Unlocking Corporate Sustainability: The Transformative Role of Digital–Green Fusion in Driving Sustainable Development Performance

Abstract

Amidst the rapid evolution of digital technologies and the strategic imperative of achieving dual-carbon objectives, this paper empirically investigates how digital–green fusion (DGF) enhances corporate sustainable development performance (SDP), fostering a “harmonious symbiosis” between economic growth and environmental protection. Utilizing data from China’s A-share listed companies over the period 2010–2022, the analysis reveals that DGF significantly boosts SDP, with results remaining robust through a series of endogeneity and robustness tests. Mechanism analysis further demonstrates that digital-green integration not only drives green technology innovation and enhances information transparency but also optimizes labor resource allocation efficiency, collectively contributing to improved corporate sustainability performance. Additionally, heterogeneity analysis indicates that the positive impact of DGF on SDP is particularly pronounced in large enterprises, state-owned enterprises, and firms operating in industries with low environmental uncertainty. This offers a strategic blueprint for harnessing digital–green fusion to achieve long-term synergies between environmental sustainability and economic growth.

1. Introduction

The 2030 Agenda for Sustainable Development, unveiled at the 70th session of the UN General Assembly, calls for nations to embrace sustainable production practices and promote inclusive economic growth. In alignment with this global agenda, China’s 20th National Congress reaffirmed the importance of advancing green development and fostering a harmonious coexistence between humanity and nature, positioning these goals as central to building a modern socialist country. Focusing on microeconomic practices at the corporate level is imperative to achieve sustainable growth at the national macroeconomic level. As the world’s largest economy among emerging markets and a key driver of global economic recovery, China is pivotal in advancing global sustainable development objectives [1].

Additionally, the first meeting of the 20th Central Financial and Economic Affairs Commission of China emphasized the need for the efficient aggregation of innovative elements, the advancement of industrial intelligence, greening, and integration, as well as the creation of a modern industrial system characterized by integrity and advanced security. Consequently, the concept of “digital–green fusion” has emerged as a prominent research focus, garnering increasing attention from both academia and industry [2]. DGF refers to the simultaneous implementation of digitalization and greening as dual strategic transformations by enterprises. This process integrates and optimizes digital resources and green production factors to fundamentally transform operational models and processes. The synergy generated fosters a strategic relationship marked by mutual reinforcement, collaboration, and dynamic interaction [3]. Specifically, digital transformation exerts an amplifying, compounding, and synergistic effect on the greening of enterprises. By providing low-carbon solutions, fostering green innovation, and optimizing operational workflows, digital transformation injects substantial momentum into the process of green transformation [4]. Indeed, not all digital technologies effectively contribute to energy savings or carbon reduction. In some cases, digital solutions may inadvertently increase energy consumption, highlighting the importance of aligning digital transformation with sustainability goals [5,6]. Meanwhile, green transformation can reciprocally enhance digitalization by promoting the application of energy-saving and low-carbon technologies within digital infrastructure. Moreover, it helps address potential “rebound effects” associated with digital technologies, thereby steering digital transformation efforts toward more sustainable outcomes [7]. In China’s socio-economic development, DGF is not a linear or additive combination of these two strategies. Rather, it embodies a dynamic and symbiotic interaction, wherein each dimension enhances the other. For example, in advanced manufacturing sectors, digital tools significantly improve green production efficiency, while green development agendas simultaneously shape the direction and principles guiding digital technology innovation [8]. As the economy shifts from rapid growth to high-quality development, the importance of harmonizing digitalization with green initiatives has become increasingly evident [9]. This dual coordination represents a new driving force for corporate and macroeconomic sustainability, advancing China’s high-quality economic growth [2,8,10].

Scholars have extensively explored digitalization and greening from various perspectives. Studies on digitalization have explored its determinants and economic outcomes, identifying internal factors such as internationalization and management structure [11,12], as well as external factors like government support and market allocation [13]. Moreover, digital transformation has been linked to improvements in financial performance, environmental outcomes, and ESG and CSR indicators [14,15,16,17]. While substantial work has examined the effects of digitalization on green practices, particularly in terms of enhancing green technological innovation [18], improving green innovation efficiency [19,20], and boosting overall green total factor productivity [4], the literature remains limited regarding the synergies between digitalization and greening. Recent work has started to recognize that there is a mutual integration rather than a unidirectional interaction between digitization and greening [8,21]. However, research on how the convergence of digitalization and greening influences corporate SDP is still scarce, with only a few studies addressing its impact on the high-quality economic development of enterprises [21]. A balanced development of environmental performance (EP) and financial performance (FP) is essential for accurately assessing whether an enterprise can meet the requirements of sustainable development [22,23]. This dual-dimensional evaluation not only reflects a company’s ability to generate profits, ensuring its long-term viability in the market, but also demonstrates the level of its production technology, which helps reduce its environmental impact. This comprehensive evaluation provides deeper insights into the alignment between a company’s operations and sustainable development strategy requirements. Accordingly, this research aims to address the following critical questions: Can DGF foster a balanced coexistence of economic growth and environmental conservation under the “dual-carbon” framework? What mechanisms underpin this relationship? These questions necessitate further in-depth investigation and discussion.

In response, this paper systematically explores the relationship between DGF and SDP from the perspective of strategic synergy theory, filling an important theoretical and empirical gap in the existing literature. First, this paper conceptualizes DGF as a strategic alignment behavior at the corporate level, integrating perspectives from dynamic capabilities theory (DCT) and the Resource-Based View (RBV). This approach reinterprets the synergistic function of digital transformation and green strategies in fostering corporate sustainable development. DGF is framed as a dynamic capability for resource integration and capability adaptation, emphasizing how companies can secure a sustainable competitive edge by aligning digital and green transformations in response to external environmental changes and internal resource reconfiguration. This perspective not only expands traditional theories that have separately studied digitalization and greening but also quantifies the synergy at the corporate level through a coupling coordination model. Second, this research offers a unified analytical framework that links DGF with SDP, advancing the theoretical understanding of how digital innovation and environmental strategies coalesce to drive corporate sustainability. The framework clarifies how digitalization, by integrating with green strategies, creates synergistic advantages that exceed simple additive effects, providing theoretical support for companies to achieve sustainable development in a complex and dynamic environment. Finally, the study identifies three key mechanisms through which DGF enhances SDP: innovation incentive, information transparency, and resource allocation efficiency (Figure 1). The quantitative analysis of these mechanisms not only empirically reveals the multi-dimensional pathways through which DGF enhances corporate SDP but also fills the gap in the existing literature by addressing the “black box” between DGF and its actual outcomes. By translating these mechanisms into actionable indicators, this paper provides robust theoretical insights and practical guidance for companies to strategically leverage DGF in achieving both competitive and ecological advantages.

