Digital Transformation of Commercial Banks and Corporate ESG Performance: Evidence from China

Abstract

Based on a sample of Chinese A-share listed companies covering the period 2013–2023, this study investigates the effect of commercial banks’ digital transformation on corporate ESG performance. The empirical results indicate that the higher the degree of commercial banks’ digital transformation, the better the corporate ESG performance. Analysis of underlying mechanisms indicates that this effect operates primarily through two channels: the mitigation of corporate financing constraints and the reinforcement of external governance. Further cross-sectional analysis shows that this positive relationship is particularly pronounced among non-state-owned enterprises, heavily polluting firms, and those characterized by higher levels of digital transformation. This study extends the literature on the determinants of corporate ESG performance and offers incremental evidence on the microeconomic effects of commercial banks’ digital transformation.

1. Introduction

As global climate change intensifies and sustainable development concepts gain widespread attention, enterprises, as key actors in economic activities, bear the responsibility of steering society toward the achievement of sustainability objectives by actively embracing and implementing ESG principles. ESG performance serves as a comprehensive indicator of a firm’s capacity for long-term sustainable development, reflecting its environmental footprint, social responsibilities, and internal governance quality. A substantial body of empirical research consistently demonstrates that corporate ESG performance has a significant influence on improving financial outcomes [1], increasing company value [2], reducing corporate financing costs [3], fostering innovation [4], and mitigating business risks [5]. However, from a cost–benefit perspective, ESG practices are characterized by substantial resource investment, long payback periods, and limited short-term financial returns, resulting in insufficient intrinsic motivation for companies and consequently constraining ESG investment. Therefore, investigating how to enhance corporate ESG performance and foster sustainable development is of considerable theoretical and practical importance.

Corporate ESG performance is primarily influenced by both internal and external factors [6]. Regarding internal determinants, existing studies indicate that sound financial performance [7], robust governance structures [8], and strategically far-sighted management [9] contribute to enhancing corporate ESG performance. With respect to external factors, existing literature demonstrates that market attention [10], environmental regulations [11], information disclosure requirements [12], tax incentives [13], and the expansion of green finance [14] can enhance corporate ESG performance. Nevertheless, limited research has been conducted on the influence of commercial banks’ digital transformation on corporate ESG performance. To address this gap, our article adopts a bank-enterprise lending relationship perspective, examining the determinants of corporate ESG.

The question of how commercial banks’ digital transformation affects corporate ESG performance is both timely and intriguing. As primary funding providers for enterprises, commercial banks can exert considerable influence on their business decisions through credit contracts [15]. We posit that commercial banks’ digital transformation affects client firms’ ESG performance through two channels. The first plausible channel is that commercial banks’ digital transformation can effectively alleviate enterprises’ financing constraints. Corporate ESG investment requires substantial capital commitment. In practice, widespread information asymmetry between lenders and borrowers prompts banks to adopt various credit rationing behaviors to mitigate lending risks, such as reducing loan amounts, shortening maturities, or demanding collateral [16]. This exacerbates financing constraints for enterprises and hinders improvements in their ESG performance. Digital transformation enables commercial banks to fundamentally reshape their credit decision-making models [17], reduce lending costs [18], and enhance risk tolerance [19], thereby alleviating corporate financing constraints. The relaxation of financing constraints can provide sufficient funding for corporate ESG investments, in turn facilitating the enhancement of corporate ESG performance.

The second plausible channel is that the digital transformation of commercial banks can enhance the external governance of enterprises. Banks have a strong motivation to improve corporate ESG performance. Commercial banks’ attention to corporate ESG performance helps prevent potential credit risks, while also serving as a crucial means for banks to manage their reputation and build social capital [20]. The commercial banks’ digital transformation can facilitate the expansion of their debt governance from post-lending supervision to real-time monitoring of various aspects of corporate operations [21], enable more effective implementation of green credit policies [22], and strengthen information disclosure quality [23]. Consequently, these improvements enhance the efficiency of bank debt governance and improve corporate ESG performance.

We examine this research question using a sample of Chinese A-share listed firms. The Chinese market is an appropriate setting for several reasons. First, the rapid digitization of China’s banking sector and the increasing attention on ESG have enhanced data availability for our empirical analysis. Second, in contrast to many developed countries, China experiences a significant information asymmetry between banks and enterprises. This asymmetry results in substantial financing constraints for firms that coexist with limited effectiveness in commercial bank debt governance. The digital transformation of commercial banks offers a favorable opportunity to address these dual challenges. Third, clarifying how bank digitalization affects corporate ESG offers valuable insights for emerging markets seeking to harness financial technology for sustainable development.

