Does green finance mitigate the effects of climate variability: role of renewable energy investment and infrastructure.

Few researches have inspected the task of green finance in reducing CO2 emissions, while earlier studies have inspected the influence of economic development on carbon emissions. A green finance development index is built using four indicators to fill in this knowledge gap: green credit, green insurance, green securities, and green investing. Using data spanning the years 2005-2019, a panel quantile regression is applied to investigate the links between green finance, renewable energy, and CO2 emissions. Increases in renewable energy use and advances in the green finance development index have contributed to a reduction in CO2 emissions from BRICS countries. CO2 emissions on the other hand slowed the growth of renewable energy use, slowed the flow of investment to green projects, and ultimately hampered the development of green finance. There was also a clear policy-driven influence on renewable energy spending in the countries of the BRICS region. Green finance policies, on the other hand, have consistently failed to have a long-term impact. Therefore, rising the consumption of renewable energy and creating a carbon trading market are all part of this study's recommendations for green finance policy improvement.

Introduction

There has been an increase in industrial activity and population growth, which has resulted in a depletion of natural resources all over the world. This has led to an increased awareness of the wealth gap, as well as social and environmental responsibility (Paramati and Shahzad 2022). It is time for a new scenario for organisations and countries that want to adopt technologies that are both environmentally friendly and economically viable (Chishti and Sinha 2022). To put it simply, green finance aims to provide financial resources for environment-friendly schemes, with ecological security as the primary motivation. The “Equator Principles” were signed on June 4, 2003, by the world’s top 10 banks in London (Rasoulinezhad and Taghizadeh-Hesary 2022). These voluntary principles are envisioned to deal with financial issues that have a social and environmental influence.

These practises are concerned with reducing environmental damage and also aim to have an impact on a variety of sectors, including energy, health, and wealth, among others (Ye et al. 2022). The use of green technologies, also referred to as Green Technology, is becoming more widespread across all sectors of a country’s economy (Li et al. 2022). Sustainable practises are promoted in this context by promoting mechanisms that reduce pollution, environmental impacts throughout the life cycle, the opening and creation of new markets, and the development of new products, services, or processes. Green technology appears in this context through the development of sustainable practices (Jin et al. 2021).

Rapid economic growth, on the other hand, is frequently accompanied by high levels of energy use and CO2 emissions. Human-caused CO2 emissions now account for nearly 2/3rds of the biosphere’s total emissions of the greenhouse gas methane. A significant rise in CO2 emissions has been observed in the BRICS nations, the world’s largest emerging economies. There were 14,759 billion tonnes of carbon dioxide emissions in 2019, accounting for 43.19% of global CO2 emissions. Some countries have made significant efforts to reduce carbon dioxide emissions, while others have taken a more moderate approach. In 2014, China released a plan to decrease its CO2 emission intensity by 40 to 45% by 2020 equating to 2005, which is a responsible country (Ning et al. 2021). This objective has been met. Paying attention to the influencing factors of carbon emissions in the BRICS nations, which contributes 2/3rds of the biosphere’s total carbon emissions, not only alleviates the pressure of reduction in global carbon emission but also helps to stimulate the sustainable growth of the nations’ economy.

Additionally, green financing has emerged as a vital means of financing environmental-related issues. This modern method of financing and managing environmental and growth-related aspects is paving the way for a global world in which economies can sustain and grow on a green basis. To put it simply, green financing is a cutting-edge method of promoting sustainable development around the world. Green growth is vulnerable by the inability to adequately fund private, public, and non-profit green initiatives (Tangonyire and Akuriba 2020). While government subsidies and public loans may still be necessary, this argument reveals new avenues for securing funding, which could eventually replace these methods. In addition, there is a pressing required to establish a more efficient market structure for financing green projects.

The newest form of financing is known as “green financing.” A lack of research in the area of green financing in renewable energy production and energy effectiveness systems necessitates further investigation to provide solutions (Jha and Gupta 2021; Liu et al. 2021a). Nonetheless, in order to present the policy implications, it is necessary to test the energy effectiveness associated with green funding and renewable electricity production. As a result, the study’s goal is to discover if there is a new relationship between these concepts and to offer the best solutions to those involved. In this study, developing and developed countries are compared in terms of energy efficiency (Irfan et al. 2022a). In addition, we came up with a set of policies.

To this day, no other study has looked at how green finance affects economic growth in such depth. So we can see if green financing actually achieves its stated objective of matching financial development and ecological affairs. The model also combines green finance, ecological benefits, and financial development into one model and draws conclusions that are currently absent from the literature. In contrast to previous studies (Ali et al. 2021; Cui et al. 2020; Falcone 2020; Li et al. 2021; Nawaz et al. 2021), this one considers and compares the BRICS nations.

