How the Transformation of Digital–Carbon Integration Is Empowering Sustainable Development: Theoretical Logic and Practical Pathways

Abstract

The paper proposes a groundbreaking strategy for merging corporate digitalization and low-carbon transition (digital–carbon integration) for Chinese companies, using data from A-share listed companies in China from 2013 to 2022. The deep integration of the digital transformation and green low-carbon development has emerged as a crucial route by which to enhance sustainable development and attain high-quality development, due to the quick iterations of digital technology and the growing severity of global climate challenges. The study uses a dual fixed effects model for regression analysis and gathers 24,074 sample observations. The findings show the following: (1) The level of digital–carbon integration has been gradually increasing, which has had a major positive impact on sustainable development. Several robustness tests confirm the validity of this conclusion. (2) Mechanism analysis shows that, by encouraging green technology innovation and increasing operational management efficiency, digital–carbon integration can improve sustainable development. (3) According to heterogeneity analysis, non-state-owned businesses and high-technology corporations are more affected by digital–carbon integration on sustainable development. This study gives a path reference for improving sustainable development and attaining high-quality growth, in addition to offering a theoretical foundation for advancing digital–carbon integration in Chinese businesses.

1. Introduction

Global environmental challenges have been brought about by the broad economic growth model in recent years, which has prompted nations to prioritize climate action. In an effort to support green transformation, China has made carbon peaking and carbon neutrality part of its national planning framework. The low-carbon transformation of businesses, which are significant market players, is essential to accomplishing these objectives and to attaining sustainable development [1,2]. Previous studies have explored corporate low-carbon practices from the green development perspective, including green governance, green innovation, and green bonds [3,4,5,6,7,8]. However, most studies measure green innovation mainly through patent data [4,9]. Stricter environmental regulations have prompted firms to allocate greater resources toward the research and development of green technologies. On the other hand, digital transformation has developed as a crucial trend that is associated with low-carbon development [10]. China’s economic growth is now being propelled by the digital economy, which is also driving global economic transformation [11]. The combination of industrial digitalization and green development has been encouraged by pertinent national policies, which have emphasized the “dual carbon” goals [12]. Digital transformation has emerged as a core driver of corporate sustainable development, fueled by technologies including cloud computing, big data, and artificial intelligence. By elevating information quality [13], boosting operational efficiency, and advancing green technology innovation [2], digitalization offers new avenues for sustainable development. Most studies have verified its positive impact on sustainable development [2,13,14,15], mainly through the promotion of green innovation, improvement of governance and information quality, and the easing of financing constraints. However, Wang and Guo [16] note that digital transformation is a double-edged sword, showing an inverted U-shaped relationship where excessive digitalization may hinder sustainable development. Existing studies mainly measure digital transformation using questionnaire surveys [17], dummy variables [18], and text-based frequency analysis from annual reports [19]. The synergistic empowerment of digitalization and low carbon transformation is crucial for green transformation and sustainable development [20]. Against the background of industrial upgrading and changing market demands, examining the coordination between digitalization and low-carbon development and its impact on corporate sustainability has important theoretical and practical significance. However, the existing research has two main limitations. First, it focuses mainly on the one-way relationship between digitalization and low carbon transformation and lacks empirical measurement of their integration. Second, most studies analyze them separately, overlooking the impact of their integrated development on corporate sustainability. Thus, using theoretical and empirical analysis, the research investigates how digital–carbon integration impacts the sustainable growth of Chinese companies.

Based on studies conducted in the area of digital–carbon integration [21,22], this paper defines digital–carbon integration as a development paradigm driven by digital technology, with data as the core element and low carbon as the objective. It reflects the in-depth coordination of digitalization and green low-carbon transformation throughout enterprise processes, aiming to break their independent development and promote joint improvements in production efficiency and carbon emission reduction, thereby boosting corporate sustainable development. The theoretical boundaries of digital–carbon integration include three aspects: a focus on micro-level enterprises (distinguishing it from macro policy coordination and industrial technology diffusion); an emphasis on mutual empowerment rather than simple superposition; and a targeting of the coordinated improvement of economic, environmental, and social performance. Additionally, it consists of three main components: the functional integration of digital technologies into carbon accounting and low-carbon production; technical support from digital technologies like big data and artificial intelligence; and factor–goal integration with data as the core factor and dual-carbon goals as the guide.

