Does institutional quality matter for renewable energy consumption and CO2 emissions? Evidence from a panel of BRICS countries

doi:10.21203/rs.3.rs-4906065/v1

The main purpose of the present study is to explore the impact of institutional quality (IQ) on renewable energy consumption (REC) and CO₂ emissions (CO₂e) in a panel of BRICS countries, spanning the period 1996Q4-2016Q4. To accomplish this, we use several panel econometric techniques on targeted models. The results of the Westerlund panel cointegration test show that there is a significant long-run equilibrium association among the variables. Furthermore, Augmented Mean Group (AMG) estimator outcomes reveal that IQ has a significant positive impact on REC. Non-renewable energy consumption (NREC) has a positive association with CO₂e, while IQ and REC significantly reduce CO₂e. Finally, we found no causal nexus between IQ, REC, and CO₂e in BRICS countries. Our novel study findings not only provide new empirical evidence to the existing literature but also help policymakers strengthen the IQ to promote the uses of renewable energy and mitigate CO₂e.

Institutional quality

renewable energy consumption

CO2 emissions

BRICS countries

The excessive use of conventional energy sources results in CO₂ emissions, eventually leading to global warming and climate change. This global threat paves the way for research in the energy economics literature on the impact of clean/ renewable energy sources on environmental degradation, which can be possible for sustainable economic growth and development (Maji et al. 2022).

Further, the institutions may also play a significant role in deploying renewable energy technologies to compact CO₂e in a country. IQ may have a positive or negative impact on the quality of the environment. The weak quality of institutions may be weakened environmental regulations by poor governance, poor judiciary system, and no protection of property rights. On the other hand, strong quality of institutions can enhance the quality of the environment through good governance, the best judiciary systems, safeguarding of property rights, no corruption, and trade policies. Hence, the stability and predictability of policy outlines are essential in order to fascinate investment, production targets, and the development of innovative technologies in the renewable energy sector to eliminate CO₂e levels. Therefore, many economists, environmental scientists, and policymakers are giving more attention to exploring the nexus between IQ and environmental degradation. For instance, Papyrakis and Gerlah (2007) argued that IQ is the main factor in determining environmental policies¹. Further, Ibrahim and Law (2014) also stated that IQ would give the solution to environmental problems. Bhattacharya et al. (2017) documented that IQ has a significantly positive impact on renewable energy use and a negative impact on CO₂e. Further, Verma et al (2024) also suggests that countries should have better institutional quality to ensure sustainable economic societies.

It is undeniable that institutions also play a vital role in the adoption of renewable energy technologies. Many governments have designed a concrete and ambitious plan to reduce greenhouse gas emissions by increasing renewable energy targets. According to the Renewable Energy Policy Network for the 21 Century (REN21, 2016), 173 nations have adopted renewable energy targets and estimated that 146 countries have renewable energy support policies. The significant role of institutional quality is that it can attract domestic and foreign capital in renewable energy projects. Therefore, both international organisations and national governments have recognised the importance and impact of IQ on the environment and have now started conceiving and financing for deployment of renewable energy technologies. Hence, in recent years, investments in renewable energy have significantly increased in the world. Therefore, this study can give answers to the following questions empirically, does IQ promote the use of renewable energy and reduce CO₂e?

The BRICS countries-Brazil, Russia, India, China, and South Africa-have been strong economic performing nations for the last few decades. These countries have significantly contributed to global CO₂e due to their high consumption of conventional energy sources. This group of countries accounts for 20% of global GDP, 37% of world energy use, and 42% of global CO₂e (Ummalla et al .2019; Ummalla and Goyari, 2021). Further, it has been forecasted that China, the U.S, India, Russia, and Japan will be contributing 59% of global CO₂e by 2030. Hence the BRICS countries had adopted targets for the deployment of renewable energy by 2030². Moreover, countries need strong institutional quality to achieve such targets. Therefore, investigating the impact of IQ on REC and CO₂e in BRICS countries is useful, and outcomes would play prominence for policymakers in achieving environmental sustainability.