Brazil, Russia, India, China, and South Africa are the BRICS countries. Jim O’Neil of Goldman Sachs came up with the acronym BRICS in a 2001 report, which is used due to its English term “brick” likeness. Twenty years ago, the idea of BRICS countries was first proposed. The BRICS nations have experienced fast financial development over the past two decades. Statistics from https://www.statista.com/studies-and-reports/ (measured on 18 June 2021) estimate the BRICS countries’ combined population at 3.178 billion people, or about 41.42% of the global population. The combined GDP of the BRICS countries is equal to approximately 22.45% of the global GDP (Hou et al. 2022). In addition, the global economy has steadily improved as nations collaborate with one another in numerous fields.

There is no research that incorporates environmental issues into finance. According to the findings of this research, therefore, has four major contributions: Our research focuses on the dynamic association between renewable energy, green finance, and CO2 emissions in the BRICS nations over the period 2010–2020. Previous studies examined the association between economic development and environmental variables, this research concentrates on green finance as an entirely separate topic. This study compares the results of OLS regression with panel quantile regression (PQR). The PQR confirms the entire conditional division of the explained variable under this condition as an alternative of proving the mean value of the explained variable under this condition. CO2 emissions are affected by different explanatory variables, which explains why regression coefficients of different quantiles tend to differ, and outliers indicate the importance of significant information. However, quantile regression does not strictly adhere to the classical econometric suppositions of a zero mean, homoscedasticity, and normal distribution in its estimation of random error terms. Quantile regression, on the other hand, yields more reliable parameter estimates for variables with abnormal distributions.

Literature review

Some academics are interested in green finance and energy efficiency. A group of researchers found that green finance is ineffective in many countries due to a number of core issues (Irfan et al. 2021a,b, 2019b; Irfan and Ahmad 2021; Saeed Meo and Karim 2021). Result of the poor private sector and insufficient financial framework, green finance instruments such as green bonds are useless in developing or less developed economies. For the same reasons as the previous studies, Zhang et al. (2021a, b) investigated whether green bonds had any effect on a variety of economic or environmental indicators and came to the conclusion that they had no such effect. Due to the absence of plans in India’s atmosphere action plan, Zhang et al. (2021b) concluded that there was no association between green bonds and sustainable development goals. On top of all of this, Wang et al. (2021) looked at how EU investment banks financed renewable energy projects over a 3-year period beginning in 2015. The findings of Tolliver et al. (2020) showed that funds were allocated inefficiently, negating the benefits of green financing for green projects.

There are studies that show the positive impact of green financing on macroeconomic variables (Dmuchowski et al. 2021; Jinru et al. 2021), in contrast to the studies (Ren et al. 2020; Zhang et al. 2021b) that found a neutral or negative influence of green finance. When comparing green bonds to traditional bonds in the COVID-19 era, researchers found that the greater transparency of interest rates and investment returns provided by green bonds made them more effective (Wang et al. 2021b). In a similar Srivastava et al. (2021) examined the time period from 2008 to 2019 in which green bonds were compared to other variables like renewable energy. There was clear proof that green bonds have an impact on the development of clean energy. According to Chen et al. (2021), they studied the green bond market in Asia and the Pacific. Green bonds in Asia tend to have greater profits, but with greater risk and greater heterogeneity, according to Mastini et al. (2021) findings. Sixty percent of all Asian green bond issuances are issued by the banking sector. A post-COVID-19 era of issuer diversification, with greater public sector participation and risk removing policies, could also be accounted for, according to Irfan et al. (2022a, b). Sustainable development goals (SDGs) relating to climate change and environmental threats were examined by van Veelen (2021). According to the findings of Iqbal et al. (2021a, b), private investors could be enticed into this market by state assisting of the banking and economic sectors in setting green financing.

As a result, the development of green financing could have a positive impact on the growth of green energy projects. Green finance, according to Khan et al. (2021), is a significant element in long-term green investments. According to them, public financial institutions play a key role in making these financing options more efficient. Green bonds, according to Liu et al. (2021b), are an effective instrument for green financing because they reduce investment risk, boost return on investment, and draw in investors from around the world. It was observed that risk management in the green bond market can improve the efficiency and effectiveness of this financing mechanism for the development of green energy projects by Ye et al. (2022). Market conditions and the green finance market mechanism, according to Rasoulinezhad and Taghizadeh-Hesary (2022), are two important factors in creating a positive connection between green finance and green energy projects. Xiong and Sun (2022) found that green finance had a positive impact on boosting small-scale green energy investments. Sustainable renewable energy development can be attained by attracting private investors, as well as creating synergy between the state and private sectors through green finance, according to Li et al., (2022). A recent study by Zhang et al. (2022a, b) found that as the green energy financing market expands in India, more green projects will be funded, increasing the share of green energy in India’s overall power mix. As Yao and Tang (2021) argued, green financing has a direct and positive impact on renewable energy development through financial market mechanisms and state legislation. All countries should implement green economic reforms, according to Huang et al., (2022a, b), in order to increase investment in green energy production and reduce pollution.

