
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12670-9
10.1016/j.heliyon.2024.e36639
e36639
Research Article
The impact of green finance on green growth: The role of green energy and green production
Nguyen Duc Trung a
Oanh Tran Thi Kim kimoanh@ufm.edu.vn
b⁎
Bui Thanh Dan a
Dao Le Kieu Oanh a
a Ho Chi Minh University of Banking, Ho Chi Minh City, Viet Nam
b University of Finance – Marketing, Ho Chi Minh City, Viet Nam
⁎ Corresponding author. kimoanh@ufm.edu.vn
22 8 2024
30 8 2024
22 8 2024
10 16 e3663913 2 2024
6 8 2024
20 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
This study examines the linkage of green finance and green growth under the regulatory role of green energy and green production in 52 countries worldwide from 2005 to 2019. Applying the Bayesian regression and GMM regression, the results of these two methods are similar. When ignoring the regulatory role of green energy and green production, green finance negatively impacts green growth. This result is entirely opposite when considering the regulatory role of green energy and green production, green finance impacts green growth positively. However, Bayesian regression is more effective when providing different posterior probability intervals and probability ranges for independent variables to affect the dependent variable. Specifically, the probability that green financial growth has a negative impact on green growth is above 75.86 %. Similarly, under the role of green energy, the probability that green finance growth has a positive impact on green growth is 80.45 % and under the role of green production, this probability is 76.64 %. These findings imply that countries should build a financial system associated with the goal of green energy and green production, thereby helping the economy become greener.

Keywords

Green finance
Green growth
Green energy
Green production
==== Body
pmc1 Introduction

In March 2016, the Department of Economic and Social Affairs Sustainable Development (SGGs) issued new sustainable development goals, prompting many countries to implement new strategies focused on green growth [1]. The core vision of global economies is inherently linked to the admirable goal of achieving sustainable development [2]. Research on green growth has accelerated in the US and China, aiming for green GDP growth rather than traditional GDP. According to Samuelson and Nordhaus (2000) [3], one of the greatest inventions of the 20th century, GDP measures the total value of all finished goods and services generated by a nation or region during a specific time period. Currently, the blind targeting of GDP growth is causing significant waste of natural resources, environmental destruction, and ecological imbalance [4]. In recent decades, global concern about achieving sustainable development has been at an all-time high due to the growing issue of environmental pollution. Addressing environmental degradation is paramount for developing nations to attain sustainable development promptly [5,6]. The increasing worries about environmental degradation necessitate achieving greater green growth [7]. The term “green GDP”, which refers to the national economy's incorporation of natural resources and environmental costs into output to make up for traditional GDP's deficiencies, was first used by the United Nations Statistics Institute in 1993. This reflects more fully and truthfully the country's development level under the influence of the environment. The process of transforming the green growth model or green GDP of countries further affirms the important role of using funds mobilized from the green financial system to aim at greening the economy [8]. Green finance, in contrast to conventional financial operations, is an expansion of energy saving and emission reduction efforts based on conventional financial services [9]. Indeed, at present, access to green finance for green GDP development is challenging in the economy for two reasons:

Firstly, according to Deng and Zhao (2022) [10], the government and large financial institutions are the centers of development spillover through the capital channel to a green economy and green financial development. The Input – Output tables (IO) are used to show the impact of green production industries and their positive effect on the environment and growth of Green GDP, i.e. the correlation between green GDP and green finance is influenced by green production of enterprises [11]. Second, Energy consumption substantially underpins all economic and human activities; hence, the extensive use of non-renewable energy sources like fossil fuels, coal, and petroleum worsens environmental degradation and slows the expansion of the green GDP [12,13]. Therefore, the current research on green energy use is always of great interest to countries.

Because of the above two reasons, the linkage of green finance and green GDP is always regulated by green energy and green production. However, current empirical studies only highlight the correlation between green finance and green GDP at the national level (mainly in the US and China) or the individual linkage of green finance and the financial development index [14], the poverty reduction effect of green finance [15], and the linkage of green GDP and ecological services [16]. To the best of our knowledge, there has been no empirical study on the correlation between green GDP and green finance under the regulation of two critical factors: green energy and green production from the perspective of countries worldwide. Moreover, the construction of green financial indicators in previous studies has not been consistent, leading to different results. In this study, we apply the Principal Component Analysis (PCA) to measure the green financial variable including credit indicators for the agricultural sector and renewable energy, the green production variable including the rate of renewable energy production and the rate of technology development related to the environment, the green growth variable (measured by green GDP) including economic indicators associated with environmental pollution reduction [17]. In addition, this study uses the Bayesian method that combines a priori information and research data to calculate the posterior distribution which is interpreted as a probability distribution, to overcome the sample size disadvantage. Besides, the study also applies the GMM method to compare with the results of the Bayesian approach.

This study makes significant contributions to the current body of literature in three key ways. Firstly, it presents compelling evidence regarding the influence of green finance on fostering green growth, with a nuanced examination of the pivotal roles played by green production and green energy. Secondly, it employs innovative Bayesian methodology to provide experimental validation, complemented by a comparative analysis with GMM regression, thereby enhancing the robustness and reliability of the findings. Thirdly, it goes beyond mere analysis to offer actionable policy implications aimed at bolstering green finance, advancing green energy initiatives, and fostering green production practices. By doing so, this study not only facilitates the promotion of green growth but also aids countries in their pursuit of sustainable development objectives.

The remaining sections of the article include: Section 2 provides a literature review of previous studies, Section 3 presents the research model and methodology, Section 4 presents the research findings. The conclusion and policy implications are presented in Section 5.

2 Literature review

This section is structured into three distinct sub-sections to provide a comprehensive exploration of our topic. Firstly, we delve into theories concerning the influence of green finance on green growth. Following this, we scrutinize this influence within the context of green production and green energy integration. Sub-section 3 offers an insightful analysis of the existing literature landscape and identifies key research gaps for further exploration.

2.1 Theory of the linkage between green finance and green growth

The “green GDP” term refers to a wide range of GDP indicators that have been updated to take into account social as well as environmental pollution and exploitation costs. Green GDP is an alternative method of calculating the financial impact of a country's economic growth caused by social and environmental damage [17]. Since the classical GDP index cannot fully assess other economic quantities in the market such as ecosystem service values, natural resource overexploitation and environmental degradation, green GDP is an essential indicator to measure sustainable development. Green GDP is set by the United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP 2013) as an indicator of green growth of countries, that is, economic growth ensures the maintenance and development of natural capital, minimizing pollution and environmental degradation, curbing the increase in greenhouse gas emissions and creating more jobs for the whole society.

The process of transforming green growth models of countries shows the important role of using the funds mobilized from the green financial system to aim at greening the economy. Countries are considered successful in transforming the green growth model when they can effectively use the mobilized green capital to build a green economy that ensures sustainable development and improves people's living standards. It also reduces greenhouse gas emissions and improves adaptability to climate change [18]. The green financial system also has the same main function as the conventional financial system. It is the movement of capital from savers to investors and thus increases the capital efficiency of the economy. However, the difference lies in the characteristics of the components involved in the capital turnover process. The mobilized green capital will be circulated through indirect financial channels through green financial intermediaries and direct financial channels through green financial markets to be used for green goals. Acknowledging the reality in some countries in the past time will help provide some policy implications for developing a green financial system to support the transition to an environmentally friendly green economy.

