Exploring the association of green banking disclosure and corporate sustainable growth: the moderating role of firm size and firm age

Abstract

This study examines the impact of green banking disclosure on firms’ sustainable growth. The methodology used is panel data analysis with a sample of 45 banks in Indonesia, a total of 578 observations from 2004 to 2021, listed on the Indonesia Stock Exchange. Data are taken from annual reports, financial reports, corporate sustainability reports, and government publications. The results show that GBDI has a negative effect on SGR, suggesting that green banking disclosures, although necessary, require resource allocation that could potentially reduce the firm’s growth capacity. This study also analyzed the moderating variables of firm size (FSIZE) and firm age (FAGE) to determine their effect on the relationship between GBDI and SGR. However, the results show that neither FSIZE nor FAGE significantly strengthen this relationship. However, FAGE strengthens the relationship between GBDI and SGR for state-owned banks. This study adds novelty to the research by highlighting the importance of understanding how sustainability disclosure affects firm growth, especially in green banking.

Keywords:

• Green banking disclosure
• corporate sustainable growth
• firm size
• firm age

JEL:

• G21
• M41
• Q56

Reviewing Editor:

• Nor Shaipah Abdul Wahab, Taylor’s University - Lakeside Campus, Malaysia

SUBJECTS:

• Business
• Management and Accounting
• Finance
• Environmental Economics

1. Introduction

Sustainable development has become a prevalent international trend, with countries worldwide increasing their focus on environmental, social, and governance (ESG) disclosures within the corporate sector (Camilleri, 2015; Cornell & Shapiro, 2021). This emphasis reflects a growing global awareness of the significance of responsible and sustainable business practices (Kopnina & Blewitt, 2014; Smith, 2010). In this context, the banking sector is a crucial financial service provider and change agent promoting sustainable development through responsible lending and environmentally sound operations (Bouma et al., 2017; Park & Kim, 2020; Weber & Feltmate, 2016). The important role of the banking sector will pave the way for new approaches to financing environmental conservation (e.g. biodiversity, climate change mitigation, etc.). It marks the intersection of sustainable finance and the urgent need for funds for environmental protection (Cosma et al., 2023). Green banking disclosure, which refers to banks and financial institutions informing the public about their social and environmental responsibility initiatives, is increasingly important (J. Chen et al., 2022; Khairunnessa et al., 2021; Sun et al., 2020). Commitment to sustainable practices can attract environmentally conscious investors and customers (Ellahi et al., 2023; Ibe-enwo et al., 2019). Additionally, applying sustainability principles in corporate valuation and green banking practices is essential for aligning financial decisions with sustainable development goals, which include environmental, social, and governance factors (Lee, 2020; Sardianou et al., 2021). The adoption of green banking practices, driven by growing stakeholder pressure and focus at the global level, represents a significant shift in the financial industry. This shift reflects changes in societal norms and expectations, as well as the increasing recognition by financial institutions of the importance of integrating sustainability principles into their operations (Bukhari et al., 2019; Masud et al., 2018; Redwanuzzaman, 2021). The benefits of sustainable finance for the banking industry are significant. These include increased long-term resilience, market share, reputation, and reduced operating costs through greater efficiency.

Sustainability practices in the banking industry, commonly known as green banking, are expected to create long-term value for companies and society (Weber & Feltmate, 2016; Yip & Bocken, 2018). Research has demonstrated that incorporating sustainability principles into banking operations not only enhances profitability and reduces risk but can also bolster the company’s reputation in the long term (Ameer & Othman, 2012; Costa-Climent & Martínez-Climent, 2018; Gomez-Trujillo et al., 2020). Furthermore, these practices contribute to sustainable economic growth and the transition to a low-carbon economy (Buallay et al., 2021; Campiglio, 2016). This underscores the importance of green banking in the broader global context. Sustainability practices in banking have also been shown to increase customer loyalty and strengthen corporate image (Ibe-enwo et al., 2019; Igbudu et al., 2018; Kartadjumena & Rodgers, 2019; Wong et al., 2019). This, in turn, can improve corporate performance in the short term. However, much of the existing research on the impact of sustainability practices on financial performance is limited to general effects and does not consider specific operating conditions (Akhter et al., 2021; Aslam & Jawaid, 2023; M. A. Hossain et al., 2020; M. S. Hossain & Kalince, 2014; Putri et al., 2022; Ratnasari et al., 2021; Siahaan et al., 2020). Few investigations have explored the impact of sustainability practices in banking under conditions where firms can achieve maximum growth without additional external financing while maintaining a constant debt-equity ratio and stable financial leverage. The Sustainable Growth Rate (SGR) is a commonly used metric to measure a company’s ability to grow sustainably without requiring additional external financing. However, previous studies have often overlooked the impact of sustainability practices on a firm’s SGR, particularly in the banking sector. Therefore, it is essential to consider sustainability practices when assessing a company’s SGR. This limitation indicates a gap in the literature, emphasizing the necessity for further investigation into the relationship between green banking practices and firms’ ability to achieve sustainable growth under optimal financial conditions. Research that fills this gap will provide valuable insights into how green banking can directly affect corporate sustainable growth rate (SGR), paving the way for more efficient and sustainable banking practices in the long run.

Green banking disclosure is essential for the banking industry to achieve sustainable growth. It provides stakeholders with vital information about a bank’s commitment to sustainability, which can influence the perceptions and trust of investors, customers, and society. Khamilia and Nor (2022) found that a company’s Sustainability Committee positively and significantly impacts green banking disclosure. The text suggests that a company’s commitment to sustainability leads to improved disclosure of sustainability practices. Winarto et al. (2021) found that green banking disclosure is crucial to a company’s operational activities, indicating that sustainability practices are essential to its strategy and operations.

The literature on green banking disclosure and sustainable growth of firms offers diverse and deep insights. According to Freeman et al. (2004) and Velte (2017), social responsibility positively impacts a bank’s financial performance. Gangi et al. (2019) and Buallay et al. (2021) also emphasize the positive influence of social practices on public perception and corporate reputation. Buallay et al. (2021) argue that social disclosure strengthens banks’ market position and increases long-term profitability. Eccles et al. (2011) add that good ESG practices and transparency improve corporate performance. Corvino et al. (2019) and Meshack et al. (2020) have identified firm size as an important moderating variable influencing firm performance. Ngatno and Dewi (2019) and Were (2022) have highlighted the role of firm age as another moderating variable in firm performance. Noor et al. (2022) and Singh et al. (2015) have also examined the interaction between firm age, firm size, and firm performance, demonstrating the significance of these variables in understanding firm performance. This literature provides a solid basis for comprehending the dynamics of green banking disclosure and the sustainable growth of firms. It particularly emphasizes the role of firm size and age as mediators.

