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Research on the Impact of ESG Factors on Bank Liquidity Risk

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14 April 2024

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15 April 2024

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Abstract
In recent years, with the increasing prominence of environmental and social issues, investors have been paying more attention to the ESG performance of enterprises, highlighting the importance of ESG factors in the financial field. This study is based on the theories of banking business models, stakeholder theory, risk management theory, and ESG investment theory. It uses the financial data and ESG scores of Chinese listed banks to deeply analyze the ESG factors and explore their impact on the liquidity risk of commercial banks. The research found that (1) good ESG performance can reduce the liquidity risk commercial banks face by improving bank value and financial performance. (2) ESG factors can also enhance the liquidity management level of commercial banks through standardization and sustainable business principles, thereby reducing the occurrence and impact of liquidity risk. Therefore, it is necessary to reduce the liquidity risk of banks and promote the sustainable development of commercial banks from four aspects: ESG performance and management, bank value, financial performance, and policy regulation.
Keywords: 
Subject: Business, Economics and Management  -   Finance

1. Introduction

ESG (Environment, Society, and Governance) was  first formally proposed in January 2004 as an initiative to incorporate ESG  factors into the capital market. This initiative clarified the meaning of  "ESG" for the first time. The report calls on financial institutions  to integrate ESG factors into the operation of the capital market. At the same  time, companies need to disclose their ESG performance according to the needs  of investors (Renneboog et al., 2008). In terms of environmental, companies  need to pay attention to the impact on the environment during operation,  including energy use, waste treatment, pollution emissions, natural resource  protection, and climate change (Jitmaneeroj, 2016). In terms of social, it  mainly involves the relationship between the company and its employees,  suppliers, customers, and the community where it is located. This includes  issues such as employee rights, occupational health and safety, diversity and  inclusion, human rights, community relations, and supply chain management. Governance  mainly focuses on the leadership structure and behavior of the company,  including board structure, executive compensation, audit process, shareholder  rights, transparency, and fair transactions (Li et al., 2021). In order to  attract investors, companies must optimize their performance in three areas:  environmental, social, and governance (ESG). It is believed that this approach  will have a significant impact on the world and create a win-win situation for  all parties involved.
In recent decades, ESG factors have aroused  widespread concern and discussion. Especially in recent years, environmental  and social issues have become increasingly prominent, and investors have paid  more attention to the ESG performance of enterprises (Avramov et al., 2022).  Twenty-six stock exchanges around the world have mandated the disclosure of ESG  information. On April 15, 2022, the China Securities Regulatory Commission  issued the "Guidelines for Investor Relations Management of Listed Companies,"  which listed "corporate environmental protection, social responsibility,  and corporate governance information" as the main content of communication  between listed companies and investors. It required listed companies to explain  ESG matters to investors (China Securities Regulatory Commission, 2022). With  the advancement of the dual-carbon goals of various countries, the importance  of ESG factors will be more widely accepted and developed into a universal  investment concept.
At the same time, the development of ESG factors in  the financial field does not only stem from the popularization of the concept  of social responsibility, but more importantly, ESG factors can provide vital  information to evaluate the risk and value of enterprises (He et al., 2022).  Environmental considerations help investors understand the environmental  threats faced by companies and their ability to develop sustainably. Social  elements reveal the relationship between the company and society, as well as the  company’s reputation and brand value. The governance factors reflect the  internal management quality and decision-making ability of the enterprise.  Combining these factors, investors can make a more comprehensive corporate risk  and value assessment and make more informed investment decisions.
In the banking sector, commercial banks face  liquidity risk, which is a significant challenge in their operations. Liquidity  risk refers to the risk of commercial banks being unable to meet the short-term  funding needs of depositors and borrowers, resulting in fund loss and increased  liquidity pressure (Yao, 1997). Currently, commercial banks face two main types  of liquidity risks: capital liquidity risks and market liquidity risks. Capital  liquidity risk occurs when commercial banks cannot raise enough funds in a  timely manner to meet the increased demand or insufficient supply of funds in  the short term. This risk often arises from improper capital management or  changes in the external environment that create a mismatch between capital  supply and demand. For example, during an economic downturn, residents may  withdraw funds, and if commercial banks have slow capital turnover, liquidity  risks increase. Market liquidity risk refers to the risk of commercial banks  facing imbalances between buyers and sellers in financial market transactions,  resulting in the inability to complete transactions or obtain sufficient market  liquidity. This risk can stem from changes in supply and demand in the  financial market or market participants’ panic about risk. Insufficient market  liquidity may prevent commercial banks from selling assets or raising funds in  a timely manner, further increasing their liquidity risk. The New Basel Accord  includes detailed provisions and requirements for bank liquidity risk. Banks  are required to maintain a sufficiently high liquidity coverage rate to ensure  they have enough high-quality liquid assets, such as cash, central bank  reserves, and marketable securities, to meet their committed cash outflows and  potential cash outflows within 30 days during severe short-term stress  (Supervision, 2010). Additionally, banks must establish a robust risk  management and supervision system, including conducting liquidity risk stress  tests, regularly reporting liquidity risk status to supervisory agencies, and developing  emergency liquidity plans.
The importance of ESG factors in the current  liquidity risks of commercial banks has become prominent. ESG, which stands for  environmental, social, and corporate governance, is now a crucial consideration  in the financial sector (Guo et al., 2023). These factors encompass the level  of environmental responsibility, social responsibility, and corporate  governance exhibited by commercial banks. Research has shown that good ESG  performance can significantly mitigate the liquidity risk faced by commercial  banks. Specifically, it can enhance bank value and financial performance,  thereby reducing liquidity risk. Moreover, ESG factors can also enhance the  liquidity management level of commercial banks through standardisation and  sustainable operation principles, ultimately minimising the occurrence and  impact of liquidity risks.
ESG factors have a significant impact on the  liquidity risk of commercial banks. Good ESG performance can reduce liquidity  risk and improve liquidity management. Therefore, it is important to strengthen  the supervision of ESG factors at the management and supervision levels.  Introducing ESG indicators as a measure of liquidity risk can promote the  sustainable development and sound operation of commercial banks. Additionally,  commercial banks should prioritize the management and practice of ESG to minimize  liquidity risks and their impact on banking operations. This study utilizes the  banking business model theory, stakeholder theory, risk management theory, and  ESG investment theory to analyze the impact of ESG factors on the liquidity  risk of listed banks in China. The findings provide suggestions for the  long-term stable development of banks and related policy supervision.

