Impact of Renewable Energy, Business Climate, and Human Capital on CO₂ Emissions: Empirical Evidence from BRICS Countries

Abstract

Since the 1950s, the remarkable amount of global environmental degradation has heightened environmental concerns at both national and international levels. This shift has spurred intensive research into the causes of environmental degradation and potential remedies, including environmental taxes, fines, education, and regulations. The drivers of CO₂ emissions have been widely explored in the literature, but the nexus between business climate, human capital, and CO₂ emissions has not been examined sufficiently. Therefore, the purpose of this study is to delve into the interplay between renewable energy, business climate, human capital, and CO₂ emissions in BRICS countries from 2000 to 2020 using panel causality and cointegration tests. Our research hypotheses suggest that there are significant mutual interactions among renewable energy, business climate, human capital, and CO₂ emissions based on the associated literature. The results of the causality test verify the research hypotheses by uncovering a bidirectional causality between business climate, renewable energy use, human capital, and CO₂ emissions. Furthermore, the cointegration analysis reveals that increases in renewable energy use and human capital decrease CO₂ emissions at the panel level, but a positive business climate increases CO₂ emissions at the panel level. However, the impact of business climate on CO₂ emissions at the country level varies among BRICS economies based on environmental policies. In conclusion, investing in green energy technologies and education is a useful tool to decrease CO₂ emissions. In addition to this, the positive effect of business climate on CO₂ emissions should be balanced by regulations to increase environmental, social, and governance awareness of firms.

1. Introduction

The environment is of vital importance for a healthy life and the presence of life on planet Earth. However, since the Industrial Revolution, the world has witnessed significant environmental degradation in terms of air and water pollution, global warming, deforestation, natural resource depletion, and a decline in biological diversity. Moreover, environmental degradation is foreseen as the top long-term risk in the world, according to the World Economic Forum [1]. Therefore, environmental sustainability has become one of the global priorities and is a constituent part of sustainable development. The environment has been a key aspect of sustainable development since the 1972 Stockholm Conference on the Human Environment [2]. Additionally, four Sustainable Development Goals (SDGs), including climate action, clean water and sanitation, life on land, and life below water, are directly related to the environment [3]. Furthermore, almost all of the remaining SDGs, such as affordable and clean energy, industry, innovation and infrastructure, and responsible consumption and production, are also significant for environmental protection.

The SDGs were adopted by the 193 UN Members in 2015. Some progress towards the achievement of the SDGs was made between 2015 and 2019, and the overall SDG index, which indicates the progress made towards all SDGs, increased from 63.8 to 66.0. However, progress markedly slowed down due to the COVID-19 pandemic, and limited progress has been made towards the environmental goals of the SDGs [4,5]. High-income countries made relatively much more progress towards the achievement of the SDGs when compared with middle-income and low-income countries between 2015 and 2023.

Another concept which is closely related to the achievement of many of the SDGs is ESG (environment, social, and governance), which was officially mentioned in a report called “Who Cares Wins” [6]. ESG indicates a firms’ commitment to environmental, social, and governance factors [7]. In this context, the environmental pillar of the ESG indicates how firms take notice of the environmental issues, such as carbon emissions, waste management, and deforestation, resulting from their business activities. The social pillar of the ESG denotes the approach of the firms towards social issues, including employees, labor standards, gender, human rights, and diversity. Lastly, the governance pillar of the ESG denotes the corporate policies of firms, such as internal audits, company leadership, shareholder rights, and transparency.

In conclusion, the environment is one of the main dimensions of both sustainable development and ESG practices. Therefore, identifying the factors behind environmental degradation is beneficial for countries and businesses to protect and improve the environment. A considerable number of studies have examined the driving micro- and macro-level factors of environmental degradation. These studies have unveiled many factors that are drivers of environmental sustainability, such as GDP per capita [8], economic growth [9,10], population [8,9], urbanization [8], institutional quality and public governance [10,11], education and human capital [11], trade liberalization [10,12], globalization and global value chains [10], industry structure [12], financial development [12], international tourism [12], environmental and market regulations [13,14,15], green innovation [14,15,16,17], natural resources [10,18], technological progress and innovation [19,20], foreign direct investments [17,20], economic openness [21], agriculture [22], renewable and non-renewable energy use and energy intensity [9,10,12,15,18,19,20,22], ESG performance [23,24], and geopolitical risks [25].