The green financial system acts as a channel for capital and resource regulation for green growth through tools such as green credit and investment, and government budget for the environment [14]. Green finance promotes the development and utilization of new energy sources, green production, and ecological agriculture for the sustainable development of the whole society through prioritizing credit granting to businesses in the agricultural sector, limiting new projects that cause pollution and applying high interest rates [11]. Therefore, for green growth to be effective, it is necessary to adopt green finance because it aids in reducing and fostering resistance to the negative effects of environmental damage that may not be addressed by traditional finance [19]. Moreover, the transition to green growth is very urgent, requiring the mobilization of huge resources in green finance for technological innovation, planning, and green infrastructure development. Therefore, in addition to the spillover from the government at public costs to the environment, the private sector also plays a role in spreading green credit when cooperating with investment funds, especially foreign investment funds.

2.2 Regulatory roles of green energy and green production in the linkage between green finance and green growth

Currently, the development of the global economy leads to increased dependence on fossil fuel energy causing environmental pollution, and increasing CO2 emissions [20]. This means energy is considered the backbone of an economy as it is linked to economic development in all human activities [21]. However, energy is divided into two categories: non-renewable and renewable. Most emerging economies depend on non-renewable energy sources to achieve rapid economic growth even though non-renewable energy sources are limited and cannot be replaced once used, including fossil fuels like oil, coal, and natural gas [22]. Using non-renewable energy also generates a lot of CO2 causing the greenhouse effect, hydrocarbon and sulfur gas which are the main causes of acid rain and pollute soil and groundwater. Climate change is taking place heavily, causing many natural disasters, unpredictable loss of people and property in the world, and threatening the lives of people and many animals in nature, because of excessive use of non-renewable energy. Meanwhile, renewable energy is used periodically and cannot be exhausted, such as energy generated from wind, sun, hydrogen, etc. It is also known as green energy. The methods and technologies used to manage, reduce and control the number of pollutants generated during industrial production is called green production. As technology advances and the economy grows, green production is also growing. Green product corporations must develop green, clean manufacturing concepts and upgrade or convert any original equipment that produces too much pollution or uses too much energy. To reduce the number of raw materials needed and the waste produced during production, businesses must take into account the expanded functionality and repurposing of green energy in their design. The purpose of green production is to produce green energy towards reducing pollution and prioritizing the use of renewable energies [23]. Therefore, green energy, green production, and green finance are considered by the SDGs (2016) as a means of green growth due to their ability to reduce emissions as well as minimize unwanted impacts on the environment [24,25]. When green financial flows into the green production and renewable energy sector, plus the government's support to promote greening the economy, national businesses use green renewable energy alternatives to the energy polluting the environment in the long term. In the short term, the cost of businesses using alternative energy may go up, leading to a decline in GDP, but in the long term when the green growth transition reaches a certain level, the development of countries is measured by Green GDP instead of GDP, green production and use of green energy help to minimize costs, thereby helping to improve green GDP significantly [17]. Thereby, the use of green finance promotes green GDP through direct and indirect channels. In the direct channel, when the green financial system of countries is solid and unified with effective green policies, green finance will help improve green growth, and vice versa when the role of the green financial system is fragmented and unstable in control, green finance cannot contribute much to green growth. In the indirect channel, green financial investment in green production and green energy helps the country improve its green GDP. Therefore, it can be seen that, under the role of green production and green energy, green finance helps green GDP significantly improve.

2.3 Empirical studies on the linkage between green finance and green growth

Studies on the correlation between green growth and green finance are still limited, mainly due to the difference in approaches to measuring representative variables. Liu et al. (2020) [8] studied the linkage between green finance and the green economy in China, using the entropy method to calculate the green financial index including a set of variables: green credit, green insurance, and green investment. Green growth is a set of 21 environmental and economic variables. Meanwhile, He et al. (2022) [26] used the PVAR method in 30 provinces in China with green growth variables represented by the set of economic size, economic structure, environmental pollution, economic benefits, and the ability to allocate resources. Rahman et al. (2022) [27] used the Content Analysis Method to calculate green financial variables representing a set of 9 different criteria such as: Energy conservation, green brick production, renewable energy, waste management, alternative energy, recyclable product production, and green facilities, environmental dimensions and sustainable performance, and green financial policy. Jiang et al. (2022) [15] applied the entropy method to measure green finance including a combination of 3 main criteria and 17 sub-criteria. The main criteria are (1) Economic-related criteria (GDP per capita, national income, unemployment rate), (2) Financial-related criteria (Number of banks and bankers, banks per population, bankers per population, credit ratio, deposit ratio, density and depth of coverage), (3) Environmental criteria (waste water rate, CO2 emission rate, energy consumption rate, solid waste rate, nature conservation rate, and national forest rate). Nahman et al. (2016) [28] calculated green finance based on 12 different criteria and green growth based on 16 different criteria such as CO2 emission rate and energy impact. Kun et al. (2022) [14] suggested that green finance is measured by 4 main indicators: Green credit (interest rates for 6 energy sectors), Green insurance (Insurance for the agricultural sector), green investment (investment costs in controlling environmental pollution) and support from the government (environmental protection budget). In addition, there are many studies such as Sonnenschein and Mundaca (2015), Yue et al. (2016), Jamie (2018), Pan et al. (2018), Alekna and Kazlauskienė (2020), Wang (2021), Zhang et al. (2022), Tang et al. (2022), Khan el al., (2024), Sharif (2024) and Syed (2024) [[29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39]] with different approaches to measure green growth and green finance.

In terms of research approach, various aspects of green growth have been addressed in separate research studies. For example, the impact of green energy on green growth has been explored by researchers such as Dai et al. (2016) [40] in the context of China. Their findings indicate that the development of renewable energy has a positive impact on green growth, benefiting upstream industries, reshaping the energy structure, and delivering significant environmental benefits. Hao et al. (2021) [41] studied the role of green growth in CO2 emissions for G7 countries from 1991 to 2017. Both theoretical and empirical findings suggest that green growth reduces CO2 emissions. Additionally, studies by Ackah and Kizys (2015) [42], Sohag et al. (2021) [43], Tawiah et al. (2021) [44], and Razzaq et al. (2023) [45] have demonstrated the role of green energy in green growth. Chishti et al. (2024) [46] conducted a study titled “Understanding the Effects of Artificial Intelligence on Energy Transition: The Moderating Role of the Paris Agreement”. Utilizing advanced methodologies, including the QVAR technique for short-term (SR), medium-term (MR), and long-term (LR) connectedness analysis and the CQ method for exploring relationships across varying market conditions and timeframes, the study investigates the interplay between artificial intelligence (AI), the Belt and Road Initiative (BRI), the Paris Agreement (PA), green technologies (GT), geopolitical risk (GPR), and ET. The relationships between AI, BRI, PA, GT, GPR, and ET are dynamic and time-varying, offering essential implications for global policies fostering sustainable energy transitions. Regarding the impact of green production and technological innovation on green growth, this has also been affirmed in several studies, including those by Dutz and Sharma (2012), Song et al. (2020), Guo et al. (2020), and Nosheen et al. (2021) [[47], [48], [49], [50]]. The common thread in these results is that green production plays a role in driving green growth. The literature review reveals that there are still existing issues and gaps as follows: (1) Green finance measurements criteria such as green investment, green credit, and the support of government for the environment are closely related to environmental protection, improving ecological efficiencies such as investment in environmental protection, investment for agriculture, R&D costs to protect ecosystems, increasing credit access for agriculture and forestry sectors, and reduce credit access for industry and services sectors; (2) The criteria are processed through suitable models such as the entropy and PCA methods to calculate green finance and green credit; (3) The previous studies were mainly carried out in the internal provinces/cities within the country and the results were not consistent. (4) There has not been any experimental research to investigate the impact of green finance on green growth with contributions from the roles of green energy and green production. Therefore, this study will clarify the following points: Based on a synthesis of previous studies, the author will propose representative variables for green finance and green growth in a more general manner for countries worldwide. It will elucidate the relationship between green finance and green growth in these countries, under the regulation of green production and green energy, as these are two influencing factors on the green growth objectives of nations. Furthermore, previous studies have all utilized models using the traditional frequency-based approach. However, testing hypotheses using this approach necessitates many assumptions that do not align with reality, making it less accurate in inference and forecasting. On the other hand, the frequency-based statistical method treats parameters as fixed but unknown values. However, these parameters may change as the sample size is updated over time. In contrast, the Bayesian method treats parameters as random variables and assigns them a distribution to represent the confidence in the parameters [51].

