Do Intelligent Manufacturing Concerns Promote Corporate Sustainability? Based on the Perspective of Green Innovation

Abstract

Using A-share listed companies in China’s all manufacturing industry (including 30 categories of manufacturing industries such as automobile manufacturing, pharmaceutical manufacturing, textiles and more) from 2010 to 2021 as a research sample, this study empirically examines the impact of intelligent manufacturing concerns on corporate sustainability based on textual analysis, and examines the impact mechanism and the moderating role of the business environment. This study found that: intelligent manufacturing concerns are significantly and positively related to corporate sustainability, i.e., corporate focus on intelligent manufacturing concerns will promote corporate sustainability; corporate focus on intelligent manufacturing concerns can promote corporate sustainability by influencing green innovation; and the business environment positively moderates the impact of intelligent manufacturing concerns on corporate sustainability. Further analysis found that ownership heterogeneity, regional heterogeneity and scale heterogeneity play a moderating role between intelligent manufacturing concerns and corporate sustainability, with intelligent manufacturing concerns contributing more to corporate sustainability in large, non-state listed companies and in the eastern and coastal regions.

1. Introduction

The manufacturing industry determines the comprehensive strength and international competitiveness of a country, is the lifeblood of the economy, and is the foundation of a nation and a strong nation [1]. The report of the 20th Party Congress proposes to adhere to the focus of economic development on the real economy, promote a new type of industrialization and accelerate the construction of a strong manufacturing country. Intelligent manufacturing is a new manufacturing model based on digital intelligence technology, which improves production efficiency, product quality and service through the use of various intelligent sensors, adaptive decision-making models, smart devices and data analysis [2,3]. Promoting the sustainable development of the manufacturing industry with intelligent manufacturing is not only the way to achieve the resilience and security of the industrial chain and supply chain of China’s manufacturing industry and to continuously enhance international competitiveness, but also the main direction of the integration of the digital economy and the real economy, and a key breakthrough to achieve high-quality economic development and a new development pattern of double circulation. For a long time, and especially in recent years, the government has gradually increased its attention and focus on intelligent manufacturing. In 2012, the “Intelligent Manufacturing Science and Technology Development of the 12th Five-Year Plan” and “High-end Equipment Manufacturing Industry of the 12th Five-Year Development Plan” were introduced to determine the importance of intelligent manufacturing technology in the future development of science and technology, and around the core of the intelligent manufacturing equipment industry for a series of deployment. At the end of 2013, the State Ministry of Industry and Information Technology issued the “Guidance on Promoting the Development of Industrial Robot Industry”, which clearly put forward the development goals of industrial robots in China and promoted the upgrading of the manufacturing industry in the direction of digitalization and intelligence. In May 2015, the State Council officially issued “Made in China 2025”, emphasizing the theme of promoting the innovative development of the manufacturing industry, accelerating the deep integration of a new generation of information technology with the manufacturing industry as the main line, and promoting intelligent manufacturing as the main direction to achieve the historical leap from big to strong manufacturing industry, and intelligent manufacturing was elevated to an unprecedented national strategic height. In December 2016, the Ministry of Industry and Information Technology and the Ministry of Finance jointly completed the “Intelligent Manufacturing Development Plan (2016–2020)”, which clarifies the guiding ideology, objectives and key tasks for the development of intelligent manufacturing in China during the “13th Five-Year Plan” period. In October 2022, in the report of the 20th National Congress of the Communist Party of China, the strategic plan of “promoting a new type of industrialization and accelerating the building of a strong manufacturing country and a digital China” was emphasized again (refer to

Table 1. The summary of the government’s legislative steps to promote intelligent manufacturing.

No	File Name	Time
1	“Intelligent Manufacturing Science and Technology Development of the 12th Five-Year Plan”	2012
2	“High-end Equipment Manufacturing Industry of the 12th Five-Year Development Plan”	2012
3	“Guidance on Promoting the Development of Industrial Robot Industry”	2013
4	“Made in China 2025”	2015
5	“Intelligent manufacturing Development Plan (2016–2020)”	2016
6	“The report of the 20th National Congress of the Communist Party of China”	2022

for details). Since then, intelligent manufacturing has risen to the new height of the strong nation strategy and is widely regarded as an important way for Chinese manufacturing enterprises to seek transformation and upgrading. Therefore, it is of great theoretical significance and practical value to study the impact of intelligent manufacturing on the sustainable development of manufacturing enterprises.

The concept of sustainable development has gone through different stages since its inception, and has also attracted various organizations to participate [4]. Over time, this concept has been constantly updated and developed, and has been adapting to the contemporary requirements of the complex global environment. However, its goals and principles have not changed, and it has always been committed to the challenges faced by human survival on Earth and the sustainable development of human society. Therefore, sustainable development should be constructed from the perspective of classical political economy through cross historical experience [5].

This study selects A-share listed companies in China’s manufacturing industry from 2010 to 2021 as the research sample to empirically test the impact of intelligent manufacturing concerns on corporate sustainability. The findings show that: intelligent manufacturing concerns are significantly and positively related to corporate sustainability, i.e., corporate concern for intelligent manufacturing promotes corporate sustainability; corporate concern for intelligent manufacturing can promote corporate sustainability by influencing green innovation; and the business environment positively moderates the impact of intelligent manufacturing concerns on corporate sustainability. Additionally, all results are valid for all type of manufacturing industries. After conducting robustness tests such as replacing explanatory variables and shortening the time lattice, the findings of this study still hold. Further analysis reveals that ownership heterogeneity and regional heterogeneity play a moderating role between intelligent manufacturing concerns and corporate sustainability, with intelligent manufacturing concerns contributing more to corporate sustainability in non-state listed companies and in the eastern and coastal regions.