In this article, we construct a bank-firm matched dataset based on loan-by-loan credit data obtained by listed companies from commercial banks between 2013 and 2023, and empirically examine how commercial banks’ digital transformation affects corporate ESG performance. We find that the higher the degree of digital transformation among commercial banks, the better the ESG performance of borrowing firms. Stated differently, commercial banks’ digital transformation positively contributes to corporate ESG performance. Our results still hold after controlling for endogeneity by employing the instrumental variable method and accounting for reverse causality. To further test the robustness of our results, we conduct several robustness tests by using alternative measures of Dig and ESG, and incorporating higher-level fixed effects. The findings remain unchanged.

Furthermore, we conduct mechanism analysis and find that this effect of commercial banks’ digitalization on corporate ESG performance can be explained by the relaxation of financing constraints and improvement of external governance. This indicates that alleviating corporate financing constraints and enhancing enterprises’ external governance are two plausible channels through which commercial banks’ digital transformation improves corporate ESG performance. In addition, considering firm characteristics, our cross-sectional tests show that the beneficial effect is more pronounced among non-state-owned enterprises, heavily polluting firms, and those with higher levels of digital transformation.

This study makes several contributions. First, it extends the literature on the micro-level determinants of corporate ESG performance from the perspective of digital transformation in commercial banks. On the determining factors of corporate ESG performance, existing literature mainly focuses on firms’ internal characteristics [7,8,9] and external environments [10,12,13]. However, these studies largely overlook the impact of commercial banks’ digital transformation. By adopting a bank-enterprise lending relationship perspective, this article systematically discusses the impact of bank digitalization on corporate ESG performance, offering empirical evidence on ESG determinants from a creditor viewpoint.

Second, this article extends relevant studies on the economic consequences of bank digitalization by focusing on ESG. Existing literature primarily examines the impact of commercial banks’ digital transformation on bank labor demand [24], risk-taking behaviors [25], liquidity hoarding [26], corporate financing constraints [27], leverage manipulation [28], and corporate innovation [29]. However, there is little literature discussing the impact of bank digitalization on ESG outcomes. To explore the underlying mechanisms, this article simultaneously considers the channels of alleviating financing constraints and enhancing external governance, thereby offering incremental evidence on the microeconomic effects of commercial banks’ digitalization.

Third, this article utilizes enterprise loan data and the PKU bank digital transformation index to construct a “bank–enterprise” matching digital transformation index, weighted by loan size. On the one hand, it captures the spillover effects of banks’ digital transformation on firms through the lending channel, thereby extending the current literature, which largely focuses on the digitalization of enterprises themselves. On the other hand, this approach helps mitigate estimation biases arising from the neglect of heterogeneity in bank–enterprise relationships—such as when different firms rely on different banks—and thus provides a more accurate assessment of the impact of bank digitalization on enterprises.

Fourth, from a policy perspective, our findings have clear policy implications. The conclusion that digitalization of commercial banks can improve corporate ESG performance not only comprehensively enhances our understanding of the relationship between bank digitalization and corporate ESG in emerging markets, but also offers practical insights for accelerating the digitalization of commercial banks, establishing sound bank-enterprise interactions, advancing corporate ESG practices, and ultimately promoting sustainable development.

The remainder of this article is structured as follows. Section 2 develops the theoretical framework and presents the research hypotheses. Section 3 details sample construction, variable definitions, and empirical model. Section 4 presents the main empirical results and robustness tests. Section 5 provides further analysis. Section 6 concludes.

2. Hypothesis Development

Within China’s bank-dominated financial system, commercial banks, serving as the primary funding providers for enterprises, play a crucial role in corporate operational decision-making [15,30]. Digital technologies—including big data, blockchain, cloud computing, and artificial intelligence—have deeply integrated with traditional commercial banks, driving the digital transformation of the banking sector and exerting a profound influence on banks’ organizational structures, business processes, and operational models. This transformation is expected to further affect corporate decision-making through the channel of credit contracts. We posit that the digital transformation of commercial banks influences corporate ESG performance through two primary channels.

2.1. The Financing Constraints Alleviation Channel

Corporate ESG investment requires sustained, large-scale capital injections, and ensuring sufficient funding is key to improving corporate ESG performance [31]. Adequate funding not only facilitates corporate investment in green technology R&D, environmental equipment upgrades, and social responsibility projects, but also enhances internal governance effectiveness by strengthening employee skills training and digital management investments, thereby improving the firm’s ESG performance. However, information asymmetry is prevalent between banks and enterprises within China’s bank-dominated indirect financing system. According to information asymmetry theory, this asymmetry constitutes the fundamental source of corporate financing constraints. Since corporate financial information does not fully reveal firms’ true operational conditions, commercial banks resort to measures such as reducing loan sizes, shortening maturities, and raising collateral standards to reduce credit risk [16,32]. Consequently, the supply of long-term loans shrinks, which in turn intensifies financing constraints for firms and thus impedes improvements in their ESG performance. Against the backdrop of a rapidly expanding digital economy, digitalization of commercial banks offers a valuable opportunity to address the challenges of costly and constrained financing for enterprises.

