Environmental Sustainability in BRICS Economies:

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1. Introduction

Concerns and knowledge about climate change, global warming, and biosystem degradation have increased in recent years. Everyone throughout the world is now aware of the problem of global warming [1]. Most people think that too much emission of greenhouse gases (GHGs), especially carbon dioxide, is the main source of this problem. To find ways to lessen the impact of this problem, numerous scholars have looked at the relationship between financial development, technological innovation, economic expansion, trade openness, carbon emissions, and renewable energy usage [2].

In the last several decades, environmental degradation and global warming have risen to the forefront of international attention. Increasing emissions of greenhouse gases are likely to be blamed for this occurrence. Because of its negative impacts on both people and the environment, carbon dioxide (CO₂) emissions are among the most harmful greenhouse gases [1]. According to Ren et al. [2], CO₂ emissions make up around 76.7 percent of the total emissions of greenhouse gases. The increasing use of conventional energy sources such as coal, gas, and oil is the main driver of carbon dioxide emissions, which harm both humans and the environment. Liu and Liu [3] report that polluting energy sources are responsible for around 68% of CO₂ emissions.

The use of renewable energy sources can significantly cut down on carbon dioxide emissions without stifling economic growth. Biomass, hydro, geothermal, solar, and wind power are environmentally beneficial alternatives to polluting fossil fuels that many countries are working hard to replace. Improving energy efficiency and introducing energy conservation measures are other priorities. Research by the scholars Guo and Hu [4], Dong et al. [5], Mensah and Abdul-Mumuni [6], Huang et al. [7], Gök [8], and Sunday Adebayo et al. [9] shows that the share of renewable energy in total energy consumption has been increasing in both developed and developing economies in recent years.

In addition, the existing literature posits that financial development significantly contributes to the production of CO₂ emissions. Theoretically, there are two ways in which changes in economic development can affect CO₂ emissions. To begin with, carbon dioxide emissions rise in response to increased direct investment across the board in the financial industry. According to Acheampong et al. [10], the tradeoff theory of business finance, which has been studied by Guo [11] and W. J. Yang et al. [12], among others, there are benefits to tax shelters. Still, there are also consequences to economic distress and debt agencies. According to Kayani et al. [13], the conventional industrial sector is notorious for its high leverage ratios, contributing to its high energy consumption and pollution levels. When businesses have looked into debt-funding sources and come up empty, direct financing can be a lifesaver. The environmental impact of large-scale direct funding may be devastating for manufacturing, especially in developing countries with lax institutional requirements [14,15].

However, rapid economic growth is often accompanied by high energy consumption and carbon dioxide emissions (CO₂). The majority of the world’s greenhouse gas emissions—about 77%—come from human activity, including carbon dioxide (CO₂) [16]. As the world’s leading developing economies, the BRICS nations have seen a dramatic increase in their CO₂ emissions. In 2019, the carbon dioxide emissions from the BRICS nations totaled 14,759 billion tons or about 43.19 percent of the world’s total. However, efforts to reduce carbon dioxide emissions by the BRICS countries have varied in intensity [17]. A conscientious nation, China, released a plan in 2014 stating its intention to cut its CO₂ emissions intensity by 40–45 percent by 2020 compared to 2005 levels [18]. The BRICS nations account for more than 40% of global CO₂ emissions; thus, understanding what drives these emissions in this region is critical. In addition to helping their economies grow sustainably, this will also reduce the burden of lowering CO₂ emissions worldwide [19].

Furthermore, technological developments substantially impact the introduction of new applications for environmental protection and the promotion of intelligent development in the governance of the environment [20]. The sustainable development goals (SDGs) require nations to work together to enhance environmental sustainability [21]. Additionally, it is generally acknowledged that understanding and resolving major environmental concerns requires technological innovation, which is credited to [22,23]. The capacity to secure a patent is a direct outcome of technological innovation, defined as improving methods, resources, and information utilized in producing goods and providing services. Obtaining a patent serves primarily to safeguard technological developments and new ideas [24]. According to recent academic research, financial development is an important factor that can significantly affect carbon emissions. Results from empirical studies could be skewed if this financial aspect is ignored [25,26,27]. As a result, numerous studies utilizing different methodologies, metrics, and populations have sought to understand the effects of these elements.

The relevant study has not yet reached a unanimous agreement because the results are still unclear. The validity of the reported coefficients and elasticities is questioned in several studies since the estimation methods used do not have a strong enough quantitative foundation [28,29]. Several recent empirical studies have included financial development as an essential explanatory variable. Unfortunately, the present empirical studies on the link between economic growth and environmental degradation have yielded unclear and inconclusive results [30,31,32,33,34]. The scholars Koc and Bulus [35], Gnangoin et al. [36], Hasan et al. [37], and Chen et al. [38] all present studies that imply a paradoxical influence on environmental degradation. The authors Nasrullah et al. [39], Çetin et al. [40], Vo et al. [41], and Erdogan et al. [42] all argue that additional empirical research is needed to clarify the conflicting results seen in these small-scale studies.

According to a literature review, academic research on technological innovation and carbon emissions has yielded useful results. However, there are still certain limitations. No one in the educational community seems to agree on how new technologies affect carbon efficiency just yet [22,23,43]. This may be because environmental factors and economic and technical conditions differ between locations. As a result, the effect of technical progress on the BRICS nations’ ability to efficiently reduce their carbon emissions requires more investigation [44,45,46].

Although the current literature highlights the significance of environmental sustainability (ES), there is a scarcity of empirical research that investigates the primary drivers of ES. The relationship between technological innovation, economic progress, and carbon emissions has been extensively investigated in previous publications [47,48,49,50]. Another approach examined the relationship between technical innovation and long-term, consistent economic development [51,52]. There is a continual debate about whether innovation and economic growth promote or demote carbon emissions. Technological innovation benefits the environment, encourages the development of clean energy projects, and aids in the resolution of dirty energy issues [53].

Similarly, studies have investigated the impact of financial development on environmental quality. Several academics have reported inconsistent findings concerning the influence of financial development (FD) on environmental deterioration and quality [54,55,56]. FD reduces the rate of environmental degradation by investing in research and development and transitioning to more environmentally friendly and sustainable energy sources. The study’s contradictory findings, as well as its core finding that financial development, or FD, have a negative influence on environmental quality, underlining the need for mitigating elements that can balance finance’s expanding dominance in the goal of ecological sustainability.

There are two schools of thought among the empirical studies on energy use and its impact on CO₂ emissions, as pointed out by [57,58]. But we still have not reached a final decision. Given these contradictory results, the researchers hope to illuminate the existing literature by offering new perspectives.

The work significantly improves upon existing empirical studies. A large amount of study has been conducted on the complex interactions between several aspects, such as energy usage, financial expansion, and technical advancement [54,56,59]. Several emotional elements (neutral, negative, zero, and positive) have been connected to these components in prior research. However, the authors contend that it is critical to acknowledge that there is a scarcity of research focusing on a certain group of economies, particularly rising economies such as the BRICS. As a result, the current study must analyze how these components interact within the framework of the BRICS paradigm. In reality, previous research has shown how restricted the connections between the factors being analyzed are [60,61]. Furthermore, the present body of literature on the BRICS states and their relationship to the aforementioned characteristics lacks empirical data. Examining how these variables impact environmental sustainability in the BRICS countries would give policymakers helpful knowledge for developing practical solutions to environmental challenges and attaining long-term economic growth.