The main contributions that this study may make are as follows: firstly, based on the theoretical perspective of management, this article explores the social factors that affect the sustainable development of enterprises. Compared to previous research on technology, this article provides a new research direction for sustainable development of enterprises and enriches relevant literature; Secondly, based on research methods, compared to previous surveys and related practices, this article uses text analysis to measure the attention of enterprises to intelligent manufacturing. The data are more objective and reliable, and can more accurately reflect the real situation of intelligent attention; Thirdly, based on practical application, in the context of China’s vigorous promotion of intelligent manufacturing and high-quality economic development, the results of this study have strong practical significance for improving enterprise sustainability, achieving high-quality economic growth and a double-cycle dual circulation.

The subsequent structure is organized as follows: the second part is literature review and hypothesis proposal; the third part is research design; the fourth part is the analysis of empirical results; the fifth part is further analysis; and the last part is conclusions and recommendations.

2. Research Hypothesis

Intelligent manufacturing generally refers to an intelligent production method that deeply integrates information technology and advanced manufacturing technology, such as intelligent sensing technology, deep learning, and adaptive decision-making models, in all aspects of research and development, production, sales, and service, so that the production and service process can be self-aware, self-learning, self-applicable, self-decision and other functions [6,7]. With the implementation of the “Made in China 2025” plan, intelligent manufacturing has gradually become an inevitable trend of manufacturing transformation and development, which has been widely concerned by manufacturing enterprises. Environment, society and governance is a new concept proposed by the United Nations Global Compact (UNGC) from the perspective of enterprises sustainable development and social responsibility to solve the symbiotic problems of environment, society and economy. ESG requires enterprises to consider not only economic benefits, but also environmental and social interests in operation, management and investment decisions, so as to promote the sustainable development of enterprises and society. Therefore, we believe that the attention and promotion of intelligent manufacturing can promote corporate sustainability. Specifically, corporate sustainability means that in the process of development, enterprises should not only consider the achievement of business objectives, but also keep the profit growth in the leading competitive areas and the future expansion of business areas, so as to ensure the sustainable survival and development of enterprises. The achievement of business goals and the sustained growth of economic performance are considered as important references for corporate sustainability [8]. In terms of the economic performance of enterprises, digital technology is an essential part of the intelligent manufacturing process. The application of digital technology enhances the ability of enterprises to access external information and resources, and enables enterprises to occupy a dominant position in the decision making of both supply and demand. Enterprises that pay more attention to intelligent manufacturing usually pay more attention to the application of data information technology [9]. Through the application of data information technology in intelligent manufacturing, enterprises can obtain more accurate information about consumers’ behavior, make more accurate predictions about consumers’ purchasing decisions, and provide consumers with products and services that are more in line with their needs. In other words, the powerful data analysis in the intelligent manufacturing process provides more personalized and targeted products and services for different consumer groups. This high degree of supply and demand matching satisfaction makes the products and services provided by intelligent manufacturing enterprises more competitive, which in turn enhances the economic performance of intelligent manufacturing enterprises. In terms of efficiency and cost, enterprises that are concerned about intelligent manufacturing will be more interested in improving the efficiency of their production and operations and reducing costs through intelligent manufacturing [10]. Intelligent manufacturing achieves the optimal allocation and utilization of enterprise resources, improves the efficiency of resource utilization, reduces the waste and loss of resources, and also reduces the cost of the production process [11,12]. In addition, the application of intelligent information technology can also realize the dynamic tracking and monitoring of the production and operation process. The enterprise reduces the generation of substandard products through the intervention of key links, thereby improving the product quality rate. The improvement of product efficiency and quality, as well as the reduction in costs, all of which have a positive impact on corporate sustainability [13]. Therefore, enterprises’ attention to intelligent manufacturing can effectively promote the sustainable development of enterprises [14,15].

Based on the above analysis, we propose the following hypothesis.

Hypothesis 1.

Enterprises’ focus on intelligent manufacturing can promote corporate sustainability.

The impact of intelligent manufacturing on green innovation. Green innovation means that enterprises solve environmental problems through innovation in green technologies, products, services, processes, etc., in order to create new market opportunities and thus provide enterprises with greater market competitive advantages. With the increasing global attention to environmental issues, green innovation has become an inevitable requirement for the development of enterprises. China takes green innovation as an important way to achieve green development and ecological civilization, and increasingly emphasizes the responsibility of enterprises for green development. In the iterative upgrading process of intelligent manufacturing, green development has become one of the important purposes of its upgrading. The business model of manufacturing industry has gradually transformed from product-driven to service-driven. Manufacturing enterprises build a consumer-centered service model, which requires manufacturing enterprises to integrate a number of technologies such as big data and artificial intelligence to provide consumers with accurate and personalized customization services. Such precise customized services can maximize the use of products, services and resources of manufacturing enterprises, and provide a better environment for green innovation. That is to say, in the process of intelligent manufacturing in the manufacturing industry, the intelligence of manufacturing enterprises helps to promote the green innovation of enterprises in many aspects such as research and development, production, sales and service [16]. Firstly, in the R & D trial stage of intelligent manufacturing, digital twin technology conducts simulation tests and trial-and-error learning for different solutions of product development, and determines the best R & D solutions in different contexts in the shortest possible time, which greatly improves the efficiency of green innovation. This reduces the waste caused by trial and error, effectively saving various resources. Secondly, from the resource perspective, intelligent manufacturing promotes the interaction and exchange of internal and external information resources of manufacturing enterprises, which further enhance their innovation capabilities through the integration and utilization of these information resources [17]. Thirdly, the application of information technology in intelligent manufacturing enhances the level of enterprise knowledge management and promotes innovative activities such as patent inventions and the development of new product technologies [18]. From the perspective of technology, the process of intelligent manufacturing inevitably involves the upgrading of technology and equipment of manufacturing enterprises, and the introduction of advanced technology and equipment helps to promote manufacturing enterprises to reduce resource input and waste, obtain higher energy efficiency output, and reduce pollutant emissions. Finally, from the perspective of human capital, intelligent manufacturing puts forward higher-quality requirements for enterprise personnel, and the introduction of high-quality personnel optimize the personnel quality structure of manufacturing enterprises and improve the human capital level of manufacturing enterprises [19]. Knowledge is an important component of human capital, and knowledgeable employees are more likely to question existing behaviors and come up with innovative ideas [20], thus promoting organizational green innovation and change. Human capital plays a key role in creating value for companies to gain competitive advantage [21]. Kianto et al. (2017) found that human capital promotes innovative performance [22]. In conclusion, enterprises that focus on and promote intelligent manufacturing typically have higher green innovation. Previous studies have found that intelligent manufacturing has a positive effect on enterprises’ innovative behavior [17,23].

