The Quantile Effects of Climate and Global Economic Policy Uncertainties on Renewable Energy Demands in the BRICS Nations: The Role of Green Ecological Policies

Abstract

Renewable energy has an important role in attaining environmental quality. Numerous studies have determined the factors driving the diminishment or promotion of renewable energy consumption (REC) globally. Despite the findings of prior studies, the specific effects of climate policy uncertainty (CPU) and global economic policy uncertainty (GEPU) on REC have not been probed in the case of BRICS economies. Furthermore, fewer considerations have been taken into account to explore whether green environmental policies (EPs) have a moderating role. Therefore, the present work contributes to filling the gap by providing an empirical model and assessing the panel quantile data and fixed-effect methods used between 2010 and 2020. The findings reveal that the expansion of the financial market, inflowing FDI, rising international remittances, developing environmental innovation, and increasing CPU are significant factors promoting REC while unfavorably increasing CO₂, economic activity, and GEPU impact. Notably, the results underscore that EPs have a significant positive effect, and stricter ecological strategies lead to a rise in REC. In addition, the findings highlight that by implementing stricter EPs, the effect of CPU on REC becomes more positive, and the negative effect of GEPU on REC is more controlled. In particular, the significant interaction effects highlight that the beneficial role of EPs is more pronounced in nations with strong EPs relative to low EPs. Overall, EPs have a substantial beneficial role in promoting REC when CPU and GEPU arise. These findings are reliable after using numerous measurements and methods, and several policy recommendations are suggested to governments, policymakers, and regulatory bodies.

1. Introduction

Over the last decade, authorities and policymakers have put into place various plans and strategic activities to promote renewable energy (RE) globally. These actions include tax benefits, cheaper credits, and incentive plans aiming to encourage households to switch to RE, motivate firms to raise R&D in green energy and encourage investors to invest in RE plans to decrease climate change, reduce CO₂ emissions, and also attain ecological sustainability [1,2,3,4,5]. Although these actions are useful for promoting RE [6], there is a significant USD 1.1 trillion in funds still required by 2030 to control greenhouse emissions and achieve sustainable development goals (SDGs) [7].

Because of rising worldwide climate change and diminishing air quality, the subject of green energy and RE has become a trending subject at the global level. Consequently, shifting economies from conventional fossil fuels to RE sources is the most important decision in their agendas to reduce climate change and accelerate achieving SDGs [8]. Unlike traditional energy, RE is replenishable and also has ecologically friendly advantages.

Therefore, a strand of the literature has been created essentially to investigate the factors that help promote or diminish renewable energy consumption (REC) in various countries using a demand modeling method. For instance, the findings of prior studies have revealed that capital development (e.g., [9,10,11]) promotes REC by providing easier external financing at lower costs for investors and households. Likewise, refs. [12,13] revealed that environmental innovations promote REC by increasing energy efficiency and accelerating the shift of the economy to a green country. Moreover, refs. [14,15] highlight that economic openness promotes REC by providing more availability to external financing.

Furthermore, prior works have revealed that economic activity (e.g., [16,17]) and international remittances (e.g., [18,19]) promote REC by increasing the income level of investors and households. Additionally, past works have documented that CO₂ [15,17] diminishes REC. Moreover, earlier studies (e.g., [11,20,21]) have shown that green ecological strategies (EPs) promote REC by enhancing RE capacity growth. However, some works (e.g., [22,23]) have underscored how global economic policy uncertainty (GEPU) diminishes REC by decreasing investor and household income (demand side) [24] and raising investment costs in the private sector [25].

What about the determinants of REC in the BRICS countries, and which factors diminish or promote REC? Understanding this question is essential since it helps policymakers to increase the proportion of REC and help accelerate the energy transition from fossil fuels to green energy, which will eventually lead to attaining ecological sustainability. Brazil, Russia, India, China, and South Africa, which are considered BRICS, represent five massive and emerging economies in the world. Over the last decades, these economies have had outstanding economic development and endeavor to attain SDGs. For example, China applied numerous environmental strategies to encourage green energy in 2006 and 2009 and also used subsidy agendas in 2003 and 2010. This led to, for instance, China increasing its regional share of hydroelectricity production from 2.9% in 1973 to 30.1% in 2019. Also, it helped China to increase its regional share of wind electricity from 1.9% in 2005 to 28.4% in 2019 (https://www.iea.org/reports/key-world-energy-statistics-2021/supply, accessed on 1 June 2024).

Although some efforts have been made in some emerging economies to attain ecological sustainability, there are some significant challenges and problems that reduce the acceleration of the transition from traditional to green energy. For example, RE sources are not fully utilized in some nations, such as India and South Africa, and their share of fossil energy is relatively high [15]. Likewise, energy demands and carbon emissions are still soaring in developing nations, like BRICS, compared to advanced nations due to the rapid growth of energy-intensive businesses and a shortage of energy productivity (e.g., [26]). For example, China accounted for 4.7% of crude oil production regionally in 2020, which was 1.9% in 1973. In addition, the investment in RE schemes decreased to USD 15.4 B in India in 2018 and USD 91.5 B in China in 2019, leading to many RE schemes remaining uncompleted [27,28]. Therefore, BRICS is an interesting case study, and it is important to identify which determinants lead to the promotion or diminishment of REC in BRICS.

This work endeavors to answer the subsequent questions in BRICS settings: (i) What are the determinants of REC? (ii) How specifically do climate policy uncertainty (CPU) and GEPU influence REC? (iii) Do EPs have a moderating effect on CPU, GEPU, and REC?

