Driving Green Transformation in Equipment Manufacturing Enterprises: The Role of Digital Technologies

Abstract

As digital technologies are increasingly applied in the equipment manufacturing industry, they play an increasingly important role in the green transformation of business development models. Digital technologies have gradually become a key driving force behind the green transformation and development of equipment manufacturing enterprises. Using empirical data from 2013 to 2022 on publicly listed equipment manufacturing companies in China’s A-share market, this study explores the mechanisms through which digital technologies influence the green transformation of these enterprises. The empirical results show that the application of digital technologies in equipment manufacturing enterprises effectively promotes their green transformation. Mechanism tests reveal that digital technologies contribute to green transformation through channels such as fostering green innovation and optimizing the market environment. The findings provide empirical support for the green transformation of equipment manufacturing enterprises. These companies should further enhance their application of digital technologies, while the government should actively introduce relevant supportive policies to create a favorable market environment, strengthen digital infrastructure, and provide extensive support for the application of digital technologies, thereby promoting the coordinated advancement of digital and green transformations in enterprises.

1. Introduction

As global climate change and sustainable development challenges become increasingly severe, green transformation has emerged as a critical goal for global industrial development. Particularly in the industrial sector, the equipment manufacturing industry, as a vital part of the world economy, has long been grappling with issues such as excessive resource consumption, high pollution emissions, and low energy efficiency. Worldwide, equipment manufacturing enterprises not only face market competition but must also undergo transformation and upgrading under the pressure of changing environmental regulations, evolving consumer demands, and energy/resource constraints. Achieving green development while maintaining economic efficiency has thus become a core issue in driving sustainable business growth [1].

Recent advancements in information technology have positioned digital transformation as a crucial solution to the green transformation challenges faced by the equipment manufacturing sector [2]. Technologies such as cloud computing, big data, artificial intelligence (AI), and the Internet of Things (IoT) are increasingly applied across industries, providing equipment manufacturers with innovative tools to optimize production, improve energy efficiency, reduce resource waste, and meet higher environmental standards. In particular, digital technologies have not only enhanced production efficiency but have also triggered significant changes in production methods, decision-making processes, and industrial structures [3]. Despite these advancements, however, many enterprises face considerable obstacles in reducing environmental pollution and energy consumption while maintaining production capacity during their green transformation efforts [4]. The widespread adoption of digital technologies often entails substantial upfront investments in technology, capital, and human resources, which may exacerbate operational costs. Additionally, balancing economic performance with environmental goals often leads to higher management expenses, further straining overall operations. In some cases, digital transformation does not yield the anticipated economic benefits, resulting in challenges such as rising management costs, insufficient R&D investment, and declining operational performance—phenomena collectively referred to as the “enterprise digitalization paradox” [5]. Moreover, while digitalization offers promising pathways for green transformation, there remains a gap in systematic theoretical and practical guidance on how to align these technologies with corporate sustainability objectives effectively.

This paper addresses these gaps by exploring the mechanisms through which digital technologies contribute to the green transformation of equipment manufacturing enterprises. Specifically, it investigates how these technologies can help enterprises overcome technical, cost, and management challenges during the green transformation process. By focusing on the practical pathways enabled by digital technologies, this study aims to reveal the underlying mechanisms driving green transformation and offer actionable insights for the sustainable development of the global equipment manufacturing industry. Digital technologies, particularly in the areas of emission reduction and energy management, can help enterprises achieve green development goals through smart optimization, precise forecasting, and real-time monitoring [6]. Furthermore, the integration of digital technologies into global supply chains has facilitated the green transformation process, alleviating the ambiguity in green innovation activities and improving resource utilization efficiency [7]. The research extends the existing literature by expanding the understanding of the spillover effects of digital technology applications in green transformation, offering both theoretical contributions and practical policy recommendations. Furthermore, this study provides essential guidance for enterprises facing the dual challenge of integrating digital technologies and meeting sustainability goals. In light of tightening global environmental regulations and accelerating technological advancements, the green transformation of equipment manufacturing enterprises has become a shared strategic objective. Digital technologies are poised to play an indispensable role in this process, offering vital support to achieve global green and low-carbon economic targets.

2. Literature Review and Research Hypotheses

2.1. Literature Review

With growing global environmental pressures, the green transformation of enterprises has become a key focus for both scholars and practitioners. Green transformation is not only a profound shift in a company’s development model but also a crucial component of the global economic transition toward sustainability. The current literature recognizes that corporate green transformation is a dynamic, multidimensional process involving green innovation, management, and production. This transformation requires a shift from traditional high-emission, high-consumption models to sustainable models characterized by low emissions, low consumption, and harmony with nature, with the aim of achieving both economic and environmental benefits [8]. Scholars agree that green transformation represents a dynamic leap from green structuration and green capabilities to green leverage, making it the ultimate goal of enterprise development [9].