This study investigates the interaction among business climate, renewable energy use, human capital, and CO₂ emissions in a sample of BRICS countries (the leading emerging markets of Brazil, Russia, India, China, and South Africa) by means of the Westerlund and Edgerton [26] cointegration test and JKS (Juodis–Karavias–Sarafidis) [27] causality test, which produce robust results with the availability of heterogeneity and cross-sectional dependency. BRICS countries make up over 42% of the world’s population, 30% of the world’s territory, 18% of global trade, and 23% of the world’s GDP and have driven the global economy in recent years [28]. Furthermore, all BRICS countries saw improvements in business climate and human capital during the 2000–2020 period. However, Russia and China, in particular, have experienced remarkable improvements in terms of their business climate, while China and India have achieved considerable progress in human capital compared to the other BRICS countries [29]. On the other hand, China, India, and the Russian Federation are also among the top five CO₂ emitters in the world, along with the USA and Japan [30]. Lastly, the Russian Federation has a very high level of human development, Brazil, China, and South Africa have high levels of human development, and India has a moderate level of human development [31]. Therefore, countries with different development levels can also benefit from the findings of this research, based on the results of country-level analyses.

This study aims to support the associated empirical literature in two ways. In the empirical literature, the studies of [32,33,34] only investigated the effect of business climate indicators on the environment in developed and developing countries and mainly uncovered that a positive business climate easing the establishment and expansion of business activities negatively affects the environment. However, the effect of the environment on business climate has not been sufficiently researched. Thus, the first empirical contribution of this article is to analyse the reciprocal nexus between business climate and CO₂ emissions, differing from the existing limited literature.

Furthermore, human capital is one of the fundamental parameters at the centre of economic activities and, in turn, may influence nearly all activities. Academics have traditionally focused on the economic effects of human capital, reflecting the knowledge, skills, and abilities of a population in an economy, while the effects of human capital on the environment have been largely overlooked until now [35,36,37]. Researchers have largely investigated the effect of human capital on the environment since 2019, and the majority of these studies denoted that human capital contributes to environmental quality [38,39,40,41,42,43,44]. However, Haini [45], Zhang et al. [46], and Sarkodie et al. [47] discovered human capital has a negative effect on the environment. The two-way association between human capital and the environment has been explored by a few researchers, and in turn, the causal relationship between these two variables is still inconclusive [38,48,49,50]. The second empirical contribution of this article is a dual analysis between human capital and CO₂ emissions, differing from the associated empirical literature.

The rest of this research article is organized as follows: Section 2 reviews the main findings of the related empirical studies on the nexus between renewable energy, business climate, human capital, and the environment. Section 3 outlines the data and methods of the study. The results and related discussion are presented in Section 4 and Section 5, respectively. The final section concludes with policy implications and recommendations.

2. Theoretical Background

The environment performs crucial functions, such as providing resources and biodiversity and assimilating wastes for a healthy world. Therefore, the protection of the environment is vital for both current and future generations. However, environmental degradation, resulting in climate change, air and water pollution, resource depletion, and a decline in biological diversity, has considerably raised environmental concerns, especially since the 1950s. This significant degradation of the environment has led researchers to explore the drivers of global environmental degradation. Empirical studies have identified many economic, social, and institutional factors as drivers of environmental degradation. This paper studies the environmental impacts of business climate, human capital, and renewable energy use.

A business climate reflects the attitudes of governments, financial institutions, and labour unions toward the private sector, including aspects like business regulations, tax rates, lending rates, and inflation [51]. Therefore, a positive business climate, including market-oriented business regulations, lower tax and lending rates, or loose labour regulations, which encourage the establishment of new firms or the expansion of the business activities of firms, is expected to impact CO₂ emissions [33]. However, the direction of the environmental effect of business climate depends on the current environmental policies of countries. On the other hand, CO₂ emissions can also affect firms through labour health, the costs of raw materials, and supply chains, which are sensitive to air and water pollution, climate change, and natural events. Thus, a bilateral or one-way interaction between business climate and CO₂ emissions is theoretically expected based on a country’s own institutional, economic, and social characteristics in light of these theoretical considerations.