2. Theoretical Analysis and Research Hypothesis

As one of the most important financial  institutions, banks have long plagued the regulatory authorities with liquidity  issues. The Basel Committee on Banking Supervision is a standing supervisory  body under the Bank for International Settlements. Its definition of liquidity  is the ability of a commercial bank to obtain funds at a reasonable cost to  respond to asset enhancements while meeting debt repayment obligations on  schedule (Fan et al.). The liquidity level of banks is changing all the time.  When banks sell assets and raise funds, their liquidity will increase. When  banks repay debts and issue loans, their liquidity will decrease. In the  Measures for the Administration of Liquidity Risks of Commercial Banks adopted  by the China Banking and Insurance Regulatory Commission, liquidity risk is  defined as the risk that commercial banks cannot obtain sufficient funds in a  timely manner at a reasonable cost to repay due debts, perform other payment  obligations and meet other capital needs for normal business development. The  core is described as the risk of not being able to complete the due payment  obligation. Commercial banks exist essentially for liquidity conversion and  liquidity creation. Therefore, liquidity risks originate from the banks  themselves, and banks need to face them all the time.
The good ESG performance demonstrates that a bank’s  positive performance in the areas of environment, society, and governance can  enhance its image among the public and investors, thereby improving its market  reputation. Banks that have higher market reputations are generally more likely  to attract investors, leading to increased market liquidity for their bonds and  equities (Uyar et al., 2022). Additionally, good ESG performance can help  mitigate the bank’s market risk associated with environmental, social, and  governance issues. For instance, if banks excel in environmental protection,  they are less likely to face fines or lawsuits, even if environmental  regulations become more stringent. This, in turn, reduces the risk of their  stock prices plummeting. Therefore, good ESG performance plays a crucial role  in minimizing the market liquidity risk for commercial banks.
Based on the above analysis, this paper proposes  hypothesis H1.
H1: ESG Performance Can Significantly Reduce  Liquidity Risk of Commercial Banks
State-owned banks have good asset quality and large  scale, which makes them more resilient in the face of market fluctuations,  thereby reducing liquidity risk. Additionally, state-owned banks are usually  subject to stronger government supervision, which also ensures their strict  implementation of ESG policies, further reducing risks (Zhang, 2023).  Non-state-owned banks are relatively small, and their asset quality and risk  management capabilities may not be comparable to those of state-owned banks (Zhu  et al., 2011). In this case, the ESG score of non-state-owned banks is  particularly important. If the non-state-owned bank has a higher ESG score, it  may attract more investors, thereby increasing the liquidity of its assets and  reducing liquidity risk. Conversely, if its ESG score is low, then investors  may have doubts about it, which will affect risk identification and loan  business.
Based on the above analysis, this paper proposes  hypothesis H2.
H2: Property Heterogeneity of ESG Performance on  Liquidity Risk of Commercial Banks
When a company performs well in environmental  protection, social responsibility, and corporate governance, it enhances its  image in the minds of the public and investors (Freeman et al., 2004), thereby  increasing its bank value. A strong corporate image increases investors’ trust  in the company and attracts more investors, thereby boosting the liquidity of  its stocks and bonds and reducing liquidity risks. Additionally, good ESG  performance helps companies avoid fines or lawsuits resulting from environmental  pollution, social problems, and governance issues, thus safeguarding their bank  value.
Companies with good ESG performance are usually  able to achieve better financial performance. Good financial performance can  increase the credit rating of an enterprise, thereby reducing the interest rate  of its bonds and reducing financial costs. At the same time, good financial  performance can also improve the profitability of enterprises, thereby  increasing the attractiveness of their stocks and improving the liquidity of  stocks (Chen et al., 2023).
Therefore, the enhancement of bank value and  improvement of financial performance through good ESG performance can  effectively reduce the liquidity risk of commercial banks. This not only  promotes the stable operation of the bank but also enhances its competitiveness  in the financial market, further strengthening its position in the eyes of  investors.
Based on the above analysis, this paper proposes  hypothesis H3.
H3: It is hypothesized that the performance of  environmental, social, and governance (ESG) factors can mitigate the liquidity  risk faced by commercial banks. This can be achieved through improvements in  bank value and financial performance.
Digital transformation refers to the ongoing  application of digital technologies such as cloud computing, the Internet of  Things, and big data. This process accelerates business optimization, upgrades,  and innovation and transforms traditional methods into new ones. It cultivates  new sources of energy and facilitates the transformation, upgrading, and  innovation process. In the case of commercial banks, digital transformation can  enhance technical infrastructure, improve risk management, and overcome limitations  of time and space. It enables the formation of an information-sharing and  innovation collaboration platform, which has a positive impact on commercial  banks (Lu, 2023).
Based on the perspective of factor allocation (Yao,  2009), digital transformation is conducive to improving the circulation speed  of internal and external factors of commercial banks, optimizing the allocation  efficiency of factors, and reducing the liquidity risk of commercial banks.  Based on the perspective of information sharing, the digital transformation of  commercial banks can improve the "information power" in the green  innovation of enterprises and significantly improve the problem of information  asymmetry (Akerlof, 1995). Specifically, banks can monitor assets and  liabilities in real-time through digitalization and discover and deal with  risks in a timely manner, thereby enhancing risk management capabilities.
Based on the above analysis, this paper proposes  hypothesis H4.
H4: Digital Transformation Level Has a Moderating  Effect between ESG Performance and Liquidity Level of Commercial Banks

