors to invest in energy development, thereby safeguarding the environment and promoting economic progress. The results of this study indicate that the natural resources in BRICS countries are responsible for environmental degradation. Therefore, it is recommended that the government supply advanced equipment for the extraction of natural resources and ensure their proper utilization in activities such as mining, deforestation, and agriculture. This will lead to an improvement in the overall quality of the environment.

The empirical findings of this paper have significant policy implications for the economies of BRICS countries in terms of quality of environment and sustainable economic growth. Unless the Kuznets curve hits its inflection point, the BRICS countries cannot depend on economic progress to decelerate pollution. It is imperative for BRICS countries to encourage the adoption of nuclear, solar, and wind energy in both residential and industrial sectors. It should promote the optimal utilization of finite resources to mitigate carbon dioxide emissions resulting from electricity production. It should also decrease the carbon dioxide emissions associated with the executive green environmental strategy aimed at improving the cleanliness of the environment. Hence, it is imperative for BRICS countries to implement a comprehensive strategy that achieves a just and enduring balance between economic expansion and ecological decline. BRICS countries should reassess their energy subsidy policies and enhance environmental rules, with a specific focus on sectors that significantly contribute to pollution. These regulations have the potential to mitigate the environmental impact. Our findings indicate that policymakers in BRICS countries should develop an environmental policy that effectively reduces CO₂ emissions while ensuring economic growth is not compromised. Adopting a low-carbon economy is the most advantageous strategic decision for tackling climate change among BRICS countries. In order to prevent pollution from occurring in the first place, it may be necessary to modify the “pollute first, then treat” approach and change the economic development model that harms the environment. Therefore, we suggest that the government support markets by establishing a strong legal structure that creates lasting benefits for reducing emissions and consistently encourages the development of innovative technologies that contribute to a less carbon-intensive economy. Furthermore, the BRICS nations’ governments can establish legislation, such as imposing a substantial carbon tax, implementing carbon capture and storage technologies, and introducing emission trading systems. These measures aim to effectively mitigate CO₂ emissions resulting from the utilization of fossil fuels in power production and industrial activities. In order to accomplish the desired outcome of decoupling at the regional level, substantial modifications are necessary in the centralized state policy, behavioral patterns, and the rate of scientific and technical advancement. The primary economic activities of the BRICS countries are the extraction of natural resources and industries, rather than the production of science-intensive goods. The exploitation of natural resources and industries is the main driver of GDP development in these countries. The primary objective in this matter is to provide government assistance for research and development, with the goal of enhancing the resource intensity and energy efficiency of production. This will involve implementing innovative approaches to modernization in order to satisfy expanding demands while also minimizing the depletion of natural resources. In addition, the BRICS countries may transition their emphasis from extensive to intensive growth and modify their economic development model by prioritizing not just economic quantity but also the enhancement of the green economy. In addition, the implementation of technological innovations in integrating renewable energy sources would lead to reduced carbon emissions rates for electricity generation in BRICS countries. Therefore, it is crucial to promote the economic shift towards renewable energy sources in order to mitigate the environmental impacts resulting from economic growth. Policymakers have the ability to foster and advocate for renewable energy firms and technology. By substituting carbon dioxide-intensive conventional energy sources, these measures would help the economy to raise the proportion of renewable energy consumption against overall energy utilization. Furthermore, it is necessary to establish institutional alignment in order to enhance the utilization of renewable energy in various economic sectors and ensure sustainable economic development in the long run. Eventually, strict compliance with environmental standards is important. These steps will guarantee that the country’s objective of achieving rapid economic growth and transformation is not achieved by sacrificing environmental quality.