This study investigates the effect of green banking disclosure on corporate sustainable growth. In this context, green banking disclosure refers to the disclosure of information by banks and financial institutions regarding their social and environmental responsibility initiatives. The main objective of this study is to understand how green banking disclosure practices can affect the sustainable growth of firms. In addition, this study will also review the role of firm size and age as moderating variables. This involves understanding how firm size and age may influence or mediate the relationship between green banking disclosures and firm sustainable growth. In this study, the sample used is banks in Indonesia from 2004 to 2021. The chosen analysis method is a static panel data regression model, which allows the examination of the effect of independent variables on the dependent variable over time. This approach was chosen for its ability to capture the dynamics of change over the period and provide a deeper understanding of the relationship between green banking disclosures and corporate sustainable growth.

This research significantly contributes to theoretical and practical green finance and banking contexts. Theoretically, this study fills a gap in the literature by investigating how green banking disclosures affect corporate sustainable growth in Indonesia. This topic needs to be explored more. By focussing on the interaction between green banking disclosures and firm size and age, this study offers a deeper understanding of the factors influencing corporate sustainable growth. From a practical perspective, this study’s findings may benefit stakeholders in the banking sector, including regulators, bank managers, and investors. This research offers insights into how green banking disclosures can be a strategic tool to improve a bank’s performance and strengthen its corporate reputation. This is important in helping banks and financial institutions craft effective sustainability strategies and improve stakeholder engagement.

This research article is organized into four main sections to ensure a logical and thorough flow. The second section reviews the relevant literature and puts forward the research hypotheses. This involves examining the current literature on green banking disclosure and its impact on corporate sustainable growth. The third section describes the data and research design, including the methodology chosen for analysis. The fourth section discusses the study’s results, including statistical analyses and discussion of the findings. Finally, the fifth section concludes with conclusions and policy recommendations based on the research results.

2. Literature review

2.1. Legitimacy theory

Legitimacy theory is essential in corporate sustainability practice, especially regarding sustainability reporting and disclosure (da Costa Tavares & Dias, 2018; Faisal, 2012; Mousa & Hassan, 2015). Legitimacy theory, proposed by Suchman (1995), defines legitimacy as the perception that an entity’s actions, such as those of an organization or organizational practice, are desirable, appropriate, and consistent with a socially established system of norms, values, and beliefs. According to this theory, sustainability disclosure is crucial for corporate legitimacy, providing a broader rationale for companies to disclose sustainability-related information (Shamil et al., 2014). This suggests that sustainability disclosure is about transparency and building and maintaining legitimacy from the public perspective. Legitimacy is crucial for an organization’s survival (Deegan, 2014; Martín et al., 2010). This resource is given to organizations by the public and is highly desired by organizations. Uniquely, unlike other resources, legitimacy is a resource that organizations can influence or manipulate through various disclosure strategies. This suggests that organizations can influence public and societal perceptions of their legitimacy.

According to legitimacy theory, managers should pursue strategies that ensure the continued supply of vital resources for organizational survival (Ruef & Scott, 1998; Scherer et al., 2012; Walker & McCarthy, 2010). Legitimacy strategies, which aim to gain, maintain, or improve legitimacy, generally rely on targeted disclosure. These strategies can involve rigorous and transparent sustainability reporting or sustainability initiatives that can attract positive attention from the public. Legitimacy theory is significant in the context of green banking. Banks and financial institutions can enhance their legitimacy by effectively implementing and disclosing green banking practices (Bose et al., 2018; Gunawan et al., 2022; Siddik et al., 2023). This is crucial as banks are critical in financing sustainable projects and supporting the transition to a low-carbon economy. Green banking disclosures serve as a tool for banks to demonstrate their commitment to sustainability practices, reflecting their social and environmental responsibilities. This enhances their legitimacy in the eyes of stakeholders. Therefore, in the context of green banking, the application of legitimacy theory can be considered a strategic approach that enables banks to fulfill the sustainability expectations of stakeholders and build a strong reputation and trust. Legitimacy theory provides a valuable framework for understanding and implementing sustainability practices in the banking sector.

2.2. Green banking and green banking disclosure

Green banking is a concept that has gained significant attention in the financial sector due to its potential to promote environmental sustainability and corporate responsibility (Aslam & Jawaid, 2023; Bouma et al., 2017). The concept involves integrating environmental considerations into banking operations and decision-making processes, promoting sustainable development and reducing the ecological footprint of financial activities (Peeters, 2005; Ziolo et al., 2019). Green banking has developed over time, reflecting the increasing recognition of financial institution’s need to align themselves with environmental goals and contribute to sustainable practices (Akomea-Frimpong et al., 2022). Various factors, including regulatory initiatives, market dynamics, and growing awareness of environmental issues, have influenced the evolution of green banking in the financial sector. Expanding the role of regulators and financial institutions has created new opportunities for private sector banks and influenced the risk management practices of individual financial institutions. Institutional policies, such as China’s Green Credit Policy, have also influenced the concept of green banking, reflecting the linkage between environmental considerations and financial practices (Park & Kim, 2020). Green banking has also gained traction in specific regional contexts, such as Bangladesh and India, where efforts have been observed to develop frameworks for assessing sustainable banking performance and implementing green banking initiatives (Kumar & Prakash, 2020; Sharma & Choubey, 2022).

Furthermore, the potential for synergies between environmental sustainability and financial governance within specific banking frameworks has been explored by integrating green banking with Islamic banking practices (Issa et al., 2022; Rahmayati et al., 2022). Green banking represents a significant shift towards greater environmental responsibility and sustainability in the financial sector. Aligning banking practices with environmental objectives, green banking has the potential to drive positive environmental outcomes, enhance corporate sustainability, and contribute to the development of new sustainable business models. It is a promising approach to promoting sustainable development.

Green banking is crucial in promoting sustainable development by integrating environmental considerations into banking operations and decision-making processes. This includes managing environmental risks, identifying green investment opportunities, and implementing environmentally responsible banking practices. Green banking disclosure is crucial to this process, requiring transparent reporting of banking institutions’ environmental initiatives, policies, and performance (Bose et al., 2018). This transparency meets regulatory demands and enhances the trust and reputation of banks in the eyes of the public and investors. The relationship between green banking practices and disclosures has been the subject of many studies and has shown mixed results. Several studies suggest a positive correlation between proactive environmental efforts by banks and a positive public perception. For instance, Sharma and Choubey (2022) found that green banking initiatives can significantly influence a bank’s green brand image. Li et al. (2022) suggest that corporate disclosure of environmental information positively correlates with bank financing, mainly when supported by formal policies such as the issuance of Green Credit Guidelines. This highlights the significance of policies and regulations in supporting green banking initiatives. Transparent disclosure of environmental initiatives and performance is crucial for building positive stakeholder relationships. This enhances the bank’s reputation and provides access to financial resources that support green banking activities.