3. Research Design and Research Data

3.1. Sample Selection and Data Sources

As of 2023, China has a total of 54 listed banks.  After excluding banks that do not have ESG-related data, this article focuses  on 41 listed banks, including Ping An Bank and Bank of China. The liquidity  indicators and financial indicators of commercial banks are measured using data  from the Wind database. The Digital Transformation Index of Peking University  Bank is obtained from the Digital Finance Research Center of Peking University.  In terms of ESG scores, the China Securities Exchange ESG rating data covers  all listed companies in China’s A-shares from 2009 to 2022, providing wider  coverage and complete data. Therefore, this paper utilizes Huazheng’s ESG  score, considering the influence of data outliers. To address this, the study  has performed 1% tailing processing on all sample data.

3.2. Variable Selection

3.2.1. Explained variable: Loan-To-Deposit Ratio (LTD)

With reference to previous studies, the  deposit-loan ratio is a key indicator to measure bank liquidity risk (Gong,  2016). Therefore, the bank’s deposit-to-loan ratio (LTD) is selected as the  explanatory variable in this article. A high deposit-to-loan ratio means that  the loan balance significantly exceeds the deposit balance, which may imply  that the bank is too biased towards lending in its capital operations. This may  bring certain risks in the short term: if there is a large-scale withdrawal of  customers or a large number of loans that have not been recovered when they  expire, the bank may not be able to meet this cash demand, which may lead to an  increase in the bank’s liquidity risk.

3.2.2. Core Explanatory Variable: HZESG

The core explanatory variable used in this article  is the ESG score of the China Politics Index. The ESG rating system of the  China Affairs Index adopts a multi-level scoring method and consists of four  levels of indicators. Specifically, there are three first-level indicators, 14  second-level indicators, 26 third-level indicators, and more than 130  fourth-level data indicators. The specific score of each indicator is based on  its contribution weight to the company’s overall ESG performance.
In order to remove the unit influence of the score  and maintain the multiple relationships of the data, a logarithmic  transformation was performed on the converted score. This treatment does not  change the relative size of the ESG score, but it can make the data conform to  the assumptions of some statistical models.

