Digital Finance, Internal and External Governance, and Corporate Environmental Information Disclosure

Abstract

Using the data of Chinese listed companies from 2011 to 2021 and the Digital Inclusive Finance Index from Peking University, this study investigates the impact of digital finance on the quality of corporate environmental information disclosure from both internal and external perspectives. The findings indicate the following: (1) Digital finance significantly enhances corporate environmental information disclosure quality, a conclusion that remains valid after a series of robustness tests. (2) Mechanism analysis shows that digital finance boosts disclosure quality by enhancing corporate environmental awareness and strengthening external oversight. (3) Heterogeneity analysis shows that digital finance more strongly enhances environmental disclosure quality for state-owned enterprises, firms in non-heavy pollution industries, and those located in regions with well-developed digital infrastructure. (4) Economic consequences analysis demonstrates that better disclosure quality, driven by digital finance, boosts a firm’s capital attractiveness, R&D investments, financing conditions, and green innovation. This process also triggers significant environmental spillover effects. The findings enrich theoretical research in digital finance and expand the discussion on enhancing environmental information disclosure.

1. Introduction

Rapid global economic development has led to severe environmental issues, including frequent extreme weather, ecosystem degradation, and biodiversity loss. These challenges pose significant barriers to green transformation and enhanced environmental protection. In this context, corporations, central to the market economy, consume vast natural resources and are primary sources of pollution. Their environmental practices are crucial to ecological sustainability [1]. Strengthening corporate environmental responsibility and promoting environmentally friendly production and management models have become a shared aspiration and practical direction across all societal sectors. Environmental information disclosure serves as a regulatory tool for pollution control, enabling the timely communication of environmental issues related to corporate activities to stakeholders, governments, and the public. By disclosing a company’s environmental impacts, conservation measures, and performance outcomes, it provides reliable data for investors, strengthens social oversight, and motivates companies to accelerate green development.

Corporate environmental information disclosure quality refers to the extent, comprehensiveness, and reliability with which firms disclose environment-related information, including environmental governance practices, pollution emissions, environmental investment, compliance status, and sustainability performance; however, corporate environmental information disclosure faces several bottlenecks in China. According to the “China Listed Companies Environmental Responsibility Information Disclosure Evaluation Report (2022)”, although there has been improvement in recent years, many companies still lack initiative, and high-quality disclosure standards are not yet widespread. Furthermore, a lack of uniformity in the content and format of disclosures leads to problems with information comparability and reliability. This issue diminishes their circulation and practical use. Therefore, standardizing corporate environmental information disclosure is crucial for enhancing business sustainability and is key to driving the economy’s green transformation, promoting harmony between humans and nature.

Digital finance refers to financial services that integrate digital technologies—such as big data, cloud computing, artificial intelligence, and mobile internet—into traditional financial activities, thereby improving the efficiency of financial intermediation, expanding financial accessibility, and enhancing information processing capabilities. Leveraging technologies such as big data and artificial intelligence, digital finance not only boosts operational efficiency and market competitiveness [2] but also integrates green concepts throughout the supply chain, from product development to after-sales service [3]. Enterprises expand their financing channels using digital financial tools, enabling diversified capital allocation [4]. They also use these tools to assess and manage environmental risks effectively, aligning economic benefits with green development. Through this process, companies achieve sustainable growth, fulfill their social responsibilities for environmental protection, and enhance their green competitiveness.

Moreover, the transparency and traceability of digital finance significantly enhance the efficiency of collecting, organizing, and disseminating corporate environmental information, thus bolstering the quality of environmental disclosure. This transformation enhances corporate environmental management levels, offering more scientific and transparent decision-making bases for investors and regulatory bodies, while also promoting the optimal allocation of resources and the deepening of societal green development. Most importantly, the digital transformation of environmental information disclosure strengthens the symmetry of information between enterprises and external stakeholders, reducing the costs and barriers in information transmission. It enhances investor and financial institution trust in enterprises, as well as the public’s trust and sense of responsibility towards businesses. Therefore, the deep integration of digital finance and environmental information disclosure provides technical support for enterprises exploring sustainable development pathways and injects vital momentum into global green transformation and ecological civilization construction.

Existing literature extensively explores the multidimensional impacts of digital finance at the corporate level, focusing primarily on financing optimization, governance enhancement, innovation stimulation, and environmental contributions. Digital finance revolutionizes traditional financing models, enhancing the accessibility and efficiency of financial services. This approach offers robust support to SMEs, helping them overcome financing challenges and broaden their access to capital [5,6]. In a broader context, such enhanced access to capital and reduced financing constraints significantly influence a company’s ability to invest in innovation and improve its outputs [7]. Further, digital finance optimizes resource allocation by redistributing funds from oversupplied sectors to those where resources are scarce, effectively mitigating capital misallocation issues [8]. This reallocation not only addresses inefficiencies but also stimulates the technological innovation potential of firms.

In terms of corporate governance, digital finance utilizes intelligent tools and technological means to enhance the precision of enterprise management, alleviate information asymmetry, and inject strong momentum into innovation and entrepreneurial activities [9,10]. Furthermore, digital finance accelerates R&D processes in startups and technology-oriented enterprises. The precise resource matching and risk management capabilities provided by digital finance help enterprises focus on core technological breakthroughs, promoting the research, development, and application of green technologies. In the environmental sector, digital finance integrates mechanisms such as green credit and green investment to enhance corporate environmental responsibility, achieving dual benefits of carbon reduction and energy efficiency enhancement [11,12].

Recent analyses focus on exploring the factors that influence the quality of environmental information disclosure from both micro and macro perspectives. From a macro perspective, the focus is on institutional regulations, social pressures, and industry competition, which serve as external environmental factors. Regulatory pressures from environmental departments, securities regulatory commissions, and local governments significantly affect corporate environmental information disclosure levels [13,14,15]. Furthermore, the introduction of mandatory reporting guidelines related to the environment not only increases the quantity of disclosures but also improves their quality [16,17]. Competition within the industry is also an important external driving force. Companies often disclose more high-quality environmental information to build competitive advantages and enhance their reputation.

From a micro perspective, the focus is on internal characteristics of companies, including enterprise size, ownership structure, profitability, corporate governance, and executive characteristics. Research indicates that state-owned enterprises, subject to stronger policy orientation and greater public supervision, tend to disclose environmental information more extensively than their non-state-owned counterparts [18,19,20]. Additionally, companies with widespread equity are more inclined to disclose environmental information due to the diversified demands of shareholders [21]. Enterprise size also plays a significant role; large enterprises, endowed with abundant resources and high social visibility, tend to be more proactive in disclosing environmental information [20], thereby demonstrating their capacity to fulfill social responsibilities and cultivate a green corporate image.

Previous research has explored how digital finance transforms enterprises and revealed the complex, diverse drivers behind environmental information disclosure. However, a significant gap remains in systematic studies on the relationship between digital finance and corporate environmental disclosure behavior. Clarifying its intrinsic mechanisms and the extent of its impact requires further exploration. Additionally, existing literature has predominantly focused on the singular effects of digital finance, overlooking its potential to enhance the quality of environmental information disclosure through synergistic internal and external governance actions. Therefore, unveiling the dual governance effects of digital finance in driving corporate environmental disclosure behaviors, as well as quantifying its specific impact, emerges as a critical issue that needs addressing.