For the equipment manufacturing sector, green transformation is particularly distinctive. It encompasses not only management and technological innovation but also significant changes in business models, resource allocation, and organizational structures [10]. This transformation requires enhancing internal green innovation capacity while also upgrading the green aspects of entire industrial and supply chains. Scholars agree that corporate green innovation is the primary driver of this transformation [11], with different types of green innovation—technological, cultural, and managerial—working together to improve resource efficiency and promote sustainability [12]. However, the process is not without challenges. It is influenced by external factors such as environmental regulations (e.g., government policies and social oversight) [13], as well as internal factors like dynamic capabilities, resource allocation, and strategic decision-making abilities [14]. Consequently, evaluating corporate green transformation performance—through both economic and environmental lenses—has become a key academic and practical concern.

In the context of widespread digital technology adoption, an increasing body of literature examines how digital tools such as big data, artificial intelligence (AI), the Internet of Things (IoT), and cloud computing are driving the green transformation in the equipment manufacturing industry. These technologies enhance management efficiency, resource utilization, and environmental performance, thereby promoting both economic and environmental gains [15,16]. Digital transformation not only streamlines management structures and improves production efficiency but also plays a crucial role in optimizing resource use, reducing pollutant emissions [17], and enabling intelligent environmental monitoring and accounting systems. Ultimately, they help enterprises improve energy efficiency, reduce material waste, and strengthen green management capabilities, minimizing human-induced pollution [18,19].

Simultaneously, the widespread application of digital technologies has sparked growing interest among scholars in understanding how digital technologies, particularly within the digital economy, contribute to the green transformation of the equipment manufacturing sector. Digital technologies are now recognized as key enablers of this sector’s green transformation, particularly in terms of technological innovation, management efficiency, and changes in production methods [2]. Research shows that these technologies, which are increasingly integrated into large-scale industrial ecosystems, reshape the industry’s structure and operations [20]. By overcoming the limitations of time and space, digital tools facilitate the shift from supply-driven to demand-driven production models, which, in turn, support green innovation initiatives within the sector [21]. Additionally, digital technologies foster collaboration among enterprises and key stakeholders, enabling better resource reconfiguration and advancing green technological developments [22]. Studies further demonstrate that digitalization can significantly enhance green innovation output, addressing the limitations and high costs that often hamper traditional green innovation efforts [23]. By utilizing conventional production factors more innovatively, digital tools help enterprises move up the value chain toward higher-value-added activities, which reduces carbon emissions and advances both green transformation and sustainable development through cost reduction and operational efficiency improvements [24]. Enterprise production and operations are becoming increasingly flexible, networked, and platform-based, which leads to lower energy consumption and reduced pollution. This transformation also drives the adoption of green production practices, such as renewable energy use and resource recycling, contributing to improved environmental performance. As a result, enterprises are receiving greater attention from environmentally conscious consumers and investors, who exert pressure on firms to strengthen environmental awareness and pursue green transformation initiatives [25].

However, the equipment manufacturing industry faces dual challenges—“carbon path dependence” and “growth at the bottom of the value chain”—which pose significant obstacles to green transformation. Scholars have suggested accelerating the penetration of digital technologies to drive transformation and upgrading within this sector [26]. At the same time, a growing number of studies have identified the phenomenon of the “digital paradox”, where excessive digital investments, while enhancing R&D, may undermine firms’ innovation capacity by creating a “crowding-out effect”. Such over-investment can hinder overall innovation performance [27]. Other challenges, such as data silos and cybersecurity risks, can impose heavy operational burdens, further complicating green innovation efforts [28]. Additionally, while digital technologies are touted to improve energy efficiency, some studies indicate a “rebound effect”, where gains in energy efficiency are offset by increased energy consumption, negating the environmental benefits of digital transformation [29,30]. This paradox suggests that the benefits of digital transformation are not guaranteed and are highly dependent on the specific context in which they are implemented.

The literature on the relationship between digital technology adoption and green transformation in manufacturing enterprises is still evolving. Although much has been said about digital technologies’ role in supporting green innovation, a comprehensive understanding of the specific mechanisms through which they enable green transformation remains underexplored. Previous studies have largely focused on the supportive role of digital technologies in green innovation or have relied on case studies to explore the interactive relationship between digital and green transformations. However, systematic quantitative evaluations and in-depth theoretical exploration of the mechanisms at play are lacking. The existing body of research has yet to provide a conclusive understanding of how, or whether, digital technologies in equipment manufacturing firms effectively promote green transformation. Addressing these gaps carries significant theoretical and practical implications for advancing sustainable development in the manufacturing sector.

Therefore, this paper aims to investigate the enabling mechanisms and pathways through which digital technologies support the green transformation of equipment manufacturing enterprises. This deeper exploration will fill the gap in the existing literature and offer both theoretical insights and practical guidance for enterprises navigating the dual challenges of digitalization and green transformation. In conclusion, while significant progress has been made in exploring the multidimensional aspects of corporate green transformation, further research is required to understand the specific mechanisms through which digital technologies enable this transformation. This study aims to bridge these gaps by providing theoretical and practical insights that will help equipment manufacturing enterprises advance their green transformation efforts and contribute to the broader academic discourse on sustainability.

