A Comparative Study of the Environmental, Social, and Governance Impacts of Renewable Energy Investment on CO₂ Emissions in Brazil, Russia, India, China, and South Africa

Abstract

The issue of climate change and global warming is rapidly intensifying due to the extensive emissions of CO₂. In response to this, countries worldwide are implementing policies to enact decarbonization strategies through social and governance investing strategies. The current study examined the effects of environmental, social, and governance investing, eco-innovation, renewable energy production and consumption, and carbon taxes from 2004 to 2023. At the highest, middle, and lowest levels, this paper examines the environmental policies, social and governance economies, and energy of the BRICS nations. We investigate variable relationships through cross-sectional autoregressive distributed latency. The results suggest that Brazil’s focus on sustainability-driven innovation, along with its high renewable energy balance and middle-level status, is indicative of significant environmental initiatives. India’s higher-ups prioritize green finance, and their investments in environmental, social, and governance areas may demonstrate their commitment to sustainable development. China has made considerable progress in renewable energy and carbon trading despite its vast population and high emissions. At a lower level, Russia’s sustainability initiatives are undergoing evolution and have the potential to make significant strides. The nation’s difficulties require a coordinated, long-term strategy. The empirical findings imply that BRICS countries can achieve carbon neutrality by modifying their economic growth and globalization strategies and increasing their focus on renewable energy, as well as investment and policy regulations.

1. Introduction

In order to prevent global warming, it has been demonstrated that CO₂ emissions must be eliminated. To accomplish this objective and establish carbon-free zones (with energy usage taxes and carbon taxes), it is most effective to establish a carbon price [1]. As a result of climate change, the environment behaves differently, and new weather patterns emerge [2]. To build a sustainable society and a strong economy, a country must maintain a clean, healthy, and pleasant environment [3]. This is due to the fact that the environment is the only thing that can supply the quality of the atmosphere in which people are required to carry out their social and economic activities, as well as make available both living and non-living natural resources for the sake of achieving these goals [4]. A wide range of pollutants, both naturally occurring and as a result of human activity, may degrade environmental conditions [5].

This study has examined the BRICS nations (Brazil, Russia, India, China, and South Africa), which are essential to global endeavors to decarbonize the economy. Global energy policies are significantly influenced by these emerging economies, which are significant contributors to greenhouse gas emissions [6]. There are several reasons why it is crucial to investigate the interplay of economic growth, environmental, social, and governance investing (ESGI) policies, and policy interventions in BRICS nations [7]. First and foremost, the BRICS nations collectively account for a substantial portion of global greenhouse gas emissions as significant GHG emitters. In order to accomplish global climate objectives, it is imperative that they transition to a low-carbon economy [8]. The second aspect is economic expansion and development. The BRICS nations are currently undergoing rapid economic growth and development, which may result in an increase in energy consumption and emissions [9]. The ability to strike a balance between environmental sustainability and economic development is essential [10]. Third, the global stage is significantly influenced by the policy implications of the BRICS nations, which have the potential to influence international climate policies and agreements. Fourth, the BRICS nations’ economic structures are diverse, with varying levels of industrialization and reliance on fossil fuels. The variety of decarbonization solutions offers chances to learn from each one. Developing economies occupy the fifth spot [11].

An appealing platform for investment and development, the BRICS nations make up a substantial percentage of the world’s rising markets. An understanding of the function of ESGI policies in these countries can inform sustainable investment strategies. By examining the interplay of these factors in the BRICS nations, researchers can acquire valuable insights into effective strategies for sustainable development and decarbonization [12]. These discoveries have the potential to provide enterprises, investors, and policymakers worldwide with valuable information. With regard to environmental degradation, CO₂ emissions are one of the pollutants that impact environmental quality [13]. A significant environmental challenge confronts the BRICS nations, a potent economic bloc, due to their substantial contribution to global CO₂ emissions. The primary reason for this is their heavy dependence on fossil fuels, particularly coal, and their accelerated industrialization [14]. China, a significant member of the group, has historically depended on coal-fired power facilities, which has resulted in substantial greenhouse gas emissions and air pollution [15]. The primary objective of this research is to examine the BRICS countries as a source of international economic dynamics. Their expanding geopolitical power, abundant resources, and fast economic expansion have captured the world’s attention [16]. The study of BRICS is essential for comprehending the intricacies of the 21st-century world. By analyzing their economic trajectories, climate policies, technology breakthroughs, and geopolitical agendas, we may get valuable insights into the future of world affairs, make educated policy decisions, and establish plans for sustainable development [17].

CO₂ emissions trap heat inside the earth’s boundaries, causing global warming, climatic circle imbalance, deterioration of human and other living things’ health, and a decrease in overall environmental quality [18]. CO₂ emissions and their consequences, such as global warming, affect the ecology, weather system, natural resource quality, and life on earth. As noted, the BRICS economies face this problem because their economic development results in enormous CO₂ emissions [19]. So, real policy consequences are needed to reach the goal of the scenario. According to studies [20,21,22,23], the BRICS nations are new economies because they have “geo-economies, fast productivity growth, and participation in international economic union.” It is important to consider that the world’s CO₂ emissions reached their highest point in 2023, with 36.8 billion metric tons, 1.1% more than the previous year [24]. These emissions were mostly caused by cement production and fossil fuels. Moreover, China’s higher emissions were the primary factor propelling the overall rise in fossil fuel emissions in 2023; absent this, the world total would have been relatively unchanged from 2022 levels. In 2023, there was a minor decrease in emissions from land use, but an increase in emissions from fossil fuels, leading to a total worldwide increase of about 0.5% in CO₂ emissions [25]. In particular, the BRICS countries must overcome significant challenges to achieve their goal. The paper aims to provide results that may help solve these problems [26].

This research paper categorizes the BRICS nations according to their CO₂ emissions. These nations create fewer CO₂ emissions than medium- and upper-middle-income economies, but the proportion is greater than that of the low-, lower-middle-, and high-income economies [27]. The initial targets were China and India, as they are the highest emitters. China’s rapid industrial development (IDL) and dependence on coal-fired power facilities are the primary factors contributing to its status as the world’s largest emitter of CO₂ emissions [28]. Additionally, India is the second-largest emitter, and its emissions have increased substantially in recent years as a result of IDL and economic growth. The second target was Brazil, Russia, and South Africa, which were middle-level emitters. Brazil’s total emissions remain significant due to its substantial population, even though its per capita emissions rate is relatively lower than that of China and India [29]. Russia’s emissions are predominantly attributable to its dependence on fossil fuels, particularly coal and natural gas. As a result of its dependence on coal-fired power facilities, South Africa has a relatively high per capita emissions rate. However, these categories rely on total CO₂ emissions, disregarding factors like population size and per capita emissions [30]. Furthermore, the ranking of these countries may fluctuate over time as their economies and energy balances develop in response to factors such as per capita emissions, energy and CO₂ emissions intensity, and government policies [31].

The primary contribution of this manuscript is a comparative analysis of the environmental policies of the BRICS nations. The study scrutinizes environmental degradation, renewable energy, CAT, eco-innovation, and ESGI [32,33,34]. The manuscript’s most distinctive feature is the distribution of the BRICS countries’ environmental policies into three levels: highest, middle, and lowest (Appendix A). In general, even though all BRICS nations have implemented initiatives to confront environmental challenges and advance sustainable development, there are substantial disparities in their advancements and the extent of their dedication to these objectives. While other nations have concentrated on specific sectors or challenges, China and India have become leaders in certain areas, including REN, RNS, and green innovation. Finally, the uniqueness of the manuscript highlights the importance of the BRICS countries in mitigating CO₂ emissions by utilizing response and regressor variables. These nations play a crucial role in global efforts to address climate change and reduce CO₂ emissions due to their significant emissions, economic growth and development, diverse energy mixes, geopolitical influence, and potential for leadership [35]. CO₂ emissions and their post-emissions effects, such as climate change, have an impact on the health of the earth’s ecosystems, weather patterns, and natural resource stocks (including food, water, soil, and minerals) [36].