3.2.3. Intermediary Variables: Bank Value (TobinQ), Profitability (ROE)

This article selects bank value (TobinQ) and  profitability (ROE) as intermediary variables. It utilizes TobinQ to assess  bank value. TobinQ is a widely used indicator for measuring the value of an  enterprise. A higher TobinQ indicates a greater recognition of the efficiency  and value of the enterprise by the market (Tobin, 1969), and vice versa. As a  real-time reflection of bank value, TobinQ aids in exploring and comprehending  the relationship between the influence of ESG ratings on bank value and bank  liquidity risk.
Profitability is often measured by the company’s  ROE (return on equity), which reflects the ability to produce profit per unit  of capital. The higher the ROE, the stronger the company’s profitability. This  is an important indicator that can directly reflect the company’s economic  benefits in its business activities (Jonathan et al., 2023). Therefore, ROE  helps to explore the relationship between the impact of ESG rating on the  profitability and operational efficiency of enterprises and the liquidity risk  of banks.

3.2.4. Moderating Variable: Peking University Bank Digital Transformation Index (PKUDBI)

The level of digitalization in banks may impact  their performance in operations, management, and risk control and subsequently  may affect the liquidity risk of the banks (Xie, 2023). This paper uses the  digital transformation index as a moderating variable, which helps to control  other factors that may affect the research results. It focuses on exploring the  relationship between ESG factors and bank liquidity risk, as well as the role  of digital transformation in this process.

3.2.5. Control Variables

In addition to the above factors, according to  previous studies, there are many factors that affect bank liquidity risk. In  this paper, considering the relevance and availability of the selected data,  the final selected control variables include "non-interest income ratio  (NIR), independent shareholder ratio (INDEP), top ten shareholder ratio  (TOP10), non-performing loan provision coverage ratio (PLLCR), year-on-year  growth of GDP in the provinces where banks are registered (PGDPG), and consumer  price index (CPI)".Among them, "the proportion of non-interest income  (NIR), the proportion of independent shareholders (INDEP), and the proportion  of the top ten shareholders (TOP10)" are characteristic variables at the  bank level; "Non-performing loan provision coverage ratio (PLLCR)" as  a regulatory factor; "The year-on-year growth of GDP in the provinces  where banks are registered (PGDPG) and the consumer price index (CPI) is  macro-influencing factors.

3.3. Model Building

This  article first analyzes the impact of ESG scores on bank liquidity risk. Select  the ESG rating published by the China Affairs Index as the explanatory  variable, and set the model (1) as follows:
L T D i t = α 0 + α 1 I n h z e s g i t + C V + ∑ y e a r + ε i t  
Among  them, the subscript i represents the i-th bank (i=1,2,...,41), t represents the  tth year (t=2009, 2010,...,2022).In order to eliminate the dimensional  difference between different data, the relatively large data (absolute value)  is taken as the natural logarithm, and the relative value (that is,  proportional or percentage data) is not processed. In formula (1), the  explained variable LTD is the bank’s deposit and loan ratio; The explanatory  variable lnhzesg is the ESG data after logarithmic processing; CONTROL  represents the control variables included in the model. ∑year represents the  fixed effect of the year, which aims to control the systematic change or trend  change between different years and avoid errors in the model due to  unconsidered factors such as inflation rate, risk-free interest rate, and  financial regulatory environment.
This  paper predicts that the α1 coefficient in model (1) is significantly negative,  indicating that banks with high ESG scores have lower deposit-to-loan ratios,  which indicates that banks with high ESG scores usually have better risk  management and are more concerned about long-term sustainability, so they will  be more cautious and tend to maintain lower deposit-to-loan ratios to prevent  credit risk and liquidity risk. At the same time, banks with high ESG scores  will attach importance to social responsibility and abide by the principle of  fair lending, and will not over-issue loans, resulting in relatively low  deposits and loans.
In  order to further explore the intermediary mechanism of ESG factor on bank  liquidity risk and test whether high ESG score can reduce bank liquidity risk,  this paper takes TobinQ and ROE as proxy variables of ESG score, and draws on  the intermediary effect model proposed in (4) to establish the following three  regression models step by step:
T o b i n Q i t = β 0 + β 1 h z e s g i t + C V + ∑ y e a r + ε i t  
R O A i t = γ 0 + γ 1 h z e s g i t + C V + ∑ y e a r + ε i t
L T D i t = ζ 0 + ζ 1 I n + ζ 2 T o b i n Q i t + δ 3 R O A i t + C V + ∑ y e a r + ε i t
Among  them, TobinQ and ROE are intermediate variables, and the definitions and  measurement methods of other variables are consistent with the above. The  coefficient α1 of the prediction model (2) is significantly negative,  indicating that from the perspective of the overall effect, good ESG  performance can significantly reduce the bank’s liquidity risk. The coefficient  β1 of the prediction model (3) is significantly positive, and the coefficient  γ1 of the prediction model (4) is significantly positive, indicating that a  high ESG score can increase the value of the bank and the return on equity of  the bank. At the same time, the coefficients δ1, δ2, and δ3 in the prediction  model (5) are significantly negative, which means that banks’ investment in  strengthening ESG performance can not only directly reduce liquidity risk, but  also indirectly reduce liquidity risk by improving bank value and bank  operating efficiency (reflected in equity return rate).
This  paper uses the digital transformation index as a moderating variable to study  the moderating effect of digital transformation and ESG on the impact of ESG on  the liquidity of commercial banks. The occasional model is shown in (6).
L T D i t = δ 0 + δ 1 h z e s g i t + δ 2 p k u d b i i t + C V + ∑ y e a r + ε i t
L T D i t = η 0 + η 1 h z e s g i t + η 2 p k u d b i i t + η 3 p k u d b i i t h z e s g i t + C V + ∑ y e a r + ε i t  