To attain these objectives, the current research uses novel data for BRICS nations between 2010 and 2020. Likewise, this research also contributes by performing both fixed effects (FEs) and quantile panel data (PQ) techniques to find reliable results. Furthermore, unlike past work (e.g., [11,17]), the current research contributes by probing the elements of the REC in BRICS environments and by focusing mainly on the roles of CPU, GEPU, and EPS, which have not been studied deeply. We employ a novel BRICS EPS index, which uses data collected from the OECD Stat website and which is broadly used for strategy examination aimed at climate change and air pollution-lessening policies. This unique research on BRICS economies can provide findings that are valuable to the work of authorities, policymakers, and executives to upsurge the share of REC and ultimately attain SDGs.

The current research yields some important highlights. The research uncovered that the expansion of the capital market, inflowing FDI, rising international remittances, boosting environmental innovation, and rising CPU are significant drivers promoting REC. This indicates that policymakers and regulatory bodies should make external financing more accessible for investors and households. This would allow them to borrow funds at lower costs, thereby expediting the completion of renewable energy schemes. Likewise, the results denote that representatives should pay more attention to R&D development and enhance country-level competitiveness factors (e.g., private and public institution qualities, economic stability, political stability, infrastructure, and education) to expedite shifting from traditional energies to green energies and attract cross-border capital inflows. Furthermore, the findings highlight that rising CO₂, economic activity, and GEPU have detrimental impacts on the REC of BRICS environments. Moreover, the findings underline that stricter EPS leads to a rise in the REC, and by implementing a stricter EPS, the impact of CPU on REC becomes more positive, and the negative impact of GEPU on REC is more controlled. The findings recommend policymakers design and implement stricter environmental policies to moderate the impact of GEPU on the REC. Policymakers, by developing effective EPS, could accelerate climate change alleviation strategies by promoting REC, particularly when CPU and GEPU arise.

The structure of this study is organized as follows: Section 2 reviews previous works. Section 3 discusses the data and methodology used. Section 4 and Section 5 detail the univariate and multivariate analyses, followed by a robustness test. Finally, Section 6 presents the conclusions.

2. Literature Review

A portion of energy research in the literature has focused on identifying the key factors that influence renewable energy consumption (REC) across various economies using demand modeling methods. Previous research has primarily indicated that several significant drivers impacting REC globally include capital market development, financial openness, international remittances, ecological innovation, carbon dioxide (CO₂) emissions, and overall economic activity. Additionally, earlier studies have shown that global Carbon Pricing Units (CPUs) and Global Environmental Policy Units (GEPU) also play an important role in affecting REC. In the following paragraphs, we will discuss in detail how these factors are connected to REC.

2.1. Country-Level Factors

According to previous studies [11,29], the development of financial markets can facilitate the availability of external funds at lower financing costs for both investors and households, thereby promoting renewable energy commercialization (REC). A country with a well-developed capital market can expedite the implementation of renewable energy (RE) projects and enhance access to a variety of financial products and services. This, in turn, can inspire firms to increase research and development (R&D) activities in clean energy, improve ecological knowledge, and expand RE initiatives.

Additionally, earlier research [30,31] demonstrated that financial openness, characterized by the inflow of foreign capital such as foreign direct investment (FDI), provides cheaper external funds for investors and households, which can also promote REC. A nation with policies supporting financial openness can facilitate the dissemination of better management practices and greener technologies, ultimately leading to an increase in both the supply and demand for renewable energy [32].

Numerous studies (e.g., [11,19]) have shown that international remittances can increase household income levels, which, in turn, may promote renewable energy consumption (REC). As remittances flow into households, they encourage families to invest in renewable energy plans. This financial support allows them to enhance their knowledge and skills related to renewable energy and implement green systems in their homes [33].

Similarly, research by [12,13] indicates that ecological innovations, which improve energy productivity, can also promote REC. Countries with high levels of technological innovation are likely to reduce the investment costs associated with new renewable energy initiatives. This shift can help transition a nation from a traditional economy to a greener one [34,35]. As a result, such changes lead to decreased CO₂ emissions and assist in achieving the Sustainable Development Goals (SDGs) [13].

Numerous studies have shown that CO₂ emissions and economic activity negatively impact renewable energy consumption (REC). This can be attributed to some governments promoting fossil fuel energy through financial incentives, which lowers the price of carbon emissions and ultimately reduces REC. However, other research indicates that economic growth can promote REC, with more developed nations requiring increased energy production from both non-renewable and renewable energy (RE) sources.

Additionally, certain studies [36,37] suggest that environmental policies, such as taxes, can encourage investors and households to support REC. Research [11] has found that implementing green ecological methods can lead to an increase in REC. Countries with stricter environmental policies are likely to see enhanced growth and consumption of renewable energy capacity [21,36]. Importantly, the works by [38,39,40] indicate that the effectiveness of ecological strategies is influenced by the type of renewable energy sources utilized.

2.2. Global-Level Factors

Several studies have examined the impact of global-level factors on renewable energy consumption (REC). For example, the earlier research by [11,23] indicated that global economic policy uncertainty (GEPU) negatively affects renewable energy from both the supply and demand perspectives. An increase in GEPU tends to lower the income levels of investors and households, which, in turn, slows down the transition to a green economy in the long run [24]. Additionally, GEPU raises investment costs for the private sector, leading to a decrease in the supply of renewable energy [25].

