Striving towards carbon neutrality target in BRICS economies: Assessing the implications of composite risk index, green innovation, and environmental policy stringency

ABSTRACT

The world governments have come together under the Paris Agreement to begin the decarbonization and transition to a zero-carbon economy. The goal of attaining low-carbon growth is not as simple as it may appear, however, because the fast developing and fossil fuel-dependent global economies are concentrated on accelerating economic expansion at the expense of catastrophic environmental repercussions. In light of these circumstances, this study aims to investigate the combined implications of composite risk (CRI), green innovation (GINOV), and environmental policy stringency (EPS) on carbon dioxide (CO₂) emissions in the context of Brazil, Russia, India, China, and South Africa (BRICS), while controlling for economic growth (GDP) and renewable energy research and development (RERD) over the period from 1960 to 2020. The study addresses the problems of cross-sectional dependence and slope heterogeneity in the data set used for analysis by using the second-generation cross-sectionally augmented autoregressive distributed lags (CS-ARDL) framework to evaluate long- and short-run models. The accompanying findings confirm cointegrating relationships between the research variables. Additionally, the results of the regression demonstrate that EPS, GINOV, and RERD contribute to a long-term reduction in CO₂ emissions. CRI and GDP, however, increase CO₂ emissions. In light of these important conclusions, it is suggested that the BRICS nations prevent environmental deterioration by strengthening the stringency of environmental policies and promoting renewable energy research and development. In addition, the BRICS authorities should encourage the use of renewable energy sources and ecologically beneficial technologies to improve environmental quality and achieve carbon neutrality target.

KEYWORDS:

• Carbon neutrality
• Composite risk index
• Green innovation
• Environmental policy
• Renewable energy R&D
• BRICS economies

JEL:

• F18
• F13
• Q56; O13; F1; F41

1. Introduction

Environmental degradation is viewed as a challenging problem that the modern world is now dealing with (Ibrahim & Mohammed, 2023; Udeagha & Breitenbach, 2023c; Yu et al., 2023). The earth’s environment and way of life are negatively impacted by rising carbon dioxide (CO₂) emissions and temperatures. Extreme weather occurrences are brought on by alarming CO₂ emissions figures and rising atmospheric pollution emissions (Ibrahim, 2022b; Sharif et al., 2023). Consequently, it is thought that a serious threat to human life is posed by climate change, and this happens as a result of unceasing pollution addition to the atmosphere without taking into account its negative impacts. Also, the environment has been severely harmed by an ongoing rise in global productivity, rapid population growth, and rising energy consumption. Many agreements have been reached in recent years to address environmental issues, find workable solutions to global warming, and safeguard the environment, such as the Kyoto Protocol in 1997 and the Conference of the Paris (COP21) in 2015 (Ibrahim, 2022a; Udeagha & Breitenbach, 2023d). To reach their goal of carbon neutrality, several nations across the world are implementing a variety of actions. Countries are attempting to offset carbon emissions in various ways in order to assure the transition to post-carbon emissions. Nonetheless, in spite of such accords, global CO₂ emissions rose at an alarming 2.7% pace in 2018 (Deng et al., 2023).

Because of contemporary society’s continuous reliance on fossil fuels, the earth is warming at a rate that has not been seen in the last 2,000 years, and its repercussions are already being felt as record-breaking droughts, wildfires, and floods decimate towns all over the world (Udeagha & Breitenbach, 2023a). Every nation is affected by environmental deterioration, making it a global issue. As major greenhouse gas (GHG) emitters, industrialized nations like BRICS countries, Germany, Japan, and the United States are responsible for safeguarding the planet. To reduce global CO₂ emissions, their commitment is crucial. Yet, reducing CO₂ emissions would result in decreased production, which would hinder economic growth because reducing CO₂ emissions is connected to energy use, which is necessary for economic growth (Zhu et al., 2023). Due to this situation, it is very difficult for these nations to agree to or carry out programs that are specifically intended to lower world CO₂ emissions. Better strategies for generating green economic growth and better environmental conditions are therefore needed. Several strategies have been used by some policymakers throughout the globe to combat environmental deterioration and global warming in this effort (Udeagha & Breitenbach, 2023e). Some of these actions, paying careful attention to composite risk (CRI), advancing green innovation (GINOV), and enforcing environmental policy stringency (EPS) are seen to be beneficial ways to improve the quality of the environment.

Composite risk (CRI), which consists of total political risk, total financial risk, and total economic risk, plays a crucial role in the policies of an economy, and the achievement of environmental sustainability (Qin et al., 2021). Each risk comprises of different components reported in Table 1. First, economic risk determines economic strengths and weaknesses of an economy. The higher the total risk point, the lower the risk, and the lower the total risk points, the higher the risk in every case (economic, political, and financial). With the expansion in economic growth, economic activities rise that generate opportunities and improve income level (Udeagha & Breitenbach, 2023b). During such expansion, economies consume more energy in order to fuel their domestic industry during transitional stage. At the same time, environmental economists state that such drastic expansions lead to a rise in carbon emissions and further deteriorate environmental quality, and also explained by EKC hypothesis in first stage; economic growth or rise in income is associated with environmental pollution. Therefore, we expect that economic risk is positively associated with carbon emissions. Second, political risk includes corruption index, government stability, investment profile, democratic accountability, law, and order. If political risk is high in an economy, policies regarding carbon neutrality would not be effectively implemented (Z. Khan et al., 2021). This leads to environmental degradation. The political risk index assesses the stability of political system of an economy that consists of 12 components. The higher political risk implies weak or unstable government, poorly or less education rate, poverty, inequalities in access to resources, risky investment background along with more corrupt officials and weak institutions (C. Wang et al., 2022). Generally, developing countries with high political risk confront multiple hardships. For instance, more corrupt officials could weaken environmental regulations that lead to illegal production and add into more emissions (Z. Wang et al., 2023). While an anti-corruption campaign is helpful to limit corruption and consequently mitigate environmental pollution, strong institutions play a crucial role in protecting environmental quality by imposing strict regulations and penalties for any activity involving illegal emissions (Amin et al., 2023). Hence, we expect that political strength of an economy play a vital role in limiting CO₂ emissions. Third, financial risk measures a country’s capacity to finance its government activities including official, commercial, and trade debt obligations (Fu & Zhu, 2023). Conclusively, the lower the financial risk of an economy, the more productive the economy is, and is capable of paying back its debt. Furthermore, countries with stable exchange rates attract foreign capital due to less financial risk. Hence, we expect that higher financial risk can trigger CO₂ emissions. The aggregate of political, financial and economic risk, called CRI is calculated by International Country Risk Guide (ICRG), and it is believed that paying close attention to CRI can help to improve environmental quality. Therefore, we conducted this research since there is disagreement among researchers regarding the effect of CRI on CO₂ emissions.

Table 1. Sub-components of composite risks

Risk	Political risk	Economic risk	Financial risk
Risk components	Government Stability	GDP per-capital	Foreign debt % of GDP
	Socioeconomic conditions	Real GDP growth	Foreign debt as % of export of goods and services
	Investment profile	Annual Inflation rate	Current account as % of exports of goods & services
	Internal conflict	Budget Balance % of GDP	Net international liquidity as months of import cover
	External conflict	Current account % of GDP	Exchange rate stability
	Corruption
	Military in politics
	Religious tensions
	Law & order
	Ethnic Tensions
	Democratic Accountability
	Bureaucracy quality

Source: International Country Risk Guide (ICRG).

Green innovation (GINOV) is a powerful tool for halting the environmental damage brought on by rising economic activity (Q. Zhang et al., 2022; Razzaq et al., 2023; Zou et al., 2023). The Schumpeter (1942) concept of ‘creative destruction’—which states that throughout the evolutionary process, ineffective sectors are removed and replaced with developed industries—is the foundation for the efficacy of GINOV. GINOV helps nations to change their economic models to rely on cleaner energy sources, enables nations to use carbon-free technology and make goods that are energy-efficient (Ibrahim, Huang, et al., 2022; Udeagha & Muchapondwa, 2023a). Environmental innovation is also the key to lessening the environmental damage brought on by an increase in economic activity. Countries all over the world have been looking for environmentally friendly technologies to slow down environmental degradation in recent years (Ibrahim, Ajide, et al., 2022; Ibrahim, Al-Mulali, et al., 2022). GINOV is another effective method of lowering CO₂ emissions that is commonly proposed, which relates to investments in novel products, R&D, and affordable technologies that improve energy efficiency, reduce the usage of fossil fuels, enhance production capacity, and supports economic progress without sacrificing the sustainability of the environment since it provides efficient energy sources (Shen et al., 2023; Udeagha & Ngepah, 2023b). The significance of GINOV, however, varies depending on the wealth and pollution levels of the country. GINOV helps the host economy cut CO₂ emissions and improves the environment. Industrial technologies used by multinational corporations have been shown to be more protective than those used by poorer countries. Because of how well GINOV can handle environmental problems, it is currently the most effective tool for ensuring economic growth while reducing emissions and resource depletion (Hao & Chen, 2023). The endogenous growth model’s findings, however, show that environmental innovation is the result of ongoing investment in R&D. As a result, there is a significant overlap between GINOV and energy investment in terms of CO₂ emissions. Public support for R&D is an efficient way to boost innovation and address the market failure issue brought on by knowledge spillovers. In order to transition the industrial structure from non-renewable to renewable energy sources through technical innovation, public assistance for R&D is necessary. Investment in energy sector also develops a reliable climate market and an effective energy system (Udeagha & Muchapondwa, 2023b). Ecologically sound products include eco-homes and mortgages for climate change, while environmentally sound services include water and waste management, recycling, testing, and consulting the environment. Among the immediate operational advantages of GINOV are improved logistics, higher resource efficiency, and cost savings. Improved relations with authorities and members of the public, a better reputation, and advantages for health and safety are examples of indirect gains. GINOV has emerged as a key idea in recent years to support the growth of a green economy, and it is viewed as any method or item that achieves resource conservation and environmental benefit (Omokanmi et al., 2022; Temesgen Hordofa et al., 2023). GINOV helps in the switch from fossil fuel energy to renewable energy sources, which can boost economic growth and lessen adverse environmental effects. The use of fossil fuels can cause environmental contamination, which can result in an inverted U-shape curve, as seen by the environmental Kuznets curve (EKC) in economic growth (Fang, 2023). Previous studies, however, shows that economic growth brought on by technological advancements may result in a decrease in CO₂ emissions (Udeagha & Ngepah, 2023a). Studies have also indicated that innovation can result in higher CO₂ emissions (Onifade et al., 2022). The vast amount of research on the link between GINOV and CO₂ emissions serves as another impetus for our analysis in this paper.