3 Methodology

3.1 Research model

Based on the study by Stjepanović et al., 2022 [17], we calculated the overall GDPGREEN of 52 countries during the period 2005–2019, and the equation is written as follows:(1) GDPGREEN = GDP – (KtCO2 * PCDM) – (Twaste*74 kWh*Pelect) – (GNI/100*%NRD)

The model to examine the impact of green finance on green growth is as follows:(2) GDPGREENi,t=βo+β1GFi,t+βxXi,t+εi,t

in which i is country and t is time.

Xi,t: is the vector of control variables.

The models shown in Eqs. (3), (4), (5) expand Eq. (2) by adding the interaction variable between green finance, green energy, and green production with consequent effects on green growth:(3) GDPGREENi,t=βo+β1GFi,t+β2GE*GFi,t+βxXi,t+εi,t

(4) GDPGREENi,t=βo+β1GFi,t+β2GP*GFi,t+βxXi,t+εi,t

(5) GDPGREENi,t=βo+β1GFi,t+β2GE*GFi,t+β3GP*GFi,t+βxXi,t+εi,t

Equation (2) represents the impact of green finance on green GDP. Equation (3) illustrates the interaction between green finance and green energy. Equation (4) demonstrates the interaction between green finance and green production. Lastly, Equation (5) showcases the role of green production and green energy.

3.2 Data and variables

In this paper, data of 52 countries were collected from 2005 to 2019. The main variables in research models are described below:

Green financial: Previous studies have calculated the green financial index through several indicators such as green credit and green investment towards the goal of increasing credit capital for the agricultural sector and the renewable energy sector, thereby minimizing environmental pollution [14,36]. This will be our approach when measuring the green finance variable.

Green production: Green production is represented by two variables: (1) the rate of renewable energy; (2) the rate of technological development related to the environment (Tsai et al., 2014).

These two variables are calculated by using the PCA method.

Green growth: Green growth is represented by the green [17]. The equation for calculating green GDP (GDPGREEN) and the variables description are shown in Table 1.(1) GDPGREEN = GDP – (KtCO2 * PCDM) – (Twaste*74 kWh*Pelect) – (GNI/100*%NRD)

Table 1 Description of representative indicators of green finance, green growth, green production and green energy.

Table 1GREEN FINANCE (GF)	
Variable name	Indicators and symbols	Measure	Source	Data sources	
Green credit	Credit for Agriculture (GRS)	Credit growth provided to the agricultural sector (%)	[53]	OECD	
Credit for renewable energy generation (GAG)	Credit growth provided for renewable energy (%)	[54]	OECD	
Green Investment	Public R&D budget for renewable energy sector (ENRVD)	Public R&D budget for renewable energy to total public R&D energy (%)	[14]	OECD	
Government support	Public R&D budget for the environment (RERD)	Environmentally related public R&D budget to total public R&D (%)	[14,36]	OECD	
GREEN GROWTH (GDPGREEN) USD	
Gross Domestic Product	GDP	Gross Domestic Product of the countries (USD)	[17]	WDI	
CO2 emissions	KtCO2	CO2 emissions/ton (Kt)	WDI	
Average price by carbon mass	PCDM	Average price by carbon mass	[67]	
Number of kilowatts of energy/1 ton of waste	74 kWh	Number of kilowatts of energy/1 ton of waste (kWh)	Australian Energy Regulator, Waste to Energy in Denmark	
Total amount of industrial and commercial waste	Twaste	Total waste (commercial and industrial) (Tons)	
Energy prices	Pelect	Average price of 1 kWh of energy per ton (USD)	Eurostat	
Total National Income	GNI	Total value added of all domestic producers plus all goods taxes (excluding subsidies not included in the value of production) plus net payments from income (employee salary and property income) from outside. (USD)	WDI	
Adjusted Savings: Natural Resource Depletion	NRD	Total mineral depletion, energy resource depletion and forest loss per country (%)	WDI	
GREEN PRODUCTION (GP)	
Renewable energy production	RER	Renewable energy supply to total energy supply (%)	[23,55]	OECD	
Technology development related to the environment	GPAT	Development of technologies related to the environment total of all technologies (%)	
GREEN ENERGY (GE)	
Total renewable energy consumption	GE	Renewable energy consumption/Total energy (%)	[56]	OECD	
Source: Author's compiled

Green energy: The green energy variable is measured by total renewable energy consumption [52].

All variables of the research model are described in Table 2.Table 2 Description of variables in the model.

Table 2Dependent variable	Symbol	Measure	Source	Data	
Green growth	GDPGREEN	Green GDP growth is calculated based on equation (1).	The author calculated according to the study of Stjepanović et al. [17]	Author's calculated	
Green Finance	GF	Calculation by PCA method with the criteria in Table 1	The author calculates according to the study of Kun et al. (2022) [14], Tang et al. (2022) [36]	Author's calculated	
Green production	GP	Calculation by PCA method with the criteria in Table 1	
Green energy	GE	Renewable energy consumption as a proportion of total energy consumption.	[21]	OECD	
Urban population	UR	The urban population as a percentage of the total population.	[9,57]	WDI	
Trade openness	OP	Total exports and imports as a percentage of GDP	[9,15,57,58]	WDI	
Inflation rate	INF	The annual Consumer Price Index (CPI) growth rate.	Saydaliev and Chin (2022) [59], Dinh el al., (2024) [57]	WDI	
Population growth rate	POP	The annual population growth rate refers to the percentage change in a population over the course of one year, considering both natural increase (births minus deaths) and net migration.	[59,60]	WDI	
Economic growth rate	EGD	The annual GDP growth rate represents the percentage change in a country's Gross Domestic Product (GDP) over the course of one year. It is a key indicator of economic performance, reflecting the rate at which the economy is expanding or contracting.	[61,62]	WDI	
Source: Author's compiled

The model to examine the impact of green finance on green growth is as follows:(2) GDPGREENi,t=βo+β1GFi,t+βxXi,t+εi,t

in which i is country and t is time.