2.2. Research Hypotheses

2.2.1. The Impact of Digital Technologies on Corporate Green Transformation

The widespread adoption of digital technologies and the development of digital transformation in equipment manufacturing enterprises provide new approaches and methods to support and empower green transformation. Digital transformation has become a key enabler for enterprises to achieve green development and tackle challenges such as environmental regulations and the improvement of total factor productivity. Existing studies suggest that digital transformation has a positive impact on enterprises’ environmental performance [31]. It effectively reduces pollutant emissions, promotes innovation in ecological processes, products, and management systems, and significantly enhances both environmental and economic outcomes, thereby serving as a key driver of organizational green transformation [32,33]. The application of digital technologies promotes economic benefits and scale growth. On one hand, digital technologies enable the collection, processing, and analysis of production data, allowing enterprises to quickly adjust their production and business models in response to market demand changes. This enhances decision-making accuracy, helps overcome resource barriers, and integrates both internal and external resources to improve resource allocation efficiency. Enterprise digital transformation can enhance environmental performance through multiple pathways, including lowering management costs, easing budget constraints, promoting human capital accumulation, and stimulating green technological innovation. For example, Zhou et al. (2023) found that digitalization improves environmental outcomes by accelerating the development of human capital, reducing financing costs, and facilitating technological progress [34]. In turn, this supports the implementation of low-cost strategies [35]. On the other hand, digital technologies provide platforms for information sharing. The universality of these technologies strengthens data and information analysis capabilities, alleviates information asymmetry in production and operations, and reduces uncertainty in research and innovation. This leads to improvements in production methods, decision-making efficiency, and information processing capabilities. The integration of digital technologies into enterprise management accelerates digitalization, optimizes process flows, improves production management, and strengthens the enterprise’s ability to adapt to environmental changes. By utilizing advanced technologies, like big data and artificial intelligence, alongside policy tools, enterprises can unlock the full potential of digital technologies to promote green transformation and drive enhanced performance and growth [36].

Efficient environmental management is key to improving green innovation capabilities, and the widespread application of digital technologies has significantly improved pollution control efforts, thus fostering the ongoing green development of enterprises [37]. However, industries with varying energy consumption levels show differences in their green transformation processes. Digital technologies exert positive spillover effects throughout the supply chain, promoting the green transformation of both upstream and downstream enterprises. Internally, digital technologies drive green transformation in manufacturing enterprises through scale and technological effects. These technologies, which are knowledge-intensive and environmentally clean, have deep integration with manufacturing industries, serving as a new engine for their growth. Their application has a significant positive impact on the green development of manufacturing sectors [38]. Based on this, we propose Hypothesis H1.

H1: 

Digital technologies facilitate the green transformation of equipment manufacturing enterprises.

2.2.2. The Mediating Role of Green Innovation in the Relationship Between Digital Technologies and Corporate Green Transformation

Digital technologies empower equipment manufacturing enterprises to enhance operational efficiency, enabling a shift from value realization to value creation, with green innovation acting as a vital conduit in this transformation. Scholars widely agree that green innovation is a critical driver for overcoming core technological bottlenecks and advancing enterprises’ green transformation [39]. It is also regarded as an effective strategy to mitigate environmental risks and reduce the negative impacts of resource consumption. In academic research, green innovation is often conceptualized as a mediating mechanism through which digital transformation enhances green technological capabilities and environmental performance [40]. Unlike general innovation, green innovation requires the integration of information across multiple dimensions—such as production processes, resource utilization, pollution control, and environmental impact assessment. It involves the creation, absorption, and dissemination of cross-domain knowledge both within and beyond organizational boundaries, placing higher demands on enterprises’ capabilities in information management, resource optimization, and collaborative sharing [22].

Through the integration of digital technologies, enterprises can harness the vast data resources enabled by big data analytics to conduct timely and systematic risk assessments, anticipate green innovation trends, and identify optimal innovation strategies. This not only allows for a more rational allocation of innovation risks but also shortens the green innovation cycle and enhances its efficiency. In turn, this motivates enterprises to intensify investment in green innovation, thereby promoting sustained green transformation [41]. The synergy between digital transformation and green innovation plays a fundamental role in accelerating green development within enterprises [42].

First, integrating digital technologies with traditional manufacturing methods improves data-driven learning, optimizes workforce skill structures, and strengthens green innovation capabilities [43]. This enhances resource substitution and waste reduction, transforming sustainability into a driving force for green production. Moreover, through vertical technological spillovers along the supply chain, digitalization further promotes green technological innovation, contributing significantly to enterprises’ green transition [23]. Empirical evidence shows that digital transformation increases the citation frequency of green patents, reflecting a growing emphasis on green innovation quality. Unlike general patents, green patents—characterized by their public goods nature, lengthy R&D cycles, and complex testing processes—have substantial spillover effects, which are crucial for the industry-wide green transition [23]. Second, by leveraging digital technologies for efficient data selection and application, enterprises can innovate across production methods, business models, and organizational structures. Tailored to industry characteristics, firms can co-create products based on user demand, enabling agile development, rapid iteration, and efficient delivery [44]. Finally, digital technologies significantly improve green management. By integrating and processing operational data, they enhance decision-making efficiency, enable comprehensive performance monitoring across all stages, and support more informed, environmentally conscious managerial decisions [45]. Based on this, we propose Hypothesis H2.

H2: 

Digital technologies promote the green transformation of equipment manufacturing enterprises through green innovation.

