Exploring the Mechanisms and Pathways Through Which the Digital Transformation of Manufacturing Enterprises Enhances Green and Low-Carbon Performance Under the “Dual Carbon” Goals

Abstract

The coordinated development of digitalization and greening is essential for economic transformation and upgrading, especially given the pressing global carbon emission challenges. China’s commitment to achieving “dual carbon” goals highlights the need for sustainable solutions, particularly in the manufacturing sector, which is a significant source of energy consumption and emissions; carbon emissions account for more than 30%. Integrating advanced digital technologies with manufacturing is critical for reducing carbon and sustainable growth. According to the research results, more than 70% of scholars believe that digital transformation boosts green innovation and low-carbon development, but the mechanisms still need to be clarified, slowing transformation efforts and reducing efficiency. Taking the intellectualization and green low-carbon development of manufacturing enterprises as latent variables, and taking the nine paths obtained by scholars’ research results and investigation interviews to promote green low-carbon performance as observation variables, this paper constructs a structural equation model and deeply explores the mechanism and paths of the intellectualization transformation of manufacturing enterprises affecting carbon reduction, emission reduction and sustainable development of enterprises. The research results show that the digital intelligent transformation of manufacturing enterprises affects the green and low-carbon performance improvement and sustainable development of enterprises through technological innovation, industrial structure transformation and upgrading, and reshaping resource allocation. These strategies lower energy use and emissions, strengthen sustainability, and improve green performance. The findings offer theoretical and practical insights, providing a roadmap for efficient digital transformation in manufacturing to achieve the “dual carbon” goals and support sustainable development.

1. Introduction

Climate change is one of the most severe environmental challenges faced globally, and its negative impacts are continuously spreading and intensifying worldwide [1,2]. The primary cause of global warming is the continuous increase in greenhouse gasses, mainly carbon dioxide, methane, nitrous oxide, hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. Since carbon dioxide accounts for the most significant proportion, greenhouse gas emissions are generally called “carbon dioxide emissions”. Currently, the comprehensive implementation of carbon reduction and green development to address global warming has become a consensus among countries which are actively committed to saving resources, optimizing energy and industrial structures, upgrading production and lifestyle methods, and achieving green and low-carbon development for both the economy and enterprises to protect the ecological environment. In December 2019, the European Commission announced the European Green Agreement to address climate change and promote sustainable development. To achieve the goal, at least 25% of the EU’s long-term budget in the future will be dedicated to climate action. The European Investment Bank has also launched the corresponding new climate strategy and energy loan policy. By 2025, the proportion of investment and financing related to climate and sustainable development will be raised to 50%. At the same time, the EU will also promote the smooth progress of EU climate action and economic transformation through internal and external policies such as taxation, trade, and public procurement. In September 2022, the US Inflation Reduction Act was formally enacted. The bill includes about USD 430 billion in the next 10 years for climate and clean energy, as well as health care. According to the bill, the US federal government will invest in climate and clean energy to support the production and investment of electric vehicles, key minerals, clean energy, and power generation facilities. Compared with the European Green Agreement and the Inflation Reduction Act, both of them have achieved emission reduction targets and net zero emission targets. The EU’s Green Agreement for Europe aims to penalize polluters, while the US Inflation Reduction Act is a supportive and incentive-driven policy [3,4].

As the world’s largest carbon emitter, China announced in September 2020 that it aims to achieve carbon peaking by 2030 and carbon neutrality by 2060 [5]. The global understanding of carbon neutrality refers to the complete offset of the total amount of carbon dioxide directly or indirectly emitted into the air by a country, enterprise, or individual within a specific time frame through means such as energy conservation, emission reduction, and absorption by green plants, achieving zero carbon dioxide emissions. Although the goal of “peak carbon dioxide emissions, carbon neutrality” put forward by the China administration is 10 years later than that of European and American countries, China is a country with a development history of more than 70 years since the founding of New China in 1949, and it is also a developing country with a large population. It is not easy to put forward the goal of double carbon. Compared with the EU’s Green New Deal, China’s goal of “double carbon” has both similarities and differences. In terms of market-oriented means, both of them have carbon emission reduction transactions, but China’s carbon market and carbon pricing mechanism achieve the goal of reducing emissions of the whole society at the lowest possible cost. In terms of green finance, Europe and the United States have established a green financial system network, and China has established a sound green financial system by expanding financial support and investment. In terms of industrial and industrial structure optimization and upgrading, the United States reduced energy use by improving the domestic automobile supply chain and the second railway revolution, while China actively promoted the transformation and upgrading of traditional industries, developed a new generation of information technology high-end manufacturing and new energy industries, and built an efficient, clean and low-carbon green system. In terms of new infrastructure, European and American countries have optimized and upgraded roads, green spaces and networks to achieve energy conservation, and emission reduction. China has led the development direction of data centers with new infrastructure+carbon neutrality and formulated a 1 + N policy system to comprehensively promote the realization of the dual-carbon goal. For China, industry is one of the pillar sectors of the national economy, and it is a crucial area characterized by high energy and resource consumption, high pollutant emissions, and the primary source of carbon dioxide emissions [6]. As an essential industry component, traditional manufacturing accounts for a large proportion of resource consumption within the entire industrial system, which has high energy demands, and consists mainly of high-pollution and high-energy-consuming enterprises [7]. Annual greenhouse gas emissions from manufacturing account for about one-quarter of global emissions [8,9]. Under pressure to achieve the “dual carbon” strategic goals, promoting the digital transformation and upgrading of manufacturing to achieve green and low-carbon development has become a topic of great interest to scholars and the business community. It is also a significant practical issue for reducing carbon dioxide emissions and achieving carbon peaking and neutrality.

According to existing research findings, most scholarly literature indicates that the digital transformation of manufacturing enterprises significantly promotes corporate green innovation and green, low-carbon, sustainable development. However, there needs to be in-depth specialized literature on the mechanisms and influence of the relationship between digital transformation and improvements in green low-carbon performance. Based on this, this paper uses a structural equation model to explore the mechanisms of how digital transformation of manufacturing enterprises impacts green low-carbon performance under the “dual carbon” goals and to propose effective pathways for promoting digital transformation to facilitate low-carbon green development. The overall structural framework studied in this paper is shown in Figure 1.

The main goal of this paper is to explore the influence mechanism of digital intelligent transformation of manufacturing enterprises on green and low-carbon development, measure different paths to improve the efficiency of green development of enterprises from the multiple paths of digital intelligent transformation of manufacturing enterprises to promote green and low-carbon development of enterprises and choose the digital intelligent transformation path that contributes greatly to the improvement of green and low-carbon performance, to achieve better and more efficient cost reduction, efficiency reduction and green sustainable development of manufacturing enterprises. The marginal contributions of this paper mainly include the following: First, compared with other kinds of literature, it innovatively explores the influence mechanism of digital intelligent transformation of manufacturing enterprises on low-carbon life’s performance under the goal of “double carbon”, and clarifies the road map of carbon reduction and emission reduction for countries all over the world, which not only provides a novel perspective for studying the relationship between digital intelligent transformation and green development, but also further supplements and enriches the theories of digital development, low-carbon economy and industrial transformation and upgrading; the second is to measure the realization path of digital intelligent transformation and the effect brought by the transformation, and the contribution to improving the green and low-carbon performance of enterprises. From the quantitative level, it innovatively explains the effectiveness of different paths of digital intelligent transformation on green and low-carbon development, and the research conclusion is more scientific and reliable and has certain universality and reference value; thirdly, according to the research results, we innovatively put forward effective measures to promote the digital intelligent transformation of manufacturing enterprises and suggestions on which aspects can effectively improve the green and low-carbon performance of enterprises, which has certain reference and guiding significance for practice.