2.3. Sustainable Growth Rate

The Sustainable Growth Rate (SGR) concept refers to the firm’s ability to maximize its sales by using internal resources without relying on external financing, making it a crucial factor for firm survival and expansion (Higgins, 1977; Mukherjee & Sen, 2018). SGR is an essential metric for evaluating a company’s ability to expand its operations without depleting its financial resources, especially for companies experiencing financial difficulties or seeking to reduce leverage (Arora et al., 2018). SGR reflects the healthy and sustainable growth of the company and provides an analytical framework to help management identify which elements in the company’s operating and financial structure to focus on to improve financial performance (Harkleroad, 1993; Wen et al., 2021). This multifaceted metric can be divided into components that reflect the company’s retention policy, net profit margin, asset turnover, and financial leverage (Altahtamouni et al., 2022). SGR allows analysts to compare company performance and identify critical elements of competitors’ strategies. It is an internal performance evaluation tool for companies and a competitive analysis tool.

2.4. Empirical literature review and hypothesis development

In the context of green banking, there is a significant relationship between green banking practices and corporate financial performance, as the literature suggests. Ratnasari et al. (2021) emphasized that the banking sector is expected to participate in environmental initiatives, although it does not directly contribute to environmental damage. The study found that green banking daily operations, green banking policy (GBP), and other factors such as capital adequacy and bank liquidity positively impact bank profitability. M. A. Hossain et al. (2020) also found a positive relationship between green banking practices and banks’ financial performance, highlighting the importance of green banking in improving economic and environmental performance. This study measured bank performance using variables such as return on assets, return on equity, and market value. Okumu (2014) analyzed the relationship between green banking and the financial performance of commercial banks in Kenya. The study utilized secondary data from the Central Bank of Kenya and the National Statistics Agency. The study revealed a significant correlation between green banking and financial performance, indicating that green banking practices positively affect bank profitability.

Zhou et al. (2021) emphasize green credit’s role in moderating the relationship between corporate social responsibility (CSR) and banks’ financial performance. The results indicate that CSR has a negative impact on banks’ financial performance in the short term but becomes positive in the long term. Additionally, Bătae et al. (2021) investigated the relationship between ESG dimensions and banks’ financial performance and found a positive correlation between emission reduction and financial performance. Lian et al. (2022) investigated the impact of green credit on the financial performance of banks in China. The study found that green credit improves banks’ financial performance, mainly through its positive effect on the rate of return on interest-earning assets. Jan et al. (2019) examined the relationship between sustainability practices and financial performance in Islamic banking. The study found a significant positive relationship between sustainability practices and financial performance indicators of Islamic banks. J. Chen et al. (2022) research highlights the positive impact of green banking practices on green financing. Nizam et al. (2019) that access to financing positively impacts banks’ financial performance. Xu et al. (2020) demonstrated a positive correlation between green financing and corporate green performance, with the moderating role of firm type and region. Miroshnychenko et al. (2017) found that internal green practices positively impact financial performance, while external green practices have a secondary role.

In the context of green banking, research shows a significant relationship between green banking practices and SGR. A literature review of related studies shows a positive correlation between sustainable practices and an increase in SGR, signaling the importance of green banking in promoting sustainable corporate growth. According to Sanoran (2023), there is evidence that companies listed as sustainable seem to achieve more sustainable growth than those not. The study also highlights the moderating influence of a firm’s industry profile in influencing the relationship between corporate sustainability and sustainable growth. Teng et al. (2021) examine the relationship between firms’ sustainable growth and ESG risks and find that ESG significantly negatively impacts firms’ sustainable growth in the upper quantile of SGR. However, there is no significant effect in the lower and median quantiles. This finding suggests that the impact of ESG risks on companies’ sustainable growth is asymmetric and influenced by the SGR distribution. The study by Sunday and Godspower (2022) shows that the COVID-19 pandemic significantly negatively impacts the sustainable growth of manufacturing companies listed in Nigeria. However, higher growth opportunities can mitigate the pandemic’s negative impact on firms’ sustainable growth. Isnurhadi et al. (2022) found that operational risk negatively impacts sustainable growth and positively impacts actual growth in the ASEAN banking industry.

The study also found that business risk has a positive effect on sustainable growth but a negative effect on actual growth. Interestingly, Chai et al. (2023) found that ESG disclosure positively affects the SGR of Chinese firms. Higher levels of ESG disclosure have been found to have a more significant promotional effect on SGR. This positive relationship is more pronounced in the presence of high media attention. Overall, the literature suggests a positive relationship between green banking and SGR. This highlights the importance of green banking practices not only for environmental sustainability but also for the sustainable growth of firms. Based on previous research findings, the following hypothesis is formulated.

H₁: Green banking disclosure has a significant positive impact on sustainable growth rate.

Firm size and age are frequently used as moderating variables, including Sustainable Growth Rate, when examining firm performance. Corvino et al. (2019) and Meshack et al. (2020) have identified firm size as a significant moderating variable that affects the operations and performance of firms. Leal-Rodríguez et al. (2015) discovered that firm size influences the relationship between organizational learning and innovation outcomes, with larger firms having a negative impact on this effect. Hernández et al. (2020) investigated the effect of firm size on the relationship between CSR and economic performance in MSMEs. The results indicate that the relationship between CSR and economic performance strengthens as firm size increases. Ngatno and Dewi (2019) and Were (2022) also emphasized the role of firm age as another moderating variable in understanding firm performance. Carr et al. (2010) investigated the moderating effect of firm age on post-internationalization survival and growth. The results indicate that firm age has a positive moderating effect on post-internationalization survival. Petruzzelli et al. (2018) examined the relationship between knowledge maturity and innovation value and how it depends on firm age and size. Yin et al. (2022) found that older firms benefit more from green innovation than younger firms. This research contributes significantly to the literature on green entrepreneurship and innovation. Based on previous research findings, the following hypothesis is formulated.

H_2a: Firm size strengthens the positive relationship between green banking disclosure and sustainable growth rate.

H_2b: Firm age strengthens the positive relationship between green banking disclosure and sustainable growth rate.

3. Data and method

3.1. Data and samples

This study analyses a sample of 45 banking companies listed on the Indonesia Stock Exchange (IDX) between 2004 and 2021, comprising 578 observations. The data were obtained from credible and reliable sources, including the official publication of the Indonesia Stock Exchange, the bank’s official websites, and publications from the Indonesian Central Bureau of Statistics. The analyzed data includes annual, financial, and corporate sustainability reports. The financial analysis will focus on financial ratios and statements obtained from the bank’s financial statements. Green banking disclosures will be analyzed using data from annual and sustainability reports.