Conversely, other studies have emphasized the significant role of global climate policy uncertainty (CPU) in stimulating both renewable energy demand and supply. For instance, research by [41,42] found that an increase in CPU encourages firms to invest more in green energy initiatives (supply side) and boosts the green energy sector supported by rising CPU. Moreover, reference [8] documented that CPU promotes REC (the demand side) in the long term. Reference [43] revealed that during extreme climate events, the causal connection between CPU and renewable energy consumption strengthens considerably. Additionally, reference [44] showed that CPU has a favorable impact on REC in the United States. A summary of the literature is presented in

Table 1. Summary of the literature.

Authors	Case-Study	Factors	Methods
[8]	USA	Economic growth, crude oil prices, CPU	Autoregressive distributed lag (ARDL)
[10]	European Union countries	Energy prices, income, financial development, FDI	FE
[11]	OECD countries	Financial expansion, FDI, remittances, environmental innovation, CO2, EPS, economic activity, GEPU	FE, PQ
[12]	OECD countries	Economic expansion, trade openness, technological innovation	ARDL, pooled mean group, mean group, FE
[14]	Asian countries	Equity market expansion, FDI, trade openness, economic growth	PQ, FE, GMM
[15]	BRICS	Financial expansion, technological innovations, FDI, economic growth, financial stability	FE, PQ
[17]	OECD countries	Geopolitical risks, CO2, economic globalization, income	GMM
[22]	USA	Economic policy uncertainty (EPU), population, real R&D expenditure, crude oil price, economic expansion	Nonparametric tests, nonparametric regressions
[23]	China	EPU, FDI, economic size, financial development	Nonlinear ARDL
[24]	Emerging markets	Financial development, geopolitical risk, FDI, energy prices, income	GMM

. Several studies have examined the impact of global-level factors on renewable energy consumption (REC). For example, the earlier research by [11,23] indicated that global economic policy uncertainty (GEPU) negatively affects renewable energy from both the supply and demand perspectives. An increase in GEPU tends to lower the income levels of investors and households, which, in turn, slows down the transition to a green economy in the long run [24]. Additionally, GEPU raises investment costs for the private sector, leading to a decrease in the supply of renewable energy [25].

Several studies have identified various country-specific and global factors that influence the rise or decline of renewable energy consumption (REC) worldwide. However, the specific impacts of Consumer Price Uncertainty (CPU) and global economic policy uncertainty (GEPU) on REC, particularly within the context of BRICS nations, have not been thoroughly examined. Furthermore, there has been limited exploration into whether economic policy stability (EPS) serves as a moderator between CPU, GEPU, and REC. This study aims to address these gaps by utilizing novel panel data from emerging BRICS countries and applying the Pooled Ordinary Least Squares (POLS) and fixed effects (FE) methods for the period from 2010 to 2020.

3. Methodology

3.1. Data Description

This research examines the factors influencing renewable energy consumption (REC) in BRICS nations over the period from 2010 to 2022. However, due to data accessibility issues and inconsistencies among various databases, the final analysis is limited to the years 2010 to 2020.

Table A1. BRICS nations and the average of factors (2010–2020).

BRICS Countries	LnREC	LnSMC	LnFDI	LnREM	LnTINV	LnCO2	LnGDPC
Brazil	3.831	3.853	1.270	−1.951	9.931	13.012	9.222
Russia	1.238	3.637	0.402	−0.841	10.518	14.306	9.375
India	3.717	4.416	0.556	1.156	10.160	14.554	7.413
China	2.801	4.055	0.787	−1.591	12.265	16.110	8.941
South Africa	2.455	5.490	−0.029	−1.427	8.862	12.967	8.852

in Appendix A lists the BRICS nations and presents the average values of the key determinants for each country. According to the data, Brazil has the highest average for LnREC (3.831) and LnFDI (1.270). South Africa, on the other hand, leads in LnSMC (5.490), indicating that it has the most developed capital market. Additionally, China has the highest averages for LnTINV (12.265) and LnCO₂ (16.110). It is noteworthy that Brazil has the lowest average for LnREM (−1.951), while Russia shows the highest average for LnGDPC (8.941). This research examines the factors influencing renewable energy consumption (REC) in BRICS nations over the period from 2010 to 2022. However, due to data accessibility issues and inconsistencies among various databases, the final analysis was limited to the years 2010 to 2020.

Following earlier studies, we identified the main factors that influence renewable energy consumption (REC). This study collected data on various factors, including renewable energy consumption (REC), capital market development (SMC), trade openness (FDI), remittances (REMs), technological innovation (TINV), carbon dioxide emissions (CO₂), and economic output (GDP) from the World Bank. Specifically, we obtained Consumer Policy Uncertainty (CPU) and Global Economic Policy Uncertainty (GEPU) data from the policy uncertainty website.

The CPU index is constructed based on reporting terms such as “uncertainty”, “carbon dioxide”, “global warming”, and “regulation”, as mentioned in articles from eight leading U.S. newspapers. The GEPU index, on the other hand, is the GDP-weighted average of national Economic Policy Uncertainty (EPU) indices for 21 nations. Each national EPU index reflects the relative frequency of articles in local newspapers that discuss three terms: economy, policy, and uncertainty.

Furthermore, for further analysis, we selected the environmental policy stringency (EPS) index from the OECD Stat website. Additionally, we gathered data for the World Uncertainty Index (WUI) and Geopolitical Risk Index (GPR) from the policy uncertainty website. The WUI is calculated based on indices of economic uncertainty for 143 nations, employing frequency counts of the term “uncertainty” in the quarterly Economist Intelligence Unit (EIU) country reports. Similarly, the GPR is derived by counting the number of articles related to adverse geopolitical events in newspapers each month.