2.2.3. The Mediating Role of the Market Environment in Corporate Green Transformation

The market environment for green transformation in equipment manufacturing enterprises includes various legal frameworks, policies, and institutional environments related to green development, as well as the degree of factor market development, government-market relationships, and the overall economic environment [46]. Government policy support and guidance are essential for green development, and the widespread use of digital technologies in equipment manufacturing enterprises strengthens factor mobility between regions, promotes the exchange of complementary elements between enterprises, and fosters value co-creation. This facilitates multi-stakeholder collaboration at different stages of the production process [47]. The application of digital technologies not only advances the green transformation of enterprises but also generates positive spillover effects on upstream and downstream enterprises and the broader industry. Digital technologies improve the flow of information and resource integration across enterprises [26], speeding up market information exchange, reducing transaction costs, and protecting intellectual property, thus optimizing the market environment. This also significantly improves the economic performance of enterprises, providing a solid foundation for green transformation. The use of digital technologies enhances enterprises’ abilities to perceive, acquire, and reconstruct information, allowing them to better plan and utilize market, product, and resource data [2]. This mitigates issues like fragmented, redundant, or asymmetric information, which often lead to low decision-making efficiency and slower responses to market changes. By improving information processing efficiency, digital technologies enable enterprises to better address the green concerns of various stakeholders, align innovation outcomes with market demand, accelerate the commercialization of innovations, alleviate financing constraints, and further promote the flow of production factors in the market. This helps optimize the relationship between government and market by implementing both formal and informal environmental regulations, reinforcing the positive impact of government regulations on corporate green transformation, and driving the green transformation of the equipment manufacturing sector [48]. Based on this, we propose Hypothesis H3.

H3: 

Digital technologies promote green transformation by optimizing the market environment.

In summary, the research framework is illustrated in Figure 1.

3. Research Design

3.1. Sample and Data Sources

This study focuses on A-share listed equipment manufacturing enterprises from 2013 to 2022, using the 2012 version of the China Securities Regulatory Commission’s industry classification guidelines as the basis for industry categorization. We selected eight major industries: metal products, general equipment manufacturing, special equipment manufacturing, automobile manufacturing, manufacturing of railway, shipbuilding, aerospace, and other transportation equipment, electrical machinery and equipment manufacturing, computer, communication, and other electronic equipment manufacturing, and instrument manufacturing. We excluded companies listed as ST and *ST, as well as those with missing key variables. The final sample consists of 381 companies with 10 annual observations each. The data primarily comes from the CSMAR database, Wind database, Zhihuya patent database, ESG reports of listed companies, and annual reports of listed companies. To mitigate the impact of extreme values, we applied a 1% tail trimming to the continuous variables in the data.

3.2. Model Specification

To analyze the mechanism by which digital technologies influence the green transformation of equipment manufacturing enterprises, we construct the following baseline model:

$${\mathrm{G}\mathrm{T}\mathrm{P}}_{\mathrm{i},\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{D}\mathrm{T}}_{\mathrm{i}.\mathrm{t}}+\mathsf{\beta }{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{l}}_{\mathrm{i}.\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(1)

where ${\mathrm{G}\mathrm{T}\mathrm{P}}_{\mathrm{i},\mathrm{t}}$ represents the degree of green transformation of the enterprise, ${\mathsf{\alpha }}_0$ is the constant term, ${\mathrm{D}\mathrm{i}\mathrm{g}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}}_{\mathrm{i}.\mathrm{t}}$ represents the degree of digital technology application, ${\mathsf{\alpha }}_1$ is the coefficient of digital technology application, ${\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{l}}_{\mathrm{i}.\mathrm{t}}$ represents the control variables, $\mathsf{\beta }$ are the coefficients of the control variables, and ${\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$ is the error term.

3.3. Variable Measurement

3.3.1. Dependent Variable: Green Transformation Degree (GTP)

In this study, we select the degree of green transformation of equipment manufacturing enterprises (GTP) as the dependent variable. Using the input-output method and the DEA-SBM model, we use the number of green patents filed and authorized by the enterprise as input indicators. The output indicators include sales revenue, the enterprise’s pollution emissions index, and the enterprise’s energy consumption index. These are combined to calculate the enterprise’s green performance, which reflects the degree of its green transformation and comprehensively mirrors both its economic and environmental performance. The pollution emissions index is calculated by considering various pollutants such as chemical oxygen demand (COD), ammonia nitrogen emissions, total nitrogen, total phosphorus, sulfur dioxide, nitrogen oxides, and particulate matter, and using the entropy method for weighting. The energy consumption index is derived from the energy conversion factors, which include water consumption, electricity usage, coal consumption, natural gas usage, gasoline, diesel, and centralized heating [49].

3.3.2. Independent Variable: Digital Technology Application Degree (DT)

The degree of digital technology application in enterprises is measured by the frequency of relevant terms disclosed in corporate annual reports [50]. These terms include “big data technology”, “IoT technology”, “artificial intelligence”, “internet technology”, and “information systems”. We construct a word frequency database for digital technologies based on the annual reports of listed companies [51]. Using Python’s Jieba segmentation function, we calculate the frequency of relevant keywords in the reports and then apply the natural logarithm (plus one) of the word frequencies to measure the degree of digital technology application. A higher value indicates a higher degree of digital technology application by the enterprise [52].

