Digital Transformation and Carbon Reduction in Chinese Industrial Enterprises: Mediating Role of Green Innovation and Moderating Effects of ESG Practices

Abstract

The urgent global challenge of mitigating climate change has intensified the need to reduce carbon emissions. China significantly contributes to greenhouse gas emissions, placing substantial pressure on its industrial sector to shift toward a low-carbon economy. However, current efforts have not yet achieved adequate progress in emission reduction. Digital Transformation (DT), involving the integration of digital technologies into business operations, offers a promising pathway for sustainable practices and emission reduction in Chinese industrial enterprises. This study investigates the impact of DT on Carbon Emissions Intensity (CEI) using data from listed companies (2013–2022) and explores the moderating role of Environmental, Social, and Governance (ESG) practices. Findings reveal that DT significantly reduces CEI, with green technological innovation (GTI) acting as a key intermediary. ESG moderates both the direct relationship between DT and CEI and indirectly influences intermediary variables like GTI, further affecting CEI. Heterogeneity analysis shows DT effectively curbs CEI in capital- and technology-intensive industries in China’s eastern and western regions, though its impact is weaker elsewhere. The study recommends that policymakers promote DT through targeted incentives, boost GTI, and strengthen ESG oversight and disclosure. These measures can help industrial enterprises leverage digitalization and sustainability to advance China’s carbon neutrality goals. The insights also provide valuable recommendations for other developing nations facing similar environmental challenges and seeking sustainable development pathways.

1. Introduction

As global climate change intensifies, mitigating carbon emissions and environmental protection have become critical global priorities, particularly in achieving the ambitious goals of the Paris Agreement’s goal of limiting warming to well below 2 °C [1]. Global emissions have increased significantly in recent decades, driven by industrial expansion, urbanization, and a growing reliance on fossil fuels [2]. In this aspect, China, the world’s largest carbon emitter, contributed 31% of global carbon dioxide emissions (CO₂) in 2023 (12.6 billion metric tons) [3] and faces significant pressure to transition to a low-carbon economy. Its industrial sector, which accounts for 37.5% of GDP [4] and 59.8% of total energy consumption [5], remains heavily reliant on fossil fuels, with coal comprising 52% of its energy mix in 2023 [6]. The industrial sector thus remains a critical driver of emissions. Industrial activities, including steel, cement, and manufacturing, are responsible for 41.5% of China’s carbon emissions [7], posing a major challenge to its pledge to peak emissions by 2030 and achieve carbon neutrality by 2060 [8]. Reducing emissions are crucial in this endeavor and present a significant challenge to achieving sustainability goals. While China actively promotes emission reduction and has committed to these ambitious climate goals [9], reaching these targets requires a multifaceted approach integrating technological innovation and green industrial transformation [10,11]. Amid growing global concerns about climate change, the digital transformation (DT) of Chinese industrial enterprises—which encompasses integrating digital technologies across various of business operations within Chinese industrial enterprises—has gained increasing attention from academia and industry as a key strategy, a promising tool, and an essential lever for fostering sustainable practices and reducing carbon emissions.

At its most basic level, the core of DT involves integrating digital technologies and computer systems to convert information into binary. More broadly, DT enhances society’s intelligence, improves information management capabilities, and optimizes resource allocation [12]. Given that digitalization provides a strategic direction for achieving sustainable development in the manufacturing sector, DT in manufacturing enterprises can be defined as the restructuring of production processes, operations, and organizational structures using digital technologies to enhance operational efficiency and competitiveness [13]. With the rapid expansion of the digital economy, intelligence, informatization, and digitalization have become essential for Chinese manufacturing enterprises, particularly in optimizing manufacturing processes and resource utilization. This was evident from the resilience demonstrated by digitally advanced enterprises, which experienced fewer disruptions and recovered faster during the COVID-19 pandemic [14]. In alignment with this, China’s 14th Five-Year Plan emphasizes improving environmental quality, accelerating the green transformation of the development model, and comprehensively enhancing resource use efficiency by promoting energy efficiency and digitalization in emerging technologies such as 5G and big data [11,12]. This plan also underscores the importance of fostering DT among enterprises to drive economic modernization and upgrading [15]. Additionally, prior research [16] indicates that DT contributes to lowering carbon emissions by facilitating technology innovation, enhancing carbon management and reduction efforts, and minimizing contractual expenses. Furthermore, green IT innovation complements DT efforts by assisting enterprises in reducing their carbon emissions.

Despite significant policy efforts to reduce carbon emissions, the pace of reduction in China’s industrial sector has been slower than expected. As mentioned earlier, the industrial sector significantly contributed to emissions in 2023. A similar trend was observed in 2022, when the total CO₂ reached 12.1 billion tons [17]. In 2022, the industrial sector accounted for approximately 56.12% of China’s total energy consumption [10] and was responsible for approximately 80% of the country’s total CO₂ [11]. These statistics highlight the crucial role of industrial enterprises in achieving national carbon neutrality goals. Despite improvements in energy efficiency, China’s industrial sector continues to face significant challenges in reducing carbon emissions, particularly in energy-intensive industries such as steel, cement, and chemical manufacturing. This raises critical questions: How effective is DT in reducing carbon emissions in the industrial sector? What key mechanisms drive this transformation? To what extent do mediating and moderating variables influence the relationship between DT and CEI? Although the potential of DT in reducing carbon emissions has been recognized, empirical evidence remains mixed. Some studies suggest a positive relationship between DT and emission reductions [15,18], while others highlight potential rebound effects where increased efficiency may lead to higher production volumes, potentially negating these benefits [19,20]. Understanding the true impact of DT on CEI in a specific industrial context is thus crucial for developing effective mitigation strategies and achieving China’s “dual carbon goal on schedule” [2]. “Dual carbon” refers to the shorthand for “carbon peak” and “carbon neutrality”. China’s Dual Carbon goal, unveiled by President Xi Jinping on 22 September 2020, during his United Nations General Assembly speech, commits the nation to peaking carbon dioxide emissions before 2030 and achieving carbon neutrality before 2060. As the world’s largest greenhouse gas emitter, China aims to address its significant environmental footprint by accelerating the transition from coal-dominated energy systems to renewables, enhancing energy efficiency, and deploying carbon capture technologies. This pledge reflects a strategic shift toward sustainable development, balancing economic growth with climate action, and has been integrated into national policies like the 14th Five-Year Plan (2021–2025). For a comprehensive analysis, see World Resources Institute, “Accelerating the Net-Zero Transition: Strategic Action for China’s 14th Five-Year Plan” (published on 2 December 2020, available at https://www.wri.org/research/accelerating-net-zero-transition-strategic-action-chinas-14th-five-year-plan, accessed on 14 March 2025) and the Guidance on Promoting High-Quality Development of Central Enterprises and Implementing Carbon Peaking and Carbon Neutrality Work [EB/OL] (27 November 2021) by the State-owned Assets Supervision and Administration Commission of the State Council (http://www.sasac.gov.cn/n2588035/c22499825/content.html), accessed on 30 December 2021.

However, a comprehensive review of the existing literature reveals that current academic research on DT primarily focuses on its economic [21,22,23], social [24,25,26], and environmental [27,28] impacts. While these perspectives provide valuable insights into DT’s potential for reducing carbon emissions, further in-depth exploration is needed. At the micro-level, the existing studies have examined the economic and social impacts of an enterprise’s DT, but research addressing its environmental effects remains limited and incomplete. At the macro level, although some studies have investigated the environmental impacts of DT, a clear consensus has yet to be reached. Therefore, using micro-level data from industrial enterprises as research samples, this paper explores the environmental and innovation-related impacts of DT, discussing whether DT can achieve environmental and technological dividends from the perspective of micro-enterprises.