This paper is structured as follows. Section 2 provides a literature review and develops the research hypotheses. Section 3 outlines the experimental design. Section 4 reports the empirical findings. Section 5 analyzes the transmission mechanisms, and the final section summarizes the findings and offers both theoretical and practical implications.

2. Literature Review and Research Hypotheses

2.1. Theoretical Background

In the context of globalization and the green economy, DGF has emerged as a vital strategy for enhancing firms’ SDP [21]. Firms’ sustainability is increasingly reliant not only on the adoption of green technologies but also on digital transformation to enhance innovation capabilities and operational efficiency [18,19].

Existing research shows that digital transformation, through cutting-edge technologies, provides businesses with unprecedented capabilities for management and decision-making [24]. In aspects like resource distribution, cost reduction, energy optimization, and production optimization, digital technologies can substantially improve efficiency and reduce energy consumption, thereby achieving green production goals. Particularly, data analytics and intelligent decision-making enable businesses to more effectively develop green products and services while simultaneously minimizing carbon emissions and environmental impact [25,26]. The core of green transformation lies in reducing energy consumption, waste emissions, and ecological damage while promoting both environmental sustainability and efficient resource utilization. Research has shown that green transformation enhances a firm’s environmental performance while concurrently delivering long-term economic benefits [27]. The adoption of green technologies, such as renewable energy and eco-friendly materials, boosts energy efficiency, lowers pollution during production, and contributes to product quality improvements and innovation [28]. Moreover, technologies like renewable energy and eco-friendly manufacturing processes help firms comply with increasingly stringent environmental regulations, reduce compliance costs, and enhance brand image [29]. By obtaining environmental certifications and green labels, firms can also improve their market competitiveness. Green transformation simultaneously strengthens corporate social responsibility, reducing negative environmental impacts and contributing to societal sustainability.

These two pathways provide firms with distinct means of achieving economic and environmental benefits. However, relying on one alone is insufficient to address the complex external challenges faced by firms. Research that focuses solely on the isolated effects of digital or green transformation overlooks the synergistic advantages of their integration [8,21]. The existing literature mainly examines individual dimensions, and there is limited systematic exploration of DGF as a composite strategy. Especially at the micro level, how DGF drives the enhancement of SDP through multidimensional mechanisms, such as resource allocation, technological synergy, and governance optimization, remains underexplored and needs further validation.

From a theoretical standpoint, DGF can be viewed as a composite dynamic capability resulting from the synergy between digitalization and green transformation, requiring insights from multiple theoretical perspectives. This study integrates the RBV, DCT, and Synergy Theory to construct an analytical framework that offers a novel perspective on DGF research. The RBV focuses on how firms create competitive advantages by leveraging unique resources, such as knowledge, technology, and human capital [30,31]. Through the integration of digital and green technologies, digital and green transformations help firms build distinct and hard-to-imitate resource advantages. For instance, data-driven applications in digital transformation and sustainable energy technologies in green transformation not only promote efficient use of internal resources but also enhance firms’ responsiveness to external market demands. However, the RBV alone cannot explain how firms dynamically adjust these resources in response to rapidly changing external environments. DCT complements the RBV by emphasizing how firms dynamically reconfigure resources and capabilities to adapt to external changes and challenges [32,33]. Specifically, DCT highlights that through the flexible integration of digital and green technologies, DGF enables firms to adjust their operational models and innovation strategies dynamically. This transformation capacity helps firms optimize resource utilization while pursuing green development objectives. Consequently, DGF transcends the limitations of traditional, single-technology innovations or green transformations. It is not merely about relying on green technologies to mitigate environmental impacts or technological innovations to enhance productivity; rather, it drives improvements across innovation, operational efficiency, and environmental performance through the dynamic fusion of digital and green technologies.

Synergy Theory further provides a practical framework to explain the value-creation mechanisms of digital–green integration. This theory emphasizes that firms can respond to internal and external environmental changes by combining and coordinating various strategies [34]. It underscores the mutual reinforcement between different strategies, with the integration of digital and green transformations embodying this synergy. Digital transformation provides the decision-making support and technological means necessary for green transformation, while green transformation drives the deeper application of digital technologies through the demand for green products and sustainable goals. This synergistic effect fosters a virtuous interaction between transformation objectives and strategic implementation.

The practical value of DGF has been preliminarily validated in several industries. For example, Cainiao presented its full-link green logistics solution at the 2024 United Nations Climate Change Conference (COP29). By leveraging digital technologies and green operations practices, Cainiao achieved sustainable ESG operations, reducing emissions by 458,000 tons in packaging decarbonization, recycling, and new energy logistics in fiscal year 2024. Through supply chain collaboration and a carbon asset management system, Cainiao built a green logistics ecosystem and contributed to the long-term development of a global smart logistics network, providing a typical case of how DGF drives SDP. Similarly, the State Grid’s Big Data Center, under the guidance of its digital department, established an energy big data application support platform to integrate energy and cross-industry data, facilitating deep-level data integration and sustainable low-carbon development. The platform provides carbon emission monitoring, analysis, and clean energy consumption services to both governments and businesses, supporting scientific decision-making and energy conservation, further demonstrating how digital transformation supports green low-carbon shifts in the energy sector.

In conclusion, this study integrates the RBV, DCT, and Synergy Theory to propose a systematic framework that not only enriches the theoretical understanding of DGF research but also reveals the underlying mechanisms through which DGF drives SDP. This framework provides a robust foundation for the development of subsequent hypotheses and offers valuable theoretical guidance for firms practicing digital–green integration.

2.2. Research Hypotheses

2.2.1. Digital–Green Fusion and Sustainable Development Performance

As two pivotal strategies for sustainable development, the convergence of digital and green transformations creates synergistic effects that enhance corporate sustainability. Digital transformation optimizes operational models through the in-depth application of information technology, enabling firms to monitor and manage production processes, minimize resource waste, optimize supply chains, and swiftly adapt to market changes [35,36]. These advancements provide essential support for greening transformation, which reflects a firm’s dedication to environmental responsibility by implementing eco-friendly technologies and streamlining production processes to mitigate their environmental footprint and support sustainability objectives [18,25,37]. Integrating digital technologies with green management practices enhances corporate sustainability performance, creating a self-reinforcing and virtuous ecosystem. This convergence catalyzes green technological advancements and stimulates broader innovations across products, services, and business models, enabling firms to deliver more sustainable solutions [36]. Furthermore, by utilizing digital technology to gather and examine vast amounts of environmental, social, and economic data, DGF revolutionizes corporate decision-making processes, allowing for more sustainable and informed strategies. In the context of globalization, DGF enhances corporate reputation through a “reputation effect”, facilitating firms to acquire more intangible assets and bolster their competitiveness while ensuring the sustainable use of local resources and environmental protection [18]. Additionally, DGF cultivates long-term competitive advantages by amplifying social and environmental impact and promoting sustainable practices throughout the supply chain, encouraging upstream and downstream enterprises to collectively enhance their sustainability efforts. Based on this, the study proposes the following hypothesis.