2. Literature Review

Digital transformation plays a significant role in promoting green and low-carbon development within manufacturing enterprises. The development of the digital economy notably advances the green transformation and upgrading of the manufacturing industry, with varying impacts across regions based on their economic and network development levels. Through enhancing green technology innovation and reshaping industrial structures, digital transformation aligns with low-carbon objectives. This corporate digital strategy drives industrial transformation, improves operational efficiency, and enables sustainable green development [10]. Digital transformation enhances green innovation in energy enterprises through improved dynamic capabilities, contributing to green and low-carbon transformation in alignment with dual-carbon goals. This effect shows notable differences between state-owned and non-state-owned enterprises, reflecting varied responses to digital transformation initiatives in the energy sector. Liu et al. (2023) investigated how digital transformation in manufacturing impacts carbon emission intensity, demonstrating that it contributes to reduced emissions through green technology innovation and moderated by absorptive capacity. It emphasizes the roles of digitalization, greening, and low-carbon strategies in achieving low-carbon goals [9]. Guo (2023) argued that digital transformation in manufacturing can address deficiencies in green development, leading to improved economic and ecological benefits. It identifies three paths for integrating digitalization and green development to enhance performance: green technology innovation, green production and supply chain innovation, and green management model innovation [11]. Hou et al. (2023) stated how digital transformation in manufacturing drives low-carbon technology innovation, highlighting that it significantly enhances low-carbon performance under carbon neutrality goals. It emphasizes the roles of knowledge reconstruction and sharing in mediating this relationship, providing insights for greener industrial practices [3]. Gan et al. (2023) show that the digital transformation of enterprises significantly promotes the adoption of energy-saving green innovation models, with digital green R&D investment serving as a mediating factor in green low-carbon performance development and government innovation subsidies playing a role in adjusting corporate green innovation strategy choices [12].

Regarding the pathways for promoting digital transformation in manufacturing enterprises, Nason R.S. (2019) pointed out that enterprises have had to pay attention to protecting the ecological environment with the successive introduction of environmental policies and strengthening environmental regulations. They have adopted measures such as increasing environmental investments, innovating production technologies, and reengineering processes for production, operation, management, and services to reduce carbon emissions, optimize business processes, and promote digital transformation and upgrading as an effective way to achieve green, low-carbon development [13]. Pettifor H et al. (2020) proposed that the industrial internet has become a new balance between improving industrial productivity and being environmentally friendly. It can significantly enhance the digitalization, networking, and intelligent development of manufacturing, helping enterprises achieve dynamic lifecycle management of the entire process, from carbon data collection, calculation, analysis, and decision-making to carbon trading [14]. Li R (2023) argued that digital transformation empowers the upgrading of manufacturing enterprises mainly through the efficient flow of data elements to integrate information and resources, driving fundamental changes in production methods, management processes, and organizational structures. This helps optimize resource allocation, enhance service capabilities, strengthen internal controls, improve the enterprise’s sustainable development capabilities, reduce carbon emissions [15,16], and enhance green, low-carbon development efficiency [17].

Regarding the effectiveness of digital transformation in manufacturing enterprises, China’s “14th Five-Year Plan” proposes “driving the transformation of production, lifestyle, and governance through comprehensive digital transformation”, providing new directions for the digital transformation and carbon reduction in enterprises. Integrating digitalization with traditional enterprises enables precise control over the production process and emission reduction through intelligent operations. It optimizes resource allocation efficiency through digital empowerment, achieving green, low-carbon development for enterprises [18]. Borenstein S (2019) found that digital transformation enhances an enterprise’s ability to acquire and integrate information resources internally and externally, promotes information exchange and sharing, reduces energy consumption in production and operations, and improves green, low-carbon performance [19]. Miao and Zhao (2023) pointed out that adopting digital information management systems significantly enhances the green transformation of manufacturing enterprises by improving digital levels, strengthening green innovation, and leveraging redundant resources, particularly under China’s dual carbon goals [20].

From the review of the existing scholarly literature, most research results indicate that promoting the digital transformation of manufacturing enterprises can effectively enhance technological innovation, reduce production and transaction costs, minimize repetitive labor and processes in production, operation, and management, optimize the combination of production factors, improve overall enterprise efficiency, reduce carbon emissions, and achieve green and low-carbon sustainable development. In other words, scholars have studied and recognized that digital transformation and upgrading of traditional manufacturing enterprises can promote green, low-carbon development [21,22]. However, how the digital transformation of traditional manufacturing enterprises affects green low-carbon operations and development and the intricate connections and influence mechanisms between the two have yet to be deeply studied. This lack of clarity has constrained the digital transformation process, affecting its impact on green, low-carbon development. Therefore, this paper uses a structural equation model to explore the pathways for digital transformation in manufacturing enterprises and the benefits brought by such transformation, aiming to understand its role in reducing carbon emissions, enhancing green low-carbon performance, and strengthening sustainable development capabilities, thereby clarifying the mutual influence mechanisms between the two. Using the entropy-weight TOPSIS method, this paper calculates the pathways for digital transformation and the effects after transformation, assessing the contribution to improving green low-carbon performance. Based on the research results, recommendations are provided for effective measures to promote digital transformation in manufacturing enterprises and suggestions on which aspects of digital transformation can more effectively enhance green low-carbon performance.

3. Research Design

3.1. Model Construction

The structural equation model (SEM) consists mainly of measurement and structural models. The measurement model is primarily used to reveal the relationship between observed and latent variables, allowing us to understand how observed variables reflect and measure latent, unobservable variables [23,24]. The structural model mainly investigates the relationships between latent variables, helping us understand their interactions and influence mechanisms. Based on the structural equation modeling process, the model for promoting the digital transformation of manufacturing enterprises to enhance green low-carbon performance is constructed as follows:

(1) Measurement Model Equation

$$\mathrm{Y}={\mathsf{\Lambda }}_{\mathrm{Y}}\mathsf{\eta }+\mathsf{\epsilon }$$

(1)

$$X={\Lambda }_X\xi +\delta$$

(2)

(2) Structural Model Equation

$$\eta =B\eta +\Gamma \xi +\zeta$$

(3)

Here, X is set as the observation indicator for the exogenous latent variable ($\mathsf{\xi }$), $\mathrm{Y}$ is set as the observation marker for the endogenous latent variable ($\mathsf{\eta }$), and ${\mathsf{\Lambda }}_{\mathrm{Y}}$ and ${\mathsf{\Lambda }}_{\mathrm{X}}$ form the coefficient loading matrix of the measurement equation. The structural equation model is constructed in a more direct and easy-to-understand way as shown in Figure 2. Among them, $\epsilon$ and $\eta$ represent the latent variables of digital intelligent transformation of manufacturing enterprises, X and Y represent the observed variables of digital intelligent transformation of manufacturing enterprises promoting green and low-carbon performance improvement, and E represents uneven items.