Xi,t: is the vector of control variables.

The models shown in Eqs. (3), (4), (5) expand Eq. (2) by adding the interaction variable between green finance, green energy, and green production with consequent effects on green growth:(3) GDPGREENi,t=βo+β1GFi,t+β2GE*GFi,t+βxXi,t+εi,t

(4) GDPGREENi,t=βo+β1GFi,t+β2GP*GFi,t+βxXi,t+εi,t

(5) GDPGREENi,t=βo+β1GFi,t+β2GE*GFi,t+β3GP*GFi,t+βxXi,t+εi,t

3.3 Analytical procedures

Green finance and green production variables are calculated based on the method of Principal Component Analysis (PCA) which is a statistical algorithm that uses an orthogonal transformation to transform aggregates data from a high-dimensional space into a new, less-dimensional space to optimize the representation of data variability (Dinh, 2024) [63].

The regression results were performed by Bayesian regression and GMM regression. In Bayesian statistics, where research data is combined with a priori information to compute a posterior distribution, and regardless of sample size, results are interpreted as a probability distribution of parameter values. Therefore, the small sample disadvantage can be solved by applying Bayesian method [64]. For GMM regression: Endogeneity may arise in a regression model if the regressors have some degree of correlation with the dependent variable or if there is an interaction between the variables, both of which lead to strongly skewed coefficients. Because it generates a weight matrix of endogenous instrumental factors linked to heteroscedasticity and autocorrelation, Arellano et al. (1991) [65] presented a more effective GMM technique (difference method) to deal with the issue of endogeneity, heteroscedasticity, and autocorrelation. The model's stringent exogenous variables and the appropriate lagged endogenous variables are both included in these instrumental variables. In this study, the author uses exogenous variables POP and OP as instrumental variables in the GMM method. Moreover, GMM approach is employed to address endogeneity, autocorrelation, multicollinearity issues, with the results also compared with Bayesian regression.

4 Research results

4.1 Principal Components Analysis results

Table 3 presents the result of the Principal Components Analysis.Table 3 PCA result.

Table 3Green Finance (GF)	
Variable	GRS	GAG	ENVRD	RERD	
	0.4044	−0.0155	−0.5849	0.7049	
Green Production (GP)	
Variable	RER	GPAT	
	0.7071	−0.7071	
Source: Author's PCA analysis

According to the results in Table 3, GF and GP are calculated by applying the following equations:GF = 0.4044*GRS – 0.0155*GAG – 0.5849*ENVRD + 0.7049*RERD

GP = −0.7071*RER + 0.7071*RER

4.2 Bayesian regression results

Table 4 shows that the average growth rate of green GDP is 8.7183 %, the average GF variable is 16.2470 %, the average GE is 35.0937 % and the GP is at the average level of 19.8220 %. The high growth rate implies that countries are paying close attention to environmental issues, especially investment costs in green energy and green production. Evaluating cross-sectional dependence (CD) is vital in panel data analysis. We employed the Pesaran test to assess CD, as ignoring it can yield inaccurate conclusions. Appendix 1 presents the results of Pesaran's cross-sectional correlation test, revealing significant cross-sectional relationships among all variables. Addressing the pseudo-regression issue involves conducting tests to ensure the stationarity of the disturbance term and examining the cointegration relationship between variables. To perform the cointegration test, each variable sequence must first undergo a unit root test to determine if it is first-order integrated. The Fisher-ADF tests are utilized for testing data stationarity, and the results indicate that all variables exhibit first-order stationarity. Subsequently, the Kao test is employed to assess cointegration (Appendix 2). The Kao ADF statistics reject the null hypothesis of no cointegration relationship between panel variables at the 1 % significance level, indicating the presence of a long-term stable relationship between variables and the absence of the pseudo-regression problem.Table 4 Descriptive statistics of variables for the period 2005–2019.

Table 4Variable	Obs	Mean	Std. Dev.	Min	Max	
GDPGREEN	780	8.7183	13.5642	−43.4777	88.7663	
GF	780	16.2470	22.8574	−27.6263	200.4865	
GP	780	19,8220	22.1155	−16.4323	116.5955	
GE	780	35.0937	26.8789	0.7000	94.7300	
UR	780	53.2848	19.8333	15,7000	91.8760	
POP	780	1.1668	1.7343	−1.8543	18.1280	
EGD	780	4.2674	3.9558	−17.0047	34,5000	
OP	780	77.8377	35.4896	0.1674	210,4002	
INF	780	6.4265	7.2105	−1.9311	63.2925	
Source: Statistical results from Stata 17.0 software

To make sure that the Bayesian inference based on the MCMC series is reasonable, we have tested the MCMC convergence of the parameter estimates through visual diagnostics using graphs. Fig. 1, Fig. 2, Fig. 3, Fig. 4 show that all the graphs of the parameters are reasonable, the trace and correlation graphs for found low autocorrelation, and the histograms’ shape are consistent and normally distributed.Fig. 1 Convergence diagnostic results of equation (2).

Fig. 1

Fig. 2 Convergence diagnostic results of equation (3).

Fig. 2

Fig. 3 Convergence diagnostic results of equation (4).

Fig. 3

Fig. 4 Convergence diagnostic results of equation (5).

Fig. 4

5 Discussion

5.1 Theoretical implications

Table 5 shows the results of Bayesian and GMM regressions. GF and POP have a negative impact on GDPGREEN while UR, EDG, OP, INF, GF*FE, and GF*GP have positive impacts on GDPGREEN. Testing for Bayesian regression shows that the average acceptance rate of models (2), (3), (4), and (5) are 0.8200, 0.8216, 0.8223, and 0.8147, respectively. Avg efficiency: min of models (2), (3), (4), and (5) are 0.0165, 0.0698, 0.1436, and 0.0556 respectively, exceeding the allowable level of 0.01, so the above models are satisfactory. Findings also indicate that the Monte-Carlo Standard Error (MCSE) of all parameters are small, confirming that the Markov chain Monte Carlo (MCMC) series are strong [66]. The results for GMM regression show that the two main tests, the Sargan test and AR (2) test, give results greater than 5 %, indicating that the GMM model is not defective. All the variables are statistically significant at the 1 % level.Table 5 Bayesian and GMM regression model results.