4. Empirical Results and Analysis

4.1. Descriptive Statistics and Correlation Analysis

Table 2 Descriptive statistical  characteristics are reported, and the number of valid samples in this  experiment is 284. In order to make the data more intuitive and easy to  understand, the original data is used in descriptive statistics. The results  show that for the explained variable ltd, the average value is 77.032, the  standard deviation is 12.956, the maximum value is 116.23, and the minimum  value is 38.97, which indicates that there is a big difference in the  loan-to-deposit ratio of the sample banks. At the same time, the distribution  map of ltd is found that the skewness of LTD data is 0. 470, which means that  the data has a certain right deviation relative to the normal distribution.  That is, more values are located on the right side of the average, which means  that more than half of the bank loan-to-deposit ratio is in a higher range, and  less than half of the bank loan-to-deposit ratio is in a lower range.
Explanatory  variable hzesg, the average value is 5.435, the standard deviation is 0.774,  the maximum value is 7, and the minimum value is 3, which indicates that the  ESG score of the sample bank is generally biased towards a higher level. At the  same time, the ESG normal distribution chart was made, and found that the  skewness is -0.653, which means that the data has a certain left deviation  relative to the normal distribution. That is, more values are located on the  right side of the average, which shows that most banks have higher ESG scores.

4.2. Benchmark Regression Results

This  article initially applies the Hashman test to assess the impact of ESG on the  liquidity of commercial banks. Subsequently, the year fixed effect model is  selected for regression analysis. To test H1, panel fixed effect regression is  conducted on model (2), while controlling for the influence of the year. The  relationship between the explanatory variable and the explained variable is  then determined. The regression results without adding control variables are  presented in Table 3, column (1), while the results  with control variables and year control are shown in column (2).
The results of the multiple regression analysis of model (2) indicate that, when controlling for the year effect, the model’s adjusted R2 is 0.68, indicating goodness of fit of 68%. The F test value is 70.98, with a p-value of <0.01. This means that the fixed effect model passes the 99% confidence interval test and can proceed to test the coefficient of a single variable. The impact coefficient of the core explanatory variable ESGhzesg is -0.174, which is statistically significant at the 1% level. This significant negative impact suggests that ESG scores have a detrimental effect on the realization of commercial bank liquidity. This may be due to the fact that high ESG scores enhance the ability of commercial banks to obtain deposits, thereby reducing the index of bank loan-to-deposit ratio. The control variables nir, pllcr, pggdpg, cpi, indep, top10 all have a significant impact, and pllcr, pggdpg have a significant negative impact, indicating that the non-performing loan provision rate increases the non-interest cost of commercial banks, which is not conducive to the realization of liquidity, while the increase in macro GDP weakens the main characteristics of commercial banks as important external financing institutions, and also hinders the realization of liquidity in disguise.Nir, cpi, indep, top10 have significant positive effects, which shows that the proportion of non-interest income and independent directors and the top ten shareholders can improve the ability of commercial banks to issue loans at the micro level and enhance market financing confidence, while the consumer price index can enhance the macro environment and the consumption willingness and scale of the real market, and stimulate the continuous realization of commercial banks’ consumer loans.

4.3. Robustness Test

4.3.1. Endogenous Analysis

From the theoretical logic analysis, it can be seen that the impact of ESG score on the liquidity risk of commercial banks is likely to be lagging. That is, there is a certain reverse causality. This will lead to a biased effect on the regression results. Therefore, in order to eliminate the possibility of this reverse causality, the core explanatory variables are lagged by one period in this study to try to solve the existing endogeneity problem. The results are listed in Table 4.
It can be seen from Table 4 that the results of hzesg lag one period are still significant at the 1% level, indicating that the lag effect has not affected the main research results. The results of the benchmark regression have a certain degree of reliability. At the same time, this significance shows that the relationship between ESG score and bank liquidity risk is significant and lasting. The bank’s ESG score does have an important and credible negative correlation with its liquidity risk.