Therefore, this research aims to explore the impact of DT on CEI in Chinese industrial enterprises. Specifically, it analyzes the heterogeneous effects of DT, investigates the mediating and moderating effects of factors linking DT and CEI, and identifies the underlying mechanisms. To achieve this goal, the study uses micro-level panel data from registered industrial enterprises spanning from 2013 to 2022. It employs econometric methodologies, including a two-way fixed effect model, a mediation effect model, and a moderation effect model, to assess the influence of DT and ESG practices (as a comprehensive performance measure of corporate environmental responsibility, social responsibility, and internal governance) on CEI. The study also examines the moderating effect of ESG practices and analyzes heterogeneity across different regions and industries. This study provides significant academic and practical contributions. Its findings enrich the existing body of knowledge regarding how DT can promote environmental sustainability in a large developing economy like China. By understanding the effectiveness of DT in reducing CEI, policymakers can develop targeted policies and incentives to encourage digital technology adoption in industries. The research provides valuable insights for policymakers and industrial practitioners, both in China and beyond, to formulate targeted strategies to combat climate change. Additionally, the results of this research can enhance best practices for digitalization and help achieve carbon neutrality in other developing countries facing similar challenges.

This study makes five significant contributions to the literature as follows. First, it investigates the carbon emission reduction effects of enterprises from the perspective of DT in listed industrial companies, broadening the research scope concerning DT’s impact on CEI in industrial enterprises and enriching the pathways and measures for CEI within enterprises. Second, it clarifies the mechanisms by which DT affects CEI and introduces GTI into the analytical framework to assess its role as a mediator in the relationship between DT and CEI. Third, it explores the relationship between ESG disclosure, DT, and CEI, further refining the mediating role of GTI. To the best of the researchers‘ knowledge, this study is among the first to examine these effects using a large sample of listed industrial firms in China, with GTI serving as a mediating variable. Fourth, it explores the regional and industry-level heterogeneity in the impact of DT on CEI, providing a nuanced understanding of how context influences outcomes. In other words, the findings offer tailored insights for policymakers and corporate leaders operating in diverse economic contexts by identifying sector-specific and geography-dependent patterns in the DT-CEI relationship. Finally, this research adopts a policy-oriented approach, offering actionable policy recommendations that bridge the gap between academic theory and practical application. This is achieved by providing valuable insights into the unique challenges and opportunities associated with implementing digital technologies, thereby informing carbon emission reduction strategies in both developed and developing economies.

The structure of the paper is as follows. Section 2 reviews the literature, while Section 3 outlines the theoretical analysis and research hypotheses. Section 4 details the research design, including sample selection, data sources, variable descriptions, and model specifications. Section 5 presents an analysis of the empirical results. Section 6 expands on the study with a mechanism analysis. Section 7 discusses further research, focusing on the moderating effects and heterogeneity analysis. Section 8 summarizes the research findings, offers policy recommendations, and provides suggestions for future research.

2. Literature Review

2.1. Carbon Emission Reduction

In recent years, the low-carbon economy has become a vital trend in global economic development [29]. In China, energy conservation and emission reduction are not only key strategies for combating climate change but also essential for promoting stable economic growth and sustainable ecological development [30]. Carbon emission reduction refers to the process of lowering greenhouse gas emissions, particularly CO₂, through various technologies and strategies [31]. This process is characterized by innovation, diversity, sustainability, systematic integration, and collaboration [32]. This multidimensional process underscores the complex yet essential nature of decarbonization efforts in contemporary economies.

A variety of factors influence carbon emission reduction. Ref. [17] suggests that when market size is limited and the cost of green innovation is high, neglecting social responsibility may help mitigate negative impacts on carbon reduction. However, incorporating social responsibility can enhance emission reduction efforts in contexts characterized by either small market size or high R&D costs. Ref. [33] argues that green finance significantly reduces regional carbon emissions, exhibiting the strongest effects in western China, followed by central regions, with comparatively weaker impacts in eastern areas. regional carbon emissions, with the strongest effects observed in western China, followed by central regions, and comparatively weaker impacts in the east. Ref. [34] also notes that the digital economy lowers industrial carbon intensity by promoting technological innovation, improving energy efficiency, and optimizing industrial structures. According to [35], upgrading industrial structures and optimizing energy consumption substantially reduce emissions, although these effects vary significantly across regions. Ref. [36] finds that narrowing the urban–rural income gap generally supports carbon reduction, especially in central and western areas; however, in the more developed eastern regions, it may inadvertently lead to increased emissions. Ref. [37] emphasizes that fiscal regulation reduces carbon emission intensity by improving fiscal resource allocation and infrastructure, particularly in areas with a high concentration of polluting industries.

2.2. Digital Transformation and Carbon Emission Reduction

The concept of DT varies widely across academic and business contexts [38]. Some scholars define it as the innovative application of digital technologies—combined with key resources such as human and financial capital—to fundamentally reshape organizational operations and support sustainable development [39]. Others describe it as a comprehensive process that enhances organizational functions by integrating information technology, computing, and communication systems [40]. Despite differing perspectives, there is broad consensus that DT enhances service delivery, drives organizational and cultural change, and creates value through digital innovation [41].

A growing body of research has examined the significant impact of DT on carbon emission reduction. Some studies focus on its direct effects. For instance, Ref. [42] found that DT significantly lowers carbon emissions in Chinese manufacturing firms, with the strongest effects observed in state-owned enterprises. Similarly, Ref. [43] reported that DT reduces carbon intensity, particularly in energy-intensive sectors under strict environmental regulations or high industry competition. Ref. [44] identified an inverted U-shaped relationship between DT and carbon emissions in manufacturing firms.

Other studies explore indirect mechanisms that affect carbon reduction. Ref. [15] argue that DT reduces emissions by enhancing technological innovation, internal controls, and environmental information disclosure. Ref. [45] observed that DT initially leads to a short-term decline in green productivity and carbon efficiency. However, in the long term, it positively impacts emissions by fostering green innovation and optimizing resource allocation, ultimately creating synergy between pollution control and emission reduction. Ref. [46] proposed a moderated and mediation model in which green technological innovation and policy incentives—such as environmental subsidies and tax benefits—mediate and moderate the impact of DT on carbon reduction. Ref. [47] emphasized that DT not only promotes green innovation but also strengthens internal controls and improves financing conditions, significantly reducing emissions, particularly in state-owned industrial enterprises. Notably, the relationship is linear in low-energy-consuming firms but follows an inverted U-shape in high-energy-consuming enterprises.

2.3. Green Technological Innovation and Carbon Emission Reduction

GTI has become a key driver of corporate sustainability and high-quality economic growth. In the current economic context, it plays a vital role in advancing technological progress, enhancing firms’ ecological and economic performance, and optimizing resource allocation [48]. With its substantial ecological benefits and positive technological externalities, GTI is instrumental in aligning economic development with environmental protection [49].

Numerous studies have examined GTI as a mediating variable, particularly concerning DT’s impact on firm and regional development. Ref. [50] found that DT enhances total factor productivity through GTI. Ref. [51] argued that GTI fully mediates the relationship between DT and firms’ financial performance. Similarly, Ref. [52] demonstrated that DT reduces ESG greenwashing by strengthening firms’ green innovation capabilities.

Other researchers have explored how GTI indirectly contributes to carbon emission reduction. Ref. [53] found that financial technology lowers regional carbon emissions via green innovation. Ref. [54] identified a threshold effect, where digitalization may increase emissions at low levels of green innovation but significantly reduce them at higher levels. Ref. [55] showed that green innovation is a key mediator between high-quality foreign direct investment (FDI) and carbon reduction. Ref. [56] highlighted the negative mediating role of green innovation in the relationship between digital finance and carbon intensity. Finally, Ref. [57] reported that the digital economy enhances provincial carbon productivity by improving GTI.