This paper systematically explores the relationship between digital finance and corporate environmental information disclosure, uncovering intrinsic interaction patterns and potential values. It delves deeply into how digital finance can enhance the quality of environmental disclosure by optimizing internal governance (enhancing corporate environmental awareness) and strengthening external governance (increasing external attention and supervision). The goal is to offer scientific and actionable recommendations for optimizing environmental disclosure mechanisms and advancing digital finance innovation in a coordinated manner. This study has the following three potential contributions:

First, this study fills a specific gap in the literature concerning the micro-governance effects of digital finance on corporate environmental information disclosure quality. Although prior studies have extensively examined the role of digital finance in alleviating financing constraints, promoting innovation, and improving ESG performance, limited attention has been paid to how digital finance influences the quality of corporate environmental information disclosure at the firm level. In particular, existing research seldom explores the governance mechanisms through which digital finance reshapes firms’ disclosure behavior. By constructing a unified analytical framework that links digital finance development to environmental disclosure quality, this study provides systematic evidence on the micro-level governance implications of digital finance and enriches the literature on environmental disclosure determinants.

Second, this paper advances the literature by empirically testing the internal and external transmission mechanisms underlying this relationship. Rather than treating digital finance as a “black box,” this study decomposes its impact into two complementary channels: internal empowerment, reflected in enhanced corporate environmental awareness, and external discipline, manifested in strengthened external oversight. By integrating these mechanisms into a coherent empirical framework, the paper offers a structured explanation of how digital finance reshapes firms’ environmental disclosure incentives. Moreover, by examining heterogeneity across ownership types, industry pollution intensity, and regional digital infrastructure levels, this study further clarifies the boundary conditions under which digital finance exerts stronger governance effects.

Third, this study extends the discussion of the economic consequences of improved environmental disclosure quality in the digital finance context. While prior research has primarily focused on the signaling or legitimacy functions of environmental disclosure, less attention has been devoted to its broader economic and environmental spillover effects. By demonstrating that disclosure improvements driven by digital finance enhance firms’ capital attractiveness, R&D investment, financing conditions, and green innovation, this study provides direct empirical evidence of the long-term value creation effects associated with disclosure enhancement. These findings deepen our understanding of how digital finance indirectly contributes to sustainable development through information governance channels.

The remaining structure of this research is as follows: Section 2 develops some hypotheses through theoretical discussion. Section 3 discusses the research design, including model construction, main variables, calculation methods, and corresponding data sources. Section 4 discusses the main baseline regression results and provides a series of robustness tests. Section 5 tests potential channels and heterogeneity. Section 6 further discusses the economic consequences, and Section 7 concludes the paper.

2. Theoretical Analysis and Research Hypothesis

Corporate environmental information disclosure is essentially an information-transmission process under conditions of information asymmetry. Managers possess private information regarding environmental performance, while external stakeholders—such as investors, creditors, regulators, and the public—face substantial uncertainty in assessing firms’ environmental behavior. According to the voluntary disclosure theory, firms choose to disclose information when the expected net benefits exceed the associated costs [22]. The disclosure decision therefore depends on two key factors: (1) the cost of producing and transmitting information and (2) the governance and market incentives linked to transparency. Digital finance fundamentally reshapes this cost–benefit structure by altering the information environment and governance mechanisms surrounding firms. When engaging in environmental information disclosure, enterprises often weigh the associated costs against the benefits, directly impacting their willingness and the quality of the disclosures. According to the voluntary disclosure theory, firms actively disclose information when the perceived benefits exceed the associated costs [22]. Digital finance facilitates high-quality environmental information disclosures by reducing costs and enhancing benefits.

From a cost perspective, environmental information disclosure typically incurs high direct costs related to data collection, processing, and reporting [23], as well as indirect costs such as legal risks, regulatory penalties, and reputational losses due to inappropriate disclosures [24,25]. Leveraging technologies such as cloud computing and machine learning, digital finance significantly improves the efficiency of environmental data management and processing by firms, thereby streamlining and enhancing the accuracy of complex data handling. Data from production and operations can be rapidly integrated by companies to automatically generate detailed environmental reports, thereby significantly reducing the time and labor costs associated with data organization and analysis. Utilizing visualization tools and technology, complex environmental data is transformed into clear and direct information, which facilitates straightforward communication of environmental policies and green strategies to external audiences. This efficient and transparent disclosure method not only reduces communication costs but also fosters enhanced trust between corporations and their stakeholders [26]. Moreover, digital finance provides environmental risk assessment and management tools that help firms effectively identify and mitigate additional costs associated with non-compliance with environmental regulations.

From a benefits perspective, the rewards of environmental information disclosure are primarily reflected in enhanced corporate image, increased market trust, and fulfillment of the capital market’s demand for green development. The advancement of digital finance optimizes resource allocation, eases financing constraints, and provides substantial support for environmental investments [8]. This resource effect alleviates financial pressures on firms, enabling them to focus on long-term sustainable development goals. According to signaling theory, firms with excellent environmental performance are more inclined to disclose information to signal their capability in green development, thereby gaining recognition from the capital market and the public [27,28]. Digital finance, by offering precise data analysis tools and diverse financing options, enables companies to more effectively demonstrate their environmental performance. This not only helps firms gain trust from investors and customers but also lowers financing costs and enhances market competitiveness, creating a positive cycle. Based on these insights, this paper proposes the following hypothesis:

H1. 

Digital finance enhances the quality of corporate environmental information disclosure.

Digital finance not only improves firms’ access to green-oriented financial instruments, such as green credit and green bonds, but also embeds environmental standards into the financing process [8]. In digital financial systems, eligibility for preferential funding conditions is increasingly contingent upon verifiable environmental performance and standardized ESG disclosures. As a result, environmental compliance is no longer merely a regulatory obligation but becomes an economically incentivized strategic choice. This financing conditionality reshapes managerial decision-making [5]. When access to capital and financing costs are linked to environmental performance, management is incentivized to internalize environmental objectives into the firm’s strategic framework. Rather than engaging in passive compliance, firms shift toward proactive environmental management in order to maintain financing advantages and long-term capital market credibility. In this context, digital finance transforms environmental responsibility from an external constraint into an internally embedded strategic priority. The strengthening of environmental consciousness within firms manifests through three interrelated governance adjustments.

First, at the managerial level, environmental performance becomes directly associated with financial outcomes, reputational capital, and investor evaluation. Management therefore attaches greater importance to the credibility and completeness of environmental information, recognizing that high-quality disclosure serves as a signal of long-term sustainability and risk management capability. Second, digital finance increases interaction between firms and ESG-oriented stakeholders, including institutional investors and financial intermediaries. The growing demand for standardized and comparable ESG information encourages firms to align disclosure practices with stakeholder expectations. This process reinforces internal commitment to environmental transparency and reduces opportunistic disclosure behavior [29]. Third, to sustain access to digital financial resources, firms strengthen internal environmental governance systems. They formalize environmental policies, integrate sustainability targets into strategic planning, and improve environmental data collection and monitoring mechanisms. Enhanced internal controls and standardized reporting procedures increase the accuracy, consistency, and reliability of environmental information disclosure.