Since its inception as an ethical and aesthetic concern for environmental well-being, green development has grown into a multifaceted support system encompassing economic, legal, and political aspects. It’s a sign of how far we have come in our understanding of human nature and interpersonal relationships. Funding for environmental protection and energy conservation projects is supported by green finance, which facilitates real economic growth. Sustainable economic growth requires a more efficient industrial structure, and green finance can help achieve that goal. Besides financing environmental governance, its role includes shifting resources from fossil-fuel-intensive industries to advanced technology ones.

Not only is it environmentally friendly, but it’s also economically sound to put money into renewable energy resources Aboramadan and Karatepe, (2021). Even though the COVID-19 pandemic has not ended, it has complicated things everywhere it has been. It is critical for proper waste management practises to employing masks made from biodegradable materials. The manufacturing costs of these facemasks and other personal protective equipment are reasonable (Irfan et al. 2019a; Mubarik et al. 2021). As a result, during a pandemic, investing in renewable energy sources benefits the environment (Irfan and Ahmad 2022; Jarboui 2021). Eco-friendly businesses can apply for green credit, which is an investment in a specific interest rate. The green economy is supported by a well-established and organised infrastructure in developed countries (Shekhar et al. 2021). Environmentally friendly approaches to starting new businesses are well-established throughout the world’s business community. Green economic projects are becoming more and more popular with corporations (Pata and Caglar 2021; Wei et al. 2022). Investment in new business ventures in developing countries such as Pakistan has been initiated, but the efforts have not yet emerged on a large scale (Nguyen et al. 2021; Tang et al. 2022). The COVID-19 pandemic has wreaked havoc on the financial and economic stability of the world. The rise in healthcare costs has had a devastating effect on the economy as a whole. Today’s businesses are struggling to deal with the current economic crisis. A new and innovative approach to supporting green credit investment initiatives is needed now (H. Liu et al. 2021a; Noureddine and Tan 2021).

Pakistan’s economic stability and long-term viability depend on green credit initiatives. Economic growth can only occur if environmentally friendly biofuels and recycled materials are used (Khan et al. 2022). Investments in green credit are advantageous for Pakistan’s economic growth (Zhou et al. 2022). Investing in green securities ensures the long-term health of the green economy. It is critical for the well-being of society as a whole that businesses use environmentally friendly materials in their manufacturing processes (W. Iqbal et al. 2021a, b; H. Zhang et al. 2022a, b). For the economic well-being of their economies, developed countries like China have put in place safe financing approaches, including companies that have appropriate strategies to support healthcare insurance and other health implications. Economic progress and prosperity cannot be sustained without environmentally sustainable options (Alsagr and van Hemmen 2021).

Furthermore, various researchers, such as Cetin et al. (2018), Charfeddine (2017), Dogan and Turkekul (2016), Hou et al. (2019), Khan et al. (2020), Setyowati (2021), Yumei et al. (2021), stress the influence of financial sector development on carbon emission. Similarly, the choice of econometric methods, the chosen countries and their economic structure, and the study period are some of the factors affecting mixed empirical findings. This study analyses previous studies, on a group of countries, such as Asia Pacific countries (Y. Zhang et al. 2022a, b), panel data for 42 countries (Xu et al. 2021), panel data from 97 countries worldwide (Iqbal et al. 2019b), Ghana (Iqbal et al. 2019a), high-income countries (bassem et al. 2022), lower income countries (X. Liu et al. 2021a, b), South Asian countries (Ahad et al. 2021; Wen et al. 2022), (OECD) countries (Zaidi et al. 2021), and European countries (Huang et al. 2021) confirm the negative correlation between financial variable and carbon emissions with different degrees, using different research methods. Although the relationship between CO2 and green finance is easily seen in the literature, none of the studies uses quantile regression approaches to empirically examine this relationship. The impact of green financing on CO2 emissions is supported by few studies; however, the researchers’ findings are inconsistent. The relationship is potentially dependent on the economic cycle and the size of green finance, which makes this approach intriguing, and therefore, changes in green finance cause CO2 emissions to respond accordingly. Hence, CO2 emissions are recorded at high values with high economic expansion, whereas recorded low with economic slumps. Consequently, the state of the economy dictates the kind of relationship between carbon emissions and green finance, regarding the complex and multifaceted nature of many factors responsible for determining its relationship with green finance. Therefore, CO2 emissions are likely to get affected by a positive change in green finance more than a negative change.

These findings show that the effects of green financing are not the same in all countries and are influenced by a variety of different factors. There are many countries that support green finance, and it would be practical to study this new financing in a group of these countries, as well as other nations that are interested in developing green finance markets. Because these economies play a critical role in achieving the United Nations’ Sustainable Development Goals (SDGs) in 2015, it is critical to investigate how this variable affects energy efficiency and green energy consumption.