Human capital is one of the key determinants of innovation, entrepreneurship, green and energy-efficient technologies, technological progress, productivity, and in turn, economic growth and development [39,42,52,53,54]. Therefore, improvements in human capital can decrease CO₂ emissions through the development of green and energy-efficient technologies and productivity theoretically. However, the interaction between the environment and economic growth differs based on the environmental Kuznets curve (EKC) [55]. The EKC hypothesis suggests an inverse U-shaped relationship between environmental degradation and economic growth. According to the hypothesis, the environment deteriorates in the early stages of economic growth but sees an improvement after a threshold, due to increases in environmental awareness and the development of green and energy-efficient technologies [18,56]. Lastly, more educated and skilled individuals usually have greater environmental awareness, avoiding attitudes and behaviours that increase CO₂ emissions, such as opting for green energy sources and energy-efficient devices and obeying environmental regulations [57,58,59]. On the other hand, increasing CO₂ emissions can also impact human capital through direct channels, such as nutrition, health, and well-being, and indirect channels involving changes in markets and economic systems [60]. In conclusion, a bilateral or one-way interaction between human capital and CO₂ emissions is theoretically expected based on the studied countries’ development levels in light of these theoretical considerations.

Last, it is predicted that more than three-quarters of global greenhouse gas emissions originate from energy consumption [61]. Therefore, renewable energy sources such as water, the sun, and wind, which emit relatively lower levels of greenhouse gases into the air, are theoretically expected to negatively impact CO₂ emissions [62].

3. Empirical Literature Review

In the literature, the nexus between business climate and the environment has been analysed only by Gani and Sharma [32], Rieger [33], and Omri and Afi [34] in a sample of developed and developing countries using regression analyses, as shown in

Table 1. A summary of the literature on business climate and CO₂ emissions.

Study	Sample; Period	Method	Effect of Business Climate Indicators on CO2 Emissions
Gani and Sharma [32]	A total of 87 developing economies and 20 developed economies; 2003	Regression	A negative relationship between the procedures to start a job and CO2 emissions
Rieger [33]	A total of 104 developing countries; 2005–2014	Regression	Positive
Omri and Afi [34]	A total of 32 developing countries; 2001–2015	Regression	Positive

. These studies have found that a positive business climate, which facilitates the establishment and expansion of business activities, has negatively affected the environment. However, the use of the regression approach in these studies does not enable us to see country-level differences. Therefore, our study will be one of the first studies to analyse the two-way relationship between business climate and CO₂ emissions, and the use of the AMG (augmented mean group) estimator in our study allows us to make country-level inferences about the nexus between business climate and CO₂ emissions, differing from the previous limited empirical studies.

In the associated literature, the nexus between renewable energy use and the environment has been widely investigated by different countries and country groups, and the studies shown in

Table 2. A summary of the literature on renewable energy CO₂ emissions.

Study	Sample; Period	Method	Effect of Renewable Energy on Greenhouse Gases/CO2 Emissions
Abbas et al. [15]	BRICS countries; 1990–2020	MG and PMG estimators	Negative
Imran et al. [19]	US, Japan, Australia, and India; 1991–2021	Method of moments quantile regression	Negative
Justice et al. [62]	Ghana; 1990–2020	Correlation analysis	Negative
Dogan and Seker [63]	40 countries with highest renewable energy; 1985–2011	Pedroni and LM bootstrap cointegration tests.	Negative
Koengkan and Fuinhas [64]	10 South American countries; 1980–2012	Regression	Negative
Ben Jebli et al. [65]	22 Central and South American economies; 1995–2010	Pedroni cointegration test and Granger causality test	Negative and unidirectional causality from renewable energy to CO2 emissions
Bayar et al. [66]	EU countries; 2004–2017	Cointegration and causality analyses	Negative
Petruška et al. [67]	22 European countries; 1992–2019	Cointegration analysis	Negative
Apergis, et al. [68]	19 developed and developing economies; 1984–2007	Vector error correction model	Insignificant
Jaforullah and King [69]	USA; 1960–2007	VAR	Negative
Ben Jebli and Youssef [70]	Tunisia; 1980–2011	Vector error correction model	Negative
Khoshnevis Yazdi and Beygi [71]	25 African countries; 1985–2015	PMG estimator and Granger causality test	Negative, but causality differs among the countries
Hasnisah et al. [72]	13 Asian developing economies; 1980–2014	Pedroni cointegration test	Insignificant
Saidi and Omri [73]	15 major renewable-energy-consuming countries; 1990–2014	Pedroni cointegrations test	Negative and bidirectional causality between renewable energy and CO2 emissions
Szetela et al. [74]	Top natural-resource-dependant countries; 2000–2015	Regression	Negative
Nan et al. [75]	China; 2000–2020	VAR and quantile regression	Negative

have largely uncovered a negative effect of renewable energy use on greenhouse gases and CO₂ emissions.

The relationship between human capital and the environment was neglected until 2019. However, several studies listed in

Table 3. A summary of the literature on human capital and CO₂ emissions.