Hypothesis 1.

DGF contributes to enhancing SDP.

2.2.2. Channel Mechanisms of Digital–Green Fusion for Sustainable Development Performance

DGF, Resource Allocation Efficiency, and SDP

Resource misallocation leads to increased costs and reduced production efficiency, thus hindering a company’s sustainable development. Consequently, improving resource allocation efficiency is a critical strategy for enhancing a firm’s sustainability performance [38]. Within the spectrum of corporate capital, human capital is of paramount importance. The synergy between digitalization and greening notably amplifies the demand for highly skilled talent, particularly those with expertise in digital technologies and environmental management. This growing demand drives improvements in a firm’s human capital allocation, enabling more effective implementation of green technological innovations and environmental management practices, thereby enhancing overall sustainability performance. Moreover, digital–green integration reduces reliance on inefficient and repetitive labor. By leveraging digital technologies, firms can automate processes and integrate intelligent systems into their workflows, thereby decreasing the need for low-skill, repetitive roles [39]. This not only improves overall competitiveness but also enhances sustainability performance by reducing inefficiencies in labor practices and allowing human resources to focus on more strategic, innovative tasks [26,40]. Finally, DGF drives internal resource reallocation during the green transition, especially in terms of personnel deployment. For instance, to meet greening requirements, firms may channel more resources and workforce into environmental management departments or green projects [27]. This reallocation strengthens internal coordination, fosters collaboration, and encourages the widespread implementation of green management practices throughout the firm, leading to improved sustainability outcomes. Based on these insights, the following research Hypothesis 2a is proposed:

Hypothesis 2a.

DGF improves labor resource allocation efficiency, thereby enhancing corporate SDP.

DGF, Green Technological Innovation, and SDP

Green technological innovation involves the adoption of environmentally sustainable materials or technologies within industrial processes and product manufacturing to minimize environmental impact and resource consumption [4]. DGF provides vital technical support and data-driven insights for these innovations [41]. Advanced technologies enable the real-time collection and analysis of environmental and resource data during production and operations [42]. These data empower firms to predict market demand and technological trends more accurately, thereby forming a robust foundation for devising targeted green innovation strategies [43,44]. For instance, real-time monitoring and analysis of critical environmental indicators like energy consumption and carbon emissions enable companies to identify inefficiencies and high-emission processes within their production models. By implementing targeted technological innovations, companies can address these issues, resulting in significant advancements in green technologies. This data-driven approach reduces resource consumption, minimizes environmental impact, and concurrently enhances production efficiency and economic performance [19]. Furthermore, DGF provides a strategic pathway for companies to navigate external environmental changes and regulatory pressures through green technological innovation. In response to increasingly stringent environmental regulations and rising consumer demand for green products, this fusion empowers enterprises to meet stricter standards and secure a competitive edge in the green market, thereby strengthening their long-term sustainability. Based on this, the study proposes Hypothesis 2b.

Hypothesis 2b.

DGF enhances green technological innovation capabilities, thereby contributing to improvements in corporate SDP.

DGF, Information Transparency, and SDP

By improving corporate information transparency, DGF introduces a new dynamic mechanism to the sustainable development of firms. Digital technologies, particularly blockchain, significantly enhance the accuracy and reliability of business environmental data by ensuring transparency and immutability during data collection and transmission. This technological advancement enables companies to swiftly and accurately disclose financial, environmental, and social responsibility information, thereby fostering greater stakeholder trust [44]. Firms demonstrating a high degree of digital–green integration tend to attract favorable attention from the public, investors, and governmental entities, motivating them to enhance disclosure quality, minimize information asymmetry, and mitigate principal–agent issues [15]. Moreover, improved information transparency benefits both external stakeholders and internal business operations. Specifically, it heightens the company’s environmental awareness, encourages a stronger focus on social responsibility and environmental performance, and bolsters the continual advancement of sustainable practices [39]. Information transparency within the context of digital-green convergence encourages cooperation and information sharing beyond just one-way disclosure. Businesses can collaborate with government platforms, research institutions, and other businesses to form innovation chains and optimize their innovation models, for instance, by using a shared data platform. This collaboration will enhance businesses’ capacity to integrate knowledge and innovate in green technologies. Furthermore, firms that effectively implement digital-green transformations are better positioned to share positive information with the public, thereby strengthening their reputation. This positive public image elevates market expectations and prompts businesses to regulate their economic and social behaviors further. Based on this, the following research Hypothesis 2c is proposed.

Hypothesis 2c.

DGF increases information transparency, thereby facilitating improvements in corporate SDP.

3. Methodology and Data

3.1. Model Specification

In this study, DGF is conceptualized as a dynamic capability that integrates insights from the DCT and RBV. By synthesizing these two perspectives, we propose that DGF, through implementing digitalization and greening as dual strategic transformations, empowers firms to enhance their sustainability performance while gaining a competitive edge in the marketplace.

To empirically test this hypothesis, a multi-dimensional panel fixed-effects model is employed. This approach extends traditional fixed-effects methods by controlling for unobserved heterogeneity across multiple dimensions, such as firm and time, allowing for more precise estimation of causal effects [45]. This approach not only addresses endogeneity more robustly but also accommodates complex data structures, making it ideal for analyzing dynamic interactions like DGF’s impact on SDP [17,19,46,47,48]. The model is specified as Equation (1).

$$SD{P}_{it}={\alpha }_0+{\alpha }_{1}DG{F}_{it}+{\displaystyle \sum Controls}+{\displaystyle \sum Firm}+{\displaystyle \sum Year}+{\epsilon }_{it}$$

(1)

In this model, i represents the firm, and t denotes the year. DGF_it captures the digital–green fusion status of firm i in year t, while SDP_it refers to corporate sustainable development performance, which functions as the dependent variable and reflects the firm’s progress toward sustainability objectives. The vector of control variables, denoted as Controls, accounts for other factors that may influence SDP. The firm and year terms account for individual fixed and time-fixed effects, respectively, while ε_it represents the random disturbance term. α₁ represents the critical coefficient of interest; a significantly positive value indicates that DGF positively impacts SDP.