Hence, three research gaps are identified, where the first one focuses on the impact of green finance on carbon emission, considering some prior studies. Moreover, it is eminent to determine the dynamics of this relationship due to the rapidly growing green finance, with BRICS countries recorded as one of the largest contributors to the global carbon emissions. The study includes renewable energy use as an important variable. Moreover, index construction is the second gap focused by this study and it is time sensitive to study green growth due to the recent attraction for this topic, where previous studies focus on the impact of financial development on environmental pressure. The study uses official documents to build a green finance development index. Furthermore, the study bridges a third gap related to the method employed in the previous studies by using fixed-effect quantile regression, to overcome this problem.

Method and data

Cross-sectional dependence test

For panel data, cross-sectional dependence (CD) of Phillips and Sul (2003) is critical, as it can lead to erroneous and inconsistent findings. Real-world connections include economic, social, political, and other channels like bilateral trade and board sharing. CD may be a result of these forms of associatively between countries. We employ Pesaran (2014) CD test and Breusch and Pagan (1980), Lagrange Multiplier (LM) test to address this issue. CD tests look for the presence of CD in data by using the equation below.

T is the period, and N is the cross-sections. The stochastic variations’ heterogeneous correlation is explained as follows: Following is an example of an LM test that uses this equation to examine panel data for CD.

where T represents the time period, and I represents cross-sections. Null hypotheses for both of these estimation methods assume the absence of cross-sectional dependence, whereas alternative hypotheses account for the presence of CD in the panel data.

Cross-sectional unit root test

Because of their low power to accommodate cross-sectional dependence, first-generation unit root tests are ineffective when dealing with cross-sectional dependence. The results are also assumed to be unaffected by cross-sectional variation, which is not the case. Because of this, Pesaran et al. (2008) developed the CIPS and CADF models, which combine the cross-sectional independence of the Pesaran-Shin and the cross-sectional augmentation of the Dickey-Fuller models. Cross-sectional and panel heterogeneity are taken into account in both of these tests For the purposes of assessing the stability of the variables, second-generation unit tests have been employed.

where denotes the variable under consideration, i indicates the cross-sections, t denotes the time period and explains the residuals of the model, respectively. The null hypothesis takes into account non-stationarity, as opposed to the alternative hypothesis, which takes into account stationarity.

Panel quantile regression model

An analysis of the effects of green finance (GF), renewable energy (RE), natural resources (NR), gross domestic product (GDP), foreign direct investment (FDI), and trade openness on CO2 emissions is carried out using a panel quantile regression model. For regression, we used a fixed-effect model. Since panel quantile regression uses fixed effect panels, we can see how CO2 emissions are distributed across the conditional distribution.

For example, Khokhar et al. (2020) proposed the use of quantile regression to investigate asymmetric distributions. These coefficients can be estimated across the various quantiles using this method. Given, the conditional quantile is:

Quantile regression does not have a problem with outliers or heavy distributions. The unobserved heterogeneity of the country is not taken into account by these methods. For this reason, some econometricians studied the theory of using quantile regressions to analyse panel data. It is possible to determine how individual heterogeneity affects the conditional heterogeneous covariance impacts of CO2 emissions drivers. Consider a fixed-effect panel quantile regression model like the one shown here:

A pure shift in the response’s conditional quantiles can be seen in formula 5. This means that covariates x_it can have varying effects based on the quantile in question. i is both a personal identification number and a chronometric identifier. The total number of observations made on i is given by the number “N.” Unobservable fixed effects were treated as dimensions to be collectively approximated with covariate impacts for dissimilar quantiles of the data at time t. An additional penalty term is included in the minimization step to account for the numerous parameters that must be estimated in this method’s parameter estimate.:

In addition, we modify the specifications of previous studies to estimate the impact on CO2 emissions of green finance (GF) and renewable energy (RE). We use quantiles with equal weights ( and set ) in this paper. We specify the conditional quantiles function for quantile as follows:

A panel quantile regression model is used in this paper to account for individual and distributional heterogeneity that cannot be directly observed. Natural resources (NR), foreign direct investment (FDI), gross domestic investment (GDP), and trade openness are selected as control variables in the model Equation in order to avoid an omitted-variable bias.

Data and variable selection

Green finance, renewable energy, natural resources, carbon dioxide emissions, gross domestic product, FDI, and trade openness are all represented by the variables GF, RE, NR, CO2, GDP, and TO. The natural logarithm is used to measure green finance (GF) in billions of dollars, and the proxy used for renewable energy (RE) is the consumption of hydroelectricity, nuclear, wind, and solar. MMT CO2 emissions, whereas GDP and FDI are expressed in constant US dollars. The World Development Indicators (WDI) provide the data for these variables. From 2005 to 2019, data on the economies of the BRICS countries were analysed. All variables are accounted for individually in Table 1.