Given the significance of institutional quality, a few studies have examined the impact of IQ on renewable energy use in the empirical domain. For example, Bhattacharya et al. (2017) in 85 developed and developing countries, Wu and Broadstock (2015) in 22 emerging market economies, and Saidi et al. (2020) in MENA countries. However, few studies have explored the effect of IQ on CO₂e. For instance, Lau et al. (2014) in Malaysia, Danish et al. (2019) in BRICS, and Hassan et al. (2020) in Pakistan. More precisely, Haldar and Sethi (2021) examined the impact of IQ and REC on CO₂e in 95 developing countries. Maji et al. (2022) probed the impact of IQ and REC on CO2e in 45 Sub-Saharan African countries. However, there has been no study conducted on the effects of IQ on REC and CO₂e in BRICS. Therefore, this present study aims to fill the research gap in the literature.

The novel contributions of this study are as follows. First, to the best of our knowledge, none of the earlier research has empirically explored the impact of IQ on REC and CO₂e in the BRICS countries. Only a single study has probed the effect of IQ on CO₂e in the BRICS countries (Danish et al., 2019). However, they have omitted the role of renewable energy use in their study, which helps to mitigate CO₂e in the economy. Therefore, we address this issue in our analysis. Second, we incorporate population, per capita income, and NREC as important variables in renewable energy and CO₂e models. The levels of renewable energy, CO2 emissions, and economic growth vary by country due to differences in institutional quality (Bhattacharya et al. 2017). Third, it applies several panel econometric techniques that account for cross-sectional dependency.

The main findings of the study show that IQ increases REC while CO₂e reduce it. The results also reveal that NREC increases CO₂e, while IQ and significantly reduce CO₂e. Finally, we did not confirm any causal relationship between IQ, REC, and CO₂e in the BRICS countries.

The remaining chapter is structured as follows: Section 2 gives a review of the literature and shows the research gap in the literature that the present study tries to fill up it. Section 3 explains the data and methodological framework. Section 4 provides empirical findings and their relevant discussion. Finally, section 5 concludes the study paper with policy implications.

2.1. Institutional quality and renewable energy consumption

In the literature, a few studies have examined the role of IQ in the promotion of renewable energy uses. For example. Apergis and Eleftheriou (2015) documented that institutional policies are crucial for public-private cooperation on renewable energies and energy-saving technologies in emerging market economies. Therefore, effective institutional policies are driving forces for the adoption of renewable energy technologies and attaining economic growth in the long run. Wu and Broadstock (2015) examined the impact of economic, financial, and institutional development on REC in 22 emerging market economies, spanning the period 1990-2010. Using the system GMM method, authors found that financial development and institutional quality have a positive effect on REC. Finally, they suggest governments should coordinate between financial development and IQ in promoting renewable energy projects. Bhattacharya et al. (2017) suggested that institutions are the main factors in promoting renewable use in all economic activities to achieve environmental sustainability. Saidi et al. (2020) found unidirectional causality from each IQ measure to renewable energy use in MENA nations from 1986 to 2015. Additionally, they conclude that the proper institutional framework can attract investment in renewable energy projects and accelerate economic growth.

2.2. Institutional quality and CO₂ emissions

In recent years, attention has been driven to institutional and policy variables to measure their impact on environmental degradation. Tamazian and Rao (2010) found that financial development and trade openness have increased CO₂e in the short run when IQ is low, whereas financial development and trade openness have reduced CO₂e in the long-run when IQ is strong in 24 transitional countries during 1993-2004. Ibrahim and Law (2016) examined the relationship among the IQ, CO₂e, and trade openness in a group of 40 Sub-Sahara African nations, spanning the period 2000-2010. They found that trade openness has increased CO₂e when IQ is low, whereas trade openness has reduced CO₂e in high IQ countries. Similarly, Solarin et al. (2017) found FDI, economic growth, financial development, and trade openness have a positive association with CO₂e, while institutional quality³ has a negative association with CO₂e in Ghana during 1980-2012.