4.3.2. Eliminate the Influence of Extreme Values

Considering that the distribution of some data may have some extreme values or outliers that affect the stability of the model. Therefore, in order to eliminate the influence of outliers, in this study, the explanatory variables and explanatory variables are truncated by 1%, and the possible regression biases are corrected by removing extreme values. The results are listed in Table 4.
It can be seen from Table 4 that the results are still significant after the tail reduction process, which shows that the estimation results of the benchmark regression model are not affected by extreme values or outliers and are stable and reliable. This also further confirms hypothesis H1: ESG performance can significantly reduce the liquidity risk of commercial banks. This result will not change due to the existence of individual outliers in the data set. This provides reliable support for the research results of this article.

4.4. Heterogeneity Test

Due to the significant differences in the nature of commercial banks, their liquidity will also have a certain degree of heterogeneity due to different natures. Therefore, according to the nature of the bank, this article divides the sample into state-owned banks and non-state-owned banks and performs regression analysis and comparison in groups. The results are listed in Table 5. It can be seen from Table 5 that whether it is a state-owned or non-state-owned bank, the ESG score has a significant negative impact on the bank’s liquidity risk. For state-owned banks, the coefficient of hzesg is -0.148, and the result is significant at the 10% significance level. For non-state-owned banks, the coefficient of hzesg is -0.410, and the result is significant at the 1% significance level. In comparison, the negative impact of ESG on the liquidity of non-state-owned banks is stronger than that of state-owned banks. The reason may be that the asset products of non-state-owned banks are more flexible and are often affected by the willingness and preference of financial investors and residents. The negative impact is obvious, while state-owned banks have stable asset scale and asset funding sources, and the stability is more obvious. The negative effect of ESG on them is lower. But on the whole, the impact of ESG score on the two types of banks is relatively insignificant, which is reflected in the correlation coefficient of less than 1 in the empirical results.

4.5. Mediating Effect Analysis

According to the previous analysis, ESG score will have an impact on the liquidity of commercial banks by enhancing bank value and financial performance.In order to test H3, this paper uses the intermediary effect model to test the intermediary mechanism of these two types of elements.Model (3), model (4), and model (5) will be regressed separately, and the regression results are shown in Table 6.
As can be seen in Table 6, the regression coefficients of the direct and indirect effects of tobinq are 0.0431 and -0.493, respectively, and both pass the significance test at the 1% level. This shows that there is a complete intermediary effect in bank value, indicating that banks with high ESG scores have higher bank value and have a positive impact on bank liquidity. The direct and indirect effects of ROE are 0.143 and -0.469, respectively, and both pass the significance test at the 1% level, which also shows that there is a complete mediating effect in financial performance, indicating that banks with high ESG scores have higher financial efficiency. This suggests that the market recognizes and has greater confidence in high-value banks, attracting more deposits and investments to banks thereby reducing liquidity risk. Banks with high financial efficiency have stronger capital allocation capabilities and effective risk management and control methods and play a more obvious role in optimizing asset-liability structure, business process risk management, and special line process management, thereby reducing liquidity risk.

4.6. Analysis of Regulatory Effects

Considering the external and policy characteristics of digital transformation in the development process of commercial banks, it is likely to play a role in the liquidity of commercial banks together with ESG. This study incorporates the interaction items of Peking University’s digital transformation level and ESG into the benchmark regression for analysis. The regression results are presented in Table 7.
Table 7 shows that in model (6), the coefficient of influence of pkudbi on ltd is 0.029. This coefficient passes the 5% significance test and is positive, indicating a significant positive correlation between the digital transformation index and the bank’s liquidity risk. This correlation can be explained by the fact that banks often require substantial upfront investment for digital transformation, such as constructing technical facilities, training personnel, and making business adjustments. These investments can consume a significant amount of cash flow and potentially increase the bank’s liquidity risk.
In model (7), the correlation coefficient of the adjustment item pkudbi_hzesg (the interactive item of digital transformation index and ESG score) is -0.559, which passes the 1% significance test and is negative. This result shows that the level of digital transformation has played a negative moderating role. That is, it has inhibited the negative impact of ESG performance on the liquidity of commercial banks, which verifies H4. This may be because the high ESG score means that the bank performs well in environmental protection, society, and governance, which improves the reputation of commercial banks in terms of non-financial indicators and reduces some liquidity risks. In addition, the continuous application of digitalization has released the vitality of the development of commercial banks and promoted the forward-looking, sustainable, and scientific development of banks under the "New Basel Accord." This in turn, provides a strong foundation for commercial banks to optimize product business lines, broaden the scope of financial services, and enhance financial market share.