The explanations and sources for these elements are detailed in

Table 2. Factors explanations.

Factors	Symbols	Definitions	Sources
Dependent variable:
Renewable energy consumption	REC	REC (% of total final energy consumption)	World Bank (https://databank.worldbank.org/)
Independent variables:
Financial development	SMC	Stock market capitalization (% of GDP)
Trade openness	FDI	FDI, net inflows (% of GDP)
International remittances	REM	Personal remittances received (% of GDP)
Technological innovation	TINV	Total number of patent applications
Carbon dioxide emissions	CO2	CO2 emissions (kt)
Economic development	GDPC	GDP per capita (current US$)
Climate policy uncertainty	CPU	CPU index	www.policyuncertainty.com
Worldwide policy uncertainty	GEPU	GEPU index

.

Figure 1 presents a scatterplot matrix illustrating the relationships among the determinants studied. Additionally, Figure 2 displays the time series data for REC, CPU, and GEPU within the BRICS countries from 2010 to 2020. As shown in Figure 2, there is a positive correlation between REC, CPU, and GEPU, suggesting that increases in CPU and GEPU lead to a rise in REC. Furthermore, Figure 2 indicates that REC showed a continuous increase throughout the study period, while CPU experienced a decline during the periods of 2010–2013 and 2017–2018.

Figure 3 highlights the potential impact of various factors on renewable energy consumption (REC) in BRICS countries. As illustrated, certain factors such as Structural Market Conditions (SMCs), foreign direct investment (FDI), Consumer Price Utility (CPU), remittances (REMs), and total investment (TINV) are anticipated to have a positive effect on REC. Conversely, other factors like Global Energy Policy Uncertainty (GEPU), gross domestic product growth (GDPC), and carbon dioxide emissions (CO₂) may impact REC both positively and negatively. The analysis presented in Figure 3 was achieved through a series of pre-estimation tests, estimation analyses, and post-estimation tests to validate the results.

Table 3. Correlation analysis.

	SMC	FDI	REM	TINV	CO2	GDPC	CPU	GEPU	VIF
SMC	1.000								1.12
FDI	−0.261 *	1.000							1.06
REM	0.062	−0.213 *	1.000						1.16
TINV	−0.277 *	0.294 *	−0.063	1.000					1.03
CO2	0.332 *	0.084 *	0.194	0.358 *	1.000				1.18
GDPC	−0.292	0.104	−0.346 *	0.091	−0.163	1.000			1.04
CPU	0.181	−0.161	0.023	−0.192	0.007	−0.005	1.000		1.09
GEPU	0.153	−0.132	0.018	−0.195	0.011	0.002	0.434 *	1.000	1.15

Note: * is statistically significant at 1%.

presents the results of the correlation analysis. It indicates that there is no evidence of multicollinearity among the factors, allowing for the inclusion of all potential determinants in the model estimates. Additionally, Table 3 shows that some correlations are statistically significant at the 1% level, with both positive and negative signs. Furthermore, the Variance Inflation Factor (VIF) suggests that the estimation models do not suffer from multicollinearity, even when incorporating all possible factors.

3.2. Model and Methodology

Equation (1) is employed to perform empirical analyses.

$${\mathrm{REC}}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{SMC}}_{\mathrm{it}}+{\mathsf{\alpha }}_2{\mathrm{FDI}}_{\mathrm{it}}+{\mathsf{\alpha }}_3{\mathrm{REM}}_{\mathrm{it}}+{\mathsf{\alpha }}_4{\mathrm{TINV}}_{\mathrm{it}}+{\mathsf{\alpha }}_5{\mathrm{C}\mathrm{O}2}_{\mathrm{it}}+{\mathsf{\alpha }}_6{\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{C}}_{\mathrm{it}}+{\mathsf{\alpha }}_7{\mathrm{C}\mathrm{P}\mathrm{U}}_{\mathrm{t}}+{\mathsf{\alpha }}_8{\mathrm{G}\mathrm{E}\mathrm{U}\mathrm{P}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{it}}$$

(1)

where country and time are represented correspondingly.

To mitigate the effect of outliers, the data elements are winsorized at the 1% level. Furthermore, each factor is normalized by applying the natural logarithm. In the estimation of Equation (1), we utilized the PQ method (see, e.g., [11,15,45]). This approach helps identify connections across multiple points in the distribution of the dependent variable (LnREC) for the estimation of Equation (1). Quantiles act as thresholds that divide a probability distribution into smaller, continuous intervals that each contain equal probabilities. The PQ technique allows us to understand the relationships among variables at different quantiles.

Additionally, we employed the fixed effects (FEs) approach while clustering standard errors at the country level to ensure reliable results. Studies conducted by [46,47] indicated that using the panel data methodology enhances estimation efficiency and addresses issues related to multicollinearity and heterogeneity. To mitigate the effect of outliers, the data elements are winsorized at the 1% level. Furthermore, each factor is normalized by applying the natural logarithm. For the estimation of Equation (1), we utilized the PQ method (see, e.g., [11,15,45]). This approach helped identify connections across multiple points in the distribution of the dependent variable (LnREC) for the estimation of Equation (1). Quantiles act as thresholds that divide a probability distribution into smaller, continuous intervals that each contain equal probabilities. The PQ technique allows us to understand the relationships among variables at different quantiles.