The impact of green innovation on the sustainable development of enterprises. Green innovation is the internal requirement of sustainable development of enterprises, and it is also an effective way of sustainable development of enterprises. Green innovation enables enterprises to acquire key resources that are difficult to imitate and valuable, enhance their core competitive advantage, seize the opportunity in the market competition, and make their development more sustainable [24,25]. According to the resource-based view, scarce, hard-to-substitute and valuable resources such as technology and knowledge are the key for enterprises to build sustainable competitive advantage [26]. Green innovation breaks through the resource constraints of a firm and brings heterogeneous resources and knowledge to the firm. These resources and knowledge brought by green innovation are in line with the characteristics of scarcity, irreplaceable and valuable in the resource-based view, which contribute to the sustainable development of enterprises [27,28]. At the same time, green innovation is inevitably accompanied by the generation of new technologies, which improve the efficiency of enterprises’ products and services, reduce operating costs, and increase operating income [29]. Singh et al. (2019) found that green innovation improved the environmental performance of enterprises and had a positive impact on the market value of enterprises [30]. Hu et al. (2021) found that green innovation helps promote the sustainable transformation and development of enterprises [31].

Based on the above analysis, we propose the following hypothesis.

Hypothesis 2.

Enterprises’ focus on intelligent manufacturing can promote corporate sustainability through green innovation, and green innovation plays a mediating role between them.

The business environment is a comprehensive ecosystem of the external environment faced by enterprises in the production and operation process. The business environment can effectively reduce the institutional costs in the market and enable enterprises to have fair access to production factors [32]. The Chinese government attaches great importance to the construction of business environment and has introduced numerous institutional policies to build a fair, transparent and stable business environment for enterprises.

In terms of the governmental environment, the governmental environment is mainly reflected in the level of governmental expenditure and good business relations. In recent years, the Chinese government has increased financial subsidies and tax incentives for intelligent manufacturing and sustainable development. These financial subsidies and preferential policies have guided enterprises to focus on technological innovation and process improvement to a certain extent [33], which have advanced the process of intelligent manufacturing and thus enhanced corporate sustainability. In addition, a good government–business relationship requires that the market mechanism plays a leading role in resource allocation, and the government plays more of a regulatory and supervisory role rather than an interventionist role. Because when government intervention increases, the production and operation of enterprises such as technological innovation may suffer from the squeeze of non-productive activities, which hinders corporate sustainability. When there is less government intervention, enterprises can invest more resources in economic performance and profitability, promoting corporate sustainability.

In terms of policy environment, when the business environment in which enterprises are located is good, a sound policy and institutional system makes enterprises face less moral hazard and adverse selection problems in the intelligent manufacturing process [34], providing a safe and stable external environment for intelligent manufacturing to promote corporate sustainability. For example, a good legal policy environment provides protection for intellectual property rights in the process of intelligent manufacturing, and enterprises can enjoy the benefits brought by intellectual property rights [35]. In order to take the lead in intelligent manufacturing, each enterprise will accelerate its own R & D process in order to obtain the legal policy protection of corresponding intellectual property rights as early as possible. Therefore, a good legal policy environment can protect and promote the intelligent manufacturing of enterprises, so as to accelerate the process of corporate sustainability.

In terms of market environment, when the business environment is high, more enterprises can be attracted to join the intelligent manufacturing, which promotes the formation of economies of scale and scope in the industry and helps to form an open and collaborative ecosystem of intelligent manufacturing industry. The interaction and collaboration of intelligent manufacturing enterprises within the system accelerate their own attention to and promotion of intelligent manufacturing, as well as the integration and utilization of internal and external resources. In other words, a high level of business environment provides important resource conditions for intelligent manufacturing to advance corporate sustainability.

Based on the above analysis, we propose the following hypothesis.

Hypothesis 3.

The business environment positively moderates the impact of enterprises’ focus on intelligent manufacturing on corporate sustainability.

3. Research Design

3.1. Data Selection

This study uses A-share listed companies in China’s manufacturing industry from 2010 to 2021 as the research sample. The data are mainly obtained from the CSMAR database, and the annual reports of listed companies in the manufacturing sector, and are collected and collated manually. Finally, 20,428 sample observations are identified, and STATA14.0 (Statistical Analysis System, A statistical analysis software, originating from StataCorp in the United States, version 14.0) is used to process and analyze.

3.2. Variable Definitions

The explained variable is corporate sustainability (Cs). Drawing on relevant research [35,36,37,38], corporate sustainability is constructed from two dimensions of economic performance and environmental social responsibility performance. The sustainable growth rate is usually used to reflect the sustainability of corporate profitability, so the sustainable growth rate is used to measure the economic performance of enterprises. At the same time, corporate environmental responsibility score and corporate social responsibility score in the Hexun.com database are used to measure corporate environmental social responsibility performance. Considering the multi-dimension and multi-level of corporate sustainability as well as the differences among different indicators, the entropy method is used to measure the corporate sustainability after the establishment of index system.