To achieve their targets of carbon neutrality and peak carbon emissions, the BRICS countries are vigorously enhancing the implementation pathway’s strategic planning. They are accelerating the development of low-carbon and environmentally friendly technology, as well as the transition to new energy and economic models. Most known research suggests a link between carbon emissions, financial development, technical innovation, economic expansion, and the usage of renewable energy. However, their points of view may differ slightly.

Previous studies have looked at different groups of countries’ economies, renewable energy, financial development, technological innovation, and CO₂ emissions in great detail [62,63]. However, this study does not consider trade openness’s mediating role. The bulk of the studies by Yao et al. [64], Long et al. [65], Olaniyi [66], Alsagr [67], Vyrostková et al. [68], Dănescu et al. [69], and Fomishyna et al. [70], as far as the researchers are aware, concentrate on reducing energy usage rather than increasing it. Growth in the economy, technological advancement, and financial development are all factors that are considered in this study [64,65]. This will substantially expand what is already known in the field. To round out the picture, the study models the relationship between these parameters and trade openness as a mediator to see how it affects the connection. Researchers, experts, politicians, and BRICS countries can all benefit from the study’s organized framework, which offers a clearer picture.

As their economies, populations, and commerce expand, the BRICS countries are aiming to reduce their dependency on non-renewable energy sources by implementing innovative and energy-saving practices. Their purpose is to build a sustainable industrial basis. Based on the most recent data available to us, this study seeks to address a significant gap, specifically in the relationships between the BRICS states. This study covers a significant knowledge gap about the link between numerous concepts. Prior studies have concentrated on certain characteristics or have not fully considered how these variables impact overall environmental sustainability. More research is also needed to investigate the numerous consequences that differ amongst nations owing to their unique political and socioeconomic situations. To strive toward a more sustainable future, stakeholders and policymakers must understand the key problems and drivers of environmental sustainability. As a result, this research is critical for addressing the current gap. The research findings provide empirical value to the corpus of literature by employing rigorous econometric approaches, assisting decision-makers in developing more appropriate policies for nations striving towards ES. This study is novel in that it evaluates the cumulative effect of numerous components rather than analyzing them independently, resulting in a more in-depth understanding of the BRICS’ specific developmental difficulties. This research is significant because it may give vital information for BRICS nations’ policy decisions.

This research examines how the environmental sustainability of BRICS nations is impacted by economic growth, financial development, technological innovation, and utilization of renewable energy sources with the mediating effect of trade openness. The primary research questions of this study are as follows:

-. How can financial development, economic growth, technological innovation, and renewable energy consumption impact the environmental sustainability in BRICS countries?
-. What is the function of trade openness in mediating the relationship between economic growth, technical innovation, financial development, renewable energy usage, and environmental sustainability in BRICS countries?

2. Literature Review

The connection between technological innovation, financial development, economic growth, renewable energy consumption, trade openness, and CO₂ emissions can be better understood within the context of endogenous growth theory. The endogenous paradigm states that spending money on people and new ideas is crucial. According to theory, internal influences are generally considered more important than external ones. To foster technological innovation, financial progress, economic growth, and the adoption of renewable energy sources, economies effectively utilize finance to lead and support enterprises through strong environmental and financial regulations.

2.1. Financial Development and Environmental Sustainability

Numerous empirical studies have examined the impact of financial development on environmental sustainability by employing a variety of straightforward indicators of this phenomenon. Xu et al. [71] looked at the 1971–2008 Indian economy to determine if there was a connection between carbon emissions and financial progress. Domestic borrowing to the business community is one indicator of financial development; the authors found that this indicator positively correlates with environmental degradation. Ali et al. [72] looked at how CO₂ emissions are associated with European financial development, specifically domestic funding to the private sector. For this study, they used the FMOLS model and the cointegration test. Their data show that progress in the financial sector harms ecological sustainability [73].

Using system-GMM, Khan et al. [74] analyzed 29 Chinese provinces to determine how GDP growth affected CO₂ emissions. Their research showed that CO₂ emissions positively correlated with financial depth, defined as the ratio of deposits and loans to GDP. Ruza and Caro-Carretero [75] looked at how financial development affected CO₂ emissions by industry and discovered that the transport, oil, and gas sectors were the most positively correlated with financial development. Musah et al. [76] used the autoregressive distributed lag (ARDL) method to analyze the correlation between the expansion of Nigeria’s financial sector and the country’s CO₂ emissions. According to a new study, financial development, as measured by the share of domestic credit going to private companies as a percentage of real GDP, is positively correlated with environmental degradation [77]. The following hypothesis can be developed from the prior discussion:

H1.

Environmental sustainability is significantly impacted by financial development.

2.2. Economic Growth and Environmental Sustainability

The gross domestic product (GDP) has been used to measure economic growth in empirical research. However, carbon dioxide (CO₂) emissions will increase as the economy grows [78]. The environment will deteriorate as a result of excessive CO₂ emissions. Questions on how CO₂ emissions affect GDP growth and how these emissions could affect the GDP growth model will follow [79]. The consequences of climate change and global warming have grown increasingly serious as globalization has advanced. Energy efficiency, pollution prevention, and sustainable development have all received more attention [80].

After looking at data from Xue et al. [81], a positive correlation between rising GDP and CO₂ emissions was found. Assuming no changes in other variables, CO₂ emissions will rise 0.305% for every 1% increase in GDP. After analyzing data, Yang and Khan [82] found that CO₂ emissions will increase in the near run due to economic growth. South Asian countries’ CO₂ emissions significantly boost economic growth over the long run. But improving environmental quality is not a good goal for the plan to boost economic growth by increasing CO₂ emissions [83]. Therefore, the relevant South Asian nations must undertake responsible domestic policies to investigate renewable energy and other alternative energy sources to lower the carbon dioxide emissions caused by energy consumption [84]. Zhang et al. [85] used data from the numbers through the NARDL model. Their findings show India’s CO₂ emissions rate grows considerably alongside its per capita GDP. In the short term, a drop in GDP could indicate a drop in demand for goods and services generally. This leads to less CO₂ being released into the atmosphere [86,87,88]. The following hypothesis can be developed from the previous discussion:

H2.

Economic growth has a significant negative impact on environmental sustainability.

2.3. Renewable Energy Consumption and Environmental Sustainability

Several studies have examined the relationship between CO₂ emissions and the percentage of energy coming from renewable and non-renewable sources in various parts of the world. Developed and developing countries’ CO₂ emissions rose between 1984 and 2007, according to research by Qudrat-Ullah and Nevo [89]; however, nuclear power reduced emissions, whereas renewable energy increased them. They argue that power generators must rely on fossil fuels without adequate storage technology to meet the massive energy demand. In their analysis of OECD countries’ CO₂ emissions and disaggregated energy consumption from 1980 to 2011, Bekele et al. [90] used the Augmented Mean Group (AMG) technique. Using renewable energy sources improves environmental quality, while using non-renewable energy sources lowers it, according to their empirical findings. Panel cointegration methods were used by Farooq et al. [91] to find that the degradation of the environment in MENA nations is positively connected with renewable energy use.