On the one hand, capturing the impact of stringency on environmental quality informs us of the effectiveness of environmental rules and regulations in reducing carbon emissions; on the other hand, it can also assist nations in quickly reaching the EKC income turning point and the early stages of economic development (Ibrahim, 2022b; Li et al., 2023). The arguments raised above are sufficient to demonstrate the significance of environmental policy stringency (EPS) and its role in reducing CO₂ emissions in the top polluting nations. EPS is one of the most significant environmental policy tools that has been put into place to confront the pressing danger to environmental sustainability and quality. By attempting to address unneeded externalities like pollution in order to foster green innovation, environmental stringency aims to promote sustainable development (Shah et al., 2023). Promoting sustainable growth and green technologies is challenging without the strict implementation of environmental laws and standards. Without implementing rigorous environmental rules and regulations as well as tightening GHG policy regimes, it will be impossible to accomplish the various CO₂ emission reduction targets, including environmental levies and renewable energy (Udeagha & Ngepah, 2022b). The EPS index’s availability makes it possible to monitor the progress being made toward the objectives of reducing CO₂ emissions, boosting green energy, and preserving an environmentally sustainable environment. EPS is thus a further tool at a nation’s disposal to combat environmental deterioration, and its goal is to increase the cost of pollution and climatic services in order to alter consumer and producer attitudes toward eco-friendly products (Udeagha & Ngepah, 2022c). This can be accomplished by placing restrictions on pollution-causing chemicals in order to increase the expense of pollution-producing activities and reduce its allure. The debate over the relationship between environmental outcome and environmental regulation was launched by Porter and Linde (1995). The industrial sector chooses environmentally friendly technologies that have a tendency to lower carbon emissions because of a well-organized environmental policy. EPS can lessen the damaging effects of CO₂ emissions by promoting environmentally friendly technologies and suppressing environmentally ‘dirty’ ones (Udeagha & Ngepah, 2022d). Hence, EPS has the power to alter consumer and producer behavior to promote energy use and output that is more environmentally friendly. It is necessary to look at how EPS helps BRICS nations reduce their CO₂ emissions for the aforementioned plausible causes. The primary goal of this study is to close this gap by using EPS as a factor in determining CO₂ emissions in BRICS nations. We believe we can test the hypothesis that a country’s environmental performance can be connected to the evolution of the stringency of its environmental laws and policies by evaluating the efficacy of the stringency of environmental rules and regulations.

Our analysis in this paper is motivated by all the aforementioned factors, including the lack of scientific consensus on the relationship between CRI, GINOV, EPS, and CO₂ emissions. The following is a summary of this paper’s significant contributions. First, the choice of the BRICS countries is based on their prominence in international discussions and agreements on carbon neutrality target and environmental sustainability (Udeagha & Ngepah, 2022a). Due to rigorous environmental regulations in advanced economies and cheap production costs in developing markets, many companies in developed economies have migrated and transferred their manufacturing units and technology to BRICS economies, which is one of the main reasons for this expanding impact (Udeagha & Muchapondwa, 2022b). These five economies’ levels of CO₂ emissions have multiplied as a result of this industrial migration, making the BRICS nations’ CO₂ emissions higher than those of other growing economies. Due to these factors, this study intends to give useful policy implications to the region’s carbon reduction goals and provides a crucial lesson for the future of the world. The relationship between some factors, such as CRI, GINOV, EPS, and environmental quality has drawn a lot of attention from academics all around the world, which brings us to our second point. Previous studies looked at the relationship between CRI and environmental quality (Z. Khan et al., 2021). A number of research also looked into how GINOV affected environmental quality (Yuan et al., 2022). However, the combined effect of GINOV, EPS, and CRI on environmental quality has typically been disregarded. By examining the impact of CRI, GINOV, and EPS on environmental quality from 1960 to 2020 in the BRICS countries while controlling for economic growth (GDP) and renewable energy R&D (RERD), our study seeks to address this gap. Our understanding of how CRI, GINOV, and EPS interact with environmental quality is improved when we model them all at once, and this is a crucial topic for industrialized nations like the BRICS nations dealing with environmental challenges. By concurrently examining the environmental impacts of CRI, GINOV, and EPS, this research specifically contributes to the body of knowledge and advances our understanding of the subject. Third, by adopting the recently constructed OECD EPS index as a surrogate for our measure of environmental regulation, we feel that the research offers few contributions to the literature on the link between CO₂ emissions and EPS. The EPS index, which ranges from 0 (not strict) to 6 (very rigorous), is a country-specific and globally comparable indicator of the severity of environmental policy. Fourth, previous empirical research produced inconsistent estimate findings because it failed to address the crucial problems of slope heterogeneity and cross-sectional dependence in panel data. This study employs recently developed econometric methods that can manage both problems, taking into account advanced panel data techniques that are resistant to the aforementioned econometric obstacles and filling in important knowledge gaps. Last but not least, the study aims to provide helpful policy recommendations on the significance of CRI, GINOV, and EPS in promoting environmental quality. The BRICS countries are working hard to find a sustainable solution to improve the quality of their environment, and they have made significant progress toward achieving ecological sustainability in the region. Additionally, the area has committed to halving CO₂ emissions by 2030 and achieving net-zero emissions by 2050. To achieve such ambitious objectives, the area has been consistently investing in R&D. The BRICS countries’ R&D intensity increased from 2016 to 2017 from 2.34% to 2.37%, showing that they are boosting their R&D spending. Additionally, their actual R&D investment rose by 3.8%. In a similar vein, public R&D spending was 1.3%, whereas private R&D spending was over 28% (Udeagha & Muchapondwa, 2022b, b). Therefore, this study is crucial for the BRICS nations under examination.

The remainder of the study is organized as follows: Section 2 summarizes earlier studies. Section 3 presents the data, resources, and methodologies. The empirical results and their discussion are provided in Section 4. The study is concluded in Section 5, which also includes policy suggestions.

2. Literature review

This section is divided into four subsections: (i) relationship between CRI and CO₂ emissions; (ii) relationship between GINOV and CO₂ emissions; (iii) relationship between EPS and CO₂ emissions; and (iv) summary of the gaps in the literature.

2.1. Relationship between CRI and CO₂ emissions

CRI, which consists of political, financial, and economic risks, is essential to the success of environmental sustainability and the economic policies of an economy. The influence of political, financial, and economic risks on CO₂ emissions have all been studied in-depth by numerous scholars, but the impact of CRI on environmental quality is less well understood. The relationship between CRI and CO₂ emissions has barely been explored in empirical investigations. For example, Z. Khan et al. (2021), who examined the effects of export diversification and CRI on CO₂ emissions for the Regional Comprehensive Economic Partnership (RCEP) countries between 1987 and 2017, discovered that lowering CRI, transitioning to renewable energy, and advancing environmentally-related technological innovations all assist in reducing CO₂ emissions in the RCEP countries. Diversifying exports, on the other hand, is proven to steadily increase CO₂ emissions. The rest of the literature review for this portion covers the available research concentrating on the environmental implications of political risk, economic risk, and financial risk because there are few studies on the relationship between CRI and CO₂ emissions.

Recently, several studies have focused on how risks—particularly political, financial, and economic risks—affect CO₂ emissions. For instance, several researchers have looked into how financial risk affects CO₂ emissions, but their results are mixed. Using a panel dataset of 111 countries, including all members of RCEP with the exception of Cambodia and Laos, W. Zhang and Chiu (2020) investigated the nonlinear effects of countries’ comprehensive risks (i.e. political risk, financial risk, and economic risk) on CO₂ emissions. They found that financial risk can have an impact on CO₂ emissions that is monotonically increasing. On the other hand, Shahbaz et al. (2016) agreed with Y. J. Zhang (2011)‘s assertion that financial stability is a key factor that enables Chinese policymakers to reduce CO₂ emissions. This viewpoint has been supported by Ngepah and Udeagha (2019), who found similar evidence in the case of African countries.

For the past three decades, a great deal of research has looked at the relationship between political risk and CO₂ emissions, but its conclusions have been mixed. For instance, using the autoregressive distributed lag (ARDL) approach, Ashraf (2023) discovered that a more favorable political climate (lower political risk) encourages economic development and reduces carbon emissions in Pakistan over the period 2000–2020. Using the ARDL method, van Vu and Huang (2020) studied the effect of political risk on CO₂ emissions in Vietnam. According to their findings, there is an association between political risk and CO₂ emissions that is statistically significant and positive. Su et al. (2021) and W. Zhang and Chiu (2020) research on the impact of political risk on CO₂ emissions in 111 countries between 1985 and 2014 supports this theory by demonstrating a statistically significant negative relationship between political risk and CO₂ emissions and refuting van Vu and Huang (2020)’s conclusion. This suggests that in the countries under study, environmental degradation is lessened by a stable environment. Political quality (reducing political risk), according to Ashraf (2022b, 2022a), improves environmental protection by reducing environmental deterioration. Similar to this, Mehmood (2023) used panel data from Pakistan, India, Bangladesh, and Sri Lanka to estimate the effects of political risk on carbon emissions while taking into account the importance of technical advancements, financial development, economic growth, and trade. In doing so, they used the cross-sectional autoregressive distributed lag (CS-ARDL) approach, an advanced econometric method, to analyze the annual data from 1984 to 2017. Their findings showed that developing South Asian nations need a stable political environment in order to achieve carbon neutrality. Moreover, Kartal et al. (2023) discovered that political stability (reduced political risk) had a statistically significant negative impact on production-based CO₂ emissions in UK utilizing quarterly data from 1995/Q1 to 2018/Q4 in a nonlinear autoregressive distributed lag (NARDL) framework. By using dynamic ARDL and BC frequency domain causality techniques, Adebayo (2022) investigated Canada from 1990 to 2018 and concluded that political stability (lower political risk) reduces CO₂ emissions. Moreover, Adebayo et al. (2022) used the moments of quantile regression technique to investigate the BRICS nations for the years 1990–2018 and discovered a negative effect between political risk and environmental degradation. A considerably better political climate (lower political risk) improves environmental quality, according to Ashraf (2022a), who used the generalized technique of moments to analyze 75 Belt and Road Initiative (BRI) nations over the years 1984–2019. Moreover, Kirikkaleli et al. (2022) used cointegrating regression and BC frequency domain causality techniques to study China between 1990/Q1 and 2018/Q4 and discovered that political stability has a substantial impact. As a result, these research reach the conclusion that political stability (reduced political risk) is a key predictor of CO₂ emissions and has a decreasing influence. Political stability (reduced political risk) is incorporated into CRI in accordance with the body of research, and it is anticipated that political stability will have a negative impact on environmental quality.

