The Growth–Energy–Emissions Trilemma: Evidence from BRICS

The Growth–Energy–Emissions Trilemma: Evidence from BRICS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Growth–Energy–Emissions Trilemma: Evidence from BRICS Sharmiladevi J.C, Arifa Haseen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7317146/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Economic development is a significant factor that indicates the health of a nation. BRICS is one of the fastest-growing regions in the current time period. It is an interesting geographical area to understand the interplay of economic development with other essential elements of development like international capital investment flow and openness to trade. At the same time, in the name of development, how much a nation is sustainable is equally important to create more desirable results in the long term, considering factors like energy utilisation and carbon emission. This study takes account of the above factors, and its objective is to understand the long-term equilibrium relationship among economic development, inward foreign direct investment, openness to trade, fossil fuel energy consumption, and carbon dioxide emission in the BRICS region, considering the data from 1990 to 2022. Data is analysed using the Johansen Fisher Panel Cointegration test and the Dumitrescu Hurlin Panel Causality Test. Results of our study indicate that there exists cointegration among the variables in the long run. Causality study indicates a relationship among fossil fuel energy consumption, economic development and carbon dioxide emission, carbon dioxide emission and inward foreign direct investment. This study emphasizes the need for BRICS countries to implement strong green policies, enhance clean energy use, and invest in technology to ensure sustainable economic growth while reducing environmental damage. economic growth carbon emission BRICS energy utilisation sustainable development inward foreign direct investment fossil fuel energy consumption Trade openness Figures Figure 1 Figure 2 1. INTRODUCTION In the current era, concerns about the impact of climate change are increasing globally, which demands sustainable development initiatives. Energy consumption, emissions, international capital flow, and openness to trade in BRICS nations (an acronym for Brazil, Russia, India, China, and South Africa) are covered in this study to help understand their nexus with economic growth for the period 1990 to 2022. BRICS plays a pivotal role in advancing clean energy technologies through collaborative frameworks and investments. BRICS nations invest heavily in renewable energy, with diverse resources contributing to clean technology development. Few studies are available that examine the economic complexity of its interlinks with international trade, capital flow, economic growth, energy, and emissions in the context of BRICS. Foreign direct investments (FDI) and environmental rhythm in BRICS is always a niche area that demands more research outcomes to better shape trade and foreign investment policies and to establish sustainable, balanced mechanisms that consider social, governance, environmental, and economic issues. India and China continue to maintain their trade relation amidst their strained political relations, Russia's long-standing military involvement with Ukraine is putting pressure on its solidarity, Brazil is reinforcing its identity as a rising economic power, and South Africa is focusing on enhancing its resource utilisation and increasing trade relations within this region. BRICS is on the verge of establishing a multipolar international order by overcoming centuries of underdevelopment and inequalities between the global north and global south. The ongoing border conflicts between China and India, the Israeli-Palestinian conflict, the Ukrainian war, de-dollarisation, and Western country influences are the challenging areas BRICS as an economic bloc must address successfully. Despite internal conflict, the BRICS alliance strives to balance political divergence with economic cooperation in a more fragmented world. As a fast-growing region, this region consumes a lot of energy for fuelling its development. Since energy consumption is the major source of emissions, the need for more sustainable alternatives must be the focus area for sustainability [ 1 ]. BRICS must implement holistic methods in the fields of green energy if they are to realize sustainable economic and environmental harmony [ 2 ]. BRICS energy resource endowment does not determine energy policy unambiguously. In terms of fossil fuels, however, as a group, the BRICS nations remain heavily reliant on them, impeding their progress towards sustainability issues. The study variables are - GDP Per capita, trade openness, inward FDI, fossil fuel energy consumption, and carbon dioxide emission same is mentioned in Fig. 1 . First, this provides the theoretical framework through which economic growth is related to CO₂ emissions. The interrelation of economic growth and carbon dioxide (CO₂) emissions is a complex and multifaceted issue when considering the case of BRICS nations. In the past three decades, these economies have grown tremendously and have progressed from emerging markets to being among the major players on the global stage. Although, this growth has not come without its environmental costs, which often correlate with greater energy use and CO₂ emission increases. Important for understanding the ecological impacts of economic policies and growth trajectories is to understand how these dynamics interact. Historically BRICS nations economic growth is related to industrialization and increased demand of energy. As a result of this environment, we have become affected with serialisation with the fossil fuels that significantly add to the CO₂ emissions. Actually, the expansion of manufacturing sectors that is by domestic and foreign investment itself contribute directly to emissions levels. For instance, with higher industrial activities goes typically higher fossil fuel consumption, which indicates dependency on energy sources in carbon intensive manner. Thus, this study questions how to policy decisions oriented towards growth promotion may lead to an aggravation of the environmental degradation. In the following sub-sections we will investigate the impacts of inward FDI onto environmental outcomes in the BRICS economies. Although FDI can spur a growth, it also poses potential environmental dangers. According to the literature, "The relationship between foreign direct investment and environmental outcomes is complex, it can both aggravate economic activity and seriously hamper environment, particularly probably in terms of CO2 emissions growth" [ 3 ]. They raise fundamental questions about the balance between locating a site that attracts investment, and location that preserves ecological integrity. Subsequently, we will analyze the contribution of trade openness towards economic growth as well as sustainability. This allows us to shed new light on the paradoxical effects of globalization in the BRICS nations through assessing the impact of growing participation in the global markets on the energy consumption patterns and the resulting CO₂ emissions. We will then examine whether trade liberalization increases environmental standards, by lifting pollution standards to our higher standards, or raises pollution, by boosting industrial activities that contribute to increased emissions. The economic development of these countries is directly connected with the increase in emissions caused by energy use, thus highly threatening sustainability. Given that both sustainable management of energy resources and economic development greatly contribute to world emissions [ 4 ], attaining a harmonious balance between these two areas is crucial. The geopolitical risks of global conflicts are major issues in BRICS -Brazil, Russia, India, China, and South Africa. The BRICS countries strongly depend on fossil fuels, particularly oil and coal, and creative climate change mitigation and energy source diversification approaches should be prioritized [ 5 ]. To make these countries green, energy secure, and economically viable in the long run [ 6 ], a shift towards sustainable energy sources is of paramount concern. Through the solution of key challenges and offering astute analyses of energy consumption trends, economic models, and sustainable development initiatives that are relevant to the BRICS countries, this paper increases the robustness of the global economy [ 7 ]. These countries combined release approximately 40% of the world's greenhouse gases, the adoption of sustainable energy policy cannot but be vital [ 8 ]. We will focus on the consumption of fossil fuels as well. However, reliance on fossil energy sources is heavy in BRICS countries raising severe challenges to their environmental sustainability. The dynamics of energy utilization and its link to CO₂ emissions will be understood, with which to articulate effective energy management strategies to conserve the environment. The reliance on fossil fuels has brought about great consequences, with CO₂ emissions being one of them. A common characteristic of high fossil fuel consumption is the utilization of them for the reason that they are all indicative of a critical nexus where economic growth coexists with environmental degradation. In BRICS nations, as [ 9 ] noted "economic growth in Saudi Arabia is linked to energy consumption and thus higher carbon emission". The BRICS countries that usually have the similar development paths coinciding with higher energy consumption are not a monopoly of Saudi Arabia. Clearly the implication is that without massive movements to alternative energy sources, efforts at stimulating economic growth will actually increase carbon emissions and put the world even more at risk toward climate change. Energy policies of some governments in BRICS countries determine how fossil fuels are consumed. Therefore, to minimize adverse environmental impact, regulations for the use of cleaner energy alternatives must be adopted. BRICS countries are beginning to address the policies for promoting the development of renewable energy and they have realized that "the environmental impacts of FDI in the mining sector of Chile and Peru are complex and contingent on circumstances," as in the case of different rules among BRICS countries. These policies differ drastically in terms of strength and consistency, thus affecting how countries switch off fossil fuels. For instance, China has invested heavily in renewable energy and electric vehicles, however, the rest of the nation is incapable of keeping pace with this adoption because of the political or financial constraints. The other factor that influences fossil fuel utilization and emissions rates of the BRICS nations is technological advancement. Energy efficiency technologies help minimizing consumption without giving up their economic growth. Emission cuts of vast amounts can be obtained by utilizing enhanced energy management system in using fossil fuels in industries. While technological advancements have taken place, there are questions surrounding the application of the technological advancements with regards to combining harmony with sustainable practices. This synergy can be enhanced by improvements in the environmental friendliness of the research and development in cleaner technologies, which in turn can have major long run economic and environmental benefits. However, the socioeconomic forces influencing the fuel consumption of fossil fuel and the respective energy policies differed from one BRICS country to the other. Each nation's energy consumption is driven by how they are affected by economic disparities, levels of industrialization and social equity. For example, India's energy policies include a challenge of widespread poverty and energy access problems that must be complemented with economic growth and achieving climate change targets. However, understanding the socio-economic conditions of these countries help explain why some countries may depart in energy consumption patterns and policies and hence different country emission levels. BRICS countries' consumption of fossil fuel is also strongly influenced by international climate agreements. The necessity of membership of global accords, for example the Paris Agreement, entices nations into making commitments to lower their greenhouse gas emissions. But the usefulness of these treaties depends also on how individual countries will tie them into national policies. Such a commitment can fail not only where competing domestic interests exist, regardless of the geostrategic sensitivity of the country, but also especially so in the developing contexts where energy security is still a key issue. The international framework is naturally ambiguous in how it influences national level energy policies and has an inherent tendency to meet with difficult political and economic challenges. Finally, this paper seeks to investigate the relationship between economic growth, energy usage, and CO₂ emissions and FDI, trade openness, and fossil fuel use in the BRICS in detail. This aims to provide comprehensive knowledge of the interconnected themes and thereby aim to provide inputs for effective research direction regarding these sustainability challenges around which the economies of these important global economies are being transformed. A trip through the economic spheres and environmental conditions in the BRICS nations guarantee important understanding of sustainable development routes by balancing economic targets and corporate ecological liabilities. 