Furthermore, the analysis will incorporate macroeconomic data, including inflation and Indonesia’s Gross Domestic Product (GDP) growth, sourced from Statistics Indonesia. The study’s sampling criteria were determined based on three main requirements. Firstly, the inclusion of banking corporations listed on the official website of the Indonesia Stock Exchange between 2004 and 2021. Secondly, companies in the banking sector that have published audited financial statements and annual reports from 2004 to 2021 were included. Finally, the selected banking corporations must have complete research data. No changes in content were made as per the instructions provided.

3.2. Operationalization of research variables

The operationalization of variables in this study is presented below in Table 1.

Table 1. Operationalisation of variables.

Variable	Operationalization
Dependent variables: Sustainable Growth Rate
Sustainable Growth Rate (SGR)	SGR can be presented with the following formula: SGR = Profit margin $\times$ Asset turnover ratio $\times$ Leverage factor $\times$ Retention ratio (Ul Ain et al., 2022).
Independent variables
Green Banking Disclosure Index (GBDI)	$\frac{{\sum }_{i=1}^{21}{d}_i}{21}$ Score $d_i=1$ if there is disclosure in the company’s annual report or sustainability report Score $d_i=0$ if there is no disclosure in the company’s annual or sustainability report (Bose et al., 2018).
Moderating variables
Firm Size (FSIZE)	Measured as the natural logarithm of the bank’s total assets (Bose et al., 2018).
Firm Age (FAGE)	Measured as the natural logarithm of the number of years since bank incorporation (Bose et al., 2018).
Control variable (bank level)
Return On Assets (ROA)	Pre-tax income is divided by total assets (Muthitacharoen, 2020).
Return On Equity (ROE)	Pre-tax income is divided by total equity (Yoon et al., 2021).
Dividend Payout Ratio (DPR)	Dividend per share divided by earnings per share (Sanoran, 2023).
Control variable (macroeconomic level)
Inflation rate (INF)	GDP per capita growth (annual %) (Le & Ngo, 2020; Petria et al., 2015).
GDP Growth (GDPGR)	Inflation, GDP deflator (annual %) (Le & Ngo, 2020; Petria et al., 2015).

The dependent variable is the Sustainable Growth Rate (SGR), measured using the formula SGR = profit margin × asset turnover ratio × leverage factor × retention ratio, following Ul Ain et al. (2022). The independent variables in this study are the Green Banking Disclosure Index (GBDI)/GBDI, which was calculated by conducting a content analysis of green banking disclosures in annual or corporate sustainability reports. The final score is obtained by comparing the existing disclosures divided by green banking indicators, as Bose et al. (2018) used. This study examines the moderating variables of Firm Size (FSIZE) and Firm Age (FAGE). Firm Size is measured as the natural logarithm of the bank’s total assets, while Firm Age is measured as the natural logarithm of the bank’s age, following the approach used by Bose et al. (2018). The study employs Return On Assets (ROA), Return On Equity (ROE), Dividend Payout Ratio (DPR), inflation rate (INF), and GDP growth (GDPGR) as control variables. ROA is calculated by dividing pre-tax income by total assets, and ROE by dividing pre-tax income by total equity. DPR is calculated as dividends per share divided by earnings per share. The inflation rate is measured using three indicators: inflation, GDP deflator (annual %), and GDP growth as GDP per capita growth (annual %). These indicators are by the studies conducted by Muthitacharoen (2020), Yoon et al. (2021), Sanoran (2023), Le and Ngo (2020), and Petria et al. (2015).

3.3. Econometric model and estimation procedure

This study employs panel data estimation to analyze the impact of green banking on corporate sustainable growth. Using panel data enables the consideration of variations across time and individuals. We refer to three main tests to select the appropriate model for panel data analysis: the Chow test, the Hausman test, and the Lagrange Multiplier (LM) test. The Chow test determines whether a Common Effect or Fixed Effect model is more appropriate. The Hausman test is used to choose between the Fixed Effect and Random Effect models. The LM test is also used to determine the most suitable model for our data, whether the Random Effect model or the Common Effect model. We use the Variance Inflation Factor (VIF) Test to check for multicollinearity issues in the model. If the VIF value is below 10, it can be concluded that there are no symptoms of multicollinearity in the model. It is essential to ensure that the independent variables in the model are not significantly correlated. This can cloud the interpretation of the regression results. The dependent variable in this study is the Sustainable Growth Rate (SGR). The independent variable is the Green Banking Disclosure Index (GBDI). The moderating variables in this study are Firm Size (FSIZE) and Firm Age (FAGE). The variable ${\phi }_{i,d}$ is a control variable consisting of Return-on-Assets (ROA), Return-on-Equity (ROE), Dividend Payout Ratio (DPR), inflation rate (INF), and GDP growth (GDPGR). This econometric model examines the effect of independent variables on the sustainable growth rate. $${\mathit{\text{SGR}}}_{i,d}={\beta }_0+{\beta }_1{\mathit{\text{GBDI}}}_{i,d}+{\beta }_2{\phi }_{i,d}+ϵ$$

Meanwhile, an econometric equation includes the moderating variable of firm size (FSIZE). $${\mathit{\text{SGR}}}_{i,d}={\beta }_0+{\beta }_1{\mathit{\text{GBDI}}}_{i,d}+{\beta }_2{\mathit{\text{FSIZE}}}_{i,d}+{\beta }_3\mathit{\text{GBDI}}\mathrm{*}{\mathit{\text{FSIZE}}}_{i,d}+{\beta }_4{\phi }_{i,d}+ϵ$$

The following is an econometric equation that includes the moderating variable of firm age (FAGE). $${\mathit{\text{SGR}}}_{i,d}={\beta }_0+{\beta }_1{\mathit{\text{GBDI}}}_{i,d}+{\beta }_2{\mathit{\text{FAGE}}}_{i,d}+{\beta }_3\mathit{\text{GBDI}}\mathrm{*}{\mathit{\text{FAGE}}}_{i,d}+{\beta }_4{\phi }_{i,d}+ϵ$$

4. Result and discussion

4.1. Descriptive statistics and correlation coefficient

This section focuses on descriptive statistical analysis based on the presented data in Table 2. The Sustainable Growth Rate (SGR) has a mean of 7.20, with a minimum value of −205.72 and a maximum of 135.50, indicating a wide variation in the sustainable growth of the studied banking companies. The Green Banking Disclosure Index (GBDI) has a mean of 0.39, a minimum value of 0.00, and a maximum value of 0.90. The data reveals that some banks have achieved 90% of the required disclosures. The metric ROA has a mean of 0.01, a standard deviation of 0.06, and a median of 0.01. The data shows that the asset performance of the average firm is relatively stable. The Return On Equity (ROE) has a mean of 0.07, a standard deviation of 0.30, and a median of 0.07, indicating considerable variation in equity performance. The Dividend Payout Ratio (DPR) has a mean of 18.06, a standard deviation of 39.28, and a median of 0.00, indicating high variability in the company’s dividend policy. The study period exhibited varying inflation conditions, with inflation (INFL) having a mean of 4.88, S.D. of 2.55, and a median of 4.28. GDP growth (GDPGR) averaged 4.72, S.D. 2.03, and median 5.07. Firm Size (FSIZE) had an average of 24.09, S.D. of 1.82, and a median of 23.98. Firm Age (FAGE) had a mean of 3.73, S.D. 0.54, and a median of 3.81.