2.2. Relationship between GINOV and CO₂ emissions

GINOV is thought to have a tight connection to CO₂ emissions. On how GINOV contributes to carbon neutrality and the reduction of global carbon emissions, there are several disagreements. According to some scholars, there is a negative association between the two. According to Yue et al. (2021), who used the cross-sectional augmented autoregressive distribution lag (CS-ARDL) approach, GINOV is helpful for lowering carbon emissions and enhancing environmental quality in Thailand. In the analysis of the G7 nations, Qin et al. (2021) also discovered the beneficial effect of GINOV on carbon emission control and highlighted the two-way causal link between them. According to Paramati et al. (2021), GINOV is the primary factor assisting OECD economies in reducing carbon emissions. According to the research conducted in Turkey by Shan et al. (2021), GINOV lowers carbon emissions. Suki et al. (2022) revealed that GINOV is negatively connected with carbon emissions over the short and long terms using the novel approach of bootstrap ARDL with Malaysia as the research context. Studies have discovered conflicting results on the link between GINOV and CO₂ emissions. Xu et al. (2021) used 218 prefecture-level Chinese cities as their research subject and discovered, using a two-way fixed effect model, instrumental variable method, spatial econometric model, and causal intermediary effect model, that GINOV and its subcategories have a positive impact on China’s carbon emission performance. Furthermore, it is made clear that GINOV greatly lowers and enhances carbon emission performance via the effects of industrial structure and foreign direct investment, respectively. In the long run, GINOV has a considerable negative influence on carbon emissions, according to Shao et al. (2021), but there is no statistically significant association between the two in the short run in N-11 nations. Khattak et al. (2022) examined the asymmetric and periodic link between green and sustainable technology innovation and carbon emissions using panel data of G7 nations from the first quarter of 1990 to the fourth quarter of 2018. It has been determined that while the beneficial effects of green and sustainable technology innovation will lower carbon emissions in economic success, the negative effects will result in carbon emissions during an economic downturn.

Also, Ahmad, Muslija, et al. (2021) found that for OECD economies, positive innovation shocks had the opposite effect of negative innovation shocks in terms of CO₂ emissions. By examining innovation shocks—both positive and negative—and figuring out how these shocks affected pollution in OECD nations, the authors significantly advanced the field of study. The authors concentrated on the CO₂ emissions and overall innovation positive and negative shocks. By focusing on the cyclical and asymmetric impacts of innovation in environmentally friendly technology, Ahmad and Zheng (2021) also significantly contributed to the research. They identified a significant and sustained positive association between CO₂ emissions during the recession and adverse shocks to innovation in environmentally friendly technology. In their study of the top 10 carbon emitting countries, Ali et al. (2021) concentrated on the relationship between innovative activity, trade, and the use of renewable energy. They discovered that over the long term, trade, GINOV, and the use of renewable energy are important factors that predict both consumption-based and territory-based carbon emissions. The authors focused on the examination of variables that affect CO₂ emissions and the part that GINOV played in encouraging ecological responsibility for the top 10 carbon emitter nations. Using panel data on 264 Chinese cities at the prefecture level from 2006 to 2017, Lin and Ma (2022) looked at how the urban innovation environment impacted the impact of green technology advancements on CO₂ emissions. According to the empirical results, different types of cities are affected by developments in green technology in a variety of ways. Similar to this, Abid et al. (2022) used data from 1990 to 2019 to analyze the effects of GINOV on carbon emission in G8 member nations and discovered that GINOV contributes to CO₂ emission reduction. On the other hand, H. Khan et al. (2022) looked at how GINOV affected environmental sustainability across 176 nations. H. Khan et al. (2022)’s main objective is to investigate the effects of GINOV, good institutions, trade openness, the use of renewable energy sources, and foreign direct investment on environmental quality. For the OECD nations between 1990 and 2017, Huang et al. (2022) investigated the impacts of GINOV, human capital, and trade openness on energy consumption at the aggregate (total) and disaggregate levels (renewable and non-renewable). The study found that increasing the use of renewable energy in the OECD area was a result of GINOV, human capital, and energy pricing.

In conclusion, previous research ignored a number of crucial issues and only looked at the relationships between trade openness, the adoption of renewable energy, and CO₂ in pieces. The varied and ambiguous findings of empirical research demand additional investigation. Unlike other studies, this study examines the combined impacts of CRI, GINOV, and EPS on environmental quality using data from 1960 to 2020. To fully appreciate the connected problems and comprehend the processes via which this connection occurs in the BRICS region, a close examination of the relationship between CO₂ emissions and these factors is required.

2.3. Relationship between EPS and CO₂ emissions

EPS shows the potential and successes in the implementation of environmental rules, and explains how economies are motivated by ecological goals in relation to the average standards of nations when implementing such regulations (Udeagha & Muchapondwa, 2022a). Kongbuamai et al. (2021) demonstrated that EPS plays a major role in accomplishing sustainable development goals while preserving ecological quality. Out of several administrative consequences, the regulation of EPS maintains the leading relevance in sanctioning certain behavior that leads to worsening environmental pollution via navigating and putting into practice purposeful environmental policies and regulations (Yirong, 2022). Among the recent works, Chu and Tran (2022) examined the nexus between EPS and CO₂ emissions utilizing the innovative Method of Moments Quantile Regression (MMQR) in 27 member countries of the organization for economic co-operation and development (OECD). The statistical investigation revealed that EPS has a significant influence in the eighth quantile to lessen the negative effects of environmental deterioration. Also, the policies recommended establishing stricter, more suitable rules on manufacturers to prevent the overuse of natural resources and reduce the ecological footprint deficit ratio. These results are comparable to those of studies by Murshed (2021), Ngepah and Udeagha (2018), and Sohag et al. (2021), which assessed the positive effects of normalizing the EPS in the industrial sectors because these units are closely linked to energy-consuming products and have an adverse effect on the global ecological footprint.

Galeotti et al. (2020) used a variety of ecological regulation metrics to examine the significance of policy indicators on environmental quality from 1995 to 2009 for 19 OECD economies. The results pointed to the critical part that environmental policies play in reducing ecological deterioration. The results showed that there is a larger consensus for using the composite environmental index and emanations-based policy measures when it comes to within-nation and between-nation disparities. Nevertheless, the empirical results varied depending on the indicators for strategic pollution reduction. The study concluded that various policy indicators provide varied results about their impact on the environment. Moreover, Kongbuamai et al. (2021) discovered that the stringency of environmental policies reduces ecological burden in the five developing BRICS countries. Their findings demonstrated that the use of renewable or non-renewable energy, industry, and the stringency of environmental regulations all had a favorable impact on ecological footprints. Rafique et al. (2022) also shown the short-term advantage of using stringent environmental regulations in enhancing ecological conditions. The study by Nathaniel et al. (2021), which examined the relationship between international trade, economic growth, and the role of environmental regulation in the N-11 nations during the time periods of 1990 and 2016, clarifies the conflicting viewpoint in their findings showing that EPS has no effect on reducing the ecological footprint in the N11 countries. A similar stance was also stated by Kampas et al. (2021).

Without a doubt, environmental contamination is a negative externality. It is inevitable that authorities will create appropriate environmental laws and regulations to deal with externality and the reduction of carbon emissions from governments, businesses, and individuals (K. M. Zhang & Wen, 2008). No matter what, groups, companies, and individuals work to maintain clean water sources, conserve arable land, and restrict pollutant discharge. Government involvement is essential to the success of such initiatives that improve environmental quality (Feng et al., 2019). Governments throughout the world may support environmental governance by implementing environmental policies (G. Zhang et al., 2019). Environmental policy stringency and regulations are strategy that may be used to combat environmental deterioration. The goal of stringent environmental policy is to increase the cost of climate services and pollution in order to change consumer and producer attitudes toward environmentally friendly products (Neves et al., 2020). Limiting the contaminating agents of pollution can accomplish this by increasing the expense of activities that cause pollution and deterring people from engaging in them (Neves et al., 2020). The overall literary work identified the important investigations of applying stringent environmental policy in various economies. But, developing nations must carefully consider how to put the EPS system into practice because doing so demands a highly mechanized industrial structure, which emerging nations do not yet have in full operation (Udeagha & Ngepah, 2019). Moreover, it is believed that using existing environmental policies would not fully take advantage of how emerging nations’ political, technological, and financial systems are changing (Udeagha & Ngepah, 2020). Due to their failure to enact ecological policies, many emerging economies are thus seen as being at fault for raising the ratio of the ecological deficit by using conventional means of attaining economic advancement. In order to achieve environmental and economic sustainability, the present study emphasizes the significance of EPS at the governmental level and its potential role in decreasing environmental deterioration.

2.4. Summary of the gaps in the literature

Several important areas of knowledge were left out due to the extensive relevance of earlier investigations. In order to address these shortcomings in our analysis, we paid close attention to it. The gaps that were identified are as follows: First, there has not been any research done to analyze the complex connections between CRI, GINOV, EPS, and environmental quality in the BRICS nations, or to pinpoint the particular processes by which these links could function. Moreover, many past investigations have shown inconsistent results about the relationships between these components. This is mostly due to the fact that very few scholars have explored the links using the methodology that is most suitable for this relationship, such as the endogenous growth model, and have instead employed a range of models. Finally, several investigations have been made to ascertain the major causes of the BRICS countries’ environmental deterioration. The precise relationships and combined consequences of CRI, GINOV, and EPS on environmental sustainability in the BRICS area have not received significant attention. The current discussion has shown that depending on the perspective from which it is seen, the influence of CRI, GINOV, and EPS differs on environmental quality. Effective steps should be put in place to improve conditions because the BRICS countries are now confronting increasing environmental difficulties. The results of the current study may thus help the governments, institutions, officials, and organizations of the BRICS countries pursue more reasonable, appropriate, and useful efforts relating to environmental safety generally and specifically in the BRICS nations. As a result, this work has produced the following noteworthy contributions: First, this study offers the environmental literature yet another novel perspective by taking into account the model’s implications of CRI, GINOV, and EPS. As far as we are aware, no study has looked at how much the combined impacts of CRI, GINOV, and EPS may account for variances in the carbon emissions of the BRICS economies. Second, this study adds to the body of literature by concentrating on developing nations with economies like the BRICS, which are the main sources of global environmental pollution. By doing this, we provide academics the opportunity to concentrate on the main economies that cause significant environmental catastrophes. Lastly, the research offers recommendations for both developed and emerging nations on how to enhance environmental sustainability by considering the BRICS economies in the theoretical framework. The current study broadens the scope of past environmental studies by looking at the complex relationships between these variables in the top economies in the world. Fourth, the study provides results that are essential for the policymakers in the BRICS countries to reflect on the progress toward environmental sustainability goals by using environmental policy stringency and green innovation as threshold variables to determine the individual heterogeneous effects of the parameters. This study also helps to consolidate the findings of other earlier studies that emphasize the importance of environmental policy stringency and green innovation in reaching the region’s goals for carbon neutrality and a green environment. Fifth, to the best of our knowledge, this is the first research to look at the dynamic link between CRI, GINOV, EPS, and CO₂ emissions in the context of the BRICS nations while taking the EKC hypothesis into account. A panel of BRIC nations (excluding South Africa) and a first-generation test were used in earlier research by Tamazian et al. (2009) and Pao and Tsai (2011), and they neglected the combined impacts of CRI, GINOV, EPS. But in this study, we included these variables in the BRICS (South Africa included) panel and used reliable estimation techniques like the CS-ARDL framework, which generates precise estimates for projecting the short- and long-term effects of the explanatory variables on the sustainability of the BRICS environment. The findings of this study will provide policymakers in the BRICS nations with a more detailed view to help them create more extended policies to reach carbon neutrality goals.