The structural equation model for promoting green low-carbon performance through digital transformation in manufacturing enterprises contains the following hypotheses:

(1) In the measurement model equation, error terms $\mathsf{\delta }$ and $\mathsf{\epsilon }$ have the same characteristics, meaning their mean values are 0.

(2) In the structural model equation, the error term $\mathsf{\zeta }$ also has the same property, with a mean value of 0.

(3) All error terms in the measurement model equation are independent of each other, with no correlations between them. Moreover, these error terms are also independent of the latent variables.

(4) The error term $\mathsf{\zeta }$ in the structural equation model is also independent of error terms $\mathsf{\delta }$ and $\mathsf{\epsilon }$ in the measurement model and the exogenous latent variable $\mathsf{\xi }$, with no correlations between them.

Therefore, in constructing the structural equation model to promote green low-carbon performance through digital transformation in manufacturing enterprises, the key to solving the model is to determine ${\mathsf{\Lambda }}_{\mathrm{Y}}$ and ${\mathsf{\Lambda }}_{\mathrm{X}}$, and the four essential coefficient matrices B and $\mathsf{\Gamma }$.

3.2. Theoretical Analysis and Hypotheses

Establishing an environmentally friendly and resource-efficient green development society is an urgent requirement for promoting the green, low-carbon transformation of China’s economic and social development and an essential task for achieving carbon peaking and carbon neutrality goals [25]. The new generation of digital technologies empowers the development of the real economy, providing significant opportunities and possibilities for the green development of industries such as production and services. The deep application and integration of digital and intelligent technologies in advanced manufacturing and modern services not only facilitate carbon reduction and environmental benefits for enterprises but also effectively promote the comprehensive efficiency of manufacturing enterprises [26,27].

Currently, two main methods for measuring green low-carbon performance in enterprises are the single-indicator and multi-dimensional indicator methods. The single-indicator method measures green low-carbon performance primarily by using a single metric, such as the number of green patents, green total factor productivity, or emissions of carbon dioxide and sulfur dioxide from fossil fuels, as the indicator of an enterprise’s green low-carbon development level [28].

The biggest problem with the single-indicator method is that it must comprehensively consider an enterprise’s green, low-carbon performance. The conclusions are limited and need more objective and accurate, resulting in significant limitations [29]. The multi-dimensional indicator method measures green low-carbon performance based on comprehensive efficiency, considering energy and resource intensity, pollutant emission reduction, low ecological damage, increased labor productivity, and robust and sustainable development capabilities [30]. Therefore, it is widely favored by scholars and practitioners in practical applications. In this paper, the multi-dimensional indicator method is used to measure the green low-carbon performance of enterprises.

At the Fourth Plenary Session of the 19th CPC Central Committee, data were defined for the first time as the fifth essential production factor, following capital, land, labor, and entrepreneurial talent [9]. The report of the 20th National Congress of the CPC also explicitly stated the need to build a digital economy with data as a critical element. The digital transformation of manufacturing enterprises involves using data as a new clean production factor to achieve carbon reduction and sustainable development. Applying new-generation digital technologies, such as big data, cloud computing, and artificial intelligence, in manufacturing enterprises can reduce repetitive production processes, optimize production workflows, and monitor precise material usage during the production process [31]. Additionally, by leveraging data elements throughout the industrial chain, digital technology enables precise carbon emission management and deep integration of emission reduction across critical areas such as energy and industry, integrating the role of digital technology throughout crucial sectors covered by the “carbon peaking and neutrality” goals. Finally, digital technologies continually reduce carbon emissions, achieving low-carbon, or even zero-carbon, outcomes. Digital transformation replaces repetitive labor [32]. Digital development promotes technological innovation and progress in manufacturing enterprises, improving green, low-carbon performance [33]. The application of digital technology in enterprises can replace low-end labor for routine and repetitive production tasks, thereby effectively reducing labor costs, lowering overall production and operational costs, encouraging enterprises to allocate saved costs to R&D and innovation, optimizing production processes and resource allocation, reducing energy consumption per unit of output, reducing carbon emissions and pollutants, and enhancing sustainable development capabilities [34,35]. Based on the above analysis, the author proposes the following research hypothesis that digital transformation and upgrading of manufacturing enterprises enhance green low-carbon performance through technological innovation:

H1. 

Digital transformation promotes precise production and energy conservation in manufacturing enterprises, effectively reducing unit energy consumption and significantly impacting the improvement of green low-carbon performance.

H2. 

Digital transformation improves the refinement of production processes and procedures in manufacturing enterprises, enhances sustainable development capabilities, and significantly impacts the improvement of green, low-carbon performance.

H3. 

Digital transformation replaces part of the routine and repetitive labor force, reducing unit energy consumption and pollutant emissions and positively impacting green, low-carbon performance.

Promoting new industrialization is to achieve Chinese-style modernization, with its main development directions being intelligent, high-end, and green [36,37]. The report of the 20th National Congress of the CPC explicitly proposed building a high-quality, efficient new system for the service industry, promoting deep integration between modern services, advanced manufacturing, and modern agriculture. Servitization of manufacturing refers to the shift from simply producing products to producing products and providing high-quality services. This involves embedding new-generation digital technologies, such as the industrial internet, into production, management, and sales, empowering new manufacturing and fostering innovative service models. It is also an essential measure for advancing the structural upgrading and modernization of manufacturing, promoting technological progress, and enhancing management efficiency to facilitate pollution reduction [38]. The development of serviced manufacturing helps enterprises move their value chain towards higher value-added areas, for instance, transforming from the production stage to extending and complementing the chain towards product R&D, brand building, personalized service production, and the design and use of digital service solutions. This transformation can mitigate massive energy consumption in manufacturing, optimize the industrial structure, promote energy conservation, and reduce pollutant emissions [39]. The organizational structure of traditional manufacturing enterprises is mostly based on functional divisions, with processes divided by departments and functions. This often leads to fragmented operations and information silos, resulting in inefficient information flow and reduced operational efficiency. The digital empowerment of enterprises aims to break these information silos, reorganize and optimize processes, and build “end-to-end” business workflows from a holistic perspective, thereby enhancing operational efficiency and improving sustainable development capabilities [3,40]. Based on the above analysis, the author proposes the following research hypothesis that digital transformation and upgrading of manufacturing enterprises enhance green low-carbon performance through industrial structure upgrading:

H4. 

Digital transformation drives the servitization of manufacturing, reducing unit energy consumption and pollutant emissions and significantly impacting the improvement of green low-carbon performance.

H5. 

Digital transformation promotes the production of high-value-added products, conserves energy, and reduces pollutant emissions, significantly impacting the improvement of green low-carbon performance.

H6. 

Digital transformation optimizes business processes, enhances sustainable development capabilities, and significantly impacts the improvement of green low-carbon performance.