Study	Sample; Period	Method	Influence of Human Capital on CO2 Emissions
Zafar et al. [38]	US; 1970–2015	ARDL	Negative
Yao et al. [39]	20 OECD members; 1870–2014	Cointegration and regression analyses	Negative
Rahman et al. [40]	Newly industrialized economies; 1979–2017	Cointegration	Negative
Hao et al. [41]	G7 economies; 1991–2017	CS-ARDL	Negative
Jahanger et al. [42]	78 developing countries; 1990–2016	Regression	Negative
Adikari et al. [43]	Sri Lanka; 1978–2019	ARDL	Negative
Zhang et al. [44]	Top ten global carbon emitters; 1990–2019	Cointegration	Negative
Haini [45]	ASEAN members; 1996–2019	Regression	Positive
Zhang et al. [46]	Pakistan; 1985–2018	ARDL	Negative for CO2 emissions but positive for ecological footprint
Sarkodie et al. [47]	China; 1961–2016	Regression	Positive
Bano et al. [48]	Pakistan; 1971–2014	Causality analysis	Bilateral causality between human capital and CO2 emissions.
Abdouli and Omri [49]	Mediterranean states	Causality analysis	Bilateral causality between human capital and CO2 emissions.
Joof and Zhakanova Isiksal [50]	MINT states; 1976–2010	Causality analysis	Bilateral causality between human capital and CO2 emissions.
Li and Ouyang [76]	China; 1978–2018	ARDL	Positive in the short-term and negative in the long-term
Ahmed et al. [77]	G7 economies; 1971–2014	Cointegration and causality analyses	Negative and a unilateral causal relationship between human capital and ecological footprint.
Sharma et al. [78]	India, Bangladesh, Nepal, Myanmar, Sri Lanka, Thailand; 1985–2019	Panel cointegration and quantile regression	Negative
Iqbal et al. [79]	126 countries; 1971–2020	Regression	Negative
Mentel et al. [80]	26 countries; 1995–2015	Cointegration	Negative
Khan et al. [81]	108 developing economies; 2000–2016	Panel cointegration and vector error correction model	Negative
Pata et al. [82]	US; 1974–2019	ARDL	Negative
Xiao et al. [83]	125 countries; 2000–2019	Regression	Inverted U-shaped interplay between human capital and CO2 emissions for upper-middle- and high-income countries.

included human capital in their empirical analyses as a determinant of the environment. These studies also analysed the drivers of environmental degradation between 2019 and 2023 for different countries and country groups, using regression, co-integration, and causality analyses as econometric techniques. A large portion of these studies revealed a mitigating effect of human capital on CO₂ emissions [38,39,40,41,42,43,44,76,77,78,79,80,81,82]. However, a few studies have also uncovered a positive influence of human capital on CO₂ emissions [45,46,47]. Furthermore, the causality between CO₂ emissions and human capital has been analysed in a small number of studies. Bano et al. [48], Abdouli and Omri [49], and Joof and Zhakanova Isiksal [50] uncovered a bilateral causal relationship between human capital and CO₂ emissions. Ahmed et al. [77] discovered a unilateral causal relationship between human capital and ecological footprint, and Zafar et al. [38] revealed an insignificant causal relationship between human capital and ecological footprint. In conclusion, our article will be one of the limited studies to investigate the mutual interaction between human capital and CO₂ emissions.

Three hypotheses in this article are created based on the associated theoretical aspects and empirical research articles.

H1. 

There is a significant interplay between renewable energy use and CO₂ emissions.

H2. 

There is a significant interplay between business climate and CO₂ emissions.

H3. 

There is a significant interplay between human capital and CO₂ emissions.

4. Materials and Methods

This research paper investigates the relationship between business climate, renewable energy use, human capital, and CO₂ emissions through Westerlund and Edgerton [26] bootstrap cointegration test and JKS [27] causality test considering the methodologies of similar recent empirical studies [15,63,84,85]. The variables used in the empirical section are displayed in

Table 4. Description of variables.

Variable Abbreviation	Data Definition	Data Source
CO2	CO2 emissions (metric tons per capita)	World Bank [86]
BUSCLIM	Private sector index	UNCTADSTAT [29]
RNWEN	Renewable energy use (percentage of total final energy use)	World Bank [87]
HCAP	Human capital index	UNCTADSTAT [29]

. In this context, environment (CO2) is represented by CO₂ emissions (metric tons per capita), obtained from the World Bank [86]. Business climate (BUSCLIM) is proxied by the private sector index from UNCTADSTAT [29]. This index indicates the ease of international trade in terms of time and related costs, as well as financial and regulatory support to businesses in terms of credit availability, the time required to start a business, and speed of contract enforcement. Renewable energy (RNWEN) is proxied by renewable energy use as a percentage of total final energy use and obtained from World Bank [87]. Last, human capital (HCAP) is represented by the index of human capital from UNCTADSTAT [29], reflecting the status of education, skills, health of individuals, and overall R&D activities in a country.