2. RESEARCH OBJECTIVES To examine the relationship between economic growth, energy utilization, and CO₂ emissions in BRICS nations. To provide policy recommendations for sustainable economic growth in BRICS nations based on empirical findings. 3. LITERATURE REVIEW 3.1 Economic Growth The effects of global climate change are seen in societies and the environment, like many unusual weather events, increasing sea levels and a decrease in food we can produce [ 10 ]. In 2024, the countries of BRICS together emitted about 51.76% of world CO₂ emissions. BRICS members in 2019 released about 14.759 billion tons of CO₂ emissions, which equals about 43.19% of the world's CO₂ emissions. There is a strongly established relationship between economic growth and carbon emissions, particularly in the short term, since industrial processes and energy consumption are central contributors to high emissions [ 11 ]. Investment in renewable energy projects and learning programs has paid off in the long term to reduce carbon emissions, as it maintains development [ 12 ]. The BRICS nations are confronted with higher emission levels because of industrialization, population, and energy intensity [ 13 ]. 3.2 Foreign Direct Investment [ 14 ] found that FDI affects economic growth differently in different economic contexts, so FDI leads to different CO2 emission results because of how it affects the sharing of knowledge. Multinational companies may manage their CO2 emissions differently by sharing non-clean or eco-friendly technologies in different countries [ 15 ]. The environmental Kuznets curve (EKC) is a hypothesized relationship between various indicators of environmental degradation and per capita income. [ 16 ] shows the inverted U structure relationship between economic growth and carbon emissions, which may indicate environmental Kuznets Curve (EKC) hypothesis particularly in presence of foreign direct investment inflow into CO2 emissions with respect to the BRICS countries. On the one hand, FDI can spurn economic growth and launch technological progress, on the other hand, it can be coincided with environmental problems. In a study of [ 17 ] it is shown that increased environmental regulatory pressure significantly increases the likelihood of foreign divestment. FDI's spatial spillover effects are significant since they increase CO2 emissions in surrounding areas while simultaneously decreasing them in surrounding areas [ 18 ]. [ 19 ] looked into the link between FDI and CO2 emissions and concluded that an increase in FDI usually leads to more environmental damage. 3.3 Trade Openness The degree to which an economy is more or less open to trade with other economies around the world is referred to as its trade openness. Growing the scale of industries, which ultimately results in higher pollution, is one of the ways that it assists nations in growing their exports, which in turn helps them to improve their domestic output [ 20 ]. [ 21 ] discussed the relationship between pollution and trade openness is favorable for nations with low incomes, whereas it is negative for countries with high and intermediate incomes. Research by [ 22 ] has shown that trade openness plays a big role in driving CO2 emissions among the key macroeconomics variables. Earlier papers in the field have used standard approaches to measure trade openness. While trade liberalization brings new economic opportunities, it also sees trade liberalization as promoting environmental degradation on the one hand and technology transfer and more sustainable practices in BRICS countries on the other hand [ 23 ]. Thus, this means that informed trade agreements can be used to facilitate for sustainable development and there should be more incentives in BRICS nations for technologies to be adopted in ways that are cleaner with economic competitiveness [ 24 ]. 3.4 Fossil Fuel Energy Consumption There have been numerous BRICS nations who have taken the initiative to launch projects with the objective of transitioning towards a low-carbon economy [ 25 ]. [ 26 ] has predicated on the belief that moving to other renewable energy sources from conventional fossil fuels will help to revolutionize the world economy. [ 27 ] start looking into energy investments in G20 countries and urge lawmakers to think about a variety of energy sources when making environmental rules. This bigger picture sets the stage for [ 28 ] more detailed study of how different factors affect the health of the environment in the BRICS economies in a way that is both changing and not the same. Their study connects the big ideas of technical progress and environmental effects, giving us useful information for making policy changes that consider the many factors that affect the quality of the environment. Efficient energy usage is generating positive change within the BRICS countries' carbon emissions landscape, consistent with the findings of [ 29 ] and their investigation of the trilemma relationship between energy consumption, carbon emissions, and economic growth. Optimizing energy use patterns, reducing waste, and transitioning to cleaner energy sources all contribute to dramatically lowering the overall carbon footprint [ 30 ]. 4. METHODOLOGY This research methodology employs a rigorous econometric framework for panel data analysis. It begins with unit root tests to assess stationarity, followed by cointegration tests to identify long-run relationships, and Panel Granger causality tests explore directional influences. Stability diagnostics and robustness checks ensure the reliability and validity of empirical findings across time and cross-sections. the econometric analysis adopted in this research shown in Fig. 2 . GDP𝑖𝑡 = 𝑓(TOP, IFDI, 𝐹FEC, CO 2 ) -------------------------------------------------------------------(1) Panel Cointegration Equation DGDP𝑖𝑡 = 𝛽0 + 𝛽1DTOP𝑖𝑡 + 𝛽2DIFDI𝑖𝑡 + 𝛽3DFFEC𝑖𝑡 + 𝛽4CO 2 𝑖𝑡 + 𝜀𝑖𝑡 ----------------- (2) 5. DATA ANALYSIS 5.1 Unit root test After ensuring stationarity and order of integration, the data is analysed. Descriptive statistics for the data are shown in Table 1 . From this table, we can understand that the data is normally distributed. First generation Panel unit root test is conducted to understand the presence of unit roots. Test statistics summary of Fisher ADF, Fisher PP, Im Pesaran & Shin, Levin Lin & Chu are checked and results are shown in Table 1 . These tests are widely known and popularly used in multiple literatures in Panel data unit root testing procedures. Table 1 First Generation Unit Root Test Variable Fisher -ADF Fisher-PP IPS LLC I(0) I(1) I(0) I(1) I(0) I(1) I(0) I(1) GDPPC 1.77 (0.99) 30.9 (0.00) 1.19 (0.99) 51.4 (0.00) 4.96 (1.00) -3.20 (0.00) 4.48 (1.00) -3.17 (0.00) TOP 10.87 (0.36) 242.5 (0.00) 9.87 (0.45) 93.09 (0.00) -0.24 (0.40) -8.53 (0.00) -0.04 (0.48) -8.11 (0.00) IFDI 20.5 (0.02) 32.3 (0.00) -1.89 (0.02) -1.88 (0.02) FFEC 4.42 (0.92) 45.76 (0.00) 5.02 (0.88) 203.0 (0.00) 1.71 (0.95) -5.26 (0.00) 0.69 (0.75) -1.10 (0.01) CO2 2.08 (0.99) 27.4 (0.00 1.31 (0.99) 88.6 (0.00 4.16 (1.00) 4.95 (1.00) -3.88 (0.00) Source: Authors’ own 5.2 Cross-Sectional Dependency Test It is conventional that countries in an economic union can exhibit similar socio-economic characteristics; also, such economies depend upon one another for gaining mutual benefits. As a result of this, these countries tend to depend cross-sectionally. While analyzing such economies, it is essential to check for the existence of cross-sectional dependencies due to the interactions of the variables examined, so as to check for bias [ 31 ]. The first-generation unit root tests may give biased results when there exists cross-sectional dependence. Two second-generation panel unit root tests, cross-section ADF (CADF) and cross-sectionally augmented IPS (CIPS), are used. Both tests were developed by [ 32 ]. As a consequence, two root unit tests of [ 32 ] of the second generation are being performed: ADF-cross section (CADF) and IPS cross-sectional increment (CIPS). The procedure for analysing cross-sectional dependency is expressed in Eq. (3). Where T denotes the time period, N is the sample size, and 𝑃𝑖𝑗 is the sample estimate of correlation errors for each cross-section of country i and j, defined as follows in Eq. (4). $$\:CSD=\sqrt[\:]{\frac{2T}{N\left(N-1\right)}}\left(\:{{\sum\:}_{i=1}^{N-1\:\:\:\:}\:\:{\sum\:}_{j=i+1}^{\:N\:\:\:\:}\:\:\:\:\:P}_{ij}^{{\prime\:}}\right)\Rightarrow\:N\left(\text{0,1}\right)-----\left(3\right)$$ T signifies the time, N denotes the sample size, and 𝑃𝑖𝑗 denotes the correlation error sample estimate for each cross-section of the country i and j as specified in Equation ___. $$\:{P}_{ij}^{{\prime\:}}=\frac{{\sum\:}_{i=1}^{T}{u}_{it}{\mu\:}_{jt}}{{\left({\sum\:}_{t=1}^{T}{u}_{it}^{2}\right)}^{1/2}{\left({\sum\:}_{t=1}^{T}{u}_{jt}^{2}\right)}^{1/2}\:\:\:\:\:\:\:\:\:\:\:\:\:\:}-----\left(4\right)$$ From Table 2 we can understand that there exist cross-sectional dependency among the variables. Table 2 Cross-Sectional Dependency Test Test Statistics Prob. Breuch-Pagan LM 106.0 0.00 Pesaran scaled LM 21.4 0.00 Pesaran CD 7.40 0.00 Source: Authors’ own Results of the second-generation unit root test are given in Table 3 . Table 3 Second Generation Unit Root Test Variable CADF CIPS I(0) I(1) I(0) I(1) GDPPC 0.12 (0.90) -1.81 ( 0.10) -2.69 (0.05) IFDI 1.19 (0.21) 9.16 (0.00) -3.32 (< 0.01) -3.82 ( = 0.10) -4.35 ( = 0.10) -2.90 (< 0.05) Source: Authors’ own As shown above, all the variables except CO2 are in I(1) integration. After validating that all the variables in this study are I(0) and I(1), the study applies four types of panel cointegration tests to determine the existence of a long-run linkage between the variables: [ 33 ], [ 34 ], and [ 35 ]. Table 4 Pedroni Cointegration Test Test Statistics Statistics Prob. Within-dimension (homogeneous) Panel v 0.944 0.17 Panel rho -1.509 0.05 Panel PP -3.810 0.00 Panel ADF -3.85 0.00 Between-dimension(heterogenous) Group rho -0.835 0.20 Group PP -4.758 0.00 Group ADF -4.681 0.00 Source: Authors’ own As shown in Table 4 , out of the seven statistics, three out of the four within-dimension statistics reject the null hypothesis of no cointegration, and two out of the three between-dimension statistics also reject the null hypothesis of no cointegration. Table 5 shows the output of Johansen-Fisher’s Cointegration test. Trace test and Maximum Eigen Value statistics indicate the presence of a maximum of four and three cointegrating equations, respectively. This result further strengthens the presence of long-term cointegration relations among the tested variables in BRICS. Table 5 Johansen Fisher Panel Cointegration Test Test Fisher Statistics (from Trace test) Prob. Fisher Statistics (from Max Eigen Value) Prob. None 75.24 0.00 48.54 0.00 At most 1 37.52 0.00 27.03 0.00 At most 2 19.40 0.03 11.20 0.34 At most 3 15.23 0.12 11.34 0.33 At most 4 17.29 0.05 17.29 0.05 Source: Authors’ own Table 6 indicates the causality test results. Dumitrescu Hurlin Panel Causality test scores indicate that economic growth is causing CO2 emission, and IFDI also causes CO2 emission in the BRICS context. Table 6 Dumitrescu Hurlin Panel Causality Test Null Hypothesis W-Stat Zbar-Stat Prob. TOP does not homogeneously cause GDP-PC GDP-PC does not homogeneously cause TOP 1.39820 1.92882 -0.73128 -0.23105 0.4646 0.8173 IFDI does not homogeneously cause GDP-PC GDP-PC does not homogeneously cause IFDI 3.71406 2.60069 1.41711 0.38260 0.1565 0.7020 FFEC does not homogeneously cause GDP-PC GDP-PC does not homogeneously cause FFEC 0.74919 1.50249 -1.34311 -0.63296 0.1792 0.5268 CO2 does not homogeneously cause GDP-PC GDP-PC does not homogeneously cause CO2 3.62844 22.9499 1.37121 19.5860 0.1703 0.0000 IFDI does not homogeneously cause TOP TOP does not homogeneously cause IFDI 2.52063 3.08637 0.30822 0.83388 0.7579 0.4043 FFEC does not Homogeneously cause TOP TOP does not homogeneously Cause FFEC 1.18286 1.84074 -0.93429 -0.31408 0.3502 0.7535 CO2 does not homogeneously Cause TOP TOP does not homogeneously cause CO2 3.74661 1.99203 1.48261 -0.17147 0.1382 0.8639 FFEC does not homogeneously cause IFDI IFDI does not homogeneously cause FFEC 2.46921 3.52783 0.26044 1.24407 0.7945 0.2135 CO2 does not homogeneously cause IFDI IFDI does not homogeneously cause CO2 1.84301 4.17835 -0.32141 1.84851 0.7479 0.0645 CO2 does not Homogeneously cause FFEC FFEC does not homogeneously cause CO2 1.52748 0.6361 -0.60941 -1.41437 0.5423 0.1573 Source: Authors’ own 6. DATA INTERPRETATION, RESULTS AND DISCUSSION The results of the data analysis indicate that the dependent variable, GDP, has a long-term cointegrating relationship among the dependent variables. We found one cointegrating equation for BRICS nations, which indicates that all the variables in these countries have an equilibrium relationship with one another in the long run. Since all BRICS nations are in the fast development phase, this long-term equilibrium relationship is relevant. However, in the short term, the results are different. Trace test statistics and max eigenvalues are not similar for all the BRICS nations. This can be because the stages of development in each of the BRICS nations are different. China is much ahead in developing technology, telecommunication, electronics and electricals, whereas India is strongly heading towards the roots of economic development. Even though these two countries are neighbors situated in the Asian region, the political systems of these two nations are different, which indicates the reason for the different facets of economic and social development. Russia situation in the Eurasian region is experiencing a different dimension of social, political and economic development when compared with China and India. Russia is more advanced in certain areas of development, including aviation, defense technology, and communication. However, the recent geopolitical disturbance has a major role in this nation's economic activities. Brazil and South Africa focus more on agrarian and related activities for achieving economic development. Though the economic profile of these two nations is not the same, the time of attaining independence, the natural resources they are bestowed with, and the socio-political arrangement match the results of our study. They are moving into highly paced industrial development in recent times. The results of the data analysis indicate that the dependent variable, GDP, has a long-term cointegrating relationship with other variables, such as trade openness, inward FDI, fossil fuel energy consumption, and carbon dioxide emission. After checking for cross-sectional dependency, first and second-generation unit root tests were conducted to identify and ensure the order of integration of variables. The variables are integrated of the order I(0) and I(1). Panel Cointegration test was done to identify the long-term equilibrium relation among the variables. From Trace test and Maximum Eigen value statistics, we came to know the presence of cointegrating relations among the variables that ensure the existence of long-term equilibrium relations among the test variables in the case of BRICS. Since all BRICS nations are in the fast development phase, this long-term equilibrium relationship is relevant. This result goes in line with [ 36 ]; [ 16 ]; [ 23 ]. After establishing the presence of cointegration among the variables, causality is checked using the Dumitrescu-Hurlin Panel Causality Test. Results of the causality test indicate that there exists unidirectional causality among GDP per capita and CO2 emission at the 5 per cent level of significance, and between inward FDI and CO2 emission at the 10 per cent level of significance. 7. CONCLUSION & POLICY IMPLICATIONS AND DIRECTION FOR FUTURE RESEARCH In conclusion, this sub-section combines the results obtained from this study in relation to the explanation of energy consumption, economic growth and CO₂ emissions in BRICS countries and the effects of the latter under diverse circumstances that FDI effects, reducing C02 and using non-conventional energy resources. The variables analysed provided an outline of the sophisticated equilibrium that must be reached between economic development and environmental sustainability in these economies in a time of flux. Thus, the entire results received reveal that the level of energy consumption and CO₂ emissions is consistently correlated to the percentage for economic growth in BRICS countries, where the latter dynamics displayed to be manifested because of certain factors moderation. FDI is prominent and in most cases linked to emission levels as being high and correlates positively with emission. It is shown that increased environmental regulatory pressure has a large effect on the foreign divestment [ 17 ]. The BRICS nations must provide utmost priority to establishing green energy ventures, environmentally conscious policies, and joint global action that will reduce their carbon footprint while ensuring economic growth [ 37 ]. Favourable studies [ 38 ]; [ 16 ]; [ 23 ]; [ 36 ] highlight that South Asian and BRICS nations should enhance growth, promote green FDI, reduce fossil fuel use, and adopt low-carbon technologies, as trade openness can lower CO₂ emissions. In contrast, [ 18 ] find FDI raises emissions in host regions but offers environmental benefits to neighbouring areas through spatial spillovers. Over the past 20 years, the BRICS have seen both their FDI flows and their share in world flows significantly rise. This is true of all BRICS countries, including considering China's weight and to a lesser degree India's global investment destinations. Apart from global policy coordination, the BRICS nations have embraced several significant projects to enhance cooperation on intra-group investment promotion in recent years. Particularly in investment facilitation, climate change, and funding in sustainable development, these projects address several important policy concerns. Among these projects are the Outlines for BRICS Investment Facilitation (2017), the BRICS MoU Trade and Investment Promotion (2019), the Strategy for BRICS Economic Partnership 2025 (2020), Intra-BRICS Cooperation for Continuity, Consolidation and Consensus (2021), and the Initiative on Trade and Investment for Sustainable Development (2022) initiatives for enhancing cooperation on pushing intra-group funding in more recent times [ 39 ]. The average (inward) FDI stock, as a proportion of GDP, increased from 20% in 2011 to 27% for the BRICS as a bloc. From 12 per cent in China and 16 per cent in India to 37 per cent in Brazil and 41 per cent in South Africa, the FDI stock to GDP ratio differs greatly at an individual economy level within the BRICS. At 29% Russia's inward FDI stock/GDP ratio is closest to the BRICS average. The contribution of FDI to Gross Fixed Capital Formation (GFCF) shows that, despite regional differences, FDI is clearly a key and rising factor in economic growth in all of the BRICS. With above 10% since the group's founding and approaching more than 22% in 2019, Brazil historically boasts the highest percentage of FDI inflows to GFCF. Brazil has set very high goals for the environment. Their main goal is to have net-zero pollution by 2050. The nation's goal is to reduce emissions by 59–67% from the levels observed in 2005 by 2035. The unwavering dedication of the Russian Federation to attain net-zero greenhouse gas emissions by the year 2060 persists, despite its significant reliance on fossil fuel resources. It is anticipated that Russia will accomplish this objective via a bifurcated strategy that includes considerable investments in carbon capture, utilization, and storage (CCUS) technologies, in coincidence with enhancements in energy efficiency. Russia's Government Approves New Energy Strategy until 2050 [ 40 ]. India has made a lot of progress in getting foreign direct investment. For example, in the first half of the current fiscal year, $ 42.1 billion came in, and since April 2000, the country has received a total of $ 1 trillion. Key factors have been things like a more competitive global market, a thriving innovation ecosystem, and a business-friendly climate. The country's proactive approach is shown by programs like "Make in India," the loosening of sectoral rules, and recent changes to policies that allow more foreign direct investment in the space industry. India is in a good position to play a bigger role in the world by following global economic trends. This would help the country expand and develop in a way that is good for everyone. India has emerged as a significant hub for investments in environmental sustainability, primarily attributable to the backing from governmental institutions. With approximately 75% FDI to GFCF ratio in the past year, India's participation is somewhat higher than the world average of 5.7%. China aspires to eradicate all carbon emissions by the year 2030 and attain carbon neutrality by 2060 by promoting multiple initiatives oriented towards clean energy solutions and alternative energy utilizations. In the sustainable technologies sector, the nation has implemented stringent environmental regulations and expanded opportunities for foreign direct investment. China is the only BRICS nation whose FDI inflows to GFCF ratio has been regularly below the world average. This indicates that domestic investment is more vital in China's economy than FDI. China's overall GFCF in 2021, for instance, was $ 7.2 trillion; in the United States, with the second biggest GFCF, it came at $ 4.9 trillion. The Combined Resource Plan for South Africa shows that the country is moving away from coal and going towards green energy sources. The dual objectives of this transition are to reduce emissions and improve energy security. South Africa often does not surpass 10% in this regard. With 6.7% of FDI inflows to GFCF over the past three years. Governments of all BRICS nations must intensify their efforts to spark the growth of renewable energy projects, forge sturdy regulatory frameworks, and embrace innovative strategies that intertwine environmental guardianship with economic flourishing [ 41 ]. The BRICS nations must provide utmost priority for establishing green energy ventures, environmentally conscious policies, and joint global action that will reduce their carbon footprint while ensuring economic growth [ 37 ]. Future explorations ought to investigate the patterns of energy usage across various sectors, uncover effective global cooperation that can nurture sustainability, and assess the influence of policies in this domain [ 5 ]. As the data available is only up to 2022, we need to restrict our study to this time period, which becomes one of this study's limitations. Future studies can estimate the effect of each of the above dependent variables on GDP to understand and identify the relationship between financial growth and urbanization with increased emissions, considering the necessity for green energy to reverse these adverse effects [ 37 ]. More studies should be taken with new members of BRICS i.e Egypt, Ethiopia, Indonesia, Iran and United Arab Emirates with the special influence of agricultural value added, absorptive capacity and