Table 5Variable	Bayesian	GMM Regression	
(2)	(3)	(4)	(5)	(2)	(3)	(4)	(5)	
Mean	MCSE	Mean	MCSE	Mean	MCSE	Mean	MCSE	Coeff	P-value	Coeff	P-value	Coeff	P-value	Coeff	P-value	
UR	0.0607 (0.0217)	0.0002	0.0659 (0.0222)	0.0002	0.0656 (0.0228)	0.0002	0.0654 (0.0201)	0.0002	0.2165 (0.0155)	0.0000	0.1811 (0.0188)	0.0000	0.2195 (0.0009)	0.0000	0.1672 (0.0201)	0.0000	
POP	−0.1882 (0.2236)	0.0021	−0.2045 (0.2265)	0.0023	−0.2040 (0.2237)	0.0022	−0.2022 (0.2269)	0.0023	−0.4799 (0.0928)	0.0000	−0.4166 (0.0479)	0.0000	−0.4842 (0.0924)	0.0000	−0.8328 (0.0837)	0.0000	
EDG	2.2687 (0.1023)	0.0010	2.2701 (0.1036)	0.0010	2.2679 (0.1041)	0.0013	2.2684 (0.1039)	0.0010	3.6395 (0.0802)	0.0000	3.6606 (0.0813)	0.0000	3.6331 (0.0848)	0.0000	3.8624 (0.1044)	0.0000	
OP	0.0099 (0.0113)	0.0001	0.0111 (0.0111)	0.0001	0.0109 (0.0114)	0.0001	0.0107 (0.0113)	0.0001	0.0388 (0.0058)	0.0000	0.0289 (0.0075)	0.0000	0.0395 (0.0059)	0.0000	0.0381 (0.0086)	0.0000	
INF	0.2248 (0.0553)	0.0005	0.2287 (0.0548)	0.0005	0.2304 (0.0551)	0.0005	0.2282 (0.0547)	0.0005	0.8225 (0.0534)	0.0000	0.7635 (0.0451)	0.0000	0.8236 (0.0532)	0.0000	0.4960 (0.0328)	0.0000	
GF	−0.01363 (0.0167)	0.0002	−0.0256 (0.0212)	0.0002	−0.0294 (0.0275)	0.0003	−0.0219 (0.0319)	0.0003	−0.0837 (0.0108)	0.0000	−0.0485 (0.0122)	0.0000	−0.0908 (0.0181)	0.0000	−0.0851 (0.0175)	0.0000	
GF*GE			0.0007 (0.0212)	0.0002			0.0102 (0.0020)	0.0000			0.0047 (0.0009)	0.0000			0.0124 (0.0025)	0.0000	
GF*GP					0.00047 (0.0007)	0.0000	0.0003 (0.0016)	0.0000					0.0003 (0.0181)	0.0000	0.0049 (0.0018)	0.0000	
Avg acceptance rate	0.8200	0.8216	0.8223	0.8147									
Avg efficiency: min	0.0165	0.0698	0.1436	0.0556									
Sargan test									0.190	0.198	0.104		0.184	
AR test(2)									0.187	0.265	0.158		0.231	
Source: Results of running Stata 17 software.

Unlike the GMM method, Bayesian regression provides an additional posterior density interval (Equal-tailed 95 % Cred. Interval), which provides a series for a parameter, and the probability that the parameter is in this series is 95 %. In addition, in addition to the standard error (Std. Dev) for the regression coefficient, the Bayesian regression results also provide a parameter of the MCSE showing the stability of the MCMC series. Table 4 suggests that all the MCSE of the three equations go to zero and are less than 5 %.

Table 6 shows the impact probability of the independent variable on the dependent variable. The probability (prob) that green financial growth negatively impact green growth is 79.32 % in equation (2), 88.45 % in equation (3), 86.08 % in equation (4), and 75.86 % in equation (5). This result is in contrast with Saydaliev and Chin (2022) [59], Liu et al. (2022) [8], Kun et al. (2022) [14], and Oanh and Dinh (2024) [68]. The cause of this inconsistency is that Saydaliev and Chin (2022) [59] studied in Asean with not long enough research time (2012–2019), while both Liu et al. (2022) [8] and Kun et al. (2022) [14] studied in China during the period 2007–2019 but only within 30 provinces/cities. On the other hand, Kun et al. (2022) [14] researched on in-depth analysis of energy intensity on financial development, and Liu et al. (2022) [8] emphasized on measures of representative variables more. In our study: (1) The research scope is in 52 countries around the world from 2005 to 2019 and the Bayesian approach is applied to provide a more robust estimation by using both the collected data and existing information. This is consistent with the sample data in the study; (2) While previous studies measure green growth by integrating composite variables related to three issues: environment, society, and economy, our study's green growth variable based Stjepanović et al. (2022) [17], that is, building on a green GDP measure that includes the resources and environmental costs in the production process of the economy to compensate for the shortcomings of traditional GDP, shows that green GDP is a better measure of a country's real growth. These are two possible reasons why the study results have not been consistent with previous studies. However, green financial growth negatively affects green growth, which shows that the amount of green credit for agriculture and the renewable energy industry in countries around the world cannot spillover to the manufacturing sector, in addition, the public spending of countries on environmental protection has not yielded the expected results. Moreover, due to the pursuit of GDP growth of countries today despite the damage to the environment, governments have not yet had a policy framework to develop a green financial system in a stable and easy way easy to apply as well as regularly review and promptly adjust problems that occur. The sources of funding for green finance in the world mainly come from the government, national development organizations, bilateral and multilateral development banks, so the issue of green financial transmission needs contributions from private investment factors in green growth. In addition, legal issues are considered to be the main challenges in promoting green finance to green development, requiring guarantees and commitments for green infrastructure projects. This is a novelty of the findings of this study compared to a handful of related studies on the subject. One potentially negative impact is that companies and financial institutions claim to engage in environmentally sustainable practices without actually making a substantial contribution. This can lead to a lack of transparency and accountability in the green finance sector, undermine the efficacy of green mainstream capital flows, and hinder the promotion of green growth.Table 6 Probability results.

Table 6Equation 2	
Variable	Mean	Std. Dev	MCSE	
Prob (GDPGREEN: GF) < 0	0.7932	0.4050	0.0040	
Prob (GDPGREEN: POP) < 0	0.7986	0.4011	0.0041	
Prob (GDPGREEN: UR) > 0	0.9977	0.0479	0.0005	
Prob (GDPGREEN: EGD) > 0	1.0000	0.0000	0.0000	
Prob (GDPGREEN: INF) > 0	1.0000	0.0000	0.0000	
Prob (GDPGREEN: OP) > 0	0.8133	0.3897	0.0037	
Equation 3	
Variable	Mean	Std. Dev	MCSE	
Prob (GDPGREEN: GF) < 0	0.8845	0.3196	0.0032	
Prob (GDPGREEN:GF*GE) > 0	0.8045	0.3966	0.0040	
Prob (GDPGREEN: POP) < 0	0.8266	0.3786	0.0037	
Prob (GDPGREEN: UR) > 0	0.9988	0.0346	0.0003	
Prob (GDPGREEN: EGD) > 0	1.0000	0.0000	0.0000	
Prob (GDPGREEN: INF) > 0	1.0000	0.0000	0.0000	
Prob (GDPGREEN: OP) > 0	0.8295	0.3761	0.0038	
Equation 4	
Variable	Mean	Std. Dev	MCSE	
Prob (GDPGREEN: GF) < 0	0.8608	0.3462	0.0035	
Prob (GDPGREEN: GF*GP) > 0	0.7664	0.4231	0.0042	
Prob (GDPGREEN: POP) < 0	0.8274	0.3779	0.0037	
Prob (GDPGREEN: UR) > 0	0.9985	0.0387	0.0004	
Prob (GDPGREEN: EGD) > 0	1.0000	0.0000	0.0000	
Prob (GDPGREEN: INF) > 0	1.0000	0.0000	0.0000	
Prob (GDPGREEN: OP) > 0	0.8283	0.3771	0.0038	
Equation 5	
Prob (GDPGREEN: GF) < 0	0.7586	0.4280	0.0043	
Prob (GDPGREEN: GF*GE) > 0	0.7036	0.4599	0.0046	
Prob (GDPGREEN: GF*GP) > 0	0.6358	0.4959	0.0049	
Prob (GDPGREEN: POP) < 0	0.8139	0.3892	0.0039	
Prob (GDPGREEN: UR) > 0	0.9989	0.0331	0.0003	
Prob (GDPGREEN: EGD) > 0	1.0000	0.0000	0.0000	
Prob (GDPGREEN: INF) > 0	1.0000	0.0000	0.0000	
Prob (GDPGREEN: OP) > 0	0.8272	0.3781	0.0037	
Source: Stata software results 17.0