Furthermore, it is noteworthy that the level of financial development plays a crucial role in determining the amount of carbon emissions in BRICS countries. Simultaneously, it interacts with other variables to elucidate the environmental deterioration in BRICS nations, underscoring the need to ensure its durability for the attainment of sustainable development in BRICS countries. This implies that by focusing on the development of its financial sector, which is the largest contributor to CO₂ emissions among BRICS countries, India might play a crucial role in reducing the impacts of climate change. The reason for this is that higher levels of financial depth operate as a conduit or catalyst for the creation of technology and innovative products and services that improve energy efficiency. Indeed, numerous studies indicate that supporting financial development is advantageous for enterprises as it alleviates credit limitations and facilitates increased investment in advanced environmentally friendly technologies. This study suggests that although financial development hinders environmental quality, financial institutions should promote investment in environmentally friendly projects by industries or firms. Additionally, they should offer credit at reduced rates to those industries or firms that are dedicated to investing in projects that promote environmental sustainability. In addition, forthcoming environmental legislation should require firms/industries to publicly declare their environmental performance. The significance of the financial and private sectors in enabling the transition to low-carbon economies in BRICS countries is inherent in this conclusion. This, in turn, has the potential to change consumption patterns, the energy usage habits of domiciles and businesses, and the overall energy utilization composition in the budget. Policymakers are likely to consider the part of the financial scheme in promoting financial activities in the private sector by increasing access to credit, which is an important part of the transition and mitigation plans. Several policy approaches can be used to promote private sector investment in low-carbon technologies through finance sector activity. Policy measures should be implemented to incentivize banks and other financial institutions to include environmental considerations in their investments and risk assessments. Additionally, they might require making incentives that promote low-carbon choices by the private sector and discourage investment in highly contaminating technologies. A robust finance system facilitates the acceptance and use of cutting-edge technologies by industries, resulting in reduced CO₂ emissions and improved compliance with environmentally friendly policies. Finally, the empirical evidence suggests that the quality of institutions has a role in the generation of CO₂ emissions. The caliber of institutions can have a substantial impact on mitigating inequality and ameliorating environmental damage. Thus, we urge the governments of the nations under study to improve the quality of their institutions, as this will promote a social inclination towards a secure environment and support for environmental legislation.

5.2. Limitations and Future Studies

The study is subject to the following constraints: The analysis was limited in duration and scope due to the unavailability of data on institutional quality and financial growth indicators. However, it has the potential to be expanded to include all countries. Furthermore, the study has exclusively utilized CO₂ as a substitute for environmental deterioration; however, additional measures of environmental degradation, such as ecological footprint, might be incorporated for a more comprehensive examination. Our study has revealed numerous inquiries that necessitate feedback from the sectors for additional investigation. If firms do not cooperate, several of these research questions may stay unaddressed. Our concentration was solely on urbanization within the realm of environmental sustainability. This endeavor may have constrained the extent and impact of our research. Future research endeavors may prioritize examining the evaluation and consequences of digital transformation in interconnected sustainability sectors, such as the economic and social realms, while also establishing a shared research plan. Additional study can be conducted in several domains to improve our comprehension of the interaction between digitization, foreign direct investment, gross domestic product per capita, renewable energy, and environmental quality in Central European countries, OECD countries, and Asian countries. Moreover, conducting an analysis of the implications specific to each sector will provide a more thorough examination of the obstacles and possibilities for achieving sustainable digital transformation and financial investment, especially in sectors like renewable energy, transportation, and manufacturing. Furthermore, this study just relies on samples from the region of China, and it is yet to be investigated whether comparable issues exist in other nations worldwide. These issues remain unresolved and will be addressed in future study. In future research, it would be advantageous to develop more comprehensive indicators for each of those ideas using a bigger sample size. This article investigated the influence of traceability procedures on eco-friendly performance, focusing on the identifying element of dynamic capacities theory. The study’s concentration on carbon emissions overlooks other significant environmental metrics, for instance, environmental footprints and various emissions like particulate matter, methane, and nitrous oxide. Upcoming research might help by integrating multiple ED indicators to confirm a complete evaluation. However, the study has met its goals regardless of these stated limitations. Further, our work can be extended by involving more indicators of financial development, green innovation, institutional frameworks, and urbanization to explore zero carbon. Modern econometric approaches include Dynamic Simulated ARDL and Spatial regression. However, this paper has some limitations. For example, this paper does not consider other indicators of financial development, such as the development of green finance.