Commercial banks’ digitalization operates through several channels to alleviate financing constraints, thereby enhancing corporate ESG performance. First, digital technologies break through the temporal and spatial constraints inherent in the traditional acquisition, processing, and transmission of information by commercial banks. This enhances their ability to acquire and process “soft information”, thereby reducing reliance on “hard information” such as corporate financial statements and collateral [17,22]. Digital transformation facilitates the shift in commercial banks’ credit decision-making models from being collateral-driven to credit-driven. This optimizes the corporate credit structure, reduces financing difficulties for firms with insufficient collateral or incomplete financial disclosure, and ultimately alleviates corporate financing constraints.

Second, digitalization of commercial banks integrates new technological means and information sources such as information technology and big data into their credit approval processes. This enhances their information screening capabilities, thereby lowering the costs of information search, processing, and verification [18]. Furthermore, digital transformation enables commercial banks to establish big-data credit and digital supply chain finance systems. This empowers them to assess corporate creditworthiness and predict default risks more accurately [33]. Such advancements not only lower the costs of traditional manual credit review, but also reduce rent-seeking opportunities for firms during the approval process [34], thereby reducing overall corporate credit transaction costs.

Third, from a risk tolerance perspective, digital transformation empowers banks to efficiently acquire and process vast amounts of multidimensional data using digital technologies [19]. This process, which involves collecting corporate ESG-related information and conducting comprehensive risk-return assessments for ESG projects via “data + algorithms”, allows banks to effectively identify and finance more promising initiatives. Therefore, digital transformation helps reduce commercial banks’ perceived uncertainty regarding ESG activities, which in turn increases their risk tolerance and lowers the cost of corporate debt financing for ESG-related investments.

2.2. The External Governance Enhancement Channel

Strong ESG performance is not only a result of a firm’s active improvements in environmental practices and social responsibility, but also a reflection of its corporate governance concept [35]. According to stakeholder theory, commercial banks’ attention to corporate ESG performance helps prevent potential credit risks, while also serving as a crucial means for banks to manage their reputation and build social capital [20]. Therefore, commercial banks have a strong motivation to improve corporate ESG performance. Based on agency theory, bank debt governance can enhance corporate governance by mitigating agency problems between shareholders and creditors, as well as between management and shareholders. However, in practice, the effectiveness of debt governance by commercial banks in China is limited, particularly because the incentive and disciplinary mechanisms of short-term debt are largely ineffective [36].

Digital transformation can enhance the effectiveness of commercial banks’ debt governance in several ways, thereby improving corporate ESG performance. First, digitalization of commercial banks can not only directly improve corporate governance performance, but also indirectly improve corporate social and environmental performance. Commercial banks have been extending their debt governance capabilities from post-lending supervision into real-time monitoring of all aspects of corporate operations, leveraging advancements in digital technologies such as big data, blockchain, cloud computing, and artificial intelligence. This helps curb the expropriation of creditor interests by shareholders and enhances corporate governance performance [21]. Moreover, digital transformation enables banks to monitor the real-time use of corporate credit funds, encouraging enterprises to balance the pursuit of economic benefits with the fulfillment of social responsibility and environmental performance.

Second, commercial banks’ digitalization facilitates the implementation of green credit policies, thereby improving corporate ESG performance. Banks’ digital transformation enhances their ability to engage with firms’ core market activities, which in turn reduces information asymmetry between banks and enterprises. This enables banks to assess and manage the environmental and social risks inherent in green credit more accurately, thus motivating firms to fully incorporate environmental considerations at the corporate governance level and actively pursue green development [22].

Third, banks’ digital transformation facilitates the improvement of corporate information disclosure quality, thereby enhancing corporate ESG performance. By enabling the multidimensional extraction and efficient processing of comprehensive corporate information, digital transformation helps overcome information asymmetry among banks and firms, firms and stakeholders, investors and regulators. This facilitates the optimization of credit allocation, allowing firms that genuinely adhere to ESG principles to gain greater access to credit resources [23]. Under digitally enabled bank credit models, firms that engage in cosmetic ESG disclosure not only fail to gain a credit advantage but may also incur credit penalties such as reduced credit availability and obstacles to loan renewal [35,37], thereby pressuring them to improve the quality of their information disclosure.

In summary, commercial banks’ digital transformation enhances corporate ESG performance through two interrelated channels: alleviating financing constraints and strengthening external governance. Accordingly, this study proposes the following research hypotheses.

Hypothesis 1.

The higher the degree of commercial banks’ digital transformation, the better the corporate ESG performance.

Hypothesis 2.

The digital transformation of commercial banks improves corporate ESG performance by alleviating financing constraints.

Hypothesis 3.

The digital transformation of commercial banks improves corporate ESG performance by enhancing external governance.