Some studies have directly probed the impact of IQ on CO₂e in the literature. Gani (2012) confirmed that political stability, the rule of law, and control of corruption have significantly reduced CO₂e in his study. Zhang et al. (2016) also suggested that more corruption always triggered more CO₂e in 19 APEC countries during 1992-2012. Halkos and Tzeremes (2013) reported that different IQ measures have different impact levels on CO₂e in G20 nations during 1996-2010. Lau et al. (2014) found that IQ (i.e., law and order) plays a significant role in reducing CO₂e in Malaysia during 1984-2008. Abid (2016) found that political stability, government effectiveness, democracy, and control of corruption impact CO₂e positively, while the rule of law and regulatory quality impact CO₂e negatively in 25 Sub-Saharan African countries during 1996-2010. Abid (2017) investigate the effect of economic, financial, and institutional development on CO₂e in 58 MENA and 41 EU countries, spanning the period 1990-2011. They reported that IQ is crucial for enhancing economic growth and reducing CO₂e. Bhattacharya et al. (2017) reported IQ has significantly reduced CO₂e in 85 developed and developing countries from 1991 to 2012. Zakaria and Bibi (2019) investigated the impact of financial development and IQ on CO₂e in South Asian countries during 1984-2015. They found that both variables have a significant negative impact on CO₂e. Salman et al. (2019) found that a significant positive relationship between IQ and CO₂e by indicating efficient institutions are important to increase economic growth and reduce CO₂e in three East Asian countries. Ali (2019) also examined the nexus between IQ (corruption, rule of law, and bureaucratic quality) and CO₂e in 47 developing countries during 2004-2010. They found that IQ has a significant negative impact on CO₂e. Danish et al. (2019) probed the impact of IQ on CO₂e in a panel of BRICS counties during 1996-2017. They concluded that IQ has a significant negative impact on CO₂e. Further, they suggest that IQ helps to the existence of the EKC hypothesis in the study period. Hassan et al. (2020) examined the nexus between IQ and CO₂e in Pakistan during 1984-2016. By using the ARDL model, they found that IQ has significantly increased CO₂e in both the short-run and long-run. Finally, their results showed that bidirectional quality between IQ and CO₂e. They suggested that IQ should strengthen to combat CO₂e. Danish and Ulucak (2020) evaluated nexus among institutional quality, renewable energy, economic growth, and CO2e in 18 Asia-Pacific Economic Cooperation Countries during 1992-2005. IQ and REC help in the reduction of CO2e. Further, IQ causes CO2e and REC.

Recently, Islam et al. (2021)⁴ reported that IQ had a positive effect on CO₂e in Bangladesh during 1972-2016. Christoforidis (2022) documented that IQ and renewable energy consumption significantly increased environmental quality in 29 OECD countries during 1984-2016. Finally, Haldar and Sethi (2021) concluded that IQ and REC significantly improved the environmental quality in 95 developing countries during 1995-2017. Acheampong et al. (2022) examine the relationship among the institutions, economic growth, renewable energy, and CO2e in 45 Sub-Saharan African countries from 1960 to 2017. The results further reveal that institutions have a negligible positive effect on renewable energy and week in mitigating CO2e. Zhang et al. (2022) examine the environmental quality by the impact of intuitional quality indices like the corruption index, law and order, government stability, and political stability in BRICS countries during 1996-2019. They reported that selected IQ variables have significantly improved. Maji et al. (2022) probed the impact of IQ and REC on CO2e in 45 Sub-Saharan African countries during 2008-2020. Their results did not show any significant role of IQ in reducing CO2e ⁵. Warsame et al. (2022) investigated the effect of renewables and IQ on CO2e in Somalia during 1990- 2017. They found REC and IQ have a significant impact on CO2e.

Overall, the review suggests that few studies have examined the nexus between IQ and REC, and IQ and CO₂e by using different IQ indicators, which is presented in Table 1. However, there is no specific study that examined the impact of IQ on REC and CO₂e in the BRICS countries by constructing the institutional quality index. This, therefore, motivates us to empirically examine the nexus among the variables in these emerging market economies.

[Insert Table 1 here]

3.1. Data and measurement

This study uses the annual data for BRICS countries (namely, Brazil, Russia, India, China, and South Africa), spanning the period 1996Q4-2016Q4⁶. The selection of the sample period is purely based on the availability of data. To meet our study objective, we collected data on REC and NREC from the US US Energy Information Administration (EIA), while population and per capita income from World Development Indicators, World Bank, 2019. CO₂e is taken from the BP Statistical Review of World Energy, 2019. Finally, IQ indicators are obtained from Worldwide Governance Indicators, World Bank, 2019.

Renewable energy consumption (REC) is measured in quadrillion Btu, non-renewable energy consumption (NREC) is the sum of total coal, natural gas, and petroleum consumption in quadrillion Btu, GDP per capita (PI) is measured in constant 2010 US$; population (POP) is measured in million; CO₂ emissions (CO₂e) measured in million tons; finally institutional quality index (IQI) constructed based on six indicators that measure the quality of institutions, such as: (i) control of correction (CC), (ii) government effectiveness (GE), (iii) political stability and absence of violence/terrorism (PS), (iv) rule of law (RL), (v) regulatory quality (RQ), finally (vi) voice and accountability (VA)⁷. To overcome the multicollinearity problem, we use principal component analysis to construct the institutional equality index.