To further investigate the mechanisms through which DGF influences SDP, the mechanism variables are integrated into the DGF-SDP framework [19,49], as outlined in Equations (2) and (3). This study mainly focuses on three key mechanisms: resource allocation, innovation incentive, and information transparency effects. By exploring these channels, our study sheds light on how DGF drives corporate sustainability, offering valuable insights for both theory and practice.

$$M_{it}={\beta }_0+{\beta }_{1}DG{F}_{it}+{\displaystyle \sum Controls}+{\displaystyle \sum Firm}+{\displaystyle \sum Year}+{\epsilon }_{it}$$

(2)

$$SD{P}_{it}={\lambda }_0+{\lambda }_{1}DG{F}_{it}+{\lambda }_2{M}_{it}+{\displaystyle \sum Controls}+{\displaystyle \sum Firm}+{\displaystyle \sum Year}+{\epsilon }_{it}$$

(3)

where M is represented by three key indicators: labor allocation efficiency, green technological innovation, and information transparency, which serve as the mechanism variables in the model. The other terms are defined in the same way as in Equation (1). The specific process is as follows (Figure 2): First, in Equation (1), if the coefficient α₁ is significant, the next step is to test the significance of the DGF coefficient (β₁) in Equation (2) and the M coefficient (λ₂) in Equation (3). Subsequently, the significance of the DGF coefficient (λ₁) in Equation (3) is examined. If the coefficients α₁, β₁, λ₁, and λ₂ are all statistically significant, and if the DGF coefficient in Equation (3) is smaller than that in Equation (1), this suggests that the proposed mechanism is valid and supports the robustness of the results [50,51].

3.2. Variable Selection

3.2.1. Dependent Variable: Sustainable Development Performance

Corporate sustainability performance is crucial for the long-term well-being of both society and the environment, aiming to achieve integrated goals of economic and environmental sustainability [52]. Drawing on the framework proposed by Alexopoulos et al. [22], SDP is conceptualized as consisting of two key dimensions: FP and EP. FP is measured using return on equity (ROE), while EP is evaluated based on the company’s environmental disclosures, sourced from the CSMAR “Environmental Research Database of Listed Companies in China.” The detailed EP evaluation criteria are provided in Appendix A. A higher EP score reflects better environmental performance. Referring to the research [23] and grounded in the organizational ambidexterity theory, the study constructs dual performance as a proxy for SDP. Dual performance captures the synergy between a company’s financial and environmental (and social) outcomes, achieved under resource constraints. To construct this composite measure, FP and EP are first normalized to a 0–1 scale, as described in Equation (4). Then, following the research methodology [53], the normalized FP and EP values are combined in Equation (5) to derive the firm’s SDP.

$$y^{*}={\displaystyle \frac{y-\min }{\max -\min }}$$

(4)

$$SDP=\left[(1-\left|FP-EP\right|)\times \sqrt{FP\times EP}\right]/1$$

(5)

3.2.2. Independent Variable: Digital–Green Fusion

The independent variable is the degree of DGF. Drawing on prior research [17], we identify 76 digital-related characteristic terms across five dimensions: artificial intelligence, blockchain, cloud computing, big data, and digital application. Using text mining techniques, the study adjusts the frequency of these aggregated terms by adding one and then applies a log transformation to derive a comprehensive indicator representing the overall level of digital transformation (DT). Similarly, based on the study [54,55], we identify 113 green-related characteristic terms, categorized into five dimensions: publicity and advocacy, strategic concepts, technological innovation, pollution control, and monitoring and management. Following the same data processing approach as for DT, these terms are aggregated, adjusted by adding one, and log-transformed to create a composite indicator for the level of green transformation (GT). To compute the degree of DGF, the study employs the coupling coordination degree model, as outlined in study [56]. This model is particularly suited for assessing the interactions between different subsystems, with a higher coupling coordination degree indicating a stronger relationship between digital and green transformations. To standardize the differing dimensions, the study normalizes the DT and GT indices using the 0–1 standardization method, as depicted in Equation (4). Subsequently, the coupling coordination degree model, as described in Equations (6)–(8), is applied to calculate the DGF, with typically α = β = 0.5.

$$C={\displaystyle \frac{\sqrt{DT\times GT}}{\left(DT+GT\right)/2}}$$

(6)

$$T=\alpha DT+\beta GT$$

(7)

$$DGF=\sqrt{C\times T}$$

(8)

3.2.3. Mechanism Variables

(1) Green technological innovation (GTI). A crucial metric for gauging a company’s green technological innovation efforts is the number of green patents it possesses. Unlike patent grants, which can be influenced by external factors, patent applications more accurately reflect the current state of technological advancement in green innovation. Therefore, to measure GTI, this study adds one to the total number of green patent applications before applying the logarithmic transformation. This adjustment ensures the robustness of the data [42].

(2) Information transparency (ITR). In line with the methodology [15], this study employs analyst attention as a proxy for a firm’s information transparency. Analyst attention is quantified by the natural logarithm of one plus the number of analysts (or analyst teams) who have tracked the company over the past year.

(3) Labor allocation efficiency (LAE). Building on the methodologies in [40,57], LAE is quantified by analyzing the surplus of employees within a company, providing a key indicator of the effectiveness in allocating labor resources. The primary determinants of an enterprise’s size typically include scale, capital intensity, growth rate, industry classification, and year. The surplus employee rate is derived using the model outlined in Equation (9).

• Firm scale (Size): Measured by the natural logarithm of total assets.
• Capital intensity (Capital): Defined as the ratio of fixed assets to total assets.
• Firm growth (Growth): Reflected by the growth rate of the company’s main business revenue.

Using these variables, the estimated number of normal employees (employee_estimate) is calculated. To determine the surplus employee rate, employee_estimate is subtracted from the actual number of employees in the enterprise, and the absolute value of this difference is taken. A lower LAE value signifies higher labor allocation efficiency within the enterprise.

$$Employe{e}_{it}={\mu }_0+{\mu }_{1}Siz{e}_{it}+{\mu }_{2}Capita{l}_{it}+{\mu }_{3}Growt{h}_{it}+{\displaystyle \sum Industry+{\displaystyle \sum Year}}+{\epsilon }_{it}$$

(9)

3.2.4. Control Variables

Building on previous research [15,42,58,59], this research incorporates several key internal and external variables that could influence a firm’s SDP, including firm age (Firmage), leverage ratio (Lev), fixed asset ratio (Fixed), management expense ratio (Mfee), board size (Board), CEO duality (Dual), the shareholding ratio of the largest shareholder (Top1), and whether the firm is audited by one of the Big Four accounting firms (Big4). The measurement methods for these variables are detailed in Appendix B.