3. Research Design

3.1. Sample and Data

Enterprise loan data is available from 2013, whereas PKU bank digital transformation index is only reported up to 2023. Therefore, this article takes A-share companies listed on Shanghai and Shenzhen stock exchanges over the period 2013 to 2023 as our research sample. Following standard research practices, we apply the following selection criteria: (1) exclude firms in the financial industry; (2) remove firms classified as ST or *ST, as well as firms with an asset–liability ratio exceeding one; (3) delete observations that do not disclose the bank name or the bank loan amount; (4) eliminate observations with missing data on the bank digital transformation index; (5) drop observations with missing values for relevant financial indicators. To reduce the influence of outliers, all continuous variables are winsorized at the 1st and 99th percentiles. These procedures yield a final sample of 11,432 firm-year observations. Data on commercial banks’ digital transformation are obtained from the Peking University China Commercial Bank Digital Transformation Index (2010–2023), compiled by the research team at the Peking University Digital Finance Research Center. Information on bank-firm lending relationships is sourced from the listed company loan database in CSMAR. Corporate ESG data are drawn from the Wind database, while data for the remaining variables are obtained from CSMAR.

3.2. Variable Definitions

3.2.1. Dependent Variable

Consistent with prior literature, the dependent variable is corporate ESG performance, measured by the ESG scores issued by Sino-Securities. This rating agency assigns scores ranging from 0 to 100 based on a comprehensive assessment of firms’ environmental, social, and governance practices. These scores are further categorized into nine grades: AAA, AA, A, BBB, BB, B, CCC, CC, and C. This study employs the continuous ESG score as a proxy for corporate ESG performance, where higher scores indicate better ESG performance. In robustness checks, we also use the nine-tier ordinal rating as an alternative measure.

3.2.2. Independent Variable

The key independent variable is the banks’ digital transformation index weighted by firm loans (Dig). Lending is the fundamental business through which firms establish relationships with commercial banks, as well as the primary channel through which they meet their financing needs. Thus, any changes in commercial banks’ operational behavior are transmitted to firms through lending activities, ultimately influencing corporate behavior. We measure bank digitalization using the Peking University China Commercial Bank Digital Transformation Index, which captures three dimensions: strategic digitalization (Dig1), business digitalization (Dig2), and management digitalization (Dig3). This index provides a reasonably comprehensive assessment of the current state and development trends of digital transformation in China’s commercial banks [38]. Following the methods of Li et al. (2023) [39], we use the annual loans a firm obtains from each bank as weights to calculate a firm-level weighted average of the digital transformation index. For each firm-year observation, the index is calculated as

$${Dig}_{i,t}={\displaystyle {\sum }_{j=1}^n({Loan}_{i,j,t}/{Loan}_{i,t}\times {Dig}_{j,t}})$$

(1)

where Dig_i,t denotes the weighted average digital transformation index of commercial banks for firm i in year t; Loan_i,j,t is the loan amount obtained by firm i from bank j in year t; Loan_i,t refers to the total loan amount obtained by firm i from all banks in year t; and Dig_j,t represents the digital transformation index of bank j in year t. A higher value of Dig implies a greater impact of commercial banks’ digital transformation on the firm.

3.2.3. Control Variables

Drawing on the relevant studies, we include a set of firm-level control variables that capture financial characteristics and corporate governance in the empirical model. These variables proxy for firm size, financial structure, profitability, growth opportunities, market valuation, ownership concentration, and board leadership structure.

Table 1. Variable definitions.

Variables	Definitions
ESG	Corporate ESG performance, Sino-Securities ESG rating score.
Dig	The bank digital transformation index weighted by firm loans, see details in Formula (1).
Size	Firm size, the natural logarithm of total assets.
Lev	Asset-liability ratio, liabilities divided by total assets.
ROA	Return on assets, net profit divided by average total assets.
Growth	Firm growth, the annual growth rate of operating revenue.
BM	The book-to-market ratio, total assets divided by market value.
Top1	The largest shareholder’s holding ratio, number of shares held by the largest shareholder divided by total number of shares.
Dual	Duality of chairman and general manager, a dummy variable that equals one if the chairman of the board and the general manager are the same person, and zero otherwise.
FC	Financing constraint index, the FC index.
IIR	Institutional investor ratio, shareholding ratio of institutional investors.
IDR	Independent director ratio, the ratio of the number of independent directors to the size of directors.
AC	Analyst coverage, the number of analysts who have conducted tracking analysis on the company.

provides detailed definitions of all variables used in the empirical analysis, and

Table 2. Descriptive statistics.

Variables	N	Mean	SD	Median	Min	Max
ESG	11,432	72.751	5.022	73.010	57.120	83.830
Dig	11,432	117.884	30.865	119.857	45.091	174.282
Size	11,432	22.195	1.096	22.078	20.119	25.232
Lev	11,432	0.440	0.193	0.432	0.078	0.936
ROA	11,432	0.036	0.066	0.037	−0.254	0.210
Growth	11,432	0.173	0.381	0.110	−0.524	2.250
BM	11,432	0.612	0.238	0.605	0.132	1.164
Top1	11,432	32.920	14.217	30.400	9.080	72.880
Dual	11,432	0.312	0.464	0.000	0.000	1.000

reports descriptive statistics of the main variables.