Table 2. Descriptive statistics.

Variable	n	Mean	S.D	Min	0.25	Mdn	0.75	Max
SGR	578	7.20	23.75	−205.72	1.09	8.11	15.67	135.50
GBDI	578	0.39	0.38	0.00	0.00	0.76	0.76	0.90
ROA	578	0.01	0.06	−1.30	0.00	0.01	0.02	0.09
ROE	578	0.07	0.30	−3.53	0.02	0.07	0.14	4.74
DPR	578	18.06	39.28	−4.21	0.00	0.00	26.01	454.67
INFL	578	4.88	2.55	1.56	3.20	4.28	6.39	13.11
GDPGR	578	4.72	2.03	−2.07	4.88	5.07	5.69	6.35
FSIZE	578	24.09	1.82	18.31	22.76	23.98	25.51	28.18
FAGE	578	3.73	0.54	1.95	3.30	3.81	4.08	4.84

The purpose of the multicollinearity test in this study is to identify any strong correlation between the independent variables. This is important because a high correlation between the independent variables can make it difficult to interpret the effect of each variable on the dependent variable. The Variance Inflation Factor (VIF) is used to conduct the multicollinearity test, where a VIF value lower than 10 does not indicate significant multicollinearity. The model testing the effect of Green Banking Disclosure had an average VIF value of 1.28, with individual VIF values ranging from 1.02 to 1.47, indicating the absence of significant multicollinearity. Therefore, this analysis concludes that all models tested in this study are free from serious multicollinearity issues, with mean VIF values below the threshold of 10 (shown in Table 3). The statement implies that the independent variables in each model can be viewed as relatively independent predictors of each other. This provides greater confidence in the interpretation of the results of our regression analysis.

Table 3. Variance inflation factor.

Variable	VIF	1/VIF
INFL	1.47	0.677969
ROE	1.31	0.762667
GDPGR	1.3	0.766611
ROA	1.29	0.774072
GBDI	1.26	0.791664
DPR	1.02	0.979903
Mean VIF	1.28

The study conducted pairwise correlation analysis and found significant results (shown in Table 4). The Green Banking Disclosure Index (GBDI) and Sustainable Growth Rate (SGR) have a negative correlation of -0.095, indicating that an increase in GBDI leads to a decrease in SGR. Return On Assets (ROA) and Return On Equity (ROE) show a significant positive correlation with SGR, at 0.214 and 0.407, respectively. This suggests that an increase in these two financial ratios correlates with an increase in SGR. The Dividend Payout Ratio (DPR) has a low correlation with SGR. In macroeconomics, Inflation (INFL) and GDP Growth (GDPGR) also have a low correlation with SGR. On the other hand, firm size (FSIZE) and firm age (FAGE) have significant positive correlations with SGR, indicating that larger and older firms tend to have higher SGR.

Table 4. Correlation matrix.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
(1) SGR	1.000
(2) GBDI	−0.095*	1.000
	(0.022)
(3) ROA	0.214*	0.020	1.000
	(0.000)	(0.639)
(4) ROE	0.407*	−0.064	−0.464*	1.000
	(0.000)	(0.126)	(0.000)
(5) DPR	0.022	0.051	0.065	0.064	1.000
	(0.596)	(0.222)	(0.117)	(0.126)
(6) INFL	−0.052	0.442*	0.044	−0.108*	−0.005	1.000
	(0.210)	(0.000)	(0.286)	(0.009)	(0.905)
(7) GDPGR	−0.020	0.292*	0.010	−0.116*	−0.031	0.466*	1.000
	(0.638)	(0.000)	(0.809)	(0.005)	(0.458)	(0.000)
(8) FSIZE	0.264*	0.313*	0.155*	0.129*	0.242*	0.453*	0.297*	1.000
	(0.000)	(0.000)	(0.000)	(0.002)	(0.000)	(0.000)	(0.000)
(9) FAGE	0.152*	0.179*	0.132*	0.041	0.067	0.259*	0.161*	0.472*	1.000
	(0.000)	(0.000)	(0.002)	(0.328)	(0.107)	(0.000)	(0.000)	(0.000)

This table presents the pairwise correlation coefficients between the variables used for hypothesis testing (p-values are in parentheses), and the * indicates significance at the 10% level.

4.2. Multivariate statistical analysis

4.2.1. The effect of the Green Banking Disclosure Index on the Sustainable Growth Rate

The discussion of the regression results in this study focuses on the effect of the Green Banking Disclosure Index (GBDI) on the Sustainable Growth Rate (SGR) of banking companies in Indonesia (shown in Table 5). Five regression models were used to analyze the relationship between variables. Based on the results of the Chow, LM, and Hausman tests, FEM was chosen as the best model for this study. Model 1, which used the Common Effect Model (CEM), revealed a significant negative effect of GBDI on SGR p < 0.05 (β = −5.054, t = −2.21). The model shows that Return On Equity (ROE) and Return On Assets (ROA) made significant positive contributions to SGR, with β = 51.15 (p < 0.01) and β = 212.3 (p < 0.01), respectively. The F-statistic value is 59.43 (p = 0.000), and R²-Adj is 0.378, indicating that this model is quite effective in explaining variations in SGR. Models 2 and 3, applying the Fixed Effect Model (FEM) and Random Effect Model (REM), respectively, confirm similar findings to Model 1 regarding GBDI, ROE, and ROA. The R²-Adj for the FEM is 0.221, indicating that this model explains about 22.1% of the variation in SGR. The F-statistic for the FEM was 35.32, indicating the reliability of this model. This demonstrates the consistency of results across different models.

Table 5. Regression results.