Table 1

Variable’s description

Variable	Mean	Std. dev	Min	Max
CO2	1.351	0.9463	− 0.343	3.194
GF	1.093	3.1538	− 9.103	4.414
NR	0.389	2.4343	− 7.437	3.444
GDP	8.773	2.167	6.822	8.876
FDI	0.387	1.432	− 4.884	2.011
TO	2.876	0.562	3.4321	3.543
RE	1.324	1.043	1.282	3.992

Results and discussion

In empirical estimation, the first step is to look for cross-sectional dependencies. The results of the cross-sectional dependence and the LM tests are presented in Table 2. It is possible to conclude that cross-sectional dependence exists as a result of the rejection of the null hypothesis for both tests. When cross-sectional dependence is present, unit root tests of the second generation should be used to examine the integration properties of the variables under consideration. This study makes use of a CADF and CIPS unit root, and the results are shown in Table 3. Model variables have a unit root at the level of the model, but they become stationary after the first difference between the two models. It is possible to detect the presence of a level and stationary first difference unit root using the CADF indicator.

Table 2

CD test

Variables	Breusch-Pagan LM	Pesaran scaled LM	Pesaran CD
CO2	515.142 ∗  ∗  ∗	117.0322 ∗  ∗  ∗	30.231 ∗  ∗  ∗
GF	515.667 ∗  ∗  ∗	42.938 ∗  ∗  ∗	8.088 ∗  ∗  ∗
TO	464.869 ∗  ∗  ∗	41.021 ∗  ∗  ∗	7.947 ∗  ∗  ∗
FDI	569.285 ∗  ∗  ∗	14.768 ∗  ∗  ∗	3.136 ∗  ∗  ∗
GDP	454.716 ∗  ∗  ∗	40.638 ∗  ∗  ∗	7.909 ∗  ∗  ∗
NR	690.089 ∗  ∗  ∗	23.101 ∗  ∗  ∗	4.953 ∗  ∗  ∗
RN	423.745 ∗  ∗  ∗	77.211 ∗  ∗  ∗	51.40 ∗  ∗  ∗

Table 3

Panel unit root tests

	CIPS		CADF
Variables	I(0)	I(1)	I(0)	I(1)
CO2	− 1.492	− 3.76*	0.000*	0.000*
GF	− 1.33	− 3.127*	0.124	0.009**
NR	− 2.814**	− 4.483*	0.788	0.015**
GDP	− 1.090	− 3.926*	0.933	0.005**
FDI	− 2.641*	− 5.418*	0.114	0.000*
TO	− 2.558*	− 4.97*	0.003**	0.000*
RE	− 2.105	− 3.74*	0.138	0.003**

***, **, and * show significance level at 1, 5, and 10%, respectively

A cross-sectional dependence issue was addressed using Pesaran et al. (2008) CIPS and ADF panel unit root test, which best addresses cross-country dependencies in the sample. It is shown in Table 3 that the analysis variables are stationary in first difference, allowing us to further investigate the cointegration between variables. Heterogeneity and cross-sectional dependences are addressed by using Persyn and Westerlund (2008) cointegration test. We can see from Table 4 that there is an integration of order one between the variables, allowing us to look into the relationship between study variables and carbon emission in the BRICS countries. Second-generation results show that all variables are either I (0) or I(1). We use the Persyn and Westerlund (2008) cointegration test to check for long-run cointegration among the study’s target variables because the variables are first-difference stationery.

Table 4

Westerlund panel cointegration test

Statistics	Test statistics	P value
Gt	− 2.364	0.980
Ga	− 1.854	1.000
Pt	− 4.072	0.966
Pa	− 2.507	0.988

***, **, and * show significance level at 1, 5, and 10%, respectively

Model comparison

In order to enable comparisons, the model is initially estimated using pooled and fixed effects OLS regression estimates. Pooled OLS regression estimates are presented in columns 1 and 2 of Table 5, correspondingly. For the purpose of estimating long-run elasticities, Pedroni (2001) used the FMOLS technique described in his paper. It has been pointed out by Pedroni (2001) that some types of cross-sectional dependency are captured by common time dummies. Column 4 summarises the FMOLS findings. According to Baltagi (2008), time-period fixed effects are used to control for all time-specific, spatially invariant variables that could bias estimates in a typical time-series study and are used to control for all time-period random effects. A fixed effect in both directions is therefore more interesting to us than the outcomes of a model with a random effect in either direction. Column 3 displays the outcomes of the two-way fixed-effects analysis. In fact, only one aspect of trade is consistent across all of the specifications: the effect of trade.