Through these channels, digital finance promotes the internalization of environmental responsibility within corporate governance structures. By strengthening managerial environmental consciousness and embedding sustainability into strategic and operational systems, digital finance enhances firms’ capacity and motivation to provide high-quality environmental information disclosure. Accordingly, this study proposes the following:

H2. 

From an internal corporate perspective, digital finance positively affects the quality of corporate environmental information disclosure by strengthening enterprises’ environmental consciousness.

Digital finance mitigates information asymmetry through digital means, significantly enhancing external supervision effectiveness over corporate governance. On the one hand, digital finance promotes efficient information sharing among financial institutions. It improves market transparency, strengthens supervision, and facilitates collaborative risk identification among institutions. Additionally, regulatory bodies now utilize the abundant data from digital finance for more effective regulation. Real-time monitoring and advanced data analysis tools swiftly identify and address abnormal trading patterns and non-compliant operations, ensuring market fairness and stability.

On the other hand, the informational advantages of digital finance effectively alleviate agency problems, enhancing the motivation for supervising corporate management and curbing managerial moral hazard [30]. Enhanced oversight encourages more transparent managerial behavior. Digital technology enables low-cost mining of operational and non-operational information, allowing financial institutions to effectively detect anomalies and risk points in investments and transactions, preventing improper managerial actions. Increased external attention and supervision raise the standards of corporate environmental information disclosure and increase the costs of concealing poor environmental behavior, reducing the likelihood of “greenwashing” and other deceptive practices.

As global awareness of sustainable development increases, companies face heightened transparency demands and societal pressure to prioritize environmental responsibilities [31]. Enterprises must provide detailed, accurate, and timely environmental information to showcase their efforts and achievements. Additionally, strengthened external supervision also prompts companies to optimize their internal governance structures. Aware of close scrutiny, management will more cautiously evaluate and manage their environmental impact, promoting an effective environmental management system. Internal transformations ensure the authenticity and reliability of environmental information, thereby enhancing disclosure quality. Based on this analysis, this paper proposes the following hypothesis:

H3. 

From an external corporate perspective, digital finance increases external attention and supervision, thereby compelling an improvement in the quality of corporate environmental information disclosure.

3. Research Design

3.1. Model Construction

To investigate the influence of digital finance on corporate environmental information disclosure quality, we designed a two-way fixed effects model for empirical evaluation:

$${Eidq}_{it}={\beta }_0+{\beta }_1{Df}_{ct}+{\beta }_2{X}_{it}+u_j+v_t+{\epsilon }_{it}$$

(1)

where i and t represent the firm and the year, respectively. The dependent variable ${Eidq}_{it}$ denotes the quality of environmental information disclosure for firm i in year t. The core explanatory variable ${Df}_{ct}$ represents the level of digital finance development in the city where the firm is located, measured by the Digital Inclusive Finance Index released by the Digital Finance Research Center of Peking University. $X_{it}$ represents a set of firm-level control variables, including return on assets, leverage ratio, firm size, ownership structure, firm age, CEO–Chair duality, board size, and the proportion of independent directors. $u_j$ and $v_t$ respectively represent industry and time fixed effects, and ${\epsilon }_{it}$ is the random error term. Finally, in the empirical analysis, standard errors are clustered at the firm level.

3.2. Definition of Variables

Dependent Variable: Quality of Corporate Environmental Information Disclosure (Eidq). In line with Yu and Jianxin [32], we employ the environmental category score from the ESG disclosure scores published by Bloomberg as the proxy variable. The ${Eidq}_{it}$ score ranges from 0 to 100, encompassing 9 primary indicators (air quality, climate change, ecology and biodiversity, ecological impact, energy management, environmental supply chain management, greenhouse gas emissions management, sustainable production, waste management, water resource management) and 21 secondary indicators. A higher score denotes superior quality of corporate environmental information disclosure.

Independent Variable: Level of Digital Finance Development (Df). Consistent with existing literature, this study measures the development of digital finance utilizing the Digital Inclusive Finance Index, released by the Digital Finance Research Center of Peking University. Derived from extensive microdata provided by Ant Group, the index encompasses three primary dimensions: Breadth, Depth, and Digitization, along with 33 specific indicators. This index has emerged as a pivotal data source for analyzing digital finance issues and serves as a critical metric for the quantitative assessment of digital finance development in China. Since its introduction in 2016, the Digital Inclusive Finance Index has been extensively employed in both research and practical applications, solidifying its status as a vital instrument for examining digital finance evolution in China.

Control Variables (X): In accordance with established literature [33,34,35], the following variables are selected as control variables: Return on Assets (Roa), defined as net profit divided by total assets; Leverage Ratio (Lev), defined as total liabilities divided by total assets; Firm Size (Size), defined as the natural logarithm of total assets; Ownership Structure (Soe), which is set to 1 for state-owned enterprises and 0 otherwise; Firm Age (Age), defined as the natural logarithm of the number of years since the company’s establishment; CEO-Chair Duality (Dual), set to 1 if the CEO is also the chairperson, otherwise 0; Board Size (Board), defined as the logarithm of the number of board members; Proportion of Independent Directors (Indep), defined as the ratio of independent directors to the total number of board members.

3.3. Sample Selection and Data Sources

We selected publicly listed companies on the Shanghai and Shenzhen A-share markets from 2011 to 2021 as the initial sample for the study, processing the data according to the following criteria: (1) Exclude companies in the financial and real estate industries. (2) Exclude samples designated as ST, PT, and *ST. ST (Special Treatment) refers to listed companies that have experienced abnormal financial conditions (e.g., consecutive losses) and are subject to special regulatory monitoring by the stock exchange. *ST indicates firms that have suffered losses for two consecutive years and face a higher risk of delisting, with the asterisk highlighting the severity of financial distress. PT (Particular Transfer) refers to firms under even more serious financial difficulty, whose shares were previously subject to special transfer arrangements due to delisting risk. (3) Exclude companies with a debt-to-asset ratio greater than 1 or less than 0. (4) Exclude samples with severe data deficiencies. The final sample of 1251 companies with 9921 firm-year observations was obtained. The environmental information disclosure data were sourced from the Bloomberg database, financial data primarily from the CSMAR database, and digital finance index data from the Digital Finance Research Center at Peking University. Additionally, to mitigate the impact of outliers on the empirical results, all continuous variables were winsorized at the 1% and 99% levels.

3.4. Descriptive Statistics Analysis

Table 1. Summary statistics.