3.3. Model Specification

To examine the effect of commercial banks’ digital transformation on corporate ESG performance and to test Hypothesis 1, we estimate the following panel regression model:

$${ESG}_{i,t}=\alpha +\beta {Dig}_{i,t}+\gamma {Controls}_{i,t}+{\displaystyle \sum Industry+{\displaystyle \sum Year}}+{\epsilon }_{i,t}$$

(2)

where ESG_i,t denotes the ESG performance of firm i in year t; Dig_i,t represents the weighted average digital transformation index of commercial banks for firm i in year t; Controls_i,t refers to a series of firm-level control variables; ΣIndustry and ΣYear denote industry fixed effects and year fixed effects, respectively, which control for time-invariant industry characteristics and common macroeconomic shocks; and ε_i,t represents a random error term. Standard errors are clustered at the firm level to address potential serial correlation within firms over time.

4. Empirical Results and Analysis

4.1. Baseline Results

Table 3. Digital transformation of commercial banks and corporate ESG performance.

	(1)	(2)	(3)	(4)
Variables	ESG	ESG	ESG	ESG
Dig	0.010 ***
	(3.26)
Dig1		0.003 ***
		(2.57)
Dig2			0.007 ***
			(2.75)
Dig3				0.006 **
				(2.08)
Size	1.152 ***	1.152 ***	1.155 ***	1.161 ***
	(12.69)	(12.67)	(12.72)	(12.79)
Lev	−5.152 ***	−5.183 ***	−5.179 ***	−5.194 ***
	(−10.79)	(−10.88)	(−10.86)	(−10.86)
ROA	13.418 ***	13.477 ***	13.467 ***	13.483 ***
	(12.45)	(12.47)	(12.48)	(12.50)
Growth	−0.672 ***	−0.672 ***	−0.672 ***	−0.681 ***
	(−4.84)	(−4.82)	(−4.82)	(−4.89)
BM	0.453	0.451	0.446	0.446
	(1.27)	(1.27)	(1.25)	(1.25)
Top1	0.013 **	0.013 **	0.013 **	0.013 **
	(2.33)	(2.38)	(2.32)	(2.40)
Dual	0.214	0.214	0.218	−0.217
	(1.51)	(1.50)	(1.53)	(1.53)
Constant	47.130 ***	47.79 ***	47.165 ***	47.684 ***
	(24.83)	(25.44)	(24.72)	(25.21)
Industry FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	11,432	11,432	11,432	11,432
Adj. R2	0.1614	0.1610	0.1612	0.1608

Notes: The numbers in parentheses denote t values which are calculated based on the standard errors clustered at the firm level. ** and *** represent significance at the 5% and 1% levels, respectively.

displays the estimation results for the relationship between commercial banks’ digital transformation and corporate ESG performance. In Column (1), the regression coefficient on Dig is 0.010 and significantly positive at the 1% level. Columns (2) through (4) show that the coefficients on Dig1, Dig2, and Dig3 are 0.003 (p < 0.01), 0.007 (p < 0.01), and 0.006 (p < 0.05), respectively. From an economic standpoint, a one-standard-deviation increase in the commercial bank digital transformation index is associated with a 0.309-point increase in the ESG score (0.010 × 30.865). This magnitude corresponds to 6.15% of the standard deviation of ESG scores (0.309/5.022), indicating a non-trivial economic effect. These findings indicate that the higher the degree of commercial banks’ digital transformation, the better the corporate ESG performance. Stated differently, the digital transformation of commercial banks makes a positive contribution to corporate ESG outcomes, providing support for Hypothesis 1. These results also carry policy implications that regulatory authorities should provide appropriate policy support for the digital transformation of commercial banks, thereby facilitating the improvement of corporate ESG performance.

4.2. Endogeneity and Robustness Tests

4.2.1. Instrumental Variable Estimation

To address potential endogeneity arising from omitted variables, we follow the approaches of Zhang et al. (2022) [40], and employ the average of commercial banks’ digital transformation index of the same type and year that have not established credit relationships with enterprises (IV_Dig) as an instrumental variable. This instrumental variable is valid, as it satisfies both the relevance and exogeneity conditions. On the one hand, commercial banks of the same type tend to exhibit similar levels of digital transformation; on the other hand, the digital transformation of commercial banks that have not established credit relationships with enterprises is unlikely to directly affect their ESG performance through credit channels. We estimate the model using the instrumental variable method of two-stage least squares (2SLS). Column (1) of

Table 4. Results of endogeneity and robustness tests.