According to research by Kirikkaleli and Adebayo [92], renewable energy sources improved environmental quality from 1981 to 2015; however, fossil fuel energy sources are a leading source of carbon dioxide emissions in the United States. The effects of renewable and non-renewable electricity generation on environmental degradation in nine Mediterranean states were investigated in the study carried out in [93]. The research examined data from 1980 to 2014 using panel econometric techniques. In contrast to the positive impact of renewable energy sources on the environment, their research showed that emissions levels rose when non-renewable energy sources were used [94,95].The impact of renewable energy on energy consumption and emissions in 22 developed developing nations was investigated by Qudrat-Ullah and Nevo [93], who also took other confounding factors into account. According to their findings, using renewable energy sources reduces energy intensity and emissions. The following hypothesis can be developed from the previous discussion:

H3.

The consumption of renewable energy significantly impacts environmental sustainability.

2.4. Technological Innovation and Environmental Sustainability

Technological innovation is acknowledged as crucial in the worldwide effort to reduce CO₂ emissions. The impact of innovation on carbon emissions has been the subject of numerous research projects, with significant conclusions [96]. A reduction in carbon dioxide emissions in eastern China from 1997 to 2008 was attributed, according to Imran et al. [97] and Raihan and Tuspekova [98], to the existence of inventions for carbon-free energy systems. Aside from that, Ahmad and Satrovic [99] used an ARDL method to look at the relationship between technological progress and CO₂ emissions in China from 1970 to 2018. According to their research, technological advancements are key to reducing carbon dioxide emissions [100].

Also, between 1980 and 2019, Raihan et al. [101] and Gao and Fan [102] used wavelet methods to examine how trade openness, technological innovation, economic development, and renewable energy sources influenced environmental degradation in Portugal’s economy. According to their study, renewable energy usage helps to regulate CO₂ emissions, whereas trade openness, technological innovation, and economic growth lead to higher emissions. From 1995 to 2016,Junsong et al. [103], Ahmad and Satrovic [104], and Majerník et al. [105] also looked at the environmental impact of key developing markets and how technical innovation relates to CO₂ emissions. Technological innovation reduces CO₂ emissions, but it has a minimal influence on the environmental impact, according to their research. Research by Xiao and Qamruzzaman [106] and Li and Qamruzzaman [107] examined the effect of technological advancements on the ecological footprints of Middle Eastern and West Asian nations from 1990 to 2017. Technological innovation helps to reduce CO₂ emissions, according to the study’s results. The following hypothesis can be developed from the previous discussion:

H4.

Technological innovation has a significant influence on environmental sustainability.

2.5. Trade Openness and Environmental Sustainability

Research has looked into the link between trade openness and carbon dioxide emissions. Research has shown mixed results on the effects of trade openness on environmental quality [108]. Some found it helps to reduce CO₂ emissions, while others claimed the opposite. According to research by Khizar and Anees [109], trade openness negatively affects carbon emissions and positively impacts economic development and energy use. The degree of trade openness also improves the state of the ecosystem as a whole, according to Akhayere et al. [110]. According to research by Haseeb et al. [111], CO₂ emissions are negatively affected by trade openness. From 1971 to 2013, researchers in eleven industrialized nations gathered data. Economic growth and reduced CO₂ emissions in industrialized countries are facilitated by trade openness, according to Khan et al. [112]. Also, similar findings were produced by the studies carried out by the scholars Li and Qamruzzaman [106], Huo et al. [113], Dam and Sarkodie [114], Siddiqui et al. [115], Abaidoo and Agyapong [116], Chikezie Ekwueme et al. [117], Barkat et al. [118], Ahmed et al. [119], and Udeagha and Ngepah [120]. In contrast, environmental quality declines due to trade openness [121]. However, countries with high and moderate or low incomes have different results. Although trade openness has a detrimental effect on environmental deterioration in high-income nations, it disproportionately affects middle- and low-income countries [122]. The following hypothesis can be developed from the previous discussion:

H5.

Environmental sustainability and trade openness are significantly associated.

2.6. Financial Development and Trade Openness

The link between monetary progress and trade liberalization has been the subject of several studies. In one study, the researcher used Ordinary Least Squares (OLS) and Instrumental Variables (IVs) estimations to look for a link between GDP growth and trade in manufactured goods [123]. In particular, this research looked at the theoretical process by which the total amount of foreign financial resources affects the trade balance in manufactured goods. We tested the theoretical model’s experimental validity using panel data from 65 nations between 1966 and 1995 [124]. Islam and Islam [125] states that countries with more developed financial systems tend to have a better trade balance in manufactured goods and a higher share of exports. This is particularly true for nations with more established financial systems. Using data from 32 manufacturing industries between 1989 and 1991, Arif et al. [126] showed that financial variables significantly affect industry specialization in 20 OECD nations. Based on these findings, countries with more developed financial systems seem to favor export industries that use financing extensively [127,128]. The following hypothesis can be developed from the previous discussion:

H6.

Trade openness and financial development are significantly associated.

2.7. Economic Growth and Trade Openness

Scholars and politicians worldwide are curious about the link between trade and economic growth. The two main areas of study are the growth-led and export-driven growth hypotheses. Numerous theoretical reasons bolster the export-led growth hypothesis (ELG) [129,130]. A country’s economy benefits in two ways from increased exports. An increase in economic production and the promotion of specialization in export-oriented businesses are their primary benefits. Economic growth and increased skill development are subsequent outcomes [131]. More productivity results from these elements, which generally benefit the economy. Buying goods, services, and foreign financial capital may be easier if exports increase because of fewer restrictions on foreign exchange [132]. The second set of research tried to determine if international trade (the total value of imports and exports) has a positive, neutral, or negative impact on GDP growth. The relationship between foreign trade and actual income is inverse, according to previous studies [133,134]. Growth in the economy also encourages people to learn new things, which boosts the economy even more and makes it easier to increase exports [127,135,136]. The following hypothesis can be developed from the previous discussion:

H7.

Trade openness and economic growth are significantly associated.

2.8. Renewable Energy Consumption and Trade Openness

While many studies have looked at trade openness generally, very few have examined how energy consumption relates to it. This is why the study zeroes in on how energy consumption and trade openness affect national economies [114]. Many studies have looked at the relationship between trade openness and energy consumption. Still, most have focused on total energy consumption or energy resources derived from traditional fossil fuels. The scholars Yang et al. [28], Afjal [33], Adebayo et al. [34], Zeren and Akkuş [137], You et al. [138], Pata and Caglar [139], Soylu et al. [140], and Khan et al. [141] are certain instances of these investigations. Using data from 25 OECD nations, Wang et al. [142] investigated the link between CO₂ emissions, GDP growth, RES consumption, NRES consumption, and trade openness. This one stands out among the earliest studies on how trade openness relates to renewable energy consumption. The researchers used Pedroni panel cointegration tests and Granger panel causality tests in their analysis of data from 1980 to 2019 [29,143,144,145]. The findings of the cointegration test show that renewable and non-renewable energy sources, along with economic factors like import and export, have a long-term relationship. The following hypothesis can be developed from the previous discussion:

H8.

Trade openness and the consumption of renewable energy are significantly associated.