Increasing green R&D investment enhances green low-carbon performance in enterprises [41]. Digital transformation drives enterprises to innovate and conduct R&D, increasing green R&D investment by integrating digital resources and achieving multi-dimensional innovations such as digital products and business models [42,43]. On the other hand, applying new digital technologies will inevitably create more new high-tech jobs, attracting high-quality human capital and leveraging the scale effect of talent. By absorbing new technologies and advanced management practices, enterprises can achieve innovation, improve innovation efficiency, and indirectly enhance green low-carbon performance [44]. Digital transformation effectively reduces the likelihood of information asymmetry. The development and application of digital technologies in manufacturing enterprises inherently promote the dissemination of information and knowledge, reduce the cost of information and technology flow, and effectively alleviate information asymmetry across departments, between enterprises and customers, and between enterprises and suppliers throughout the industry chain [45]. This promotes convenient development in technology cooperation and talent exchange between enterprises and industries, reduces communication and learning costs, accelerates knowledge flow, promotes the technology spillover effect, and enhances enterprise innovation capabilities—digital transformation forces enterprises to increase environmental governance investments. The pressure to achieve the “dual carbon” goals drives traditional manufacturing industries to undergo digital transformation and upgrading. They are reducing unit energy consumption and pollutant emissions, forcing enterprises to increase R&D and innovation efforts and enhancing their sustainable development capabilities. On the other hand, environmental regulations driven by environmental policies a nd requirements also force manufacturing enterprises to allocate more resources to green technology R&D, enhancing their green innovation capabilities. This means that the impact of environmental regulations on digital transformation in manufacturing has shifted from a “compliance cost effect” to an “innovation compensation effect”. Based on the above analysis, the author proposes the following research hypothesis that digital transformation and upgrading of manufacturing enterprises enhance green low-carbon performance by reshaping resource allocation:

H7. 

Digital transformation increases R&D investment, reduces unit energy consumption, decreases pollutant emissions, and enhances sustainable development capabilities, significantly impacting the improvement of green low-carbon performance.

H8. 

Digital transformation reduces information asymmetry in manufacturing enterprises, enhances sustainable innovation capabilities, and significantly impacts the improvement of green low-carbon performance.

H9. 

Digital transformation, by increasing environmental regulation and investment, reduces unit energy consumption and decreases pollutant emissions, significantly impacting the improvement of green low-carbon performance.

Based on the above theoretical analysis and hypotheses, there are four latent variables in the structural equation model for promoting green low-carbon performance through the digital transformation of manufacturing enterprises: technological innovation, industrial structure transformation, upgrading, reshaping resource allocation, and green low-carbon performance. The observed variables for technological innovation include precise production and energy saving, improving production process refinement, and replacing part of the routine and repetitive labor. The observed variables for industrial structure transformation and upgrading include promoting the servitization of manufacturing, producing high-value-added products, and optimizing business processes. The observed indicators for reshaping resource allocation include increasing green R&D investment, reducing information asymmetry, and increasing environmental investment. The observed variables for green low-carbon performance include reduced energy consumption per output unit, reduced pollutant emissions, and enhanced sustainable development capability. According to the principles of the structural equation model, the schematic diagram of the structural equation model for promoting green low-carbon performance through the digital transformation of manufacturing enterprises is shown in Figure 3.

3.3. Data Collection and Analysis

Questionnaires and field interviews were primarily used to collect data for this study on the impact of digital transformation in manufacturing enterprises on green low-carbon performance under the “dual carbon” goals. The Likert 7-point scale was adopted, inviting five universities from Northwest University, Xi’an University of Architecture and Technology, La Trobe University, Anhui University of Finance and Kunming University of Science and Technology, mainly engaged in the research direction of digital economy, enterprise innovation management, digital technology application and management, 20 experts and scholars with doctoral degrees or senior titles, 80 enterprise managers, and 200 manufacturing professionals from 24 manufacturing enterprises, including Shaanxi Drum Power, Shaanxi Automobile Group, Xifei Company, Fast Group, Yanchang Petroleum Group and Shaanxi Nonferrous Metals Holding Group Co., Ltd., are engaged in the digital transformation or digital manufacturing of manufacturing enterprises, which will bring about technological innovation, industrial structure transformation and upgrading, and reshape the observed variables contained in enterprise resource allocation, and promote enterprises.

(1) Questionnaire Design and Revision. The questionnaire on the impact of digital transformation on green low-carbon performance in manufacturing enterprises was divided into two parts. The first part provided instructions for filling out the questionnaire, including the meaning of different values on the Likert 7-point scale and the indicators for measuring green low-carbon performance. The second part contained the primary survey, consisting of 9 questions that covered nine observed variables related to technological innovation, industrial structure transformation and upgrading, and reshaping resource allocation, driven by digital transformation, to promote green, low-carbon performance. Respondents were invited to rate these variables using objective questions, resulting in evaluation data.

(2) Questionnaire Distribution and Collection. To ensure alignment between the questionnaire design and the research questions, the initial draft was tested on a small sample, and experts reviewed the preliminary survey results to guide necessary revisions. The formal questionnaire distribution lasted for 10 days, using face-to-face, online, and WeChat methods. Three hundred questionnaires were distributed, 291 were returned, and 286 valid questionnaires were retained after removing invalid ones, yielding an effective response rate of 95.3%. These questionnaires served as the sample data for evaluating the impact of digital transformation on green, low-carbon performance in manufacturing enterprises.

(3) Data Sorting and Analysis. To ensure that data collection matched the research questions, the research team conducted statistical analysis and sorting, focusing on the mean, maximum and minimum values, skewness, and kurtosis of the data. This ensured that the processed data maintained high reliability and validity, providing a scientific and objective basis for subsequent research.

1) Statistical Description of Data

The sample data collected to evaluate the impact of digital transformation on green low-carbon performance in manufacturing enterprises was analyzed using descriptive statistics with SPSS (version 22), and the results are shown in

Table 1. Descriptive statistics results.

	N	Min	Max	Mean	SD
Precise production saves energy	286	2	7	6.1333	0.96956
Improve the refinement of production technology and flow	286	3	7	5.9500	1.04399
Replace part of the programmed and repetitive production labor force	286	2	7	6.0417	1.07215
Promote the service of the manufacturing industry	286	2	7	5.8167	1.19511
Production of high-value-added products	286	2	7	5.8500	1.08194
Business process optimization	286	2	7	5.9250	1.09362
Increase investment in green R&D	286	4	7	6.1083	0.89627
Reduce information asymmetry	286	2	7	5.8917	1.11367
Increase investment in environmental protection	286	2	7	5.9750	1.04891
Effective N	286

.

As can be seen from the data shown in Table 1, the average values of the nine observed variable data have little difference, are relatively stable, the standard deviation is relatively concentrated, and the discreteness of the observed variable data is not great. The obtained nine observed variable data can scientifically and effectively reflect the current situation of the impact of the digital intelligence transformation of manufacturing enterprises on the low-carbon life performance of enterprises. Overall, the selected observed variables are reasonable and, to some extent, reflect the significant impact of digital transformation on green, low-carbon performance in manufacturing enterprises.

2) Reliability and Validity Test

① Reliability Test

The Cronbach method was used to conduct a reliability test on the sample data collected to evaluate the impact of digital transformation on green low-carbon performance in manufacturing enterprises.

The Cronbach’s alpha coefficient was tested using SPSS (version 22), a social science statistical analysis software (

Table 2. Reliability statistics results.

Cronbach’s Alpha	Cronbach’s Alpha Based on Standardized Items	Number of Items
0.900	0.900	9

).

Table 2 shows that the overall reliability coefficient for the impact of digital transformation on green low-carbon performance in manufacturing enterprises is 0.900, greater than the generally recognized reliability value of 0.8, indicating that the obtained data are reliable.

② Validity Analysis

Using SPSS (version 22), a social science statistical analysis software, exploratory factor analysis was conducted to analyze the validity of the data regarding the impact of digital transformation in manufacturing enterprises on green low-carbon performance.