	(1)	(2)	(3)	(4)	(5)	(6)
	First Stage	Second Stage
Variables	Dig	ESG	ESG	ESG	ESG_rank	ESG
IV_Dig	0.675 **
	(2.18)
Dig		0.009 ***			0.002 ***	0.011 ***
		(3.12)			(2.99)	(3.43)
L.Dig			0.013 ***
			(3.01)
Dig_mean				0.011 ***
				(3.47)
Constant	5.67 ***	38.548 ***	43.560 ***	44.671 ***	−1.118 ***	46.938 ***
	(8.25)	(20.11)	(18.13)	(20.26)	(−2.83)	(18.41)
Control	YES	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
Region × Year FE	NO	NO	NO	NO	NO	YES
Observations	11,432	11,432	7340	11,432	11,432	11,099
Adj. R2	0.2156	0.1534	0.1653	0.1459	0.1482	0.1745

Notes: The numbers in parentheses denote t values which are calculated based on the standard errors clustered at the firm level. ** and *** represent significance at the 5% and 1% levels, respectively.

reports the first-stage results. The coefficient on IV_Dig is positive and significant at the 5% level, confirming a strong correlation with the endogenous variable Dig. Meanwhile, the Kleibergen–Paap rk Wald F-statistic is 459.305, well above the 10% critical value of 16.38, rejecting the null hypothesis of weak identification and supporting the relevance of the instrument. Column (2) presents the second-stage estimates. The coefficient on Dig remains positive and statistically significant at the 1% level, reinforcing our baseline findings.

4.2.2. Reverse Causality

To mitigate concerns that our results may be driven by reverse causality, we replace Dig with its one-period lag (L.Dig). As shown in Column (3) of Table 4, the coefficient on L.Dig is positive and significant at the 1% level, further supporting our baseline results.

4.2.3. Alternative Measure of Dig

To exclude the possible impact of bias in the selection of independent variable indicators, we replace the loan-weighted index Dig with the mean of the digital transformation indices of all loan banks (Dig_mean). Column (4) of Table 4 reports a positive and statistically significant coefficient (p < 0.01) for Dig_mean, confirming the robustness of our core finding.

4.2.4. Alternative Measure of ESG Performance

We also examine whether our results are sensitive to the measurement of ESG performance. Following prior studies, we replace the continuous ESG score with an ordinal ESG ranking (ESG_rank), which assigns values from 1 to 9 corresponding to the nine rating categories (C to AAA). Column (5) of Table 4 shows that the coefficient on Dig remains positive and significant at the 1% level, indicating that our baseline results are still valid.

4.2.5. Inclusion of Region × Year Fixed Effect

To account for time-varying unobservable factors at the region level, we augment the baseline specification with Region × Year fixed effects. The results, reported in Column (6) of Table 4, show that the coefficient on Dig continues to be positive and significant at the 1% level, indicating that our baseline results still hold.

5. Further Analysis

5.1. Mechanism Analysis

5.1.1. Mechanism Analysis of Alleviating Financing Constraints

Our theoretical framework suggests that the digital transformation of commercial banks can alleviate the financing constraints of enterprises, thereby providing the funding necessary for ESG investment, and ultimately improving the ESG performance of enterprises. If this financing constraints channel holds, we anticipate that the positive effect of commercial banks’ digital transformation on ESG outcomes will be more pronounced among firms facing severe financing constraints, where the marginal benefit of constraint alleviation is greater.

To test this prediction, we employ the FC index to measure the degree of firms’ financing constraints. The larger the FC index, the higher the degree of corporate financing constraints. We split the sample into high and low constraint groups based on the annual industry median of the FC index and re-estimate the baseline model for each subsample. As reported in

Table 5. Results of mechanism analysis based on alleviating financing constraints.

	(1)	(2)	(3)
	High FC	Low FC
Variables	ESG	ESG	FC
Dig	0.013 ***	0.006	−0.005 ***
	(2.92)	(1.43)	(−3.11)
Constant	63.359 ***	35.337 ***	5.2531 ***
	(17.90)	(12.06)	(99.37)
Control	YES	YES	YES
Industry FE	YES	YES	YES
Year FE	YES	YES	YES
Observations	5954	5478	11,020
Adj. R2	0.1440	0.1973	0.8537

Notes: The numbers in parentheses denote t values which are calculated based on the standard errors clustered at the firm level. *** represents significance at the 1% level.

, Column (1) shows that for firms with relatively high financing constraints, the coefficient on Dig is positive and statistically significant at the 1% level. In contrast, Column (2) reveals an insignificant coefficient for the low-constraint group. Column (3) shows that the coefficient on Dig on FC is negative and statistically significant at the 1% level. These results are consistent with the view that commercial banks’ digital transformation does have an effect on alleviating financing constraints. Stated differently, the digital transformation of commercial banks may effectively improve corporate ESG performance by alleviating financing constraints. The findings are consistent with the research Hypothesis 2.