3.3. Data Sources

This study draws on data from China’s A-share listed companies over the period from 2010 to 2022. The data processing procedure involves several steps: (1) excluding financial firms from the sample; (2) eliminating observations with missing key variables; (3) retaining only firms that remained continuously listed throughout the entire sample period; and (4) applying winsorization at the 1% and 99% percentiles to continuous variables in order to minimize the influence of outliers. The final dataset comprises 2896 listed companies, resulting in 29,090 firm-year observations. These observations are distributed across 31 provinces and 372 cities, providing a comprehensive representation of firms operating in various regions of China. Green technological innovation data are sourced from the CNRDS database, while financial and corporate governance information, including analysts’ attention and control variables, are retrieved from the CSMAR database. Information on digital and green transformation is extracted from the firms’ annual reports. Descriptive statistics for the primary variables are provided in

Table 1. Descriptive statistics.

Variable	N	Mean	SD	Min	Median	Max
SDP	29,090	0.1875	0.2052	0.0000	0.1067	0.7468
DGF	29,090	0.3770	0.3116	0.0000	0.5009	0.9777
Firmage	29,090	2.9467	0.3290	1.7918	2.9957	3.5553
Lev	29,090	0.4644	0.2101	0.0663	0.4588	0.9803
Fixed	29,090	0.2239	0.1674	0.0013	0.1914	0.7085
Mfee	29,090	0.0892	0.0918	0.0072	0.0655	0.6504
Board	29,090	2.1386	0.1982	1.6094	2.1972	2.7081
Dual	29,090	0.2381	0.4259	0.0000	0.0000	1.0000
Top1	29,090	0.3490	0.1520	0.0863	0.3267	0.7500
Big4	29,090	0.0684	0.2524	0.0000	0.0000	1.0000

.

4. Empirical Results

4.1. Benchmark Regression

To ensure the reliability of parameter estimation, this study first conducts a multicollinearity assessment using the Variance Inflation Factor (VIF). The analysis reveals that all variables have VIF values below 2, indicating no significant multicollinearity within the dataset. Detailed results are provided in Appendix C. Additionally, correlation coefficients between variables are calculated, and a correlation coefficient matrix is generated (Figure 3). The results display that all correlation coefficients are below 0.3, further confirming that multicollinearity is not a concern in this analysis.

Table 2. Benchmark regression results.

	(1)	(2)
	SDP	SDP
DGF	0.0200 ***	0.0207 ***
	(4.95)	(5.11)
Firmage		0.0811 ***
		(5.73)
Lev		−0.0124 *
		(−1.85)
Fixed		0.0382 ***
		(3.84)
Mfee		−0.0876 ***
		(−6.93)
Board		−0.0003
		(−0.04)
Dual		−0.0071 ***
		(−2.69)
Top1		0.0395 ***
		(3.28)
Big4		0.0169 **
		(2.43)
Constant	0.1800 ***	−0.0670
	(106.39)	(−1.46)
Firm/Year FE	Yes	Yes
Adj.R2	0.6283	0.6299
N	29,090	29,090

Note: T-values are in parentheses. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Same for the table below.

illustrates the baseline regression outcomes that evaluate how DGF affects SDP. Column (1) displays the univariate regression findings of DGF for SDP, while column (2) incorporates the control variables identified in the study. The results indicate that boosting corporate sustainability performance is greatly supported by DGF (β_DGF = 0.0207, p < 0.01). The economic interpretation of this result is that SDP improves by 2.07% with each incremental rise in the degree of DGF. Thus, H1 is validated. DGF expedites technological application and market expansion while dramatically optimizing resource allocation and facilitating the diffusion of green innovations. By synergizing digital and green resources, enterprises can accomplish environmentally friendly operations while retaining high efficiency, consequently bolstering their market competitiveness.

4.2. Robustness Check

4.2.1. Alternative Dependent Variable

To further mitigate potential biases related to measuring the dependent variable, this study utilizes return on assets (ROA) as an alternative indicator for corporate financial performance (FP_1) [15,60]. Additionally, the environmental pillar score disclosed in the Huazheng ESG index is employed as a substitute for environmental performance (EP_1) [58]. These alternative measures are then used to calculate a proxy for corporate sustainable development performance, denoted as SDP_1. Column (1) of

Table 3. Robustness check results.

	(1)	(2)	(3)	(4)	(5)
	Replacing Variable Measurement			Tobit	High-Dimensional Fixed
	SDP_1	SDP	SDP	SDP	SDP
DGF	0.0086 ***			0.0177 ***	0.0208 ***
	(2.60)			(3.12)	(5.03)
DGF_1		0.0194 ***
		(5.04)
DGF_2			0.0219 ***
			(5.15)
Constant	−0.0035	−0.0665	−0.0673	−0.2036 ***	−0.0501
	(−0.10)	(−1.45)	(−1.47)	(−5.36)	(−0.70)
Controls	Yes	Yes	Yes	Yes	Yes
Firm/Year FE	Yes	Yes	Yes	Yes	Yes
Province *Year					Yes
Industry *Year					Yes
Adj.R2	0.4679	0.6299	0.6300		0.6438
N	27,893	29,090	29,090	29,090	29,090

Note: Significance levels: *** p < 0.01. The parentheses in column (4) of Table 3 report the Z-values.

’s results demonstrates that even when substituting the dependent variable with alternative measures, the positive impact remains both substantial and statistically significant.

4.2.2. Adjusting Weight Assignments

When calculating the DGF degree using the coupling coordination model, this study initially assigns equal weights of 0.5 to both DT and GT. To ensure the robustness of the results and to avoid potential bias from weight assignments, this study follows the methodology in [56] and explores two alternative weight configurations: (1) 1/3 and 2/3, and (2) 2/3 and 1/3. These configurations are then used to compute alternative proxy variables, DGF_1 and DGF_2. The regression results for these proxies are shown in columns (2) and (3) of Table 3. The coefficients for DGF_1 and DGF_2 closely align with the baseline results in magnitude and direction.

4.2.3. Changing the Regression Model

Considering that the distribution of the corporate sustainable development performance variable exhibits a distinct right-censored tail (0 ≤ SDP < 1), this paper replaces the baseline regression model with a Tobit model to account for the right-censoring. Column (4) of Table 3 depicts the results of the Tobit model, which imply that the DGF estimations remain consistent with those in Table 2. This consistency highlights that the study’s core conclusions are reliable and unaffected by potential biases in the model selection process.