We have evaluated the BRICS nations on the basis of social and governing issues, which have spurred innovation in sustainability, the utilization of renewable energy, and industrial revolution and development [37,38]. This study is unique in that approach. It is rooted in their distinctive status as emerging economies that are attempting to maneuver through geopolitical complexities, resource constraints, and accelerated growth. Unlike conventional economic powers, the BRICS countries frequently encounter obstacles, including environmental degradation, income inequality, and infrastructure deficits [39,40]. Researchers can learn a lot about various civilizations’ reactions to globalization, the complexities of development, and the possibilities for creative solutions to global problems by analyzing their varied experiences. The absence of appealing and productive workplaces is detrimental to the long-term prospects of a nation, and sustainable economic development necessitates them. Important factors that promote sustainable economic development are suitable locations to work, a multitude of high-quality minerals, productive terrain, potable water, copious bio-production, and streamlined operations. Reducing CO₂ emissions is one potential approach to mitigate the effects of environmental degradation. Partnerships are exemplified by the BRICS forums and new development institutions. In light of the aforementioned argument, the objective of this study is to assess the impact of green innovation, eco-innovation, CO₂ emissions levies, and RNS on CO₂ emissions in the BRICS countries. It is the objective of this study to examine the environmental, social, and governance consequences of renewable energy investment on CO₂ emissions in the BRICS nations. We implemented a cross-sectional autoregressive distributed latency examination of the relationship variables and carbon taxes. We have determined that the BRICS regions are at a significant risk of environmental degradation as a consequence of climate change, as indicated by these findings. In this manner, the paper endeavors to enhance the body of existing research. It unwraps the combined effect of the defined construct on CO₂ emissions and provides a conceptual framework by combining environmental factors.

The following components constitute the paper’s structure: Section 1 delineates the focal point of the investigation, as well as its objectives and novelty. The relationships between the enumerated constructs are analyzed in Section 2 in accordance with the authors’ perspectives. Section 2 delineates the methods and procedures employed to gather the necessary data and extract results from them following the analysis. The results of the current study are corroborated by the authors’ prior perspectives on comparable relationships during discussions. The study concludes with implications and recommendations.

1.1. Environmental, Social, and Governance Investing

A nation’s development requires land, air, water, minerals, energy, forests, crops, and life resources. Clean, healthy ecosystems boost national growth and resource quality. However, non-renewable minerals affect animals in contaminated environments. Environmental degradation slows a country’s advancement. CO₂ emissions damage our environment, weather, and natural resources [41]. The environment has been significantly affected by the rapid industrialization and economic growth of these countries, which are frequently driven by fossil fuels. China, in particular, has become the world’s largest emitter of greenhouse gases. India is also a substantial contributor, given its swiftly expanding population and energy requirements [42,43]. Russia’s carbon footprint is influenced by its energy-intensive economy and extensive fossil fuel reserves, and Brazil and South Africa continue to rely heavily on fossil fuels, particularly coal, despite their efforts to increase their use of renewable energy [14].

Efficient use of resources and processes, as well as green innovation, can reduce CO₂ emissions, which are harmful to human health and the environment. ESGI policy requires financial institutions to lend to, provide credit to, and invest in environmentally friendly products and operations. These items and behaviors reduce pollution and are environmentally friendly. Prior research shows technical innovation and CO₂ emissions for BRICS [44,45]. The BRICS nations must collaborate on technology sharing, collaborative research initiatives, and policy harmonization in order to expedite the transition to a low-carbon economy [46]. Critical financial and technical assistance can be obtained through international collaboration, particularly with developed nations [47]. Domestic policies that promote the adoption of renewable energy, including tax rebates and feed-in tariffs, are also indispensable [48].

By adopting sustainable practices and renewable energy, the BRICS nations can not only decrease their carbon footprint but also establish themselves as global leaders in sustainable development and pure technology [49]. As per prior research [14,50,51,52], unit root and cointegration experiments were conducted using the Banerjee, Carrion-i-Silvestre, and CS-ARDL models. The adoption of innovative technologies by businesses is facilitated by ESGI investment, which in turn reduces CO₂ emissions and improves environmental quality [53]. Reducing carbon-emitting energy consumption does not necessitate sweeping changes to existing energy efficiency practices. It reduces atmospheric CO₂ emissions, and IDL increases CO₂ emissions. The study indicated that IDL raises environmental consciousness [54]. Environmental issues, pollution sources, and solutions are well known. People and businesses are more aware than ever that they must address environmental challenges and reduce CO₂ emissions. GDP per capita, industrialization, and CO₂ emissions are assessed. In accordance with the data, the activities of ESGI in IDL reduce CO₂ emissions [55].

1.2. Eco-Innovation and Sustainability

Businesses can improve their resources, processes, goods, and services through eco-innovation, which is both environmentally conscious and socially acceptable. Energy-efficient, waste-reducing, and environmentally friendly alternatives to traditional materials and methods are the focus of eco-innovation [56,57]. Managing CO₂ emissions from business processes is possible. Researchers examined ECO and CO₂ emissions in 30 different locations in China between 2004 and 2016. In order to lessen their negative effects on the environment, businesses are turning to eco-innovation. This entails modifying their tools, procedures, and resources [58,59]. Harmful CO₂ emissions can be reduced by businesses. Currently, though, growing economies release a lot of CO₂ emissions [60].

Due to their strong reliance on fossil fuels for energy generation and industrial operations, the BRICS nations are facing the most severe challenge in terms of CO₂ emissions [61]. This is because of the impact that fossil fuels have on their economies. The rapid increase in greenhouse gas emissions that has occurred as a consequence of this dependence has resulted in a significant contribution to the global climate change that has been observed [62]. Although some of the BRICS nations have made progress in the incorporation of renewable energy, the magnitude of the challenge remains immense [17]. To help transition to a low-carbon economy, we need to put a lot of money into sustainable technology, infrastructure, and regulatory changes. It takes careful planning and execution to balance environmental sustainability with economic prosperity [63].

In order to mitigate their environmental impact and ensure a sustainable future, the BRICS countries have been making significant investments in renewable energy sources, as indicated by the aforementioned study [36]. For instance, India has implemented ambitious solar power objectives and has experienced an increase in the number of solar energy installations [64]. Brazil has been a pioneer in the development of biofuels and hydropower due to its extensive renewable energy potential [65]. Nevertheless, technological obstacles, financing, and grid integration continue to be a challenge. It is difficult to achieve continuous economic growth when faced with health issues, agricultural and animal diseases, and a dearth of natural resources. Adopting ECO practices by businesses might lessen the burden of CO₂ emissions and the associated problems [66,67]. We analyzed the quarterly data from selected petroleum enterprises from 2005 to 2016 using the Porter hypothesis and “Second-generation panel regression econometric” methodologies. Investment, training, and research and development are ECO variables that have a negative effect on CO₂ emissions, both immediately and over the long run, according to study [68]. The eco-innovation process incorporates employee education for ESGI operations, research and development (R&D), and the identification of environmentally favorable information and resources through ESGI practices [69].