Table 5

Model comparison

Variable	OLS pooled	OLS one-way fixed effect	OLS two-way fixed effect	FMOLS
GF	− 0.0388***	− 0.0402***	− 0.0315***	− 0.0549***
	(− 2.964)	(− 3.236)	(− 2.238)	(− 6.516)
RE	− 0.2893***	− 0.4468***	− 0.02479***	− 0.2987***
	(1.1721)	(1.8548)	(0.0622)	(1.3295)
NR	0.3759***	2.1535***	0.8464***	0.9553***
	(0.3742)	(1.8563)	(0.6677)	(0.4644)
TO	0.2320***	0.2348***	0.2896***	0.0862***
	(2.3619)	(2.4948)	(2.3424)	(0.8259)
GDP	0.7121***	0.4899***	0.5157***	0.1924***
	(2.3996)	(1.7092)	(1.5182)	(0.9539)
FDI	0.9975***	0.8774***	0.7416***	0.5514***
	(3.3798)	(3.0746)	(2.3580)	(2.0575)
Constant	3.798***	4.4759***	3.551***	3.9272***
	0.3467)	(1.8602)	(0.1732	(0.0767)

These numbers are t-values, which indicate statistical significance at the 1% level of significance. Probability of being significant at a level of 5%. Statistical significance at a 10% level of confidence

Quantile regression results

The quantile regression with fixed effects in Lamarche (2010) is used to control for the distributional heterogeneity. An important source of our concentration on the quantile regression method with a two-way fixed effect is that the absence of time-period fixed effects may lead to biased findings in a typical study of time series. Results of the panel quantile regression estimation are shown in Table 6. All of the results are presented for the 5th and 10th percentiles of the conditional emissions distribution. According to the statistics, there is obvious heterogeneity in the effects of numerous factors on carbon emissions.

Table 6

Panel quantile regression estimations

Variable	5th	10th	20th	30th	40th	50th	60th	70th	80th	90th	95th
GF	− 0.049*	− 0.371*	− 0.245**	− 0.398***	− 0.516***	− 0.600**	− 0.616***	− 0.592***	− 0.606**	− 0.759***	− 0.203***
	(0.361)	(1.843)	(1.107)	(1.421)	(2.110)	(2.537)	(2.907)	(3.682	(3.568	(4.374	(0.924)
RE	− 0.750*	− 0.978*	− 1.160**	− 0.905**	− 0.778	− 0.694***	− 0.565***	− 0.667***	− 0.432***	− 0.133***	− 1.090***
	(2.909)	(4.733)	(5.776)	(4.039)	(4.111)	(4.181)	(3.336)	3.537	1.547	0.282	(1.856)
NR	0.602**	1.001**	0.842***	1.602***	1.556*	1.961*	1.422*	1.230***	1.831***	1.482***	2.429***
	(0.291)	(0.484)	(0.355)	(0.721)	(0.637)	(1.175)	0.639	1.480	1.368	1.895	(3.589
TO	0.506**	0.396**	0.284**	0.239	0.131	0.087	0.110	0.085***	0.105***	0.386***	0.695***
	(3.603)	(2.970)	(1.993)	(2.079)	(1.105)	(0.757)	1.052	0.704	0.711	1.988	(2.640)
GDP	0.132***	0.082	0.135**	0.343	0.212	0.192*	0.040***	0.024***	0.012***	0.443***	− 2.880
	(0.771)	(0.484)	(0.839)	(1.740)	(1.062)	(1.444)	0.323	0.226	0.055	0.577	(2.867)
FDI	0.005*	0.003**	0.004	0.003	0.002***	0.003**	0.004	0.005	0.006***	0.014**	0.025*
	(0.839)	(0.775)	(0.976)	(1.412)	(1.140)	(1.094)	1.294	1.888	3.139	2.761	(2.818)
Constant	0.071***	0.035***	0.005***	0.024***	0.047***	0.029***	0.031***	0.046***	0.047***	0.054***	0.109***
	(2.830)	(1.699)	(0.279)	(1.565)	(3.292)	(2.187)	2.298	2.874	2.300	1.086	(1.369)

Green finance

The findings of the study indicate that green finance and carbon emissions are shown to be negatively associated. Green finance and carbon emissions in the middle and upper quantiles have a particularly strong negative impact. There is, however, a negative correlation between the lower and higher quantiles of green finance (from 5 to 40th and 60th to 95th). According to these findings, green finance in the BRICS countries reduces CO2 emissions. Even though there is a non-uniform association between CO2 emissions and demand for green investment, when CO2 emissions rise, so does the demand for green investment. The green payment and credit business, containing home mortgages and project credits, is the primary focus of the term “green credit.” According to United Nations Environment Programme (2017), however, the country only began to truly unify statistical standards and increase the quality of green finance data in 2014 despite the fact that China’s banks began issuing social responsibility reports in 2006. In China, data on green credits has been collected over time, but there are a number of problems, such as incomplete disclosure, a brief period of disclosure, and inconsistent statistical standards. As a result, the green credit variable in this study is defined as the total green credit of listed firms divided by the total credit of listed firms. The following are some of the benefits of making use of this index. For starters, the sample spans a significant amount of time and is highly representative. In 2000, China had only 1086 publicly traded companies; by the end of 2018, that number had risen to 3,549, with the firms spread across a wide range of industries and regions (Anser et al. 2020; HUANG et al. 2022a, b; Khokhar et al., n.d.). To begin with, the information is of a high standard because it was independently verified by reputable accounting firms to ensure that the amounts and purposes of bank loans disclosed by publicly traded companies in their financial reports were accurate. The use of this index was deemed appropriate for this study based on these considerations.