2.4. Literature Review Summary

Although existing research has established that DT contributes to carbon emission reduction through mechanisms such as technological innovation, internal control, and financing improvements, limited attention has been paid to GTI as a key mediating variable. Prior studies have primarily explored how DT, mediated by GTI, influences outcomes like ESG greenwashing, financial performance, and total factor productivity, while other research highlights the indirect role of GTI in linking financial technology, digitalization, FDI, and digital economy to carbon reduction. However, few studies have integrated these dimensions into a comprehensive analytical framework. Addressing this gap, the present study advances the literature by explicitly examining the mediating role of GTI in the relationship between DT and CEI, incorporating ESG disclosure, and accounting for regional and industry-level heterogeneity using micro-level panel data from registered industrial enterprises. This approach not only enriches the understanding of carbon emission reduction pathways within industrial enterprises but also offers policy-relevant insights tailored to diverse economic contexts, bridging theoretical research with practical applications.

3. Theoretical Analysis and Research Hypotheses

3.1. Digital Transformation and Carbon Emission Intensity of Industrial Enterprises

DT refers to the process through which industrial enterprises implement intelligent production processes, optimize supply chains, and facilitate data-driven decision-making by adopting information technology and digital tools. This is achieved through various mechanisms. First, DT improves the production efficiency of industrial enterprises by integrating advanced automation and intelligent systems, optimizing processes, minimizing resource waste, and reducing energy consumption. This enhances production planning accuracy and flexibility while lowering inventory and waste generation, ultimately reducing carbon emissions [58]. Second, DT optimizes supply chains by leveraging digital technology to enhance information management and networking, improving logistics efficiency and resource utilization [59]. This facilitates precise supply–demand matching, thereby mitigating resource waste and carbon emissions caused by information asymmetry [56,60]. Finally, DT improves the accuracy and evidence-based nature of decision-making [61]. By leveraging digital tools to collect, analyze, and mine extensive data, enterprises can gain a better understanding of their carbon emissions and the influencing factors. This enables the formulation of more effective and targeted carbon emission reduction strategies, thereby lowering carbon emission intensity. However, while digital transformation positively reduces carbon emissions per unit of product, its potential to increase overall production could lead to an expansion of economic activities, thereby triggering a rebound effect in carbon emissions [62]. Despite this risk, prudent policy guidance and continuous technological innovation can effectively mitigate this rebound effect and ensure a reduction in overall carbon emissions levels [63]. Therefore, this paper proposes

Hypothesis 1. 

DT significantly reduces the CEI of industrial enterprises.

3.2. The Mediating Role of Green Technology Innovation

By implementing GTI, industrial enterprises can adopt more energy-efficient and environmentally friendly technologies and processes, thereby reducing both energy consumption and waste emissions, which ultimately lowers CEI. These GTIs encompass innovations in clean energy utilization, waste reuse, and pollution control. These changes alter enterprise production methods and management models, thereby enhancing carbon emission reduction effects. Furthermore, GTI enhances industrial enterprises’ production efficiency and competitiveness, providing crucial technological support and innovation impetus for DT [64]. Through integrating digital systems, enterprises can monitor and analyze carbon emissions in real-time during the production process, accurately track energy consumption situations, and implement corresponding energy-saving and emission-reduction measures. Ref. [65] finds that the combination of DT and GTI effectively improves resource utilization efficiency and reduces CEI in industrial enterprises. Moreover, GTI promotes the sustainable and high-quality development of industrial enterprises. Actively incorporating GTI within DT not only lowers CEI but also enhances a company’s brand value and corporate image. This alignment with stakeholder and consumer expectations supports both sustainability goals and high-quality industrial development [66,67]. This strengthens the company’s social legitimacy and competitive advantage. Consequently, this paper proposes

Hypothesis 2. 

GTI mediates the relationship between DT and the CEI of industrial enterprises, implying that DT’s impact on CEI is transmitted through the mechanism of GTI.

3.3. The Regulatory Role of Environmental, Social, and Governance (ESG) Practices

ESG practices emphasize the environmental responsibility, social responsibility, and good corporate governance that industrial enterprises undertake during the process of DT, all of which significantly influence the resulting CEI. First, ESG practices emphasize environmental responsibility, prompting industrial enterprises to adopt energy-efficient technologies, sustainable practices, and technological innovations to reduce CEI throughout the DT process [28]. Second, ESG practices highlight social responsibility, encouraging industrial enterprises to prioritize social responsibility and sustainable development during DT [68], thereby enhancing their social reputation and brand value [67]. According to [69], strong ESG performance reflects a firm’s ability to effectively balance environmental sustainability, social responsibility, and sound corporate governance, indicating a greater potential for long-term sustainable growth and a stronger focus on carbon emission reduction. Finally, ESG practices promote good corporate governance by encouraging industrial enterprises to enhance management efficiency, increase transparency and accountability during DT [70], and ensure effective management of carbon emission-related risk. These measures support informed decision-making [64] and promote the realization of CEI effects. Therefore, this paper proposes

Hypothesis 3. 

ESG practices moderate the relationship between DT and the CEI of industrial enterprises, meaning that DT’s influence on CEI is transmitted through ESG.

Moreover, in addition to moderating the DT–CEI relationship, ESG also influences CEI indirectly by shaping intermediate variables such as green technological innovation (GTI) [71,72]. While enterprises adopt GTI to reduce CEI, its implementation requires compliance with environmental laws and policies and the fulfillment of social responsibilities [19]. ESG, through regulatory and constraint mechanisms, motivates companies to prioritize environmental protection and social responsibility during DT, thereby regulating the impact of GTI [73]. Additionally, as DT enables companies to enhance resource utilization efficiency, optimize production processes, and reduce CEI, ESG practices by emphasizing environmental and social responsibility, encouraging companies to undertake sustainable development actions, and promoting the implementation of GTI [25,74]. Hence, this paper proposes

Hypothesis 4. 

ESG practices moderate the role of GTI in DT, thereby influencing the CEI of industrial enterprises.

This study investigates how DT influences CEI by positioning GTI as a mediating variable and ESG performance as a moderating variable. Specifically, it addresses four research aspects: (1) the direct effect of DT on CEI; (2) the mediating role of GTI in the DT-CEI relationship; (3) the moderating influence of ESG on the direct DT-CEI link; and (4) ESG’s moderating effect on the indirect pathway through which DT impacts CEI via GTI. The theoretical framework, illustrated in Figure 1, clarifies these interconnected relationships, emphasizing how ESG performance may amplify or attenuate both the direct and indirect mechanisms linking DT to reduced carbon emissions.

4. Research Design

4.1. Sample Selection and Data Sources

To ensure data integrity, continuity, accessibility, and validity, this study selected listed industrial enterprises in the Shanghai A-share, Shenzhen A-share, and the Growth Enterprise Market (GEM) from 2013 to 2022 as the initial sample. The selection criteria were as follows: (1) companies with significant missing key data were excluded; for enterprises with partially missing data, interpolation methods, such as the difference method and the trend method, were used to impute the missing values; (2) ST companies (companies with special treatment due to financial difficulties) were excluded; and (3) continuous variables underwent two-way trimming at the 1st percentile to mitigate outliers. The final sample comprised 1446 industrial enterprises across 41 industrial sectors, totaling 14,459 observation samples. Enterprise-level data were primarily obtained from the Guotai An (CSMAR), Wind, and Markit databases. Industry and regional data were sourced from the China Industrial Statistical Yearbook, China Statistical Yearbook, and China Energy Statistical Yearbook.