4. Univariate and Multivariate Analyses

Table 4. Descriptive statistics (2010–2020).

Elements	Mean	Median	St.Dev	Min	Max
LnREC	2.808	2.748	0.964	1.172	3.890
LnSMC	4.290	4.192	0.699	2.931	5.777
LnFDI	0.597	0.636	0.596	−0.824	1.420
LnREM	−0.931	−1.402	1.135	−2.227	1.327
LnTINV	10.347	10.294	1.170	8.736	13.174
LnCO2	14.190	14.297	1.172	12.882	16.208
LnGDPC	8.761	8.999	0.731	7.208	9.679
LnCPU	4.810	4.741	0.327	4.351	5.387
LnGEPU	5.095	5.035	0.322	4.663	5.768

presents the descriptive statistics of determinants from 2010 to 2020. The average of LnCO₂ is 14.190, which is the highest, with values ranging from 12.882 to 16.208. Conversely, the average of LnREM is −0.931, and the lowest average ranges from −2.227 to 1.327. Following that, LnFDI has an average of 0.597, with a range between −0.824 and 1.420.

Additionally, Table 4 indicates that LnREM has the lowest minimum value at −2.227, while LnCO₂ has the highest maximum value at 16.208. Moreover, LnCO₂ exhibits the highest variation with a standard deviation of 1.172, followed closely by LnTINV at 1.170. In contrast, LnGEPU and LnCPU display the lowest variations, with standard deviations of 0.322 and 0.327, respectively.

4.1. Multivariate Results

Before estimating the models, this study conducted cross-sectional dependence (CD) and panel unit root tests.

Table 5. Cross-sectional dependence.

	REC	SMC	FDI	REM	TINV	CO2	GDPC	CPU	GEPU
Pesaran’s test	13.802 **	24.725 *	38.551 **	44.275 *	19.459 *	33.239 *	24.337 **	19.117 *	42.722 *

Note: * and ** reveal statistical significance at the 1% and 5%, correspondingly.

shows that the test values for all factors are statistically significant, indicating that any variation in one country will definitely impact the others in the sample. In the robustness section, we employed the CD post-estimation test to examine whether the estimated model encounters any dependency issues.

We also conducted the panel unit root test as recommended by sources [48,49] to examine the stationarity of the factors. Specifically, the suggested methods allowed us to assess the stationarity of the factors while considering both trend and cross-dependence alternatives. As shown in Panels (A) and (B) of

Table 6. Panel unit root tests.

Factors	Panel (A): [48]		Panel (B): [49]
	With Trend	With CD	With Trend	With CD
LnREC	−5.316 *	−4.325 *	−5.418 *	−6.343 *
LnSMC	−4.432 *	−6.437 *	−4.522 *	−4.532 *
LnFDI	−5.247 *	−4.662 *	−5.433 *	−2.217 **
LnREM	−4.566 *	−5.343 *	−4.684 *	−4.623 *
LnTINV	−6.323 *	−2.162 **	−6.341 *	−5.315 *
LnCO2	−4.242 *	−4.527 *	−4.737 *	−3.224 *
LnGDPC	−5.538 *	−4.679 *	−6.354 *	−5.743 *
LnCPU	−5.477 *	−6.292 *	−2.213 **	−4.435 *
LnGEPU	−2.225 **	−2.212 **	−3.714 *	−2.146 **

Note: * and ** reveal statistical significance at the 1% and 5%, correspondingly.

, the variables under investigation were stationary (I(1)), indicating that the panels did not contain unit roots.

We conducted a Granger causality test to examine the possibility of reverse causality among the factors. As shown in

Table 7. Granger causality test.

H0			F-Statistics	[Prob]	Result
LnSMC	→	LnREC	6.421 *	[0.000]	Yes
LnFDI	→	LnREC	5.668 *	[0.000]	Yes
LnREM	→	LnREC	2.103 **	[0.024]	Yes
LnTINV	→	LnREC	5.451 *	[0.001]	Yes
LnCO2	→	LnREC	4.873 *	[0.000]	Yes
LnGDPC	→	LnREC	5.932 *	[0.002]	Yes
LnCPU	→	LnREC	2.202 ***	[0.068]	Yes
LnGEPU	→	LnREC	2.224 **	[0.019]	Yes

Note: *, **, and *** denote 1%, 5%, and 10% statistical significance levels, respectively.

, the F-statistics were significant at the 1%, 5%, and 10% significance levels, indicating that causality flows from LnSMC, LnFDI, LnREM, LnTINV, LnCO₂, LnGDPC, LnCPU, and LnGEPU to LnREC for the BRICS economies. This suggests that reverse causality is likely to occur, and the tested model is not affected by potential endogeneity.

4.1.1. Estimation Results

Table 8. The determinants of REC: the role of CPU and GEPU.