3. Methodology

3.1. Theoretical framework

The environmental Kuznets curve hypothesis argues that regional environmental pollution rises in step with regional GDP growth (Udeagha & Breitenbach, 2021). The usage of both renewable and non-renewable energy sources is anticipated to rise along with GDP. Increased economic production in nations with significant non-renewable energy usage further increases the strain on energy supply. A significant percentage of the energy required by expanding economic activity is provided by fossil fuels. Demand for fossil fuels is increasing, which is unfriendly to the environment and opens the door to a high rate of carbon emissions into the atmosphere (M. C. Udeagha & N. Ngepah, 2021). Consequently, based on this supposition, we anticipate that GDP will have a positive influence on CO₂ emissions in the chosen nations, i.e. $\left({\beta }_1=\frac{\alpha C{O}_2}{\alpha GDP}>0\right)$.

To check for the existence of the EKC hypothesis for the BRICS countries, the GDP-squared factor is introduced to the model. Therefore, it is anticipated that the GDP-squared element will have a negative impact on CO₂ emissions if the EKC hypothesis is true, i.e. $\left({\beta }_2=\frac{\alpha C{O}_2}{\alpha GDPS}<0\right)$.

Porter (1996) and Porter and Linde (1995) make the assertion that environmental policies may be advantageous to industries and that well formulated environmental policies encourage innovation, which improves enterprises’ private benefits by raising productivity. Environmental regulations would therefore benefit enterprises as well as society as a whole. A supportive audience for this perspective has emerged among decision-makers and the broader public. It makes sense that stringent environmental restrictions deter CO₂ emissions, which ultimately results in a decrease in environmental pollution. Environmental policies and regulations that are strict are being put into place to fight environmental deterioration. These policies increase the cost of pollution and other environmental services in an effort to shift consumer and producer behavior toward more environmentally friendly goods. In order to achieve this, limits are placed on polluting agents to raise the cost and reduce the allure of polluting operations. The argument between environmental regulation and environmental outcomes has been at the forefront of the regulation-environmental-outcome nexus since the pioneering work of Porter and Linde (1995), which stresses that an environmental policy that is correctly crafted can assist firms in adopting environmentally friendly practices that can reduce emissions. By encouraging ecologically beneficial technologies and inhibiting environmentally ‘dirty’ ones, stringent environmental policies can reduce the deleterious impacts of pollution. Therefore, like environmental taxes, strict environmental laws and regulations have the capacity to influence producers’ and consumers’ behaviour to promote the production and use of energy products in an environmentally sustainable manner. Environmental policy stringency (EPS) is therefore anticipated to result in a reduction in CO₂ emissions, i.e.$\left({\beta }_3=\frac{\alpha C{O}_2}{\alpha EPS}<0\right)$.

In order to achieve environmental sustainability, economic strategies must take into account the composite risk index (CRI), which combines all political, financial, and economic risks. First, an economy’s strengths and weaknesses are determined by economic risk. The risk decreases with increasing total risk point and increases with decreasing total risk point in every instance (Economic, Political, and Financial). Economic activities that provide possibilities and raise income levels increase with the expansion of economic growth. In order to support their domestic sector throughout the transitory stage, economies with such expansions use more energy. In addition, environmental economists assert that such sweeping expansions increase carbon emissions and worsen environmental quality, which is likewise described by the EKC hypothesis in the first stage; economic growth or an increase in income is linked to environmental degradation. Consequently, we anticipate that economic risk will be positively correlated with carbon emissions. Second, political risk encompasses factors like the corruption index, the stability of the government, the investment profile, democratic accountability, and law and order. Political risk makes it difficult to adopt carbon neutrality rules in an economy. Deterioration of the environment results from this. The political risk index evaluates the political system’s stability in an economy through 12 different components. Higher political risk is associated with weaker or more unstable governments, lower or less educated populations, poverty, unequal access to resources, riskier investment history, more corrupt officials, and weaker institutions. Developing nations with significant political risk typically face a number of challenges. For instance, more dishonest government representatives can undermine environmental laws, allowing for unlawful manufacturing and increasing emissions (Chen et al. 2018). Strong institutions are crucial for protecting environmental quality because they impose strict regulations and penalties for any activity involving illegal emissions, while an anti-corruption campaign is helpful to reduce corruption and subsequently mitigate environmental pollution (Zhou et al. 2020). Therefore, we anticipate that political stability in an economy will be crucial in regulating carbon dioxide emissions. Third, financial risk gauges a nation’s ability to finance its government’s operations, including obligations under official, commercial, and trade debt. An economy is more productive and better able to repay its debt if its financial risk is lower. Additionally, because there is less financial risk in nations with stable currency rates, international investment flows there. An economy’s ability to be productive and repay its debt depends on how high the financial risk is for that economy. Furthermore, because they pose less of a risk to investors, nations with stable exchange rates attract foreign capital. So, we anticipate that more financial risk may cause carbon emissions. In conclusion, International Country Risk Guide (ICRG) uses the formula ‘A’ to determine the composite risk, which is the sum of political, financial, and economic risks. It is therefore predicted that CRI is positively related to carbon emissions, i.e.$\left({\beta }_4=\frac{\alpha C{O}_2}{\alpha CRI}>0\right)$.

The most important element influencing sustainable development and environmental sustainability is thought to be technological innovation (M. C. Udeagha & N. N. Ngepah, 2021; M. C. Udeagha & N. Ngepah, 2021). Dietz and Rosa (1994) developed the STIRPAT model in the theoretical research and included technology as one of the three key elements that affect environmental sustainability. Numerous studies based on various nations or areas have concluded in the empirical investigation that technological innovation encourages sustainable development. The conflict between carbon emissions and economic growth can be more immediately resolved by green technological innovation that prioritizes clean production and energy efficient utilization. There are two different ways that green innovation affects carbon emissions: directly and indirectly. First, through increasing green productivity, having diverse spillover effects, reducing costs, specialization, and other factors, green innovation may directly reduce carbon emissions. Second, the primary source of carbon emissions in high carbon sectors is the combustion of fossil fuels. By improving the industrial structure of the pollution industry, green innovation can indirectly raise energy consumption rate and accomplish energy conservation aim. The two ways clearly divide the work, which has a double impact on carbon emissions. Therefore, encouraging green innovation has turned into a crucial tool for attaining green development goals and is crucial to cutting emissions. Furthermore, the ecological modernization theory (EMT) suggests that increasing resource efficiency (renewable energy) through technological innovation might aid in reducing the environmental risks brought on by economic expansion. We establish relationships between the development of technology and CO₂ emissions in light of the aforementioned ideas. Through technological advancement, energy usage can be reduced, energy efficiency can be raised, and the environment can be enhanced. Advanced technologies would be used in order to promote sustainable manufacturing and enhance environmental sustainability. This study looks at how green technological innovation affects environmental quality since it has the potential to make renewable energy sources like solar and wind less wasteful and more ecologically friendly. Modern energy-intensive industrial equipment is being replaced with greener, more efficient alternatives, which eases the burden on the environment and the economy. As a result, green technological innovation (GINOV) is anticipated to reduce CO₂ emissions, i.e. $\left({\beta }_5=\frac{\alpha C{O}_2}{\alpha GINOV}<0\right)$.

One of the key factors in a nation’s ability to fulfil its sustainable development goals (SDGs) and grow its economy is the use of renewable energy. By minimizing the burning of fossil fuels, renewable energy deployment can help ensure the energy supply and be a potential solution in addressing the climate change challenges. Theoretically, renewable energy research and development (RERD) is one of the strategies for addressing excessive carbon emissions to achieve carbon neutrality or a low-carbon economy (M. C. Udeagha & N. N. Ngepah, 2021). RERD can be viewed as one of the game changers for the economic development and achievement of sustainable development goals in a country. It is crucial to switch from conventionally non-renewable energy sources to renewable ones in order to attain carbon neutrality, and R&D cannot be disregarded in this process. Investment in RERD substantially promotes environmentally friendly innovation and technology, contributes to economic growth and reduces CO₂ emissions in the atmosphere, leading to achieving carbon neutrality. Therefore, to achieve carbon neutrality target, it is important to increase investment in RERD to avoid future adverse effects of CO₂ emissions. Consequently, it is projected that RERD will decrease CO₂ emissions, i.e.$\left({\beta }_6=\frac{\alpha C{O}_2}{\alpha RERD}<0\right)$.