4.2.4. Considering High-Dimensional Fixed Effects

Expanding on the baseline regression, this study further incorporates high-dimensional fixed effects by considering province-year and industry-year interactions. This refinement aims to mitigate potential biases stemming from time-varying omitted variables at the regional level. The findings in column (5) of Table 3 reveal that DGF has markedly improved SDP by 2.08%. Even after controlling for high-dimensional fixed effects, the positive influence of DGF on corporate SDP persists, thereby reinforcing the validity of H1.

4.3. Addressing Endogeneity

Reverse causality problems and bias due to omitted variables could affect the regression model. This study adopts the instrumental variable approach to resolve these concerns. The average DGF for firms in the same location, industry, and year is chosen as the instrumental variable (IV) in accordance with the technique [15]. This instrumental variable reflects external drivers of DGF, influenced by the shared environment, policies, technological advancements, and market demands within a given region and industry. The IV exhibits a strong correlation with the DGF of individual firms by capturing broader regional and industry trends rather than firm-specific behaviors. Moreover, as it is insulated from direct influence by the SDP of any single firm, it serves as a robust exogenous variable. The results in

Table 4. Results of instrumental variable test.

	(1)	(2)
	First Stage	Second Stage
	DGF	SDP
DGF		0.0139 **
		(2.18)
IV	0.9207 ***
	(121.51)
Constant	0.2375 ***	0.1384 **
	(3.31)	(2.48)
Anderson LM		10,492.04 ***
Cragg-Donald Wald F statistics		14,765.50 [16.38]
Controls	Yes	Yes
Firm/Year FE	Yes	Yes
Adj.R2		0.6670
N	29,090	29,090

Note: Significance levels: *** p < 0.01, ** p < 0.05. Critical values for the Stock–Yogo test at the 10% level are presented in brackets.

indicate that the instrumental variable does not suffer from weak instrument or overidentification issues. In the second-stage regression, the coefficient for DGF remains significant (β_DGF = 0.0139, p < 0.05). These findings underscore that, despite accounting for endogeneity concerns, the principal conclusions of this study continue to hold with considerable robustness.

4.4. Heterogeneity Analysis

4.4.1. Heterogeneity Based on Firm Size

The influence of DGF on SDP is likely modulated by firm size. Firm size not only encapsulates the extent of resource availability but also determines the strategies and practices that can be employed during a company’s digital and green transformation initiatives. To examine this heterogeneous impact, firm size is quantified using the natural logarithm of total annual assets [54]. The sample is then bifurcated into large and small firms based on the median value. The regression outcomes are detailed in columns (1) and (2) of

Table 5. Heterogeneity analysis.

	(1)	(2)	(3)	(4)	(5)	(6)
	Small Enterprises	Large Enterprises	Non-SOEs	SOEs	Low-Industry Uncertainty	High-Industry Uncertainty
DGF	0.0039	0.0262 ***	0.0187 ***	0.0238 ***	0.0268 ***	0.0135 ***
	(0.80)	(3.95)	(3.63)	(3.65)	(3.85)	(2.68)
Constant	0.0230	−0.0477	−0.1291 **	−0.0662	0.0352	−0.0656
	(0.39)	(−0.57)	(−2.34)	(−0.78)	(0.46)	(−1.05)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Firm/Year FE	Yes	Yes	Yes	Yes	Yes	Yes
p-value	0.0000 ***		0.0001 ***		0.0000 ***
Adj. R2	0.6091	0.6319	0.6195	0.6370	0.6292	0.6373
N	14,397	14,429	16,483	12,539	13,554	14,940

Note: T-values are in parentheses. Significance levels: *** p < 0.01, ** p < 0.05.

. In small enterprises, the DGF coefficient is 0.0039 and lacks statistical significance. Conversely, in large enterprises, the DGF coefficient is 0.0262 (p < 0.01). Large enterprises typically possess more abundant resources, allowing them to allocate substantial capital, advanced technology, and human resources to achieve a greater degree of digital–green integration. This scale advantage boosts the efficiency and economic feasibility of green innovation and digital transformation, significantly improving sustainability outcomes. By contrast, although small enterprises can also derive benefits from digital-green convergence, their limited resources and capabilities may dampen the effect, resulting in a less pronounced enhancement in SDP. Figure A1, included in Appendix D, visually illustrates these heterogeneous effects across firm sizes.

4.4.2. Heterogeneity Based on Ownership Structure

Given the substantial differences in strategic objectives, resource allocation, governance structures, and external environments between state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs), this study categorizes firms by ownership structure, with the regression analysis for each group shown in columns (3) and (4) of Table 5. Regardless of ownership categories, DGF significantly enhances SDP, with a more pronounced effect observed in SOEs. This is likely due to the policy support and resource allocation advantages that SOEs benefit from. In pursuing digital and green transformations, SOEs are better positioned to implement these strategies on a larger scale and in a more systematic manner. Additionally, they often receive more substantial government funding, technical support, and policy incentives, facilitating a smoother and more effective digital-green convergence, leading to a more marked improvement in SDP [19,61]. Compared to SOEs, non-SOEs are typically more market-driven and agile, compelled by competitive forces to exhibit greater innovation and adaptability in their digital–green synergy initiatives. However, they may face constraints related to financing. The p-value from intergroup difference tests further substantiates the notable variation in how DGF affects SDP across different ownership structures, with these heterogeneous effects visually depicted in Figure A1 in Appendix D.

4.4.3. Heterogeneity Based on Environmental Uncertainty

In industries with high environmental uncertainty, the unpredictability of future conditions may complicate a firm’s investment in and strategic adjustments for digital–green convergence. This added complexity can, in turn, affect the effectiveness of DGF and its impact on boosting SDP. Following prior studies [62,63,64], this study measures industry environmental uncertainty using the industry-adjusted standard deviation of corporate revenue over the past five years. Firms are classified into high- and low-industry uncertainty categories based on the median value, with the corresponding regression results displayed in Table 5, columns (5) and (6). Figure A1, presented in Appendix D, visually depicts the heterogeneous effects of DGF across different levels of environmental uncertainty. The findings reveal that in environments characterized by low industry uncertainty, the coefficient for DGF is 0.0268 (p < 0.01), suggesting that in relatively stable industry contexts, firms are better positioned to systematically and continuously implement digital and green transformation strategies, thereby effectively integrating resources and achieving synergistic gains in both financial and environmental performance. In contrast, under conditions of high industry environmental uncertainty, while DGF remains statistically significant, its impact is comparatively weaker. This attenuation may result from the heightened challenges and unpredictability that firms encounter in more volatile and complex external environments, which can hinder the efficacy of digital–green integration. The p-value from the intergroup difference tests further corroborates the significant presence of this heterogeneity.