Variable	N	Mean	Sd	Max	Min
Eidq	9921	9.1325	12.2991	53.0051	0
Df	9921	226.1227	74.1490	351.5300	56.8600
Breadth	9921	226.1708	73.2494	361.6500	57.1100
Depth	9921	220.4965	76.4509	350.0200	54.9700
Digitization	9921	236.1936	84.8123	336.4700	20.4100
Size	9921	23.1596	1.2943	26.8971	20.4875
Roa	9921	0.0467	0.0594	0.2192	−0.1851
Lev	9921	0.4668	0.1952	0.8758	0.0687
Soe	9921	0.5209	0.4996	1	0
Dual	9921	0.2099	0.4072	1	0
Board	9921	2.1760	0.2073	2.7081	1.6094
Indep	9921	0.3759	0.0558	0.5714	0.3125
Age	9921	0.3768	0.0556	0.5714	0.3333

presents the descriptive statistics of the variables. For the quality of corporate environmental information disclosure (Eidq), scores range from 0 to 53.0051, with a substantial standard deviation highlighting considerable variability among companies. Given that the full score for Eidq is 100, the maximum of 53.0051 underscores a generally low level of disclosure. The average level of digital finance development is 226.1227, ranging from 56.8600 to 351.5300, illustrating significant regional disparities in digital finance development, with some areas exhibiting advanced stages. The mean scores for the breadth, depth, and digitization aspects of digital finance all surpass 220, though the depth score is marginally lower, suggesting incomplete penetration of digital finance within some firms and potential areas for enhancement.

4. Results

4.1. Baseline Regression

This paper empirically tests the hypotheses proposed in the theoretical analysis section using Model (1).

Table 2. Baseline results.

Variable	Eidq
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Df	0.0613 ***				0.0394 ***
	(0.0129)				(0.0113)
Breadth		0.0438 ***				0.0255 ***
		(0.0099)				(0.0087)
Depth			0.0511 ***				0.0365 ***
			(0.0109)				(0.0096)
Digitization				0.0276 ***				0.0208 **
				(0.0097)				(0.0091)
Size					3.5966 ***	3.6207 ***	3.5938 ***	3.6899 ***
					(0.2464)	(0.2477)	(0.2455)	(0.2518)
Roa					6.6962 **	6.7378 **	6.3921 **	7.0714 **
					(2.9644)	(2.9651)	(2.9638)	(2.9599)
Lev					−3.7069 ***	−3.8512 ***	−3.5853 **	−4.1433 ***
					(1.4047)	(1.4101)	(1.4011)	(1.4287)
Soe					−0.1524	−0.1369	−0.1487	−0.0930
					(0.5158)	(0.5164)	(0.5147)	(0.5181)
Dual					0.2937	0.3196	0.2894	0.3407
					(0.4835)	(0.4832)	(0.4823)	(0.4812)
Board					0.8518	0.7801	0.9129	0.5914
					(1.2414)	(1.2430)	(1.2403)	(1.2511)
Indep					4.4118	4.2025	4.5908	3.8242
					(3.9813)	(3.9851)	(3.9863)	(4.0177)
Age					0.1228	0.1093	0.0627	−0.0115
					(0.7047)	(0.7048)	(0.7029)	(0.6971)
Constant	−4.7395 *	−0.7660	−2.1417	2.6122	−85.4895 ***	−82.5890 ***	−84.6489 ***	−82.3384 ***
	(2.8631)	(2.1830)	(2.3434)	(2.2661)	(7.0999)	(6.8443)	(6.9315)	(6.8637)
Controls	YES	YES	YES	YES	YES	YES	YES	YES
Industry	YES	YES	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES	YES	YES
N	9921	9921	9921	9921	9921	9921	9921	9921
F	22.52 ***	19.60 ***	22.07 ***	8.08 ***	30.33 ***	30.12 ***	30.56 ***	29.84 ***
$Adj\_R^2$	0.2400	0.2378	0.2407	0.2318	0.3343	0.3329	0.3355	0.3312

Notes: Standard errors of estimated parameters are shown in parentheses; *, ** and *** denote significant levels of 10%, 5% and 1%, respectively.

reports the baseline regression results. Columns (1) to (4) solely include the core explanatory variable without control variables, whereas columns (5) to (8) incorporate these controls. The results in columns (1) and (5) show that the coefficient for the core explanatory variable (Df) is significantly positive at the 1% level, suggesting that the development of digital finance significantly enhances the quality of corporate environmental information disclosure. This finding implies that digital finance not only serves as a financial innovation tool but also functions as an institutional force that reshapes firms’ disclosure incentives. By reducing information-processing costs and strengthening governance and supervisory pressures, digital finance promotes higher levels of environmental transparency, thereby contributing to more credible corporate sustainability practices and improving the effectiveness of market-based environmental governance.

To further capture the specific impacts of different dimensions of digital finance on disclosure quality, digital finance is divided into breadth of coverage (Breadth), depth of usage (Depth), and degree of digitization (Digitization). The results from columns (2) to (4) and columns (6) to (8) demonstrate that all three dimensions positively influence the disclosure quality, thereby providing preliminary support for Hypothesis H1. Additionally, the regression analysis of other control variables reveals that companies with larger sizes and higher returns on assets typically exhibit higher disclosure quality. Conversely, companies with higher leverage ratios have lower disclosure quality, aligning with findings from existing literature.

4.2. Endogeneity Tests

The quality of corporate environmental information disclosure can also impact the extent to which a firm accesses digital finance. For instance, high-quality disclosures often enhance a company’s credibility and market image, thereby facilitating easier access to digital finance. Consequently, this study may encounter an endogeneity problem due to potential reverse causality, where areas with superior environmental disclosure quality exhibit enhanced digital finance development. To address this issue, all core explanatory variables and their sub-indicators in the baseline regression model are lagged by one period, as recommended by prior research. The results in

Table 3. Endogeneity test: lag one period.

Variable	Eidq
	(1)	(2)	(3)	(4)
L.Df	0.0422 ***
	(0.0125)
L.Breadth		0.0274 ***
		(0.0096)
L.Depth			0.0397 ***
			(0.0105)
L.Digitization				0.0165 *
				(0.0097)
Constant	−93.4715 ***	−90.5271 ***	−92.7750 ***	−89.2525 ***
	(7.7433)	(7.4983)	(7.5740)	(7.4201)
Controls	YES	YES	YES	YES
Industry	YES	YES	YES	YES
Year	YES	YES	YES	YES
N	8584	8584	8584	8584
F	30.97 ***	30.80 ***	31.24 ***	30.52 ***
${Adj\_R}^2$	0.3382	0.3368	0.3395	0.3345

Notes: Standard errors of estimated parameters are shown in parentheses; *and *** denote significant levels of 10% and 1%, respectively.

indicate that digital finance variables—Breadth, Depth, and Digitization—significantly enhance the quality of corporate environmental information disclosure, further corroborating the baseline regression results.

To further address the issue of endogeneity, this study implements the instrumental variable technique. The spherical distance between the city where the company is located and Hangzhou is selected as the instrumental variable. Since the digital inclusive finance data are derived from transaction data by the Ant Group, headquartered in Hangzhou, we follow the research methodology of Fu and Huang [36]. This methodology involves using the spherical distance from the city to Hangzhou as a geographical feature instrumental variable. Calculated based on each city’s latitude and longitude, this distance satisfies the instrumental variable requirements of relevance and exogeneity.