This paper makes three contributions. First, under the framework of “digital empowerment and low carbon leadership”, it constructs an indicator system to evaluate enterprise digital–carbon integration. Utilizing a decade of micro data from A-share listed companies in Shanghai and Shenzhen, this study assesses the integration level of digitalization and low-carbon development among Chinese listed firms. Second, this paper reveals the synergy between digitalization and low carbon transformation in enterprise development and examines the heterogeneous effects of enterprise ownership and the level of high technology development. Third, it investigates how innovative green technology and effective operational management might mediate the incorporation of digital–carbon. All things considered, this study gives empirical evidence for corporate digital–carbon integration as well as references that governments can use to develop unique macro strategies and regulations.

2. Research Design

2.1. Research Hypotheses

Enterprises undergoing digital transformation improve their sustainable development through increased technological innovation [23,24]. According to Li et al. [13], digital transformation boosts ESG performance by fostering green innovation and social responsibility. Green innovation speeds up the attainment of green low-carbon transformation objectives by reducing carbon emissions and resource use [25]. The use of green low-carbon production helps to reduce these negative externalities [26]. Digital–carbon integration is also, through green innovation, technologically driven to improve production efficiency, reduce pollution, and promote enterprises and thereby to fulfill their environmental responsibilities. Digital–carbon integration facilitates green innovation by applying digital technologies to carbon emission management in two main ways. First, it supports the development of high-performance, low-carbon, and environmentally friendly products. Second, digital–carbon integration, grounded in information asymmetry and knowledge spillover theories, improves both internal and external information connectivity, thereby fostering green technology innovation. Internally, improved information sharing and data integration reduce information asymmetry within firms, optimize resource allocation, and enhance the efficiency of green research and development. Externally, stronger information connectivity promotes knowledge spillovers and collaborative innovation among firms, research institutions, and other stakeholders, which further accelerates the development and diffusion of green technologies. Through these mechanisms, digital–carbon integration improves production efficiency and ultimately promotes corporate sustainable development. Consequently, this investigation puts forward the following hypothesis.

H1. 

Digital–carbon integration fosters corporate green technology innovation, thereby advancing corporate sustainable development.

Research shows that corporate digital transformation also improves ESG performance by improving decision-making and operation management efficiency [2] and strengthening internal control [24]. By increasing operational and managerial efficiency, digital–carbon integration can support the sustainable growth of businesses as an extension of digital transformation in the area of environmental management. Specifically, digital–carbon integration uses the low-carbon concept to promote digitization, help enterprises to effectively monitor environmental impacts, improve resource utilization, optimize business processes, and reduce costs. In addition, digitization empowers low-carbon management, promotes internal management innovation and information sharing, improves operational efficiency and market competitiveness, and thus improves economic efficiency and sustainable development. Thus, hypothesis 2 is suggested.

H2. 

Digital–carbon integration can improve the efficiency of a corporation’s operation and management, so as to promote the sustainable development of enterprises.

A firm’s ownership structure shapes how digital–carbon integration influences corporate sustainable growth. Green innovation plays a more critical role in underpinning digital transformation within non-state-owned firms [20]. Relative to state-owned enterprises, non-state-owned firms exhibit greater agility, enabling them to quickly deploy new technologies and respond to market changes. Although state-owned enterprises may benefit from more favorable financing conditions [27], financing constraints compel non-state-owned firms to place greater emphasis on the returns to investments in digitalization and low-carbon transformation and to enhance their brand image and market position through the fulfillment of corporate social responsibility. Accordingly, the promoting effect of digital–carbon integration is expected to be stronger for non-state-owned enterprises.

Additionally, the influence of digital–carbon integration could change depending on the level of high-technology development within firms. High-technology enterprises possess stronger R&D capabilities and are better able to adopt advanced digital tools to optimize production processes [12], improve production efficiency, reduce energy consumption, and promote sustainable development. Furthermore, technological innovation in high-technology firms tends to follow an upward spiral trajectory [28], which facilitates the thorough merging of digital and green technologies. Therefore, high-technology firms are predicted to benefit more from digital–carbon integration concerning sustainable growth. Accordingly, the following theories are put forth in this study:

H3a. 