5. Mechanism Analysis

Building on the theoretical framework outlined earlier, this study investigates the impact of DGF on corporate SDP from three key dimensions: resources, technology, and governance. Within these dimensions, the enhancement of SDP through DGF is driven primarily by mechanisms such as the optimization of resource allocation efficiency, the stimulation of green technological innovation, and the improvement of information transparency. Accordingly, this study systematically evaluates each of these mechanisms to provide a comprehensive understanding of how DGF contributes to SDP.

5.1. Resource Allocation Effect

Table 6. Results of the resource allocation effect test.

	(1)	(2)
	LAE	SDP
DGF	−0.0229 *	0.0203 ***
	(−1.95)	(5.02)
LAE		−0.0061 ***
		(−2.86)
Constant	0.5006 ***	−0.0605
	(3.78)	(−1.32)
Controls	Yes	Yes
Firm/Year FE	Yes	Yes
Adj.R2	0.5873	0.6298
N	29,024	29,024

Note: T-values are in parentheses. Significance levels: *** p < 0.01, * p < 0.1.

provides the results of the resource allocation effect analysis. Column (1) reveals that the coefficient for DGF is −0.0229 (p < 0.1), indicating that DGF enhances labor allocation efficiency by reducing the firms’ over-employment rates. In column (2), both DGF and LAE show statistical significance at the 1% level (β_DGF = 0.0203; β_LAE = −0.0061), demonstrating that DGF not only directly improves the SDP of enterprises but also amplifies this effect by mitigating over-employment issues. H2a is verified. During the digital–green convergence process, integrating digital technologies with green management practices substantially diminishes reliance on inefficient and redundant labor, thereby optimizing labor resource allocation within enterprises. Concurrently, this integration facilitates a more nuanced management of operational processes, minimizing resource waste and redundancy. Consequently, enterprises enhance their operational flexibility and adaptability in responding to market fluctuations.

5.2. Innovation Incentive Effect

Table 7. Results of the innovation incentive mechanism test.

	(1)	(2)
	GTI	SDP
DGF	0.0455 ***	0.0203 ***
	(3.25)	(5.03)
GTI		0.0079 ***
		(4.40)
Constant	0.0762	−0.0676
	(0.48)	(−1.48)
Controls	Yes	Yes
Firm/Year FE	Yes	Yes
Adj.R2	0.6682	0.6302
N	29,090	29,090

Note: T-values are in parentheses. Significance levels: *** p < 0.01.

delineates the regression results pertaining to the innovation incentive mechanism. The outcomes exhibit that the GTI and DGF coefficients are both considerably positive, as evidenced in columns (1) and (2). Additionally, column (2)’s DGF coefficient is lower in comparison to the baseline regression, illustrating that DGF drastically impacts SDP via GTI. This verifies H2b and validates the green technical innovation pathway. As Chinese enterprises increase their participation in the global market, the international community’s demands for green products and sustainable development have become a critical consideration for their growth strategies. DGF drives green technological innovation, enhancing resource efficiency and reducing production costs. This optimization of resources and costs not only directly boosts the economic benefits of enterprises but also indirectly improves their sustainability performance through more efficient resource allocation and environmental management. Furthermore, China’s advancements in digital infrastructure, exemplified by the extensive deployment of 5G networks and the widespread adoption of artificial intelligence and big data technologies, have laid a robust groundwork for enterprises to pursue digital–green integration. These technological advancements empower firms to more precisely identify and capitalize on opportunities for green technological innovation, leading to facilitating the effective synergy between DGF and SDP.

5.3. Information Transparency Effect

Table 8. Results of the information transparency effect test.

	(1)	(2)
	ITR	SDP
DGF	0.1187 ***	0.0134 **
	(4.60)	(2.43)
ITR		0.0116 ***
		(6.98)
Constant	2.2584 ***	0.0738
	(7.95)	(1.21)
Controls	Yes	Yes
Firm/Year FE	Yes	Yes
Adj.R2	0.5609	0.6379
N	19,255	19,255

Note: T-values are in parentheses. Significance levels: *** p < 0.01, ** p < 0.05.

details the findings related to the effect of information transparency. Column (1) demonstrates that DGF significantly increases ITR by 11.87%. In column (2), both DGF and ITR show significant positive effects (β_DGF = 0.0134, p < 0.05; β_ITR = 0.0116, p < 0.01), with the DGF coefficient notably reduced compared to the baseline regression. This reduction indicates a substantial mediating role of ITR in the relationship between DGF and SDP. H2c is corroborated. According to reputation theory, as societal focus on ethical practices and moral standards grows, reputation increasingly becomes a critical strategic asset for businesses. In this regard, DGF is instrumental in cultivating and maintaining a robust corporate image. By enhancing information transparency, DGF allows firms to more effectively demonstrate their dedication to environmental stewardship and long-term sustainability. This enhanced visibility not only reinforces the company’s ethical stance but also bolsters its capacity to attract investors and customers. Amid increasingly stringent regulatory demands for corporate information disclosure, heightened investor and public expectations for transparency, and intensifying competition-driven reputational pressures, DGF not only directly enhances SDP but also amplifies this effect through the mediating role of information transparency.

6. Conclusions and Implications

6.1. Conclusions

This study, utilizing data from A-share listed companies in China over the period 2010–2022, empirically investigates the effects of DGF on SDP. The principal findings are as follows.

First, by shifting the focus to the micro-level, the study constructs a dual framework of sustainability performance that incorporates both financial and environmental metrics. By addressing endogeneity concerns with instrumental variables and conducting rigorous robustness tests, the findings consistently underscore the pivotal role of DGF in advancing corporate sustainability, filling a critical gap in the literature on digitalization and greening. This dual framework offers a more comprehensive evaluation of corporate sustainability, enabling a nuanced understanding of how digital and green transformations jointly influence both economic and environmental outcomes.

Second, this study delves into the impact of DGF on SDP from three critical perspectives: resources, technology, and governance. The analysis systematically elucidates how DGF enhances sustainability by optimizing labor allocation efficiency, fostering green technological innovation, and improving information transparency. These insights illuminate the multifaceted pathways through which digital–green integration empowers corporate sustainability and deepens our understanding of the mechanisms underpinning sustainable development.