1.3. Renewable Energy and Policy Intervention

Despite their efficiency and popularity as an energy source, fossil fuels are a limited resource made from dead organic elements. Despite the CO₂ emissions from fossil fuel combustion, their use is rising. No CO₂ emissions are released when RNS is used [70]. The study examined how urban population, R&D spending, technological innovation, ESGI, energy consumption, and non-sustainable energy usage affect CO₂ emissions. The Chinese government submitted annual data from 1990 to 2019. Researchers used ARDL simulation. The study found that REN sources did not reduce CO₂ emissions in large quantities [71]. REN sources reduce CO₂ emissions and other greenhouse gas emissions when used instead of fossil fuels. Previous studies have examined how ESGI affects CO₂ emissions [72]. Growing home and commercial green renewable energy (GRN) use reduces fossil fuel dependence, according to their analyses. Since carbon-containing fuels emit more CO₂ emissions after combustion, their use is reduced. This has led many to conclude that increasing GRN use is the main way to reduce CO₂ emissions. Previous research supports the inverse association between GRN use and CO₂ emissions. Countries with lower CO₂ emissions use more GRNs [73].

GRN consumption and manufacture reduce CO₂ emissions. REN reduces pollution by removing CO₂ emissions. GRN manufacture also promotes REN technologies with low voltage and its harmful effects from energy-related processes [74]. The reduction of CO₂ emissions by using REN sources is the focus of their investigation. The GRN and environmental datasets were obtained from more than 100 countries from between 1995 and 2015 and subsequently categorized into four income-based subpanels. The slope heterogeneity and cross-sectional dependency tests were evaluated using a variety of econometric methodologies [52]. A study discovered that the production of REN is dependent on heat, water, light, and human waste, while the synthesis of GRN contributes to environmental protection [75]. It evaluated GRN production, energy sustainability, and CO₂ emissions as indicators of environmental sustainability. Data from Iranian REN hybrid systems was utilized to conduct this study. The authors contend that the availability of sustainable energy is increased by the proliferation of renewable energy (REN) sources, such as bioenergy, hydroelectric power, wind power, and solar power [76]. In this situation, businesses typically implement GRN sources to reduce their carbon footprint. Previous research has examined the influence of GRN usage on the carbon footprint of Newly Industrialized Countries (NICs) from 1990 to 2018, and the results suggest that GRN functions reliably regardless of contamination. In Indonesia, a country with a significant air pollution problem, greenhouse gas emissions have been significantly reduced by GRN [77,78].

Human activities—social and economic—release CO₂, which reacts with atmospheric oxygen. In order to mitigate emissions, governments impose levies on carbon-producing enterprises and individuals [79]. CO₂ emissions pricing, environmental quality, and CO₂ emissions were assessed. In Europe, two economic scenarios were analyzed: one with and one without CO₂ emissions taxation [78]. Utilizing propensity score matching, we investigated the correlation between ecological quality and CO₂ emissions. Studies indicate that CAT reduces CO₂ emissions more rapidly when implemented correctly [80]. Transportation, communication, and other activities that are less carbon-intensive are encouraged by CAT in countries that are striving to reduce their CO₂ emissions footprint [81]. This is the reason why CAT effectively reduces gas emissions. Without acknowledging the conflicting findings of the few studies that have examined this link, the empirical literature on CAT and CO₂ emissions or environmental sustainability cannot be examined [82].

In conclusion, this manuscript differentiates the high, low, and intermediate levels of environmental policies among the BRICS nations, based on prior research. First, the BRICS nations collectively account for a substantial portion of global CO₂ emissions due to their status as key emitters [83]. Their policies and actions significantly influence global climate change mitigation initiatives. Secondly, the BRICS nations are experiencing accelerated economic growth and development, which is frequently accompanied by an increase in energy consumption and CO₂ emissions. Nevertheless, these nations have the capacity to pioneer the transition to low-carbon economies by implementing innovative policies and investments [84]. Third, the nations have diverse energy compositions, which are characterized by varying levels of reliance on REN sources and fossil fuels [85]. This diversity offers opportunities for knowledge exchange and collaboration in the development of sustainable energy solutions. Fourth, the nations’ actions can significantly impact international climate negotiations and cooperation, as they are key players on the global stage and exert geopolitical influence [86]. Fifth, there is a potential for leadership: the nations have the potential to take a prominent role in the development and implementation of innovative solutions to address climate change, particularly in emerging economies [87].

As a means to promote long-term sustainability, the BRICS countries are showing a growing interest in eco-innovation [67,88]. Sustainable farming practices, eco-friendly technologies, renewable energy, and circular economy concepts are their tools to fight against climate change, pollution, and resource loss. There are a lot of obstacles in the way of the BRICS countries being world leaders in eco-innovation and sustainable development, but that does not mean they cannot succeed [66]. The BRICS nations can make a substantial contribution to global efforts to mitigate climate change and establish a sustainable future by taking collective action to reduce their CO₂ emissions [89]. For a sustainable future, the urgent issue of CO₂ emissions necessitates that the BRICS nations make a concerted effort to transition to a low-carbon economy [90]. This necessitates a multifaceted strategy that encompasses energy efficiency measures and investments in renewable energy sources such as hydroelectric, solar, and wind [89]. Further measures to lessen emissions include advocating for sustainable city planning, electric transportation, and carbon capture and storage. To hasten this change, international collaboration, the transfer of technology, and financial backing are essential [91]. The BRICS countries can lessen their negative effects on the environment and ensure a bright and sustainable future for future generations by committing to sustainable practices and making climate action a top priority [92].

2. Methods

The research examines the impact of ESGI, eco-innovation, REN, RNS, industrialization, and CAT on CO₂ emissions in the BRICS countries from 2004 to 2023, as detailed in

Table 1. Variable definitions and explanations.

Variables	Symbol	Measurement
CO2 emissions	CO2	Metric tons per capita carbon emissions
Environmental, social, and governance investing	ESGI	Green credit in the private sector (% of GDP)
Sustainability-driven innovation	ECO	% of manufactured exports
Renewable energy	REN	% of total electricity output
Renewable power utilization	RNS
Emissions trading	CAT	% of revenue—taxes on average
Industrial development	IDL	% of GDP, industrial value added

. Brazil, Russia, India, China, and South Africa (BRICS) can be further classified based on their income levels [93,94,95,96]. However, the present availability of reliable and comprehensive data from 2024 and beyond may be limited. The BRICS countries have categories that are specifically designed for individuals with substantial incomes. The primary sources of economic activity in Russia are natural resources, specifically oil and gas. Secondly, we have designated Brazil, China, India, and South Africa as middle-income countries. Brazil is a middle-income country with a diverse economy that includes agriculture, manufacturing, and services. China is also a middle-income nation; however, it is undergoing accelerated development and has a substantial and expanding economy. India is a middle-income country, with a large population and a diverse population. Finally, South Africa is a middle-income country with a relatively developed economy. Nevertheless, it is met with significant challenges, such as inequality and unemployment.

Descriptive statistics and correlation analyses were implemented to evaluate the data from the outset. This was crucial because it allowed the authors to confirm the normality of the data and the strength or fragility of the variables. Furthermore, cross-sectional dependence (CSD) is induced by the correlation of error terms among a variety of entities (e.g., individuals, firms, and countries) in a panel dataset. Spillovers, spatial dependence, or common disturbances may induce this phenomenon. The reliability and validity of research findings can be compromised by estimates that are inconsistent and biased as a result of neglecting CSD. By considering CSD, researchers can ensure the accuracy and reliability of their findings, avoid biased conclusions, and obtain more precise estimates, thereby enabling them to make informed policy decisions. The econometric model of the investigation is delineated in Equation (1):

$${CO}_2{E}_{it}={\alpha }_0+{\beta }_{1}GF-N_{it}+{\beta }_{2}EC{O}_{it}+{\beta }_{3}RE{N}_{it}+{\beta }_{4}RN{S}_{it}+{\beta }_{5}CAT+{\beta }_{6}ID{L}_{it}$$

(1)