Digital–carbon integration has a stronger positive impact on sustainable development in non-state-owned enterprises relative to state-owned enterprises.

H3b. 

Digital–carbon integration yields a more notable positive effect on sustainable development in high-technology enterprises compared with non-high-technology enterprises.

2.2. Data Sources

Using Shanghai and Shenzhen A-share listed businesses as the study object and choosing data from 2013 to 2022, this paper focuses on China. We maintain samples that have no missing values for five years in a row, exclude samples from the financial industry, special treatment samples (STs), and delisted samples within the time, and compile a total of 24,074 sample observations. Data on corporate sustainable development performance are drawn from the ESG rating system of Huazheng. Data regarding firms’ digital–carbon integration are derived from textual mining and analyzing word distances in their annual reports. Information such as company finance data can be found on the official websites of the Shenzhen Stock Exchange, the Shanghai Stock Exchange, and the CSMAR database.

2.3. Variables Design

2.3.1. Explained Variable

Corporate sustainable development (SD). Corporate governance, social responsibility, and environmental responsibility are all included in the broad idea of business sustainable development [29]. Many people consider environmental, social, and corporate governance (ESG) performance to be a key metric for assessing corporate sustainability [30]. According to Wang [31], this study uses the Huazheng ESG score as a proxy for corporate sustainable development. The index is based on the characteristics of various listed companies and the realities of China’s capital market. The rating system is based on three pillars: environmental, social, and corporate governance. Firms are classified into nine rating levels and are evaluated four times a year. In this study, we convert these ratings into numerical scores ranging from 1 (lowest) to 9 (highest), and the annual average score of each firm is adopted to reflect its sustainable development performance in that year [2]. In the subsequent robustness test, the ESG rating is regressed as an ordered variable, and the main conclusions are still robust.

2.3.2. Explanatory Variable

Corporate digital–carbon integration level (lnDCI). Following Wu et al. [19] in measuring the level of digital transformation, this study constructs an index system for corporate digital–carbon integration by combining text mining and word distance analysis [32], supported by Python 3.14.0based web crawling techniques, and derives a digital–carbon integration development index. First, textual mining is conducted on the annual reports of Chinese A-share listed firms on the Shanghai and Shenzhen stock exchanges from 2013 to 2022 to identify keywords related to digitalization and low carbon. Two sets of keywords are defined. The first set consists of digitalization-related keywords, such as blockchain, cloud computing, artificial intelligence, big data, and digital technology applications. The second set comprises low-carbon-related keywords, including green and low-carbon technologies, low-carbon energy transition, green technology innovation, carbon peaking, and carbon neutrality. Second, in order to evaluate the degree of digital–carbon integration, word distance statistics are used, that is, digital and low-carbon keywords appear simultaneously within 500 word distances, which is recorded as 1 instance. Crucially, we further conduct semantic screening and exclude negative contexts that indicate no intention, rejection, or suspension of digital–carbon practices (e.g., statements containing “no,” “not,” “never,” “no interest,” “suspended”). We only retain text segments with positive or neutral semantics that reflect the firm’s real actions, plans, or operational intentions regarding digital–carbon integration. Finally, the total number of word distances in an annual report is counted, and the total number of digital–carbon integration of each enterprise is added to 1, before being logarithmically processed to measure the level of the digital–carbon integration development of enterprises.

2.3.3. Control Variables

In order to increase the accuracy of the empirical analysis, this study follows the approaches of Hu et al. [2], Zhai et al. [33], and Lei et al. [34] by incorporating a set of control variables that may affect corporate sustainable development. Firm age (age), leverage ratio (lever), board size (board), percentage of independent directors (indep), CEO duality (dual), ownership concentration (top1), and fixed asset ratio (fixed) are some of these variables.

Table 1. Variables definition.

Variable Type	Variable	Variable Definition
Explained variable	SD	Annual average of Huazheng ESG quarterly ratings (1–9)
Explanatory variable	lnDCI	Text mining and word distance analysis, log-transformed
Control variables	age	ln (current year–year of firm listing +1)
	lever	Total corporate liabilities/total corporate assets
	board	ln (number of board members)
	indep	Number of independent directors/total number of board members
	dual	A dummy variable equal to 1 if the chairman and the CEO positions are held by the same individual, and 0 otherwise
	top1	Shareholding ratio of the largest shareholder
	fixed	Fixed assets/total assets

provides comprehensive definitions for every variable.