2.9. Technological Innovation and Trade Openness

Investigations on the link between innovation and trade openness have recently dominated the empirical literature. Innovation activities (including R&D spending and patent applications) and trade openness were studied in eleven European economies by [146]. The analysis was conducted using the ARDL paradigm. Their findings showed that innovations significantly affect trade openness. For developing nations to become more open to trade and integrated into global value chains, Ullah et al. [147] said that they should strengthen their creative capacities. When looking for a link between innovation and trade openness, researchers dug deep [120]. Innovation stocks and competitiveness are the main factors that affect the export growth of Taiwan, Korea, Japan, and Singapore, as shown by [148]. Research into the effect of global trade on innovations has been limited. Increased trade openness, for example, has been found to increase competition, which in turn encourages inventions inside the domestic sector [149]. Osabuohien-Irabor et al. [150] looked at OECD nations to see if trade openness correlated with creativity. According to the panel vector auto-regressive model’s results, neither economic growth nor trade openness was significantly associated with innovation or creative thinking. The following hypothesis can be developed from the previous discussion:

H9.

Trade openness and technological innovation are significantly associated.

2.10. Mediating Role of Trade Openness

Businesses are seen as pivotal to combat climate change and advance environmentally conscious, resilient, and inclusive sustainable development [151]. The relational relationships between financial development, economic growth, energy use, and environmental degradation are explored in this study with the mediating effect of trade openness [152]. The hypothesis’s integrity has been the subject of heated controversy among environmental economists for quite some time. Multiple studies have shown that the EKC hypothesis establishes a broad range of relationships between economic growth/development and environmental degradation [153,154,155,156,157].

Latif et al. [158] looked at the link between industrialization, urbanization, energy consumption, carbon dioxide emissions, and economic growth. They found that carbon dioxide emissions slow economic expansion [159]. Using data collected in India between 1971 and 2018, Chang and Lai [160] looked into the connections between urbanization, energy usage, innovation, and GDP development. The ARDL model was used for the analysis [161]. Scientists have found a unidirectional connection between energy consumption and the expansion of cities and economies. According to a new study by Wang et al. [162], countries with high or upper-middle-income levels see a considerable reduction in carbon emissions as a result of trade openness, while those with lower or middle-income levels have little to no effect. On the flip side, low-income nations’ carbon emissions have increased due to the extent of trade openness [163].

Additionally, it is becoming more difficult to disentangle economic growth from carbon emissions due to rising populations and per capita incomes. The separation of economic expansion from the emission of carbon emissions was greatly influenced by using renewable energy sources and the increase in oil costs. Separate research by Hasan and Du [164] found that rising incomes, more urbanization, trade openness, and energy use positively affect CO₂ emissions. On the other hand, it is negatively affected by trade openness [165]. Several variables, including trade openness, energy consumption, urbanization, and carbon dioxide emissions, have been examined in depth concerning economic growth [148]. The correlation between trade openness, energy and urbanization, and economic growth is also investigated in the study of [166].

Trade openness has a negative impact on environmental degradation in high-income countries, but it disproportionately affects middle- and low-income countries [122]. Huo et al. [113] empirically analyzed the relationship between trade openness and financial and economic growth using data collected from 80 countries between 1960 and 1994. They demonstrated how exposure to foreign competition and external disturbances are two of the hazards that can increase with more open trade. Trade openness correlates positively with long-term economic and financial development in both high- and low-income economies [128]. Arif et al. [126] used data from 32 manufacturing industries between 1989 and 1991 to show that financial variables significantly affected industry specialization in 20 OECD nations. Based on these findings, countries with more developed financial systems seem to favor export industries that use financing extensively [127].

The following hypothesis can be developed from the previous discussion.

H10.

Trade openness has a mediation role between environmental sustainability and financial development.

H11.

Trade openness has a mediation role between environmental sustainability and economic growth.

H12.

Trade openness has a mediating role between the consumption of renewable energy and environmental sustainability.

H13.

Trade openness mediates between environmental sustainability and technological innovation.

3. Research Methodology

3.1. Data Sources and Variables Measurement

This study looks at how the environmental sustainability of BRICS countries is affected by economic growth, financial development, new technologies, and the use of renewable energy with the mediating effect of trade openness. Due to data restrictions, this analysis specifically focused on the BRICS countries from 2004 to 2023 to perform panel data analysis. The BRICS countries included South Africa, Brazil, Russia, India, and China. The BRICS countries have a large population, a lot of land, and a lot of energy. Achieving sustainable global energy growth during the energy transition can be greatly aided by improving collaboration across various sectors and maximizing their unique strengths. Out of all the developing nations globally, the economies of the BRICS countries are the fastest-growing and largest. A large increase has been observed in the carbon dioxide emissions of the BRICS nations over the last 40 years, with the most notable increase occurring in the previous 20 years. Despite efforts to advance the economy, differences of opinion have arisen due to the rise in carbon emissions [47,75,119,167,168].

Data on trade openness, financial development, technological innovation advances, economic growth, and renewable energy utilization came from the World Developed Indicators. All values are then gathered in real time. The data’s availability determined the choice of countries and periods. With panel data, researchers may simulate behavioral variances within groups more flexibly and efficiently [169,170,171].

The first step of the statistical evaluation was to conduct preliminary tests to find the best estimator to use with the empirical models. Panel data methods that do not account for cross-sectional dependence may yield the wrong conclusions because of all the globalization. To determine if there are cross-sectional dependencies, assessing the degree of connectivity between the selected nations is crucial when employing panel data approaches.

Panel data analysis has many benefits compared to time series analysis. Among these advantages is the possibility of evaluating with a smaller dataset and aggregating results from different nations or businesses. Having cross-sectional and time components in the data is the main advantage of panel data analysis. To determine the link between the panels, the first step of this inquiry was to look at the cross-section dependence [78,113,172,173].

There are several benefits to using panel data estimation methods instead of time series data [174]. When applied to problems involving endogeneity, heterogeneity, and multicollinearity, these models produce reliable outcomes. One possible source of bias in fixed-effect regression is that it accounts for unobserved time-invariant individual attributes. Table 1 displays the variables measurement and their data source used in this study:

3.2. Empirical Model and Analysis

The CO₂ emission model built in this study is as follows:

CO_2it = f (EC_it, TINN_it, FinD_it, GDP1_it, OPP2_it)

In this context, CO₂ stands for carbon dioxide emissions, EC for consumption of renewable energy, TINN for technological advancements, FinD for financial development, GDP1 for economic growth, and OPP2 for trade openness. Based on the existing literature, variables are selected with the sophisticated economy group in mind.

i indicates country (i = 1, …, N);

t indicates time (t = 2004 … 2023).

To analytically examine how financial development, technological advancements, economic growth, trade openness, and renewable energy consumption affect environmental sustainability, the baseline model employs a panel data regression with fixed effects. Although the intercept may differ between individuals, the term “fixed effects” is used since the intercept for each individual remains consistent throughout time [174,175].

There are several reasons why a fixed-effect model is appropriate for working with cross-national data. At first, we presume that these fixed features are unique to that nation and have nothing to do with the traits of other countries. Because of these inherent differences between nations, it is possible that the error term and constant, which stand in for certain national characteristics, will not have any correlation with one another. The researcher also postulate that different variables inside each nation, such as GDP or carbon emissions, can affect or introduce bias to the outcome variables that serve as predictors. Although it is not possible to quantify the probability of this effect, we can control it by using a fixed-effect model. Instead of a random-effect model, a fixed-effect model would be more appropriate here. The second model has less variance because it estimates the parameter value more efficiently. On the other hand, it may be more biased than the fixed-effect model due to its lack of consistency. The STATA 14.2 program was used to analyze the data from this research.