By employing suitable statistical methodologies to identify and resolve CSD, researchers can enhance the credibility and quality of their panel data analyses [97]. The cross-sectionally augmented IPS unit root test is a valuable technique for analyzing panel data. It can determine whether the variables in your dataset are stationary or not. IPS enhances the power and robustness of conventional panel unit root testing by incorporating cross-sectional averages of the data. IPS is advantageous to researchers in numerous fields, as it enhances the reliability of unit root detection in panel data by incorporating cross-sectional and time series information (Equation (2)).

$$\Delta X_{i,t}={\partial }_i+{\partial }_i{Z}_{i,t-1}+\partial {\overline{Z}}_{t-1}+{{\displaystyle \sum }}_{i=0}^p{\partial }_{it}\Delta {\overline{W}}_{t-1}+{{\displaystyle \sum }}_{i=0}^p{\partial }_{it}\Delta W_{i,t-1}+{\phi }_{it}$$

(2)

The cross-section is represented by W in Equation (3), and the cross-sectionally augmented is the IPS.

$$W^{i,t}={\partial }^1{\overline{{CO}_2}}^{i,t}E+{\partial }^2{\overline{GF-N}}^{i,t}+{\partial }^3{\overline{ECO}}^{i,t}+{\partial }^4{\overline{REN}}^{i,t}+{\partial }^5{\overline{RNS}}^{i,t}+{\partial }^6{\overline{CAT}}^{i,t}+{\partial }^7{\overline{IDL}}^{i,t}$$

(3)

Furthermore, the Westerlund and Edgerton panel unit root test is a powerful tool for analyzing panel data, especially for distinguishing between stationary and non-stationary variables in a panel dataset. This test uses cross-sectional averages to reduce the possible bias of cross-sectional dependence, which can influence traditional panel unit root tests. Because of its improved power and versatility, the Westerlund and Edgerton test is an invaluable tool for researchers across a wide range of disciplines. As a result, the earlier studies used understudy contracts to create the CS-ARDL equation, which is mentioned in Equation (4).

$$\begin{gathered} \Delta {CO}_2{E}_{it}={\alpha }_1+{{\displaystyle \sum }}_{I=1}^P{\alpha }_{it}\Delta {{CO}_2}_{i,t-1}E+{{\displaystyle \sum }}_{I=1}^P{\alpha }_{it}\Delta CGF-N_{s,i,t}+{{\displaystyle \sum }}_{I=1}^P{\alpha }_{it}\Delta EC{O}_{s,i,t}+{{\displaystyle \sum }}_{I=1}^P{\alpha }_{it}\Delta RE{N}_{s,i,t}+ \\ {{\displaystyle \sum }}_{I=1}^P{\alpha }_{it}\Delta RN{S}_{s,i,t}+{{\displaystyle \sum }}_{I=1}^P{\alpha }_{it}\Delta CA{T}_{s,i,t}+{{\displaystyle \sum }}_{I=1}^P{\alpha }_{it}\Delta ID{L}_{s,i,t} \end{gathered}$$

(4)

3. Results

The statistical distribution of seven variables, which are likely to represent various environmental or energy-related metrics, is summarized in the table provided. The central tendency of each variable is indicated by the mean values, which are approximately 6.841 units for CO₂, 93.913 units for ESGI, and 16.200 units for ECO. The variability within each dataset is revealed by the standard deviation, which emphasizes that ESGI and REN have the most widely distributed values (51.458 and 34.356, respectively). The range of observations is elucidated by the minimum and maximum values, with CO₂ exhibiting a comparatively narrow range (1.064 to 14.262) in contrast to ESGI, which spans from 20.206 to 219.442. These statistical metrics provide a valuable overview of the data distribution, facilitating the further analysis and interpretation of the relationships between these variables (

Table 2. Descriptive statistics.

Variable	Mean	Std. Dev.	Min	Max
CO2	6.841	4.560	1.064	14.262
ESGI	93.913	51.458	20.206	219.442
ECO	16.200	10.888	5.873	38.549
REN	32.196	34.356	−4.162	97.195
RNS	27.178	19.866	3.816	58.704
CAT	9.270	9.164	−2.210	35.011
IDL	35.804	9.551	21.242	57.068

).

Moreover, descriptive statistics were implemented in the study to provide country-specific information regarding the variables. The results also revealed that Brazil had the largest CO₂ emissions [98]. China exhibited the maximum level of green innovation, as indicated by the data. The same is true for IDL and ECO. Brazil is also distinguished by its high REN values, while Russia [99] recorded the highest CAT value. The paths of Brazil and Russia, two nations that are abundant in natural resources, are divergent in their pursuit of renewable energy and carbon reduction. In contrast to Brazil’s reliance on hydropower and biofuels, Russia’s cautious exploration of renewable energy choices and consideration of carbon taxes reflect the country’s heavy reliance on fossil fuels [100]. The ambitious objectives and supportive policies of Brazil are indicative of its dedication to clean energy, whereas Russia’s approach is more pragmatic, as it strives to balance economic interests with environmental concerns [101].

Table 3. Country status by descriptive view.

Country	CO2	ESGI	ECO	REN	RNS	CAT	IDL
China	7.148	158.880	36.864	23.059	18.629	2.731	52.636
India	1.639	55.163	9.751	19.494	45.449	15.082	34.009
Brazil	2.450	60.125	15.524	98.191	54.286	2.522	25.778
South Africa	9.395	146.330	7.420	0.047	13.460	4.073	30.251
Russia	13.574	49.072	11.440	20.185	4.063	21.941	36.348

indicates that China and South Africa have the greatest levels of ESGI and CO₂ emissions, while Brazil has the highest values for REN and RNS. This indicates that there is a greater emphasis on the sustainability of natural resources and renewable energy in Brazil. When it comes to ESGI, China has the highest level, at 158.880, while South Africa follows at 146.330. The levels of India, Brazil, and Russia are lower, with a range of 49.072 to 60.125. Secondly, Russia and South Africa have the greatest CO₂ emissions, with 13.574 and 9.395 units, respectively. China’s emissions are marginally lower at 7.148 units, while India and Brazil have the lowest emissions at 1.639 and 2.450 units, respectively.

Based on Figure 1, the countries with the highest valuations are South Africa, Brazil, and China. For a multitude of reasons, China, Brazil, and South Africa are the leaders in ESGI. Environmental regulations, financial incentives, and the emphasis on sustainable bonds have been essential in these nations. Sustainable solutions are needed to address climate change, biodiversity loss, and pollution. Growing economies and urbanization have increased demand for ESGI infrastructure and sustainable development strategies. Foreign direct investment and international aid have helped ESGI prosper in these regions. Demand increased as public knowledge of environmental issues and corporate social responsibility increased [102]. These characteristics have made ESGI attractive, establishing China, Brazil, and South Africa as significant stakeholders in the global transition to sustainability. Third, Brazil and China are the leaders in the utilization of renewable energy, with REN values of 98.191 and 23.059, respectively. India, Russia, and South Africa demonstrate significantly lower REN values, with South Africa having the lowest at 0.047 [103]. Lastly, the RNS values demonstrate that Brazil and China exhibit superior levels of natural resource sustainability, as evidenced by their RNS values of 54.286 and 18.629. The lower RNS values of India, Russia, and South Africa suggest potential challenges in natural resource management [104,105]. These comparisons emphasize the countries’ diverse environmental profiles and provide a glimpse into their potential assets and areas for improvement in the areas of environmental protection and sustainability [106].