4.2. Variable Description

4.2.1. Dependent Variable

The dependent variable in this study is the CEI of industrial enterprises. Following the research of Shen et al. [75], it is measured as the ratio of carbon dioxide emissions of industrial enterprises to their operating income. The carbon dioxide emissions of industrial enterprises are calculated using the following equation:

$$CEI={\displaystyle \frac{TECI\times {CO}_2 as CF\times OCIE}{OCIL}}.$$

(1)

In Equation (1), the CEI represents Carbon Emission Intensity; TECI is the total energy consumption of the industry where the industrial enterprise is located; CO₂ as CF is the carbon dioxide conversion factor; OCIE denotes the operating costs of the industrial enterprise; and OCIL refers to the operating costs of the industry where the industrial enterprise is located. The carbon dioxide conversion factor is set at 2.493 based on the Xiamen Energy Conservation Center calculation standard [13].

4.2.2. Independent Variable

The independent variable in this study is DT. Following the approach of Wu et al. [76], the frequency of DT-related keywords in the annual reports of listed companies is used as a proxy. These keywords—artificial intelligence, big data, blockchain, and cloud computing—are selected for their pivotal roles in digital transformation processes. Using Python (Version 3.11.3), text analysis is performed on the Management Discussion and Analysis sections of the annual reports to extract the frequency of these keywords. The raw word frequency data are then subject to a plus-one adjustment and logarithmic transformation to construct the DT index used in the empirical analysis

4.2.3. Control Variables

To enhance the accuracy of the analysis and control for operational characteristics and activities of listed industrial enterprises that may affect CEI, this study adopts the approaches of Sun et al. [77] and Liu et al. [78] by including the following control variables: firm size (BA), represented by the logarithm of the total output value at the end of the period; leverage ratio (LR), defined as the ratio of total liabilities to total assets at the end of the period; equity concentration (EC), measured using the Herfindahl index; and return on assets (ROA), which is the ratio of net profit to total assets. These control variables are included to better account for factors that may influence the CEI of industrial enterprises (see

Table 1. List of Variables According to Models and Data Sources.

Variables Name	Proxy	Variable Symbol	Type of Variable	Data Source	Reference
Carbon Emission Intensity	The ratio of carbon dioxide emissions to enterprise revenue	CEI	D.V	Guotai An Database (CSMAR), WIND, Markit Database, China Industrial Statistical Yearbook, China Statistical Yearbook, China Energy Statistical Yearbook	[79,80,81,82]
Digital Transformation	Digitization transformation word frequency measurement results in annual reports of listed companies	DT	I.V		[83]
Business Scale	Logarithm of total output at the end of the period	BA	C.V		[84]
Leverage Ratio	Herfindahl index of the ratio of total liabilities to total assets at the end of the period	LR	C.V		[85,86]
Equity Concentration	Herfindahl Index	EC	C.V		[87]
Return on Assets	The ratio of net profit to total assets	ROA	C.V		[88]
Green technology innovation	Green patent applications	GTI	MEV		[89]
ESG performance	Hua Zheng ESG Rating	ESG	MOV		[90]

Note: D.V, I.V, C.V, MEV, and MOV indicate the dependent variable, independent variable, control variable, mediating variable, and moderating variable, respectively.

).

4.3. Model Specification

To empirically test the effect of DT on CEI of industrial enterprises, this study follows the methodological framework of Shang et al. [15]. It constructs a two-way fixed-effect benchmark regression model, as shown in Equation (2):

$${CEI}_{i,t}=a_0+a_1{DT}_{i,t}+a_2{BA}_{i,t}+a_3{LR}_{i,t}+a_4{EC}_{i,t}+a_5{ROA}_{i,t}+u_i+{\delta }_t+{\epsilon }_{i,t} ,$$

(2)

where i and t represent industrial enterprises and years, respectively, which are the two fixed dimensions of the model (this model selects the enterprise dimension and the year dimension); $a_0$ represents the constant term representing the baseline level without any explanatory variables (control and focus variables); and $a_1$ is the influence coefficient of DT, which is the main parameter of the study. If the coefficient is significantly less than 0, it means that DT helps reduce industrial enterprises’ CEI. $a_2$, $a_3$, $a_4$, and $a_5$ are the coefficients of control variables, which are used to adjust other factors that may affect CEI and ensure the accuracy of $a_1$ estimation. $u_i$, and ${\delta }_t$ represent the individual fixed effect and time fixed effect of industrial enterprises, respectively, which are used to control individual characteristics that do not change over time and time trends or shocks common to all individuals. ${\epsilon }_{i,t}$ represents the random error term, capturing random fluctuations that the model cannot explain (e.g., measurement errors or unobserved transient shocks). It is a standard component of regression models, ensuring the error structure adheres to Gauss–Markov assumptions. The term is not directly “extracted” but inferred residually after accounting for all modeled fixed and control effects. Overall, this is a paradigm for model construction, and the model is built and explicated based on the approach proposed by [15].

5. Empirical Results Analysis

Table 2. Descriptive Statistics and Correlation Coefficient Matrix of Variables.

Variables	Obs.	Min	Max	Avg.	Std. Dev.	Mean
CEI	14,460	0.004	599.88	1.158	14.946	0.247
DT	14,460	0.000	6.63	2.303	1.319	2.303
BA	14,459	16.412	28.64	22.441	1.307	22.281
LR	14,460	−6.328	178.35	0.449	1.535	0.428
EC	14,460	0.039	1.00	0.139	0.113	0.085
ROA	14,460	−30.688	7.45	0.024	0.444	0.032
GTI	14,460	0.000	7.34	0.570	1.025	0.000
EGS	14,460	41.190	90.93	72.470	5.716	72.770
Variables	CEI	DT	BA	LR	EC	ROA	GTI	EGS
CEI	1
DT	0.0070	1
BA	0.0050	0.137 **	1
LR	0.0090	−0.024 **	0.0050	1
EC	0.096 **	−0.057 **	0.033 **	0.075 **	1
ROA	0.0000	0.031 **	0.074 **	−0.683 **	−0.062 **	1
GTI	−0.022 **	0.179 **	0.383 **	0.0140	−0.0140	0.0150	1
EGS	−0.0130	0.161 **	0.291 **	−0.057 **	0.0060	0.095 **	0.221 **	1

Source: Prepared by the Author. Note: ** represents significance levels of 5%.

presents descriptive statistics and correlation coefficients for the study variables, based on 14,459 observations. CEI exhibits a low mean value of 1.158 with high variation. Significant positive correlations are observed between EGS, BA, and GTI, while ROA demonstrates a strong negative correlation with LR. EC is positively correlated with several variables, including DT and LR. The data suggest various interrelationships among the variables, with notable significance at the 1%, 5%, and 10% levels.

5.1. Benchmark Regression Results Analysis

This study tests the effect of DT on CEI in industrial enterprises based on the regression model outlined in Equation (2), as shown in

Table 3. Benchmark Regression Analysis Results of CEI Using Two-Way Fixed Effect Model (Focusing on Testing Hypothesis 1).