5. V. Research Findings and Implications

5.1. Conclusion of the Study

This study conducted an in-depth discussion on the impact of ESG factors on banks’ liquidity risk, and reached the following conclusions:
(1)
The ESG performance of commercial banks can significantly mitigate liquidity risk. Banks with stronger ESG performance demonstrate enhanced stability and resilience when confronted with liquidity challenges.
(2)
The ESG performance has a heterogeneous impact on the liquidity risk of commercial banks. The liquidity of non-state-owned banks is more negatively affected by ESG factors compared to state-owned banks.
(3)
ESG performance can mitigate the liquidity risk for commercial banks by enhancing bank value and financial performance. Banks that exhibit better ESG performance generally have higher bank value and financial performance, which in turn enhances their ability to manage risks and liquidity.
The level of digital transformation has a negative regulatory effect on the ESG factor. In other words, it inhibits the negative impact of ESG performance on the liquidity of commercial banks.

5.2. Policy Recommendations

Based on the findings of this study, the following policy recommendations are made.
(1)
Accelerate the construction of ESG disclosure of Chinese commercial banks. Research shows that ESG performance can significantly reduce the liquidity risk of commercial banks. Regulators can encourage banks to formulate and implement corresponding ESG policies and conduct regular ESG reports and assessments. At the same time, regulatory agencies should set up corresponding ESG indicators and standards as a reference basis for measuring bank liquidity risk. This improves the bank’s ESG performance and reduces the level of liquidity risk.
(2)
Continue promoting the reform of the differentiated liquidity risk system of Chinese commercial banks. Given the heterogeneity of property rights in the liquidity risk of commercial banks, special attention should be given to the liquidity risk of non-state-owned banks. Building upon the stability of the deposit-loan ratio of state-owned banks, the reform of the liquidity risk system of commercial banks should be developed with differentiation. Regulatory agencies should promote the interconnection between ESG factors and bank liquidity risks based on their own conditions.
(3)
Continuously consolidate the guiding role of digital transformation in China’s commercial banks. Commercial banks in China should start by enhancing the value of informatization, establishing a digital internal control management process for commercial banks, and playing the role of ESG in regulating the liquidity level of commercial banks through data sharing and technological innovation. This will help improve the bank’s ability to monitor and predict ESG factors and provide more accurate and reliable information for liquidity risk management.
(4)
Actively promote the government and regulatory agencies’ efforts in educating and training on liquidity risk and ESG. On the one hand, it is necessary to provide comprehensive training and guidance programs for the ESG evaluation system to help bank personnel better understand and apply ESG factors, thereby enhancing their ability to manage liquidity risks. On the other hand, it is important to promote the public dissemination of ESG-related knowledge and improve awareness and understanding of ESG ratings throughout society.
(5)
Continuously strengthen international cooperation among banks and promote the global adoption and implementation of ESG factors. By enhancing cross-border collaboration and sharing information, a unified ESG standard and guidelines will be established to enhance the overall ESG level and liquidity risk management capabilities of the global banking industry.

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Table 1. Variable Explanation.
Table 1. Variable Explanation.
Variable Type Indicator Variable Symbol Measurement Method
Dependent Variable Loan-to-Deposit Ratio LTD Bank Loans / Bank Deposits
Independent Variable Hua Zheng ESG Score HZESG Natural Logarithm After Conversion
Mediating Variable Bank Value TobinQ (Market Value + Bank Liabilities) / Book Value
Profitability ROE End-of-Period Net Profit / End-of-Period Total Assets
Moderating Variable Peking University Bank Digital Transformation Index PKUDBI Natural Logarithm of Peking University’s Digital Inclusive Finance Index
Control Variable Non-interest Income Ratio NIR Non-interest Income / Total Income
Proportion of Independent Shareholders INDEP Number of Independent Shareholders / Total Number of Company Shareholders
Proportion of Top 10 Shareholders TOP10 Number of Shares Held by the Top 10 Shareholders / Total Company Share Capital
Non-performing Loan Provision Coverage Ratio PLLCR Bank Provisions for Non-performing Loans / Total Non-performing Loans
Year-on-Year Growth of Bank’s Registered Province GDP PGDPG China National Bureau of Statistics
Consumer Price Index CPI China National Bureau of Statistics
Table 2. Descriptive Statistics of Variables.
Table 2. Descriptive Statistics of Variables.
Variable Obs Mean Std. Dev. Min Max
hzesg 285 5.435 .774 3 7
ltd 285 77.032 12.956 38.97 116.23
tobinq 285 .995 .02 .93 1.06
pkudbi 285 95.634 40.201 3 184
roe 285 .144 .043 .06 .25
nir 285 23.419 8.696 5.82 51.09
coir 285 30.035 4.985 18.93 59.01
indep 285 .369 .048 .1 .56
top10 285 .662 .208 .25 .99
pllcr 285 261.578 97.179 132.44 567.71
pggdpg 285 7.086 2.144 1.2 13.9
cpi 285 2.27 1.007 .9 5.4
Table 3. Baseline Regression Results.
Table 3. Baseline Regression Results.
(1) (2)
VARIABLES ltd ltd
     