2.4. Empirical Model

To examine the impact of digital–carbon integration on corporate sustainable development, this study constructs the following baseline regression model:

$${\mathrm{SD}}_{\mathit{it}}={\alpha }_0+{\alpha }_1{\mathrm{lnDCI}}_{\mathit{it}}+\sum _{\mathrm{j}=1}^{\mathrm{n}}{\gamma }_j{\mathrm{Control}}_{\mathit{ij}}+{\delta }_i+{\phi }_t+{\eta }_{\mathit{it}}$$

(1)

where ${\mathrm{SD}}_{\mathit{it}}$ indicates the firm’s sustainable development performance in year t, and ${\mathrm{lnDCI}}_{\mathit{it}}$ indicates the firm’s degree of digital–carbon integration in the same year. To reduce the possibility of data volatility, the natural logarithm is used. Firm age (age), leverage ratio (lever), board size (board), percentage of independent directors (indep), CEO duality (dual), ownership concentration (top1), and fixed asset ratio (fixed) are among the control variables denoted by $\mathrm{Control}$. Firm fixed effects and year fixed effects are both included in the baseline regression. Industry fixed effects and geographical fixed effects are also incorporated in robustness checks. In particular, ${\delta }_i$ is the error term, ${\phi }_t$ indicates year fixed effects, and ${\eta }_{\mathit{it}}$ indicates firm-specific fixed effects. The extent of the impact of digital–carbon integration on corporate sustainable development is captured by the coefficient ${\alpha }_1$. This study also uses a two-stage mediation model based on Equation (1) to investigate the mediating impacts of digital–carbon integration on corporate sustainable development, as follows:

$${\mathrm{M}}_{\mathit{it}}={\alpha }_2+{\alpha }_3{\mathrm{lnDCI}}_{\mathit{it}}+{\delta }_i+{\phi }_t+{\epsilon }_{\mathit{it}}$$

(2)

$${\mathrm{SD}}_{\mathit{it}}={\alpha }_4+{\alpha }_5{\mathrm{M}}_{\mathit{it}}+{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\gamma }_j{\mathrm{Control}}_{\mathit{ij}}+{\delta }_i+{\phi }_t+{\mu }_{\mathit{it}}$$

(3)

When two sets of mediating variables (M) are used. The total number of green invention and green utility model patents the company has applied for serves as the first mediator. The firm’s level of green technology innovation is measured using the natural logarithm of this total (lnenvpatr). The company’s net profit margin on sales (margin), which measures management and operational effectiveness, is the second mediator. The other variables’ definitions align with those already mentioned.

3. Empirical Results and Analysis

3.1. Descriptive Statistics and Correlation Analysis

3.1.1. Descriptive Statistics

Table 2. Results of descriptive statistics.

Variable	Obs	Mean	SD	Min	Max
SD	24,074	4.162	0.949	1.000	7.750
lnDCI	24,074	0.404	0.797	0.000	5.690
age	24,074	2.198	0.824	0.000	3.497
lever	24,074	0.414	0.196	0.008	1.380
board	24,074	2.280	0.255	1.386	3.401
indep	24,074	0.386	0.076	0.188	0.800
dual	24,074	0.231	0.422	0.000	1.000
top1	24,074	33.330	14.524	0.290	89.990
fixed	24,074	0.205	0.155	0.000	0.954

presents the descriptive statistics. The explained variable, corporate sustainable development (SD), has a mean of 4.162, a standard deviation of 0.949, a minimum value of 1, and a maximum value of 7.750. With a mean of 0.404, a standard deviation of 0.797, a minimum of 0, and a maximum of 5.690, the explanatory variable, digital–carbon integration development (lnDCI), shows significant variation in enterprises’ degrees of digital–carbon integration.

3.1.2. Correlation Analysis

The results of the correlation analysis outcome are shown in

Table 3. Results of correlation analysis.