Independent Factors	PQ				FE
	Q.25	Q.50	Q.75	Q.95	Coefficients
LnSMC	0.034	0.038	0.056 **	0.053 ***	0.022
	(1.44)	(0.78)	(2.14)	(1.68)	(1.18)
LnFDI	0.025	0.043 **	0.059 *	0.048 **	0.067 *
	(0.74)	(2.04)	(5.15)	(2.11)	(4.86)
LnREM	0.101 ***	0.122 **	0.138 **	0.144	0.011
	(1.73)	(2.15)	(2.08)	(1.33)	(1.54)
LnTINV	0.034 **	0.051 *	0.048 ***	0.042 *	0.028 **
	(2.18)	(3.87)	(1.71)	(4.66)	(2.19)
LnCO2	−0.216	−0.354 **	−0.361 ***	−0.411 *	−0.244 **
	(−1.43)	(−2.14)	(−1.73)	(−5.18)	(−2.05)
LnGDPC	−0.041 **	−0.035 **	−0.026 *	−0.061 ***	−0.022
	(−2.14)	(−2.19)	(−4.18)	(−1.73)	(−1.43)
LnCPU	0.122 **	0.252 *	0.318 ***	0.311 ***	0.405 *
	(2.02)	(5.22)	(1.68)	(1.71)	(4.18)
LnGEPU	−0.232 **	−0.348 *	−0.316 **	−0.269 ***	−0.288 **
	(−2.12)	(−5.16)	(−2.02)	(−1.73)	(−2.19)
CD-test	—	—	—	—	(0.393)
Dummies	✓	✓	✓	✓	✓

Note: *, **, and *** denote 1%, 5%, and 10% statistical significance levels, respectively.

presents the determinants of renewable energy consumption (REC). The results indicate that the natural logarithm of Social Market Capitalization (LnSMC) has a positive effect on LnREC. However, the coefficient is statistically significant only in the 75th percentile (Q.75) at a significance level of 5% (α = 0.056) and in the 95th percentile (Q.95) at a level of 10% (α = 0.053). These findings are consistent with previous studies (e.g., [9,10,11,15]), suggesting that the development of financial markets plays a crucial role in promoting REC in BRICS economies. A well-developed financial market leads to greater availability of external financing and lower borrowing costs. As a result, investors are more likely to engage in renewable energy projects, which ultimately boosts REC and enhances ecological quality (e.g., [26,29]). Furthermore, a developed financial market encourages firms to invest more in research and development (R&D) for renewable energy initiatives, clean energy, and increasing environmental awareness, thereby contributing to higher REC in the long term.

The results further highlight that foreign direct investment (LnFDI), remittances (LnREM), and total investment (LnTINV) have a positive impact on renewable energy consumption (LnREC), with statistically significant coefficients across various quantiles. Based on the estimates from the fixed effects (FEs) method, a 1% increase in FDI and TINV results in an increase of 0.06% and 0.02% in renewable energy consumption, respectively. In agreement with previous studies, these findings indicate that cross-border capital inflows play a crucial role in enhancing renewable energy consumption in BRICS economies. The openness of these economies allows for foreign capital inflows, enabling investors to access additional external funds for renewable energy projects. Furthermore, this also facilitates the transfer of improved management practices and greener skills, which contribute to the growth of renewable energy both in the host country and globally [50]. The results indicate that international remittances play a significant role in promoting renewable energy consumption (REC) in BRICS economies, which is consistent with previous studies (e.g., [11,15,19,51,52]). Increased remittances raise household income levels, enabling families to invest in renewable energy technologies and fund renewable energy initiatives more easily. This shift gradually reduces the reliance on fossil fuels while increasing the use of renewable energy sources.

Additionally, the findings suggest that technological innovation is a crucial driver of REC in BRICS economies, aligning with earlier research ([12,53]). Enhanced technological innovation improves energy efficiency and accelerates the transition from conventional energy sources to clean and green alternatives. Consequently, this transition contributes to a decline in CO₂ emissions and supports the achievement of Sustainable Development Goals (SDGs) [13,54].

In contrast, Table 8 indicates that LnCO₂ has a negative impact on LnREC. According to the estimations from the fixed effects (FEs) method, a 1% increase in CO₂ results in a 0.2% decrease in renewable energy consumption (REC). These findings align with previous studies (e.g., [17]) suggesting that CO₂ is a significant factor adversely affecting REC in BRICS economies. This situation can be attributed to many governments promoting fossil fuel energy. For instance, OECD countries allocated nearly USD 108 billion to fossil fuel extraction in 2019, providing financial incentives for investments in this sector. This investment leads to a reduction in carbon emissions prices, undermining the effectiveness of ecological initiatives and obstructing the transition to a clean economy [55].

Additionally, consistent with the findings of Ref [14], gross domestic product per capita (GDPC) is also a significant factor negatively impacting REC in BRICS economies. However, some studies (e.g., [16,34,56]) have documented that REC can increase with a rise in real per capita income. Similarly, the research in Ref. [17] emphasizes that GDP per capita has a positive effect on REC in OECD nations.

Table 8 shows that LnCPU has a positive effect on LnREC at various quantiles in the BRICS nations: Q.25 (α = 0.122), Q.50 (α = 0.252), Q.75 (α = 0.318), and Q.95 (α = 0.311). According to the estimates from the fixed effects method, a 1% increase in CPU leads to a 0.4% increase in REC. This aligns with previous studies (e.g., [8,41]), indicating that CPU is a significant driver of REC in BRICS economies. An increase in CPU encourages firms to boost their investments in clean energy initiatives, which contributes to a higher REC in the long term. Furthermore, the research by [42] suggested that the green energy sector was enhanced by increasing CPU and the Paris Agreement (2015).

The results indicate that LnGEPU has a negative impact on LnREC at various quantiles: Q.25 (α = −0.232), Q.50 (α = −0.348), Q.75 (α = −0.316), and Q.95 (α = −0.269). Specifically, a 1% increase in GEPU is associated with a 0.28% reduction in REC within the BRICS nations. This aligns with previous findings from sources [11,22,23], suggesting that GEPU adversely affects REC. The rise in economic policy uncertainty (EPU) on an international scale negatively influences the income levels of investors and households, making them less motivated to replace fossil fuels with renewable energy sources (REs). Notably, the higher levels of GEPU result in a slower transition from traditional energy sources to REs. It is important to mention that all estimated models incorporate time and country dummies, although their coefficients are not reported to save space. Additionally, the cross-sectional dependence (CD) post-estimation test produced a p-value of 0.393, which is not statistically significant, indicating that the estimation results are robust.