Renewable energy

A negative sign indicates that the estimated coefficient of renewable energy consumption reduces carbon dioxide emissions, even at a level of 1% in all quantiles. According to Irfan et al. (2022b;) and Wen et al. (2022), renewable energy consumption cuts carbon emissions significantly in the current research sample countries using a panel quantile regression model for BRICS countries. Reducing carbon emissions through the utilization of renewable energy is also supported by Zhao and Taghizadeh-Hesary (2022).

Natural resources

In the lower half of the quantiles, the natural resources (NR) significantly increase CO2 emissions. The BRICS countries’ NR CO2 emissions are indicated by a positive coefficient. Natural resource extraction can result in higher CO2 emissions, as this study demonstrates. BRICS economies’ rising greenhouse gas emissions may be linked to an increase in natural resource extraction and unsustainable use, according to our findings. In addition, the country’s reliance on fossil fuel imports worsens the environment by causing emissions of greenhouse gases.

Next, the results of the control variables also show significant impact on CO2 emissions. We can observe that the influence of FDI on CO2 emissions is clearly heterogeneous when it comes to positive coefficient is only marginally significant at the 10% level in the 5th quantile. In low-emission countries, the positive FDI coefficient is not enough to boost the pollution haven hypothesis. It is clear that foreign direct investment (FDI) has a negative influence on CO2 emissions and that this impact is greater in nations with high emissions than in nations where emissions are low. Other coefficients are negative and become significant at higher quantiles (the 60th, 70th, 80th, and 90th quantile). In countries with high emissions, these findings lend credence to the halo effect hypothesis. Because FDI has a negligible effect at the low quantile, it’s safe to assume that the vast majority of it goes into non-polluting industries in low-emissions nations like China and India. High-emission countries, on the other hand, may place greater emphasis on environmental issues and enact stricter environmental regulations. Through backward and forward linkages, foreign direct investment (FDI) in high-emission countries may help to create advanced management, specialised technical, and innovation in the production process; these technologies may also be passed on to domestic enterprises. Multinational corporations in high-emission countries may have access to more advanced technology, and they are more likely to disseminate environmentally friendly technology. A rise in foreign direct investment (FDI) improves environmental quality in high-emission countries, as illustrated in the graph below. Halo-effect hypothesis is valid in high-emission BRICS countries, according to the results. Results are consistent with those of Hamid et al. (2022) that states how can one analyse how foreign direct investments (FDI) are linked with pollution in BRICS nations using panel analysis. The authors’ outcomes endorse the halo effect and do not support the FDI’s negative environmental impact.

The openness of the global economy to trade and the consumption of renewable energy as a percentage of GDP are the metrics; we used to gauge international trade and renewable energy consumption. Studies of Pata and Caglar (2021) who used trade percent of GDP are just two examples that have used various variables to gauge economic growth. The economic growth measured by Evangelista et al. (2022) was based on GDP per capita and industry value added. Only at the 95th quantile does the coefficient of GDP have a positive sign, which initially rises and then falls with the increase in the CO2 quantiles. When it reaches the 80th quantile, it is no longer significant, but when it reaches the 95th quantile, it is significant once more. At the 90th and 95th percentiles, GDP2’s coefficients are statistically significant. EKC is not applicable to BRICS countries because the GDP quadratic term indicates that the relationship between economic growth and CO2 emissions has been monotonic in the BRICS nations in the past. Perhaps the BRICS countries did not get to where they needed to be in terms of economic development. As previously reported empirically by Usman et al. (2022), our study’s findings contradict the EKC hypothesis, which states that pollution levels rise with income before levelling off and eventually declining. According to Usman et al. (2022), our findings are consistent with theirs. A closer look at economic growth and pollution emissions provides a more complete picture of economic growth than previous research has shown. Carbon emissions can be reduced by increased economic growth in high-emission countries, according to our results, which express a negative and significant coefficient of GDP for the 95th percentile.