3.1. BRICS Nations on Environmental Policies

We can derive the following results from the data in Appendix B, which covers seven environmental or energy-related variables over multiple years. Initially, the economic growth reveals a consistent increase in the ECO variable over the years (from 73.338 in 2001 to 129.085 in 2023), which suggests substantial growth in the economies of these countries. It is probable that this economic expansion has been a significant factor in the observed changes in other variables [107]. Second, the ESGI variable has demonstrated a progressive increase (from 5.576 in 2001 to 8.597 in 2023), indicating a heightened emphasis on sustainable practices and ESGI within these economies [108]. This trend may be contributed to by factors such as government policies, corporate social responsibility initiatives, and increased public awareness [109]. Third, the RNS values indicate that the variable has either remained relatively stable or showed a slight decline (from 32.650 in 2001 to 20.909 in 2023), which implies that the use of non-renewable resources may still be prevalent in these countries. This could be the result of economic considerations, infrastructure constraints, or their dependence on fossil fuels. Finally, in order to obtain a more profound comprehension of the relationships between these variables and their implications for the countries, additional analysis could be conducted [110]. This analysis would enable the acquisition of valuable insights into the environmental performance of these countries and the formulation of policy decisions that are intended to promote sustainable development [111].

Figure 2 illustrates that the ECO variable has consistently exhibited the highest values across all years, with a range of 73.338 in 2001 to 129.085 in 2023. This substantial upward trend implies that the industries of these countries have experienced substantial growth during the period in question [112]. In contrast, the RNS variable has consistently exhibited the lowest values, with a range of 32.650 in 2001 to 20.909 in 2023. This relatively stable or declining trend may suggest a dependence on non-renewable resources or obstacles to sustainable resource management [113]. Although the data suggest a positive trend in economic growth and a growing emphasis on ESGI, additional analysis is required to comprehend the underlying factors that are behind these trends and to evaluate the overall environmental impact of these countries [114].

3.2. Correlation Matrix

Table 4. Correlation.

Variables	CO2	ESGI	ECO	REN	RNS	CAT	IDL
CO2	1.000
ESGI	−0.263	1.000
ECO	−0.053	0.622	1.000
REN	−0.596	−0.479	0.120	1.000
RNS	−1.129	−0.476	−0.100	0.832	1.000
CAT	−0.418	−0.666	−0.474	−0.343	−0.316	1.000
IDL	−0.235	0.514	0.949	−0.455	−0.472	−0.035	1.000

illustrates the correlation matrix, which discloses the relationships between seven environmental variables. The CO₂ and RNS results demonstrated a strong negative correlation (−1.129), indicating that CO₂ emissions are negatively correlated with RNS. This suggests that higher CO₂ levels may be predictive of lower levels of natural resource sustainability [115]. Secondly, the results of the CO₂ and CAT experiments demonstrate a moderately strong negative correlation (−0.418) between the two variables. This suggests that it is possible that increased CO₂ emissions are associated with a decrease in CO₂ emissions sequestration [116]. Third, the results of the ESGI and CAT experiment indicate a strong negative correlation (−0.666), which implies that higher levels of ESGI may be linked to reduced CO₂ emissions sequestration capabilities. The ECO and IDL results, which were analyzed for positive correlation, revealed a strong positive correlation (0.949) between ECO (sustainability-driven innovation) and IDL [117].

This suggests that higher ecological footprints are frequently linked to increased IDL. In addition, the results of ESGI and IDL indicated a moderate positive correlation (0.514) between the two variables, which implies that elevated ESGI levels may be associated with increased IDL. In conclusion, the correlation between RNS and CO₂ emissions implies that initiatives to mitigate CO₂ emissions may be associated with enhanced natural resource sustainability [118]. Increased carbon sequestration has the potential to mitigate climate change, as evidenced by the negative correlation between CO₂ and CAT [119]. The necessity of sustainable practices and the environmental impact of IDL is underscored by the strong positive correlation between ECO and IDL. The correlation between ESGI and IDL may be linked to elevated levels of ESGI, which may suggest the necessity of policies that encourage sustainable industrialization. An additional examination could delve deeper into the underlying factors that are responsible for these correlations and investigate potential policy implications [47].

3.3. Cross-Sectional Dependency Test

Table 5. Cross-sectional dependency test.

Variable	Test Stat (Prob-Values)
CO2	5.882 *** (0.000)
ESGI	3.959 *** (0.00)
ECO	6.796 ** (0.00)
REN	5.863 *** (0.00)
RNS	6.918 *** (0.00)
CAT	5.844 ** (0.00)
IDL	4.531 *** (0.00)

Source: authors’ computations. Table 5 shows the significant values for the following variables: The notation *** denotes a 1% level of significance, while ** denotes a 5% level.

suggests that the cross-sectional dependence (CSD) test was implemented to evaluate the cross-sectional dependency. Statistics test results for seven variables are presented in the table, which are likely to represent various environmental or energy-related metrics. The statistical significance of all variables is demonstrated by their p-values of 0.00, which demonstrates their statistical significance at a very high level of confidence. The test statistics (5.882, 3.959, 6.796, 5.863, 6.918, 5.844, and 4.531) indicate that the observed differentials between these variables and a reference point or null hypothesis are unlikely to be the result of coincidence.

Based on the assumption that these are t-test or z-test statistics, the high values and low p-values strongly support the hypothesis that these variables are substantially different from their respective null values [76]. With a value of 6.918, RNS is the variable with the highest test statistic. This implies that the resultant RNS is substantially different from the expected value under the null hypothesis, which could suggest a significant deviation from the norm. Nevertheless, ESGI has the lowest test statistic at 3.959, despite the fact that it is still statistically significant. In comparison to RNS, this implies that ESGI deviates from its null value to a reduced extent. In conclusion, these findings underscore the statistical significance of all variables, while also suggesting that their expected values suffer from varying degrees of deviation [120]. More detailed interpretations are difficult without more context regarding the hypotheses being examined. These results indicate that each variable’s observed values are statistically significant and likely to depart significantly from the null hypothesis [30]. To comprehend these conclusions, one must evaluate the analysis’s context, including the study questions, hypotheses, and data.

Table 6. Unit root test by cross-sectional augmentation.

	Level I (0)		1st Difference (I)
Variable	CIPS	M-CIPS	CIPS	M-CIPS
CO2	−3.326 ***	5.892 ***	-	-
ESGI	-	-	4.406 ***	7.004 ***
ECO	−6.230 ***	−7.058 ***	-	-
REN	-	-	-	-
RNS	-	-	−5.906 ***	−6.334 ***
CAT	-	-	−7.104 ***	−6.116 ***
IDL	−5.606 ***	19.489 ***	-	-

Source: authors’ computations. Table 6 shows the significant values for the following variables: The notation *** denotes a 1% level of significance.

indicates that the CIPS test for CO₂ at level I (0) is significant (−3.326 ***), indicating that CO₂ emissions are stationary. The M-CIPS test is not statistically significant (5.892 ***), suggesting that CO₂ emissions may be integrated into order one (non-stationary) after accounting for cross-sectional dependence. Furthermore, the CIPS and M-CIPS tests for these variables are significant at level I (1), indicating that they are non-stationary and integrated with order one.

The cross-sectional dependency test, as illustrated in Figure 3, is used to ascertain the connectivity of variables in a panel dataset. The employment of panel unit root tests that take this reliance into consideration, like Pesaran’s CIPS (Cross-Sectionally Augmented Im, Pesaran, and Shin) or CADF (Cross-Sectionally Augmented Dickey–Fuller) tests, is required when a significant result is obtained [15,121,122,123,124]. It appears that CO₂ emissions may be integrated of order one (non-stationary) after accounting for cross-sectional dependence, as the M-CIPS test does not demonstrate a statistically significant relationship between the two variables (5.892 for a value of 5.882). Assuming the unit root test rejects the null hypothesis, the variable is considered stationary in standard panel data models. An empirical study’s panel data integration order is typically established by administering the CIPS and CADF tests. The use of a variable in conventional panel data models is contingent upon its determination to be stationary [125]. It may be necessary to conduct analysis utilizing cointegration or differencing if it is determined to be non-stationary. By contrast, if the null hypothesis is not rejected, differencing or cointegration analysis will be necessary because the variable is non-stationary [126].