Variable	SD	lnDCI	Age	Lever	Board	Indep	Dual	Top1	Fixed
SD	1.000
lnDCI	0.084 ***	1.000
age	−0.058 ***	0.022 ***	1.000
lever	−0.047 ***	0.022 ***	0.341 ***	1.000
board	−0.031 ***	−0.009	0.210 ***	0.155 ***	1.000
indep	0.074 ***	0.018 ***	−0.119 ***	−0.072 ***	−0.190 ***	1.000
dual	−0.018 ***	0.034 ***	−0.195 ***	−0.087 ***	−0.140 ***	0.101 ***	1.000
top1	0.064 ***	−0.088 ***	−0.031 ***	0.055 ***	0.000	0.005	−0.029 ***	1.000
fixed	−0.060 ***	−0.157 ***	0.088 ***	0.051 ***	0.109 ***	−0.035 ***	−0.091 ***	0.097 ***	1.000

Notes: *** p < 0.01.

. The higher the level of lnDCI, the better the sustainable development, according to the correlation coefficient between lnDCI and SD, which is 0.084 and considerably positive at the 1% level. There is no multicollinearity and every variable passes the correlation test.

3.2. Benchmark Regression Results

Before conducting the regression analysis, a VIF test was performed. The findings indicate that there are no multicollinearity problems between the VIF value of each variable ranging from 1 to 2. The null hypothesis that the random effects model is better than the fixed effects model is rejected by the Hausman test results, which indicate that the Hausman statistic is significant at the 1% level. Thus, the impact of digital–carbon integration on corporate sustainable growth is examined in this study using the fixed effects model. The baseline regression analysis findings are shown in

Table 4. Estimated results of the benchmark regression.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
lnDCI	0.016 **	0.020 ***	0.021 ***	0.021 ***	0.021 ***	0.021 ***	0.022 ***	0.022 ***
	(0.008)	(0.008)	(0.008)	(0.008)	(0.008)	(0.008)	(0.008)	(0.008)
age		−0.244 ***	−0.200 ***	−0.199 ***	−0.200 ***	−0.200 ***	−0.194 ***	−0.185 ***
		(0.018)	(0.018)	(0.018)	(0.018)	(0.018)	(0.019)	(0.019)
lever			−0.475 ***	−0.470 ***	−0.469 ***	−0.469 ***	−0.471 ***	−0.464 ***
			(0.051)	(0.051)	(0.051)	(0.051)	(0.051)	(0.051)
board				−0.144 ***	−0.163 ***	−0.164 ***	−0.162 ***	−0.164 ***
				(0.025)	(0.025)	(0.025)	(0.025)	(0.025)
indep					0.347 ***	0.348 ***	0.345 ***	0.351 ***
					(0.076)	(0.076)	(0.076)	(0.076)
dual						−0.021	−0.021	−0.022
						(0.018)	(0.018)	(0.018)
top1							0.002 *	0.002 *
							(0.001)	(0.001)
fixed								−0.354 ***
								(0.075)
Observations	24,072	24,072	24,072	24,072	24,072	24,072	24,072	24,072
R-squared	0.580	0.584	0.585	0.586	0.587	0.587	0.587	0.587
ID FE	YES	YES	YES	YES	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES	YES	YES	YES	YES

Note: t statistical value in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

. At the 5% significance level, the regression findings with just the primary independent variable, digital–carbon integration, shown in column (1), reveal a favorable impact on business sustainable development. The stepwise regression results with gradually additional control variables are shown in columns (2), (3), (4), (5), (6), (7), and (8). The findings demonstrate that, at the 1% significance level, digital–carbon integration strongly encourages corporate sustainable development, indicating that businesses’ efforts to combine digitalization and low-carbon development greatly improve their sustainability.