5.1.2. Mechanism Analysis of Enhancing External Governance

The preceding theoretical analysis posits that the digital transformation of commercial banks can alleviate information asymmetry between banks and enterprises, enhance the effectiveness of external governance, and ultimately improve corporate ESG performance. If this external governance channel holds, we reasonably expect that its effect will be stronger when firms’ existing governance mechanisms are weak (i.e., when the level of corporate governance is relatively low). In such cases, the digital transformation of commercial banks should have a greater impact on governance improvement and, consequently, a more pronounced effect on enhancing corporate ESG performance.

To proxy for corporate governance quality, we use three indicators: institutional investor ratio, independent director ratio, and analyst coverage. The higher the value of each indicator, the higher the level of corporate governance. We split the sample into high and low governance groups based on the annual industry median of each indicator separately and re-estimate the baseline model for each subsample. The results are reported in

Table 6. Results of mechanism analysis based on enhancing external governance.

	(1)	(2)	(3)	(4)	(5)	(6)
	Institutional Investor Ratio		Independent Director Ratio		Analyst Coverage
	High	Low	High	Low	High	Low
Variables	ESG	ESG	ESG	ESG	ESG	ESG
Dig	0.007	0.015 ***	0.007	0.013 ***	0.005	0.014 ***
	(1.23)	(3.46)	(1.50)	(3.04)	(0.96)	(3.46)
Constant	40.192 ***	53.897 ***	43.591 ***	50.349 ***	64.068 ***	41.817 ***
	(15.76)	(16.59)	(17.05)	(18.13)	(21.00)	(17.96)
Control	YES	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
Observations	5553	5878	4758	6671	4231	7200
Adj. R2	0.1778	0.1468	0.1734	0.1429	0.1219	0.1785

Notes: The numbers in parentheses denote t values which are calculated based on the standard errors clustered at the firm level. *** represents significance at the 1% level.

. For firms with relatively high governance quality—i.e., high institutional ratio (Column 1), high independent director ratio (Column 3), or high analyst coverage (Column 5)—the coefficients on Dig are statistically insignificant. In contrast, for firms with relatively low governance quality—i.e., low institutional ratio (Column 2), low independent director ratio (Column 4), or low analyst coverage (Column 6)—the coefficients on Dig are positive and significant at the 1% level.

Table 7. Results of intermediate regressions based on enhancing external governance.

	(1)	(2)	(3)
Variables	IIR	IDR	AC
Dig	0.016 ***	0.001 ***	0.015 ***
	(2.71)	(3.12)	(2.91)
Constant	−160.677 ***	36.234 ***	−109.957 ***
	(−19.01)	(14.70)	(−27.14)
Control	YES	YES	YES
Industry FE	YES	YES	YES
Year FE	YES	YES	YES
Observations	11,716	11,716	7820
Adj. R2	0.4067	0.4673	0.4302

Notes: The numbers in parentheses denote t values which are calculated based on the standard errors clustered at the firm level. *** represents significance at the 1% level.

shows that the coefficient on Dig on IIR, IDR, and AC are positive and statistically significant at the 1% level. These results are consistent with the view that commercial banks’ digital transformation has an effect on enhancing external governance. Stated differently, bank digital transformation may effectively improve ESG performance by enhancing external governance. The findings are consistent with the research Hypothesis 3.

5.2. Cross-Sectional Analysis

5.2.1. Property Rights Nature

Ownership discrimination is a well-documented phenomenon in China’s credit market. Compared with state-owned enterprises (SOEs), private firms often face greater difficulty in obtaining bank loans due to factors such as less complete information disclosure and limited collateral capacity, which can substantially constrain the improvement of their ESG performance. By changing banks’ credit decision-making models, digital transformation has a larger marginal effect on alleviating financing constraints for private enterprises, thereby promoting improvements in their ESG performance. Consequently, we expect the effect of bank digitalization to be stronger for non-state-owned enterprises (non-SOEs).

To test whether the nature of corporate property rights moderates the relationship between commercial banks’ digital transformation and corporate ESG performance, we split the sample into SOEs and non-SOEs and re-estimate the baseline model. As shown in Columns (1) and (2) of

Table 8. Results of cross-sectional analysis.

	(1)	(2)	(3)	(4)	(5)	(6)
	Property Rights Nature		Pollution Intensity		Digital Connection
	SOE	Non-SOE	Heavy	Non-Heavy	High	Low
Variables	ESG	ESG	ESG	ESG	ESG	ESG
Dig	0.013	0.007 **	0.016 ***	0.007	0.018 ***	0.005
	(1.52)	(1.98)	(2.71)	(1.42)	(3.69)	(1.28)
Constant	35.873 ***	53.131 ***	41.735 ***	48.387 ***	44.983 ***	49.252 ***
	(10.00)	(22.16)	(11.96)	(23.62)	(17.31)	(20.97)
Control	YES	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
Observations	3498	7930	2956	8473	5003	6428
Adj. R2	0.2156	0.1665	0.1558	0.1777	0.1765	0.1602

Notes: The numbers in parentheses denote t values which are calculated based on the standard errors clustered at the firm level. ** and *** represent significance at the 5% and 1% levels, respectively.