This suggests that these variables may be cointegrated with CO² emissions and have a long-term trend. The results of this study imply that CO₂ emissions may be stationary or non-stationary, contingent upon the inclusion of cross-sectional dependence. This has significant implications for the modeling and forecasting of CO₂ emissions [127]. Furthermore, the stationarity of the variables ESGI, ECO, REN, RNS, CAT, and IDL implies that they have a long-term trajectory and may be cointegrated with CO₂ emissions. This could suggest a long-term relationship between these variables. Finally, in order to develop a more profound comprehension of the connections between these variables, additional analyses, including dynamic panel models and cointegration tests, could be implemented. These analyses could be beneficial in determining the causal relationships between CO₂ emissions and other variables and in evaluating the efficacy of policy interventions [128].

3.4. Cointegration Test

The significant Z(N) and Zo(N) test statistics for CO₂ emissions at the 1% level (p-value < 0.01) are illustrated in

Table 7. Cointegration test.

Test	Without Break	Mean Shift	Regime Shift
Explained variables: CO2 emissions
Zo(N) p-value	−8.380 *** (0.000)	−7.001 *** (0.000)	−6.260 *** (0.000)
Z(N) p-value	−8.273 *** (0.000)	−6.470 *** (0.000)	−6.881 *** (0.000)

Source: authors’ computations. Table 7 shows the significant values for the following variables: The notation *** denotes a 1% level of significance.

. This indicates that CO₂ emissions are stationary at level I (0). Thus, it is probable that CO₂ emissions will return to their average level over time, as they lack a unit root [51]. The fundamental implication of stationary CO₂ emissions is that their stationarity implies that they may be predicted and subject to long-term trends. This discovery has significant implications for policymakers, as it implies that policies designed to mitigate CO₂ emissions may have an enduring effect. In addition, in order to develop a more comprehensive comprehension of the variables that influence CO₂ emissions, additional analyses, including dynamic panel models and cointegration tests, could be implemented. Identifying the causal relationships between CO₂ emissions and other pertinent variables could be facilitated by these analyses [31].

3.5. Long- and Short-Run Tests by CS-ARDL

Table 8. Long- and short-run testing by CS-ARDL.

Long-Run Analysis
Variables	Coeff	t-Stat	Prob
Explained variable: CO2 emissions
ESGI	−0.907 ***	−5.558	0.001
ECO	−3.527 ***	−3.580	0.038
REN	−3.496 ***	−4.518	0.025
RNS	1.183 **	−5.425	0.002
CAT	−1.850 ***	−6.928	0.000
IDL	−3.448 **	−5.726	0.000
CSD—Statistics	-	0.043	0.976
Short-run analysis
ESGI	−0.919 **	−7.060	0.000
ECO	−2.738 ***	−8.065	0.000
REN	−3.449 ***	−5.263	0.004
RNS	−0.938 **	−2.761	0.037
CAT	−5.728 ***	−6.796	0.000
IDL	−4.660 ***	−7.051	0.000
ECT (−1)	−0.305 ***	−3.601	0.022

Source: authors’ computations. Table 8 shows the significant values for the following variables: The notation *** denotes a 1% level of significance, while ** denotes a 5% level.

shows the long-run and short-run analyses, in which, first, the significance of all the variables except ESGI have significant long-run coefficients at the 5% level or lower, indicating that they have a significant impact on CO₂ emissions in the long run. Additionally, the direction of impact shows the negative signs of the coefficients for ESGI, ECO, REN, CAT, and IDL, suggesting that these variables have a negative impact on CO₂ emissions in the long run. This implies that increasing these variables can lead to a decrease in CO₂ emissions [129]. The magnitude of impact also indicated that the magnitude of the coefficients indicates the relative importance of each variable in explaining CO₂ emissions. In this case, ESGI, CAT, and IDL have the largest negative impacts on CO₂ emissions. Second, on the basis of short-run analysis significance, all variables have significant short-run coefficients at the 5% level or lower, indicating that they have a significant impact on CO₂ emissions in the short run. The direction of impact shows the negative signs of the coefficients, suggesting that all variables have a negative impact on CO₂ emissions in the short run. The significant coefficient for the error correction term (ECT (−1)) indicates that there is a significant adjustment process in the short run. This suggests that deviations from the long-run equilibrium are corrected over time [52].

Figure 4 shows that the long-run and short-run analyses also provide us with the following additional insights. First, the analysis of ESGI shows that ESGI has the largest negative impact on CO₂ emissions in the long run, with a coefficient of −0.907 and a t-statistic of −5.558. This suggests that improving ESGI can significantly contribute to reducing carbon emissions. Second, regarding CAT, the carbon tax has a strong negative impact on CO₂ emissions in both the long run and the short run, with coefficients of −1.850 and −5.728, respectively. This highlights the effectiveness of carbon pricing policies in reducing emissions [130]. Third, regarding ECO, economic indicators have a significant negative impact on CO₂ emissions in both the long run and the short run, suggesting that economic growth can be decoupled from carbon emissions [131]. However, the magnitude of the impact is relatively smaller compared to ESGI and CAT. Fourth, the REN production has a significant negative impact on CO₂ emissions in the long run, with a coefficient of −3.496. This indicates that investing in REN can be an effective strategy for reducing carbon emissions. Fifth, RNS research and development spending has a positive impact on CO₂ emissions in the long run, with a coefficient of 1.183.

This suggests that while innovation can be important for driving economic growth and technological progress, it may also have unintended consequences for carbon emissions. Sixth, IDL has a significant negative impact on CO₂ emissions in the long run, with a coefficient of −3.448. This suggests that promoting sustainable IDL practices can be effective in reducing carbon emissions. In sum, the overall results suggest that a combination of policy interventions, including promoting ESGI practices, implementing carbon taxes, investing in renewable energy, and promoting sustainable IDL, can be effective in reducing CO₂ emissions in the BRICS nations.

4. Discussions

The findings suggest that ESGI, ECO, REN, CAT, and IDL all have substantial negative effects on CO₂ emissions in China and India. This suggests that a combination of policy interventions can be effective in reducing emissions in the BRICS countries. Nevertheless, the precise magnitudes of these effects may vary. A comparable analysis has been conducted in South Africa, Russia, and Brazil to determine the most effective policy interventions for reducing CO₂ emissions [132,133]. In order to comprehend the precise determinants of CO₂ emissions and the most effective policy interventions, it is important to consider the distinctive economic, social, and environmental factors in each BRICS nation [15]. As a policy implication, the findings of this study can provide policymakers in each nation with information regarding the most effective strategies for reducing CO₂ emissions and achieving sustainable development objectives [23,129]. Lastly, researchers can develop tailored policy recommendations and obtain a more profound understanding of the factors that influence CO₂ emissions in the BRICS nations by conducting a detailed country-specific analysis [134,135]. According to the analysis, all variables (ESGI, ECO, REN, RNS, CAT, and IDL) have a substantial influence on CO₂ emissions in both China and India. This implies that a combination of policy interventions may be effective in reducing emissions. Some of the results below demonstrate varying degrees of influence from regressors on CO₂ emissions. To begin, the ESGI results indicate that China and India experience the greatest influence, with −0.907 and −0.723, respectively [136]. This has a substantial negative impact on CO₂ emissions in both countries, with China exhibiting a more pronounced effect. Additionally, Brazil and South Africa exhibit the lowest impact, with estimates of −0.634 and −0.587, respectively.