3.2. Model specification

It is becoming more usual to conduct studies on the variables affecting carbon emissions in an effort to pinpoint any potential unique characteristics impacting CO₂ emissions. The most popular model for tackling this problem is STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology). The linear, easy-to-read, and simple-to-estimate STIRPAT model assists in estimating how much a certain human activity affects the environment. The model computes the total effect of each anthropogenic component on the ecosystem and allows testing of hypotheses. In prior studies, the STIRPAT model was employed to investigate the effects of various factors on CO₂ emissions. Nasir et al. (2021) utilized the STIRPAT model to analyze the relationship between trade openness, industrialization, economic growth, energy consumption, and CO₂ emissions for Australia. Pham et al. (2020) used the STIRPAT model to analyze the short- and long-term impacts of energy, economic, and social factors on environmental pollution for 28 European countries. York et al. (2003) examined the relationship between population and carbon emissions using the STIRPAT model. Wang et al. (2013) examined the impacts of economic development, technology, population, urbanization, industrialization, service level, trade, and energy structure on CO₂ emissions in China from 1980 to 2010. They used the enlarged STIRPAT model to do so. The STIRPAT model was used by Zhang and Lin (2012) to investigate the impact of urbanization on China’s energy consumption. Using the STIRPAT model, Yeh and Liao (2017) looked at the relationship between population growth, economic development, and CO₂ emissions for emerging economies. Following Cheng, Fan, Meng, et al. (2020, b), the STIRPAT model is presented as follows:

(1) $I_t=a{P}_t^b{A}_t^c{T}_t^d{\mu }_t$(1)

where I denotes the total environmental impact, P indicates population size, A signifies affluence, and T represents technology or the effect per unit of socioeconomic activity. With the exception of population size and affluence, which have an influence on ‘I’, ‘T’ may be divided into a variety of categories depending on the environmental impact being examined (York et al. 2003). In the equation above, ‘a’ stands for a constant, ‘b’, ‘c’, and ‘d’ are, respectively, the exponents of ‘P’, ‘a’, and ‘T’, and ‘μ’ is the error term in period ‘T’ Although the technological component ‘T’ may be broken down into a number of factors, the variables chosen depend on the environmental effect being studied. Most importantly, the STIRPAT model enables us to assess if the EKC hypothesis is applicable. We added a quadratic GDP element to the model to see if there is a non-linear relationship between GDP and CO₂ emissions. The updated STIRPAT model is, therefore, provided as follows:

(2) $logC{O}_{2it}={\alpha }_{it}+{\beta }_{1}logGD{P}_{it}+{\beta }_{2}logGDP{S}_{it}+{\beta }_{3}logE{P}_{it}+{\beta }_{4}logCR{I}_{it}+{\beta }_{5}logGINO{V}_{it}+{\beta }_{6}logRER{D}_{it}+{\mu }_{it}$(2)

where CO₂ represents CO₂ emissions as a proxy for environmental quality; GDP denotes gross domestic product; GDPS is the square of GDP; EP stands for environmental policy; CRI represents composite risk index; GINOV captures green innovation; and RERD denotes research and development in renewable energy. The letters ‘i’ and ‘t’ in the subscript stand for the cross-section and the time or year, respectively. All the variables are expressed in their logarithmic terms. Regarding estimates, the study uses the particular to general strategy to progressively examine every explanatory variable’s impact on the CO₂ emissions.

3.3. Data sources and its definitions

Table 2 lists the sources of data and definitions of the variables. The statistics for the most industrialized BRICS economies from 1960 to 2020 were used in this analysis. These nations are, specifically, South Africa, Brazil, Russia, India, and China.

Table 2. Variable definition and sources

Variable	Definition	Unit	Source
CO2	Carbon dioxide emissions: caused by any event by using fossil fuel	Kiloton(kt)	WDI
GDP	Gross Domestic Product: the sum of the economies all final goods and services	Constant US 2010	WDI
GDPS	Gross Domestic Product Square	Constant US 2010	WDI
CRI	Composite Risk Index: consists of total political, financial, and economic risk indicators	Index	Political Risk Services (PRS)
GINOV	Green Innovation: any software or hardware innovation related to green products and processing	Environmental innovations	WDI
EPS	Environmental Related Policy: measured as environmental- related taxes	Environmental taxes	WDI
RERD	Research and development relevant to the field of renewable energy	Percentage of total energy R&D	WDI

WDI: World Development Indicator

3.4. Econometric techniques

3.4.1. Slope heterogeneity and cross-section dependence of the panel

We check the panel data’s cross-section dependence and slope heterogeneity as the first steps in our research. Nations on the panel may mirror one another in certain ways while differing in others. In contrast, homogeneous attributes in empirical model may result in skewed estimates, especially for panel estimates. As a result, the group of nations in question has to be homogenized (i.e. BRICS economies). In this context, we used the slope coefficient homogeneity (SCH) test proposed by M. H. Pesaran and Yamagata (2008) while taking into account coefficients parallel to the null hypothesis. Equation (3)(3) ${\tilde{\mathrm{\Delta }}}_{SCH}={\left(N\right)}^{\frac{1}{2}}{\left(2k\right)}^{-\frac{1}{2}}\left(\frac{1}{N}\tilde{S}-k\right)$(3) and Equation (4)(4) ${\tilde{\mathrm{\Delta }}}_{ASCH}={\left(N\right)}^{\frac{1}{2}}{\left(\frac{2k\left(T-k-1\right)}{T+1}\right)}^{-\frac{1}{2}}\left(\frac{1}{N}\tilde{S}-2k\right)$(4) are general equations for the earlier tests.

(3) ${\tilde{\mathrm{\Delta }}}_{SCH}={\left(N\right)}^{\frac{1}{2}}{\left(2k\right)}^{-\frac{1}{2}}\left(\frac{1}{N}\tilde{S}-k\right)$(3)

(4) ${\tilde{\mathrm{\Delta }}}_{ASCH}={\left(N\right)}^{\frac{1}{2}}{\left(\frac{2k\left(T-k-1\right)}{T+1}\right)}^{-\frac{1}{2}}\left(\frac{1}{N}\tilde{S}-2k\right)$(4)

where ${\tilde{\mathrm{\Delta }}}_{SCH}$ denotes slope coefficient homogeneity from Equation (3)(3) ${\tilde{\mathrm{\Delta }}}_{SCH}={\left(N\right)}^{\frac{1}{2}}{\left(2k\right)}^{-\frac{1}{2}}\left(\frac{1}{N}\tilde{S}-k\right)$(3) ; ${\tilde{\mathrm{\Delta }}}_{ASCH}$ represents slope coefficient homogeneity after adjustment in Equation (4)(4) ${\tilde{\mathrm{\Delta }}}_{ASCH}={\left(N\right)}^{\frac{1}{2}}{\left(\frac{2k\left(T-k-1\right)}{T+1}\right)}^{-\frac{1}{2}}\left(\frac{1}{N}\tilde{S}-2k\right)$(4) ; N is the cross section dimension; T denotes time series dimension; $\tilde{S}$ represents Swamy’s test statistic; and k is the mean bias.

The second-generation procedures is used in place of the first generation testing methods if the data exhibits homogeneity features. The worldwide village we live in makes it possible for a nation to become more and more dependent on other nations. However, if the cross-section dependence problem is disregarded, this might result in erratic and false estimations (Campello et al., 2019). In order to examine cross-section reliance among the BRICS nations, we used the M. Pesaran and Smith (2004) cross-section dependence (CD) test. The relevant test is presented in its general form as Eq. (4), with the independence of the cross-sections serving as the null hypothesis.

(5) $C{D}_{Test}=\sqrt{\frac{2T}{N\left(N-1\right)}\sum _{i=1}^{N-1}\sum _{j=i+1}^N{\hat{\rho }}_{ij}}$(5)

where $C{D}_{Test}$ is the cross-section dependence test; T denotes time dimension of the panel; N represents cross section dimension; ${\hat{\rho }}_{ij}$ is the sample estimate of the pair-wise correlation of the residuals. According to a specific ‘connection or spatial matrix’ that describes the pattern of spatial dependence in accordance with a pre-established set of principles, the degree of cross section dependence is quantified in the literature on spatial statistics. A connection matrix’s (i, j) elements, for instance, might be set to 1 if the ith and jth regions are connected and to zero otherwise.

3.4.2. Unit root

We proceeded on to look for the unit root or stationarity in the chosen panel after the findings for cross-section dependence and heterogeneity were obtained. Dealing with data that includes both cross sections and time series at once requires constant attention. In order to address the problem of the heterogeneous panel and resolve the cross-section dependence dilemma between the components, we employed the panel unit root test, such as IPS (2003) provided by Im et al. (2003) and CIPS (2007) established by M. H. Pesaran (2007). The null hypothesis for these tests is that the unit root exists in the data.

3.4.3. Panel cointegration test

We use the Westerlund (2007) error correction model (ECM) to determine the cointegration among the heterogeneous variables after the stationarity or unit root is checked. The issue of cross-section dependence and heterogeneous slope parameters are both addressed by Westerlund (2007). In order to determine the long-term connection between variables, we also used the Kao residual cointegration test advanced by Kao et al. (1999).

3.4.4. Cross-sectionally augmented autoregressive distributed lags (CS-ARDL)

Prominent events associated to the cross-section-dependence problem include the oil price shocks of 1997–1999 and the global financial crises of 2008–2009. It can result in inaccurate estimations if these elements were not recognized in association to the regression. The CS-ARDL is a practical choice to utilize since it makes use of a dynamic common correlated effects estimator to get beyond the cross-section dependence problem, slope heterogeneity, non-stationarity, and endogeneity (S. A. R. Khan et al., 2020; Yao et al., 2019). In comparison to common correlated effect mean group (CCEMG), augmented mean group (AMG), pooled mean group (PMG), and mean group (MG), Chudik and Pesaran (2015) introduced the CS-ARDL, that is regarded as effective for better result analysis and accurate control (Li et al., 2020, Danish, 2020). The CS-ARDL is typically presented as Equation (5)(5) $C{D}_{Test}=\sqrt{\frac{2T}{N\left(N-1\right)}\sum _{i=1}^{N-1}\sum _{j=i+1}^N{\hat{\rho }}_{ij}}$(5) in the following format as below:

(6) $Y_{it}=\sum _{I=0}^{P_w}{\alpha }_{I,t},Y_{i,t-I}+\sum _{I=0}^{P_z}{\beta }_{I,i}{Z}_{i,t-I}+\sum _{I=0}^{P_x}{\gamma }_i,I{\bar{X}}_{t-I}+{\epsilon }_{i,t}$(6)

The extended version of CS-ARDL is given by the equation (6)(6) $Y_{it}=\sum _{I=0}^{P_w}{\alpha }_{I,t},Y_{i,t-I}+\sum _{I=0}^{P_z}{\beta }_{I,i}{Z}_{i,t-I}+\sum _{I=0}^{P_x}{\gamma }_i,I{\bar{X}}_{t-I}+{\epsilon }_{i,t}$(6) above, where ${\bar{X}}_{t-I}={\bar{Y}}_{i,t-I,}{\bar{Z}}_{i,t-I}$ reflects the means of the variables under consideration for both the dependent and independent variables. The $P_w,P_z,and{P}_w$, on the other hand, denote the lags of every element. Additionally, $Y_{it}$ denotes the response variable in this instance, which is CO₂ emission. $Z_{it}$ simultaneously lists all the considered factors being taken into account, including the research and development of renewable energy sources, environmental taxes, green innovation, the composite risk index, and GDP.

3.4.5. Robustness and causality tests

When using the conventional methodologies, a number of panel data concerns, such as cross-section dependence and slope heterogeneity, may result in erroneous results (Çoban and Topcu, 2013). This work used the dynamic ordinary least square (DOLS) method to address the problems of non-stationarity, cross-section dependence, and slope heterogeneity. By taking into account all of these factors, the estimation approach offers thorough evaluation and better results. Lastly, we used the Granger causality heterogeneous panel test proposed by Dumitrescu and Hurlin (2012) to determine if there could be a causal relationship among these variables involved. If the cross-sections and time series are not comparable, the technique is effective (i.e. T≠ N). Additionally, the technique simultaneously handles the panel data’s cross-section dependence and heterogeneity. The following part includes the findings, interpretations, and discussions (Section-4).