Variables	(1)	(2)
$\mathrm{D}\mathrm{T}$	−0.0141 *** (−6.5225)	−0.0079 *** (−3.7689)
$\mathrm{B}\mathrm{A}$		−0.0302 *** (−7.3214)
$\mathrm{L}\mathrm{R}$		0.1274 *** (7.9587)
$\mathrm{E}\mathrm{C}$		−0.1066 *** (−3.2139)
$\mathrm{R}\mathrm{O}\mathrm{A}$		−0.6534 *** (−24.3576)
Cons	0.6012 *** (116.2948)	1.2446 *** (13.8505)
$R^2$	0.0138	0.0058
$F$-statistic	42.5424 ***	206.3752 ***
Individual Fixed Effects	YES	YES
Time Fixed Effects	YES	YES
OBS	14,459	14,459

Source: Prepared by the Authors. Notes: 1. *** represents significance levels of 1%; 2. The values in parentheses are t-values.

. In the benchmark regression model, Column (1) presents the core explanatory variable, DT, with a coefficient of −0.0141, which is statistically significant at the 1% level. This suggests that a one-unit increase in DT is associated with a 0.0141 decrease in CEI, indicating that DT plays a significant and negative role in reducing environmental impacts. In Column (2), additional control variables—such as firm size, leverage ratio, equity concentration, and return on assets—are included. The coefficient for DT is reduced to −0.0079, but it remains statistically significant at the 1% level. This reinforces the robustness of the relationship between DT and CEI, showing that even after accounting for other firm-specific factors, DT remains a significant driver of environmental improvement. These findings are consistent with global research. For example, studies by [91,92] analyze the impact of DT on carbon intensity reduction in the transportation industry and manufacturing sectors across 43 economies. They found that adopting DT reduces CEI. These studies highlight that the positive impact of DT on reducing carbon emissions is not unique to China but rather aligns with broader global trends, emphasizing the role of DT in achieving sustainability goals across different regions.

From a practical perspective, these findings suggest that increasing DT efforts in industrial enterprises can substantially contribute to environmental sustainability. Specifically, as industrial firms adopt more advanced digital technologies, the efficiency of production processes, resource utilization, and management improves, reducing environmental impact. Moreover, DT fosters GTI, enhances environmental monitoring, and strengthens management capabilities, leading to reductions in CEI while also driving higher economic and social benefits. Furthermore, the real-world implications are clear: firms aiming to reduce their CEI should prioritize DT as a key strategy. By integrating digital tools and technologies into their operations, businesses can not only improve operational efficiency but also contribute to broader sustainability goals. These results support the validity of the first hypothesis of this study, confirming that higher levels of digital transformation significantly reduce CEI and offering valuable insights for industrial enterprises and policymakers seeking to promote environmental sustainability.

5.2. Robustness Tests

To ensure the reliability of the research results, a series of robustness tests were conducted using four different regression models, as shown in

Table 4. Robustness Tests Using Two-Way Fixed Effect Model and Instrumental Variable Model.

Variables	Replace Dependent Variable (1)	Replace Independent Variable 1 (2)	Replace Independent Variable 2 (3)	Sample Size Reduction (4)	Instrumental Variable 1 (5)	Instrumental Variable 2 (6)
$\mathrm{D}\mathrm{T}$	−0.0073 * (−1.7712)	−0.0048 ** (−2.0782)	−0.0084 *** (−7.3271)	−0.0078 *** (−3.4095)	−0.1659 *** (−9.3862)	−0.2916 *** (−15.8445)
$\mathrm{B}\mathrm{A}$	0.8049 *** (99.9642)	−0.0313 *** (−7.6016)	−0.0302 *** (−7.3271)	−0.0274 *** (−5.8383)	0.0940 *** (14.8466)	0.1178 *** (18.0244)
$\mathrm{L}\mathrm{R}$	0.3800 *** (12.1637)	0.1299 *** (8.1208)	0.1273 *** (7.9502)	0.1356 *** (7.5580)	0.1892 *** (5.0820)	0.0957 ** (2.4486)
$\mathrm{E}\mathrm{C}$	0.1783 *** (2.7546)	−0.1068 *** (−3.2176)	−0.1064 *** (−3.2070)	−0.1205 *** (−3.2728)	−0.5931 *** (−12.4611)	−0.6867 *** (−13.0037)
$\mathrm{R}\mathrm{O}\mathrm{A}$	−0.1467 *** (−2.8020)	−0.6570 *** (−24.5023)	−0.6533 *** (−24.3541)	−0.6648 *** (−23.7031)	−0.6490 *** (−6.5611)	−0.6628 *** (−6.5439)
cons	3.2351 *** (18.4486)	1.2540 *** (13.9456)	1.2449 *** (13.8553)	1.1843 *** (11.5650)	−1.1396 *** (−9.9104)	−1.3297 *** (−11.3259)
$R^2$	0.5268	0.0016	0.0053	0.0075	0.1218	0.0437
$F-statistic$	2469.5329 ***	204.2434 ***	206.4199 ***	192.4497 ***	727.4639 ***	813.0021 ***
Individual Fixed Effects	YES	YES	YES	YES	YES	YES
Time Fixed Effects	YES	YES	YES	YES	YES	YES
OBS	14,459	14,459	14,459	13,013	14,459	14,459

Source: Prepared by the Authors. Note: 1. *, **, *** represent significance levels of 10%, 5%, 1%, respectively; 2. The values in parentheses are t-values.

. First, the dependent variable was replaced with carbon dioxide emissions (CO2), which directly reflect the environmental burden of enterprises. This approach follows the method of [93], who used CO₂ as an alternative measure of CEI for industrial enterprises in their regression analysis, as shown in Column (1). Second, the core explanatory variable was divided into two dimensions: DT’s development and DT’s application. This differentiation, based on [94], captures the varying impacts of DT at the levels of technological innovation and practical application, with results presented in Columns (2) and (3). The DT’s development dimension includes indicators such as the frequency of terms related to artificial intelligence, big data, cloud computing, and blockchain technology. In contrast, the DT’s application dimension uses terms related to actual digital technology applications as indicators.

Third, the sample size was reduced by excluding data from 2013, following the approach of [95], with results shown in Column (4). Fourth, the Instrumental Variable Method was applied for regression analysis, using the number of broadband internet users and mobile phone penetration rate (log-transformed) as instruments, based on [96], with results displayed in Columns (5) and (6). The regression results of −0.0073, −0.0048, −0.0084, −0.0078, −0.1659, and −0.2916 all pass significance level tests. These results are consistent with the DT outputs of benchmark regression in Table 3, columns 1 and 2, with a slight difference in coefficient size, confirming that the negative impact of DT on the CEI of industrial enterprises is robust and reliable.

6. Mechanism Analysis

To explore the underlying mechanism through which DT affects the CEI reduction effects of industrial enterprises, this study adopts the research framework of [97]. Drawing on theoretical insights from previous literature, GTI is identified as an important mediating variable that influences the relationship between CEI and DT reduction. An intermediate effect model is constructed, as shown in Equations (2) and (3), to examine whether and how DT impacts CEI reduction through the promotion of GTI, as detailed in

Table 5. Mechanism Testing DT’s Impact on Industrial Enterprises’ CEI Levels Using Mediation Effect Model (Focusing on Testing Hypothesis 2).

Variables	(1)	(2)
DT	0.1015 *** (17.1039)	−0.1417 *** (−34.9093)
$\mathrm{G}\mathrm{T}\mathrm{I}$		−0.0400 *** (−7.1084)
cons	−5.6664 *** (−39.1060)	−1.3360 *** (−12.9519)
Control Variables	Control	Control
$R^2$	0.1552	0.1264
$F-statistic$	531.1471 ***	348.3791 ***
Bootstrap	PASS	PASS
Existence of Direct Effect	YES	YES
OBS	14,459	14,459

Source: Prepared by the Authors. Note: 1. *** represents significance levels of 1%; 2. The values in parentheses are t-values.