When considering the role of green energy (GE*GF), findings imply that green finance brings high efficiency, as evidenced by the probability (prob) of financial growth. Green finance under the role of green energy positively affects green growth at 80.45 % and under the role of green production (GF*GP), green finance positively affects green growth at 76.64 %. In Eq. (5), green financial growth under the role of green energy is positively correlated with green growth at 70.36 % and under the role of green production (GF*GP), green finance also impacts green growth positively at 63.58 %. This is an important contribution of the research results that the previous studies are limited: under the role of green energy and green production, the green finance promotes green growth, suggesting that the governments of countries should invest more in these two areas, because of the current difficulties and obstacles in the transition from traditional energy to green energy is more costly and takes time to expand when large-scale projects are still limited.

It can be seen that using renewable energy assists countries in reducing the risk of environmental problems, the costs and damage to the environment. We can also see that in this period, green energy is accelerating to assert the upper hand, making an important contribution to maintaining sustainable and meaningful socio-economic development in the role of green energy. Green finance has brought about positive effects and contributed significantly to green growth. The role of green production is equally important, showing that businesses in other countries are choosing to increase the production of renewable energies to replace traditional energies, under the impact of green production, green finance positively impacts green growth.

In addition, the findings also confirm that urbanization rate (UR), trade openness (OP), inflation rate (INF), and economic growth rate (EGD) positively contribute to the process of green growth; on the contrary, the population growth rate hinders green growth. We find that in the current development trend, in order to realize green growth goals, the urban system plays a key role. Urban areas are increasingly contributing a larger share of economic growth globally. In addition, the process of decentralization of the production cycle further promotes the participation of developing countries in the global value chain. The economic growth rate and inflation rate at the allowable level make countries more confident in transitioning to a green economy. Besides, the process of decentralization of the production cycle is more promotes the participation of countries in the global value chain. Currently, the rapid population growth seriously affect the environment, causing resources to gradually become exhausted and generate more waste, polluting the environment, and leading to a lot of costs for environmental problems. This negatively impacts green growth.

5.2 Policy implications

The above results imply that governments worldwide should develop and complete specific legal frameworks on green finance and business investment projects that affect the environment. Central banks of countries need to set standards on green credit, a list of green industries/fields for common and uniform application. More specifically, the development of separate policies for green credit activities should be associated with green goals including green energy and green production. At the same time, it is necessary to raise the awareness of all people, agencies, and organizations about the importance and role of green finance in promoting the effectiveness of green growth, raise people's consciousness in the use and saving of green finance, save energy, and protect the environment. In addition, the improvement of green production and green energy is extremely necessary because it makes a positive contribution to the harmonious and balanced development between the economy and the environment, minimizing the negative impacts of production and business activities.

5.3 Limitation and future research

This study is limited in some aspects of the measurement variables due to the access to data: When the green growth process requires a large spillover, not only in green investment from the government sector but also from green investment in the region. While the private sector contribution is not large, it contributes to a more complete assessment of green finance. However, with the current global research scope, we cannot fully access the data and this is considered a good idea for future research.

6 Conclusion

Research on the linkage between green finance and green growth under the regulatory role of green energy in 52 countries around the world in the period 2005–2019, Bayesian regression and GMM regression have the following similarities: Green finance is negatively correlated with green growth, but under the role of green energy and green production, this relationship becomes positively. For GMM regression, all variables are statistically significant and both AR (2) and Sargan tests meet the test requirements. However, Bayesian regression is more efficient at providing a posterior probability interval (Equal-tailed 95 % Cred. Interval), which provides a sequence for the parameters in models, and probabilities to the parameter variables in this series are 95 %. The results of the convergent diagnostics of the 4 equations show that all the graphs of the parameters in the model have a consistent shape and are normally distributed. The probability that independent variables impact the dependent variable is more than 63 %.

The results of the 3 equations regression also show that the green financial transmission has not been used effectively by countries. On the contrary, it also reduces the level of green growth; but under the regulatory role of green production and energy, the impact of green credit on the economy is getting greener.

Data availability statement

Data are available upon request.

CRediT authorship contribution statement

Duc Trung Nguyen: Software, Investigation, Conceptualization. Tran Thi Kim Oanh: Writing – original draft, Project administration, Conceptualization. Thanh Dan Bui: Writing – review & editing, Resources, Methodology. Le Kieu Oanh Dao: Writing – original draft, Validation, Resources.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix 1 Pesaran's cross-sectional correlation test and unit root tests

Variable	Pesaran CD Test	ADF Test	
GDPGREEN	131.233***	76.164***	
GF	260.765***	76.116***	
GP	135.131***	95.144***	
GE	130.131***	98.321***	
UR	50.167***	96.649***	
POP	25.164***	73.174***	
EGD	120.996***	67.164**	
OP	81.116***	70,888***	
INF	396.464***	99.331***	
Note: *, **, *** represent signiﬁcant at 1 %, 5 %, and 10 % inspection levels, respectively. Source: Author's calculations.

Appendix 2 Kao panel cointegration tests results

	Statistic	P-value	
ADF	2.1455**	0.0159	
Residual variance	0.0025		
HAC variance	0.0019	
Note: *, **, *** represent signiﬁcant at 1 %, 5 %, and 10 % inspection levels, respectively. Source: Author's calculations.

Acknowledgements

The author acknowledges being supported by the University of Finance - Marketing, Viet Nam.
==== Refs
References