4. Empirical results and discussions

The calculated results, interpretations, and discussions of the findings are presented in this part. The slope coefficient homogeneity and cross-section dependence test results from M. H. Pesaran and Yamagata (2008) are presented in Table 3. To cope with biased cointegration and unit root estimates, the cross-section dependence must be addressed (Salim et al., 2017; Westerlund, 2007). In terms of practical assessments, the findings for RERD, GINOV, CRI, EP, GDPS, and GDP respectively, support the diverse character of the slope coefficients at various significance levels. The alternative hypothesis is therefore acceptable, and the null hypothesis of slope homogeneity is discarded as a result. This demonstrates the diverse properties of every research variable in our analysis. Additionally, the M. Pesaran and Smith (2004) estimated findings, which do not include cross-sectional dependence as a null hypothesis, are disproved and the alternative acknowledged as the potential substitute. Our findings suggest that each country’s dependence on the other is confirmed by the fact that all relevant variables are determined to be significant at 1%, 5%, and 10%, respectively. This further explains why the augmentation or decrease of CO₂ emissions could not be managed entirely. Instead, other nations would have an impact on the goals of a particular nation.

Table 3. Cross-section dependence and heterogeneous slope coefficient

Slope heterogeneity
Models	$\bar{\mathrm{\Delta }}$	${\bar{\mathrm{\Delta }}}_{Adjusted}$
$C{O}_{2it}=f\left(GD{P}_{it},GDP{S}_{it}\right)$	21.731***	23.841***
$C{O}_{2it}=f\left(GD{P}_{it},GDP{S}_{it},EP{S}_{it}\right)$	20.646***	22.610***
$C{O}_{2it}=f\left(GD{P}_{it},GDP{S}_{it},EP{S}_{Sit},CR{I}_{it},\right)$	18.052***	20.831***
$C{O}_{2it}=f\left(GD{P}_{it},GDP{S}_{it},EP{S}_{it},CR{I}_{it},GINO{V}_{it},\right)$	15.137***	17.426***
$C{O}_{2it}=f\left(GD{P}_{it},GDP{S}_{it},EP{S}_{it},CR{I}_{it},GINO{V}_{it},RER{D}_{it}\right)$	13.215***	15.218***
Cross-section Dependence
CO2	GDP	GDPS
9.830***	24.741***	24.853***
EPS	CRI	RERD
13.963***	14.741***	11.053***
GINOV
24.041***

Note: *, ** & *** is for the significance level of 10%, 5% and 1%.

Im, Pesaran, and Shin (IPS, 2003)’s and Pesaran (CIPS, 2007)‘s unit root test estimations are shown in Table 4. The data, at level I (0) and the first difference I(1) are processed using these two procedures. According to the previous report (IPS, 2003), all of the variables’ data are insignificant at I(0) but significant at I(1). This confirms stationarity at first difference, according to the variables. The variables, however, are significant at the 10%, 5%, and 1%, according to the CIPS (2007) estimates, with the exception of GDP and environmental policy (EP), which are very significant at I(1). Moreover, this supports the data’s stationarity at I(0) and I(1), respectively. As a result, the null hypothesis of the unit root existence is rejected for the variables RERD, GINOV, CRI, and CO₂ while being confirmed for GDP and EPS at level. GDP and EPS at I(1), however, were among the variables for which the null was rejected.

Table 4. Panel unit root tests

Im, Pesaran and Shin (IPS, 2003)
Variables	Trend and intercept
	level	First Difference
CO2	1.057	−6.731***
CRI	0.481	−7.184***
EPS	1.520	−4.177***
GDP	−0.301	−4.739***
GINOV	1.103	−4.831***
RERD	−0.318	−7.453***
M. H. Pesaran (2007) CIPS
CO2	−4.173***	-
CRI	−6.510***	-
EPS	−1.394	−5.403***
GDP	−3.041	−5.836***
GINOV	−2.863***	-
RERD	−3.057***	-

Note: *, ** & *** is for the significance level of 10%, 5% and 1%.

Table 5 provides statistical findings for cointegration using the residual cointegration test developed by Kao et al. (1999) and Westerlund (2007). In a conditional panel ECM, Westerlund (2007) evaluated the test’s hypothesis of whether the error correction term is zero (i.e. ECT = 0). For each of the five models, the numbers from the table strongly indicate a significant outcome at the 1%, 5%, and 10% levels. `These figures reveal the error correction for the mean group as well as the panel, or $G_{\tau }\text{amp}{G}_{\alpha }$ and $P_{\tau }\text{amp}{P}_{\alpha }$ respectively. The long-run relationship between RERD, GINOV, CRI, EPS, GDPS, GDP, and CO₂ is also shown by the Kao-residual cointegration test, which shows a significant value at 5% level of significance. The null hypothesis that there is no cointegration between the variables was thus rejected. As an alternative, it is possible to adopt the long-run cointegration presence hypothesis. Consequently, it is demonstrated that all factors have an impact on CO₂ emissions rather than just one. Since all of the variables and nations have long-run associations or cointegrations that are proven, we now investigate the link between CO₂ emission and other related indicators over the short and long terms.

Table 5. Cointegration tests

Westerlund (2007)
Models	Gt	Gα	Pt	Pα
$C{O}_{2it}=f\left(GD{P}_{it},GDP{S}_{it}\right)$	−9.430***	−48.618***	−22.315***	−49.715***
$C{O}_{2it}=f\left(GD{P}_{it},GDP{S}_{it},EP{S}_{it}\right)$	−9.562***	−41.841***	−23.530***	−48.840***
$C{O}_{2it}=f\left(GD{P}_{it},GDP{S}_{it},EP{S}_{it},CR{I}_{it},\right)$	−6.710***	−39.015***	−20.061***	−44.142***
$C{O}_{2it}=f\left(GD{P}_{it},GDP{S}_{it},EP{S}_{it},CR{I}_{it},GINO{V}_{it},\right)$	−8.425***	−41.430***	−23.946***	−46.416***
$C{O}_{2it}=f\left(GD{P}_{it},GDP{S}_{it},EP{S}_{it},CR{I}_{it},GINO{V}_{it},RER{D}_{it}\right)$	−8.534***	−39.133***	−21.621***	−40.313***
Kao Residual Cointegration Test
ADF	t-statistics			Prob.
	−1.604**			0.0301

Note: *, ** & *** is for the significance level of 10%, 5% and 1%.

Table 6 presents the outcomes of the CS-ARDL framework. Our findings indicate that economic growth (GDP) and economic growth squared (GDPS) have, respectively, positive and negative effects on environmental quality. While GDP degrades environmental quality, GDPS helps the BRICS economies’ environmental sustainability. The empirical finding thus supports the EKC theory’s applicability to the BRICS nations. The results are relevant to the region’s fundamental transformation and technological development. When environmental awareness increases, environmental rules are put into place to encourage the adoption of eco-friendly technology and reduce emissions. These findings corroborate Udeagha and Breitenbach (2021), which show the EKC theory’s applicability to the Southern African Development Community (SADC) from 1960 to 2014. Alharthi et al. (2021) for the Middle East and North Africa (MENA) region validate the EKC hypothesis in this region. Our findings support Ahmed et al. (2022)’s observation that Pakistan has an EKC since the income coefficient is positive but its quadratic component is negative from 1984 to 2017. The analysis conducted for South Africa by Udeagha and Ngepah (2019) further supported our finding. Similar to this, Ahmad, Jiang, et al. (2021) showed that EKC occurred in 11 emerging economies from 1992 to 2014 using balanced yearly panel data. In contrast, EKC was not valid in the US, UK, Japan, Italy, Germany, and Canada, according to Işık et al. (2019), who verified these findings for France in their examination of G7 nations from 1995 to 2015. Udeagha and Muchapondwa (2022a) also claim that between 1960 to 2020, EKC was uncovered in South Africa (2022b). Our findings add to Murshed (2021)’s findings for six South Asian economies. The findings, however, contradict those of Minlah and Zhang (2021), who found that the EKC hypothesis was not true for Ghana. Similar results from Ozturk and Al-Mulali (2015), Sohag et al. (2019), Tedino (2017), and Mensah et al. (2018) demonstrate the invalidity of the EKC hypothesis.

Table 6. CS-ARDL estimation results

Variables	Short-Run
	Model-1	Model-2	Model-3	Model-4	Model-5
	Coefficients	Coefficients	Coefficients	Coefficients	Coefficients
	[Std Error]	[Std Error]	[Std Error]	[Std Error]	[Std Error]
ΔGDP	0.135*** [0.248]	0.106*** [0.153]	0.140** [0.058]	0.151* [0.093]	0.158*** [0.015]
ΔGDPS	−0.259***	−0.271***	−0.204**	−0.211	−0.208**
	[0.037]	[0.068]	[0.039]	[0.098]	[0.047]
ΔEPS	-	−0.150**	−0.170***	−0.148***	−0.136
	-	[0.031]	[0.016]	[0.027]	[0.099]
ΔCRI	-	-	0.163*	0.140***	0.139
	-	-	[0.086]	[0.015]	[0.098]
ΔGINOV	-	-	-	−0.018***	−0.017***
	-	-	-	[0.027]	[0.025]
ΔRERD	-	-	-	-	−0.021***
	-	-	-	-	[0.016]
ECM(−1)	−0.831***	−0.805***	−0.813***	−0.892***	−0.807***
	[0.028]	[0.017]	[0.024]	[0.019]	[0.013]
Long-Run
GDP	0.203**	0.214***	0.205	0.281***	0.253***
	[0.423]	[0.038]	[0.961]	[0.028]	[0.016]
GDPS	−0.142**	−0.133***	0.105***	−0.131***	−0.129***
	[0.301]	[0.028]	[0.025]	[0.019]	[0.028]
EPS	-	−0.205**	−0.218***	−0.253*	−0.281**
	-	[0.049]	[0.014]	[0.138]	[0.126]
CRI	-	-	0.204***	0.218***	0.240**
	-	-	[0.018]	[0.025]	[0.031]
GINOV	-	-	-	−0.183***	−0.175***
	-	-	-	[0.014]	[0.026]
RERD	-	-	-	-	−0.057***
	-	-	-	-	[0.017]

Note: *, ** & *** is for the significance level of 10%, 5% and 1%.