3.3. Conceptual Framework

The conceptual framework serves as a theoretical model that determines the relationship between variables which guides and help in explaining the results and understanding and evaluating these linkages by arranging the study variables. In this study, the independent variables are economic growth, financial development, new technologies, and renewable energy consumption. The dependent variable is environmental sustainability and the mediating variable is trade openness. The conceptual model of the study is illustrated in Figure 1.

4. Data Analysis

4.1. Descriptive Statistics

The descriptive statistics in Table 2 show the nature of variables employed in a research initiative conducted in the BRICS countries from 2004 to 2023 comprising technological innovation (TINN), energy consumption (EC), financial development (FinD), economic growth (GDP1), CO₂ emission (CO₂), and trade openness (OPP). This holds specific information for each variable, including the number of observations (Obs), mean, standard deviation (Std. Dev.), minimum (Min), and maximum (Max). TINN, another contingency factor indicating the level of technological innovation, has a mean score of 109 with a standard deviation of 67.62, with a minimum value of 1 and a maximum value of 226, which shows that the variation in the number of valuable products is significant. As for the Renewable Energy Consumption (EC) variable, it also reveals a mean of 98. The questionnaire average is 85, and the standard deviation is calculated to be 66.328;as statistical values for the TSC-ES located in the province range from 1 to 214. Depending on the study, financial development (FinD) stands at a mean of 114. This is with a mean of 588 and a standard deviation of approximately 69.28 with values from 1 to 234 observed. The mean economic growth (GDP1) is in the middle of 115. There is a mean employment of 388 and a standard deviation of 68.134, and therefore the range of economic growth within the sample ranges between 1 and 234, which highlights the high variability of this aspect. The average of CO₂ emissions (CO₂) is 101 with CO₂ = 101 t. The mean number of cigarettes is 375, and the SD is 68—062 with an interval of 1 and a maximum of 220. For the variable trade openness (OPP), it is observed that the mean value is 107.879 with a standard deviation of 68 distances or values, which is common with this type of distribution. The Info2 total count was 814, with values between 1 and 227. The co-difficulty and disease confirm the median and standard deviation of each variable describing technological innovation, energy utilization, financial development, GDP growth rate, CO₂ emission, and trade openness of the BRICS countries during the analysis period.

This means that there is a large disparity between the BRICS countries regarding these indicators, which is further supported by the fluctuations that have been observed in these parameters. For instance, in the technological innovation variable (TINN) there is a substantial spread, causing a diversity of innovation activities in these countries. Likewise, the variance in the terms of EC is rather vast and cannot help but show the dissimilarities in energy usage patterns, which prove the more widely varying degrees of industrialization and energy conservation. It is also important to note that the concept of FinD also shows a wide range, which indicates that the financial structures as well as the required facilities in these countries may also be different. GDP1 as a transit coefficient reveals fluctuating trends in the economies of the member countries and their growth rates during the period under analysis.

4.2. Pairwise Correlations

The pairwise correlations in Table 3 illustrate the relationships between the study variables: TINN represented technological innovativeness, EC was renewable energy consumption, FinD was financial development, GDP1 was economic growth, CO₂ was CO₂ emissions, and OPP was trade openness. TINN correlates with a close to zero negative value of −0.072. Looking at the results, TINN has a positive coefficient of 0.678, which means it shares a positive relationship with financial development (FinD). A result of 230 implies a positive correlation between them. On regressing TINN with economic growth (GDP1), the resultant R-value is moderately negative at −0. On the contrary, a score of 245 was observed, reflecting the level of negative correlation. This suggests a fairly positive and weak relationship between TINN and CO₂ emissions, with a correlation coefficient score of 0.238, meaning a relatively small positive correlation exists between the two variables when assessed from a direct perspective. In other words, it is less than perfect, but it is still positive. On the same note, although OPP and TINN have negative coefficients, the strength of their relationship is moderate at −0.260. This paper reveals that renewable energy consumption (EC) has a negative but insignificant qualitative relationship with financial development (FinD) with a correlation coefficient of −0.175 and it has a very low negative relationship with economic growth (GDP1), Est = −0.012. The results indicate a positive relationship. EC stands at 0 with the CO₂ emissions. This has been found to have a small direct relationship with the DEM at 178, and OPP has a small direct correlation with it at 0.319. Therefore, there is evidence of a weak negative link between FinD and GDP1 such that FinD has a coefficient of 0.061 and a trade openness of 20.091 with an insignificant negative estimate of OPP, −0.102. However, GDP at constant prices, economic growth (GDP1), has a moderate negative correlation with CO₂ emissions, representing −0.2342. Thus, we conclude that economic growth and emissions have an inverse relationship. The regression comparison of trade openness (OPP) and GDP1 is positive at 0.365, and from this, it can be inferred that trade liberalism enhances the economic growth rate. These correlations outline how these variables relate, in essence helping to determine the strength of the linkage between the variables in a bid and determine the effect of one variable on another.

Figure 2 presents the time series of technological innovation (TI), financial development (FD), renewable energy consumption (REC), gross domestic product (GDP), CO₂ emissions (CO₂), and trade openness (TR) for the BRICS countries, namely Brazil, China, India, Russia, and South Africa, during the 2004–2023 period. Indeed, the Brazil analysis reveals a positive correlation between TI and CO₂ emissions over time, which may imply that as technology grows, so do emissions. It is also shown that FD, REC, and GDP fluctuate less compared to trade openness. In most provinces of China, TI has been rising gradually since 2010 along with the CO₂ emission rates, while FD and REC exhibit a gradual tendency to ascend, and GDP reveals a continual escalation. Trade openness, however, continues to be low, although it seems to have stabilized. Carrying out the analysis, it can be seen that India exhibits the least fluctuations in most of the variables; however, there are slight positive shifts in the TI and FD, REC and CO₂ are almost constant, and there is a slight rise in GDP. Trade openness remains constant. In Russia, TI and CO₂ emissions have been rising since 2010 with slight fluctuations, and the tendency of FD, REC, and GDP seems rather stable. In South Africa, there is a hint of an increase in TI, but it appears around 2010 and increased CO₂ emissions accompany it. As for FD, REC, and GDP, there is comparatively less fluctuation. These trends generally show that these countries are at different stages of development and have other environmental effects.

4.3. Shapiro–Wilk W Test for Normal Data

Table 4 presents the Shapiro–Wilk W test for normal data, assessing the residuals’ normality in the regression analysis. The table includes 240 observations (Obs), with the W statistic being 0.992, very close to 1, indicating homoscedasticity and an approximate normal distribution of residuals. The V statistic is 0.576, and the z value is −1.213. The probability value (Prob > z) is 0.887, higher than the common significance level of 0.05, thus failing to reject the null hypothesis that the residuals are normally distributed. These results suggest that the assumptions for regression analysis are met, confirming the normality of the residuals and ensuring the validity of statistical inferences derived from the regression analysis.