Finally, the cross-sectional analysis delineates the boundary conditions affecting the effectiveness of DGF for SDP. Specifically, DGF strategies are more successful in large enterprises, state-owned enterprises, and those operating in low-uncertainty environments. These findings offer valuable implications for policymakers and corporate managers, emphasizing the need for tailored strategies that consider firm size, ownership structure, and industry characteristics.

6.2. Implications

6.2.1. Theoretical Implications

First, this study introduces the concept of DGF, which combines digital and green transformations into a dynamic, integrated process at the corporate level. Moreover, this study positions DGF as a complex dynamic capability, building on the RBV and DCT. The RBV highlights the uniqueness of DGF’s resource base, which includes digital technologies (e.g., artificial intelligence, big data) and green initiatives (e.g., pollution control, sustainable resource management). However, the static nature of the RBV is insufficient to capture the dynamic challenges faced by firms. The DCT complements this by emphasizing the importance of continuously reconfiguring these resources to adapt to evolving market demands, regulatory pressures, and technological advancements. The complexity of DGF stems from its dual focus on internal coordination and external adaptability. Internally, firms must foster cross-functional collaboration to integrate digital and green objectives, overcoming organizational silos and aligning workflows with sustainability goals. Externally, firms must dynamically respond to shifting regulatory requirements, technological innovations, and market expectations. This dual interaction creates a virtuous cycle, wherein digital innovations accelerate green advancements, while green objectives guide the application and refinement of digital tools. By integrating the RBV and DCT, this study not only provides a deeper theoretical understanding of DGF but also reveals how it enables firms to achieve long-term competitive advantages through resource optimization and continuous innovation.

Second, the study examines the relationship between DGF and SDP. By integrating digital and green transformations, DGF enables firms to pursue both financial and environmental goals simultaneously, rather than treating them as independent strategies. This approach emphasizes the synergy between digital and green initiatives, where the interaction between them creates mechanisms such as green technological innovation and resource optimization, which ultimately enhance firms’ SDP. The study reveals how the dynamic interplay between digital and green transformations fosters a feedback loop, where digital advancements accelerate green progress, while green goals help shape and refine digital tools. This relationship underscores how DGF serves as a strategic framework that not only drives technological innovation but also supports a balanced pursuit of both financial performance and environmental sustainability. By conceptualizing this dual transformation, the study broadens the theoretical understanding of how firms can effectively align their digital and green strategies to improve overall sustainability outcomes.

Finally, this study refines the theoretical framework by identifying the boundary conditions of DGF strategies. The findings indicate that factors such as firm size, ownership structure, and industry characteristics significantly influence the implementation effectiveness of DGF strategies. Firms must align their DGF initiatives with specific organizational attributes and external environmental contexts to effectively enhance their SDP through mechanisms such as green technological innovation, information disclosure quality, and resource allocation optimization. This discovery offers a more nuanced perspective for academia and provides practical guidance for firms in designing and implementing DGF strategies that maximize the integrated impact on financial and environmental sustainability, thus supporting the realization of global sustainable development objectives.

6.2.2. Policy Implications

The managerial policy implications are as follows.

For policymakers, it is critical to establish a robust and coherent policy framework that supports the integration of digital and green strategies. Such a framework should emphasize the need to harmonize digitalization with environmental sustainability, guiding economic actors to align with the goals of digital–green convergence while addressing any potential conflicts between these dual objectives. To facilitate corporate investment in this integration, governments can introduce financial incentives such as tax reductions, subsidies, and low-interest loans, thereby alleviating the financial and technological burdens faced by firms. Additionally, policymakers must lead efforts to develop industry standards for digital–green convergence and foster international collaborations to introduce and disseminate cutting-edge technologies and best practices. These global partnerships will be crucial in advancing the green and digital transformation of enterprises across global supply chains.

Industry associations, in collaboration with leading firms, should provide practical guidelines for implementing dual synergies, offering enterprises actionable frameworks for integrating digital and green initiatives. Recognizing that large enterprises and SOEs are better positioned for digital–green integration, targeted support policies are needed for small and medium-sized enterprises (SMEs) and non-SOEs. These policies should focus on overcoming resource and technological constraints, thus enhancing SMEs’ capacity for digital–green integration and enabling their pursuit of sustainable development. Special attention should be given to digitalization efforts in high-carbon industries to facilitate their transition toward low-carbon operations, with sectors such as energy and transportation serving as initial demonstration areas. Additionally, the government can prioritize the development of digital zero-carbon industrial parks to ensure that green transitions progress in tandem with economic growth. Pilot projects for digital–green integration, particularly in intelligent green manufacturing, can be launched, with firms excelling in these initiatives receiving recognition through awards or honorary titles. To further incentivize sustainability, establishing green certification or digital–green integration evaluation systems will encourage firms to incorporate sustainability goals into their long-term strategic planning.

For enterprises, embedding DGF into their strategic core is crucial. Companies must develop a comprehensive, long-term plan that aligns digital and green initiatives. Executive management is crucial in defining the importance of this convergence for sustainability, cultivating a robust green culture, and regularly disclosing both environmental performance and progress in digital–green integration. Such transparency not only enhances market competitiveness but also reinforces corporate social responsibility and public perception, thereby building greater trust among consumers and investors. Firms should increase their investments in R&D to advance both green and digital technologies, ensuring that sustainability principles are integrated from the design phase onward. Additionally, companies must focus on upgrading production equipment through intelligent technologies and implementing comprehensive monitoring and energy management systems, thus embedding sustainability throughout their operational processes. Establishing a supply chain data-sharing platform will enable dynamic resource optimization and promote full lifecycle green management across the supply chain. Furthermore, enterprises should invest in developing their employees’ competencies in both green technologies and digital skills. Comprehensive training and re-education programs are essential to ensuring employees are aligned with the goals of DGF. Collaboration with academic institutions is critical for cultivating multidisciplinary talent, and firms should consider establishing training centers to support skill transitions. Tailored strategies for digital–green integration should be developed based on firm size, ownership structure, and industry sector. Large enterprises and SOEs can leverage their resource advantages to expedite integration, while SMEs may benefit from collaborations with government agencies and research institutions to access technological and financial support, gradually advancing toward sustainability.

6.3. Limitations and Future Research

While this study has undergone meticulous refinement, several aspects merit further exploration. Firstly, this research is confined to firms in China, an emerging economy, which may limit the generalizability of the results. Future studies should consider expanding the sample to include firms from diverse countries and regions to assess the broader applicability of the findings. Secondly, due to constraints in data availability, this study relies primarily on publicly disclosed information from listed companies. Future research could benefit from incorporating field studies and interview data to more comprehensively examine the role of internal management practices in digital–green integration.