labour productivity. Declarations Declaration of Competing Interest: The authors declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding: No funding or grants were received to assist with the preparation of this manuscript. Ethics approval and Consent to Participate: Not applicable Acknowledgement: Not applicable Clinical Trial: Not applicable Author Contribution Dr. Sharmiladevi J.C. (Corresponding Author): Conceptualized the research framework, designed the methodology, conducted econometric analysis including unit root tests, cointegration analysis, and causality testing, interpreted results, developed policy recommendations, and led the manuscript writing and revision process.Arifa Haseen: Conducted a comprehensive literature review, performed data collection and preliminary analysis, contributed to the theoretical framework development, and assisted in manuscript preparation and formatting.Both authors have read and approved the final manuscript and take responsibility for the integrity and accuracy of the research presented. Acknowledgement NA Data availability: This research used only secondary data available in public domain from authentic sources. Data used for this study can be available on request to the authors. Consent for Publication: Authors give their consent to this publication. Conflict of Interest/Competing Interest: The authors do not have any relevant financial or non-financial interests to disclose. References Zardoub, A. (2024). Impact of economic growth, renewable energy consumption and energy intensity on CO2 emissions in BRIC countries: An application of CS-ARDL. Journal of Infrastructure, Policy and Development , 8(8), 4312. Asif, M., Li, J. Q., Zia, M. A., Hashim, M., Bhatti, U. A., Bhatti, M. A., & Hasnain, A. (2024). Environmental sustainability in BRICS economies: The nexus of technology innovation, economic growth, financial development, and renewable energy consumption. Sustainability , 16(16), 6934. Acharyya, J. (2009). FDI, growth and the environment: Evidence from India on CO2 emission during the last two decades. Journal of Economic Development , 34(1), 43. United Nations Environment Programme. 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The impact of economic growth, trade openness and manufacturing on CO2 emissions in India: an autoregressive distributive lag (ARDL) bounds test approach. Journal of Economics, Finance and Administrative Science , 26(52), 376-389. Shekhawat, K. K., Yadav, A. K., Sanu, M. S., & Kumar, P. (2022). Key drivers of consumption-based carbon emissions: empirical evidence from SAARC countries. Environmental Science and Pollution Research , 29(16), 23206-23224. Dalei, N. N., & Roy, H. (2021). The empirical relationship between carbon emission and energy use of BRICS nations. Journal of Public Affairs , 21(1), e2154. Sun, Y., Gao, P., Tian, W., & Guan, W. (2023). Green innovation for resource efficiency and sustainability: Empirical analysis and policy. Resources Policy , 81, 103369. Ajide, K. B., & Mesagan, E. P. (2022). Heterogeneous analysis of pollution abatement via renewable and non-renewable energy: lessons from investment in G20 nations. Environmental Science and Pollution Research , 29(24), 36533-36546. Ibrahim, R. L., & Ajide, K. B. (2021). The dynamic heterogeneous impacts of nonrenewable energy, trade openness, total natural resource rents, financial development and regulatory quality on environmental quality: Evidence from BRICS economies. Resources Policy , 74, 102251. Nawaz, M. A., Hussain, M. S., Kamran, H. W., Ehsanullah, S., Maheen, R., & Shair, F. (2021). Trilemma asAsociation of energy consumption, carbon emission, and economic growth of BRICS and OECD regions: quantile regression estimation. Environmental Science and Pollution Research , 28, 16014-16028. Pata, U. K. (2021). Linking renewable energy, globalization, agriculture, CO2 emissions and ecological footprint in BRIC countries: A sustainability perspective. Renewable Energy , 173, 197-208. Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies , 47(1), 239-253; Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross‐section dependence. Journal of Applied Econometrics , 22(2), 265-312. Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross‐section dependence. Journal of Applied Econometrics , 22(2), 265-312. Pedroni, P. (2001). Fully modified OLS for heterogeneous cointegrated panels. In Nonstationary panels, panel cointegration, and dynamic panels (pp. 93-130). Emerald Group Publishing Limited. Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics , 90(1), 1-44. Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control , 12(2-3), 231-254. Aye, G. C., & Edoja, P. E. (2017). Effect of economic growth on CO2 emission in developing countries: Evidence from a dynamic panel threshold model. Cogent Economics & Finance , 5(1), 1379239. Ul-Haq, J., Visas, H., Umair, M., Hye, Q. M. A., & Khanum, S. (2024). Toward sustainable development: Exploring the relationship between economic fitness and carbon emissions in BRICS. Sustainable Futures , 7, 100226. Ozturk, I., Farooq, S., Majeed, M. T., & Skare, M. (2024). An empirical investigation of financial development and ecological footprint in South Asia: Bridging the EKC and pollution haven hypotheses. Geoscience Frontiers , 15(4), 101588. United Nations Conference on Trade and Development. (2023). World investment report 2023: Investing in sustainable energy for all (UNCTAD/DIAE/2023/1). United Nations. https://unctad.org/system/files/official-document/diae2023d1_en.pdf Net zero targets. (n.d.). Retrieved May 22, 2025, from https://climateactiontracker.org/countries/russian-federation/net-zero-targets/; Russia's government approves new energy strategy until 2050 | Enerdata. (2025, April 16). https://www.enerdata.net/publications/daily-energy-news/russias-government-approves-new-energy-strategy-until-2050.html World Bank. (2023). Bosnia and Herzegovina economic update, Fall 2023 (Report No. 186593). https://documents1.worldbank.org/curated/en/099030009272214630/pdf/BOSIB0db37c9aa05a0961a08a83a0ea76ea.pdf Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7317146","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504503209,"identity":"e343c3cc-3247-4631-9bc3-e12e0644ca10","order_by":0,"name":"Sharmiladevi J.C","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYDACCRBhYMHAcACIPqBIMePVIgHWcnAG8VoYIFqYeYhxl/zs5mefbhRIyPMd7zE8bPPHLlp+do/Zgw8MdvIM7LwHsGkxuHPMeHaOgYThzDNnDA7ntiXnbrhzxtxwBkOyYQMzXwJWLRIJxsxALYwbbqQlHM5tOJC7QSLHTJqHgTkB6E4DrA6bkf4ZpMV+w/1nCYct/hzInT8DqOUPQz1OLQw3csC2JG64wXzgMAPbgdyGG0AtDAyHcWoxuJFTDNKSPPNM8oGDvWC/HCuT7DE4btiG22GbmXP+2Nj2HT/Y/OHHH7vc+bObt0n8qKiW5+c/g91hmAASuQwMbESqh2kZBaNgFIyCUYAAAMz5Xq4eH6qIAAAAAElFTkSuQmCC","orcid":"","institution":"Symbiosis International (Deemed University","correspondingAuthor":true,"prefix":"","firstName":"Sharmiladevi","middleName":"","lastName":"J.C","suffix":""},{"id":504503212,"identity":"1fe9ed16-91d0-4401-b4bc-6a4cd47d89da","order_by":1,"name":"Arifa Haseen","email":"","orcid":"","institution":"Symbiosis International (Deemed University","correspondingAuthor":false,"prefix":"","firstName":"Arifa","middleName":"","lastName":"Haseen","suffix":""}],"badges":[],"createdAt":"2025-08-07 09:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7317146/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7317146/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89831722,"identity":"37e96111-a3a0-4122-a5e4-d7732cb4acd7","added_by":"auto","created_at":"2025-08-25 13:43:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27565,"visible":true,"origin":"","legend":"\u003cp\u003eVariables\u003c/p\u003e\n\u003cp\u003eSource: by authour\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7317146/v1/33308a1d224bd00821cb5cd1.png"},{"id":89831721,"identity":"d1b5802d-8ae0-4a52-9901-cd5d85122ae0","added_by":"auto","created_at":"2025-08-25 13:43:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33805,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMethodological Sequence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Authors’ own\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7317146/v1/1366ecc098aa56bfd4528504.png"},{"id":91879209,"identity":"2b10995c-7e9d-4c56-a061-19232bc2c75b","added_by":"auto","created_at":"2025-09-22 14:48:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":993133,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7317146/v1/e20d97ff-31d8-468a-a168-9f968290b289.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Growth–Energy–Emissions Trilemma: Evidence from BRICS","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eIn the current era, concerns about the impact of climate change are increasing globally, which demands sustainable development initiatives. Energy consumption, emissions, international capital flow, and openness to trade in BRICS nations (an acronym for Brazil, Russia, India, China, and South Africa) are covered in this study to help understand their nexus with economic growth for the period 1990 to 2022.\u003c/p\u003e\u003cp\u003eBRICS plays a pivotal role in advancing clean energy technologies through collaborative frameworks and investments. BRICS nations invest heavily in renewable energy, with diverse resources contributing to clean technology development. Few studies are available that examine the economic complexity of its interlinks with international trade, capital flow, economic growth, energy, and emissions in the context of BRICS. Foreign direct investments (FDI) and environmental rhythm in BRICS is always a niche area that demands more research outcomes to better shape trade and foreign investment policies and to establish sustainable, balanced mechanisms that consider social, governance, environmental, and economic issues.\u003c/p\u003e\u003cp\u003eIndia and China continue to maintain their trade relation amidst their strained political relations, Russia's long-standing military involvement with Ukraine is putting pressure on its solidarity, Brazil is reinforcing its identity as a rising economic power, and South Africa is focusing on enhancing its resource utilisation and increasing trade relations within this region. BRICS is on the verge of establishing a multipolar international order by overcoming centuries of underdevelopment and inequalities between the global north and global south. The ongoing border conflicts between China and India, the Israeli-Palestinian conflict, the Ukrainian war, de-dollarisation, and Western country influences are the challenging areas BRICS as an economic bloc must address successfully. Despite internal conflict, the BRICS alliance strives to balance political divergence with economic cooperation in a more fragmented world. As a fast-growing region, this region consumes a lot of energy for fuelling its development. Since energy consumption is the major source of emissions, the need for more sustainable alternatives must be the focus area for sustainability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. BRICS must implement holistic methods in the fields of green energy if they are to realize sustainable economic and environmental harmony [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. BRICS energy resource endowment does not determine energy policy unambiguously. In terms of fossil fuels, however, as a group, the BRICS nations remain heavily reliant on them, impeding their progress towards sustainability issues.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe study variables are -\u003c/b\u003e GDP Per capita, trade openness, inward FDI, fossil fuel energy consumption, and carbon dioxide emission same is mentioned in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFirst, this provides the theoretical framework through which economic growth is related to CO₂ emissions. The interrelation of economic growth and carbon dioxide (CO₂) emissions is a complex and multifaceted issue when considering the case of BRICS nations. In the past three decades, these economies have grown tremendously and have progressed from emerging markets to being among the major players on the global stage. Although, this growth has not come without its environmental costs, which often correlate with greater energy use and CO₂ emission increases. Important for understanding the ecological impacts of economic policies and growth trajectories is to understand how these dynamics interact. Historically BRICS nations economic growth is related to industrialization and increased demand of energy. As a result of this environment, we have become affected with serialisation with the fossil fuels that significantly add to the CO₂ emissions. Actually, the expansion of manufacturing sectors that is by domestic and foreign investment itself contribute directly to emissions levels. For instance, with higher industrial activities goes typically higher fossil fuel consumption, which indicates dependency on energy sources in carbon intensive manner. Thus, this study questions how to policy decisions oriented towards growth promotion may lead to an aggravation of the environmental degradation.