The explanatory variable is intelligent manufacturing concerns (Imc). Intelligent manufacturing concerns are measured by using corporate intelligent manufacturing concerns, and information related to intelligent manufacturing concerns is extracted from the text of annual reports of listed manufacturing companies. The specific steps are as follows: firstly, construct a keyword list for intelligent manufacturing concerns. Drawing on the research of Guo Lei et al. [39], we extracted the similar words of “intelligent manufacturing” in the context of annual reports through deep learning technology with the help of “WinGo similar words database”, and finally obtained a set of 57 keywords on the topic of “intelligent manufacturing”, including “Made in China 2025”, “Industry 4.0”, “industrial robot”, “intelligent terminal” and “high-end equipment manufacturing”. Secondly, measure the attention of enterprises to intelligent manufacturing. Based on the term “intelligent manufacturing” and its 57 keywords, analysis the development of intelligent manufacturing concerns. Based on the constructed keyword list, the Python language is used to analyze the text of the annual report of listed manufacturing companies, and the word frequency of each keyword and “intelligent manufacturing” appeared in the annual reports of listed companies in the manufacturing industry are counted, and the word frequencies of all keywords are summed up by company and year, respectively, as a measure of intelligent manufacturing concerns. At the same time, the ratio of the frequency of “intelligent manufacturing” to the total number of words was calculated and tested for robustness.

The mediating variable is green innovation (Inno). The main measure is from an output perspective, drawing on relevant scholarly research [39,40,41,42] and using the natural logarithm of the number of green patents granted in the year for listed manufacturing companies to measure green innovation.

The moderating variable is the business environment (Mark). Drawing on the research of Lin S. et al. [32,43,44,45], adopt the marketization index of the provinces and cities where the listed manufacturing companies are located.

Control variables. Through reviewing the existing literature, it is found that there are many factors affecting corporate sustainability. The basic characteristics of enterprises and differences in governance institutions will all have different effects on corporate sustainability. In view of this, select the size of assets (Size), time of establishment (His), profitability (Roa), debt paying ability (Lev), ownership concentration (Top1), proportion of independent directors (Indep), concurrent appointments of chairman and general manager (Dual) as control variables. In addition, the annual effect (Year) and individual effect (Ind) are also controlled. The detailed definition and measurement of variables are shown in

Table 2. Variable definition and interpretation.

Variables	Name	Symbol	Definition or Measurement
The explained variable	Corporate sustainability	Cs	comprehensive indicator constructed by the entropy method
The explanatory variable	Intelligent manufacturing concerns	Imc	enterprise intelligent manufacturing concerns concerns constructed using text analytics
The mediating variable	Green innovation	Inno	the natural logarithm of the number of green patents granted by the enterprise in the year
The moderating variable	Business environment	Mark	marketability index of the province and city where the company is located
Control variables	Size of assets	Size	the natural logarithm of total assets at the end of the period
	Time of establishment	His	the natural logarithm of the difference between the year under study and the year the enterprise was established
	Profitability	Roa	net profit rate of total assets, that is, the ratio of net profit to total assets at the end of the year
	Debt paying ability	Lev	asset–liability ratio, that is, the ratio of total liabilities to total assets at the end of the year
	Ownership concentration	Top1	proportion of the largest shareholder
	Number of independent directors	Indep	the proportion of the number of independent directors in the total number of directors
	Concurrent appointments of chairman and general manager	Dual	if the two positions of chairman and general manager are combined, the value is 1; otherwise, the value is 0
	Year dummy variable	Year	controlling for year effects
	Individual dummy variable	Ind	controlling for individual effects

.

3.3. Model Building

To test hypothesis 1, model (1) was constructed to empirically test the impact of intelligent manufacturing concerns on corporate sustainability.

$${Cs}_{i,t}=\alpha +{\beta }_1{Imc}_{i,t}+{\beta }_2{Size}_{i,t}+{\beta }_3{His}_{i,t}+{\beta }_4{Lev}_{i,t}+{\beta }_5{Roa}_{i,t}+{\beta }_6{Top1}_{i,t}+{\beta }_7{Indep}_{i,t}+{\beta }_8{Dual}_{i,t}+Year+Ind+{\mathcal{E}}_{i,t}$$

(1)

To test hypothesis 2, models (2) to (3) were constructed by drawing on the “three-step approach” for testing intermediary effects proposed by Wen Zhonglin and others [46,47,48].

$${Inno}_{i,t}=\alpha +{\beta }_1{Imc}_{i,t}+{\beta }_2{Size}_{i,t}+{\beta }_3{His}_{i,t}+{\beta }_4{Lev}_{i,t}+{\beta }_5{Roa}_{i,t}+{\beta }_6{Top1}_{i,t}+{\beta }_7{Indep}_{i,t}+{\beta }_8{Dual}_{i,t}+Year+Ind+{\mathcal{E}}_{i,t}$$

(2)

$${Cs}_{i,t}=\alpha +{\beta }_1{Imc}_{i,t}+{\beta }_2{Inno}_{i,t}+{\beta }_3{Size}_{i,t}+{\beta }_4{His}_{i,t}+{\beta }_5{Lev}_{i,t}+{\beta }_6{Roa}_{i,t}+{\beta }_7{Top1}_{i,t}+{\beta }_8{Indep}_{i,t}+{\beta }_9{Dual}_{i,t}+Year+Ind+{\mathcal{E}}_{i,t}$$

(3)

To verify hypothesis 3, the business environment and the interaction between business environment and intelligent manufacturing concerns are introduced, and model (4) is constructed.

$${Cs}_{i,t}=\alpha +{\beta }_1{Imc}_{i,t}+{\beta }_2{Mark}_{i,t}+{\beta }_3{Im}_{i,t}\ast {Mark}_{i,t}+{\beta }_4{Size}_{i,t}+{\beta }_5{His}_{i,t}+{\beta }_6{Lev}_{i,t}+{\beta }_7{Roa}_{i,t}+{\beta }_8{Top1}_{i,t}+{\beta }_9{Indep}_{i,t}+{\beta }_{10}{Dual}_{i,t}+Year+Ind+{\mathcal{E}}_{i,t}$$

(4)

where $\alpha$ is the intercept term and ${\mathcal{E}}_{i,t}$ is the random error term; at the same time, the year fixed effect ($Year$) and individual fixed effect ($Ind$) are controlled bidirectionally.