The BRICS economies, consisting of Brazil, the Russian Federation, India, China, and South Africa, are the focal point of this study, covering the period from 2000 to 2020. This timeframe was chosen because the business climate variable is available from 2000, and the CO₂ emissions and renewable energy use variables are available up to 2020. The econometric analyses are implemented using Stata 17.0, Gauss 12.0, and EViews 13.0 statistical packages. Based on the descriptive statistics given in

Table 5. Summary indicators of CO2, BUSCLIM, RNWEN, and HCAP.

Characteristic	CO2	BUSCLM	RNWEN	HCAP
Mean	5.589	51.517	22.739	45.663
Median	6.077	49.653	14.810	48.548
Maximum	11.885	79.796	50.050	61.202
Minimum	0.884	31.575	3.180	26.861
Std. Dev.	3.783	11.081	16.880	9.163
Skewness	0.248	0.6194	0.299	−0.307
Kurtosis	1.596	3.073	1.417	1.783

, the average value of the CO2 variable is 5.589 metric tons per capita, the average value of the BUSCLIM index is 51.517, the average value of RNWEN is 22.739%, and the average value of the HCAP index is 45.663. The variables of RNWEN, BUSCLIM, and HCAP exhibit notable volatility during the period between 2000 and 2020.

The effect of business climate, renewable energy use, and human capital on CO₂ emissions in BRICS states is scrutinized within the scope of the model described in Equation (1). The dependent variable is CO₂ emissions (CO2), and the explanatory variables are business climate (BUSCLIM), renewable energy use (RNWEN), and human capital (HCAP).

$${CO2}_{it}={\alpha }_i+{\beta }_1{BUSCLIM}_{it}+{\beta }_2{RNWEN}_{it}+{\beta }_3{HCAP}_{it}+{\epsilon }_{it}$$

(1)

where i and t, respectively, represent the countries and years.

In the empirical analysis, the methodology presented in Figure 1 is followed [85]. First, LM, LM_adj., and LM cross-section dependency (CD) tests are conducted, which are, respectively, suggested by Breusch and Pagan [88], Pesaran et al. [89], and Pesaran [90]. Then homogeneity tests of delta tilde by Pesaran and Yamagata [91] are conducted to determine the presence of homogeneity or heterogeneity. The stationarity of the variables is examined by means of panel CIPS test of Pesaran [92] considering the presence of heterogeneity among the series. The short-run and long-run interactions among business climate, renewable energy use, human capital, and CO₂ emissions are, respectively, investigated using the JKS [27] causality test and AMG estimator of Eberhardt and Bond [93].

The short-run interaction among business climate, renewable energy use, human capital, and CO₂ emissions is investigated using JKS [27] causality test in view of the fact that there is heterogeneity and cross-sectional dependence among the series. The causality test examines whether one variable is useful in predicting another variable [94]. JKS [27] causality test regards heterogeneity and produces robust results under the presence of cross-sectional dependence. Furthermore, the half-panel jackknife (HPJ) technique of Dhaene and Jochmans [95] used in the test mitigates Nickell bias. Consequently, the JKS [27] test is a robust causality test, especially in complex models for which traditional methods might fall short.

On the other hand, cointegration test determines whether series under consideration moves together or displays a common long-term trend. In other words, two series with stochastic trends are accepted as cointegrated if the linear combination of two variables eliminates the trends or becomes I(0) [96]. In this context, the JKS [27] causality test, which is developed for heterogeneous panel datasets, is used to determine the presence of a short-term interplay of business environment, renewable energy use, human capital, and CO₂ emissions. The test rests on the linear dynamic panel data model, as shown in Equation (2):

$$y_{it}={\varnothing }_{0i}+{\displaystyle \sum _{p=1}^P}{\varnothing }_{p,i}{y}_{i,t-p}+{\displaystyle \sum _{p=1}^P}{\beta }_{p,i}{x}_{i,t-p}+{\epsilon }_{it}$$

(2)