3.3. Robustness Tests

To further confirm the reliability of the baseline regression findings, we conduct a series of robustness checks. First, to address potential biases from unobserved industry- and region-level factors, we additionally control for industry fixed effects and regional fixed effects in the baseline model. Column (1) of

Table 5. Results of the robustness test.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
lnDCI	0.018 **	0.019 **	0.013 *	0.022 ***	0.020 ***	0.025 ***	0.099 ***	0.057 **	0.065 ***	0.015 ***
	(0.008)	(0.008)	(0.008)	(0.008)	(0.008)	(0.008)	(0.015)	(0.023)	(0.009)	(0.005)
Control	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
ID FE	YES	YES	YES	YES	YES	YES	NO	YES	YES	YES
YEAR FE	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
REGION FE	NO	YES	NO	NO	NO	NO	NO	NO	NO	NO
INDUSTRY FE	YES	NO	NO	NO	NO	NO	YES	NO	NO	NO
Observations	24,072	24,001	24,072	24,072	22,304	21,345	24,072	24,072	24,072	24,072
R-squared	0.593	0.592	0.592	0.587	0.597	0.634	0.318	0.179	0.824	0.753

Note: t statistical value in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

presents results with industry fixed effects, while the second column incorporates regional fixed effects. Second, we introduce firm size, measured as the natural logarithm of total assets, as an additional control variable in the third column, and remove the fixed asset ratio from the set of controls in the fourth column. Finally, we perform subsample tests by excluding observations from 2013 and 2022, respectively, with results shown in the fifth and sixth columns, to test the stability of the core relationship. Column (7) shows the robustness test results using ESG rating (SD) as an ordinal variable, using the ordered logit model and controlling the fixed effects of year and industry. Column (8) reports the robustness test results with ESG rating (SD) treated as an ordinal variable using the ordered logit model, while controlling for individual and year fixed effects. Columns (9) and (10) replace the keyword frequency measure of digital–carbon integration with alternative patent-based indicators. Specifically, we use the count of digital economy patent applications as an alternative measure in the ninth column, while the tenth column adopts the number of green low-carbon patent applications. Across all of these robustness checks, the coefficient of lnDCI remains consistently positive and statistically significant, which confirms that the positive impact of digital–carbon integration on corporate sustainable development persists robustly even after incorporating a variety of additional controls and alternative measurement strategies. This further confirms the validity of the relationship between digital–carbon integration and corporate sustainable development.

3.4. Endogeneity Tests

This study initially uses a two-stage least squares (2SLS) regression to solve the endogeneity problem between digital–carbon integration and corporate sustainable development. As the influence of digital–carbon integration on corporate sustainable development may have a lag effect, the lagged value of digital–carbon integration (L.lnDCI) is used as an instrumental variable and reintroduced into the model for regression. The results of the regression are shown in

Table 6. Results of endogeneity tests.

Variable	(1)	(2)	(3)	(4)	(5)
	First	Second	PSM	PSM	PSM
	lnDCI	SD	1: 1 Nearest Neighbor Matching	Radius Matching	Nuclear Matching
lnDCI		0.113 ***	0.019 *	0.021 ***	0.021 ***
		(3.103)	(0.010)	(0.008)	(0.008)
L.lnDCI	0.236 ***
	(19.606)
Control	YES	YES	YES	YES	YES
ID FE	YES	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES	YES
Observations	21,309	21,309	10,851	24,072	24,072

Note: t statistical value in parentheses; *** p < 0.01, * p < 0.1.

. Due to its highly positive coefficient in column (1), the instrumental variable L.lnDCI meets the relevance criterion. The L.lnDCI coefficient in column (2) is 0.113 and statistically significant at the 1% level, suggesting that digital–carbon integration greatly enhances a company’s sustainable development. These findings confirm that, even after accounting for potential endogeneity, the favorable effects of digital–carbon integration on corporate sustainable growth remain robust. To further mitigate sample selection bias, this study employs the propensity score matching (PSM) method. Following the approach of Zeng et al. [35], we partition the sample into treatment and control groups based on firms’ engagement in digital–carbon integration. Control variables are included as covariates, and matching is implemented via 1:1 nearest neighbor matching, radius matching, and kernel matching. We re-estimate the model for the matched samples. Column (3) presents the results from 1:1 nearest neighbor matching, while columns (4) and (5) report estimates derived from radius matching and kernel matching, respectively. The evidence verifies that, even when using propensity score matching to address endogeneity, the impact of digital–carbon integration on corporate sustainable development remains statistically significant.