Specifically, the proximity to Hangzhou may reflect a company’s potential access to and reliance on digital financial resources, given that the digital inclusive finance index is constructed from transaction data provided by Ant Group, which is headquartered in Hangzhou. Firms located closer to Hangzhou are more likely to be embedded in the early diffusion network of digital financial services, benefit from stronger financial technology spillovers, and experience lower information and transaction frictions in adopting digital financial tools. Such geographical proximity may therefore be systematically associated with higher levels of digital finance development, satisfying the relevance requirement of the instrumental variable. Conversely, geographical distance is a predetermined natural characteristic that is unlikely to directly influence a firm’s environmental information disclosure quality. Corporate environmental disclosure is primarily shaped by internal governance structures, managerial incentives, regulatory intensity, industry norms, and stakeholder pressures, rather than by mere physical distance from a specific city. After controlling for regional fixed effects and other observable firm-level characteristics, the spherical distance to Hangzhou does not plausibly affect environmental disclosure quality through channels other than digital finance development. Therefore, the instrumental variable satisfies the exogeneity condition.

Assuming the instrumental variable relevance hypothesis is met, the first-stage regression results in

Table 4. Endogeneity test: Instrumental Variables.

Variable	First-Stage Regression
	Df	Breadth	Depth	Digitization
	(1)	(2)	(3)	(4)
IV_distance	−0.0124 ***	−0.0088 ***	−0.0247 ***	−0.0021 ***
	(0.0009)	(0.0011)	(0.0011)	(0.0004)
Controls	YES	YES	YES	YES
Industry	YES	YES	YES	YES
Year	YES	YES	YES	YES
N	9918	9918	8583	8583
F statistic	185.90 ***	61.36 ***	488.31 ***	30.66 ***
Cragg-Donald Wald F statistic	1272.27 [16.38]	396.05 [16.38]	4088.17 [16.38]	49.39 [16.38]
Kleibergen-Paap Wald rk F statistic	185.90 [16.38]	61.36 [16.38]	488.31 [16.38]	30.66 [16.38]
Kleibergen-Paap rk LM statistic	129.17 ***	55.33 ***	212.013 ***	27.31 ***
Variable	Second-stage regression
	Eidq	Eidq	Eidq	Eidq
	(5)	(6)	(7)	(8)
Df	0.0826 ***
	(0.0313)
Breadth		0.1160 ***
		(0.0451)
Depth			0.0415 ***
			(0.0156)
Digitization				0.4986 **
				(0.2074)
Controls	YES	YES	YES	YES
Industry	YES	YES	YES	YES
Year	YES	YES	YES	YES
N	9918	9918	9918	9918
F	30.57 ***	29.15 ***	30.87 ***	27.36 ***
$R^2$	0.1295	0.0949	0.1368	0.3205

Notes: Standard errors of estimated parameters are shown in parentheses; ** and *** denote significant levels of 5% and 1%, respectively.

show that the coefficients of the instrumental variable IV_distance are significantly negative at the 1% level. This indicates a negative correlation between the spherical distance from the city of the company’s location to Hangzhou and the development of digital finance, along with its sub-indices. Building on these findings, further tests include the calculation of two key statistics: the Cragg–Donald Wald F statistic and the Kleibergen–Paap Wald rk F statistic. Both statistics significantly exceed the Stock-Yogo critical value of 16.38 at the 10% bias level, affirming the strength of the instrumental variable and ruling out the presence of a weak instrument. Additionally, the Kleibergen–Paap rk LM statistic rejects the hypothesis of insufficient identification at the 1% level, further verifying its validity.

The second-stage regression results, post-addressing potential endogeneity issues, reveal significantly positive estimated coefficients for the levels of digital finance development, breadth of coverage, depth of usage, and degree of digitization. These findings confirm that, even after accounting for endogeneity, the various dimensions of digital finance substantially enhance the quality of corporate environmental information disclosure, thus supporting the research hypothesis.

4.3. Robustness Tests

4.3.1. Replacing the Independent Variable

In the baseline regression, we selected the digital finance development level at the prefecture-city level as the independent variable to measure its impact on the quality of environmental information disclosure. Considering potential differences in digital finance development between prefecture-city and provincial levels, and the more comprehensive reflection of regional development status by provincial-level data, this paper adjusts the level of the explanatory variable from prefecture-city to provincial. Following this adjustment, we reanalyzed the model through regression. According to the regression results shown in column (1) of

Table 5. Robustness tests.

Variable	Replacing the Explanatory Variable	Replacing the Dependent Variable	Changing Fixed Effects		Changing Clustering Approaches		Changing Regression Samples	Excluding Municipalities Directly Under the Central Government
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Df	0.0256 ***	0.0281 **	0.0449 **	0.0471 **	0.0449 ***	0.0449 **	0.0548 ***	0.0252 **
	(0.0084)	(0.0116)	(0.0209)	(0.0209)	(0.0249)	(0.0205)	(0.0128)	(0.0125)
Constant	−83.4742 ***	18.5893 ***	18.5893 ***	−76.0275 ***	−85.4895 ***	−85.4895	−83.2766 ***	−73.6275 ***
	(6.9411)	(6.0356)	(6.0356)	(12.8599)	(8.5339)	(10.5201)	(7.9363)	(7.8626)
Controls	YES	YES	YES	YES	YES	YES	YES	YES
Firm	NO	NO	YES	YES	YES	YES	NO	NO
Industry	YES	YES	NO	YES	NO	NO	YES	YES
Year	YES	YES	YES	YES	YES	YES	YES	YES
N	9921	9921	9921	9921	9921	9921	7832	7427
F	30.34 ***	13.89 ***	10.24 ***	10.53 ***	7.65 ***	9.64 ***	26.30 ***	21.18 ***
${Adj\_R}^2$	0.3338	0.1503	0.6590	0.6612	0.6590	0.6590	0.3210	0.3265

Notes: Standard errors of estimated parameters are shown in parentheses; ** and *** denote significant levels of 5% and 1%, respectively.

, even at the provincial level, digital finance significantly enhances the quality of corporate environmental information disclosure at the 1% level. This finding further validates the significant impact of digital finance on improving environmental information disclosure quality, thus strengthening the robustness of our research conclusions.

4.3.2. Replacing the Dependent Variable

To enhance robustness, the environmental score from the Huazheng ESG database is introduced as an alternative dependent variable. Measured on a 100-point scale, the Huazheng ESG database scores capture specific differences in corporate environmental information disclosure and performance. These scores exist in continuous numerical form, avoiding the potential concealment of subtle performance differences between companies, a common issue with discrete ratings. Furthermore, the scoring design comprehensively considers multi-dimensional evaluations of environmental performance, more fully displaying the actual contributions and performances of companies in environmental protection and sustainable development. Therefore, using the Huazheng ESG environmental score as an alternative indicator overcomes potential biases from single data sources and discrete ratings, providing detailed, multidimensional research support. Results in column (2) of Table 5 indicate that digital finance significantly promotes environmental information disclosure quality at the 5% level, reinforcing the robustness of the baseline regression results.

4.3.3. Changing Fixed Effects and Clustering Approaches

To further validate the reliability of the baseline regression results, multidimensional robustness checks are conducted by altering the fixed effects and clustering methods. In the fixed effects setting, the baseline regression model controls for both individual and time fixed effects. This setup effectively eliminates potential interferences from individual heterogeneity and time trends, ensuring the results primarily reflect the direct impact of the independent variables on the dependent variables. Recognizing that industry characteristics might also influence corporate behavior, this paper additionally incorporates industry fixed effects. Specific methods involve controlling fixed effects for industry and time separately, as well as controlling triple fixed effects for individual, industry, and time simultaneously. Concerning the clustering method, the baseline regression model clusters standard errors at the individual level to account for heterogeneity and potential autocorrelation among firms. Considering the varying impacts of clustering approaches on standard error estimation and model robustness, this paper explores clustering at industry and regional levels in its robustness checks. Whether adjusting fixed effects settings or changing clustering methods, the regression results show good robustness.