3.3.3. Mediating Variables: Green Innovation (GI) and Market Environment (ME)

The green innovation efficiency of equipment manufacturing enterprises is selected as the main indicator for measuring the level of green innovation. This is calculated using the number of R&D staff and R&D expenditure as input and the number of green patents filed and authorized as output, based on the “International Patent Classification Green List”. Green patents are a direct manifestation of the enterprise’s green management and technology and reflect the enterprise’s green innovation. Therefore, the higher the green innovation efficiency, the greater the level of green innovation in the enterprise. Considering that the impact of digital technology on green innovation may be delayed, we use lagged green innovation data for robustness checks. The market environment is measured using the provincial-level marketization index, which can be obtained from the China Marketization Index Database.

3.3.4. Control Variables

The control variables in this study include enterprise size (Size), which is measured by the natural logarithm of total assets at the end of the year; financial leverage (Lev), which is measured by the ratio of total assets to total liabilities; growth potential (Grow), which is measured by the growth rate of operating income; ownership concentration (Shrcr), which is measured by the ratio of the largest shareholder’s holdings to total shares; duality (Presmn), which is a dummy variable where Presmn = 1 if the chairman and CEO roles are combined, and Presmn = 0 otherwise; and listing age (Age), which is measured by the natural logarithm of the difference between the target year and the listing year. In addition, year (Year) and industry (Ind) dummy variables are also controlled for in the study. The definitions of the variables are shown in

Table 1. Variable definitions.

Variable Type	Variable Name	Variable Symbol	Variable Definition
Dependent Variable	Green Transformation Degree	GTP	Comprehensive evaluation of the enterprise’s economic and environmental performance
Independent Variable	Digital Technology Application Degree	DT	Natural logarithm of the frequency count of digital technology-related terms in the annual report plus 1
Mediating Variable	Green Innovation	GI	Assessment outcomes regarding the green innovation efficiency of enterprises
	Market Environment	ME	Provincial marketization index obtained from the China Marketization Index Database
Control Variable	Enterprise Size	Size	Natural logarithm of total assets at the end of the year
	Enterprise Leverage Ratio	Lev	Ratio of total assets to total liabilities
	Growth Potential	Grow	Growth rate of operating income
	Ownership Concentration	Shrcr	Percentage of shares held by the largest shareholder
	Dual Role	Presmn	1 if the chairman and CEO roles are combined, otherwise 0
	Listing Age	Age	Natural logarithm of the number of years since the enterprise was listed
	Industry	Industy	Controls for industry factors
	Year	Year	Controls for year factors

.

4. Empirical Results Analysis

4.1. Descriptive Statistical Analysis

After obtaining the statistical data for the variables based on their definitions, a descriptive statistical analysis of the variables was conducted.

Table 2. Descriptive statistics and correlation matrix of variables.

Variable	GTP	DT	GI	ME	Size	Lev	Grow	Shrcr	Presmn	Age
GTP	1.000
DT	0.145 ***	1.000
GI	0.380 ***	0.164 ***	1.000
ME	0.237 ***	0.188 ***	0.249 **	1.000
Size	0.149 ***	0.180 **	0.165 ***	0.050 **	1.000
Lev	0.117 ***	0.019	0143 ***	0.100 **	0.550 ***	1.000
Grow	−0.040 **	0.051 ***	−0.048 ***	−0.129 ***	−0.062 ***	−0.054 ***	1.000
Shrcr	−0.113 ***	−0.085 ***	−0.109 ***	−0.078 ***	−0.031 *	−0.039 **	0.001	1.000
Presmn	0.0131	0.070 ***	−0.033 **	0.118 ***	−0.095 ***	−0.070 ***	−0.020	0.042 ***	1.000
Age	0.322 ***	0.169 ***	0.325 ***	0.135 ***	0.528 ***	0.426 ***	−0.032 *	−0.159 ***	−0.136 ***	1.000
Mean	0.583	1.875	0.584	10.23	22.14	0.397	0.299	29.53	0.336	2.351
S.D.	0.186	1.407	0.190	1.305	1.156	0.178	0.793	13.27	0.472	0.493

Note: *** p < 0.01, ** p < 0.05, and * p < 0.1.

presents the descriptive statistics and the correlation matrix of the variables. As shown in Table 2, the correlation coefficient between the dependent and independent variables is positive, which meets the requirements for the subsequent baseline regression analysis. A multicollinearity test was conducted on the model, and the results indicate that the Variance Inflation Factor (VIF) values are all below 2, suggesting that there is no multicollinearity among the variables in the model.

4.2. Benchmark Regression Results

Based on this, a Hausman test was conducted, and the null hypothesis was rejected, so the fixed-effects model was chosen. The benchmark regression results using the fixed-effects model are shown in Column (1) of

Table 3. Benchmark regression results.