4.4. Cross-Sectional Independence Test

Table 5 presents the results of two non-parametric tests for cross-sectional independence: Pesaran’s and Friedman’s tests. Both tests evaluate whether the residuals across the cross-sections (BRICS countries) are random. Pesaran’s test yields a statistic of 4.388 with a p-value of 0.0000, indicating that the null hypothesis of cross-sectional independence is rejected, confirming dependence among the residuals. Similarly, Friedman’s test provides a statistic of 37.210 with a p-value of 0.0001, leading to rejecting the null hypothesis and suggesting cross-sectional dependence. These results indicate that the residuals in the regression models for the BRICS countries are not independent, necessitating addressing this cross-sectional dependence in subsequent analyses to ensure valid statistical inferences.

4.5. Testing for Slope Heterogeneity

Table 6 presents the results of the slope heterogeneity test using the method, examining whether the slope coefficients are equal across all BRICS countries. The Delta statistic is 6.394 with a p-value of 0.000, and the adjusted Delta statistic is 7.931, also with a p-value of 0.000. Both p-values are below the 0.05 threshold, leading to the rejection of the null hypothesis that the slope coefficients are constant. This indicates significant slope heterogeneity, meaning that technological innovation, renewable energy consumption, financial development, and economic growth impact CO₂ emissions and environmental sustainability across the BRICS countries. Recognizing and addressing this heterogeneity is crucial to ensure accurate and representative conclusions in the regression analysis, as these variables’ effects differ from country to country.

4.6. Hausman Specification Test

The Hausman test in Table 7 evaluates whether the random-effects model is preferred, assuming that individual effects are uncorrelated with other explanatory variables. With a p-value of 0.004, significantly below the 0.05 threshold, the test provides strong evidence against the null hypothesis, indicating that at least one variable is structurally related to the others. This substantial difference between the fixed-effects and random-effects models suggests that the fixed-effects model is more appropriate for this dataset. Consequently, the Hausman test results recommend using the fixed-effects model to analyze the relationships between variables in the BRICS countries from 2004 to 2023, as it accounts for individual differences and considers that the individual-specific effects influencing the dependent variable are related to the explanatory variables, thereby enhancing the precision and reliability of the estimates.

4.7. Fixed-Effect Models Results

The findings of the fixed-effects model in Table 8 establish the positive correlation of different independent variables with CO₂ emission levels in BRICS nations. In the first model, technological innovation has a positive coefficient, showing that CO₂ emissions increase with technological innovation. Thus, a unit increase in technological innovation increases CO₂ emissions by 0. In conclusion, there is an increase of 2716 CO₂ emissions per 1000 units of GDP. This relationship is highly noteworthy. Likewise, the EC > CO₂ equation reveals a positive and significant impact of renewable energy consumption (EC) on emissions, with a coefficient of 0.385 to support the hypothesis that high uptake of renewable energy sources implies high CO₂ emission. Similarly, financial development (FinD) also has positive and significant coefficients on CO₂ emissions, experiencing a coefficient of 0.2361. On the other hand, GDP negatively influences CO₂ emission (GDP1) with a coefficient of −0. Spearman rank order correlation = −0.237, suggesting that as the rate of economic growth increases, the amount of emissions of CO₂ decreases. The fixed part or the intercept is 55. The slope is constant, which means that its value does not change. Indeed, 63 is very meaningful; the model accounts for 63% of the total. It explains only 2% of the total variance in CO₂ emissions, which is indicated by the R² of 0.632.

In the second model, the factors of trade openness (OPP) are also considered in the analysis. Technological innovation (TINN) in the analysis remains statistically significant and positive with CO₂ emissions, albeit slightly less so, with a coefficient of 0.2545. For EC, the coefficient is positive and remains significantly related to CO₂ emissions; hence, the relationship between renewable energy consumption and CO₂ emissions holds. Financial development (FinD) is still positive and statistically significant, though with a slightly lower proportion coefficient of 0.2135.First, economic growth in terms of GDP1 remains to exert a persistent but strong and statistically significant negative effect on CO₂ emissions with a coefficient of −2418. When adding trade openness (OPP) in this model, it shows a positive but significant sign with CO₂ emission, a = 0. The following is the estimated model: 230. Analysis of automobile insurance in Uganda shows a constant term of 38. Sample 32 must be significant, and the model percentiles show 65%. Consequently, the model has demonstrated an R-squared figure of 0.06%, seeking to explain 6% of the total variance of the CO₂ emissions.

The third model analyzes the direct and indirect relationship between trade openness and technological innovation, the so-called OPPTINN model. However, technological innovation has a slightly less positive effect on CO₂ emission, yet it maintains a positive coefficient of 0.2368. The results indicate that renewable energy consumption (EC) is strongly and significantly correlated with CO₂ emissions as in previous studies; the relevant coefficient is identically 0.385 as in the first model, although some additional layers of complexity are involved in the algorithmic analysis. The second model of the network also indicates a positive and significant connection between CO₂ emission and financial development with a coefficient value of 0.2349. Independent variable (GDP1) CO₂ emissions = 3. Economic growth appears to remain an important determinant of CO₂ emissions, hence the coefficient of −0.2342. The coefficient for the interaction terms for trade openness with technological innovation (OPPTINN) is a significantly large positive value of 0.633. In this case, the CO₂ emissions equal 30,373, which means that when enhancing trade openness and technological innovation, there is a great impact on the rapid increase in CO₂ emissions. The constant is 55. The value of 31 is significant; by using this, it can be noted that 0.03 has been established as an R-squared value, pointing to the fact that this model only explains 3% of the total variance in CO₂ emissions.

In general, the effectiveness of technological innovation, renewable energy consumption, and financial development enhances CO₂ emissions, whereas economic growth has the effectiveness of reducing emissions as signed from the models Vern3, Harn4, and Harn5. In addition, incorporating trade openness and trade openness interacting with technological innovation helps to explain the determination of CO₂ emission in BRICS countries effectively.

The scatter plots in Figure 3 display the residuals from the regression of CO₂ emissions on the independent variables: technological innovation (TINN), renewable energy consumption (EC), financial development (FinD), economic growth (GDP1), and trade openness (OPP), with each plot showing the regression line, coefficients, standard errors, and t-values. The first plot reveals a strong positive correlation between CO₂ emissions and technological innovation, indicated by a coefficient of 0.17165037, a standard error of 0.06510296, and a t-value of 2.64. The second plot shows a significant positive correlation between CO₂ emissions and renewable energy consumption, with a coefficient of 0.26078472, a standard error of 0.06671071, and a t-value of 3.91, suggesting that higher renewable energy use is associated with increased CO₂ emissions, possibly due to the concurrent use of traditional energy sources. The third plot illustrates a very weak and almost non-existent correlation between CO₂ emissions and financial development, with a coefficient of 0.03613442, a standard error of 0.06159836, and a t-value of 0.59. The fourth plot indicates a negative but statistically insignificant relationship between CO₂ emissions and economic growth, with a coefficient of −0.11880412, a standard error of 0.06621193, and a t-value of −1.79, implying that higher economic growth is somewhat related to lower CO₂ emissions. The fifth plot demonstrates a strong negative association between CO₂ emissions and trade openness, with a coefficient of −0.18376557, a standard error of 0.06924467, and a t-value of −2.65, suggesting that countries with greater international trade tend to use more environmentally friendly products and techniques, resulting in lower CO₂ emissions.