4.1.2. Additional Analysis: The EPS Interaction Effects with CPU and GEPU

This sub-section seeks to determine whether the environmental policy stringency (EPS) acts as a moderator between the relationships of Clean Production Utilization (CPU) and Green Energy Production Utilization (GEPU) in the context of Resource Efficiency and Conservation (REC). Understanding this issue carries significant policy implications for governments, as it can help improve REC by implementing effective green policies when both CPU and GEPU are present. To achieve this objective, we gathered EPS data from the OECD.Stat website. According to a report from the OECD Nations Department, the EPS index is calculated based on three sub-indices: market-based policies, non-market-based policies, and policies that support technology.

Table 9. The nexus between EPS, CPU, GEPU, and REC.

Independent Factors	EPS		BRICS Economies
	Low EPS	High EPS
LnCPU	0.118	0.233	0.338 **
	(2.02)	(1.17)	(2.11)
LnGEPU	−0.143 **	−0.231	−0.122 **
	(−2.14)	(−1.11)	(−2.18)
LnEPS	0.246	0.327 **	0.188 *
	(0.71)	(2.14)	(4.19)
LnCPU*LnEPS	0.028 **	0.033 *	0.017 ***
	(2.11)	(4.16)	(1.73)
LnGEPU*LnEPS	0.016 **	0.044 ***	0.022 *
	(2.19)	(1.73)	(2.02)
Control factors	✓	✓	✓
Dummies	✓	✓	✓

Note: *, **, and *** denote 1%, 5%, and 10% statistical significance levels, respectively.

examines the interaction effects of environmental policy strategies (EPS) with Carbon Pricing Units (CPUs) and Green Energy Policy Units (GEPUs) on renewable energy consumption (REC) in BRICS nations, taking into account various control variables. The findings indicate that EPS has a positive and statistically significant effect on REC. According to the existing literature, implementing green environmental strategies contributes to an increase in REC (e.g., [11,20,21]). Studies by [36,38] demonstrate that EPS effectively enhances REC, while the success of ecological strategies is influenced by the type of renewable energy sources utilized. Furthermore, reference [57] revealed that countries with lower levels of REC experience positive impacts from EPS.

Furthermore, Table 9 indicates that the coefficients for the interaction effects of (LnEPS*LnCPU) (α = 0.017) and (LnEPS*LnGEPU) (α = 0.022) are both positive and statistically significant in BRICS nations. This suggests that implementing stricter Environmental Policy Standards (EPSs) enhances the positive effect of CPU on renewable energy consumption (REC) and mitigates the negative impact of GEPU on REC. Therefore, EPS plays a crucial role in promoting REC when CPU and GEPU are present.

The analysis reveals that when data are divided based on the average Earnings Per Share (EPS), the interaction effects are significantly different between economies with low and high EPS. Specifically, the magnitude of the interaction effects for LnEPS*LnCPU (α = 0.028) and LnEPS*LnGEPU (α = 0.016) is relatively smaller in low EPS economies. By contrast, for high EPS economies, the coefficients are larger: LnEPS*LnCPU (α = 0.033) and LnEPS*LnGEPU (α = 0.044).

These findings suggest that the efficiency of EPS is crucial. The results indicate that CPU has a more positive effect on renewable energy consumption (REC), while GEPU has a less negative impact on environments with stricter green policies. Overall, the evidence demonstrates that strengthening EPS can promote REC by enhancing the positive effects of CPU and mitigating the negative effects of GEPU. Therefore, we recommend that policymakers design and implement stricter EPS to achieve higher levels of REC.

5. Robustness Check

First, we utilized alternative measurements, specifically the “stock market turnover ratio” (LnSMT) and the “world uncertainty index” (LnWUI), to assess financial market development and geopolitical economic policy uncertainty (GEPU), respectively. The data for these measurements were gathered from the World Bank and the policy uncertainty website.

Second, following the findings from the work referenced in [17], we estimated Equation (1) by introducing a new variable: the “geopolitical risk index” (LnGPR), with data collected from the same policy uncertainty website.

Third, we estimated Equation (1) using both pooled quantile (PQ) and fixed effects (FEs) regression methods to check for consistency in our results.

Finally, we conducted the CD post-estimation test [58] to determine whether the estimated model exhibited any dependency issues.

The findings presented in

Table 10. Robustness test.