Variable	(1)	(2)	(3)
	GTP	GTP	GTP
DT	0.023 *** (6.68)	0.014 *** (3.72)	0.150 *** (4.25)
Size		−0.005 (−1.29)	−0.008 * (−1.88)
Lev		−0.010 (−0.66)	0.011 (0.84)
Grow		−0.008 (−1.69)	−0.009 * (−1.89)
Shrcr		−0.001 *** (−4.32)	−0.001 *** (−4.48)
Presmn		0.019 ** (3.05)	0.018 ** (2.70)
Age		0.122 *** (15.86)	0.125 *** (12.88)
Year	Yes	Yes	Yes
Ind	Yes	Yes	Yes
Constant	0.563 *** (94.56)	0.454 *** (5.04)	0.512 *** (5.44)
Observations	3810	3810	3429
R2	0.028	0.124	0.107

Note: The values in parentheses are t-values. *** p < 0.01, ** p < 0.05, and * p < 0.1.

. From the results, it can be seen that without introducing control variables, the coefficient of digital technology application on corporate green transformation is 0.023 and is significant at the 1% level, indicating that the application of digital technology in equipment manufacturing enterprises has a significant positive impact on corporate green transformation. Specifically, a 1% increase in digital technology applications can lead to a 0.023% improvement in the green transformation of equipment manufacturing enterprises. Column (2) of Table 3 shows the benchmark regression results after control variables were introduced. The results indicate that, after adding control variables, the coefficient of digital technology application on green development is 0.014, which is significant at the 1% level. This suggests that the higher the level of digital technology application in enterprises, the more advanced the green transformation in equipment manufacturing enterprises, establishing a significant positive correlation between the two. Therefore, Hypothesis H1 is verified. Column (3) represents the robustness check. To account for the possible lag effect of digital technology application, the previous period’s explanatory variable data were used in the benchmark regression. From the results in Column (3) of Table 3, it can also be seen that the coefficient of digital technology application is positive, indicating that the hypothesis is robust.

4.3. Endogeneity Test

In empirical research, endogeneity issues, such as sample selection or reverse causality, may arise, which can affect the consistency of parameter estimates. Therefore, it is essential to address endogeneity in the data. In this study, due to missing data on indicators such as the number of green patent applications and green patent grants by firms, the data did not cover all the equipment manufacturing companies listed on the Shanghai and Shenzhen stock exchanges. Consequently, relevant listed companies were excluded during sample selection. However, this exclusion may still lead to endogeneity issues due to sample selection bias.

To address this endogeneity problem, this study applies the Heckman two-step method. The Probit model is used to estimate the selection equation and calculate the Inverse Mills Ratio (IMR), which is then added to the regression model along with the control variables to correct the results. The corrected regression results are presented in

Table 4. Endogeneity test.

Variable	Heckman Two-Step Method		Instrumental Variable Method
	Result Equation		Selection Equation
DT	0.011 *** (2.90)	0.168 * (1.85)		0.018 *** (4.50)
IV			0.836 *** (104.90)
IMR	−0.316
Year	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes
Constant	0.534 (3.86)	−12.041 (−3.57)	−0.889 *** (−5.85)	0.528 *** (5.86)
Observations	3886	3902	3429	3429

Note: The values in parentheses are t-values. *** p < 0.01 and * p < 0.1.

. As shown, the estimated coefficient of IMR is significantly negative, indicating that the Heckman two-step method is effective. The regression coefficients for digital technology application are all significantly positive, confirming that the basic hypotheses proposed in the earlier sections remain valid.

After addressing the endogeneity issue arising from sample selection, the study also considers reverse causality as a potential source of endogeneity. Specifically, the application of digital technology may promote green transformation, and as companies’ economic and environmental performance improve, they may further drive the application of digital technology, which could result in reverse causality.

To test for this potential reverse causality, this study uses the lagged value of digital technology application as an instrumental variable and performs a two-stage least squares (2SLS) test. The results are shown in Table 4. In the first stage, the coefficient of the instrumental variable is 0.836 and is significant at the 1% level. In the second stage, the coefficient for digital technology application is 0.018, also significant at the 1% level, indicating that the basic hypotheses proposed earlier remain valid.

4.4. Mechanism Verification

4.4.1. Mediating Effect of Green Innovation

Based on the theoretical analysis in previous sections, the application of digital technologies in equipment manufacturing enterprises is expected to enhance their green innovation capabilities, thereby driving their green transformation. To verify the mediating effect of green innovation, this study employs stepwise regression analysis. The regression results are shown in Column (1) and Column (2) of

Table 5. Mechanism verification.

Variable	(1)	(2)	(3)	(4)
	GI	GTP	ME	GTP
DT	0.017 *** (6.26)	0.009 *** (3.28)	0.146 *** (6.25)	0.010 ** (3.09)
GI		0.289 *** (12.79)
ME				0.026 *** (11.88)
Control Variables	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes
Constant	0.426 *** (6.93)	0.332 *** (4.24)	11.823 *** (10.25)	0.140 *** (1.94)
Observations	3810	3810	3810	3810

Note: The values in parentheses are t-values. *** p < 0.01 and ** p < 0.05.

. In Column (1), green innovation is set as the dependent variable to analyze the impact of digital technology application on green innovation. The regression result in Column (1) shows that the coefficient for digital technology application is 0.017, which is significant at the 1% level. This indicates that the application of digital technologies has a positive impact on green innovation in equipment manufacturing enterprises.