4. Empirical Analysis

4.1. Variable Description

Table 3. Summary statistics.

Variables	Obs	Mean	Median	Std. Dev	Min	Max
Cs	20,428	0.050	0.048	0.082	−0.335	0.307
Imc	20,428	3.366	3.401	1.167	0.693	6.006
Inno	20,428	1.407	1.386	1.341	0.000	5.252
Mark	20,428	8.789	9.300	1.710	4.390	11.310
Size	20,428	8.106	7.961	1.135	6.145	11.448
His	20,428	2.859	2.890	0.334	1.792	3.466
Lev	20,428	0.358	0.335	0.187	0.044	0.777
Roa	20,428	0.056	0.048	0.043	−0.021	0.209
Top1	20,428	0.356	0.342	0.143	0.091	0.752
Indep	20,428	3.178	3.000	0.576	0.000	8.000
Dual	20,428	0.302	0.000	0.459	0.000	1.000

reports the results of descriptive statistics for all variables. The table shows that the mean value of corporate sustainability (Cs) is 0.050 and the standard deviation is 0.082, indicating that the sustainable development level of listed manufacturing companies in China is low and varies widely. Intelligent manufacturing concerns (Imc) has a maximum value of 6.006, a minimum value of 0.693 and a standard deviation of 1.167, indicating that there is also a wide variation in the current focus on intelligent manufacturing concerns among listed manufacturing companies. The existence of good dispersion between the moderating variables and each of the control variables indicates that the variables were selected in a reasonable manner, which is conducive to the regression analysis later on.

There are many ways in which listed companies with high and low intelligent manufacturing concerns can differ significantly. To provide a clearer picture of the differences between the two different types of companies, the groups were next grouped according to the median of intelligent manufacturing concerns and t-test analyses of the differences between the groups were conducted.

Table 4. Grouping descriptive statistics according to green innovation.

Variables	Imc ≤ 3.401			Imc > 3.401			Mean Diff
	Obs	Mean	Std. Dev	Obs	Mean	Std. Dev
Cs	10,246	0.043	0.027	10,182	0.050	0.019	−0.007 ***
Inno	10,246	1.185	0.030	10,182	1.627	0.037	−0.442 ***
Mark	10,246	8.454	0.045	10,182	9.122	0.041	−0.668 ***
Size	10,246	8.020	0.028	10,182	8.190	0.029	−0.170 ***
His	10,246	2.836	0.009	10,182	2.881	0.008	−0.046 ***
Lev	10,246	0.348	0.005	10,182	0.367	0.005	−0.019 ***
Roa	10,246	0.055	0.044	10,182	0.056	0.001	−0.001
Top1	10,246	0.352	0.003	10,182	0.360	0.004	−0.009 *
Indep	10,246	3.226	0.016	10,182	3.130	0.013	0.096 ***
Dual	10,246	0.285	0.451	10,182	0.320	0.012	−0.035 **

Note: *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively, the same below.

reports the results of the univariate analysis. It can be seen that when intelligent manufacturing concerns are low, the mean value of corporate sustainability (Cs) is 0.043; when intelligent manufacturing concerns are high, the mean value of corporate sustainability (Cs) is 0.050; the difference between groups is significant at the 1% level. The above results indicate that listed companies with a higher focus on intelligent manufacturing concerns also have a higher level of sustainable development. It can also be found that when intelligent manufacturing concerns are low, the mean value of green innovation (Inno) is 1.185; when intelligent manufacturing concerns are high, the mean value of green innovation (Inno) is 1.627; the difference between groups is significant at the 1% level. The above results indicate that listed companies with a higher focus on intelligent manufacturing concerns also have a higher level of green innovation.

4.2. Benchmark Test

Figure 1 shows a scatter plot of intelligent manufacturing concerns and corporate sustainability. It can be seen that the horizontal axis represents the explanatory variable intelligent manufacturing concerns and the vertical axis represents the explained variable corporate sustainability. It is also noticeable that most of the samples are concentrated in the lower right of the scatter plot. This suggests that intelligent manufacturing concerns and corporate sustainability are largely aligned trends, with a relatively small slope between the two. That is, corporate sustainability gradually increases with intelligent manufacturing concerns.

Table 5. Benchmark test.

Variables	Cs	Cs	Cs
	(1)	(2)	(3)
Imc	0.103 ***	0.101 *	0.152 ***
	(3.29)	(1.85)	(3.25)
Size		0.048 ***	0.586 ***
		(9.50)	(9.03)
His		0.015	−0.061
		(0.91)	(−0.15)
Lev		0.048 **	0.592 **
		(2.47)	(2.44)
Roa		0.018	1.125 ***
		(0.32)	(3.02)
Top1		0.095 ***	0.371
		(3.14)	(0.53)
Indep		0.004	−0.045
		(0.88)	(−0.65)
Dual		−0.004	0.053 *
		(−0.75)	(1.83)
Intercept		0.298 ***	4.213 ***
		(6.94)	(3.09)
Control year	Yes	No	Yes
Control individual	Yes	No	Yes
Observations	20,428	20,428	20,428
R2	0.363	0.107	0.290