For i = 1, …, N and t = 1, …, T, $x_{it}$ is postulated to be scalar for simplification. The parameters of ${\varnothing }_{0i}$ and ${\varnothing }_{p,i}$, respectively, indicate individual-specific effects and heterogeneous autogressive coefficients (p = 1, …, P). Furthermore, ${\beta }_{p,i}$ are the Granger causality parameters, and ${\epsilon }_{it}$ are the error terms [97]. JKS [27] causality test allows the HPJ estimator to tackle the pooled estimator’s bias problem, and HPJ estimator’s variance is derived from bootstrapping if cross-sectional dependence exists. The null hypothesis, which suggests that $x_{it}$ is not Granger cause of $y_{it}$, and the alternative hypothesis are formulated as follows:

$$\begin{array}{c}{H}_0:{\beta }_{p,i}=0, for all i and p\\ H_1:{\beta }_{p,i}\ne 0, for some i and p\end{array}$$

The long-run interaction among business climate, renewable energy use, human capital, and CO₂ emissions is analysed by Westerlund and Edgerton’s [26] bootstrap cointegration test, which is derived from Equation (3), because the test considers heterogeneity and generates robust results for small samples.

$$y_{it}={\alpha }_i+x_{it}{\beta }_{it}+Z_{it}$$

(3)

$$Z_{it}={\mu }_{it}+V_{it}={\sum }_{J=1}^t{ŋ}_{ij}$$

(4)

where i and t, respectively, represent the cross-section and time dimensions of the dataset. $Z_{it}$ is the disturbance term. The LM statistic is calculated as follows:

$${LM}_N^{+}={\displaystyle \frac{1}{N{T}^2}}{\displaystyle \sum _{i=1}^N}{\displaystyle \sum _{t=1}^T}{\widehat{w}}_i^{-2}{s}_{it}^2$$

(5)

In Equation (5), $s_{it}^2$ is the partial sum of $Z_{it}$, and ${\widehat{w}}_i^{-2}$ is the long-run variance of ${\mu }_{it}$. Both are generated from cointegration model via fully modified ordinary least squares regression. The H0 of the cointegration test suggests the presence of cointegration relationship among the series under consideration [26]. The bootstrapping critical values are regarded if cross-sectional dependence exists. Otherwise, asymptotic probability values from normal distribution are taken into account. Last, cointegration coefficients are estimated through the AMG (augmented mean group) estimator of Eberhardt and Bond [93] to determine the presence of heterogeneity and countries’ cointegration coefficients.

5. Results

In the results section, the subsistence of CD and heterogeneity is examined through econometric tests first. The subsistence of CD among CO2, BUSCLIM, RNWEN, and HCAP is investigated using the LM, LM_adj., and LM CD tests, and their results are exhibited in

Table 6. Results of LM, LM_adj, and LM CD tests.

Cross-Section Dependence Tests	Test Statistics	Probability Values
LM	17.289	0.004
LMadj.	19.537	0.000
LM CD	18.102	0.015

. The null hypothesis in terms of cross-section independence is negated because the probability values of the three tests are lower than 5%. In conclusion, the cross-sectional dependence among CO₂ emissions, business climate, renewable energy use, and human capital is unveiled.

The subsistence of homogeneity is also examined using two delta tilde tests, and these two tests’ results are indicated in

Table 7. Delta tilde tests’ results.

Test	Test Statistic	Probability Values
$\widetilde{∆}$	26.193	0.002
${\widetilde{∆}}_{adj.}$	28.505	0.011

. The null hypothesis in terms of homogeneity is negated for both tests, and the availability of heterogeneity is unveiled. Therefore, econometric tests of unit root, cointegration, and causality developed for heterogeneous panel datasets can lead us to attain relatively stronger results.

The stationarity of CO2, BUSCLIM, RNWEN, and HCAP is investigated by means of a panel CIPS test developed for the series with CD, and the test statistics are reported in

Table 8. Results of panel CIPS test.

Variable	Level		1. Level
	Constant	Constant + Trend	Constant	Constant + Trend
CO2	−1.167	−1.214	−5.782 ***	−6.731 ***
BUSCLM	−1.092	−1.188	−6.921 ***	−7.832 ***
RNWEN	−0.947	−1.036	−8.117 ***	−9.104 ***
HCAP	−0.884	−0.972	−7.479 ***	−8.693 ***

*** significant at 1%.

. The results of the tests show that the variables of CO2, BUSCLIM, RNWEN, and HCAP are not stationary at their level values, but the first differences of these series have become stationary.