	Model 1	Model 2	Model 3	Model 4	Model 5
	CEM	FEM	REM	FEM	FEM
GBDI	−5.054**	−7.953***	−5.054**	−50.05	−36.70
	(−2.21)	(−2.65)	(−2.21)	(−1.07)	(−1.55)
GBDI*FSIZE				1.600
				(0.85)
FSIZE				4.658**
				(2.60)
GBDI*FAGE					7.674
					(1.27)
FAGE					−4.095
					(−0.50)
ROE	51.15***	43.66***	51.15***	43.78***	43.56***
	(17.44)	(13.37)	(17.44)	(4.44)	(4.43)
ROA	212.3***	157.7***	212.3***	157.7***	159.4***
	(13.99)	(9.09)	(13.99)	(5.11)	(5.17)
DPR	−0.0286	−0.0558**	−0.0286	−0.0596***	−0.0583***
	(−1.43)	(−2.52)	(−1.43)	(−4.33)	(−3.36)
INFL	−0.0351	−0.192	−0.0351	−0.919	−0.210
	(−0.09)	(−0.49)	(−0.09)	(−0.97)	(−0.26)
GDPGR	0.877**	0.847*	0.877**	0.417	0.889*
	(2.00)	(1.96)	(2.00)	(1.07)	(1.90)
_cons	0.826	4.198*	0.826	−101.3**	19.18
	(0.38)	(1.84)	(0.38)	(−2.49)	(0.66)
N	578	578	578	578	578
R^2-Adj	0.378	0.221		0.292	0.280
F_Stat	59.43	35.32		12.67	11.45
Prob > F	0.000	0.000	0.000	0.000	0.000
Chow Test	Prob > F = 0.0091 (Fixed Effect Model)
LM Test	Prob > chibar2 = 1.0000 (Random Effect Model)
Hausman Test	Prob > chi2 = 0.0013 (Fixed Effect Model)

The table displays the regression outcomes that investigate the effect of green banking disclosure on sustainable growth rate regarding the moderating influence of firm age and size. Models 1, 2, and 3 use the equation. ${\mathit{\text{SGR}}}_{i,d}={\beta }_0+{\beta }_1{\mathit{\text{GBDI}}}_{i,d}+{\beta }_2{\phi }_{i,d}+ϵ$, Models 4 uses the equation ${\mathit{\text{SGR}}}_{i,d}={\beta }_0+{\beta }_1{\mathit{\text{GBDI}}}_{i,d}+{\beta }_2{\mathit{\text{FSIZE}}}_{i,d}+{\beta }_3\mathit{\text{GBDI}}*{\mathit{\text{FSIZE}}}_{i,d}+{\beta }_4{\phi }_{i,d}+ϵ$, and Models 5 use the equation ${\mathit{\text{SGR}}}_{i,d}={\beta }_0+{\beta }_1{\mathit{\text{GBDI}}}_{i,d}+{\beta }_2{\mathit{\text{FAGE}}}_{i,d}+{\beta }_3\mathit{\text{GBDI}}*{\mathit{\text{FAGE}}}_{i,d}+{\beta }_4{\phi }_{i,d}+ϵ$. The variable ${\phi }_{i,d}$ is a control variable that comprises factors such as Return-on-Assets (ROA), Return-on-Equity (ROE), Dividend Payout Ratio (DPR), inflation rate (INF), and GDP growth (GDPGR). The table includes regression coefficients and t-statistics $\left(\frac{b}{t_{\mathit{\text{stat}}}}\right)$. Robust regressions have been presented to account for heteroscedasticity and autocorrelation. t Statistics in parentheses. The significance levels are denoted by ***, **, and *, corresponding to 1%, 5%, and 10% respectively. *p < 0.1, **p < 0.05, ***p < 0.01.

Model 4, which also uses FEM, introduces the moderating variable Firm Size (FSIZE). The results show that FSIZE is insignificant in moderating the relationship between GBDI and SGR (β = 1.600, p > 0.1). However, FSIZE alone significantly affects SGR (β = 4.658, p < 0.05). The model has an R²-Adj of 0.292 and an F-statistic of 12.67, demonstrating its effectiveness in explaining variation in SGR. Model 5, also using FEM, includes the moderating variable firm age (FAGE). In this model, the interaction between GBDI and FAGE shows an insignificant effect (β = 7.674, p > 0.1). Model 5 has an R²-Adj of 0.280 and an F-statistic of 11.45, showing consistency in explaining variations in SGR.

The study’s estimation results, excluding the moderating variables FSIZE and FAGE, indicate that the Green Banking Disclosure Index (GBDI) negatively affects the Corporate Sustainable Growth Rate (SGR). This relationship suggests that green banking policies, which require initial investment and significant operational changes, may impact the company’s short-term financial performance. Zhou et al. (2021) emphasize that corporate social responsibility (CSR), including green banking practices, may have a negative impact on banks’ short-term financial performance but can have a positive impact in the long run. Therefore, GBDI may reflect the initial costs and investments for green banking practices that are yet to yield immediate results. Meanwhile, it was found that FSIZE and FAGE did not significantly moderate the relationship between GBDI and SGR. This suggests that the effect of green banking on sustainable growth may not be significantly influenced by firm size or age. However, the results indicate that FSIZE has a significantly positive effect on SGR. It can be inferred that larger firms possess greater resources and capacity to implement green banking practices. This, in turn, can enhance operational efficiency and long-term financial performance, as evidenced by Miroshnychenko et al. (2017). This, in turn, can enhance operational efficiency and long-term financial performance, as evidenced by Miroshnychenko et al. (2017). This, in turn, can enhance operational efficiency and long-term financial performance, as evidenced by Miroshnychenko et al. (2017).

The study’s findings reject Hypothesis 1 (H₁), which suggests that green banking disclosure significantly impacts the Sustainable Growth Rate (SGR). Instead of a positive relationship, the results indicate a negative impact of green banking disclosure on SGR. Despite their positive impact on sustainability, green banking practices may cause short-term burdens that could affect a company’s financial performance. The findings reject Hypothesis 2a (H_2a), which suggests that firm size strengthens the positive relationship between green banking disclosure and SGR. This indicates that firm size does not significantly influence the effect of green banking disclosures on SGR. The results suggest that the firm’s size does not influence the impact of green banking disclosures on SGR.

Additionally, the findings reject Hypothesis 2b (H_2b), which proposes that the firm’s age strengthens the positive correlation between green banking disclosures and SGR. This indicates that the firm’s age does not significantly moderate the relationship between green banking disclosures and SGR. The impact of green banking disclosures on SGR appears consistent across companies of different ages.

To provide further insights, this study conducts additional analyses by distinguishing between the sample of banks that are State-Owned Enterprises (SOEs) and those that are not. The chosen method for this analysis is Ordinary Least Squares (OLS), which uses a robust approach to ensure the results are robust to various forms of misspecification. This analysis is presented in Table 6.

Table 6. Regression results.