. Following the methodology of [98], the number of green patent applications—including both independent and joint green invention patents and green utility model patents— is used as the indicator for GTI. The number of green patents is increased by 1 and then logged to form the measure for GTI. Control variables are included to account for other factors that may influence the results.

$${CI}_{i,t}={\beta }_0+{\beta }_1{DT}_{i,t}+{\beta }_{2}C{ontrols}_{i,t}+{\mu }_i+{\delta }_t+{\epsilon }_{i,t}$$

(3)

$${CEI}_{i,t}={\gamma }_0+{\gamma }_1{DT}_{i,t}+{\gamma }_2{GTI}_{i,t}+{\gamma }_3{Controls}_{i,t}+{\mu }_i+{\delta }_t+{\epsilon }_{i,t}$$

(4)

Table 5 highlights the significant role of DT in promoting GTI and reducing CEI. In Column (1), the regression coefficient for DT is 0.1015, statistically significant at the 1% level. This result indicates that DT positively influences the development and application of green technologies, enhancing the capacity of industrial enterprises to adopt more sustainable practices. In Column (2), both DT and GTI are found to significantly reduce CEI, with coefficients of −0.1417 for DT and −0.0400 for GTI, both passing the 1% significance threshold. These findings suggest that DT helps lower CEI not only by improving operational efficiency but also indirectly through the promotion of GTI. The mediation effect of GTI, with a coefficient of −0.0041, accounts for 2.7882% of the total effect, underscoring GTI’s critical role in linking digitalization to carbon reduction. These results confirm Hypothesis 2 of the study, aligning with theoretical expectations that digitization supports sustainability efforts through green technological advancements. The positive impact of DT on GTI and the subsequent reduction in CEI is consistent with findings from prior studies. For instance, [57,99,100]—focusing on China and GCC countries—demonstrated that DT and innovation significantly mitigate carbon emissions by improving operational efficiency and fostering green technology adoption, with GTI functioning as a key mediating mechanism.

In real-world applications, these findings emphasize the importance of advancing DT in industrial enterprises. By enhancing production efficiency and management practices, DT not only contributes to economic growth but also indirectly supports environmental protection through the promotion of GTI. Industrial enterprises should prioritize both technological progress and environmental responsibility, ensuring that economic growth is aligned with sustainability objectives. The real-world implications of these findings are substantial. Policymakers should support this transition by providing incentives such as tax breaks and subsidies to encourage businesses to invest in digital solutions and green technologies. This will help drive collective progress toward carbon reduction and sustainable development, ensuring that industrial enterprises play an active role in achieving the Sustainable Development Goals (SDGs).

7. Further Research

7.1. ESG Moderating Effects

To further validate the moderating effect of ESG on the impact of DT on the CEI reduction in industrial enterprises, a moderating effect model is constructed, as shown in Equation (4). Additionally, to verify the moderating role of ESG in the mediating effect of GTI on the relationship between DT and CEI reduction, an extended moderating effect model is developed, as shown in Equation (5). The regression results are presented in

Table 6. Moderating Effect Test Results of CEI Using Moderation Effect Model (Focusing on Testing Hypotheses 3 and 4).

Variables	(1)	(2)	(3)
$\mathrm{D}\mathrm{T}$	−0.1441 *** (−35.5820)	−0.1437 *** (−35.4454)	−0.2772 *** (−5.5410)
$\mathrm{E}\mathrm{S}\mathrm{G}$	−0.0038 *** (−3.8417)	−0.0037 *** (−3.7291)	−0.0058 *** (−3.2102)
$\mathrm{D}\mathrm{T}\times \mathrm{E}\mathrm{S}\mathrm{G}$		0.0012 * (1.7378)
$\mathrm{G}\mathrm{T}\mathrm{I}$			0.1661 ** (2.2681)
$\mathrm{E}\mathrm{S}\mathrm{G}\times \mathrm{G}\mathrm{T}\mathrm{I}$			−0.0027 *** (−2.8068)
Cons	−1.5650 *** (−15.0342)	−1.5662 *** (−15.0461)	−1.0290 *** (−6.6469)
Control Variables	YES	YES	YES
$R^2$	0.1242	0.1243	0.1276
$F-statistic$	341.4029 ***	293.1034 ***	234.8554 ***

Source: Prepared by the Authors. Note: 1. *, **, *** represent significance levels of 10%, 5%, 1%, respectively; 2. The values in parentheses are t-values.

. In this context, ESG refers to a company’s environmental, social, and governance practices. Following the approach of [101], Huazhong ESG ratings are used to measure a company’s ESG practices. The Huazhong ESG ratings consist of nine levels: “AAA, AA, A, BBB, BB, B, CCC, CC, C” with corresponding values of “9, 8, 7, 6, 5, 4, 3, 2, and 1”, respectively.

$${CEI}_{i,t}={\varnothing }_0+{\varnothing }_1{DT}_{i,t}+{\varnothing }_2{ESG}_{i,t}+{\varnothing }_3{DT}_{i,t}\times {ESG}_{i,t}+{\varnothing }_4{Controls}_{i,t}{+\mu }_i+{\delta }_t+{\epsilon }_{i,t}$$

(5)

$${CEI}_{i,t}={\vartheta }_0+{\vartheta }_1{DT}_{i,t}+{\vartheta }_2{GTI}_{i,t}+{\vartheta }_3{ESG}_{i,t}+{\vartheta }_4{ESG}_{i,t}\times {GTI}_{i,t}+{\vartheta }_{5}C{ontrols}_{i,t}+{\mu }_i+{\delta }_t+{\epsilon }_{i,t}$$

(6)

Table 6 provides key insights into the relationship between DT, ESG practices, and CEI. In Column (1), the regression results confirm that DT significantly reduces CEI, with a coefficient indicating its effectiveness in lowering CEI. This highlights that DT is an important tool for reducing environmental impacts within companies. In Column (2), the regression results show that as ESG scores rise, the negative impact of DT on CEI weakens. This suggests that companies with higher ESG ratings mitigate environmental pressures by adopting proactive sustainability measures, such as green policies, resource-efficient practices, and environmental compliance. The interaction effect coefficient of 0.0012 further confirms that strong ESG practices help offset the environmental drawbacks associated with DT, highlighting the complementary role of ESG and DT in reducing CEI. These results strongly support and validate Hypothesis 3 of the study. In Column (3), the moderating role of ESG on GTI is explored. The results reveal that ESG practices not only directly reduce the CEI impact of GTI but also weaken its effect on emission intensity through an interaction term with a coefficient of −0.0027. This suggests that while high ESG-rated firms prioritize environmental goals, the compliance costs and management complexities associated with stringent ESG standards may limit their capacity for GTI. These findings provide strong support for Hypothesis 4, suggesting that ESG considerations can have unintended consequences on innovation and emission reductions—particularly when firms must balance sustainability goals with operational efficiency.

From an implementation perspective, these results emphasize the need for carefully companies to integrate ESG considerations into their DT strategies. While ESG practices can help mitigate the environmental drawbacks of DT, they may also limit the effectiveness of GTI, particularly when firms face high compliance costs and management complexities. Therefore, companies should aim to balance ESG commitments with technological progress, ensuring that the two do not work at cross-purposes. The real-world implications are significant. Policymakers should recognize that higher ESG ratings do not always directly enhance technological innovation or environmental outcomes. Instead, policies should encourage companies to adopt practical technological and management solutions that maximize the environmental benefits of DT and green technologies. Setting appropriate ESG targets and providing targeted incentives—such as subsidies for green innovation and support for streamlining ESG compliance—will be essential for fostering sustainable industrial development. By addressing the potential trade-offs between ESG practices and technological innovation, policymakers can better support firms in maximizing their contributions to sustainable development.