\u003c/p\u003e\u003cp\u003eIn the following sub-sections we will investigate the impacts of inward FDI onto environmental outcomes in the BRICS economies. Although FDI can spur a growth, it also poses potential environmental dangers. According to the literature, \"The relationship between foreign direct investment and environmental outcomes is complex, it can both aggravate economic activity and seriously hamper environment, particularly probably in terms of CO2 emissions growth\" [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. They raise fundamental questions about the balance between locating a site that attracts investment, and location that preserves ecological integrity.\u003c/p\u003e\u003cp\u003eSubsequently, we will analyze the contribution of trade openness towards economic growth as well as sustainability. This allows us to shed new light on the paradoxical effects of globalization in the BRICS nations through assessing the impact of growing participation in the global markets on the energy consumption patterns and the resulting CO₂ emissions. We will then examine whether trade liberalization increases environmental standards, by lifting pollution standards to our higher standards, or raises pollution, by boosting industrial activities that contribute to increased emissions. The economic development of these countries is directly connected with the increase in emissions caused by energy use, thus highly threatening sustainability. Given that both sustainable management of energy resources and economic development greatly contribute to world emissions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], attaining a harmonious balance between these two areas is crucial. The geopolitical risks of global conflicts are major issues in BRICS -Brazil, Russia, India, China, and South Africa. The BRICS countries strongly depend on fossil fuels, particularly oil and coal, and creative climate change mitigation and energy source diversification approaches should be prioritized [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. To make these countries green, energy secure, and economically viable in the long run [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], a shift towards sustainable energy sources is of paramount concern. Through the solution of key challenges and offering astute analyses of energy consumption trends, economic models, and sustainable development initiatives that are relevant to the BRICS countries, this paper increases the robustness of the global economy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These countries combined release approximately 40% of the world's greenhouse gases, the adoption of sustainable energy policy cannot but be vital [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe will focus on the consumption of fossil fuels as well. However, reliance on fossil energy sources is heavy in BRICS countries raising severe challenges to their environmental sustainability. The dynamics of energy utilization and its link to CO₂ emissions will be understood, with which to articulate effective energy management strategies to conserve the environment. The reliance on fossil fuels has brought about great consequences, with CO₂ emissions being one of them. A common characteristic of high fossil fuel consumption is the utilization of them for the reason that they are all indicative of a critical nexus where economic growth coexists with environmental degradation. In BRICS nations, as [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] noted \"economic growth in Saudi Arabia is linked to energy consumption and thus higher carbon emission\". The BRICS countries that usually have the similar development paths coinciding with higher energy consumption are not a monopoly of Saudi Arabia. Clearly the implication is that without massive movements to alternative energy sources, efforts at stimulating economic growth will actually increase carbon emissions and put the world even more at risk toward climate change.\u003c/p\u003e\u003cp\u003eEnergy policies of some governments in BRICS countries determine how fossil fuels are consumed. Therefore, to minimize adverse environmental impact, regulations for the use of cleaner energy alternatives must be adopted. BRICS countries are beginning to address the policies for promoting the development of renewable energy and they have realized that \"the environmental impacts of FDI in the mining sector of Chile and Peru are complex and contingent on circumstances,\" as in the case of different rules among BRICS countries. These policies differ drastically in terms of strength and consistency, thus affecting how countries switch off fossil fuels. For instance, China has invested heavily in renewable energy and electric vehicles, however, the rest of the nation is incapable of keeping pace with this adoption because of the political or financial constraints.\u003c/p\u003e\u003cp\u003eThe other factor that influences fossil fuel utilization and emissions rates of the BRICS nations is technological advancement. Energy efficiency technologies help minimizing consumption without giving up their economic growth. Emission cuts of vast amounts can be obtained by utilizing enhanced energy management system in using fossil fuels in industries. While technological advancements have taken place, there are questions surrounding the application of the technological advancements with regards to combining harmony with sustainable practices. This synergy can be enhanced by improvements in the environmental friendliness of the research and development in cleaner technologies, which in turn can have major long run economic and environmental benefits.\u003c/p\u003e\u003cp\u003eHowever, the socioeconomic forces influencing the fuel consumption of fossil fuel and the respective energy policies differed from one BRICS country to the other. Each nation's energy consumption is driven by how they are affected by economic disparities, levels of industrialization and social equity. For example, India's energy policies include a challenge of widespread poverty and energy access problems that must be complemented with economic growth and achieving climate change targets. However, understanding the socio-economic conditions of these countries help explain why some countries may depart in energy consumption patterns and policies and hence different country emission levels.\u003c/p\u003e\u003cp\u003eBRICS countries' consumption of fossil fuel is also strongly influenced by international climate agreements. The necessity of membership of global accords, for example the Paris Agreement, entices nations into making commitments to lower their greenhouse gas emissions. But the usefulness of these treaties depends also on how individual countries will tie them into national policies. Such a commitment can fail not only where competing domestic interests exist, regardless of the geostrategic sensitivity of the country, but also especially so in the developing contexts where energy security is still a key issue. The international framework is naturally ambiguous in how it influences national level energy policies and has an inherent tendency to meet with difficult political and economic challenges.\u003c/p\u003e\u003cp\u003eFinally, this paper seeks to investigate the relationship between economic growth, energy usage, and CO₂ emissions and FDI, trade openness, and fossil fuel use in the BRICS in detail. This aims to provide comprehensive knowledge of the interconnected themes and thereby aim to provide inputs for effective research direction regarding these sustainability challenges around which the economies of these important global economies are being transformed. A trip through the economic spheres and environmental conditions in the BRICS nations guarantee important understanding of sustainable development routes by balancing economic targets and corporate ecological liabilities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. RESEARCH OBJECTIVES","content":"\u003col\u003e\n \u003cli\u003eTo examine the relationship between economic growth, energy utilization, and CO₂ emissions in BRICS nations.\u003c/li\u003e\n \u003cli\u003eTo provide policy recommendations for sustainable economic growth in BRICS nations based on empirical findings.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"3. LITERATURE REVIEW","content":"\u003ch2\u003e3.1 Economic Growth\u003c/h2\u003e\u003cp\u003eThe effects of global climate change are seen in societies and the environment, like many unusual weather events, increasing sea levels and a decrease in food we can produce [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In 2024, the countries of BRICS together emitted about 51.76% of world CO₂ emissions. BRICS members in 2019 released about 14.759\u0026nbsp;billion tons of CO₂ emissions, which equals about 43.19% of the world's CO₂ emissions. There is a strongly established relationship between economic growth and carbon emissions, particularly in the short term, since industrial processes and energy consumption are central contributors to high emissions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Investment in renewable energy projects and learning programs has paid off in the long term to reduce carbon emissions, as it maintains development [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The BRICS nations are confronted with higher emission levels because of industrialization, population, and energy intensity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003e3.2 Foreign Direct Investment\u003c/h2\u003e\u003cp\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] found that FDI affects economic growth differently in different economic contexts, so FDI leads to different CO2 emission results because of how it affects the sharing of knowledge. Multinational companies may manage their CO2 emissions differently by sharing non-clean or eco-friendly technologies in different countries [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe environmental Kuznets curve (EKC) is a hypothesized relationship between various indicators of environmental degradation and per capita income. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] shows the inverted U structure relationship between economic growth and carbon emissions, which may indicate environmental Kuznets Curve (EKC) hypothesis particularly in presence of foreign direct investment inflow into CO2 emissions with respect to the BRICS countries.\u003c/p\u003e\u003cp\u003eOn the one hand, FDI can spurn economic growth and launch technological progress, on the other hand, it can be coincided with environmental problems. In a study of [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] it is shown that increased environmental regulatory pressure significantly increases the likelihood of foreign divestment. FDI's spatial spillover effects are significant since they increase CO2 emissions in surrounding areas while simultaneously decreasing them in surrounding areas [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] looked into the link between FDI and CO2 emissions and concluded that an increase in FDI usually leads to more environmental damage.