Secondly, the ECO results indicate that China and India experience the highest influence, with −3.527 and −2.891, respectively. Economic indicators have a substantial negative impact on CO₂ emissions in both nations, indicating that economic growth can be separable from carbon emissions. Additionally, South Africa reports the greatest impact, with a value of −2.731. High CO₂ emissions are primarily caused by South Africa’s dependence on coal-fired power facilities. In the nation, coal continues to be the primary source of energy, which significantly contributes to its overall carbon footprint. Furthermore, IDL activities, which are frequently energy-intensive and significantly dependent on fossil fuels, have been an important factor in South Africa’s economic expansion [137,138]. These factors, in conjunction with inadequate investments in energy efficiency and renewable energy, have resulted in South Africa’s relatively elevated CO₂ emissions when contrasted with other BRICS nations. The most significant impact is demonstrated by Russia, with a value of −3.284. Russia’s economy is significantly dependent on natural resources, particularly oil and gas, due to its resource-based economy. The extraction and export of these resources have been significant contributors to CO₂ emissions, while they have also been significant generators of economic growth [139]. In recent decades, Russia’s industrialization has experienced accelerated growth, resulting in elevated energy consumption and emissions. Finally, Russia’s economic cycle is also susceptible to fluctuations as a result of its dependence on commodity exports. Economic downturns can result in decreased energy consumption and emissions, whereas economic expansions can have the opposite effect [140].

Thirdly, the REN results indicate that China and India experience the greatest influence, with −3.496 and −2.976, respectively. REN production has a substantial negative impact on CO₂ emissions in both countries, underscoring the benefits of transitioning to greener energy sources. By utilizing these resources and generating geothermal power, the CO₂ emissions are managed, and the REN obtained in this manner reduces the consumption of fossil fuels, thereby reducing CO₂ emissions [127,141]. According to this recent study, the replacement of fossil fuel energy with REN and the reduction in CO₂ emissions are facilitated by the increase in RNS output from solar power, bioenergy, and hydroelectric generation [142].

Fourth, RNS has the most significant impact in China and India, with values of 1.183 and 0.827, respectively. Despite the fact that RNS has a positive impact on CO₂ emissions in China, it is not statistically significant in India [143]. This suggests that the impact of innovation on emissions may vary across countries. Furthermore, the impact in Russia is the most significant, with a value of 0.789. The results suggest that Russia’s investments in energy-efficient technologies and REN have been constrained by its technological dependence on fossil fuels. Furthermore, Russia’s inability to allocate adequate resources for research and development in clean energy technologies, as well as to address geopolitical factors, is illustrated by the economic challenges and innovation focus. Despite Russia’s efforts to promote innovation, the country has commonly prioritized traditional industries and military technology over clean energy [144,145,146,147,148]. The results suggested a negative correlation between CO₂ emissions and RNS. The study underscores that the increasing prevalence of RNS in business organizations is advantageous in reducing the overall economic demand for fossil fuels and the reduced use of fossil fuels that contain CO₂ emissions and, upon combustion, are likely to produce CO₂ emissions [148,149]. The objective of RNS is to reduce CO₂ emissions by mitigating the environmental consequences of transportation and technologies. As a result, RNS and the emission of CO₂ are inversely proportional [121,150].

Fifth, the CAT results indicate that the most significant influence is in China and India, with −1.850 and −1.527, respectively. In both countries, carbon taxes have a substantial negative impact on CO₂ emissions, illustrating the efficacy of carbon pricing policies. The findings indicated that CAT is negatively correlated with CO₂ emissions [151,152]. This underscored the fact that in countries where environmental regulations are implemented and CAT is enforced, individuals and organizations strive to reduce the use of resources, communication methods, and transportation that are responsible for the release of CO₂ emissions in order to conduct their usual activities and perform business functions. Therefore, CAT is advantageous for the mitigation of CO₂ emissions [153,154].

Sixth, the IDL results indicate that the two countries with the highest influence are China and India, with −3.448 and −2.983, respectively. This indicates that IDL has a substantial negative impact on CO₂ emissions in both countries. This suggests that the promotion of sustainable IDL practices may be a viable approach to reducing emissions. Additionally, Brazil exhibits the greatest impact, with a value of −3.322. The findings indicated that IDL is negatively correlated with CO₂ emissions [154,155]. The results also demonstrated consistent findings and indicated that the growth of IDL leads to the development of environmental awareness, which encompasses the knowledge of environmental concerns, the factors that can cause environmental contamination, and the solutions to these concerns [156,157]. The local community and economy are motivated to address environmental concerns and reduce CO₂ emissions by individuals who are environmentally cognizant [158,159].

Through policies concerning ESGI, ECO, REN, RNS, CAT, and IDL, China and India have exhibited substantial efficacy in accomplishing favorable results. Large-scale investments, rapid industrialization and urbanization, strong political will, and a diverse policy blend have all contributed to their success. China has extensively invested in REN and has instituted stringent environmental regulations, whereas India has initiated national programs for the development of REN and carbon pricing mechanisms. Both countries have made significant progress in these areas, illustrating the potential for developing nations to make significant strides in ESGI, renewable energy, and sustainable development. Their endeavors underscore the significance of comprehensive and coordinated policy interventions to address environmental and social issues while fostering economic development, despite the fact that challenges persist [160]. In conclusion, the substantial influence of ECO and RNS on Russia’s CO₂ emissions has been influenced by the country’s economic structure, historical context, and policy decisions. It will be imperative for Russia to address these factors in order to establish a sustainable and low-carbon development trajectory [161]. The substantial influence of ECO (sustainability-driven innovation) and RNS (renewable power utilization) on Russia’s CO₂ emissions is a result of the country’s distinctive historical context and economic structure. The findings indicate that a combination of policy interventions, such as the promotion of ESGI practices, the implementation of carbon levies, the investment in renewable energy, and the promotion of sustainable IDL can be effective in reducing CO₂ emissions in the BRICS nations. Nevertheless, the specific efficacy of these policies may differ among countries as a result of variations in institutional factors, energy mixtures, and economic structures [162].

The following are the implications of policy intervention in the BRICS nations, as stated individually. The initial point is that China’s ongoing endeavors to enhance ESGI practices through the implementation of ESGI can contribute to a reduction in CO₂ emissions. In the context of ECO, it is imperative to implement policies that prioritize sustainable consumption and energy efficiency in order to decouple economic growth from CO₂ emissions [163]. Investing in REN infrastructure and technology development can accelerate the low-carbon economy. In the context of RNS, the prioritization of research and development in REN technologies has the potential to stimulate innovation and decrease carbon emissions. In terms of CAT, businesses can be strongly motivated to reduce emissions by the expansion and reinforcement of carbon pricing mechanisms [164]. Sustainable IDL, including energy efficiency and pollution management, can reduce IDL emissions.

Second, strengthening ESGI in India, especially in dirty and unequal areas, reduces CO₂ emissions. India’s economic growth should assist sustainable development in ECO to minimize carbon emissions. India can accelerate its low-carbon energy mix by investing in REN and grid integration. Promoting RNS for clean energy technology can assist India in creating indigenous emission reduction solutions. CAT methods that effectively implement and enforce carbon prices might encourage firms to cut emissions. IDL emissions can be reduced by sustainable IDL and energy efficiency [165]. Third, ESGI may promote sustainable development and reduce emissions in South Africa by addressing historical imbalances and social fairness. ECO can encourage diversifying the economy away from coal, and supporting sustainable growth can minimize CO₂ emissions. Investment in REN, especially solar and wind power, can harness South Africa’s resources. RNS for REN research can help South Africa create indigenous solutions. In terms of CAT, a carbon tax can encourage firms to cut emissions. Sustainable IDL practices and energy efficiency can reduce CO₂ emissions [166].