The estimated long-run coefficient on the composite risk index (CRI), which is found to be positive and statistically significant at 1% and 10% levels, suggests that the CRI has a detrimental impact on carbon emission in both the short and long terms in the BRICS countries. Political risk makes up the majority of the CRI (50%) while the remaining portions (25%) and (25%) are made up of financial risk and economic risk, respectively. As a result, both short-term and long-term carbon emissions increase with increasing CRI. However, if a varied range of political figures unite together to address the issue of climate change, this might reduce the risk of instability and ultimately result in the creation of a carbon-neutral world. (Karhunmaa, 2019). Our findings are in line with those of Z. Khan et al. (2021), who looked at how export diversification and CRI influenced CO₂ emissions for the RCEP nations and found that a greater CRI adversely affects the environment. Ashraf (2022b, 2022a), who noted that growing CRI deteriorates environmental protection through increasing environmental degradation, also lends credence to our conclusion. Mehmood (2023), who estimated the impacts of CRI on carbon emissions using panel data from Pakistan, India, Bangladesh, and Sri Lanka, made similar findings. In addition, greater CRI results in a large rise in production-based CO₂ emissions in the UK, according to Kartal et al. (2023). Our results, however, do not support Adebayo (2022) that CRI lowers CO₂ emissions in Canada. Ashraf (2022a), who noted that in 75 countries participating in the Belt and Road Initiative (BRI), a much lower CRI enhances environmental quality found similar results. Additionally, Kirikkaleli et al. (2022) discovered same outcomes in the case of China. Furthermore, Z. Khan et al. (2021), who studied the effects of export diversification and CRI on CO₂ emissions for the RCEP countries, found that lowering CRI, switching to renewable energy, and advancing environmentally conscious technological advancements all help to lower CO₂ emissions in the RCEP countries.

The calculated coefficient on environmental policy stringency (EPS) is found to be statistically significant, indicating that an increase in EPS of 1% results in a reduction in CO₂ emissions of 0.205%. This result implies that stringent environmental policy aids the BRICS region in reducing CO₂ emissions, and it demonstrates the viability and success of putting environmental regulations into practice. It also explains how the BRICS economies are driven by ecological goals relative to the average standards of nations when putting environmental regulations into practice. Our result is in line with those of Kongbuamai et al. (2021), who showed that EPS is essential for achieving sustainable development objectives while maintaining ecological quality. Chu and Tran (2022), who investigated the relationship between EPS and CO₂ emissions, revealed comparable findings in the cases of 27 Organization for Economic Co-operation and Development member nations (OECD). Ngepah and Udeagha (2018), who found that EPS significantly influences the eighth quantile to minimize the harmful impacts of environmental degradation, provide more support for our finding. In order to avoid the exploitation of natural resources and lower the ecological footprint deficit ratio, the policies also suggested imposing stronger, more appropriate regulations on industries. The findings of research by Murshed (2021) and Sohag et al. (2021), which evaluated the advantages of normalizing the EPS in the industrial sectors and found that these units have a negative impact on the ecological footprint of the earth’s surface, are similarly equivalent to the findings of this study. Galeotti et al. (2020), which examined the relevance of policy indicators on environmental quality for 19 OECD economies using a number of ecological regulation variables, found a similar conclusion. The findings demonstrated how important environmental regulations are in preventing ecological decline. The findings indicated that, when it comes to within-nation and between-nation differences, there is a greater agreement in favor of utilizing the composite environmental index and emanations-based policy approaches. Furthermore, Kongbuamai et al. (2021)’s observation that the five BRICS emerging nations’ strict environmental regulations lessen environmental burden supports our finding. Additionally, Rafique et al. (2022) have demonstrated the immediate benefit of implementing stringent environmental regulations in improving ecological circumstances. Similar findings were made in the investigation conducted by Nathaniel et al. (2021) regarding the N-11 countries. However, our finding contradicts those of Shah et al. (2023), who found that EPS degrades environmental quality in BRICS economies.

The estimated coefficient for research and development in renewable energy (RERD) is statistically significant and negative in the long run. In the BRICS countries, a 1% rise in RERD is strongly correlated with a 0.057% decrease in CO₂ emissions at a 1% level of significance. This empirical evidence suggests that the research and development (R&D) in the use of renewable energy improves environmental sustainability in the region. The significant expenditures made in RERD by the BRICS nations are beginning to yield results. They are therefore presently moving toward completing the Sustainable Development Goals (SDGs) by 2030. The region employs a variety of strategies to reduce emissions and combat climate change, including a transportation sector that is environmentally friendly, a varied energy mix, and a dependable energy infrastructure. The best approach to the issues of electricity production and global warming is to encourage the development of renewable resources. It is significant that regulations for renewable energy are created and implemented because the BRICS region is one of the top producers and users of energy in the world. The zone places a high emphasis on the development of green sources because they are the biggest producers and consumers of energy in the world. They have devised a variety of long-term, intensive major projects to achieve this. Our findings support M. C. Udeagha and N. Ngepah (2021, b) that the use of green energy, including hydroelectric power, super capacitors, solar energy, biogas, wave power, and other sources, has the potential to greatly reduce global CO₂ emissions. Additionally, our finding is consistent with Ponce and Khan (2021), who revealed that RERD improves the quality of the environment in nine developed European and non-European countries are in line with the findings from our study. In a similar vein, RERD, according to Udeagha and Ngepah (2022d), aids in raising environmental standards in South Africa. The results, however, disagree with those of Boluk and Mert (2014), who uncovered that RERD lowers environmental quality in 16 EU member states. Pata and Caglar (2021), who discovered evidence to back the assertion that RERD has no impact on China’s carbon footprint, came to similar findings.

Over the long term, it is demonstrated that the computed coefficient on green innovation (GINOV), which shows that a 1% rise in GINOV causes a 0.183% reduction in CO₂ emissions at a 1% level of significance, is statistically significant and negative. This empirical evidence reveals that green technological innovation has benefited the BRICS region, which has passed many legislative measures intended to boost GINOV in support of environmental sustainability. Among the BRICS countries, GINOV promotes energy efficiency, lowers the cost of access to renewable energy sources, and improves the environment. By improving energy efficiency through a variety of techniques, such as changing the fuel mix, adopting energy-efficient industrial processes, and utilizing end-of-pipe technology, GINOV aids in reducing CO₂ emissions in the BRICS countries. More importantly, the high levels of R&D expenditure and technological innovation are the fundamental components in the environmental stewardship of the zone, which has put in place a number of programs to increase government R&D involvement, enabling it to gradually shift its industrial activities from very energy-intensive coal-powered technologies to processes driven by technological advancements that use less energy. The BRICS region’s capacity to decrease environmental pollution has been significantly boosted by some of these institutional initiatives that support green technological innovation. Our results are in agreement with those of Erdogan (2021) and Guo et al. (2021), who found that GINOV promotes an environment that encourages a reduction in energy consumption, an increase in energy efficiency, and ultimately a reduction in greenhouse gas emissions for China and the BRICS countries, respectively. Anser et al. (2021) for EU nations provide additional support for these findings. Nevertheless, our results do not support the claims made by Dauda et al. (2021) that GINOV in Sub-Saharan African countries undermines environmental health. For Asian nations, Usman and Hammar (2021) discovered comparable findings. The results of Udeagha and Ngepah (2022a) for South Africa and Ngepah and Udeagha (2018, 2019) for Sub-Saharan Africa provide more evidence for this assertion.

For robustness check, the dynamic ordinary least square (DOLS) method is used and its results are presented in Table 7. The DOLS estimations have verified that GDP and CRI have adverse effects on ecological quality. The other factors, such as RERD, GINOV, EPS, and GDPS are indicated to have contributed to improve ecological quality and achieve carbon neutrality target in the BRICS nations. We found evidence of little to no change in the estimated coefficients when the findings of the two approaches (CS-ARDL and DOLS) are compared, notably with regard to their signs and magnitudes. While keeping their signs, the majority of the variables are statistically significant, though in some cases, their magnitudes change slightly from one another.

Table 7. Dynamic ordinary least square (DOLS) for robustness

Variables	Model-1	Model-2	Model-3	Model-4	Model-5
	Coefficients	Coefficients	Coefficients	Coefficients	Coefficients
	[Std Error]	[Std Error]	[Std Error]	[Std Error]	[Std Error]
GDP	0.259** [0.110]	0.232*** [0.034]	0.243 [0.973]	0.280*** [0.023]	0.251*** [0.018]
GDPS	−0.130*** [0.019]	−0.121*** [0.023]	0.121*** [0.028]	−0.132*** [0.018]	−0.124*** [0.020]
EPS	--	−0.234** [0.048]	−0.237*** [0.015]	−0.279* [0.134]	−0.284** [0.120]
CRI	--	--	0.216*** [0.019]	0.214*** [0.020]	0.244** [0.032]
GINOV	--	--	--	−0.186*** [0.014]	−0.173*** [0.027]
RERD	--	--	--	--	−0.053*** [0.011]

Note: *, ** & *** is for the significance level of 10%, 5% and 1%.

Finally, it is crucial to identify the factors that are causal as well as how they affect the outcome of policies intended to promote economic growth and environmental preservation. The Dumitrescu and Hurlin (2012) approach is used to evaluate the suggested causal relationships between the variables in this study. Table 8 depicts a one-way causal link between CO₂ emissions and GDP, GDPS, EPS, CRI, GINOV, and RERD demonstrating that any short-term policy change in the aforementioned variables will have a long-term effect on the pollution levels in the BRICS countries.

Table 8. Dumitrescu – Hurlin causality testing method for heterogeneous panel

Causality direction	W-statistics	$\bar{Z}-statistics$	p-value
GDP→CO2	3.481***	4.015	0.000
GDPS→CO2	4.104***	3.618	0.000
EPS→CO2	2.610***	2.413	0.005
CRI→CO2	4.120***	3.483	0.000
GINOV→CO2	4.429***	5.714	0.000
RERD→CO2	4.821***	5.260	0.000

Note: 1%, 5%, and 10% significance levels are captured by the asterisks ***,**,* respectively. The right-pointing arrow denotes unidirectional causation between independent and dependent variables. There is no bidirectional causation, or the flow of information from dependent to independent factors.

5. Conclusion and policy implications

5.1. Conclusion

Innovation in technology has significantly increased economic participation and global output, which has significantly increased the consumption of fossil fuels and raised significant environmental concerns. Due to recent developments in environmentally friendly technologies, the economic structure has changed away from non-renewable energy sources and toward sustainable energy sources like renewable energy. Environmentally friendly practices have greatly increased the sustainability of the environment in developed nations since the start of the fourth industrial revolution. As a result, this study used the BRICS region as an example to examine the joint effects of green innovation (GINOV), composite risk index (CRI), and environmental policy stringency (EPS) on CO₂ emissions over the period from 1960 to 2020 in the context of economic growth (GDP) and renewable energy research and development (RERD). For solutions to be developed that maintain environmental sustainability, understanding the relationship between these factors is essential. The following are the main conclusions drawn from the research findings: (i) in the long term, GINOV, CRI, EPS, RERD, GDP, and CO₂ emissions are cointegrating; (ii) EPS, GINOV, and RERD contribute to improve environmental sustainability; (iii) CRI degrades environmental quality; and (iv) GDP heightens environmental degradation while its square aids in preventing it, showing evidence of EKC hypothesis.