\u003c/p\u003e\u003ch2\u003e3.3 Trade Openness\u003c/h2\u003e\u003cp\u003eThe degree to which an economy is more or less open to trade with other economies around the world is referred to as its trade openness. Growing the scale of industries, which ultimately results in higher pollution, is one of the ways that it assists nations in growing their exports, which in turn helps them to improve their domestic output [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] discussed the relationship between pollution and trade openness is favorable for nations with low incomes, whereas it is negative for countries with high and intermediate incomes. Research by [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] has shown that trade openness plays a big role in driving CO2 emissions among the key macroeconomics variables. Earlier papers in the field have used standard approaches to measure trade openness. While trade liberalization brings new economic opportunities, it also sees trade liberalization as promoting environmental degradation on the one hand and technology transfer and more sustainable practices in BRICS countries on the other hand [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Thus, this means that informed trade agreements can be used to facilitate for sustainable development and there should be more incentives in BRICS nations for technologies to be adopted in ways that are cleaner with economic competitiveness [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003e3.4 Fossil Fuel Energy Consumption\u003c/h2\u003e\u003cp\u003eThere have been numerous BRICS nations who have taken the initiative to launch projects with the objective of transitioning towards a low-carbon economy [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] has predicated on the belief that moving to other renewable energy sources from conventional fossil fuels will help to revolutionize the world economy. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] start looking into energy investments in G20 countries and urge lawmakers to think about a variety of energy sources when making environmental rules. This bigger picture sets the stage for [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] more detailed study of how different factors affect the health of the environment in the BRICS economies in a way that is both changing and not the same. Their study connects the big ideas of technical progress and environmental effects, giving us useful information for making policy changes that consider the many factors that affect the quality of the environment. Efficient energy usage is generating positive change within the BRICS countries' carbon emissions landscape, consistent with the findings of [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and their investigation of the trilemma relationship between energy consumption, carbon emissions, and economic growth. Optimizing energy use patterns, reducing waste, and transitioning to cleaner energy sources all contribute to dramatically lowering the overall carbon footprint [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e"},{"header":"4. METHODOLOGY","content":"\u003cp\u003eThis research methodology employs a rigorous econometric framework for panel data analysis. It begins with unit root tests to assess stationarity, followed by cointegration tests to identify long-run relationships, and Panel Granger causality tests explore directional influences. Stability diagnostics and robustness checks ensure the reliability and validity of empirical findings across time and cross-sections. the econometric analysis adopted in this research shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGDP𝑖𝑡 = 𝑓(TOP, IFDI, 𝐹FEC, CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e) -------------------------------------------------------------------(1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel Cointegration Equation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDGDP𝑖𝑡 = 𝛽0 + 𝛽1DTOP𝑖𝑡 + 𝛽2DIFDI𝑖𝑡 + 𝛽3DFFEC𝑖𝑡 + 𝛽4CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e𝑖𝑡 + 𝜀𝑖𝑡 ----------------- (2)\u003c/strong\u003e\u003c/p\u003e"},{"header":"5. DATA ANALYSIS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Unit root test\u003c/h2\u003e\u003cp\u003eAfter ensuring stationarity and order of integration, the data is analysed. Descriptive statistics for the data are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. From this table, we can understand that the data is normally distributed. First generation Panel unit root test is conducted to understand the presence of unit roots. Test statistics summary of Fisher ADF, Fisher PP, Im Pesaran \u0026amp; Shin, Levin Lin \u0026amp; Chu are checked and results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These tests are widely known and popularly used in multiple literatures in Panel data unit root testing procedures.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eFirst Generation Unit Root Test\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eFisher -ADF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eFisher-PP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eIPS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eLLC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDPPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.77\u003c/p\u003e\u003cp\u003e(0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.9\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003cp\u003e(0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51.4\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.96\u003c/p\u003e\u003cp\u003e(1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3.20\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.48\u003c/p\u003e\u003cp\u003e(1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-3.17\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTOP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.87\u003c/p\u003e\u003cp\u003e(0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e242.5\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.87\u003c/p\u003e\u003cp\u003e(0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e93.09\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003cp\u003e(0.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-8.53\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003cp\u003e(0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-8.11\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIFDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.5\u003c/p\u003e\u003cp\u003e(0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.3\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.89\u003c/p\u003e\u003cp\u003e(0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.88\u003c/p\u003e\u003cp\u003e(0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFFEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.42\u003c/p\u003e\u003cp\u003e(0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.76\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.02\u003c/p\u003e\u003cp\u003e(0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e203.0\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003cp\u003e(0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-5.26\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003cp\u003e(0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-1.10\u003c/p\u003e\u003cp\u003e(0.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003cp\u003e(0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.4\u003c/p\u003e\u003cp\u003e(0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003cp\u003e(0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88.6\u003c/p\u003e\u003cp\u003e(0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.16\u003c/p\u003e\u003cp\u003e(1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.95\u003c/p\u003e\u003cp\u003e(1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-3.88\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eSource: Authors\u0026rsquo; own\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Cross-Sectional Dependency Test\u003c/h2\u003e\u003cp\u003eIt is conventional that countries in an economic union can exhibit similar socio-economic characteristics; also, such economies depend upon one another for gaining mutual benefits. As a result of this, these countries tend to depend cross-sectionally. While analyzing such economies, it is essential to check for the existence of cross-sectional dependencies due to the interactions of the variables examined, so as to check for bias [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The first-generation unit root tests may give biased results when there exists cross-sectional dependence. Two second-generation panel unit root tests, cross-section ADF (CADF) and cross-sectionally augmented IPS (CIPS), are used. Both tests were developed by [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As a consequence, two root unit tests of [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] of the second generation are being performed: ADF-cross section (CADF) and IPS cross-sectional increment (CIPS). The procedure for analysing cross-sectional dependency is expressed in Eq.\u0026nbsp;(3). Where T denotes the time period, N is the sample size, and \u0026#119875;\u0026#119894;\u0026#119895; is the sample estimate of correlation errors for each cross-section of country i and j, defined as follows in Eq.\u0026nbsp;(4).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:CSD=\\sqrt[\\:]{\\frac{2T}{N\\left(N-1\\right)}}\\left(\\:{{\\sum\\:}_{i=1}^{N-1\\:\\:\\:\\:}\\:\\:{\\sum\\:}_{j=i+1}^{\\:N\\:\\:\\:\\:}\\:\\:\\:\\:\\:P}_{ij}^{{\\prime\\:}}\\right)\\Rightarrow\\:N\\left(\\text{0,1}\\right)-----\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eT signifies the time, N denotes the sample size, and \u0026#119875;\u0026#119894;\u0026#119895; denotes the correlation error sample estimate for each\u003c/p\u003e\u003cp\u003ecross-section of the country i and j as specified in Equation ___.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{P}_{ij}^{{\\prime\\:}}=\\frac{{\\sum\\:}_{i=1}^{T}{u}_{it}{\\mu\\:}_{jt}}{{\\left({\\sum\\:}_{t=1}^{T}{u}_{it}^{2}\\right)}^{1/2}{\\left({\\sum\\:}_{t=1}^{T}{u}_{jt}^{2}\\right)}^{1/2}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:}-----\\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e we can understand that there exist cross-sectional dependency among the variables.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eCross-Sectional Dependency Test\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStatistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProb.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreuch-Pagan LM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e106.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePesaran scaled LM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePesaran CD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eSource: Authors\u0026rsquo; own\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eResults of the second-generation unit root test are given in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eSecond Generation Unit Root Test\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCADF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eCIPS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDPPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003cp\u003e(0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.81\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.02\u003c/p\u003e\u003cp\u003e(0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.86\u003c/p\u003e\u003cp\u003e(0.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTOP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003cp\u003e(0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.25\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.19\u003c/p\u003e\u003cp\u003e(\u0026gt;\u0026thinsp;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.69\u003c/p\u003e\u003cp\u003e(0.