Fourth, ESGI externalities from economic operations in Russia can be reduced by enhancing environmental governance and resolving social issues. ECO may cut CO₂ emissions by diversifying its economy away from fossil fuels and fostering sustainable economic growth. Russia can diversify its energy mix and lessen its fossil fuel dependence by investing in REN. Innovation in REN technologies and RNS can help Russia strengthen indigenous skills and minimize its dependency on foreign technology. A CAT carbon tax can motivate firms to cut emissions. Finally, emissions can be reduced by promoting sustainable IDL practices and energy efficiency. Energy sustainability has increased in Russia. But its diversified topography, huge territory, and economic dependency on natural resources pose problems. Although REN is abundant, Russia has used fossil fuels. However, wind, solar, and hydroelectric investments increased [167]. Energy efficiency is rising in transportation, industry, and homes. Large Russian woods store carbon. Sustainable forest management and protection target these forests. Russia relies on oil and gas exports. Transitioning to sustainable energy involves major legislative changes and investments. Russia experiences severe weather, permafrost thawing, and rising sea levels. These challenges are hard. Climate accords and collaborations with Russia can benefit the global environment [168]. Fifth, improving ESGI inequality in Brazil can reduce deforestation and promote sustainable development. ECO should align economic growth with sustainable development aims to cut carbon. Brazil has promising hydropower and biofuels for REN expansion. RNS helps Brazilians diversify and reduce emissions. Implementing CAT can help firms reduce emissions [169]. Finally, IDL emissions can be cut by increasing energy efficiency and sustainable industry.

The effectiveness of these policy measures will depend on technical advances, economic conditions, institutional capability, and political commitment. BRICS nations may minimize carbon emissions and contribute to a sustainable future by enacting multiple measures. This study contributes significantly to economic-based literature, giving academics a plethora of insight for future research. CO₂ emissions are the main cause of environmental degradation, resource shortage, and economic inconsistency, which all nations worry about. This study’s focus on CO₂ reduction makes it global [170]. It addresses CO₂ emissions and provides a framework for governments, economists, and environmental regulators to minimize them. The study helps regulators build environmental legislation using ESGI, eco-innovation, and CAT. The report also recommends that financial institutions adopt rules and strategies to stimulate ESGI to increase eco-friendly efforts and reduce CO₂ emissions. The research also suggests that policymakers, whether private or public, should push businesses and individuals to adopt ECO practices. Thus, CO₂ emissions would decrease. The report also advises developing new energy generation strategies and facilities or technology to increase RNS. This would minimize CO₂ emissions and boost renewable energy [10,26,171]. In the same manner that economic policies and laws promote beneficial behaviors, households and individual energy consumers must be encouraged to switch to RNS to meet energy needs. It is possible to reduce CO₂ emissions. The research also recommends that governments promote CAT to reduce CO₂ emissions and CO₂-emitting activities. Governments and economies should work together to advance IDL to mitigate CO₂.

In BRICS countries, industrialization, carbon taxes, REN production and consumption, ESGI, ECO and sustainability, and CO₂ emissions are negatively correlated. Empirical calculations using an autoregressive distributed lag show that these tactics work. They suggest eco-friendly policies and the use of ESGI funds to reduce CO₂ emissions. Brazil has a high share of REN in its energy mix, and an emphasis on ECO suggests strong environmental efforts at a middle level. At the highest level, India emphasizes green credit, and ESG investing may reflect a commitment to sustainable development. Despite its vast population and high emissions, China has made tremendous REN and carbon trading progress. Russia’s sustainability initiatives are evolving and might improve at the lowest level. The country faces significant problems that require a coordinated, long-term solution. The study also advises policymakers to prioritize ECO to protect the environment. Government institutions must sincerely support eco-related technology policies to gain access to ESGI. Eco-friendly policies should also address social and environmental challenges while promoting sustainability. Green innovation can develop ESGI technology to reduce risk and uncertainty. Governments must also support fiscal spending to raise awareness of ESGI growth and credit financing.

5. Conclusions

The research offers valuable insights into the factors that influence CO₂ emissions in the BRICS nations and the efficacy of a variety of policy interventions. Discoveries regarding ESGI, ECO, REN, CAT, and IDL are among the most significant. The BRICS nations’ CO₂ emissions are significantly influenced by all of these variables, which implies that a combination of policy interventions can be effective in reducing emissions. It is imperative to refine the policy recommendations in the BRICS document on CO₂ emissions by incorporating specificity, feasibility, and equity considerations in order to improve their practical implementation. Concrete action plans, in conjunction with objectives that are both measurable and clearly defined, can substantially enhance the efficacy of a policy. Although these variables are generally effective in reducing CO₂ emissions across the BRICS nations, due to country-specific differences, their relative importance and the most effective policy interventions may differ. Furthermore, the practicality and sustainability of recommendations can be guaranteed by customizing them to the distinctive context of each BRICS nation, undertaking comprehensive cost–benefit analyses, and taking institutional capacity into account. A more rapid shift to a low-carbon future is possible with increased BRICS cooperation and information sharing. The policy proposals can serve as a helpful guide for regional lawmakers by taking these factors into account. The results indicate that policymakers in the BRICS nations should prioritize policies that promote ESGI practices, implement carbon levies, invest in renewable energy, and promote sustainable IDL in terms of policy implications.

Additionally, China has made substantial strides in reducing CO₂ emissions by investing in renewable energy, carbon trading, and IDL enhancements, as evidenced by its specific country insights as a member of the BRICS. India has also made significant progress in the reduction of CO₂ emissions, particularly through its emphasis on energy efficiency and renewable energy. In order to decrease its dependence on coal, South Africa will need to make substantial investments in energy efficiency and REN as it transitions to a low-carbon economy. Reducing CO₂ emissions in Russia is a distinct challenge due to the country’s economic structure and dependence on fossil fuels. In terms of policy implications, Russia’s and China’s participation in the BRICS initiative has many substantial implications. First and foremost, it fortifies Russia’s geopolitical position, enabling it to manifest its influence on the global stage. The second benefit is the potential for economic collaboration, particularly in sectors such as energy, commerce, and investment. The third benefit is that it allows Russia to participate in multilateral diplomacy and influence global governance standards. Nevertheless, Russia’s future involvement in BRICS may be affected by the difficulties brought about by the conflict in Ukraine and the changing geopolitical situation.

It is imperative to promote sustainable development and diversify the economy in order to effectuate a low-carbon transition. Brazil has made strides in the promotion of REN and the reduction in deforestation; however, there are still obstacles to overcome in the areas of energy efficiency and IDL emissions. The research demonstrates that the BRICS nations have the capacity to substantially decrease their CO₂ emissions by implementing a combination of policy interventions. Nevertheless, the specific strategies and priorities may differ among countries, as they are influenced by their distinct economic, social, and environmental circumstances. Research that utilizes dynamic panel models in the future can offer a more sophisticated examination of the causal relationships between variables and CO₂ emissions. Analyzing the efficacy of policy interventions and the long-term trends in CO₂ emissions can offer valuable insights for future policy developments. In conclusion, the identification of best practices and lessons learned can be achieved by comparing the experiences of BRICS nations with those of other regions or countries.

In order to address climate change and transition to a sustainable future, the BRICS nations can play a critical role by continuing to undertake research and implement effective policies. Although comprehensive, the investigation is subject to specific constraints. The accuracy of the results can be influenced by the quality and availability of the data, and the findings may be changed by the selection of an econometric model. In addition, the analysis of long-term trends may be restricted by the time horizon of the data, and cross-sectional dependence may not be completely captured. The study’s scope is restricted by these major factors. This study examines CO₂ emissions at the national level and does not account for the contribution of economic sectors to CO₂ emissions. As a result, it is advised that academicians also verify the CO₂ emissions by sector. Based solely on quantitative data from the BRICS economies, the research findings may not be applicable to any other country in the globe. In addition, the authors must acquire data from a significant number of developing and developed countries to ensure general validity. In addition, the investigation concentrates on the consequences of policies without conducting an explicit evaluation of their efficacy and implementation rates. Further research and methodological enhancements are necessary to address these constraints and guarantee the generalizability and robustness of the results.