Note: *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively; The numbers in brackets are “t” of the estimated coefficients; R² is the goodness of fit of the model.

reports the basic regression results for the impact of intelligent manufacturing concerns on corporate sustainability. As seen in column (1), without considering the influence of control variables, the correlation coefficient between Cs and Imc is 0.103 and is significant at the 1% level, indicating that intelligent manufacturing concerns are significantly and positively correlated with corporate sustainability, i.e., corporate focus on intelligent manufacturing concerns will promote corporate sustainability, and hypothesis 1 is preliminarily verified. In column (2), when the influence of control variables is considered, but the year and individual fixed effect are not considered, the correlation coefficient between Cs and Imc is 0.101, which is significant at the level of 10%, indicating that intelligent manufacturing concerns are significantly positively correlated with corporate sustainability after considering the influence of control variables. In column (3), when the influence of control variables is considered and the year and individual fixed effects are controlled bidirectionally, the correlation coefficient between Cs and Imc is 0.152, which is still significant at the 1% level, again indicating that intelligent manufacturing concerns are significantly positively correlated with corporate sustainability. Hypothesis 1 is verified. In addition, it can be observed that the R² of the model improves from 0.107 to 0.290 after controlling for the year and industry variables, indicating that the goodness of fit of the model is enhanced.

Columns (1) and (2) in

Table 6. Test of influence mechanism and moderating effect.

Variables	Inno	Cs	Cs
	(1)	(2)	(3)
Imc	0.206 **	0.125 ***	0.195 ***
	(2.49)	(3.26)	(3.78)
Inno		0.141 **
		(2.43)
Market			0.141 **
			(2.01)
Imc*Market			0.049 ***
			(3.04)
Size	0.659 ***	0.558 ***	−0.570 ***
	(19.47)	(7.91)	(8.77)
His	−0.040	−0.103	−0.084
	(−0.18)	(−0.25)	(−0.20)
Lev	0.120	0.588 **	0.603 **
	(0.95)	(2.43)	(2.49)
Roa	0.239 *	1.167 ***	1.005 ***
	(1.77)	(3.15)	(2.69)
Top1	1.866 ***	0.440	0.325
	(5.13)	(0.63)	(0.47)
Indep	−0.009	−0.047	−0.046
	(−0.26)	(−0.68)	(−0.66)
Dual	0.058 *	0.058 *	0.033 *
	(1.84)	(1.80)	(1.75)
Intercept	1.438 **	3.828 ***	2.591 *
	(2.11)	(2.92)	(1.77)
Control year	Yes	Yes	Yes
Control individual	Yes	Yes	Yes
Observations	20,428	20,428	20,428
R2	0.269	0.288	0.291

Note: *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.

show the test results of the impact mechanism of intelligent manufacturing concerns on corporate sustainability. As seen in column (1), the correlation coefficient between Inno and Imc is 0.206 and is significant at the 5% level, indicating that intelligent manufacturing concerns are significantly and positively correlated with green innovation, i.e., companies’ focus on intelligent manufacturing concerns will promote green innovation. Meanwhile, as can be seen in column (2), the correlation coefficient between Cs and Inno is 0.141 and is significant at the 5% level after the introduction of green innovation; the correlation coefficient between Cs and Imc is 0.125 and remains significant at the 1% level. This suggests that green innovation plays a partially mediating role in intelligent manufacturing concerns and corporate sustainability, i.e., corporate concern for intelligent manufacturing concerns can promote corporate sustainability through influencing green innovation, and hypothesis 2 is verified.

Column (3) in Table 6 shows the test results of the moderating effect of business environment on intelligent manufacturing concerns and corporate sustainability. It can be seen that the correlation coefficient between Cs and Imc is 0.195 and is significant at the 1% level after the introduction of the interaction term between business environment and intelligent manufacturing concerns; the correlation coefficient between Cs and the interaction term Mark * Imc is 0.049 and is significant at the 1% level; indicating that business environment positively moderates the promotion effect of intelligent manufacturing concerns on the corporate sustainability, and hypothesis 3 is verified.

4.3. Robust Test

4.3.1. Replace Method of the Explanatory Variable

In order to verify the robustness of the regression findings, the explanatory variable is then replaced and the ratio of the frequency of the word “intelligent manufacturing concerns” to the total number of words (Rim) is used as a measure of intelligent manufacturing concerns, and the results of the robustness tests after replacing the explanatory variables are reported in

Table 7. Robust test: replace method of the explanatory variable.

Variables	Cs	Inno	Cs	Cs
	(1)	(1)	(3)	(4)
Rim	0.161 ***	0.250 **	0.136 ***	0.135 ***
	(3.17)	(2.53)	(3.39)	(3.78)
Inno			0.187 **
			(2.26)
Market				0.115 *
				(1.80)
Rim*Market				0.019 ***
				(3.40)
Size	0.587 ***	0.170 **	0.588 ***	0.581 ***
	(9.01)	(2.01)	(9.01)	(8.92)
His	−0.101	−1.511 ***	−0.090	−0.049
	(−0.24)	(−2.77)	(−0.22)	(−0.12)
Lev	0.618 **	−0.352	0.621 **	0.624 **
	(2.54)	(−1.11)	(2.55)	(2.57)
Roa	1.174 ***	0.218	1.173 ***	1.152 ***
	(3.16)	(0.45)	(3.16)	(3.09)
Top1	0.481	0.199	0.479	0.275
	(0.69)	(0.22)	(0.69)	(0.39)
Indep	−0.050	0.102	−0.050	−0.046
	(−0.72)	(1.14)	(−0.73)	(−0.66)
Dual	0.060 *	0.049 *	0.060 *	0.059 *
	(1.79)	(1.74)	(1.78)	(1.76)
Intercept	4.268 ***	4.527 ***	4.237 ***	4.624 ***
	(3.27)	(2.67)	(3.24)	(3.40)
Control year	Yes	Yes	Yes	Yes
Control individual	Yes	Yes	Yes	Yes
Observations	20,428	20,428	20,428	20,428
R2	0.235	0.286	0.235	0.238

Note: *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.