4.3.4. Changing Regression Samples

In the baseline regression results, companies that did not disclose environmental information were assigned a value of zero, potentially underestimating their disclosure level and leading to biased estimation results. To ensure research robustness, the robustness checks excluded these non-disclosing firms from the sample and conducted a reanalysis with the remaining samples. Results in column (7) of Table 5 show that, even after excluding these samples, the positive impact of digital finance on information disclosure remains significant, confirming the robustness and reliability of the baseline regression results.

4.3.5. Excluding Municipalities Directly Under the Central Government

Municipalities directly under the central government, with more developed economies and significant advantages in policy systems, financial support, and innovation capabilities, may perform differently in environmental information disclosure compared to other cities. Including digital finance development from all prefecture-level cities and above in the regression analysis could introduce estimation biases, affecting the accuracy. To address this, this paper excludes samples from municipalities directly under the central government during robustness checks, retaining only data from other cities. According to column (8) of Table 5, although excluding these municipalities reduces the significance level of digital finance development’s (Df) positive impact on environmental information disclosure quality, the effect remains significant at the 5% level, confirming the robustness of the adjusted sample analysis.

5. Mechanism and Heterogeneity Analysis

5.1. Mechanism Tests

In the theoretical section, we propose that digital finance can enhance information disclosure by boosting the company’s own environmental awareness and strengthening external oversight. Building on this, the study employs a stepwise regression method based on the research approach of Wen et al. [37] to test for mediating effects. An extended model is constructed to examine the following transmission mechanisms.

$${Mech}_{it}={\beta }_0+{\beta }_1{Df}_{ct}+{\beta }_2{X}_{it}+u_j+v_t+{\epsilon }_{it}$$

(2)

$${Eidq}_{it}={\beta }_0+{\beta }_1{Df}_{ct}+{\beta }_2{Mech}_{it}+{\beta }_3{X}_{it}+u_j+v_t+{\epsilon }_{it}$$

(3)

where Mech represents the mediating variables, including corporate environmental awareness and external oversight. This paper utilizes the CNRDS database’s Environment, Social, and Governance (ESG) datasets, as well as corporate annual reports, social responsibility reports, and sustainability reports from the CSRC Information Network. Indicators related to corporate-level environmental awareness were constructed following the approach of Wang et al. [35]. Additionally, the external oversight mechanism was developed based on the number of securities analysts and research reports focusing on the same publicly listed company.

5.1.1. Internal Mechanism: Corporate Environmental Awareness

Table 6. Mechanism Test: Corporate Environmental Awareness.

Variable	(1)	(2)	(3)	(4)
	EnerSaving	Eidq	GreWorking	Eidq
Df	0.0012 **	0.0326 ***	0.0011 ***	0.0335 ***
	(0.0005)	(0.0105)	(0.0004)	(0.0109)
EnerSaving		5.8667 ***
		(0.3970)
GreWorking				5.2245 ***
				(0.4643)
Constant	−2.9098 ***	−68.4186 ***	−1.1403 ***	−79.5319 ***
	(0.2529)	(6.5531)	(0.2498)	(6.9615)
Controls	YES	YES	YES	YES
Industry	YES	YES	YES	YES
Year	YES	YES	YES	YES
N	9921	9921	9921	9921
F	35.99 ***	45.49 ***	5.02 ***	39.38 ***
${Adj\_R}^2$	0.1592	0.3788	0.0946	0.3618
Sobel	Z = 4.849 *** P = 0.000		Z = 5.234 *** P = 0.000

Notes: Standard errors of estimated parameters are shown in parentheses; ** and *** denote significant levels of 5% and 1%, respectively.

shows the test results for the internal mechanism, where the mediating variable Mech in Model (2) represents corporate environmental awareness. The presence of energy-saving policies, relevant technology equipment purchases, or the introduction of advanced technologies is assessed; the variable EnerSaving is assigned a value of 1 if implemented or purchased, and 0 otherwise. The existence of green office policies or measures is also considered; the variable GreWorking is assigned a value of 1 if present, and 0 if not.

Regression results in columns (1) and (3) indicate that the coefficients (Df) for digital finance are significantly positive, indicating that digital finance promotes energy-saving and green office practices. Columns (2) and (4) display the joint impact of the core explanatory variable and mediating variables on environmental information disclosure in Model (3). Here, the significantly positive coefficient β2 suggests that enhanced corporate environmental awareness improves environmental information disclosure quality. These results demonstrate that digital finance effectively boosts corporate environmental consciousness, encouraging practical environmental actions and building a green external reputation, thereby enhancing environmental information disclosure quality. Thus, the internal transmission pathway is validated.

5.1.2. External Mechanism: External Attention and Supervision

Table 7. Mechanism Test: External Oversight.

Variable	(1)	(2)	(3)	(4)
	Analyst	Eidq	Report	Eidq
Df	0.0020 *	0.0378 ***	0.0024 *	0.0378 ***
	(0.0010)	(0.0113)	(0.0013)	(0.0113)
Analyst		0.7725 ***
		(0.1948)
Report				0.6727 ***
				(0.1546)
Constant	−7.4208 ***	−79.7567 ***	−9.3269 ***	−79.2152 ***
	(0.5199)	(7.2046)	(0.6460)	(7.1504)
Controls	YES	YES	YES	YES
Industry	YES	YES	YES	YES
Year	YES	YES	YES	YES
N	9921	9921	9921	9921
F	215.75 ***	28.63 ***	214.00 ***	28.84 ***
$Adj\_R^2$	0.4315	0.3374	0.4310	0.3380
Sobel	Z = 3.544 *** P = 0.000		Z = 3.565 *** P = 0.000

Notes: Standard errors of estimated parameters are shown in parentheses; * and *** denote significant levels of 10% and 1%, respectively.

presents the test results for the external mechanism where the mediating variable (Mech) in Model (2) denotes corporate external supervision. External supervision intensity is measured by the logarithm of one plus the number of securities analysts and research reports focused on the same publicly listed company. Results indicate significantly positive coefficients of Df in columns (1) and (3) are significantly positive, showing that digital finance increases external attention. Results in columns (2) and (4) further suggest that enhanced external supervision improves the quality of corporate environmental information disclosure.

This effect primarily stems from digital platforms and big data technologies enhancing information circulation and transparency. In practice, many listed firms have actively adopted big data technologies in their operations. For example, Haier Smart Home has developed the COSMOPlat industrial internet platform to integrate production and user data, while Ping An Insurance applies big data analytics in risk management and customer profiling. These real-world cases indicate that big data technologies have been widely implemented among Chinese listed companies. First, digital finance grants investors and analysts easy access to real-time corporate data analysis. The widespread dissemination encourages companies to focus more on disclosing accurate and timely environmental information. Through digital channels, detailed analysis and reporting by analysts alert corporate management to the importance of public environmental disclosure, avoiding market misconceptions or negative evaluations. Second, digital finance enables securities analysts to efficiently collect, integrate, and disseminate corporate environmental performance data. As analyst involvement and research reports increase, external pressure on companies rises, which enhances disclosure quality, particularly of environmental information, to maintain a positive market image. Moreover, environmental information disclosure enhances company competitiveness in the capital market and earns investor trust. Therefore, by increasing securities analyst engagement and focusing on corporate environmental information, digital finance promotes more standardized and transparent disclosures, improving overall disclosure quality.