The test results, shown in

Table 3. KMO value and Bartlett’s test.

Kaiser–Meyer–Olkin Measure of Sampling Adequacy	0.873
Bartlett’s Test of Sphericity	574.561
Df	36
Significance	0.000

, showed a KMO value of 0.873 and Bartlett’s test of sphericity value of 574.561.

The calculated value of KMO is greater than the generally accepted validity value of 0.7, indicating that the research design’s observation indicator system has good construct validity and that the impact of digital transformation in manufacturing enterprises on green, low-carbon performance has strong explanatory power.

The reliability and validity analysis results of the data collected on the impact of digital transformation in manufacturing enterprises on green low-carbon performance indicate that the reliability and quality of the collected survey data are dependable, and the construct validity is good, supporting the subsequent research on the impact of digital transformation on green low-carbon performance in manufacturing enterprises.

3.4. Model Solution and Results

Based on the research hypothesis that digital transformation and upgrading of manufacturing enterprises enhance green low-carbon performance through technological innovation and combined with the principles of the structural equation model, the structural equation model for promoting green low-carbon performance in manufacturing enterprises was constructed using AMOS software, as shown in Figure 4.

The data obtained through surveys was standardized using SPSS software and subjected to reliability and validity tests before being imported into AMOS software for model fitting. In the discrepancy section, the maximum likelihood method was selected for parameter estimation.

To verify the reliability of the driving factors and the scientific validity of the constructed structural equation model, some fit indices were examined and calculated, with the following results.

Notes for Model (Default model)

Computation of degrees of freedom (Default mode)

Number of distinct sample moments: 78

Number of distinct parameters to be estimated: 33

Degrees of freedom (78–33): 45

Result (Default model)

Minimum was achieved

Chi-square = 59.593

Degrees of freedom = 45

Probability level = 0.071

The value of CMIN (Chi-square value) in the calculation results is shown in

Table 4. CMIN (chi-square value).

Model	NPAR	CMIN	DF	P	CMIN/DF
Default model	33	59.593	45	0.071	1.324
Saturated model	78	0.000	0	-	-
Independence model	12	631.348	66	0.000	9.566

.

The chi-square value of CMIN reflects the difference between the sample covariance matrix and the implicit covariance matrix. Generally speaking, the smaller the chi-square value, the smaller the difference between them, that is, the better the model fits. The table shows that the CMIN (chi-square value) of the structural equation model for promoting green low-carbon performance in manufacturing enterprises through digital transformation is 1.324, indicating that the model fit is good.

The value of RMSEA in the calculation results is shown in

Table 5. RMSEA value.

Model	RMSEA	LO90	HI90	PCLOSE
Default model	0.052	0.000	0.085	0.434
Independence model	0.268	0.249	0.288	0.000

.

From Table 5, it can be seen that the RMSEA value of the structural equation model for promoting green low-carbon performance in manufacturing enterprises through digital transformation is 0.052 < 0.08, indicating that the relationship analysis between the structural equation model components is precise, and the data collected provides valid measurement and fitting results for the model.

Based on the data collected on promoting green, low-carbon performance through digital transformation in manufacturing enterprises, AMOS software was used for model fitting.

Table 6. Regression weights of observed variables for latent variables.

			Estimate	S.E.	C.R.	P
Low-carbon life performance		Technical innovation	2.325	4.695	0.495	0.621
Low-carbon life performance		Transformation and upgrading of industrial structure	−2.491	4.315	−0.577	***
Low-carbon life performance		Reshaping enterprise resource allocation	1.581	1.918	0.824	***
Increase investment in environmental protection		Reshaping enterprise resource allocation	1.156	0.169	6.821	0.153
Reduce information asymmetry		Reshaping enterprise resource allocation	1.339	0.188	7.118	***
Increase investment in green R&D		Reshaping enterprise resource allocation	1.000	—	—	—
Business process optimization		Transformation and upgrading of industrial structure	0.901	0.102	8.858	***
Production of high-value-added products		Transformation and upgrading of industrial structure	0.876	0.097	9.065	0.098
Promote the service of the manufacturing industry		Transformation and upgrading of industrial structure	1.000	—	—	—
Replace part of programmed and repetitive production labor force		Technical innovation	1.208	0.162	7.444	***
Improve the refinement of production technology and flow		Technical innovation	1.339	0.159	8.432	0.206
Precise production saves energy		Technical innovation	1.000	—	—	—
Energy consumption per unit output value decreased		Low-carbon life performance	1.000	—	—	—
Pollutant emission reduction		Low-carbon life performance	−0.692	0.397	−1.744	***
Strong ability of sustainable development		Low-carbon life performance	0.212	0.221	0.961	***

Annotate: *** indicates significant results.

shows the regression weights of the observed variables for the latent variables.

“S.E.” in Table 6 is the abbreviation of Standard Error, which is used to measure the accuracy of the estimated values of model parameters (such as path coefficient) and reflects the dispersion degree of the sampling distribution of the estimated values of parameters. The smaller the standard error, the higher the reliability of parameter estimation. According to the calculation results, although the S.E. values of the latent variable technological innovation, industrial structure transformation and upgrading, and reshaping enterprise resource allocation on green and low-carbon performance are relatively high, they are still within the acceptable range. The S.E. values of the other nine observed variables corresponding to the latent variables are all low, and the estimated values of the parameters are highly reliable. C.R. is the abbreviation of Critical Ratio, which is used to measure the ratio between non-standardized load coefficient and Standard Error, to evaluate whether the direct effect between variables is significant. C.R. is used to calculate the p value to judge whether the path coefficient is statistically significant. According to the results of the p value, the significance of the measurement results of the three observation variables, namely, improving the production process and process refinement, producing high value-added products, and increasing environmental protection investment, to the latent variables are 0.206, 0.098 and 0.153, respectively, and the significance level is greater than 0.05, which is not significant. The rest are significant and accept the original hypothesis. The regression weights of the observed variables for the latent variables obtained from Table 6 were standardized, resulting in the standardized regression weights as shown in

Table 7. Standardized regression weights (group number 1-default model).

			Estimate
Low-carbon life performance		Technical innovation	2.559
Low-carbon life performance		Transformation and upgrading of industrial structure	−3.853
Low-carbon life performance		Reshaping enterprise resource allocation	1.703
Increase investment in environmental protection		Reshaping enterprise resource allocation	0.715
Reduce information asymmetry		Reshaping enterprise resource allocation	0.782
Increase investment in green R&D		Reshaping enterprise resource allocation	0.724
Business process optimization		Transformation and upgrading of industrial structure	0.767
Production of high-value-added products		Transformation and upgrading of industrial structure	0.756
Promote the service of the manufacturing industry		Transformation and upgrading of industrial structure	0.780
Replace part of the programmed and repetitive production labor force		Technical innovation	0.747
Improve the refinement of production technology and flow		Technical innovation	0.855
Precise production saves energy		Technical innovation	0.684
Energy consumption per unit output value decreased		Low-carbon life performance	0.561
Pollutant emission reduction		Low-carbon life performance	−0.461
Strong ability for sustainable development		Low-carbon life performance	0.159

.