. It can be seen that the correlation coefficient between Cs and Rim in column (1) is 0.161 and remains significant at the 1% level, indicating that intelligent manufacturing concerns are significantly and positively related to corporate sustainability and hypothesis 1 is verified. The correlation coefficient between Inno and Rim in column (2) is 0.250 and is significant at the 5% level, indicating that intelligent manufacturing concerns are significantly and positively correlated with green innovation. The correlation coefficient between Cs and Inno in column (3) is 0.187 and is significant at the 5% level; the correlation coefficient between Cs and Rim is 0.136 and is still significant at the 1% level. This indicates that green innovation plays a partly mediating role in the sustainable development of intelligent manufacturing concerns and enterprises, and Hypothesis 2 is again verified. The correlation coefficient between Cs and Rim in column (4) is 0.135 and is significant at the 1% level; the correlation coefficient between Cs and the interaction term Mark* Rim is 0.019 and is significant at the 1% level; indicating that the business environment positively moderates the role of intelligent manufacturing concerns in promoting the corporate sustainability, and Hypothesis 3 is again verified.

4.3.2. Shorten the Time Pane

Considering that after the release of Made in China 2025 by the State Council in 2015, the manufacturing industry has focused on improving its core competitiveness and brand building capabilities, the focus on intelligent manufacturing concerns has increased significantly. In order to eliminate the impact of policy release, the time pane is shortened, data before 2015 are eliminated, and only data from 2016 to 2021 are used for regression.

Table 8. Robustness test: shorten the time pane.

Variables	Cs	Inno	Cs	Cs
	(1)	(2)	(3)	(4)
Imc	0.192 ***	0.173 ***	0.180 ***	0.228 **
	(3.99)	(4.58)	(4.02)	(2.03)
Inno			0.144***
			(3.60)
Market				0.118 *
				(1.85)
Im*Market				0.082 ***
				(2.90)
Size	0.321 **	0.144	0.319 **	0.325 **
	(2.36)	(0.64)	(2.35)	(2.39)
His	−0.651	−0.787	−0.640	−0.519
	(−1.03)	(−0.76)	(−1.01)	(−0.82)
Lev	0.174	−0.104	0.175	0.262
	(0.46)	(−0.17)	(0.47)	(0.69)
Roa	0.401	1.860	0.376	0.313
	(0.52)	(1.47)	(0.49)	(0.41)
Top1	−2.275 **	−0.562	−2.268 **	−2.169 *
	(−2.04)	(−0.31)	(−2.04)	(−1.94)
Indep	−0.010	0.057	−0.010	−0.005
	(−0.09)	(0.34)	(−0.10)	(−0.05)
Dual	0.066	−0.090	0.068	0.068
	(0.83)	(−0.68)	(0.85)	(0.85)
Intercept	3.470 *	4.576 *	3.408 *	3.535 *
	(1.75)	(1.80)	(1.72)	(1.66)
Control year	Yes	Yes	Yes	Yes
Control individual	Yes	Yes	Yes	Yes
Observations	14,003	14,003	14,003	14,003
R2	0.355	0.347	0.351	0.334

Note: *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.

reports the robustness test results after shortening the time pane. It can be seen that the regression results are consistent with the above, indicating that the regression conclusion is robust.

5. Further Analysis

5.1. Ownership Heterogeneity

Considering the special institutional context in China, where there are significant differences between state-owned and non-state-owned enterprises in terms of policy support and target missions, further analysis of the moderating effect of ownership heterogeneity follows, and

Table 9. Test of ownership heterogeneity.

Variables	Cs
	State-Owned Enterprise	Non-State-Owned Enterprise
	(1)	(2)
Imc	0.102 **	0.126 ***
	(2.15)	(2.92)
Size	−0.671 ***	−0.319 ***
	(−7.58)	(−3.35)
His	0.452	−0.056
	(0.87)	(−0.07)
Lev	0.874 ***	0.658 *
	(2.70)	(1.83)
Roa	1.439 ***	−0.172
	(2.63)	(−0.34)
Top1	−1.237	2.378 **
	(−1.38)	(2.20)
Indep	−0.078	−0.076
	(−0.92)	(−0.63)
Dual	0.114	−0.053
	(1.36)	(−0.64)
Intercept	2.691	2.432
	(1.64)	(1.04)
Control year	Yes	Yes
Control individual	Yes	Yes
Observations	6230	14,198
R2	0.351	0.391

Note: *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.

reports the results of the test of ownership heterogeneity. It can be seen that the correlation coefficient between Cs and Imc is 0.102 among state-owned listed companies and is significant at the 5% level, while among non-state-owned listed companies, the correlation coefficient between Cs and Imc is 0.126 and is significant at the 1% level. It shows that enterprises’ focus on intelligent manufacturing concerns promotes corporate sustainability in both state-owned and non-state-owned listed companies; at the same time, it can be found that intelligent manufacturing concerns promotes corporate sustainability more strongly in non-state-owned listed companies. This may be due to the fact that state-owned enterprises, as the “eldest son” of the Republic, occupy an important position in the national economy and social life, and have always been the object of government support and cultivation, with strong financial strength, and the level of corporate sustainability is itself relatively high, so the role of intelligent manufacturing concerns in promoting corporate sustainability is weaker.