	NONSOE	SOE	NONSOE	SOE	NONSOE	SOE
	Model a	Model b	Model c	Model d	Model e	Model f
GBDI	−5.931	−44.04	−36.16	−51.83**	−18.04	−51.01***
	(−0.16)	(−0.55)	(−1.46)	(−2.45)	(−1.07)	(−2.82)
GBDI*FSIZE	−0.0571	1.705
	(−0.04)	(0.53)
FSIZE	2.076***	2.508			1.887**	2.336
	(2.78)	(0.71)			(2.55)	(1.32)
GBDI*FAGE			8.141	12.52**
			(1.23)	(2.50)
FAGE			0.330	−4.790	−1.247	−4.218
			(0.20)	(−1.34)	(−0.89)	(−1.35)
GBDI*FSIZE*FAGE					0.124	0.483***
					(0.66)	(2.82)
ROE	46.24***	152.4***	47.97***	95.70***	46.14***	135.6***
	(4.64)	(3.15)	(4.87)	(2.66)	(4.65)	(4.10)
ROA	190.9***	−525.7	198.6***	268.8	191.1***	−322.4
	(5.81)	(−1.24)	(6.14)	(0.97)	(5.84)	(−0.80)
DPR	−0.0351**	−0.0849***	−0.0212	−0.0828***	−0.0341*	−0.0934***
	(−2.06)	(−3.57)	(−1.37)	(−4.78)	(−1.96)	(−4.36)
INFL	−0.717	−0.235	−0.470	0.509	−0.777	−0.257
	(−1.03)	(−0.36)	(−0.73)	(1.23)	(−1.10)	(−0.43)
GDPGR	0.584	1.124***	0.789*	1.549**	0.607	1.243**
	(1.41)	(2.89)	(1.78)	(2.62)	(1.46)	(2.46)
_cons	−43.32***	−64.14	1.724	10.33	−34.25**	−44.75
	(−2.72)	(−0.79)	(0.31)	(0.64)	(−2.12)	(−1.20)
N	499	79	499	79	499	79
R^2-Adj	0.378	0.499	0.371	0.541	0.378	0.573
F_Stat	16.84	13.56	9.069	17.46	18.47	17.36
Prob > F	0.000	0.000	0.000	0.000	0.000	0.000

This table presents the results of a robust Ordinary Least Squares (OLS) regression analysis investigating the effect of green banking disclosure on sustainable growth rate concerning the moderating influence of firm age and firm size. Models a and b use the equation ${\mathit{\text{SGR}}}_{i,d}={\beta }_0+{\beta }_1{\mathit{\text{GBDI}}}_{i,d}+{\beta }_2{\mathit{\text{FSIZE}}}_{i,d}+{\beta }_3\mathit{\text{GBDI}}*{\mathit{\text{FSIZE}}}_{i,d}+{\beta }_4{\phi }_{i,d}+ϵ$, Models c and d use the equation ${\mathit{\text{SGR}}}_{i,d}={\beta }_0+{\beta }_1{\mathit{\text{GBDI}}}_{i,d}+{\beta }_2{\mathit{\text{FAGE}}}_{i,d}+{\beta }_3\mathit{\text{GBDI}}*{\mathit{\text{FAGE}}}_{i,d}+{\beta }_4{\phi }_{i,d}+ϵ$, and Models e and f ${\mathit{\text{SGR}}}_{i,d}={\beta }_0+{\beta }_1{\mathit{\text{GBDI}}}_{i,d}+{\beta }_2{\mathit{\text{FAGE}}}_{i,d}+{\beta }_3{\mathit{\text{FSIZE}}}_{i,d}+{\beta }_4\mathit{\text{GBDI}}*\mathit{\text{FSIZE}}*{\mathit{\text{FAGE}}}_{i,d}+{\beta }_5{\phi }_{i,d}+ϵ$. The variable ${\phi }_{i,d}$ is a control variable that comprises factors such as Return-on-Assets (ROA), Return-on-Equity (ROE), Dividend Payout Ratio (DPR), inflation rate (INF), and GDP growth (GDPGR). The table includes regression coefficients and t-statistics $\left(\frac{b}{t_{\mathit{\text{stat}}}}\right)$. Robust regressions have been presented to account for heteroscedasticity and autocorrelation. t Statistics in parentheses. The significance levels are denoted by ***, **, and *, corresponding to 1%, 5%, and 10% respectively. *p < 0.1, **p < 0.05, ***p < 0.01.

The results of this study show that the effect of the Green Banking Disclosure Index (GBDI) on the Sustainable Growth Rate (SGR) is negative for both state-owned and nonstate-owned firms (shown in Table 6). This is consistent with the regression results for the full sample. The fact that GBDI has a negative effect on SGR suggests that efforts to implement green banking practices may not have been fully integrated or valued in the context of firms’ sustainable growth. This could be due to the initial investment in significant green initiatives or a lack of market awareness and appreciation of sustainability practices. Furthermore, firm size (FSIZE) was found to have no significant role as a moderating variable in the relationship between GBDI and SGR. This suggests that banks, regardless of size, experience similar effects of green banking practices on their growth rates. In other words, neither large nor small banks have been able to use their size to enhance the positive effects of green banking practices on sustainable growth.

Interestingly, in the context of non-SOEs, firm age (FAGE) also did not moderate the relationship between GBDI and SGR, suggesting that banks’ experience and maturity in operations do not significantly influence how they use green banking practices for sustainable growth. However, for state-owned enterprises, FAGE moderation showed a significant positive effect. This could mean that older SOEs may be better able to integrate green banking practices into their growth strategies, perhaps due to government support or more sustainability-oriented policies. Interestingly, the effect is positive when FAGE and FSIZE are simultaneously moderated in the relationship between GBDI and SGR. This is significant for SOEs but not for NONSOEs. This phenomenon suggests that the maturity and size of SOEs may provide more favorable conditions for implementing effective green banking practices to achieve sustainable growth.

4.2.2. Robustness test

To ensure the robustness of the results in the context of green banking and corporate valuation, a robustness test was conducted using X. Chen et al. (2021) Sustainable Growth Rate (SGR) proxy (results presented in Table 7). The SGR proxy used by X. Chen et al. (2021) is as follows: $${\mathit{\text{SGR}}}_t=\frac{{\mathit{\text{ROE}}}_t}{1-{\mathit{\text{ROE}}}_t}$$

Table 7. Regression results.

	CEM	FEM	REM	CEM	CEM	CEM
	SGR2	SGR2	SGR2	SGR2	SGR2	SGR2
GBDI	−0.0591**	−0.0816***	−0.0662**	−0.236	−0.297	−0.159
	(−2.46)	(−3.04)	(−2.37)	(−0.84)	(−1.59)	(−1.32)
GBDI*FSIZE				0.00666
				(0.62)
FSIZE				0.0293***		0.0275***
				(5.32)		(4.54)
GBDI*FAGE					0.0626
					(1.40)
FAGE					0.0277**	0.0117
					(2.19)	(1.14)
GBDI*FAGE*FSIZE						0.000909
						(0.83)
ROE	0.224*	0.117	0.175	0.167	0.206*	0.165
	(1.81)	(0.86)	(1.40)	(1.32)	(1.66)	(1.31)
ROA	2.154***	1.653***	1.933***	1.896***	2.060***	1.883***
	(6.66)	(5.07)	(7.87)	(5.73)	(6.32)	(5.68)
DPR	0.000373**	−0.000121	0.000146	0.0000750	0.000324**	0.0000817
	(2.11)	(−1.23)	(1.23)	(0.83)	(2.14)	(0.87)
INFL	−0.000728	−0.00443**	−0.00266*	−0.00934***	−0.00375*	−0.00954***
	(−0.55)	(−2.15)	(−1.84)	(−2.89)	(−1.93)	(−3.06)
GDPGR	0.00457	0.00351	0.00408	0.000914	0.00392	0.000970
	(0.82)	(0.84)	(0.85)	(0.21)	(0.76)	(0.22)
_cons	0.0521***	0.103***	0.0714***	−0.577***	−0.0314	−0.579***
	(3.23)	(9.63)	(4.46)	(−4.72)	(−0.62)	(−4.17)
N	578	578	578	578	578	578
R^2-Adj	0.511	0.439		0.592	0.542	0.596
F_Stat	37.12	78.73		102.9	46.06	94.52
Prob > F	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000

t Statistics in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01.