In the structural equation model (SEM), “Estimate” usually refers to the estimated parameters, which are used to describe the relationship between latent variables and observed variables. Parameter estimates can help researchers understand the direct and indirect effects between variables and the statistical significance of these effects. If the p-value corresponding to the estimated value is less than the preset significance level (usually 0.05), the path can be considered significant, that is, there is a statistically significant correlation between variables. From Table 6 and Table 7, the measurement of observed variables for latent variables in the digital transformation of manufacturing enterprises promoting green low-carbon performance, as well as the measurement of relationships between latent variables, indicate that the industrial structure transformation and resource reallocation brought about by digital transformation significantly enhance green low-carbon performance. In other words, digital transformation and upgrading in manufacturing enterprises effectively promote green, sustainable development through structural transformation and resource reallocation. From the measurement results of observed variables, such as reduced energy consumption per unit output, reduced pollutant emissions, and enhanced sustainable development capability, it can be seen that digital transformation and upgrading in traditional manufacturing enterprises significantly and effectively improve green low-carbon performance by reducing unit energy consumption, decreasing pollutant emissions, and enhancing sustainable development capability.

From the measurement results of observed variables on latent variables such as technological innovation, industrial structure transformation and upgrading, and resource reallocation in promoting green low-carbon performance through digital transformation in manufacturing enterprises, it can be seen that the measurement results for precise production and energy saving, replacement of part of routine and repetitive labor, promotion of servitization of manufacturing, business process optimization, increased green R&D investment, and reduction in information asymmetry are significant. This effectively supports the original hypotheses, meaning Hypotheses H1, H3, H4, H6, H7, and H8 are accepted. The significance levels for the measurement results of three observed variables on latent variables, namely improving production process refinement, high-value-added product production, and increasing environmental investment, are 0.206, 0.098, and 0.153, respectively, all greater than 0.05. Based on the principles of analyzing structural equation model measurement results, these measurement results do not support the original hypotheses, meaning that Hypotheses H2, H5, and H9 are rejected. Based on the measurement and analysis results, the accepted and rejected hypotheses are presented in tabular form, as shown in

Table 8. Hypothesis test results.

Hypotheses		Test Result
H1	Digital transformation promotes precise production and energy saving in manufacturing enterprises, effectively reducing unit energy consumption and significantly improving green low-carbon performance.	Supported
H2	Digital transformation improves production process refinement in manufacturing enterprises, enhances sustainable development capabilities, and significantly improving green, low-carbon performance.	Not Supported
H3	Digital transformation replaces part of routine and repetitive labor, reduces unit energy consumption and pollutant emissions, and significantly improving green low-carbon performance.	Supported
H4	Digital transformation drives the servitization of manufacturing, reducing unit energy consumption and pollutant emissions, and significantly improving green low-carbon performance.	Supported
H5	Digital transformation drives the servitization of manufacturing, reducing unit energy consumption and pollutant emissions and significantly improving green, low-carbon performance.	Not Supported
H6	Digital transformation optimizes business processes, enhances sustainable development capabilities, and significantly improving green, low-carbon performance.	Supported
H7	Digital transformation increases R&D investment, reduces unit energy consumption, decreases pollutant emissions, and enhances sustainable development capabilities, significantly improving green, low-carbon performance.	Supported
H8	Digital transformation reduces information asymmetry in manufacturing enterprises, enhances sustainable innovation capabilities, and significantly improving green, low-carbon performance.	Supported
H9	Digital transformation, by increasing environmental regulation and investment, reduces unit energy consumption and decreases pollutant emissions, significantly improving green low-carbon performance.	Not Supported

.

3.5. Robustness Test of the Model

When testing the robustness of the structural equation model (SEM) of manufacturing enterprises’ digital intelligent transformation promoting green and low-carbon performance improvement, the method of changing the sample range is mainly adopted. In the original model, 286 valid data were obtained through questionnaires and interviews, and it was concluded that hypotheses H1, hypothesis H3, hypothesis H4, hypothesis H6, hypothesis H7, and hypothesis H8 were accepted, while hypothesis H2, hypothesis H5 and hypothesis H9 were rejected. Now, the sample data will continue to be collected by the same method, and the number of experts and scholars in colleges and universities will be expanded to 32 in three universities, including Xi ’an Jiaotong University, Anhui University, and Laurel University, and the managers and business personnel of the digital intelligent transformation of manufacturing enterprises will be expanded to 435 in total, including Xidian Switch Electric Company, Shaanxi Coal Chemical Industry Group Company, and Maike Metal International Group Co., Ltd., and a total of 452 survey and interview data will be collected, including 448 valid questionnaire data, with an effective recovery rate of 95.9%. After testing the reliability and validity of the data, the conclusion of accepting and rejecting the original hypothesis is consistent with the previous one by using AMOS software, which shows that the structural equation model of manufacturing enterprises’ digital and intelligent transformation promotes the green and low-carbon performance of enterprises to maintain good stability.

3.6. Results Analysis and Discussion

Based on the above measurement and analysis results, digital transformation in manufacturing enterprises can promote precise production and energy saving, replace part of the routine and repetitive labor, drive the servitization of manufacturing, optimize business processes, increase green R&D investment, and reduce information asymmetry. This effectively reduces unit energy consumption and pollutant emissions, enhances sustainable development capability, and significantly promotes green, low-carbon performance improvement. The acquisition of this research conclusion not only effectively verifies the scholars’ view that the transformation of digital intelligence can promote enterprises to reduce costs and increase efficiency, reduce carbon and reduce emissions, and enhance their green and low-carbon sustainable development ability, but also has important scientific guiding value for enterprises engaged in manufacturing production, especially for improving the managers’ understanding of applying the new generation of digital technology to production, management, and operation, promoting the transformation and development of digital intelligence, strengthening the managers’ belief in digital intelligence transformation and reducing the resistance of employees to the transformation of digital intelligence. From the nine paths of digital intelligent transformation of manufacturing enterprises to promote green and low-carbon performance, six assumed paths were obtained according to the measurement results, which put forward a clearer direction and a more effective digital intelligent transformation path for manufacturing enterprises to improve green and low-carbon performance and have important guiding value for manufacturing enterprises to apply new generation information technologies such as artificial intelligence and industrial internet to enterprises, develop intelligent production, information management, optimize business processes, and realize energy conservation, emission reduction and green sustainable development.

Meanwhile, an analysis of the unsupported original hypotheses related to improving production process refinement, promoting high-value-added product production, and increasing environmental regulation and investment suggests that refining production processes is part of business process optimization. It involves optimizing and reengineering production, management, sales, and service processes in traditional manufacturing enterprises, partially refining production processes, and managing each step in more detail. The relationship between improving production process refinement and business process optimization has already been fitted and analyzed in the structural equation model fitting process. Promoting high-value-added product production during advanced stages of digital transformation can effectively reduce carbon emissions and unit energy consumption while enhancing green, low-carbon performance. However, at the current stage, increasing the value-added of products requires more resources, workforce, and financial investment, and the results may need to be more evident. During the fitting measurement, even though it is a valid observed variable, experts and practitioners considered the current circumstances of manufacturing enterprises when providing survey data. Increasing environmental regulation and investment can effectively reduce carbon and pollutant emissions. However, as an external policy measure, environmental regulation imposes policy pressure on enterprises, requiring them to allocate more resources and invest more workforce, financial, and material resources to meet pollutant reduction requirements. As a result, unit energy consumption per output may increase rather than decrease due to short-term actions taken by enterprises to meet environmental requirements, which weakens their sustainable development capability. In summary, although these three observed variables serve as important indicators for measuring and fitting digital transformation in manufacturing enterprises to promote green, low-carbon development, they contribute to reducing carbon emissions and pollutant emissions to some extent in specific aspects, stages, or steps. However, given the practical realities of production, operation, and management in enterprises, certain deficiencies and limitations lead to the fitting results not supporting the original hypotheses.