4. Further Analysis

4.1. Mechanism Analysis

After a series of robustness checks, the above analysis empirically confirms that digital–carbon integration can effectively enhance corporate sustainable development. On this basis, this study further explores the mechanisms through which digital–carbon integration affects corporate sustainable development. This study uses a two-stage mediation model to further investigate the internal pathways by which digital–carbon integration promotes corporate sustainable development via green technology innovation and firms’ operational efficiency, building on the methodological approach of Hu et al. [2].

Table 7. Results of mechanism analysis.

Variable	Green Technology Innovation		Corporate Operation Management
	(1)	(2)	(3)	(4)
lnDCI	0.030 *** (0.006)		0.004 *
			(0.002)
lnenvpatr		0.041 ***
		(0.010)
margin				0.050 **
				(0.023)
	(0.004)	(0.081)	(0.012)	(0.082)
Observations	24,072	24,072	24,072	24,072
R-squared	0.735	0.587	0.299	0.587

Note: t statistical value in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

presents the comprehensive empirical findings. First, green technology innovation is examined. In accordance with Song et al. [36], this study counts the number of green invention and green utility model patents that each A-share listed company submitted for between 2013 and 2022 using patent classification data from the China National Research Data Service Platform (CNRDS). To measure firms’ green technology innovation, we sum the number of utility and green invention patents, add one, and then take the natural logarithm of this total. The regression findings displayed in column (1) of Table 7 indicate that the coefficient of digital–carbon integration on green technology innovation is 0.030 and is considerably positive at the 1% level. This suggests that digital–carbon integration greatly improves firms’ capacity for green technology innovation. Additionally, column (2) demonstrates that business sustainable development is greatly enhanced by green technology innovation, suggesting that green technology innovation encourages firms to assume greater environmental responsibility and thereby promotes sustainable development. Therefore, digital–carbon integration enhances corporate sustainable development through green technology innovation, providing empirical support for hypothesis H1.

Second, corporate operational management is analyzed. This study uses firms’ net profit margin on sales as a proxy for operational management performance. The coefficient of digital–carbon integration on operational management is 0.004 and is significantly positive at the 10% level, according to the regression results in column (3) of Table 7, suggesting that digital–carbon integration increases enterprises’ operational and management efficiency. Additionally, column (4) demonstrates that the operational management efficiency coefficient on corporate sustainable development is 0.050, which is highly favorable at the 5% level, indicating that improved operational management promotes corporate sustainable development by enhancing firms’ net profit margins. Therefore, digital–carbon integration enhances corporate sustainable development through improved operational management, thereby validating hypothesis H2.

4.2. Heterogeneity Analysis

4.2.1. Heterogeneity Based on Enterprise Ownership

Subgroup regressions based on ownership structure are performed in this study for both state-owned and non-state-owned businesses. Only non-state-owned businesses exhibit a statistically significant beneficial impact from digital–carbon integration on corporate sustainable development, as indicated by the regression results presented in

Table 8. Results of heterogeneity analysis.

Variable	(1) State-Owned Enterprises	(2)	(3)	(4)
		Non-State-Owned Enterprises	High-Technology	Non-High-Technology
lnDCI	0.014	0.025 ***	0.020 **	0.021
	(0.013)	(0.009)	(0.009)	(0.014)
Chow test	14.540 ***		8.750 ***
Control	YES	YES	YES	YES
ID FE	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES
Observations	7929	16,093	15,498	8560
R-squared	0.639	0.578	0.568	0.642

Note: t statistical value in parentheses; *** p < 0.01, ** p < 0.05; the Chow test represents the Chow test statistic.

. State-owned businesses do not exhibit any statistically significant benefit. In particular, the coefficient of lnDCI in the non-state-owned firm group is 0.025 and is considerably positive at the 1% level. With a test statistic of 14.54 and a p-value of 0.000, significant heterogeneity between the two groups is confirmed by the Chow test for coefficient differences between groups, which also shows statistical significance at the 1% level. These findings indicate that digital–carbon integration yields a more pronounced boosting effect on sustainable development among non-state-owned enterprises relative to state-owned enterprises. The following are some possible explanations. State-owned enterprises are subject to direct or indirect government intervention, which may weaken innovation incentives and lead to more conservative operational behavior. Moreover, high internal agency costs in state-owned enterprises may dampen employees’ motivation to innovate. Non-state-owned businesses, on the other hand, are typically more adaptable and open, which allows them to quickly adopt cutting-edge techniques and new technologies. Facing more intense market competition, their employees tend to exhibit stronger innovation incentives and are more inclined to enhance corporate sustainable development through digital–carbon integration, thereby strengthening market competitiveness and more effectively attracting investors and consumers.