, the coefficient on Dig is positive and significant only for non-SOEs (Column 2), while it is insignificant for SOEs (column 1). The above results indicate that the impact of commercial bank’s digital transformation on ESG performance is more pronounced for non-state-owned enterprises than for state-owned enterprises. This finding carries a policy implication that commercial banks should deepen the integration of financial technology into the process of green loans and enhance the accessibility of credit for private enterprises.

5.2.2. Pollution Intensity

Heavily polluting enterprises have substantially higher funding needs for green technology research and development, environmental protection equipment updates, and related activities compared to other enterprises. The digital transformation of commercial banks can more effectively alleviate their funding bottlenecks by easing financing constraints. Meanwhile, heavily polluting enterprises face greater environmental compliance and social supervision pressures, and commercial banks’ digital transformation can enhance their ESG performance through improved debt governance. Therefore, we expect the positive effect of commercial banks’ digital transformation on ESG performance to be more pronounced for heavily polluting enterprises.

To test whether this effect varies with corporate pollution intensity, we split the sample into heavily polluting and non-heavily polluting firms according to their pollution status. The results, reported in Columns (3) and (4) of Table 8, show that the coefficient on Dig is positive and significant at the 1% level for heavily polluting enterprises (Column 3), but statistically insignificant for non-heavily polluting enterprises (Column 4). The above results suggest that the ESG-enhancing effect of commercial bank’s digital transformation is concentrated among heavily polluting enterprises. The policy implication is that commercial banks should leverage digital technology, strengthen ESG governance oversight for heavily polluting enterprises, and encourage them to accelerate green innovation.

5.2.3. Digital Connection

As capital demanders, firms that undergo digital transformation themselves can transmit information to commercial banks more efficiently, thereby further reducing information asymmetry and enabling banks to evaluate ESG projects and extend loans more quickly and accurately. Consequently, we expect that the beneficial effect of commercial banks’ digital transformation on ESG performance will be more pronounced for enterprises with higher levels of digital maturity.

To test whether this effect varies with digital connection, we group the sample by the annual industry median of firms’ digital transformation index. The results, reported in Columns (5) and (6) of Table 8, indicate that for firms with relatively high digital transformation levels, the coefficient on Dig is positive and significant at the 1% level (Column 5); for firms with relatively low digital transformation levels, the coefficient on Dig is statistically insignificant (Column 6). The above results suggest that the ESG benefits of commercial banks’ digital transformation are amplified when enterprises themselves are digitally sophisticated. The policy implication for enterprises is that they should enhance the transparency of their financial and ESG data through digital transformation, leverage the digital linkages between banks and firms, and thereby reduce information asymmetry.

6. Conclusions

Using a sample of A-share firms listed on the Shanghai and Shenzhen stock exchanges covering the 2013–2023 period, this study explores the relationship between commercial banks’ digital transformation and corporate ESG performance. Building on a systematic theoretical analysis of the underlying mechanisms, we assess the impact of commercial banks’ digital transformation on ESG outcomes and identify the channels through which this effect operates. Our main findings are threefold. First, we document that there exists a positive association between the degree of commercial banks’ digital transformation and corporate ESG performance. In other words, digitalization of commercial banks positively contributes to corporate ESG performance. These results remain robust after a series of endogeneity and robustness tests. Second, digital transformation of commercial banks improves corporate ESG performance primarily by alleviating financing constraints and enhancing external governance. This indicates that bank digitalization can effectively alleviate the financing constraints of enterprises, provide sufficient financial support for ESG investments, enhance the effectiveness of bank debt governance, and strengthen external governance of enterprises, thereby improving corporate ESG performance. Third, the effect of commercial banks’ digital transformation on corporate ESG performance varies systematically with firm characteristics. Specifically, the positive relationship is more pronounced for non-state-owned enterprises, heavily polluting firms, and those with relatively high levels of digital transformation.

These findings carry several important implications for policy and practice. First, regulatory authorities should provide appropriate policy support for commercial banks to facilitate the orderly implementation of digital transformation within the banking sector. Moreover, regulators should encourage banks to develop differentiated digital strategies tailored to their specific circumstances and to establish diverse models of digital transformation. Second, commercial banks should deepen the integration of financial technology into the full process of green lending and improve the quality and efficiency of financial services for the real economy. They should fully leverage digital technologies—such as big data, blockchain, cloud computing, and artificial intelligence—to design algorithm-based financing solutions, improve the accessibility of credit for private enterprises, and strengthen ESG governance oversight for heavily polluting enterprises. Third, enterprises should pursue sustainable development goals through technological empowerment, data transparency, and ecological collaboration. In particular, private enterprises should enhance the transparency of financial and ESG data through digital transformation, capitalize on the digital linkages between banks and enterprises, and reduce information asymmetry. Heavily polluting enterprises should actively embrace the principles of green and sustainable development, accelerate green technology innovation, and strengthen environmental information disclosure.