4. Mechanism Analysis

A structural equation model was constructed to promote green, low-carbon performance improvement through digital transformation in manufacturing enterprises. Data were collected through surveys and interviews and based on the measurement results and the above analysis, the mechanism by which digital transformation and upgrading in manufacturing enterprises influence green low-carbon performance improvement and sustainable development was explored, as shown in Figure 5.

With the rapid development and widespread application of new-generation digital technologies such as artificial intelligence, 5G, big data, and the industrial internet, deeply integrating digital technology with the real economy has become an essential part of economic development in the present era. In China, empowering manufacturing enterprises with digital and intelligent technology throughout the entire production, operation, management, and service processes, as well as the whole industry chain, and promoting the digital transformation and upgrading of traditional manufacturing enterprises have significant value in promoting national economic development. From the external environment perspective, factors such as the emergence of personalized consumer demands, the rapidly changing external environment, industrial structure optimization and upgrading, optimal allocation of external resources, the arrival of the AI era, the pressure from the government to promote digital transformation in traditional manufacturing, and supportive digitalization policies, all necessitate the digital transformation and upgrading of manufacturing enterprises. From the external feasibility perspective, the emergence, rapid development, and widespread application of new-generation digital technologies, the supply of digital talents, and the construction of national and regional digital infrastructure make the digital transformation and upgrading of manufacturing enterprises possible. Internally, the need for optimizing the allocation of existing resources, adjusting industrial structures, optimizing organizational setups, addressing imbalances in production capacity, reducing energy consumption, lowering costs, and improving efficiency all require manufacturing enterprises to undergo digital transformation and upgrading. According to the structural equation model fitting results, hypotheses H1, H3, H4, H6, H7, and H8 are accepted, meaning the hypotheses are valid. Precise production and energy saving, replacing part of routine and repetitive labor, promoting servitization of manufacturing, optimizing business processes, increasing green R&D investment, and reducing information asymmetry can significantly and positively promote green low-carbon performance improvement. This means that digital transformation and upgrading in manufacturing enterprises influence green, low-carbon, sustainable development through technological innovation, industrial structure transformation, and resource reallocation.

4.1. Digital Transformation Strengthens Technological Innovation to Improve Green Low-Carbon Performance

With new-generation digital technologies such as artificial intelligence and the industrial internet in traditional manufacturing enterprises, digital transformation enhances green, low-carbon performance by strengthening technological innovation. The main mechanisms and pathways include precise production, energy saving, and replacing routine and repetitive labor. In promoting production technology advancement, management optimization, and business process improvement, enterprises can be provided with real-time monitoring data on energy consumption. Through digital platform feedback and precise management, inputs such as raw materials, labor, materials, and funds can be adjusted and optimized, ensuring production goals are met while reducing overcapacity, achieving precise management, and saving energy [46]. This effectively reduces unit energy consumption and significantly promotes green, low-carbon performance. By empowering clever platform construction with digital technology, enterprises can control machine operations to replace repetitive production processes traditionally performed by humans, freeing up labor for more valuable and irreplaceable tasks. At the same time, by collecting and analyzing large amounts of data and optimizing business processes, organizations can effectively reduce repetitive labor, improve labor efficiency, and decrease error rates. This, in turn, enhances their ability to achieve sustainable development, lowers carbon emissions, and fosters green, sustainable growth.

4.2. Digital Transformation Promotes Traditional Industrial Upgrading to Improve Green Low-Carbon Performance

Digital transformation in manufacturing enterprises promotes traditional industrial upgrading, with mechanisms and pathways including promoting the servitization of manufacturing and optimizing business processes, significantly enhancing green low-carbon performance. Digital transformation in manufacturing enterprises enables the deep integration of digital frontier technologies, such as the metaverse and artificial intelligence, into the entire industry chain of manufacturing enterprises, embedding digital technologies in more servitization-oriented manufacturing fields. Based on the characteristics of the manufacturing industry and the requirements for “chain extension, chain supplementation, and chain expansion”, digital empowerment drives industrial enterprises’ R&D, lifecycle management, supply chain management, customized services, and collaborative networked manufacturing, effectively improving the level of servitization and continually advancing green low-carbon practices throughout the entire industrial chain and enterprise lifecycle [47]. In terms of business process optimization, digital transformation in manufacturing enterprises enables more efficient data management, process coordination, and resource allocation through automated business decisions, effectively reducing management and operational costs, decreasing the error rate in production processes, reducing waste in all aspects, lowering unit energy consumption, and enhancing sustainable development capabilities [48]. At the same time, by transforming existing business processes and integrating digital technology platforms, manufacturing enterprises can collect and analyze more data, better understand market trends, understand customer needs, and make more informed decisions, significantly promoting green, low-carbon performance.

4.3. Digital Transformation Reshapes Resource Allocation to Improve Green Low-Carbon Performance

Manufacturing enterprises implementing or completing digital transformation and upgrading can improve green low-carbon performance by influencing resource allocation, primarily through increased green R&D investment and reduced information asymmetry [49,50].

Digital transformation effectively enhances the continuous R&D capabilities of manufacturing enterprises, develops technologies with higher energy efficiency and better environmental performance, improves resource utilization in production, and reduces pollutant and carbon emissions. Increased R&D efforts, improved product structures, and higher product quality make products more competitive in the environmental market.

Digital transformation in manufacturing enterprises reduces internal information asymmetry, effectively improving information processing capabilities, stabilizing production and operations, enhancing sustainable innovation, and promoting low-carbon transformation.

Additionally, digital transformation in manufacturing enterprises improves information transparency to some extent, alleviating information asymmetry between banks and enterprises. Banks can use digital financial platforms to expand their service range, lower financing costs, optimize resource allocation, and provide financial support for low-carbon transformation and green development, thereby significantly promoting green, low-carbon performance in manufacturing enterprises.

From the influence mechanism of digital intelligent transformation on green and low-carbon development, we can also see that the digital intelligent transformation of traditional manufacturing enterprises is not only influenced by external conditions such as the development and application of digital technology but also mediated by internal factors such as enterprise managers’ ideas and organizational structure. Judging from the current development situation of the digital intelligent transformation of manufacturing enterprises to promote the green and low-carbon performance of enterprises, there are still some weak links and obstacles in the process of digital intelligent transformation, mainly as follows:

First, the digital literacy level of management needs to be improved, and the organizational management model needs to be further optimized. Through investigation and interview, it is known that although most enterprise managers understand that digital intelligent transformation is the future development direction of enterprises, the leadership of manufacturing enterprises still difficult to adapt to digital management thinking and working methods, resulting in great resistance to digital intelligent transformation. There are still some manufacturing enterprises, especially small and medium-sized manufacturing enterprises, whose existing organizational management mode is traditional and backward, and they do not have modern enterprise management structure and thinking, and there are problems such as lack of functions and incomplete functions, which hinder the transformation and development of digital intelligence to a certain extent.