hzesg -0.363*** -0.174***
  (0.0504) (0.0350)
nir   0.151***
    (0.0345)
pllcr   -0.0154***
    (0.00317)
pggdpg   -1.066***
    (0.114)
cpi   0.0103***
    (0.000736)
indep   0.0950**
    (0.0468)
top10   0.115**
    (0.0480)
Constant 2.146*** 1.965***
  (0.0370) (0.0469)
     
Observations 284 284
Number of id 41 41
R-squared 0.176 0.680
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Table 4. Lag Effect Test (1-2), Exception handling (3-4).
Table 4. Lag Effect Test (1-2), Exception handling (3-4).
(1) (2) (3) (4)
VARIABLES ltd ltd ltd ltd
         
L.hzesg -0.381*** -0.104*** -0.359*** -0.174***
  (0.0493) (0.0384) (0.0489) (0.0338)
nir   0.215***   0.159***
    (0.0373)   (0.0334)
pllcr   -0.0187***   -0.0151***
    (0.00339)   (0.00306)
pggdpg   -1.079***   -1.030***
    (0.138)   (0.110)
cpi   0.00975***   0.0101***
    (0.000823)   (0.000710)
indep   0.0825   0.0677
    (0.0508)   (0.0455)
top10   0.0948*   0.111**
    (0.0532)   (0.0465)
Constant 2.167*** 1.929*** 2.144*** 1.973***
  (0.0362) (0.0513) (0.0359) (0.0453)
         
Observations 242 242 283 283
R-squared 0.227 0.677 0.182 0.686
Number of id 37 37 41 41
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Table 5. Heterogeneity Test.
Table 5. Heterogeneity Test.
State-Owned Non-State-Owned
VARIABLES ltd ltd
     
hzesg -0.148* -0.410***
  (0.0831) (0.0593)
CONTROL Control Control
     
Constant 1.973*** 2.186***
  (0.0609) (0.0436)
     
Observations 62 222
R-squared 0.054 0.205
Number of id 6 35
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Table 6. Mediation Effect Analysis.
Table 6. Mediation Effect Analysis.
(3) (4) (5)
VARIABLES tobinq roe ltd
       
hzesg 0.0431*** 0.143*** -0.0885**
  (0.0131) (0.0229) (0.0352)
tobinq     -0.493***
      (0.163)
roe     -0.469***
      (0.0937)
nir   -0.124*** 0.0835**
    (0.0212) (0.0338)
indep -0.0343*   0.0830*
  (0.0178)   (0.0439)
top10 -0.0390**   0.0852*
  (0.0172)   (0.0448)
pllcr 0.00479*** 0.0134*** -0.00706**
  (0.00120) (0.00200) (0.00321)
pggdpg 0.348*** 0.832*** -0.514***
  (0.0426) (0.0724) (0.136)
cpi -0.00344*** -0.00616*** 0.00582***
  (0.000273) (0.000479) (0.000987)
Constant 0.969*** -0.0187 2.446***
  (0.0178) (0.0182) (0.164)
       
Observations 283 283 283
Number of id 41 41 41
R-squared 0.538 0.693 0.727
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Table 7. Moderation Effect Analysis.
Table 7. Moderation Effect Analysis.
(6) (7)
VARIABLES ltd ltd
     
pkudbi 0.029** 0.028**
  (0.0139) (0.0138)
hzesg -0.154*** -0.125***
  (0.0350) (0.0359)
pkudbi_ hzesg   -0.559***
    (0.1997)
nir 0.099** 0.113***
  (0.0391) (0.0389)
pllcr -0.014*** -0.015***
  (0.0031) (0.0031)
pggdpg -0.806*** -0.806***
  (0.1401) (0.1381)
cpi -0.009*** -0.002***
  (0.0087) (0.0087)
indep 0.097** 0.072**
  (0.0457) (0.0459)
top10 0.092* 0.095**
  (0.0476) (0.0476)
Constant 1.834*** 1.826***
  (0.0591) (0.0583)
     
Observations 284 284
Number of id 41 41
R-squared
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
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