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIFDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003cp\u003e(0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.16\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.32\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.82\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFFEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003cp\u003e(0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003cp\u003e(0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.07\u003c/p\u003e\u003cp\u003e(\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.35\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.59\u003c/p\u003e\u003cp\u003e(0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.82\u003c/p\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.57\u003c/p\u003e\u003cp\u003e(\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.90\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Authors\u0026rsquo; own\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown above, all the variables except CO2 are in I(1) integration. After validating that all the variables in this study are I(0) and I(1), the study applies four types of panel cointegration tests to determine the existence of a long-run linkage between the variables: [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003ePedroni Cointegration Test\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest Statistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStatistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProb.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithin-dimension (homogeneous)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePanel v\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePanel rho\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePanel PP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-3.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePanel ADF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-3.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBetween-dimension(heterogenous)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup rho\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup PP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-4.758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup ADF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-4.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eSource: Authors\u0026rsquo; own\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, out of the seven statistics, three out of the four within-dimension statistics reject the null hypothesis of no cointegration, and two out of the three between-dimension statistics also reject the null hypothesis of no cointegration. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the output of Johansen-Fisher\u0026rsquo;s Cointegration test. Trace test and Maximum Eigen Value statistics indicate the presence of a maximum of four and three cointegrating equations, respectively. This result further strengthens the presence of long-term cointegration relations among the tested variables in BRICS.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eJohansen Fisher Panel Cointegration Test\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFisher Statistics (from Trace test)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProb.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFisher Statistics (from Max Eigen Value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProb.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt most 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Authors\u0026rsquo; own\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicates the causality test results. Dumitrescu Hurlin Panel Causality test scores indicate that economic growth is causing CO2 emission, and IFDI also causes CO2 emission in the BRICS context.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eDumitrescu Hurlin Panel Causality Test\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNull Hypothesis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eW-Stat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZbar-Stat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProb.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTOP does not homogeneously cause GDP-PC\u003c/p\u003e\u003cp\u003eGDP-PC does not homogeneously cause TOP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.39820\u003c/p\u003e\u003cp\u003e1.92882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.73128\u003c/p\u003e\u003cp\u003e-0.23105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4646\u003c/p\u003e\u003cp\u003e0.8173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIFDI does not homogeneously cause GDP-PC\u003c/p\u003e\u003cp\u003eGDP-PC does not homogeneously cause IFDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.71406\u003c/p\u003e\u003cp\u003e2.60069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.41711\u003c/p\u003e\u003cp\u003e0.38260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1565\u003c/p\u003e\u003cp\u003e0.7020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFFEC does not homogeneously cause GDP-PC\u003c/p\u003e\u003cp\u003eGDP-PC does not homogeneously cause FFEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.74919\u003c/p\u003e\u003cp\u003e1.50249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.34311\u003c/p\u003e\u003cp\u003e-0.63296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1792\u003c/p\u003e\u003cp\u003e0.5268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 does not homogeneously cause GDP-PC\u003c/p\u003e\u003cp\u003eGDP-PC does not homogeneously cause CO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.62844\u003c/p\u003e\u003cp\u003e22.9499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.37121\u003c/p\u003e\u003cp\u003e19.5860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1703\u003c/p\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIFDI does not homogeneously cause TOP\u003c/p\u003e\u003cp\u003eTOP does not homogeneously cause IFDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.52063\u003c/p\u003e\u003cp\u003e3.08637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.30822\u003c/p\u003e\u003cp\u003e0.83388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7579\u003c/p\u003e\u003cp\u003e0.4043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFFEC does not\u003c/p\u003e\u003cp\u003eHomogeneously\u003c/p\u003e\u003cp\u003ecause TOP\u003c/p\u003e\u003cp\u003eTOP does not homogeneously\u003c/p\u003e\u003cp\u003eCause FFEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.18286\u003c/p\u003e\u003cp\u003e1.84074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.93429\u003c/p\u003e\u003cp\u003e-0.31408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3502\u003c/p\u003e\u003cp\u003e0.7535\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 does not homogeneously\u003c/p\u003e\u003cp\u003eCause TOP\u003c/p\u003e\u003cp\u003eTOP does not homogeneously cause CO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.74661\u003c/p\u003e\u003cp\u003e1.99203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.48261\u003c/p\u003e\u003cp\u003e-0.17147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1382\u003c/p\u003e\u003cp\u003e0.8639\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFFEC does not homogeneously\u003c/p\u003e\u003cp\u003ecause IFDI\u003c/p\u003e\u003cp\u003eIFDI does not homogeneously cause FFEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.46921\u003c/p\u003e\u003cp\u003e3.52783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.26044\u003c/p\u003e\u003cp\u003e1.24407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7945\u003c/p\u003e\u003cp\u003e0.2135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 does not homogeneously\u003c/p\u003e\u003cp\u003ecause IFDI\u003c/p\u003e\u003cp\u003eIFDI does not homogeneously\u003c/p\u003e\u003cp\u003ecause CO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.84301\u003c/p\u003e\u003cp\u003e4.17835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.32141\u003c/p\u003e\u003cp\u003e1.84851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7479\u003c/p\u003e\u003cp\u003e0.0645\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2 does not\u003c/p\u003e\u003cp\u003eHomogeneously cause FFEC\u003c/p\u003e\u003cp\u003eFFEC does not homogeneously cause CO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.52748\u003c/p\u003e\u003cp\u003e0.6361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.60941\u003c/p\u003e\u003cp\u003e-1.41437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5423\u003c/p\u003e\u003cp\u003e0.1573\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Authors\u0026rsquo; own\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"6. DATA INTERPRETATION, RESULTS AND DISCUSSION","content":"\u003cp\u003eThe results of the data analysis indicate that the dependent variable, GDP, has a long-term cointegrating relationship among the dependent variables. We found one cointegrating equation for BRICS nations, which indicates that all the variables in these countries have an equilibrium relationship with one another in the long run. Since all BRICS nations are in the fast development phase, this long-term equilibrium relationship is relevant. However, in the short term, the results are different. Trace test statistics and max eigenvalues are not similar for all the BRICS nations. This can be because the stages of development in each of the BRICS nations are different. China is much ahead in developing technology, telecommunication, electronics and electricals, whereas India is strongly heading towards the roots of economic development. Even though these two countries are neighbors situated in the Asian region, the political systems of these two nations are different, which indicates the reason for the different facets of economic and social development. Russia situation in the Eurasian region is experiencing a different dimension of social, political and economic development when compared with China and India. Russia is more advanced in certain areas of development, including aviation, defense technology, and communication. However, the recent geopolitical disturbance has a major role in this nation's economic activities. Brazil and South Africa focus more on agrarian and related activities for achieving economic development. Though the economic profile of these two nations is not the same, the time of attaining independence, the natural resources they are bestowed with, and the socio-political arrangement match the results of our study. They are moving into highly paced industrial development in recent times.\u003c/p\u003e\u003cp\u003eThe results of the data analysis indicate that the dependent variable, GDP, has a long-term cointegrating relationship with other variables, such as trade openness, inward FDI, fossil fuel energy consumption, and carbon dioxide emission. After checking for cross-sectional dependency, first and second-generation unit root tests were conducted to identify and ensure the order of integration of variables. The variables are integrated of the order I(0) and I(1). Panel Cointegration test was done to identify the long-term equilibrium relation among the variables. From Trace test and Maximum Eigen value statistics, we came to know the presence of cointegrating relations among the variables that ensure the existence of long-term equilibrium relations among the test variables in the case of BRICS. Since all BRICS nations are in the fast development phase, this long-term equilibrium relationship is relevant. This result goes in line with [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]; [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]; [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. After establishing the presence of cointegration among the variables, causality is checked using the Dumitrescu-Hurlin Panel Causality Test. Results of the causality test indicate that there exists unidirectional causality among GDP per capita and CO2 emission at the 5 per cent level of significance, and between inward FDI and CO2 emission at the 10 per cent level of significance.