Conclusion and policy recommendation

Conclusions

PQR was applied to empirically investigate the function of green finance and renewable energy use in reducing CO2 emissions in BRICS countries from 2005 to 2019. On the basis of four indicators from the regulation on setting a green financial system, a green finance development index was created to more accurately represent green finance. We came to the following conclusions. A rise in the green finance development index and the percentage of renewable energy utilization participated in a decrease in CO2 emissions, which was found to have a long-term equilibrium association with CO2 emission, green finance, and renewable energy spending in the first place. As a result, the green finance development index fell as CO2 emissions rose. This hampered the growth of renewable energy and reduced green finance investment. Green finance and CO2 emissions had a substantial influence on both short-term and long-term renewable energy consumption, while the development of renewable energy depends on policy support. Fourth, although the green financial policy of the BRICS nations had a significant impact on carbon mitigation, its results were inconsistent and unreliable. BRICS nations’ CO2 emissions have fluctuated slightly over time, making it difficult to reduce CO2 emissions in a short period of time.

Implications for policy and future research

The key policy implication is that eco-friendly economic policy should be more stable and long-term. Significant fluctuations in green finance have a negative influence on CO emissions and the renewable energy industry, as shown in the analysis above. Because of this, it is critical to have a stable and long-term green financial policy in place. The advancement of energy conservation can be ensured through large-scale green financing. The green financial policy system of the BRICS nations needed to be developed as a comprehensive industry chain. A green development funds and system and green finance were first proposed in China’s 13th Five-Year Plan, which was first implemented in 2008. There are three ways in which green economic policy can be advanced.

An improved legal framework for the development of a green financial system is needed. Green investment, green bonds, green loans, and green securities should all be part of a comprehensive financial service system.

Green securities and bonds require a rating system tailored to the specific needs of the BRICS countries. First, a commanding 3rd party must make sure that the tools are truly green and steer systematic evaluations of the environmental advantages of projects before they can be issued. It is also necessary to assemble a team of experts who are familiar with the level of green funds and who can analyse and observe the precise attributes of these spendings used to backing the green business.

Green credit’s policies, processes, and procedures could use some work. In order to enable energy-saving landscaping and the purchase of green homes and new energy vehicles in accordance with national building certification criteria, specific consumers should be identified and provided with special loans.

It follows that green financing for renewable energy industries should be bolstered as a second policy outcome. Currently, renewable energy development is hampered by the higher unit costs associated with renewables than with non-renewable sources. Solar, wind, and nuclear power can all help reduce carbon emissions over the long term because of the long-term cointegration equation. Renewable energy projects, on the other hand, necessitate large investments and long payback periods. The development of renewable energy industries should be supported by flexible and diverse service schemes for green finance products and services, specifically the following:

Providing loans at less interest to ease the financial needs of investigation and verification and reduce the time it takes for credit approval should be added to the support for green loaning for renewable energy proposes. Fiscal takeoff interest, decrease in tax, tax freedom, pre-tax facility, and intended write-off of bad debt should be implemented simultaneously for non-fossil industries.

The securities market’s importance should be emphasised. Additionally, a significant number of green businesses that focus on renewable energy should be established.

BRICS nations should lower the barrier to entry for renewable energy companies in their stock markets. In the case of company securities issuance, less project profits should be required for review and approval. Environmentally friendly companies should be allowed to raise a reasonable quantity and proportion of their working capital or to repay their bank debts.

As a third and final policy implication, the carbon market’s green finance application process should be loosened up. The utilization of economic derivatives to restrict CO2 and other greenhouse gas emissions is one way that carbon trading manifests itself in the financial market. Currently, the CO2 market in the BRICS nations is not fully in effect, and there are many misconceptions about CO2 emission rights and their financial aspects for institutions and businesses. In order to help build a national carbon market, financial regulators should get involved. The establishment of a healthy carbon promotion market will be facilitated by active financialization of the carbon market. Additionally, a carbon investment fund based on carbon financial markets should be established to strengthen the financial strength of emission reduction projects. It is important for commercial banks to encourage the inclusion of carbon emission rights in the scope of pledged loans and to offer a higher collateral ratio in the early stages of carbon finance. The development of green financing products and the establishment of a green channel for special approval should be done in tandem. Investing in carbon asset securitizations, reviving existing carbon assets, and increasing their efficiency should be promoted by the capital market. The carbon connections should be stimulated to develop low-carbon derivative financial tools and build a low-carbon index system of such instruments. Financial leasing and low-carbon funds would benefit from this approach.

The following is a list of upcoming research opportunities. (1) The green finance index could be better-quality in this paper. If a green finance index is properly created, it will help future research, which is currently lacking due to the difficulty and novelty of creating one. It is possible to conduct a more thorough investigation of the relationship between variables using advanced methods and tools. The threshold model, for example, could be applied to recognize the nexus’s stopping points. A cross-nation analysis, focusing on economies with well-established green financial regularities, could be added as a third option. This would allow for a comparison of the advantages and differences between various countries.