Second, the supply of digital talents, especially the shortage of compound talents who understand business and digital technology, has restricted the process of digital intelligent transformation of manufacturing enterprises to some extent. Judging from the current situation of higher education, although more than 200 colleges and universities across the country have setup digital economy majors, most of them are currently in the student stage, with fewer undergraduate graduates and fewer graduate students, and the supply of talents is insufficient. In terms of personnel training, it is not deep enough to integrate traditional engineering teaching materials and technology teaching with the subject of digital economy, so it is more difficult to train engineers with digital professional knowledge, and it is even more difficult to train outstanding engineers with high digital literacy and mastery of digital technology.

Third, the lag of enterprise digital infrastructure is an important factor restricting the transformation of digital intelligence. According to scholars’ research results, there is a positive correlation between enterprise information technology and information infrastructure and the digital transformation and innovation of intelligent manufacturing, and digital infrastructure plays an important role in accelerating the digital transformation and intelligent development of enterprises. However, the existing manufacturing enterprises, especially small and medium-sized manufacturing enterprises, have weak information and digital infrastructure, insufficient investment in construction, the existing foundation is difficult to support the operation of the digital platform, and there are problems such as data islands and data security risks, which are the big problems that hinder the digital transformation of manufacturing enterprises. The above-mentioned problems and obstacles in the digital intelligent transformation of manufacturing enterprises to promote green and low-carbon development need to be solved by actively linking internal and external enterprises, changing management concepts, optimizing organizational models, increasing capital investment, and accelerating the training of digital talents.

5. Conclusions and Recommendations

Manufacturing is the foundation of China’s real economy, the lifeline of the national economy, and an essential area for building a modern industrial system. The deep integration of new-generation digital technologies with manufacturing enterprises is essential for promoting the transformation and upgrading of traditional manufacturing industries, improving energy efficiency, fostering green innovation, achieving carbon reduction, cost savings, efficiency improvements, and green sustainable development. It is also crucial for China to achieve its “dual carbon” goals. This paper constructs a structural equation model to promote green low-carbon performance improvement through digital transformation in manufacturing enterprises, examining the mechanisms and pathways by which digital transformation affects carbon reduction and sustainable development. Mechanism analysis shows that digital transformation and upgrading in manufacturing enterprises affect green low-carbon performance and sustainable development through technological innovation, industrial structure transformation, and resource reallocation. Specific impact pathways include precise production and energy saving, replacing part of routine and repetitive labor, promoting the servitization of manufacturing, optimizing business processes, increasing green R&D investment, and reducing information asymmetry, which effectively reduces unit energy consumption and pollutant emissions, enhances sustainable development capabilities, and significantly promotes green low-carbon performance improvement.

The conclusion of this paper is consistent with the existing research results of digital technology enabling green development of enterprises, and there are also innovative results. Scholars and enterprise digital intelligent production managers, business workers, and article research results are consistent in the view that digital intelligent transformation can realize enterprise cost reduction, efficiency increase, carbon reduction and emission reduction, and green sustainable development. The main performance contents of obstacles and obstacles encountered in the process of digital intelligent transformation of manufacturing enterprises are similar. These consistent research results show that the research direction of the article is correct, and the research foundation and conclusions are reliable, convincing, and valuable for reference. At the same time, the article summarizes the scattered and fragmented paths of scholars’ research achievements in promoting green and low-carbon development through the digital intelligent transformation of manufacturing enterprises, which is not only a summary of the existing literature achievements, but also a comb and promotion of the existing literature. Constructing the structural equation model of manufacturing enterprises’ intelligent transformation to promote their green and low-carbon performance, and quantitatively measuring and analyzing the path efficiency, not only provides a new research perspective but more importantly, gives a clear road map of carbon reduction and emission reduction for the realization of carbon neutrality goals in all countries of the world, which has important theoretical guidance and practical reference value for manufacturing enterprises’ intelligent transformation to promote more effective path selection of green and low-carbon development.

Based on the above analysis of mechanisms and pathways, the following policy recommendations are proposed:

Firstly, actively promote the digital transformation and upgrading of traditional manufacturing enterprises. Integrating and applying digital technologies can help manufacturing enterprises capture various internal and external information elements more precisely, efficiently, and promptly, laying the foundation for core digital intelligence technologies and achieving the goal of carbon reduction, emission reduction, and efficiency improvement. From an external environment perspective, the government should strengthen policy support and guidance for the digital transformation of enterprises, establish a digital property rights security system, invest heavily in the transformation and construction of regional digital infrastructure, provide financing support for enterprises’ low-carbon production, adjust credit resource allocation, and increase the amount of green credit issuance to create a favorable external environment for the digital transformation and upgrading of manufacturing enterprises [51]. Higher education institutions should accelerate the construction and adjustment of disciplines and majors, strengthen the training of digital composite talents and high-end talents in the digital economy and digital technology, and provide talent support for the digital transformation of enterprises. From an internal enterprise perspective, senior managers should uphold the concept of digital transformation, create a positive atmosphere for digital transformation within the enterprise, accurately formulate forward-looking digital transformation strategies, optimize the organizational structure and management mode, and deeply integrate the application of digital technologies into multiple vital areas such as production, operations, sales, and R&D [52]. Strengthen the recruitment and training of digital talent, increase R&D investment in digital intelligence fields, actively promote the transformation of digital innovation achievements, and implement optimization and reengineering of existing business processes throughout the entire chain. Actively promote the transition of traditional manufacturing enterprises from single product production to both product production and service promotion, thereby achieving industrial structure transformation and upgrading, reducing unit energy consumption, reducing pollution and carbon emissions while improving efficiency, and enhancing the green low-carbon performance and sustainable development capabilities of manufacturing enterprises.

Secondly, optimize business processes, increase investment in green and intelligent research and development, and vigorously develop green technologies and a green economy. Through the innovation and application of green patent technology, the existing business will be connected with the digital intelligence platform. This will optimize or even recreate the existing business process to better understand the market and consumer demand, optimize the product structure, increase the added value of products, and introduce more high-value-added products. By introducing more high-value-added products, the market competitiveness of products will be improved, enhancing the sustainable development ability of enterprises. Strengthening the transformation of green R&D achievements and using green digital technologies will improve the automation, modernization, and intelligence level of production and manufacturing, replacing programmed complicated labor. This will facilitate the integration and transformation of digitalization and low-carbon initiatives, reduce energy consumption, and achieve cost reduction, efficiency improvement, and carbon emission reduction for enterprises.

Thirdly, enhance the transparency of information, and actively construct the disclosure and feedback mechanism of environmental intelligent management in manufacturing enterprises. Build an intelligent data-sharing platform, open up the link path of the management information system, break the “data island”, realize data sharing and intelligent analysis, and provide services and support for scientific decision-making. Through digital technology to strengthen the monitoring of pollutant emissions, carbon emissions, and other data, through digital platform information feedback and accurate production management, mediation, and optimization of the proportion of people and property investment, to ensure the achievement of production targets, reduce overcapacity, achieve accurate management and energy conservation, and effectively reduce the unit energy consumption of enterprises. Accelerate the improvement of environmental standards and regulatory systems compatible with green transformation and upgrading, promote cooperation between manufacturing enterprises and other participants in the digital technology ecosystem, and safeguard the coordinated development of enterprises’ digital and green transformation.