3.2. Methodology

The prime aim of this study is to examine the effect of IQ on REC and CO₂e in BRICS countries. To attain this objective of this study, we use following equations.

$$\:{REC}_{it}=f\left({{{CO}_{2e,it,}POP}_{it},{PI}_{it},\:{IQI}_{it},\:NREC}_{it},\:{V}_{i}\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)\:$$

$$\:{CO}_{2e,it}=f\left({{POP}_{it},{PI}_{it},\:{IQI}_{it},\:NREC}_{it},\:{{REC}_{it},\:V}_{i}\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$

Where REC, CO₂e, POP, PI, IQI, and NREC represent renewable energy consumption, CO₂ emissions, population, per capita income, institutional quality index, non-renewable energy consumption, respectively, while $\:{\text{V}}_{\text{i}}$ indicates individual fixed country effects. Similarly, countries are indicated by the subscript $\:i$ ($\:\text{i}=1,\dots\:.,\text{N})\:$and $\:\text{t}$ represents the time period$\:(\text{t}=1,\dots\:.,\text{T})$.

4.1. Results of cross-sectional dependence (CD) test

Before commencing any econometric tests on panel data series, we better verify whether a cross-sectional dependency exists or not. Further, conventional unit root tests can give invalid results when the given variable is a cross-sectional dependence. Therefore, in our study, we apply the Pesaran (2004) cross-sectional dependence (CD) test to examine the existence of cross-sectional dependence among the variables. The results of the CD test are reported in Table 1. It shows that the null hypothesis of cross-sectional independence is rejected at the 1% level of significance. Based on this result, we can observe that there is strong evidence of cross-sectional dependence in all selected variables.

[Insert Table 1 here]

4.2. Results of panel unit root test

Next step, we employ the Pesaran (2007) cross-sectional augmented IPS (CIPS) test that accounts for cross-sectional dependence in the series. The CIPS results are documented in Table 2. These results reveal that the null hypothesis of unit root cannot be rejected for all the variables except the intuitional quality index and population. However, at the first difference, the null hypothesis of a unit root can be rejected at the 5% level of significance. The CIPS results suggest that all variables are non-stationary except intuitional quality index and population variables at levels, and all variables became stationary at their first differences. Therefore, our results suggest that selected variables are followed the mixed order of integration, i.e., I (0) and I (1). However, instead of Pedroni and Fisher-Johanson cointegration tests, better to apply a second-generation cointegration test like Westerlund (2007) panel cointegration test in the presence of cross-sectional dependence.

[Insert Table 2 here]

4.3. Results of panel cointegration test

In order to analyse the long-run relationship among the variables, we use Westerlund (2007) panel cointegration test on equations (1) and (2). The results are presented in Table 3. It shows that two statistics out of four are statistically significant at the 5% level, as null hypothesis of no no-cointegration is rejected in both equations. Based on these results, we can conclude that a long-run equilibrium exists among the variables in our study.

[Insert Table 3 here]

4.4. Results of long-run elasticities

This study applies the AMG estimation approach suggested by Eberhardt and Bond (2009) and Eberhardt and Teal (2010) to examine the impact of selected independent variables on REC and CO₂e in the BRICS countries. The findings of AMG long-run estimations are documented in Table 4.

The long-run estimates of REC reveal that a 1% increase in IQ raises 0.534% of REC, while a 1% increase in CO₂e reduces REC by 0.393% in the BRICS countries. More specifically, CO₂e have a negative impact on REC, while IQ has a positive effect on it. This result implies that increasing IQ significantly increases REC in a panel of BRICS countries. Therefore, governments and policymakers should strengthen the IQ to encourage the use of renewable energy in these emerging market economies. These findings are similar with Wu and Broadstock (2015) who found that IQ (political stability) has a positive impact on REC in 22 emerging market economies.