The residual plot in Figure 4 illustrates the residuals and fitted values of CO₂ emissions from the linear regression model using the independent variables of the BRICS countries. This plot helps to identify non-linearity, heteroscedasticity, and outliers affecting the regression model’s validity. In the plot, residuals are on the y-axis and fitted values on the x-axis; ideally, residuals should scatter randomly around the y = 0 line, indicating the model’s assumptions hold. Although the plot does not confirm linearity, it reveals some distortion patterns in residuals at different fitted value levels, suggesting heteroscedasticity where residual variation depends on the independent variables’ values. This issue can lead to inefficient regression estimates and invalid hypothesis tests. Additionally, a few residuals lie significantly farther from the majority, indicating the presence of outliers, which can influence the regression outcomes and should be assessed for their impact.

5. Discussion, Conclusions, and Implications

5.1. Discussion

Companies must ensure their production methods are compatible with the new sustainable economy to survive today’s competitive business environment. These findings show that using renewable energy sources, technological innovation, and financial development all have a significant and positive impact on environmental sustainability. Nevertheless, ecological sustainability is significantly and negatively impacted by economic growth. Furthermore, trade openness functions as an important mediator between them. Unlike earlier studies that zeroed in on particular nations or areas, the researchers employed panel data to examine this correlation. The researchers also used several proxy variables inside a single framework to look into how different parts of financial development affected carbon emissions. Therefore, our study adds to the existing empirical evidence on this subject while providing a more holistic view than previous research. Although our results were consistent with some research [1,2,3,4], they differed considerably from other reports [5,6,7,8].

This study’s findings concerning the relationship between patents and patent squares agree with those of [9]. Study after study in China [10], across a wide range of countries [11], and in the context of MENA countries [12] provides strong evidence that technological innovation contributes to a decrease in CO₂ emissions. According to research, green technology advancements significantly reduce CO₂ emissions, especially in economies [13].

Inverse relationships between GDP and CO₂ emissions have been found in several research studies. Sari et al. [14] emphasize the significance of population growth, and evidence for this link is provided by [15,176]. Tao et al. [1] add to the evidence by arguing that GDP per capita and CO₂ emissions are inversely related. Our study’s findings show that renewable energy usage significantly impacts CO₂ emissions. This aligns with earlier studies showing that many variables affect how effective renewable energy is at improving environmental quality [2]. The use of renewable energy sources and reductions in CO₂ emissions are intricately related and situationally dependent. While the use of renewable energy affects CO₂ emissions, the impact is small compared to the economy’s overall growth and the increasing use of non-renewable energy sources [3]. Renewable energy sources may not be as effective in improving the environment as they may be due to technical hurdles that are being investigated [4]. Nonetheless, the environmental and economic benefits of using renewable energy sources, such as lowering CO₂ emissions, have been brought to light by [5,6,7,8,9].

Due in large part to inefficient policies that promote the use of renewable energy sources, the finding shows that commercial activity has a notable and beneficial influence on environmental sustainability. According to earlier studies by Acheampong et al. [10], Guo [11], and Yang et al. [12], national policies determine how commerce affects environmental quality. Developing nations, which comprise the bulk of the countries we looked at, do not have laws that encourage using renewable energy or improve environmental quality.

Carbon dioxide emissions are directly proportional to the degree to which commerce is open; in other words, CO₂ emissions rise with trade openness. Still, there is the possibility that trade openness can lower emissions in the long run. Domestic carbon dioxide (CO₂) emissions are significantly impacted by global trade, which means that they must be included in climate change discussions [13].

The results show that more trade openness mediates the relationship between the variables and helps to improve environmental quality. Our findings are consistent with those of [14] for CIS nations, [15] for Sweden, [176] for India, and [27] for 208 countries globally.

5.2. Conclusions

Using panel data estimate methodology, this study looked at the BRICS nations’ environmental sustainability from 2004 to 2023, looking at how financial development, economic growth, renewable energy usage, and technological innovation affected it. The findings show that using renewable energy sources, technological innovation, and financial development all have a significant and positive impact on environmental sustainability. Nevertheless, ecological sustainability is significantly and negatively impacted by economic growth. Furthermore, trade openness functions as an important mediator between them.

According to the study’s results, CO₂ emissions in BRICS nations are rising, partly due to the increasing weight of financial sector expansion and its parts. These consequences are fascinating in and of themselves. Still, they take on added interest when one considers that a more robust role for the financial sector may be able to lower the existing environmental cost. Using cutting-edge technology initially causes emissions to rise, but they level off after reaching a certain threshold. According to the study’s empirical findings, financial development, renewable energy consumption, and innovation positively affect carbon emissions. Growth in the economy, on the other hand, reduces emissions of carbon dioxide. As a mediator between these characteristics, trade openness also has a crucial influence. This suggests that CO₂ emissions and GDP growth are inversely related. There is a strong association between rising energy consumption and increasing pollution, as shown by the positive coefficient in energy utilization. This suggests that energy use is a major driver of CO₂ emissions in the BRICS countries. If the coefficient of trade openness is positive, trade contributes to the rise in CO₂ emissions. Previous studies have found a correlation between commercial activity and CO₂ emissions, so this makes sense [13,14,15,25,26,27,176]. Even so, trade can help to reduce CO₂ emissions by promoting cleaner production processes and energy-efficient technology. Advanced technology and procedures that help to reduce CO₂ emissions can be more widely disseminated through trade.

Prompting the use of energy-saving measures and promoting the use of renewable energy sources might lead to long-term reductions in the use of fossil fuels and carbon dioxide emissions. Sustainable urbanization solutions should be implemented to lessen the negative effects of urbanization on the environment. These include pushing for greener city planning and transport options [10,11,12]. According to the results of this study, BRICS nations should make reducing CO₂ emissions a top priority by enacting sustainable policies. Investments in green technology and advancing renewable energy sources are two possible ways to do this.

5.3. Implications

A major environmental issue that has gained traction in the last several decades is the steadily rising levels of carbon emissions. World Bank statistics show that carbon emissions per capita have increased dramatically from 4.19 metric tons in 1990 to 4.97 metric tons in the last several years. From the BRICS countries’ point of view, financial development, technological innovation, and energy consumption all have a positive effect on carbon emissions, contrary to the claims of some studies that have shown a negative correlation between financial development and carbon emissions in different countries or regions [177,178,179,180,181].

Governments should consider sustainable economic growth and environmental preservation as they craft energy consumption rules, so that their area can make the most of its renewable energy sources and maximize energy efficiency [182]. This will help to mitigate the global and local environmental impacts of our heavy reliance on traditional energy sources in the long run [183]. There is a two-way street between energy use and international trade; therefore, reducing energy use to save money will have the opposite effect on trade volume. It is believed that commerce with the global community will promote economic growth because of the symbiotic relationship between international trade and revenue. Using the knowledge and experience of the industrialized economies in ecological industrial technology, the BRICS nations can strengthen their renewable energy industries [182].

The government should encourage investments in environmentally friendly technology to reduce CO₂ emissions [184]. It is essential to tighten trade regulations to ensure that companies are operating in an eco-friendly way. Policies that encourage the use of more sustainable production and consumption practices should be put in place. Governments should do more than promote eco-friendly habits; they should also tackle the root sources of carbon dioxide emissions, like our reliance on fossil fuels. To this end, policymakers have considered implementing carbon dioxide (CO₂) taxes and cap-and-trade systems to regulate emissions and facilitate emission trading among businesses.