In Column (2), both digital technology application and green innovation are included as independent variables to analyze their effects on green transformation. The regression result shows that the coefficient for green innovation is 0.289, significant at the 1% level, and the coefficient for digital technology is 0.009, also significant at the 1% level. This suggests that green innovation promotes green transformation and that green innovation plays a mediating role between digital technology and green transformation. Combining the results from Column (1) and Column (2), we can conclude that digital technologies promote green transformation in enterprises through the enhancement of green innovation, thereby validating Hypothesis H2.

4.4.2. Mediating Effect of Market Environment

The mediating effect of the market environment is also analyzed using stepwise regression, and the results are shown in Column (3) and Column (4) of Table 5. In Column (3), market environment is set as the dependent variable to examine the impact of digital technology application on the market environment. The regression result in Column (3) shows that the coefficient for digital technology application is 0.146, significant at the 1% level, indicating that the application of digital technologies has a positive impact on the market environment in equipment manufacturing enterprises.

In Column (4), both digital technology application and market environment are included as independent variables to analyze their effects on green transformation. The regression result shows that the coefficient for market environment is 0.026, significant at the 1% level, and the coefficient for digital technology is 0.010, significant at the 5% level. This suggests that the market environment positively influences green transformation and that digital technology contributes to green transformation by optimizing the market environment. Combining the results from Column (3) and Column (4), it can be concluded that digital technology promotes green transformation through improving the market environment, thus validating Hypothesis H3.

5. Conclusions

As the global economy shifts towards high-quality development, achieving harmonious growth between the economy, society, and the environment has become an urgent issue for governments and enterprises worldwide. The equipment manufacturing industry, as a key engine for global economic growth and industrial development, faces significant environmental pressures and resource consumption from traditional production methods. Consequently, green transformation has become crucial for its sustainable development. However, this transformation requires not only policy guidance but also technological innovation, particularly the widespread application of digital technologies, to drive the industry towards a green, low-carbon, and efficient future.

Based on the analysis of theories related to the green transformation of equipment manufacturing enterprises and the application of digital technologies, this study, using data from 381 equipment manufacturing enterprises in Shanghai and Shenzhen from 2013 to 2022, explores the mechanisms through which digital technologies facilitate green transformation. The findings indicate that the application of digital technologies significantly promotes the green transformation of enterprises. As the core driving force of the current global industrial transformation, digital technology is profoundly reshaping the transformation trajectories of equipment manufacturing enterprises. Both green innovation and the market environment serve as mediating mechanisms linking the application of digital technologies to green transformation. By integrating digital technologies, enterprises can revamp traditional production methods, optimize operational processes and management models, reduce resource consumption and environmental pollution, and enhance production efficiency. Digitalization enables equipment manufacturing firms to respond more effectively to dynamic market demands, stimulate green innovation, and improve both economic and environmental performance—thus accelerating the achievement of green transformation goals. Among the two mediators, green innovation plays a more prominent role. Specifically, digital technologies strengthen firms’ green technological innovation capabilities, upgrade production and management practices, and enhance the efficiency of resource allocation, all of which serve to further promote the realization of green transformation.

The findings of this study not only provide theoretical support and empirical evidence for empowering green transformation in equipment manufacturing enterprises through digital technologies but also offer important policy insights. Specifically, the following policy recommendations are crucial for promoting green transformation in equipment manufacturing enterprises:

Firstly, enhance the application of digital technologies. Equipment manufacturing enterprises should actively leverage advanced digital technologies—such as cloud computing, the Internet of Things (IoT), big data, and artificial intelligence (AI)—to build a sustainable green innovation system underpinned by digitalization. By harnessing the enabling roles of digital simulation and big data analytics in green innovation and decision-making, enterprises can significantly improve information processing efficiency and decision-making accuracy. Digital technologies should be positioned as key drivers of green governance and green production. To this end, enterprises must increase investment in digital transformation, accelerate the adoption and diffusion of digital technologies, and embed them across all dimensions of production and operational management—particularly throughout the entire lifecycle of green technological innovation. This will promote the deep integration of digitalization and greening, unlock the potential of data-driven factors, and enhance digital productivity. Furthermore, it will reinforce the catalytic role of digital technologies in green innovation while strengthening firms’ capabilities in technological integration and application. At the same time, enterprises should establish comprehensive energy consumption monitoring systems, strengthen energy management across all operational stages, and develop robust internal and external information-sharing platforms. These efforts will improve the transparency and efficiency of information exchange—especially in areas such as production, energy control, and across industrial and supply chains—thus enhancing the efficiency of green innovation and continually elevating enterprises’ levels of digitalization and green innovation capacity.

Secondly, promote green technological innovation and enhance management capabilities. Equipment manufacturing enterprises should develop long-term digitalization strategies and establish sustainable green development goals, ensuring the effective execution of strategic decisions. In addition to increasing investment in green technological innovation, enterprises should prioritize strengthening their green management capabilities. By integrating information and leveraging data analytics, digital technologies can improve green management practices, optimize resource allocation, and reduce pollution emissions. To achieve this, enterprises must enhance their green technological innovation capacities through digital transformation, fostering innovations in green products, processes, and business models. This approach enables the simultaneous achievement of green development and economic benefits. By utilizing digital technologies, enterprises can establish a comprehensive digital management platform that monitors the entire lifecycle of production materials, large-scale equipment, and supporting facilities. This platform enhances both the efficiency and precision of production processes. Moreover, through specialized management modules—such as those for waste recycling and reuse, energy optimization, and strategic resource planning—enterprises can strengthen cost management and operational efficiency. These efforts will improve raw material and infrastructure utilization rates while simultaneously reducing energy consumption and labor costs, thereby promoting sustainable development.