The long-run estimates of CO₂e reveal that a 1% rise in NREC increases CO₂e by 0.859 %, while IQ and REC reduce CO₂e by 0.120 and 0.050, respectively. These results indicate that NREC significantly increases CO₂e, while IQ and REC mitigate CO₂e in a panel of BRICS countries. These results suggest that these emerging market economies should strengthen IQ and promote renewable energy uses for a cleaner environment. Our findings are similar with Bhattacharya et al. (2017) who found that NREC increases CO₂e, and IQ and REC reduce it in 85 developed and developing countries. More precisely, this outcome is in line with Danish et al. (2019), Zakaria and Bibi (2019), Hassan et al. (2020), Haldar and Sethi (2021), Zhang et al. (2022), who reported that IQ has a significant negative impact on CO₂e in their studies. However, this finding is not similar to Hassan et al. (2020), and Islam et al. (2021), who found that IQ significantly increases CO₂e emissions.

[Insert Table 4 here]

4.6. Panel causality test results

Since our results confirmed a long-run equilibrium association among the variables, we expect that there must be the existence of one-way directional causality. In this context, we apply the Dumistrescu-Hurlin (2012) panel causality test to explore the direction of causality among variables⁸. The results of Granger causality are presented in Table 5. The results show that REC Granger causes economic growth and bidirectional causality between REC and CO₂e. This result is not in line with Kutan et al. (2018), who found unidirectional causality from REC to CO₂e in BICS countries. It implies that the rapidly rising demand for renewable energy not only achieves economic growth targets but also mitigates BRICS’s CO₂e in the future. Further, results reveal that bidirectional causality between IQ and population and one-way causality moving from NREC to population and IQ. But we found no causal nexus between IQ, REC, and CO₂e. These results contradict with Saidi et al. (2020) and Hassan et al. (2020). They found unidirectional causality from IQ to REC and bidirectional causality between IQ and CO₂e in MENA countries and Pakistan, respectively. Overall, our short-run panel Granger causality test suggests that there is two-way causality between REC and CO₂e.

[Insert Table 5 here]

This paper has examined the impact of IQ on REC and CO₂e in BRICS countries during the period 1996Q4-2016Q4. The study has used CD test for cross-sectional dependency, CIPS panel unit test for check the stationarity of the variables, the Westerlund (2007) panel cointegration test to explore the long-run relationship among the variables, and finally AMG estimations to examine the long-run impact of selected independent variables on REC and CO₂e in the BRICS countries.

The main findings of the present study are as follows. First, the results of CD test have confirmed the strong cross-sectional dependence in all the selected variables due to globalization and financial integration in the world. Second, panel cointegration test has confirmed the long-run equilibrium association among the variables in both Equations (1) and (2). Third, panel AMG estimations have shown the IQ has a significant positive impact on REC, while CO₂e has a negative impact on it. Further, this study has indicated that NREC increases CO₂e, while IQ and REC mitigate CO₂e in a panel of BRICS countries. Fourth, panel Granger causality test has provided no causal relationship among the IQ, REC, and CO₂e.

The empirical findings of this study have highlighted the following important policy implications: First, the IQ increases the use of renewable energy and reduces CO₂e in BRICS countries. Therefore, these countries should strengthen the institutions to promote the use of renewable energy. Further, the development of strong quality institutions can help to reduce CO₂e and improve the quality of the environment. Second, governments should increase public awareness on environmental issues; hence people will demand a clean environment that might increase institutional performance to promote renewable energy use. Third, having strong institutions in these BRICS countries can improve the quality of the environment by good governance, quality of judiciary systems, protection of property rights, and absence of corruption. Fourth, better institutions, especially government effectiveness, control of corruption and political stability, can attract more foreign and domestic investment in renewable energy projects, which eventually mitigates CO₂e.

The present study has been conducted on BRICS countries only. However, future studies can be examined the impact of IQ on REC and CO₂e in developed and developing countries. It helps researchers to understand the larger impact of IQ. Furthermore, a future paper can also investigate the role of IQ in REC and CO₂e in democratic and non-democratic countries in the world.

Author Contribution

MU: Conceptualization, Data curation, Methodology, Software, Writing- original draft. PG: Writing-review and editing, Supervision, Validation.

Acknowledgement

The authors would like to thank the Editor and anonymous reviewers for their helpful comments and constructive feedback. Further, this study has not received any funding.

Data Availability

Data will be available freely from the mentioned sources in the manuscript.