5.4. Limitations and Suggestions for Future Research

Further investigations can fill in all of the study’s flaws. To begin with, the data presented here only apply to the BRICS nations; however, it is important to remember that individual nations may have varied outcomes. Therefore, future researchers may use this framework to group countries into other groups and see whether they can achieve similar results. Looking at the results from different places is something we think is beneficial. There are other ways to measure environmental quality; one of these is the ecological footprint, which considers a wider range of indicators of human activity. For that reason, it needs to be considered in future studies. When thinking about the connection between innovation and pollution, it is vital to consider more than just financial development, economic expansion, technical innovation, and energy use. This category includes things like green innovation, inclusive finance, high-quality institutions, and the idea of a circular economy.

Only the BRICS countries are included in this study. Additional countries can be included in subsequent research. In addition, researchers may try to reproduce this study in the future to see if there is a correlation between the variables for high-income and middle- to low-income countries. Furthermore, future research must investigate how human capital and institutional quality impact the emissions of greenhouse gases. Future studies may also include the evaluation of the reciprocal effects between factors. To improve the reliability of trade data selection and reduce the impact of missing variables, future studies should consider adding other economic factors like urbanization and carbon emissions to the mix, along with the amount of international trade. Governments must prioritize the use of renewable energy resources and invest in them. Improving environmental sustainability worldwide will be greatly aided by this. Sustainable economic growth and climate change mitigation in these nations can only be achieved by promoting the efficient use of energy resources and ensuring that natural resources are utilized optimally.

Author Contributions

Conceptualization, M.H. and A.H.; Methodology, M.A., J.-Q.L. and M.A.Z.; Software, M.H.; Validation, M.A.Z., U.A.B. and A.H.; Formal analysis, J.-Q.L.; Investigation, M.A.B. and A.H.; Resources, J.-Q.L. and M.A.B.; Data curation, U.A.B. and M.A.B.; Writing—original draft, M.A. and J.-Q.L.; Writing—review & editing, M.A., J.-Q.L., M.A.Z. and M.H.; Visualization, M.A., M.A.Z. and A.H.; Supervision, M.A.Z. and M.H.; Project administration, M.H. and U.A.B. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Conceptual framework.Source: Developed by the researchers.

Figure 2. Line graphs of countries.

Figure 3. Regression diagnostic graphs.

Figure 4. Residuals vs. fitted values.

Table 1

Variables, measurements, and data sources for this study.

Variables	Abb.	Variables Measurement	Data Source
Technological Innovations	TINN	Individuals using the Internet (% of the population)	WDI (World Development Indicators)
Renewable Energy Consumption	EC	% of total final energy consumption	WDI (World Development Indicators)
Financial Development	FinD	ICT service exports (% of service exports, BOP)	WDI (World Development Indicators)
Economic Growth	GDP1	GDP growth (% annual)	WDI (World Development Indicators)
Trade Openness	OPP	Trade as a % of GDP	WDI (World Development Indicators)
Environmental Sustainability	CO₂	CO₂ Emissions (metric tons per capita)	WDI (World Development Indicators)

Source: Developed by the researchers.

Table 2

Descriptive statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
TINN	100	109.117	67.62	1	226
EC	100	98.85	66.328	1	214
FinD	100	114.588	69.28	1	234
GDP1	100	115.388	68.134	1	234
CO₂	100	101.375	68.062	1	220
OPP	100	107.879	68.814	1	227

Table 3

Pairwise correlations.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
(1) TINN	1.000
(2) EC	−0.072	1.000
(3) FinD	0.230	−0.175	1.000
(4) GDP1	−0.245	−0.012	−0.061	1.000
(5) CO₂	0.238	0.178	0.058	−0.234	1.000
(6) OPP	−0.260	0.319	−0.102	0.365	−0.196	1.000

Table 4

Shapiro–Wilk W test for normal data.

Variable	Obs	W	V	z	Prob > z
residuals	100	0.992	0.576	−1.213	0.887

Table 5

Cross-sectional independence test.

Pesaran’s test of cross-sectional independence = 4.388, Pr = 0.0000

Friedman’s test of cross sectional independence = 37.210, Pr = 0.0001

Table 6

Testing for slope heterogeneity.

	Delta		p-Value
	6.394		0.000
adj		7.931		0.000

Variables partially separated out: constant.

Table 7

Hausman specification test.

	Coef.
Chi-square test value	4.622
p-value	0.004

Table 8

Fixed-effect models results.

	(1)	(2)	(3)
Variables	CO₂	CO₂	CO₂
TINN	0.2716 ***	0.2545 ***	0.2368 **
	(0.0481)	(0.0479)	(0.0722)
EC	0.385 ***	0.360 ***	0.385 ***
	(0.0499)	(0.0501)	(0.0499)
FinD	0.2361 ***	0.2135 ***	0.2349 ***
	(0.0449)	(0.0451)	(0.0450)
GDP1	−0.2370 ***	−0.2418 **	−0.2342 ***
	(0.0442)	(0.0436)	(0.0445)
OPP		0.230 ***
		(0.0458)
OPP∗TINN			0.30373 ***
			(0.0575)
Constant	55.63 ***	38.32 ***	55.31 ***
	(9.749)	(11.58)	(9.775)
Observations	100	100	100
R-squared	0.632	0.656	0.633
Number of countries	5	5	5

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

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Abstract

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The long-term development goals of most countries face significant challenges in reducing emissions, improving environmental sustainability, and mitigating the negative effects of climate change. This study looks at how the ecological sustainability of BRICS countries is affected by economic growth, financial development, new technologies, and renewable energy consumption with the mediating effect of trade openness. The study covers the years 2004–2023, and it was based on fixed-effect models that use static panel data. Data were collected from the World Development Indicators website. The countries and time frame for this study were selected on the basis of data availability. These findings show that the use of renewable energy sources, technological innovation, and financial development all have a significant and positive impact on environmental sustainability. Nevertheless, environmental sustainability is significantly and negatively impacted by economic growth. Furthermore, trade openness functions as a significant mediator between them. Based on empirical evidence, the paper suggests that the BRICS nations seek sustainable economic development. Moreover, government agencies need to accurately evaluate the connection between financial development and emission reduction when formulating programs to cut emissions.

Details

Title

Environmental Sustainability in BRICS Economies: The Nexus of Technology Innovation, Economic Growth, Financial Development, and Renewable Energy Consumption

Author

Asif, Muhammad¹

; Jian-Qiao, Li²; Muhammad Azam Zia³; Hashim, Muhammad⁴

; Uzair Aslam Bhatti⁵; Mughair Aslam Bhatti⁶

; Hasnain, Ahmad⁷

¹ School of Media, Hunan University of Science and Engineering, Yongzhou 425199, China; [email protected]
² School of Economics and Management, Hunan University of Science and Engineering, Yongzhou 425199, China
³ Department of Computer Science, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
⁴ Faisalabad Business School, National Textile University Faisalabad, Faisalabad 37610, Pakistan; [email protected]
⁵ School of Information and Communication Engineering, Hainan University, Haikou 570228, China; [email protected]
⁶ School of Geography, Nanjing Normal University, Nanjing 210098, China; [email protected]
⁷ Department of Atmospheric and Oceanic Sciences, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China; [email protected]

First page

6934

Publication year

2024

Publication date