7.2. Heterogeneity Analysis

7.2.1. Heterogeneity Analysis Based on Different Regions

Considering regional differences in resource endowment, DT, and CEI, this study divides China into four regions (Eastern, Central, Western, and Northeastern) based on the Opinions of the CPC Central Committee and the State Council on Promoting the Rise of Central China. The regression results in

Table 7. Regional Heterogeneity Analysis: Regression Results of DT on Industrial Enterprise CEI Levels Using Two-way Fixed Effect Model.

Variables	Eastern Region (1)	Central Region (2)	Western Region (3)	Northeast Region (4)
$\mathrm{D}\mathrm{T}$	−0.0051 ** (−2.3156)	−0.0077 (−1.2985)	−0.0133 * (−1.8786)	−0.0148 (−0.9221)
$\mathrm{B}$A	−0.0228 ** (−5.0754)	−0.0329 *** (−2.9529)	−0.0458 *** (−3.5134)	−0.0945 *** (−2.7533)
$\mathrm{L}\mathrm{R}$	0.0427 ** (2.4173)	0.2260 *** (5.0332)	0.2316 *** (4.9944)	0.6810 *** (5.0141)
$\mathrm{E}\mathrm{C}$	−0.1300 *** (−3.9103)	−0.1457 (−1.3523)	−0.0559 (−0.04678)	−0.4850 (−1.4331)
$\mathrm{R}\mathrm{O}\mathrm{A}$	−0.5395 *** (−18.2678)	−1.0379 *** (−13.5721)	−0.5592 *** (−8.3465)	−0.7090 *** (−3.5437)
cons	1.0212 *** (10.4820)	1.3655 *** (5.5326)	1.7330 *** (6.1004)	2.6627 *** (3.6007)
$R^2$	0.0516	0.1193	0.0716	0.1045
$F$-statistic	98.5617 ***	62.3825 ***	36.0256 ***	16.6319 ***
Individual Fixed Effects	YES	YES	YES	YES
Time Fixed Effects	YES	YES	YES	YES
OBS	8949	2530	2340	640

Source: Prepared by the Authors. Note: 1. *, **, *** represent significance levels of 10%, 5%, 1%, respectively; 2. The values in parentheses are t-values.

show that DT significantly reduces CEI in industrial enterprises located in the Eastern and Western regions, with coefficients significant at the 5% and 10% levels, respectively. This suggests that DT has a more pronounced impact on reducing CEI in these regions. In contrast, while DT has a negative impact on CEI in the Central and Northeastern regions, the results do not pass the significance test. This highlights a regional disparity in the effectiveness of DT on CEI, which can be attributed to differences in industrial structure. In terms of actionable outcomes, the Central and Northeastern regions are primarily dominated by heavy industries that typically have higher carbon emissions. In these regions, the short-term effectiveness of DT in reducing CEI may be limited, as such industries face challenges in rapidly adopting DT—often better suited to high-tech or service-oriented sectors. On the other hand, the Eastern and Western regions have more diverse economies, with a higher concentration of high-tech and service sectors. These sectors offer greater opportunities for DT to drive significant CEI reductions, as digital solutions can be more easily integrated into less carbon-intensive industries.

These findings provide valuable contextual insights into the relationship between regional economic structures and the effectiveness of DT in reducing environmental impacts. While DT can generate substantial environmental benefits, its impact is highly contingent upon the regional industrial context. The real-world implications suggest that policymakers should recognize these regional disparities and tailor their support for DT accordingly. In regions with heavy industrial bases, the focus could be on upgrading existing technologies and promoting the development of green technologies to reduce emissions. In contrast, in regions with more diversified economies, policies can aim to accelerate the adoption of digital solutions and green technologies across sectors, thereby maximizing the carbon reduction potential of DT.

7.2.2. Heterogeneity Analysis Based on Different Industries

Given the varying effects of DT on CEI across industries, this study classifies listed industrial enterprises into four sectors: labor-intensive, capital-intensive, technology-intensive, and resource-intensive, based on the National Bureau of Statistical Bulletin of National Economic and Social Development (see Appendix A). The regression results in

Table 8. Regression Results of Industry Heterogeneity Analysis of DT on Industrial Enterprise CEI Levels Using Two-way Fixed Effect Model.

Variables	Labor-Intensive (1)	Capital-Intensive (2)	Technology-Intensive (3)	Resource-Intensive (4)
$\mathrm{D}\mathrm{T}$	0.0279 (0.3148)	−0.0212 *** (−3.6551)	−0.0010 ** (−2.2111)	−0.0089 (−0.9338)
$\mathrm{B}$A	−0.3821 * (−1.7076)	−0.0146 (−1.3443)	−0.0076 *** (−8.5951)	−0.0309 * (−1.9025)
$\mathrm{L}\mathrm{R}$	2.0466 *** (2.9354)	0.2553 *** (5.9522)	0.0313 *** (8.8051)	0.1239 * (1.7870)
$\mathrm{E}\mathrm{C}$	5.6038 *** (6.4917)	−1.2779 *** (−12.1799)	0.0463 *** (3.2182)	0.0890 (0.7573)
$\mathrm{R}\mathrm{O}\mathrm{A}$	0.5046 (0.3821)	−1.1268 *** (−16.0891)	−0.1647 *** (−30.0506)	−1.5087 *** (−12.3198)
cons	8.6581 * (1.7835)	1.7526 *** (7.4285)	0.3207 *** (16.6511)	1.7865 *** (4.8689)
$R^2$	0.0103	0.2288	0.1683	0.1437
$F-statistic$	9.6807 ***	124.2464 ***	275.9134 ***	44.3908 ***
Individual Fixed Effects	YES	YES	YES	YES
Time Fixed Effects	YES	YES	YES	YES
OBS	2860	3599	6500	1500

Source: Prepared by the Authors. Note: 1. *, **, *** represent significance levels of 10%, 5%, 1%, respectively; 2. The values in parentheses are t-values.

show that DT significantly reduces CEI in capital-intensive and technology-intensive industries, with coefficients statistically significant at the 1% and 5% level, respectively. Specifically, DT has a negative impact on CEI in these industries, suggesting that DT is an effective tool for reducing environmental impact in sectors that require substantial investment in advanced technologies and equipment. However, in labor-intensive and resource-intensive industries, the carbon reduction effect of DT shows mixed results: labor-intensive industries exhibit a positive impact, while resource-intensive industries show a negative but also statistically insignificant impact. These disparities can be attributed to industry-specific production characteristics. Capital-intensive and technology-intensive industries typically require significant investments in equipment and advanced technologies, where DT facilitates the adoption of intelligent monitoring and automated control systems, improving energy efficiency and reducing emissions. In contrast, labor-intensive industries, which rely heavily on human labor, may experience a positive impact on productivity through the introduction of automation and robotics. However, these technological upgrades can also lead to increased energy consumption, potentially offsetting the environmental benefits of DT.

Similarly, in resource-intensive industries, new technologies introduced through DT may intensify resource extraction processes, potentially counteracting the energy-saving and emission-reduction benefits typically associated with DT. These factors explain why DT’s overall carbon reduction effect in these sectors remains insignificant. From a policy standpoint, these findings underscore the need for industry-specific strategies when promoting DT. While DT clearly drives significant environmental benefits in capital- and technology-intensive industries, the impact in labor- and resource-intensive sectors may be more complex. Policymakers and business leaders should therefore consider the unique characteristics of each industry when implementing DT strategies. For labor-intensive industries, the focus could be on integrating energy-efficient automation that reduces energy consumption, while for resource-intensive sectors, promoting green technologies and sustainable practices alongside DT may be key to achieving environmental goals.