Thirdly, optimize the policy environment to support green transformation. Governments should design and implement targeted policies that promote and facilitate the widespread adoption of digital technologies in the green transformation process. First, governments should prioritize the development of digital infrastructure, strengthening the advancement of key technologies such as the Internet of Things (IoT), big data, cloud computing, and artificial intelligence (AI). They should also focus on promoting and disseminating these technologies to help high-carbon equipment manufacturing enterprises transition to more sustainable production methods. By addressing resource bottlenecks, accelerating the integration of digital technologies into production processes, and fostering the optimal allocation and efficient utilization of production resources, the government can significantly support sustainable industrial development. Second, a comprehensive policy framework must be established to facilitate the digital and green transformation of enterprises. This framework should include fiscal and tax incentives tailored to support digital and green transformation, as well as strategies for cultivating and attracting digital technology talent. Governments should also develop flexible reform plans, customized to the characteristics of different industries and enterprises, to encourage the deep integration of digital technologies and green transformation. This approach would create a more favorable environment for business development. Governments must play a pivotal role in driving the digital and green transformation of enterprises by promoting the integrated use of advanced intelligent equipment, core software, and industrial internet solutions. This will enhance the overall digital service capabilities of enterprises. For critical technological breakthroughs that drive transformation, the government should provide robust support through mechanisms such as procurement contracts, financial subsidies, preferential loans, and targeted tax incentives. Additionally, the government should lead initiatives focused on aligning supply and demand for advanced technologies, fostering technology exchanges, and managing relevant projects. Furthermore, it is essential to develop and refine environmental policies that complement strategies for digital technology adoption. This includes strengthening regional cooperation and coordination, establishing government-enterprise collaboration platforms, and fostering closer partnerships with enterprises. Investments should be directed toward energy-efficient, innovative sectors. Moderate environmental regulations, such as curbing excessive energy consumption and emissions, should be implemented. Policies like emission trading systems, environmental taxes, and tax incentives for green projects will motivate enterprises to pursue green transformations. These measures will reinforce corporate commitments to sustainable practices and social responsibility, ultimately fostering the harmonious co-development of environmental and economic performance.

Finally, create a green transformation ecosystem. Globally, green transformation is not the sole responsibility of individual enterprises; it is a collective endeavor involving industry chains, supply chains, and ecosystems. First, governments should strengthen the development of innovation platforms and collaborative mechanisms, promoting cooperation and coordination between upstream and downstream enterprises within the industry chain to drive green transformation collectively. By establishing cross-industry platforms for digital technology and green transformation, as well as comprehensive supply chain data-sharing and resource-trading platforms, enterprises can share experiences and technologies. This enables enterprises to stay informed about the supply and demand dynamics of upstream and downstream products, enhances their resource allocation capabilities, optimizes internal resource distribution in real-time, improves decision-making efficiency, alleviates inventory pressure, reduces overproduction, and ultimately elevates the overall green performance of the industry. Second, a collaborative development mechanism for the green transformation of equipment manufacturing enterprises should be established, leveraging the extensive penetration and integration capabilities of digital technologies. Creating a cross-regional digital service platform for resource trading will allow enterprises to broaden their resource demand sources, achieve precise matching between upstream and downstream trading partners, optimize supply chain resource allocation, and innovate resource supplydemand models. This approach will promote the green, low-carbon, and high-quality development of equipment manufacturing enterprises. Third, enterprises should establish a digital R&D system to enhance the sharing of general-purpose technologies both internally and externally. By promoting collaborative R&D among enterprises, universities, and research institutes in the green industry–university–research domain and encouraging cooperation in critical areas such as carbon reduction and core technologies, enterprises can form a green technology industry–university–research innovation alliance. This collaboration will support equipment manufacturing enterprises in building an autonomous, controllable, and internationally advanced networked collaborative innovation and digital platform. A robust industry–university–research cooperation mechanism should be established to foster a green transformation ecosystem within the enterprise sector. Additionally, governments should leverage their external supervisory role to enhance corporate transparency. This includes refining existing environmental information disclosure standards and imposing stricter penalties on equipment manufacturing enterprises that exceed emission limits. Such measures will prevent opportunistic behavior driven by the cost of governance exceeding the penalties for breaches of contract.

Furthermore, the global green transformation of the equipment manufacturing industry requires not only the efforts of enterprises but also the joint efforts of governments, industry associations, and international organizations. As digital technologies continue to advance, more innovative technologies and management models will emerge, providing new opportunities and challenges for the green transformation of equipment manufacturing enterprises. In the face of global environmental changes and the pressure for sustainable development, enterprises must accelerate their digital transformation, achieve green development through technological innovation, and secure a competitive edge in the global marketplace.