5.2. Policy implications

This section explores the context of the BRICS countries specifically as well as the implications for sustainability (more generally) and the Sustainable Development Goals (SDGs). This part focuses on the study’s importance (and what it means for policy) as well as what may be learnt about sustainable development. The United Nations’ Sustainable Development Goals (SDGs) provide a helpful framework for addressing developmental difficulties in order to achieve a low-carbon economy free of societal, economic, and ecological imbalances and therefore guarantee a healthy and sustainable quality of the environment. The BRICS countries have thrived in all five of the goals—advancement of climate action, justice and social institutions, life on land and peace, industry, innovation and infrastructure, and access to affordable and clean energy—and have made considerable progress toward all of them. The findings of this research are extremely pertinent to the thirteenth UN Goal (Climate Action). First, the BRICS countries should vigorously promote and put into practice green innovation techniques in order to ensure environmental sustainability. On the one hand, it is critical to continue developing the industry’s green standard system, find additional green factories, processes, products, parks, and supply chains, and create a green manufacturing system during the course of the sector’s existence. On the other hand, it is critical to uphold intellectual property rules, focus on urgent problems like energy conservation, carbon emission reduction, environmental pollution control and governance, and provide targeted financial and tax help to encourage green innovation. The BRICS states should use top-level planning to create a list of prospective smart technological solutions for carbon reduction while also establishing technical value criteria. Furthermore, it is crucial to assist companies in selecting technologies and formulating business strategies based on the characteristics and application of certain technologies, as well as the potential for emission reduction and potential profit margins of smart carbon reduction solutions. In partnership with governmental agencies, energy providers, and other stakeholders, platforms are being developed for monitoring carbon emissions at the industrial level. The platform can analyze data on the impact of smart carbon reduction technology and monitor the carbon emissions of energy-intensive companies in real time, giving evidence in favor of technological adoption and the development of appropriate international regulations. It is crucial to create a green supply chain with significant companies at its core. Leading companies in the green manufacturing sector should get assistance and encouragement in order to actively create a green supply chain, take the lead, and motivate firms upstream and downstream to implement energy-saving and environmental initiatives. The implementation of ecological design, the development of green goods, the promotion of green manufacturing, and the provision of green consumption advice should all be promoted by major businesses. Also, businesses should be encouraged to adopt long-term low-carbon development strategies and roadmaps toward carbon neutrality. The BRICS governments should enhance aid and advice, industry promotion, and the depth of enterprise collaboration in order to fully capitalize on the leading position of big companies. Moreover, given that green innovation is environmentally benign in the BRICS nations, BRICS authorities should urgently concentrate on optimizing the ecological effect of green technologies in their efforts to strengthen and promote sustainable economies. In order to promote environmental advancements and associated technology, the BRICS government should also subsidize programs like the adoption of green practices and make a deliberate effort to reform all pertinent legislation (Udeagha & Breitenbach, 2023d). Adopting technology-friendly regulations, putting green policies into practice, and improving the environment are all important to support sustainable growth, solve ecological and sustainability challenges, and improve the environment. As more responsible technical infrastructures and innovation are built with the inclusion of green solutions, it will be possible to control the risk factors linked with innovations and technological achievements. While selecting environmentally friendly standards for technology that might increase environmental sustainability, authorities must also have a set of criteria in place.

Second, policymakers should manage political unrest, economic crises, and financial instability to reduce CO₂ emissions since the composite risk index (CRI) increases CO₂ emissions in the BRICS countries. Political stability should theoretically result in higher income levels, which will raise people’s awareness of environmental contamination. Policymakers are often under more pressure to create a better atmosphere in this circumstance. It follows that reducing political risk in a nation can cut CO₂ emissions, suggesting that political risk is probably a significant component in the deterioration of ecosystems and the environment. Also, while formulating policies that promote development at the expense of environmental sustainability, the BRICS countries should exercise caution. Innovations in carbon capture and storage that can lower CO₂ emissions from the production of power and airborne processing processes utilizing fossil fuels should also be adopted by governments. In order to limit environmental harm, decision-makers should also take into account imposing a carbon price on polluters in the short term to control CO₂ pollution.

Third, raising environmental taxes and tightening environmental rules and regulations can both be useful tools for lowering CO₂ emissions since environmental policy stringency (EPS) enhances environmental quality. These two tools, however, are not enough to completely offset the negative consequences of energy use and CO₂ emissions. The relevance of the conflict between fostering economic growth and protecting the environment has been further underscored by our findings. The fundamental tenet of sustainable development for BRICS nations should be lowering total emissions while maintaining high levels of economic development. The BRICS nations should strike a balance between fostering economic development and preserving their environmental quality. Their long-term objective of protecting the environment should be an effective strategy of tightening their environmental rules and regulations while also supporting renewable energy, changing the energy mix to a fossil fuel-free economy, and improving energy efficiency. Our research further reveals that the best method to minimize CO₂ emissions is to increase the use of renewable energy sources. Over time, increasing the use of renewable energy both lowers emissions and supports a sustainable energy supply for a zero-emission plan. The BRICS nations should provide incentives for their citizens to buy more environmentally friendly products and services. In their efforts to create sustainable energy sources and improve environmental quality, they should also benefit from attracting foreign direct investment that supports cutting-edge green technology and renewables. The BRICS nations should improve their capacity for imitating advanced nations’ green innovations and team up to promote clean technologies. Because CO₂ emissions are a global issue, there must be a global response. In order to reduce pollution, the BRICS nations should actively participate in international collaboration. Future clean growth should follow a path involving increased energy efficiency and the development of renewable energy sources. In order to stop environmental damage, the BRICS area should also tighten environmental regulations. Environmental rules that are more stringent will encourage businesses and consumers to adopt environmentally favorable behavior. In this regard, it is important to effectively promote and execute recent commendable regulatory improvements in the area, such as the Revised Environmental Protection Law that went into effect in 2014 and the Environmental Protection Tax Law that went into effect in 2018. Also, the current level of emissions should be taken into account when determining how strict environmental rules should be in the BRICS nations.

Finally, there is no disputing the role that renewable energy sources play in environmental protection. It is advised to increase green investment in order to move away from traditional energy manufacturing methods and to modernize and better the processes used to produce green energy. More emphasis should be placed on the production of nuclear, biomass, and solar thermal energy. The BRICS countries should increase green funding scope and volume to help the generation of renewable energy. The BRICS countries’ next move in terms of policy will be to focus on making a major adjustment to their entire energy mix by raising the share of renewable sources in the overall domain of energy consumption. Similar to this, it is critically important to plan well for technological advancements and improvements in the energy sector to boost carbon sequestration and storage in order to avert ecological harm. In order to promote a green environment, growing green investment is crucial. Another suggestion is to create different credit or green credit policies or procedures that would allow for varying interest rates for various firms depending on how big of an impact they have on environmental degradation and carbon emissions. More polluting companies might face higher interest rates on their debt. Businesses that generate more pollution might face greater borrowing costs, and vice versa. All sectors will be compelled to develop sustainable or renewable energy sources to the utmost degree practicable as a result. Similarly, industries that produce little or no carbon dioxide should be given incentives like tax cuts or lower tax rates. Importers should be given incentives to simultaneously bring in green energy products. These concepts show how three important Sustainable Development Goals (SDGs)—improving economic growth (SDG no. 8), taking into account environmental degradation and enhancing environmental sustainability (SDG no. 13), and ensuring widespread access to affordable green energy (SDG no. 7)—can cooperate.

5.3. Limitations of the study

Although the current inquiry produced robust empirical findings in the instance of the BRICS countries, the research investigation had several drawbacks that might be taken into account in further investigative work. The inadequate availability of the data outside of the sample period, which limits the use of panel data, is one of the investigation’s key limitations. Nevertheless, this study assessed the combined impact of CRI, EPS, and GINOV on environmental quality in BRICS nations using up-to-date panel data. Further research utilizing other econometric methods or micro-disaggregated pertinent data may focus on other emerging regions. Other growth-related elements that were not taken into account in this study, such as institutional quality and natural resources, can be examined in future research. Nonetheless, CO₂ was employed in this study as an indicator of the quality of the environment. Additional research is required to determine whether consumption-based carbon emissions or other metrics of carbon footprints, such as chlorofluorocarbons, volatile organic compounds, hydrocarbons, and other transient climatological shocks, are better indicators of environmental quality in the BRICS countries. The present research examines CO₂ emissions as a reflection of environmental quality even though they are not the only cause of ecological pollution. Future research should study this connection by taking into account additional ecological contamination factors, such as water pollution and hazardous contaminants. By combining time series data with panel estimation techniques, further research may compare country-specific results to generic panel outputs using far more advanced strategies. This can assist illuminate the existing evidence by providing a comparison analysis with the findings of this inquiry. Another significant weakness in the inquiry is its narrow analysis of a single region. Thus, future study should concentrate on examining the combined impact of CRI, EPS, and GINOV on environmental quality for a wider perspective in the African panel setting and other regions of the world. Furthermore, using aggregate-level data, this study has examined the combined effects of CRI, EPS, and GINOV on CO₂ emissions in the BRICS economies. This is also another drawback. By combining time series data with panel estimation techniques, further research may compare country-specific results to generic panel outputs using far more advanced strategies. This can assist illuminate the existing evidence by providing a comparison analysis with the findings of this inquiry. The inquiry ignored any potential national disparities within each country in doing this. In light of this, future research may look into analyzing the combined environmental effects of CRI, EPS, and GINOV using statistics at the national level (as opposed to state- or provincial-level). The combined impact of CRI, EPS, and GINOV on emissions in various industries within the BRICS economies was ignored by using aggregate-level data in this analysis. The sectoral variations in the analysis could thus be another direction for future research. Lastly, future studies might think about looking into how certain factors like economic development and renewable energy on the environment interact with CRI, EPS, and GINOV.

Availability of data and materials

The datasets used are publicly available from the World Bank World Development Indicators, which can be accessed https://databank.worldbank.org/source/world-develop ment-indicators

Author information

Maxwell Chukwudi Udeagha and Nicholas Ngepah contributed equally to this work.

Contributions

MC conceptualised the study idea, drafted the paper, collected data, analysed data, wrote the introduction section, organised the literature review, drafted the methodology section, interpreted the results and provided the discussions, concluded the study with policy implications and organised the reference list. N conceptualised the study idea, drafted the paper, collected data, analysed data, wrote the introduction section, organised the literature review, drafted the methodology section, interpreted the results and provided the discussions, concluded the study with policy implications and organised the reference list.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data relevant to this research is publicly available from the XXX.

Additional information

Funding

This study was not funded by any organisation.