8. Research Conclusions and Policy Recommendations

8.1. Research Conclusions

This study analyzes the panel data from industrial enterprises of listed companies on the Shanghai A-share, Shenzhen A-share, and the Growth Enterprise Market from 2013 to 2022. Using SPSSAU (version 26) and STATA (version 16) software and applying benchmark regression models, mediation models, moderation models, and moderated mediation extension models, the study theoretically analyzes and empirically tests the CEI reduction effects of DT in industrial enterprises. The following conclusions are as follows:

Firstly, the benchmark regression results indicate that DT significantly reduces the CEI of industrial enterprises. This finding is robust, as confirmed through various robustness checks, regional heterogeneity analysis, and industry-level heterogeneity analysis. It suggests that adopting DT in industrial enterprises is an effective strategy to lower CEI and improve sustainability practices.

Secondly, the mediation effect regression results show that the inhibitory effect of DT on CEI in industrial enterprises operates partly through indirect pathways—particularly GTI. This finding highlights the critical role of GTI in enabling industries to achieve carbon reduction goals through technological advancements, underscoring the importance of fostering innovation as part of the digital transformation process.

Thirdly, the moderation effect regression results show that ESG practices not only directly moderate the relationship between DT and CEI reduction but also indirectly moderate the impact through intermediary variables like GTI. This demonstrates that strong ESG practices are essential in enhancing the positive effects of DT, providing a foundation for sustainable development while improving the environmental footprint of industrial enterprises.

The insights from this study carry significant implications for multiple stakeholders, including policymakers, business leaders, and technology developers. For Policymakers, the findings emphasize the importance of encouraging DT and the adoption of green technologies within industrial sectors. Policymakers can design policies incentivizing businesses to adopt digital solutions and green technologies, such as tax breaks or grants for companies investing in digital tools and sustainable practices. Moreover, integrating ESG practices can further amplify the positive effects of digital transformation on sustainability. Regulations that promote a combination of digital and green technological investments can further accelerate the transition to a more sustainable industrial economy. For business leaders, this study underscores the strategic value of DT in reducing operational costs, improving efficiency, and, importantly, lowering carbon emissions. This study highlights that businesses can benefit from investing in green technologies, which not only help reduce environmental impact but also contribute to long-term competitiveness. Additionally, integrating ESG practices into business operations can further strengthen the impact of DT on sustainability goals, positioning companies as leaders in environmental responsibility while enhancing their brand reputation and aligning with growing consumer demand for sustainable practices. For technology developers, the results emphasize the role of technology development, particularly in the areas of DT and GTI. Technology developers can focus on creating solutions that help businesses optimize energy usage, improve resource efficiency, and integrate sustainability into their operations. This can include developing smart technologies, AI-powered systems, or renewable energy solutions that aid in carbon emission reduction. Moreover, the study underscores the need for technologies that support the implementation of ESG frameworks, helping companies track and improve their environmental, social, and governance performance.

While this study highlights the significant positive impact of DT on reducing CEI in industrial enterprises, we acknowledge potential trade-offs associated with its implementation. Specifically, the widespread adoption of digital technologies may result in job displacement, particularly in sectors where automation and AI replace manual labor. This could cause short-term disruptions in the labor market, especially for workers in low-skilled positions. Moreover, the implementation costs of DT can pose significant challenge, particularly for small firms with limited financial resources. Adopting green technologies and digital solutions often requires substantial investment in infrastructure, training, and ongoing maintenance—investments that may be difficult for smaller firms to afford. While our study demonstrates the overall benefits of DT in promoting sustainability and operational efficiency, we recognize these trade-offs and suggest that future research should explore strategies to mitigate them. Such strategies could include targeted retraining programs for displaced workers and financial incentives for small firms to adopt digital transformation and green technologies.

In summary, this study not only contributes to the academic understanding of how digital transformation influences carbon emission intensity in industrial enterprises but also provides actionable insights for policymakers, business leaders, and technology developers. By aligning digital transformation with green technology innovation and ESG practices, stakeholders can collectively promote industrial sustainability and contribute to global efforts in carbon reduction and environmental protection.

8.2. Policy Recommendations

Based on the research findings, this study highlights the complex relationship between DT and ESG practices, emphasizing the need to reduce CEI in industrial enterprises to support sustainable economic development. These findings provide valuable policy insights for pollution reduction and the green transition of enterprises in China and other emerging economies, as outlined below:

Firstly, the study underscores the CEI reduction effects of DT and proposes utilizing emerging technologies—such as artificial intelligence, big data, cloud computing, and blockchain in in advancing green production. This is consistent with best practices observed in countries like Germany, where the Industrie 4.0 initiative successfully integrates digital transformation with sustainability goals. By promoting digitalization alongside green technologies, Germany has demonstrated success in reducing emissions while enhancing productivity. The study advocates for strengthening both research and application of these technologies to achieve a similar synergistic development between digitalization and greening. This vision also aligns with the global momentum behind Industry 4.0 as a sustainability driver.

Secondly, the study recommends formulating incentive and support policies for industrial enterprises to accelerate DT, reflecting successful strategies in South Korea. South Korea’s Green New Deal policy focuses on integrating digital and green initiatives through fiscal subsidies, tax breaks, and targeted support for technological innovation. In line with this, our study proposes region-specific support, training programs, and talent acquisition policies, similarly to South Korea’s emphasis on fostering a skilled workforce in green technologies.

Thirdly, we suggest increasing support and investment in green technology innovation (GTI). International models, like the European Union’s Horizon 2020 program, demonstrate the effectiveness of establishing research centers, providing funding, and fostering technology transfer to drive green innovation. Our study supports similar measures to promote industry collaboration, facilitate knowledge exchange, and accelerate the dissemination of green technologies, aligning with global efforts to foster innovation in industrial sectors.

Fourthly, strengthening ESG supervision and disclosure is crucial. This recommendation draws on best practices from the United States, where companies are increasingly required to report their environmental impacts and sustainability measures. For example, the U.S. Securities and Exchange Commission (SEC) mandates the disclosure of climate-related risks, and many corporations voluntarily publish detailed ESG reports to improve transparency and accountability. In line with this, our study proposes the development of a robust and comprehensive ESG evaluation system to enforce compliance with environmental and social responsibilities during the DT process.

Finally, we stress the importance of international cooperation and knowledge sharing, as exemplified by the Paris Agreement and UN Sustainable Development Goals (SDGs), which encourage global collaboration on climate change and carbon reduction initiatives. By learning from successful international experiences in DT and CEI reduction, China can foster partnerships with international organizations and governments. These collaborations can facilitate the exchange of best practices and innovative solutions to accelerate the Country green transition.

In brief, by drawing comparisons to international best practices, these expanded policy recommendations provide a broader perspective on how China and other emerging economies can effectively navigate their green transition through digital transformation.

8.3. The Study Limitation

This research has several limitations that necessitate further investigation and offer specific suggestions for future research. First, future studies could explore the broader economic impacts of carbon performance, such as cost reduction and market support, through qualitative case studies. Second, to improve the representativeness of the study sample, we suggest including small and medium-sized enterprises (SMEs) in future research, particularly through targeted surveys or in-depth case studies. Third, to handle missing data more effectively, advanced econometric techniques like imputation methods or structural equation modeling could be employed. Additionally, alternative methods for measuring CEI—such as life-cycle analysis or environmental input-output analysis—may provide new insights. Future research should also investigate the bidirectional effects between ESG practices and DT, potentially through longitudinal data or qualitative interviews. Lastly, expanding the scope to include other mechanisms—such as technological integration, managerial efficiency, and employee training—would provide a more comprehensive understanding of DT’s environmental impact.