The cointegration interplay between human capital, business climate, renewable energy use, and CO₂ emissions is analysed using the Westerlund and Edgerton [26] bootstrap cointegration test. The test statistics, asymptotic, and bootstrap probability values are introduced in

Table 9. Westerlund and Edgerton cointegration test results.

Constant			Constant and Trend
LM Statistic	Asymptotic p Value	Bootstrap p Value	LM Statistic	Asymptotic p Value	Bootstrap p Value
5.885	0.318	0.409	7.102	0.391	5.885

. As a result of the cointegration analysis, the H0 hypothesis, which indicated the presence of a long-term relationship between the variables, is accepted (p > 0.05).

The cointegration coefficients are forecasted by means of the AMG estimator and reported in

Table 10. Cointegration coefficients.

Countries	BUSCLIM	RNWEN	HCAP
Brazil	0.037	−0.317 ***	−0.103 **
China	0.041 ***	−0.428 ***	−0.093 ***
India	0.028	−0.297 ***	−0.086 ***
Russian Federation	0.025 **	−0.414 ***	−0.097
South Africa	0.017 **	−0.469 **	−0.075 **
Panel	0.036 **	−0.413 ***	−0.084 ***

*** and ** indicate significance at 1% and 5% levels, respectively.

. The estimated cointegration coefficients indicate that human capital and renewable energy negatively impact CO₂ emissions, but business climate positively influences CO₂ emissions on the panel level. However, the BRICS countries’ cointegration coefficients indicate that renewable energy use negatively impacts CO₂ emissions in all countries, and human capital also affects CO₂ emissions negatively in all of the countries except Russia. On the other hand, business climate increases CO₂ emissions in China, Russia, and South Africa.

The causal relationship among business climate, renewable energy use, human capital, and CO₂ emissions in the BRICS countries’ economies between 2000 and 2020 is analysed using the JKS [27] panel causality test, and the findings are reported in

Table 11. JKS Granger non-causality test.

H0	HPJ Wald Test	Probability Values
BUSCLIM ↛ CO2	19.2755	0.0000
CO2 ↛ BUSCLIM	49.0675	0.0000
RNWEN ↛ CO2	17.4132	0.0000
CO2 ↛ RNWEN	34.2613	0.0000
HCAP ↛ CO2	11.9784	0.0005
CO2 ↛ HCAP	3.9219	0.0477

. The test findings point out that the bilateral causal connection among BUSCLIM, RNWEN, HCAP, and CO2 is present. In other words, a reciprocal relationship between business climate, renewable energy, human capital, and CO₂ emissions exists.

6. Discussion

Business climate is a significant factor in the decision to start a business or expand business activities. According to this connection, more market-oriented business regulations, fewer procedures to start a business, lower tax and lending rates, and loose labour regulations, which represent a positive business climate, are expected to increase the establishment of companies or the business activities of existing firms. Therefore, a positive business climate can impact CO₂ emissions through the channel of economic growth theoretically, because economic growth and CO₂ emissions are closely related [98]. However, the interaction between business climate and CO₂ emissions can change depending on national environmental policies and ESG practices. Our results indicate that the business climate increases CO₂ emissions in China, Russia, and South Africa in the long run because the mean values of the environmental policy stringency index for China, Russia, and South Africa over 2000–2020 are, respectively, 0.84, 0.82, and 0.71 (on a scale from 0, not stringent, to 6, the highest degree of stringency) [99]. In conclusion, a positive business climate, coupled with loose environmental regulations, results in increased CO₂ emissions in China, Russia, and South Africa. However, the relationship between business climate and CO₂ emissions in Brazil and India is found to be insignificant, which might result from their current business climate and relatively lower level of economic development. The findings of Imran et al. [100] on BRICS countries also support our findings, and our findings are also consistent with the theoretical expectation and the results of Gani and Sharma [32], Rieger [33], and Omri and Afi [34], who revealed a positive relationship between business climate and CO₂ emissions. Last, the bidirectional causality between business climate and CO₂ emissions is compatible with theoretical considerations, because CO₂ emissions can also impact the business climate through labour health, the costs of raw materials, and supply chains.