1 Dmuchowski P. Dmuchowski W. Baczewska D. Abrowska A.H. Gworek B. Green economy—growth and maintenance of the conditions of green growth at the level of polish local authorities J. Clean. Prod. 301 2021 126975 10.1016/j.jclepro.2021.126975
2 Xia X. Chishti M.Z. Dogan E. Transition towards the sustainable development: unraveling the effects of mineral markets, Belt & Road Initiative, and the Paris Agreement on green economic growth Resour. Pol. 91 2024 104896 10.1016/j.resourpol.2024.104896
3 Samuelson P. Nordhaus W. Economics 1995 Graw-Hill New York
4 Lee C. Chuan L. Chien C. How does green finance affect green total factor productivity? Evidence from China Energy Econ. 107 2022 105863 10.1016/j.eneco.2022.105863
5 Anwar A. Barut A. Pala F. Kilinc-Ata N. Kaya E. Lien D.T.Q. A different look at the environmental Kuznets curve from the perspective of environmental deterioration and economic policy uncertainty: evidence from fragile countries Environ. Sci. Pollut. Res. 2023 1 20 10.1007/s11356-023-28761-w
6 Durani F. Bhowmik R. Sharif A. Anwar A. Syed Q.R. Role of economic uncertainty, financial development, natural resources, technology, and renewable energy in the environmental Phillips curve framework J. Clean. Prod. 420 2023 138334 10.1016/j.jclepro.2023.138334
7 Anwar A. Huong N.T.T. Sharif A. Kilinc‐Ata N. Çitil M. Demirtaş F. Is a green world real or a dream? A look at green growth from green innovation and financial development: evidence from fragile economies Geol. J. 59 1 2024 98 112 10.1002/gj.4844
8 Liu N. Liu C. Xia Y. Ren Y. Liang J. Examining the coordination between green finance and green economy aiming for sustainable development: a case study of China Sustainability 12 9 2020 3717 10.3390/su12093717
9 Guo C.Q. Wang X. Cao D.D. Hou Y.G. The impact of green finance on carbon emission-analysis based on mediation effect and spatial effect Front. Environ. Sci. 10 2022 844988 10.3389/fenvs.2022.844988
10 Deng L. Zhao Y. Investment lag, financially constraints and company value – evidence from China Emerg. Mark. Finance Trade 58 2022 3034 3047 10.1080/1540496X.2021.2025047
11 Xu L. On the evaluation of performance system incorporating “green credit” policies in China's financial industry J. Financ. Risk Manag. 2 2 2013 33 37 10.4236/jfrm.2013.22005
12 Nawaz M.A. Seshadri U. Kumar P. Aqdas R. Patwary A.K. Riaz M. Nexus between green finance and climate change mitigation in N-11 and BRICS countries: empirical estimation through difference in differences (DID) approach Environ. Sci. Pollut. Res. Int. 28 6 2021 6504 6519 10.1007/s11356-020-10920-y 32997248
13 Sarma P. Roy A. A scientometric analysis of literature on green banking (1995- March 2019) J. Sustain. Financ. Invest. 11 2 2021 143 162 10.1080/20430795.2020.1711500
14 Kun Lv Yu S. Fu S. Wang J. Wang C. Pan J. The impact of financial development and green finance on regional energy intensity: new evidence from 30 Chinese provinces Sustainability 14 15 2022 9207 10.3390/su14159207
15 Jiang L. Wang H. Tong A. Hu Z. Duan H. Zhang X. Wang Y. The measurement of green finance development index and its poverty reduction effect: dynamic panel analysis based on improved entropy method Discrete Dynam Nat. Soc. 1 2020 8851684 10.1155/2020/8851684
16 Xu L. Yu B. Yue W. A method of green GDP accounting based on eco-service and a case study of Wuyishan, China Procedia Environ. Sci. 2 2010 1865 1872 10.1016/j.proenv.2010.10.198
17 Stjepanović S. Tomić D. Škare M. A new database on Green GDP; 1970-2019: a framework for assessing the green economy Oeconomia Copernicana 13 4 2022 949 975
18 Aneja R. Kappil S.R. Das N. Banday U.J. Does the green finance initiatives transform the world into a green economy? A study of green bond issuing countries Environ. Sci. Pollut. Res. 30 2023 42214 42222 10.1007/s11356-023-25317-w
19 Desalegn G. Tangl A. Enhancing green finance for inclusive green growth: a systematic approach Sustainability 14 2022 7416 10.3390/su14127416
20 Stern D.I. The role of energy in economic growth Ecol. Econ. Rev. 1219 1 2011 26 51 10.1111/j.1749-6632.2010.05921.x
21 Li Y. Li H. Chang M. Qiu S. Fan Y. Razzaq H.K. Sun Y. Green energy investment, renewable energy consumption, and carbon neutrality in China Front. Environ. Sci. 10 2022 960795 10.3389/fenvs.2022.960795
22 Hanif I. Faraz Raza S.M. Gago-de-Santos P. Abbas Q. Fossil fuels, foreign direct investment, and economic growth have triggered CO2 emissions in emerging Asian economies: some empirical evidence Energy 171 2019 493 501 10.1016/j.energy.2019.01.011
23 Tsai S.B. Xue Y.Z. Huang P.Y. Zhou J. Li G.D. Guo W.F. Lau H. Shang Z.W. Establishing a criteria system for green production, proceedings of the institution of mechanical engineers, Part B J. Eng. Manuf. 229 8 2015 1395 1406 10.1177/0954405414535923
24 Bekhet H.A. Harun N.H. Elasticity and causality among electricity generation from renewable energy and its determinants in Malaysia Int. J. Energy Econ. Pol. 7 2 2017 202 216
25 Dogan E. Seker F. Determinants of CO2 emissions in the European union: the role of renewable and non-renewable energy Renew. Energy 94 2016 429 439 10.1016/j.renene.2016.03.078
26 He Y. Zhang J. Feng J. Shi G. Dynamic relationship between green economy and energy utilization level: evidence from China Energies 15 2022 5927 10.3390/en15165927
27 Rahman S. Moral I.H. Hassan M. Hossain R.S. Perveen R. A systematic review of green finance in the banking industry: perspectives from a developing country Green Financ 4 3 2022 347 363
28 Nahman A. Mahumanib B.K. Langec W.J. Beyond GDP: towards a green economy index Dev. South Afr. 33 2 2016 215 233 10.1080/0376835X.2015.1120649
29 Sonnenschein J. Mundaca L. Decarbonization under green growth strategies? The case of South Korea J. Clean. Prod. 123 1 2015 180 193 10.1016/j.jclepro.2015.08.060
30 Yue S. Yang Y. Hu Y. Does foreign direct investment affect green growth? Evidence from China's experience Sustainability 8 2016 158 10.3390/su8020158
31 Jamie N.A. Financing green economy impact on sustainable development Int. J. Bus. Adm. 9 2 2018 123 128
32 Pan W. Pan W. Hu C. Tu H. Zhao C. Yu D. Xiong J. Zheng G. Assessing the green economy in China: an improved framework J. Clean. Prod. 209 1 2019 680 691 10.1016/j.jclepro.2018.10.267
33 Alekna R. Kazlauskienė E. Evaluation indicators of green economic development: the case of the baltic countries Econ. Cult. 17 1 2020 150 163
34 Wang Y. Research on the relationship between green energy use, carbon emissions and economic growth in henan province Front. Energy Res. 9 2021 701551 10.3389/fenrg.2021.701551
35 Zhang C. Cheng X. Ma Y. Research on the impact of green finance policy on regional green innovation-based on evidence from the pilot zones for green finance reform and innovation Front. Environ. Sci. 10 2022 896661 10.3389/fenvs.2022.896661