4.2.2. Heterogeneity Based on the High-Technology Development Level of Enterprises

To do a heterogeneity analysis on the firms’ degrees of high-technology development, this study separates the sample into high-technology enterprises and non-high-technology organizations. Columns (3) and (4) present the regression results. Only high-technology companies benefit greatly from digital–carbon integration in terms of corporate sustainable development. In particular, the coefficient of lnDCI is insignificant in the non-high-technology sample, but it is 0.020 in the high-technology sample and considerably positive at the 5% level. A substantial difference between the two groups is indicated by the Chow test for intergroup coefficient differences, which provides a test statistic of 8.75 with a p-value of 0.000, which in turn is statistically significant at the 1% level. This suggests that high-technology companies benefit more from digital–carbon integration in terms of sustainable development than non-high-technology companies. Possible explanations are as follows. High-technology firms possess advantages in technology innovation and market adaptability, enabling them to utilize digital–carbon integration technologies more efficiently so as to improve operational efficiency and resource utilization while reducing environmental impacts. Compared with non-high-technology firms, high-technology firms face greater market pressure and thus have stronger incentives to enhance their sustainable development performance in order to meet stakeholder expectations, attract capital, and strengthen their brand image. Moreover, high-technology firms typically have more mature data-driven decision-making systems, which allow them to advance sustainability initiatives more precisely, foster innovative business models, and enhance the transparency and efficiency of corporate governance.

5. Conclusions and Policy Implications

A crucial tactic for advancing Chinese corporate sustainable growth is digital–carbon integration, which blends digitalized manufacturing methods with green and low-carbon development concepts. This study uses data from Chinese A-share listed businesses on the Shanghai and Shenzhen stock exchanges between 2013 and 2022 to examine the effects of digital–carbon integration on corporate sustainable development. The findings demonstrate that, by promoting green technology innovation and increasing operational efficiency, enterprises’ digital–carbon integration transformation greatly improves sustainable development performance. Heterogeneity study further reveals that high-technology companies and non-state-owned businesses benefit more from digital–carbon integration in terms of sustainable growth.

There are still several limitations with this study, despite the fact that it offers fresh empirical data on corporate sustainability and digital–carbon integration. First, digital–carbon integration is mainly measured via textual analysis of annual reports. While patent-based indicators are used for robustness tests, constructing a precise measure of cross-domain (digital–green low-carbon) patents requires more extensive data work, which could be refined in future research. Second, although 2SLS and PSM mitigate endogeneity, unobservable factors may still bias results. Third, the sample is confined to Chinese A-share listed firms, limiting generalizability to other contexts. Future research could extend findings with more detailed data, quasi-natural experiments, and cross-country comparisons.

This study suggests the following policy implications in light of the aforementioned findings. First, one should promote the coordinated development of digitalization and low-carbon transformation in enterprises. The findings demonstrate that enterprises’ success in sustainable development is greatly enhanced by digital–carbon integration. Governments should therefore improve policy support for digital and low-carbon transformation and establish a more scientific evaluation system to guide firms in continuously enhancing their level of digital–carbon integration. Second, one should seek to strengthen the role of digital–carbon integration in promoting green technology innovation. Empirical results indicate that green innovation is an important mechanism through which digital–carbon integration enhances sustainable development. Governments can encourage firms to conduct green R&D and promote technology commercialization through policy tools such as fiscal subsidies and tax incentives. Third, one should enhance firms’ operational efficiency through digital–carbon integration. The findings show that improved operational efficiency is another key channel linking digital–carbon integration and sustainable development. Firms should leverage digital technologies to optimize production processes and resource allocation, while governments should continue to improve digital infrastructure to support digital and low-carbon management. Fourth, one should implement differentiated policies to promote digital–carbon integration across different types of firms. According to heterogeneity studies, high-technology and non-state-owned businesses benefit more. Policy design should therefore account for firm heterogeneity by encouraging these firms to play a leading role while providing more targeted support for state-owned and non-high-technology firms.