Renewable energy sources, which emit relatively lower amounts of greenhouse gases emissions than fossil energy sources, are expected to decrease CO₂ emissions. According to this connection, a negative effect of renewable energy use on CO₂ emissions is theoretically expected. Our results indicate that renewable energy use has a negative effect on CO₂ emissions in all BRICS countries in the long run. Therefore, our findings are consistent with the related theoretical considerations and results of Abbas et al. [14], Imran et al. [21], Justice et al. [62], Dogan and Seker [63], Koengkan and Fuinhas [64], Ben Jebli et al. [65], Bayar et al. [66], Petruška et al. [67], Jaforullah and King [69], Ben Jebli and Youssef [70], Khoshnevis Yazdi and Beygi [71], Saidi and Omri [73], Szetela et al. [74], and Nan et al. [75]. However, few studies have investigated the causality between renewable energy use and CO₂ emissions. In this context, Ben Jebli et al. [65], Khoshnevis Yazdi and Beygi [71], and Saidi and Omri [73] discovered different causal relationship between the two variables, but our findings are consistent with the results of Saidi and Omri [73]. Therefore, our results together with the limited empirical literature indicate that renewable energy use is also effective to counter environmental degradation as approximated by CO₂ emissions in the short term.

Lastly, human capital is one of the main drivers of production and consumption and may impact CO₂ emissions through environmental awareness, entrepreneurship, innovation, green and energy efficient technologies, labour productivity, and physical capital investment. But the effect of human capital on CO₂ emissions can be changed theoretically based on countries’ own characteristics. Our findings denote that improvements in human capital reduce CO₂ emissions in all BRICS countries except Russia in the long run. Our findings might be due to the fact that Brazil, China, India, and South Africa achieved significant progress in human capital, but Russia already had a high human capital during 2000–2020 [29]. In conclusion, our results are consistent with the related theoretical views and the results of [38,39,40,41,42,43,44,76,77,78,79,80,81,82]. Furthermore, our bilateral causality between CO₂ emissions and human capital is compatible with the results of Bano et al. [48], Abdouli and Omri [49], and Joof and Zhakanova Isiksal [50].

7. Conclusions, Policy Implications, and Limitations

The world has achieved significant progress in terms of welfare since the Industrial Revolution, but this has been accompanied by noteworthy environmental degradation. Consequently, concerns about environmental problems have intensified since the 1950s, leading researchers and policymakers to focus on factors that can disrupt the environment. This article examines the influence of business climate, renewable energy use, and human capital on CO₂ emissions in the BRICS countries’ economies, which are also among the top global CO₂ emitters. The study addresses the insufficient number of studies on the nexus between business climate, human capital, and CO₂ emissions, despite their critical importance for all economic activities, through causality and cointegration tests that account for cross-sectional dependency and heterogeneity.

The results of the causality test indicate a bidirectional causal relationship among business climate, renewable energy use, human capital, and CO₂ emissions. On one hand, business climate, renewable energy use, and human capital have a significant influence on CO₂ emissions. On the other hand, CO₂ emissions also have a significant impact on business climate, renewable energy use, and human capital. In other words, there exists a feedback interaction among these factors in the short term. In the long-term analysis, improvements in renewable energy use support the decrease in CO₂ emissions in all BRICS countries, and improvements in human capital also decrease CO₂ emissions in all BRICS countries except Russia. However, the effect of human capital on CO₂ emissions is much higher than that of renewable energy use in all BRICS countries’ economies. Lastly, having a positive business climate enhances CO₂ emissions in China, India, and Russia, which have loose environmental regulations. On the other hand, the results of the causality test indicate a mutual interaction between business climate, renewable energy use, human capital, and CO₂ emissions and support these long-term findings.

Based on the results of our research, three policy recommendations are made to decrease CO₂ emissions, as follows:

First, human capital has the largest impact on CO₂ emissions when compared with the impacts of renewable energy and business climate because human capital can impact CO₂ emissions through multiple channels. Therefore, it is recommended that environmental awareness among individuals should be fostered by education policies in terms of lifelong educational and training programs. All of the countries except Russia can keep decreasing their CO₂ emissions by improving their human capital through educational investments.

Secondly, renewable energy use is an effective tool to decrease CO₂ emissions in all BRICS countries. Therefore, the clean energy transition should be accelerated to reduce CO₂ emissions in all BRICS countries.

Last, business climate is revealed to increase CO₂ emissions in China, Russia, and South Africa, which had fewer environmental regulations during 2000–2020. All countries, especially China, Russia, and South Africa, should encourage firms to move towards ESG practices through business climate and environmental policies.

This article has several limitations that could guide future studies. First, the sample is limited to BRICS countries and could be expanded to include developing and developed countries. Secondly, the availability of business climate and CO₂ emissions data restricts our analysis to the period between 2000 and 2020. Thirdly, the study investigated the overall effect of human capital, renewable energy use, and business climate on CO₂ emissions. Future research could focus on the influence of secondary, tertiary, and vocational education on the interplay between human capital and CO₂ emissions.