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They have treated corruption as the institutional quality.
Brazil (45%), Russia (14%), India (40%), China (30%) and South Africa (45%)
They have estimated institutional quality by calculating the averages of two components-Political rights and Civil liberties.
They have considered political terror scale as the institutional quality.
They have used six indicators as institutional quality. But they did not construct the index.
By using the linear interpolation method, we have converted the annual data into the quarterly time series data. Because the high-frequency data increases the power of a statistical test and provide robust results (Zhou, 2001). The quarterly interpolation method has been widely used in the empirical analysis (Tang & Chua, 2012; Shahbaz et al. 2014; Paramati et al. 2018).
The missing values in the institutional quality indicators are filled by the linear interpolation method. The previous studies (e.g., Danish et al. 2018 and Danish et al. 2019) have used this method in their studies.
To apply this test, we require stationary data. But our variables have mixed order of integration, i.e. I (0) and I (1). Therefore, for a constituency in the measurement of variables, we converted all selected variables into first difference before conducting the panel Granger causality test. This procedure has been done in few studies (e.g., Adams et al. 2018; Hafeez et al.2018; Koçak and Şarkgüneşi, 2017; Paramati and Nguyen, 2019).

Table 1: Cross-sectional dependence (CD) test results

Variables	CD-test	Prob
REC	7.468***	0.000
CO₂e	22.510***	0.000
IQI	5.856***	0.000
PI	27.453***	0.000
NREC	26.722***	0.000
POP	7.593***	0.000

Notes: *** indicates the rejection of null hypothesis of cross-sectional independence (CD test) at the 1% significance level.

Table 2: Panel unit root test (CIPS) results

Variable	Level		First Difference
	Zt-bar	Prob	Zt-bar	Prob
REC	6.978	1.000	-4.843***	0.000
CO₂e	1.785	0.962	-3.502***	0.000
IQI	-3.813***	0.000	-5.296***	0.000
PI	2.700	0.996	-1.932**	0.026
NREC	1.394	0.918	-4.054***	0.000
POP	-11.728***	0.000	-3.182***	0.000

Notes: ** and *** indicate the rejection of null hypothesis of unit root at the 5% and 1% significance levels, respectively.

Table 3: Westerlund (2007) cointegration test result

Test	Value	Z-value	P-value

Gt	-3.157	-2.107	0.018**
Ga	-9.574	0.626	0.734
Pt	-6.071	-1.525	0.064*
Pa	-8.501	-0.132	0.448
)
Gt	-3.027	-1.822	0.034**
Ga	-6.245	1.595	0.945
Pt	-6.969	-2.260	0.012**
Pa	-5.508	0.710	0.761

Notes: * and ** indicate the rejection of no-cointegration null hypothesis at the 10% and 5% significance levels, respectively.

Table 4: Augmented Mean Group Estimations

Variable	Coefficient	Prob

CO₂e	-0.393**	0.058
POP	-0.777	0.738
PI	0.684	0.421
IQI	0.534***	0.001
NREC	0.525	0.133
)
POP	0.712	0.294
PI	0.132	0.436
IQI	-0.120**	0.027
NREC	0.859***	0.000
REC	-0.050	0.137

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

Table 5: Dumitrescu - Hurlin panel causality test results

Dependent Variable	Independent Variable
	REC	CO₂e	POP	PI	IQI	NREC
REC	-	1.925** (0.035)	3.208 (0.205)	2.969 (0.155)	7.694 (0.100)	2.746 (0.117)
CO₂e	1.170*** (0.009)	-	3.798 (0.376)	3.463 (0.271)	6.703 (0.317)	11.827 (2.E-0)
POP	1.835** (0.003)	4.208 (0.536)	-	6.430 (0.410)	18.355*** (0.000)	1.879** (0.033)
PI	2.153** (0.051)	3.790 (0.374)	7.605 (0.113)	-	5.180 (0.990)	3.365 (0.244)
IQI	3.444 (0.265)	3.761 (0.364)	2.192** (0.054)	2.942 (0.150)	-	1.545** (0.019)
NREC	7.356 (0.154)	3.118 (0.185)	3.523 (0.288)	5.675 (0.739)	2.677 (0.107)	-
Directional of causality: REC ↔ CO₂e, REC → PI, NREC → IQI, POP, IQI →POP

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

No competing interests reported.

Does institutional quality matter for renewable energy consumption and CO2 emissions? Evidence from a panel of BRICS countries

Status:

Version 1

Abstract

1. Introduction

2. Review of Literature

3. Data and Methodology

3.1. Data and measurement

3.2. Methodology

4. Empirical findings and discussion

5. Conclusion and policy implications

Declarations

Author Contribution

Acknowledgement

Data Availability

References

Footnotes

Tables

Additional Declarations

Status:

Version 1