36 Tang D. Zhong H. Zhang J. Dai Y. Boamah V. The effect of green finance on the ecological and environmental quality of the yangtze river economic Belt Int. J. Environ. Res. Publ. Health 19 2022 12492 10.3390/ijerph191912492
37 Khan K.A. Thang P.D. Uyen P.T.M. Anwar A. Abbas A. From brown to green: are Asian economies on the right path? Assessing the role of green innovations and geopolitical risk on environmental quality Environ. Sci. Pollut. Res. 2024 1 13 10.1007/s11356-023-31613-2
38 Sharif A. Sofuoglu E. Kocak S. Anwar A. Can green finance and energy provide a Glimmer of hope towards sustainable environment in the midst of chaos? An evidence from Malaysia Renew. Energy 223 2024 119982 10.1016/j.renene.2024.119982
39 Syed Q.R. Durani F. Kisswani K.M. Alola A.A. Siddiqui A. Anwar A. Testing natural resource curse hypothesis amidst geopolitical risk: global evidence using novel Fourier augmented ARDL approach Resour. Pol. 88 2024 104317 10.1016/j.resourpol.2023.104317
40 H. Dai, X. Xie, Y. Xie, J. Liu, T. Masui, Green growth: The economic impacts of large-scale renewable energy development in China, Appl. Energy 162 (2–16) 435-449. 10.1016/j.apenergy.2015.10.049.
41 Hao L.N. Umar M. Khan Z. Ali W. Green growth and low carbon emission in G7 countries: how critical the network of environmental taxes, renewable energy and human capital is? Sci. Total Environ. 752 2021 141853 10.1016/j.scitotenv.2020.141853
42 Ackah I. Kizys R. Green growth in oil producing African countries: a panel data analysis of renewable energy demand Renew. Sustain. Energy Rev. 50 2015 1157 1166 10.1016/j.rser.2015.05.030
43 Sohag K. Husain S. Hammoudeh S. Omar N. Innovation, militarization, and renewable energy and green growth in OECD countries Environ. Sci. Pollut. Res. 28 2021 36004 36017 10.1007/s11356-021-13326-6
44 Tawiah V. Zakari A. Adedoyin F.F. Determinants of green growth in developed and developing countries Environ. Sci. Pollut. Res. 28 2021 39227 39242 10.1007/s11356-021-13429-0
45 Razzaq A. Sharif A. Ozturk I. Skare M. Asymmetric influence of digital finance, and renewable energy technology innovation on green growth in China Renew. Energy 202 2023 310 319 10.1016/j.renene.2022.11.082
46 Chishti M.Z. Xia X. Dogan E. Understanding the effects of artificial intelligence on energy transition: the moderating role of Paris Agreement Energy Econ. 131 2024 107388 10.1016/j.eneco.2024.107388
47 Dutz M.A. Sharma S. Green Growth, Technology and Innovation 2012 World Bank Policy Research Working Paper 5932
48 Song M. Zhu S. Wang J. Zhao J. Share green growth: regional evaluation of green output performance in China Int. J. Prod. Econ. 219 2020 152 163 10.1016/j.ijpe.2019.05.012
49 Guo M. Nowakowska-Grunt J. Gorbanyov V. Egorova M. Green technology and sustainable development: assessment and green growth frameworks Sustainability 12 16 2020 6571 10.3390/su12166571
50 Nosheen M. Iqbal J. Abbasi M.A. Do technological innovations promote green growth in the European Union? Environ. Sci. Pollut. Res. 28 2021 21717 21729 10.1007/s11356-020-11926-2
51 Binh N.T. Oanh T.T.K. Dinh L.Q. Ha N.T.H. Nonlinear impact of financial inclusion on tax revenue: evidence from the Monte–Carlo simulation algorithm under the Bayesian approach J. Econ. Stud. 2024 10.1108/JES-01-2024-0010
52 Rasoulinezhad E. Taghizadeh-Hesary F. Role of green finance in improving energy efficiency and renewable energy development Energy Effic 15 14 2022 10.1007/s12053-022-10021-4
53 Galan J.E. Tan Y. Green light for green credit? Evidence from its impact on bank efficiency Int. J. Financ. Econ. 29 1 2024 531 550 10.1002/ijfe.2697
54 Chen C. Zhang Y. Bai Y. Li W. The impact of green credit on economic growth—the mediating effect of environment on labor supply PLoS One 16 9 2021 e0257612 10.1371/journal.pone.0257612
55 Baines T. Lightfoot H.W. Servitization of the manufacturing firm: exploring the operations practices and technologies that deliver advanced services Int. J. Oper. Prod. Manag. 34 1 2013 2 35 10.1108/IJOPM-02-2012-0086
56 Ahmed Z. Ahmad M. Rjoub H. Kalugina O.A. Hussain N. Economic growth, renewable energy consumption, and ecological footprint: exploring the role of environmental regulations and democracy in sustainable development Sustain. Dev. 30 4 2022 595 605 10.1002/sd.2251
57 Dinh L.Q. Oanh T.T.K. Ha N.T.H. Financial stability and sustainable development: perspectives from fiscal and monetary policy Int. J. Financ. Econ. 2024 1 18 10.1002/ijfe.2981
58 Wu Y. Anwar A. Quynh N.N. Abbas A. Cong P.T. Impact of economic policy uncertainty and renewable energy on environmental quality: testing the LCC hypothesis for fast growing economies Environ. Sci. Pollut. Res. 2023 1 12 10.1007/s11356-023-30109-3
59 Saydaliev H.B. Chin L. Role of green financing and financial inclusion to develop the cleaner environment for macroeconomic stability: inter-temporal analysis of ASEAN economies Econ. Change Restruct. 56 2022 3839 3859 10.1007/s10644-022-09419-y
60 Oanh T.T.K. Van L.T.T. Dinh L.Q. Relationship between financial inclusion, monetary policy and financial stability: an analysis in high financial development and low financial development countries Heliyon 9 6 2023 e16647 10.1016/j.heliyon.2023.e16647 16647
61 Ngo T.Q. Doan P.N. Vo L.T. Hai T.T.T. Nguyen D.N. The influence of green finance on economic growth: a COVID-19 pandemic effects on Vietnam Economy Cogent Bus. Manag. 8 1 2021 2003008 10.1080/23311975.2021.2003008
62 Fu W. Irfan M. Does green financing develop a cleaner environment for environmental sustainability: empirical insights from association of southeast asian nations economies Front. Psychol. 13 2022 904768 10.3389/fpsyg.2022.904768
63 Dinh L.Q. The relationship between digital financial inclusion, gender inequality, and economic growth: dynamics from financial development" Journal of Business and Socio-economic Development 2024 10.1108/JBSED-12-2023-0101 ahead-of-print No. ahead-of-print
64 Zondervan-Zwijnenburg M. Peeters M. Depaoli S. Van de Schoot R. Where do priors come from? Applying guidelines to construct informative priors in small sample research Res. Hum. Dev. 14 4 2017 305 320 10.1080/15427609.2017.1370966
65 Arellano M. Bond S. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations Rev. Econ. Stud. 58 2 1991 277 297 10.2307/2297968
66 Flegal J.M. Haran M. Jones G.L. Markov chain Monte Carlo: can we trust the third significant figure? Stat. Sci. 23 2008 250 260 https://www.jstor.org/stable/27645897
67 Capoor K. Ambrosi P. State and Trends of the Carbon Market 2007 2007 World Bank Institute, World Bank Retrieved from http://documents.worldbank.org/curated/en/416871468138574709/State-and-trends-of-the-carbon-market-2007(8.11.2020
68 Oanh T.T.K. Dinh L.Q. Digital financial inclusion, financial stability, and sustainable development: evidence from a quantile-on-quantile regression and wavelet coherence Sustain. Dev. 2024 1 15 10.1002/sd.3021 (Early View)