Where ROE is the ratio of pre-tax income divided by total equity, the test revealed that the Green Banking Disclosure Index (GBDI) significantly negatively affects SGR, which is consistent with previous results. In the context of firm size (FSIZE), the robustness test results confirm the previous finding that FSIZE has a significant positive effect on SGR, highlighting the critical role of firm size in sustainable growth. The analysis shows that firm age (FAGE) significantly affects SGR, consistent with previous findings. This suggests that firm age may contribute to sustainable growth. The robustness test results maintain that FSIZE does not strengthen the relationship between GBDI and SGR, confirming the results of previous studies. The robustness test results show that FAGE strengthens the positive relationship between GBDI and SGR, which is consistent with previous findings.

4.2.2.1. The effect of control variables

The Return on Equity (ROE) and Return on Assets (ROA) significantly positively affect SGR. This can be explained by higher returns on equity and assets reflecting management’s efficiency and effectiveness in using resources. This, in turn, strengthens the company’s financial position and supports sustainable growth. Additionally, the Dividend Payout Ratio (DPR) has a negative effect on SGR. The argument is that high dividend distribution may reduce the funds available for corporate investment and expansion, limiting the company’s ability to grow sustainably in the long run.

Additionally, a high inflation rate (INFL) significantly negatively affects SGR. High inflation creates an uncertain economic environment, increases operating costs, and reduces purchasing power. This could impede the company’s expansion and decrease profits that could be reinvested for sustainable growth. Finally, the Gross Domestic Product Growth Rate (GDPGR) positively impacts SGR. Strong economic growth generally creates more business opportunities, increases demand, and supports corporate expansion. In this context, a high GDP growth rate can catalyze sustainable growth.

5. Conclusions

This study examines the impact of green banking disclosure (GBDI) on corporate sustainable growth (SGR). The findings indicate that GBDI has a negative effect on SGR, suggesting that while important, green banking disclosures may require resource allocation that could potentially reduce a bank’s capacity to grow. The study also analyzed the moderating variables of firm size (FSIZE) and firm age (FAGE) to determine their impact on the relationship between GBDI and SGR. However, the results showed that neither FSIZE nor FAGE significantly strengthened this relationship. However, FAGE strengthened the relationship between GBDI and SGR in state-owned enterprises. When FSIZE and FAGE were analyzed, they further strengthened the relationship between GBDI and SGR in state-owned enterprises.

This research contributes significantly to green finance, specifically in the context of green banking disclosure (GBDI) on firms’ sustainable growth (SGR). The finding that GBDI negatively affects SGR adds a new perspective to the literature, suggesting that while essential, green banking practices can have complex consequences on firm growth. Additionally, the study investigates the moderating variables of firm size (FSIZE) and firm age (FAGE), providing new insights into how internal factors influence the relationship between green banking practices and a bank’s growth.

The study has three main limitations. Firstly, the sample only includes banks in Indonesia, which limits the generalizability of the results to banks in other countries or a global context. Firstly, the sample only includes banks in Indonesia, which limits the generalizability of the results to banks in other countries or a global context. Secondly, the study only focuses on banks listed on the Indonesia Stock Exchange, which may need to reflect the dynamics and practices of non-listed banks. The third limitation concerns the use of green banking disclosure proxies, which may require further customization to be more relevant to the context and practices in Indonesia. Future research in green banking and corporate valuation could assess the impact of green banking disclosure on a global scale to gain a more comprehensive understanding of how green banking practices contribute to sustainable growth in countries with varying economic characteristics. Furthermore, research may encompass unlisted banks better to understand green banking in the broader banking sector. It is also necessary to adapt green banking disclosure proxies to account for variations in practices and regulations across different countries.

6. Policy recommendations

Several policy recommendations can be implemented to promote the growth of green banking and enhance corporate valuation. Firstly, as a banking regulator, the Financial Services Authority of the Republic of Indonesia should pay close attention to how green banking disclosures can impact the Sustainable Growth Rate (SGR). Regulators should encourage banks to adopt schemes that benefit the environment and the company. Regulations are necessary to balance environmental interests and business sustainability without imposing excessive burdens on companies. Additionally, banking companies should promote more effective sustainability disclosures. Although sustainability practices are implemented, without sufficient disclosure, these efforts may not gain enough legitimacy from the public and investors. Therefore, banks should use effective communication strategies to convey their sustainability practices to the public and stakeholders. This can enhance the bank’s image and provide a competitive advantage in the market. Thirdly, closer cooperation is needed between regulators, banks, and other stakeholders to develop clear standards and frameworks for disclosing green banking practices. With a consistent and transparent framework, banks will find it easier to follow guidelines and meet the expectations of regulators and the public. Additionally, banks should continue to innovate and explore new sustainability solutions. Continuous research and development can help banks integrate sustainability practices into their business models, improving SGR and overall firm value.

Authors contributions

Conceptualization, A.F.; methodology, N.D.K., A.F.; validation, A.F.; investigation, A.F., N.D.K.; data analysis, N.D.K.; writing—original draft preparation, A.F.; writing—review and editing, A.F., N.D.K.; visualization, N.D.K., A.F.; supervision, A.F., N.D.K. All authors have read and agreed to the published version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data supporting this study are openly available from https://www.idx.co.id/id.

Additional information

Notes on contributors

Amrie Firmansyah

Dr. Amrie Firmansyah, a distinguished academic from Universitas Pembangunan Nasional Veteran Jakarta, holds a Doctorate in Accounting. His research focuses on financial, public sector, and sustainability accounting. He possesses profound skills in financial reporting, financial statement analysis, management accounting, financial management, and sustainability reporting, contributing valuable insights to the field.

Nafis Dwi Kartiko

Nafis Dwi Kartiko is an employee of the Ministry of Finance of the Republic of Indonesia. His research interests include financial management, tax accounting, corporate governance, sustainability accounting, and economics.