5.2. Heterogeneity Analysis

5.2.1. Digital Infrastructure Construction Level

According to the 2013 “State Council Notice on Issuing the ‘Broadband China’ Strategy and Implementation Plan”, China designated three batches of pilot cities for the “Broadband China” initiative. To explore how different levels of digital infrastructure construction affect digital finance’s impact on environmental information disclosure, the sample was divided into “Broadband China” pilot and non-pilot cities, emphasizing the variations in digital infrastructure. Results in

Table 8. Heterogeneity Analysis.

Variable	“Broadband China” Pilot Cities	“Non-Broadband China” Pilot Cities	Heavy Pollution Industries	Non-Heavy Pollution Industries	State-Owned Enterprises	Non-State-Owned Enterprises
	(1)	(2)	(3)	(4)	(5)	(6)
Df	0.0455 ***	0.0259	0.0230	0.0498 ***	0.0572 ***	0.0277 *
	(0.0146)	(0.0219)	(0.0224)	(0.0126)	(0.0166)	(0.0160)
Constant	−90.5371 ***	−72.7325 ***	−72.1521 ***	−91.9919 ***	−88.8116 ***	−88.7539 ***
	(8.3601)	(13.8617)	(13.6675)	(8.1509)	(9.4550)	(11.2939)
Controls	YES	YES	YES	YES	YES	YES
Industry	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES
N	7480	2441	2628	7293	5168	4753
F	23.30 ***	6.96 ***	7.47 ***	24.68 ***	19.92 ***	14.79 ***
$Adj\_R^2$	0.3323	0.3825	0.3527	0.3246	0.3550	0.3369

Notes: Standard errors of estimated parameters are shown in parentheses; * and *** denote significant levels of 10% and 1%, respectively.

, columns (1) and (2), show that in “Broadband China” pilot cities, digital finance plays a more significant role in enhancing the quality of corporate environmental information disclosure.

The reasons for these differences are as follows. First, well-developed digital infrastructure provides solid technical support for digital finance growth. In cities with advanced digital infrastructure construction, fast information transmission, and strong data processing capabilities help financial institutions can accurately assess and manage corporate information, enhancing capital allocation efficiency. Additionally, advanced digitalization also reduces the transaction costs of green financial products, enabling more SMEs and individual entrepreneurs to engage in green development. Second, regions with well-developed digital infrastructure typically attract more fintech talent and professional resources. The aggregation of these resources and talents aids enterprises in better understanding and complying with information disclosure norms. Financial institutions use these resources to develop and implement more complex and precise environmental benefit assessment models, enhancing the professionalism and accuracy of information disclosure. Lastly, in areas with developed digital infrastructure, governments and regulatory bodies often provide more policy support and incentives, encouraging active environmental information disclosure by enterprises. These areas may also have more mature and comprehensive regulatory frameworks, which help drive enterprises to enhance the standards and quality of environmental information disclosure.

5.2.2. Inherent Attributes of Corporate Pollution Emissions

Due to varying regulatory scrutiny, numerous laws specifically target heavy pollution industries for environmental information disclosure. Notable examples include the “Guidelines for Environmental Information Disclosure of Listed Companies (Draft for Comments)” introduced in 2010 and the “Environmental Protection Law of the People’s Republic of China” implemented in 2015. Research by Kong et al. [38] and Zhu et al. [39] shows that enterprises in heavily polluted industries are key monitoring targets for national environmental departments, often required to maintain high environmental disclosure standards and transparency. This suggests that the enhancing effect of digital finance may be less pronounced in these companies.

Conversely, companies in less polluted industries often use competitive strategies to enhance their environmental disclosures and reputations. Thus, digital finance significantly helps these companies enhance environmental performance and disclosure quality. According to the classification by Pan et al. [40], companies in the thermal power, steel, and coal industries are classified as heavy pollution enterprises, while others are considered non-heavy pollution enterprises. Results shown in columns (3) and (4) of Table 8 indicate that digital finance more significantly improves environmental disclosure quality in non-heavy pollution enterprises, aligning with expectations.

5.2.3. Ownership Nature

State-owned enterprises (SOEs) play a crucial role in China’s national economy, especially in heavily polluted industries. Compared to non-state-owned enterprises, SOEs bear more economic and social responsibilities. Expectations for SOEs’ environmental management and protection are higher from both government and societal perspectives [41,42]. However, the motivations for disclosing social responsibilities differ between state-owned and non-state-owned enterprises. SOEs are primarily driven by government regulations and monitoring pressure, whereas non-state-owned enterprises are more influenced by their own interests and governance levels [43,44].

This study categorizes companies as state-owned or non-state-owned to explore how digital finance influences their environmental information disclosure. Regression results in columns (5) and (6) of Table 8 indicate significant heterogeneity between state-owned and non-state-owned enterprises. Digital finance has comprehensively enhanced environmental information disclosure quality in SOEs. However, although the impact on non-state-owned enterprises is also positive, this effect is not significant.

6. Extended Analysis of Economic Consequences

6.1. Economic Consequences for Enterprises

Enhancing the quality of environmental information disclosure strengthens enterprises’ ability to attract capital and secure favorable financing conditions, which in turn spurs investments in research and promotes green innovation, advancing sustainable development comprehensively. Firstly, improved transparency reduces the search costs for investors and enhances the ability of enterprises to attract capital. This ease of identification provides a stable funding source for enterprises, as investors can readily select companies that align with their sustainability criteria.

Secondly, transparent environmental information helps alleviate financing constraints for enterprises. Increasing trust from financial institutions secures better loan conditions, lowering financing costs and bolstering financial support for development. Transparent environmental information also highlights an enterprise’s innovation in green technologies, attracting investment and potentially securing government R&D subsidies or tax incentives. These resources facilitate rapid technological innovation and product upgrades, enhancing market competitiveness.

Finally, high-quality information disclosure effectively promotes green innovation. Enterprises that actively disclose environmental information are more likely to obtain bank loans at lower interest rates, thereby helping SMEs to reduce costs and increase efficiency in green R&D and innovation activities under higher uncertainty. Therefore, this paper investigates the economic consequences of enhancements in environmental information disclosure driven by digital finance across four dimensions: entry of green investors, financing constraint alleviation, R&D investment, and green innovation, constructing the following model:

$${Ecoconsequences}_{i,t}={\beta }_0+{\beta }_1\gamma {Eidq}_{it}+{\beta }_2{X}_{it}+u_j+v_t+{\epsilon }_{it}$$

(4)

where Ecoconsequences is the metric for measuring economic consequences, comprising the entry of green investors (Greeninvestor), financing constraints (SA), enterprise research and development investment (R&D), and the level of green innovation (Greenp). Specifically, these are assessed by the presence of green investors, the SA index, R&D expenditure, and the number of green patent filings by the enterprise. The explanatory variable $\gamma Eidq$ (derived from the coefficients in the baseline regression) represents the level of improvement in environmental information disclosure quality induced by digital finance.