\u003c/p\u003e"},{"header":"7. CONCLUSION \u0026 POLICY IMPLICATIONS AND DIRECTION FOR FUTURE RESEARCH","content":"\u003cp\u003eIn conclusion, this sub-section combines the results obtained from this study in relation to the explanation of energy consumption, economic growth and CO₂ emissions in BRICS countries and the effects of the latter under diverse circumstances that FDI effects, reducing C02 and using non-conventional energy resources. The variables analysed provided an outline of the sophisticated equilibrium that must be reached between economic development and environmental sustainability in these economies in a time of flux. Thus, the entire results received reveal that the level of energy consumption and CO₂ emissions is consistently correlated to the percentage for economic growth in BRICS countries, where the latter dynamics displayed to be manifested because of certain factors moderation. FDI is prominent and in most cases linked to emission levels as being high and correlates positively with emission. It is shown that increased environmental regulatory pressure has a large effect on the foreign divestment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The BRICS nations must provide utmost priority to establishing green energy ventures, environmentally conscious policies, and joint global action that will reduce their carbon footprint while ensuring economic growth [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Favourable studies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]; [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]; [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] highlight that South Asian and BRICS nations should enhance growth, promote green FDI, reduce fossil fuel use, and adopt low-carbon technologies, as trade openness can lower CO₂ emissions. In contrast, [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] find FDI raises emissions in host regions but offers environmental benefits to neighbouring areas through spatial spillovers.\u003c/p\u003e\u003cp\u003eOver the past 20 years, the BRICS have seen both their FDI flows and their share in world flows significantly rise. This is true of all BRICS countries, including considering China's weight and to a lesser degree India's global investment destinations. Apart from global policy coordination, the BRICS nations have embraced several significant projects to enhance cooperation on intra-group investment promotion in recent years. Particularly in investment facilitation, climate change, and funding in sustainable development, these projects address several important policy concerns. Among these projects are the Outlines for BRICS Investment Facilitation (2017), the BRICS MoU Trade and Investment Promotion (2019), the Strategy for BRICS Economic Partnership 2025 (2020), Intra-BRICS Cooperation for Continuity, Consolidation and Consensus (2021), and the Initiative on Trade and Investment for Sustainable Development (2022) initiatives for enhancing cooperation on pushing intra-group funding in more recent times [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe average (inward) FDI stock, as a proportion of GDP, increased from 20% in 2011 to 27% for the BRICS as a bloc. From 12 per cent in China and 16 per cent in India to 37 per cent in Brazil and 41 per cent in South Africa, the FDI stock to GDP ratio differs greatly at an individual economy level within the BRICS. At 29% Russia's inward FDI stock/GDP ratio is closest to the BRICS average. The contribution of FDI to Gross Fixed Capital Formation (GFCF) shows that, despite regional differences, FDI is clearly a key and rising factor in economic growth in all of the BRICS. With above 10% since the group's founding and approaching more than 22% in 2019, Brazil historically boasts the highest percentage of FDI inflows to GFCF. Brazil has set very high goals for the environment. Their main goal is to have net-zero pollution by 2050. The nation's goal is to reduce emissions by 59\u0026ndash;67% from the levels observed in 2005 by 2035. The unwavering dedication of the Russian Federation to attain net-zero greenhouse gas emissions by the year 2060 persists, despite its significant reliance on fossil fuel resources. It is anticipated that Russia will accomplish this objective via a bifurcated strategy that includes considerable investments in carbon capture, utilization, and storage (CCUS) technologies, in coincidence with enhancements in energy efficiency. Russia's Government Approves New Energy Strategy until 2050 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. India has made a lot of progress in getting foreign direct investment. For example, in the first half of the current fiscal year, \u003cspan\u003e$\u003c/span\u003e42.1\u0026nbsp;billion came in, and since April 2000, the country has received a total of \u003cspan\u003e$\u003c/span\u003e1 trillion. Key factors have been things like a more competitive global market, a thriving innovation ecosystem, and a business-friendly climate. The country's proactive approach is shown by programs like \"Make in India,\" the loosening of sectoral rules, and recent changes to policies that allow more foreign direct investment in the space industry. India is in a good position to play a bigger role in the world by following global economic trends. This would help the country expand and develop in a way that is good for everyone. India has emerged as a significant hub for investments in environmental sustainability, primarily attributable to the backing from governmental institutions. With approximately 75% FDI to GFCF ratio in the past year, India's participation is somewhat higher than the world average of 5.7%. China aspires to eradicate all carbon emissions by the year 2030 and attain carbon neutrality by 2060 by promoting multiple initiatives oriented towards clean energy solutions and alternative energy utilizations. In the sustainable technologies sector, the nation has implemented stringent environmental regulations and expanded opportunities for foreign direct investment. China is the only BRICS nation whose FDI inflows to GFCF ratio has been regularly below the world average. This indicates that domestic investment is more vital in China's economy than FDI. China's overall GFCF in 2021, for instance, was \u003cspan\u003e$\u003c/span\u003e7.2 trillion; in the United States, with the second biggest GFCF, it came at \u003cspan\u003e$\u003c/span\u003e4.9 trillion. The Combined Resource Plan for South Africa shows that the country is moving away from coal and going towards green energy sources. The dual objectives of this transition are to reduce emissions and improve energy security. South Africa often does not surpass 10% in this regard. With 6.7% of FDI inflows to GFCF over the past three years.\u003c/p\u003e\u003cp\u003eGovernments of all BRICS nations must intensify their efforts to spark the growth of renewable energy projects, forge sturdy regulatory frameworks, and embrace innovative strategies that intertwine environmental guardianship with economic flourishing [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The BRICS nations must provide utmost priority for establishing green energy ventures, environmentally conscious policies, and joint global action that will reduce their carbon footprint while ensuring economic growth [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Future explorations ought to investigate the patterns of energy usage across various sectors, uncover effective global cooperation that can nurture sustainability, and assess the influence of policies in this domain [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As the data available is only up to 2022, we need to restrict our study to this time period, which becomes one of this study's limitations. Future studies can estimate the effect of each of the above dependent variables on GDP to understand and identify the relationship between financial growth and urbanization with increased emissions, considering the necessity for green energy to reverse these adverse effects [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. More studies should be taken with new members of BRICS i.e Egypt, Ethiopia, Indonesia, Iran and United Arab Emirates with the special influence of agricultural value added, absorptive capacity and labour productivity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eNo funding or grants were received to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and Consent to Participate: \u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial: \u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eDr. Sharmiladevi J.C. (Corresponding Author): Conceptualized the research framework, designed the methodology, conducted econometric analysis including unit root tests, cointegration analysis, and causality testing, interpreted results, developed policy recommendations, and led the manuscript writing and revision process.Arifa Haseen: Conducted a comprehensive literature review, performed data collection and preliminary analysis, contributed to the theoretical framework development, and assisted in manuscript preparation and formatting.Both authors have read and approved the final manuscript and take responsibility for the integrity and accuracy of the research presented.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003ch2\u003eData availability:\u003c/h2\u003e\n\u003cp\u003eThis research used only secondary data available in public domain from authentic sources. Data used for this study can be available on request to the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e Authors give their consent to this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest/Competing Interest: \u003c/strong\u003eThe authors do not have any relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZardoub, A. (2024). Impact of economic growth, renewable energy consumption and energy intensity on CO2 emissions in BRIC countries: An application of CS-ARDL. \u003cem\u003eJournal of Infrastructure, Policy and Development\u003c/em\u003e, 8(8), 4312.\u003c/li\u003e\n\u003cli\u003eAsif, M., Li, J. Q., Zia, M. A., Hashim, M., Bhatti, U. A., Bhatti, M. A., \u0026amp; Hasnain, A. (2024). 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Bosnia and Herzegovina economic update, Fall 2023 (Report No. 186593). https://documents1.worldbank.org/curated/en/099030009272214630/pdf/BOSIB0db37c9aa05a0961a08a83a0ea76ea.pdf\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"economic growth, carbon emission, BRICS, energy utilisation, sustainable development, inward foreign direct investment, fossil fuel energy consumption, Trade openness","lastPublishedDoi":"10.21203/rs.3.rs-7317146/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7317146/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEconomic development is a significant factor that indicates the health of a nation. BRICS is one of the fastest-growing regions in the current time period. It is an interesting geographical area to understand the interplay of economic development with other essential elements of development like international capital investment flow and openness to trade. At the same time, in the name of development, how much a nation is sustainable is equally important to create more desirable results in the long term, considering factors like energy utilisation and carbon emission. This study takes account of the above factors, and its objective is to understand the long-term equilibrium relationship among economic development, inward foreign direct investment, openness to trade, fossil fuel energy consumption, and carbon dioxide emission in the BRICS region, considering the data from 1990 to 2022. Data is analysed using the Johansen Fisher Panel Cointegration test and the Dumitrescu Hurlin Panel Causality Test. Results of our study indicate that there exists cointegration among the variables in the long run. Causality study indicates a relationship among fossil fuel energy consumption, economic development and carbon dioxide emission, carbon dioxide emission and inward foreign direct investment. This study emphasizes the need for BRICS countries to implement strong green policies, enhance clean energy use, and invest in technology to ensure sustainable economic growth while reducing environmental damage.\u003c/p\u003e","manuscriptTitle":"The Growth–Energy–Emissions Trilemma: Evidence from BRICS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 13:43:49","doi":"10.21203/rs.3.rs-7317146/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"372ccca0-c5ab-45ab-9958-76e97af3ac66","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T14:40:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-25 13:43:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7317146","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7317146","identity":"rs-7317146","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}