Independent Factors	PQ				FE
	Q.25	Q.50	Q.75	Q.95	Coefficients
LnSMT	0.026	0.043 *	0.058 **	0.062 **	0.038 ***
	(0.78)	(5.66)	(2.08)	(2.13)	(1.73)
LnFDI	0.018	0.036 **	0.047	0.056 ***	0.034
	(1.15)	(2.14)	(0.88)	(1.68)	(1.24)
LnREM	0.121 ***	0.119 **	0.094	0.138 **	0.127 ***
	(1.71)	(2.14)	(1.42)	(2.13)	(1.73)
LnTINV	0.029 *	0.036 **	0.021 **	0.047 ***	0.012
	(4.16)	(2.19)	(2.05)	(1.69)	(0.44)
LnCO2	−0.106	−0.155	−0.211	−0.388 **	−0.125
	(−1.12)	(−1.38)	(−1.58)	(−2.14)	(−1.41)
LnGDPC	−0.017	−0.024 **	−0.028 **	−0.043 ***	−0.035 *
	(−1.23)	(−2.17)	(−2.02)	(−1.71)	(−4.66)
LnCPU	0.117 ***	0.225 **	0.269 *	0.306 ***	0.241 **
	(1.71)	(2.12)	(4.76)	(1.69)	(2.11)
LnWUI	−0.224 **	−0.265 **	−0.327 ***	−0.237 **	−0.351 *
	(−2.13)	(−2.04)	(−1.69)	(−2.17)	(−4.88)
LnGPR	−0.121	−0.147	−0.172 **	−0.168 ***	−0.148 **
	(−0.77)	(−1.36)	(−2.12)	(−1.69)	(−2.14)
CD-test	—	—	—	—	(0.318)
Dummies	✓	✓	✓	✓	✓

Note: *, **, and *** denote 1%, 5%, and 10% statistical significance levels, respectively.

align with previous studies, indicating that LnSMT, LnFDI, LnREM, LnTINV, and LnCPU have positive and statistically significant effects on renewable energy consumption (REC). Our results provide substantial evidence that improving access to external funds by developing financial markets and attracting foreign direct investment (FDI), increasing household income through international remittances, enhancing environmental innovation, and raising consumer purchasing power (CPU) contribute to promoting REC in BRICS countries.

Conversely, the data reveal that LnCO₂, LnGDPC, LnWUI, and LnGPR negatively impact REC. Specifically, our findings demonstrate that increases in CO₂ levels, economic output, global uncertainty, and geopolitical risk contribute to a decline in REC within BRICS nations.

6. Conclusions

Several researchers (e.g., [11,15]) have examined the elements of renewable energy consumption (REC) in various countries. However, there has been less focus on this topic in the context of BRICS nations. To the best of the authors’ knowledge, this study may be the first to investigate the moderating role of environmental policies (EPs) between Clean Production Units (CPUs), Green Energy Policy Uncertainty (GEPU), and REC in BRICS economies. This research aims to address these gaps by offering an empirical model that explores the determinants of REC in BRICS countries, utilizing traditional control factors. To achieve these objectives, this study employed the pooled quantile (PQ) and fixed effects (FEs) methods to analyze annual data from 2010 to 2020.

The findings indicate that the expansion of the capital market, an influx in foreign direct investment (FDI), increasing international remittances, advancements in environmental innovation, and rising capacity utilization (CPU) are significant factors that promote renewable energy consumption (REC). However, rising carbon dioxide (CO₂) levels, economic growth, and an increase in global economic policy uncertainty (GEPU) negatively affect REC in BRICS countries.

Additionally, the results emphasize that environmental policy stringency (EPS) has a positive and significant impact on REC, suggesting that stricter ecological policies enhance REC. Notably, EPS plays a significant moderating role between CPU, GEPU, and REC. This means that by implementing stricter EPS, the positive effect of CPU on REC can be amplified, while the negative impact of GEPU on REC can be mitigated.

Consequently, EPS has a crucial role in promoting REC when CPU and GEPU are present. Specifically, the interaction effects highlight that the positive influence of EPS is more pronounced in countries with high EPS compared to those with low EPS. The findings are robust, having been validated through multiple measures and various methodologies.

6.1. Policy Recommendations

Here, we provide several policy recommendations. First, the statistically significant findings regarding SMC, FDI, and REM suggest that policymakers should improve access to external funds for investors and households to promote renewable energy consumption (REC). This can be achieved by developing financial markets, fostering open economies, and enhancing the inflow of international remittances. By doing so, investors and households can access and borrow funds at lower costs, which encourages greater investment in renewable energy schemes.

Furthermore, the findings indicate that policymakers aiming to facilitate capital inflows into a host country should focus on enhancing competitiveness at the country level. This includes improving the quality of private and public institutions, ensuring financial and economic stability, maintaining political stability, and developing infrastructure (e.g., [59]).

Secondly, the significant positive impact of technological innovation (TINV) indicates to policymakers the importance of focusing on research and development (R&D) and financing renewable energy (RE) technologies. This focus can help expedite the transition from conventional energy sources to clean energy and contribute to achieving the Sustainable Development Goals (SDGs).

Moreover, the statistically significant findings regarding Environmental Policy Structures (EPSs) suggest that policymakers should design and implement stricter environmental policies. These policies can be market-based, non-market-based, or involve technological support, all aimed at promoting renewable energy consumption (REC). In countries with stricter environmental regulations, growth in RE capacity and consumption could be enhanced. This environment would also encourage both investors and households to actively promote REC.

Additionally, consistent with the study in ref [11], stricter environmental policies can help moderate the influence of Global Energy Price Uncertainty (GEPU) on the demand for RE. Therefore, by developing effective Environmental Policy Structures, policymakers can accelerate climate change mitigation strategies, particularly when confronted with Consumer Price Uncertainty (CPU) and GEPU.

6.2. Limitations of Study

Further studies could explore the findings of this research across both developing and developed economies. Additionally, it would be valuable to test the proposed model with various renewable energy (RE) sources. Incorporating the impact of economic globalization and sovereign Environmental, Social, and Governance (ESG) factors [60,61] on renewable energy consumption (REC) and different RE sources would also be beneficial. Moreover, future research should examine the short- and long-term relationships between these variables and REC.