The results in

Table 9. Economic Consequences Analysis for Enterprises.

Variable	(1)	(2)	(3)	(4)
	Greeninvestor	SA	R&D	Greenp
$\gamma Eidq$	0.0310 **	−0.0525 ***	0.5049 ***	0.2953 ***
	(0.0149)	(0.0084)	(0.1676)	(0.0550)
Constant	−2.3054 ***	4.1150 ***	−10.2018 ***	−5.5294 ***
	(0.1914)	(0.1476)	(2.8996)	(0.7547)
Controls	YES	YES	YES	YES
Industry	YES	YES	YES	YES
Year	YES	YES	YES	YES
N	9921	9921	9921	9921
F	121.67 ***	152.68 ***	22.10 ***	15.68 ***
$Adj\_R^2$	0.2390	0.7227	0.6468	0.3064

Notes: Standard errors of estimated parameters are shown in parentheses; ** and *** denote significant levels of 5% and 1%, respectively.

show that the coefficients of the core explanatory variable $\gamma Eidq$ are significantly positive in columns (1), (3), and (4), indicating that the improvement in disclosure quality induced by digital finance helps attract green investors, boost R&D investment, and promote green innovation. In column (2), the coefficient of the core explanatory variable $\gamma Eidq$ is significantly negative, suggesting that digital finance’s enhancement of disclosure quality alleviates financing constraints. Therefore, improvements in environmental information disclosure driven by digital finance yield multiple positive economic outcomes for enterprises, supporting their green transformation and sustainable development.

6.2. Environmental Consequences for Enterprises

The enhancement of corporate environmental information disclosure has optimized information transparency and authenticity, effectively aligning with external resources and motivating internal shifts towards green transformation and innovation. Driven by digital finance, improvements in disclosure quality strengthen internal and external supervision, motivating enterprises to optimize their environmental management practices. On the one hand, high-quality information disclosure increases transparency in environmental compliance, placing greater social and market pressure on enterprises. To address this pressure, companies often escalate their investment in environmental management to ensure the credibility and competitiveness of their disclosures.

On the other hand, improved disclosure quality also encourages enterprises to strengthen internal green operational management. By publicly reporting key data on resource consumption, carbon emissions, and pollution control, companies raise environmental accountability among management and provide a quantitative basis for formulating green strategies. For instance, enterprises may optimize their energy structure, reduce waste emissions, or implement green office policies through these measures.

These actions directly enhance environmental performance and lay the foundation for a green culture by boosting employee awareness and capabilities. Therefore, to investigate whether improvements in environmental information disclosure quality, driven by digital finance, can enhance corporate environmental advantages, the following model is constructed:

$${Envconsequences}_{i,t}={\beta }_0+{\beta }_1\gamma {Eidq}_{it}+{\beta }_2{X}_{it}+u_j+v_t+{\epsilon }_{it}$$

(5)

where Envconsequences is the metric for measuring corporate environmental advantages, including whether the company offers environmentally beneficial products (Greenproduct), uses renewable energy (Renergy), practices green office policies (Greenoffice), and has received environmental accolades (Encom).

It is evident from

Table 10. Environmental Consequences Analysis for Enterprises.

Variable	(1)	(2)	(4)	(5)
	Greenproduct	Renergy	Greenoffice	Encom
$\gamma Eidq$	0.1893 ***	0.2165 ***	0.2041 ***	0.1108 ***
	(0.0174)	(0.0161)	(0.0176)	(0.0140)
Constant	−1.7778 ***	−1.6357 ***	−0.2923	−1.0981 ***
	(0.2195)	(0.1931)	(0.2265)	(0.1668)
Controls	YES	YES	YES	YES
Industry	YES	YES	YES	YES
Year	YES	YES	YES	YES
N	9921	9921	9921	9921
F	43.30 ***	52.98 ***	20.53 ***	22.99 ***
${Adj\_R}^2$	0.1714	0.1907	0.1307	0.1112

Notes: Standard errors of estimated parameters are shown in parentheses; *** denote significant levels of 1%.

that the coefficients of the core explanatory variable $\gamma Eidq$ are significantly positive, demonstrating that improvements in information disclosure quality driven by digital finance led to significant environmental spillover effects, reflecting effective environmental governance.

7. Conclusions and Policy Recommendations

7.1. Conclusions

As the global economy evolves rapidly, it faces significant environmental challenges hindering sustainable development. In this context, environmental information disclosure serves as a key tool for environmental governance, crucial for enhancing transparency and public participation. Concurrently, the emergence of digital finance offers new opportunities for environmental information disclosure. Based on data from 2011–2021 from publicly listed companies and the digital inclusive finance index compiled by Peking University, this paper explores the impact of digital finance on environmental information disclosure, both theoretically and empirically, arriving at the following conclusions.

First, digital finance significantly enhances the quality of corporate environmental disclosures. To further capture the effects of different dimensions of digital finance, this study divides digital finance into breadth of coverage, depth of usage, and degree of digitization, finding that all three dimensions positively impact environmental information disclosure. Second, mechanism tests reveal that digital finance boosts environmental information disclosure primarily by enhancing corporate environmental awareness and strengthening external supervision. Third, heterogeneity tests show that compared to non-state-owned enterprises and heavily polluted industries, digital finance has a more significant positive impact on the environmental information disclosure of state-owned and non-heavily polluted ones. Furthermore, in cities with advanced digital infrastructure construction, the effects of digital finance on improving local enterprises’ environmental information disclosure quality are also more pronounced. Fourth, an extended analysis of economic consequences indicates that the improvements in environmental information disclosure quality driven by digital finance yield multiple positive economic outcomes for enterprises, along with significant environmental spillover effects.

7.2. Policy Recommendations

First, expand the breadth and depth of digital finance coverage and usage. The government and relevant departments should enhance the promotion and application of digital financial tools, especially among small and medium-sized enterprises (SMEs) and non-state-owned enterprises. Through policy incentives and financial support, enterprises should be encouraged to adopt digital financial services, thereby improving their environmental transparency and sense of environmental responsibility.

Second, optimize digital infrastructure with a focus on environmentally sensitive areas. Given that digital finance has a more significant impact on improving environmental information disclosure in regions with advanced digital infrastructure, the government should prioritize the construction and improvement of digital infrastructure in heavily polluted and environmentally sensitive areas. This will not only enhance the quality of environmental information disclosure by local enterprises but also strengthen the supervision and control of environmental risks in these regions.

Third, strengthen external supervision and foster corporate environmental awareness. Research shows that digital finance can improve the quality of environmental information disclosure by enhancing corporate environmental awareness and external supervision mechanisms. Therefore, regulatory agencies should leverage digital financial tools to conduct more effective environmental supervision while encouraging enterprises to enhance their internal environmental awareness through training and education. The government can implement relevant incentive measures, such as tax reductions and financial subsidies, to encourage enterprises to proactively improve their environmental information disclosure practices.