5.2. Regional Heterogeneity

Considering that there are significant differences between the eastern, central and western regions of China in terms of the level of economic development, industrial policies and the degree of openness to the outside world, the impact of intelligent manufacturing concerns on corporate sustainability may also vary significantly depending on the region in which the listed companies are located. The regressions are further grouped according to the provinces where the listed companies are located, and are divided into three sub-samples, namely the eastern and coastal regions, the central region and the western region, according to the National Development and Reform Commission’s classification of the eastern, central and western regions (Eastern and coastal regions include Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan; central regions include Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei and Hunan; western regions include Sichuan, Chongqing, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang and Guangxi).

Table 10. Test of regional heterogeneity.

Variables	Cs
	Eastern Region	Central Region	Western Region
	(1)	(2)	(3)
Imc	0.169 ***	0.040 *	0.035 *
	(3.30)	(1.84)	(1.76)
Size	0.511 ***	1.044 ***	0.428 ***
	(6.35)	(5.98)	(3.08)
His	−0.138	1.044	−0.388
	(−0.28)	(0.93)	(−0.30)
Lev	0.199	1.913 ***	1.208 **
	(0.68)	(3.08)	(2.05)
Roa	1.198 ***	2.296 **	−0.441
	(2.65)	(2.18)	(−0.54)
Top1	−0.019	2.841	−3.152 *
	(−0.02)	(1.54)	(−1.67)
Indep	−0.102	0.070	0.239
	(−1.24)	(0.38)	(1.27)
Dual	0.114 *	0.102 *	0.075 *
	(1.75)	(1.82)	(1.74)
Intercept	3.277**	4.240	4.032
	(2.07)	(1.30)	(1.05)
Control year	Yes	Yes	Yes
Control individual	Yes	Yes	Yes
Observations	14,462	3513	2453
R2	0.291	0.362	0.383

Note: *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.

reports the results of the test of regional heterogeneity. It can be seen that in the Eastern and Coastal regions, the correlation coefficient between Cs and Imc is 0.169 and is significant at the 1% level; in the Central region, the correlation coefficient between Cs and Imc is 0.040 and is significant at the 10% level; in the western region, the correlation coefficient between Cs and Imc is 0.035 and is significant at the 10% level. It can be seen that intelligent manufacturing concerns promotes corporate sustainability in both the eastern and coastal regions, the central region and the western region; the eastern and coastal regions have the strongest contribution of intelligent manufacturing concerns to corporate sustainability, and there are significant differences with the central and western regions. This is probably because the eastern and coastal regions are better developed than the central and western regions, both in terms of ecological environment and resource endowment, as well as in terms of economic development level and business environment. Therefore, the eastern and coastal regions have a better environment for intelligent manufacturing concerns and corporate sustainability, so when enterprises pay more attention to intelligent manufacturing concerns, they can achieve better results, and the promotion of corporate sustainability is stronger.

5.3. Scale Heterogeneity

Considering that listed companies of different sizes have great differences in financial strength, human resources and development prospects, the median number of employees of listed companies is further grouped for regression.

Table 11. Test of scale heterogeneity.

Variables	Cs
	Large Scale	Small Scale
	(1)	(2)
Imc	0.210 ***	0.012 *
	(2.62)	(1.72)
Size	−0.055 ***	−0.046 ***
	(−7.11)	(−4.90)
His	−0.196 ***	0.090
	(−3.67)	(1.62)
Lev	0.086 ***	0.041
	(2.87)	(1.30)
Roa	0.049	−0.096
	(0.65)	(−1.05)
Top1	0.106 ***	0.026
	(2.90)	(0.42)
Indep	−0.003	0.018 **
	(−0.53)	(2.13)
Dual	0.004	−0.014 *
	(0.50)	(−1.67)
Intercept	1.011 ***	0.040
	(5.81)	(0.23)
Control year	Yes	Yes
Control individual	Yes	Yes
Observations	10,386	10,042
R2	0.464	0.354

Note: *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.

reports the test results of scale heterogeneity. It can be seen that the correlation coefficient between Cs and Imc is 0.210 in large-scale listed companies, and it is significant at 1% level; the correlation coefficient between Cs and Imc is 0.012 in large-scale listed companies, and is significant at the 10% level. It shows that in large-scale listed companies, intelligent manufacturing concerns plays a stronger role in promoting corporate sustainability. This may be because the large-scale listed companies have strong financial strength and abundant human resources, which provide sufficient funds and human resources for corporate sustainability, so intelligent manufacturing concerns has a strong role in promoting corporate sustainability.

6. Discussion

In recent years, the development of intelligent manufacturing concerns has increasingly affected the production of enterprises. Through intelligent manufacturing concerns, enterprises can greatly improve their production efficiency and greatly save their production costs. Therefore, this study selects A-share listed companies in China’s manufacturing industry from 2010 to 2021 as the research sample to explore the relationship between intelligent manufacturing concerns and corporate sustainability, and analyzes the relevant research results as follows:

Firstly, this study tests the positive influence of intelligent manufacturing concerns on corporate sustainability. Intelligent manufacturing concerns are not simply replacing manpower with machines, but using intelligent equipment as a carrier to promote the reconstruction of the innovation ecosystem, so that enterprises can produce the ability to digest, absorb and reinvent, thus promoting the long-term corporate sustainability by improving innovation performance [49]. Therefore, enterprises’ concerns to intelligent manufacturing concerns can make enterprises fully realize the advantages of intelligent manufacturing concerns, although the current intelligent manufacturing concerns requires enterprises to conduct large-scale research and development and investment, but from the perspective of the long-term development of enterprises, intelligent manufacturing concerns has great appeal to manufacturing enterprises, especially in areas such as sustainable development, the reasons as follows. First, intelligent manufacturing concerns can not only enhance the information processing ability of enterprises, but also promote the knowledge management ability of enterprises, integrate external information knowledge, and facilitate enterprise innovation. On the one hand, the information technology of intelligent manufacturing concerns can enhance the absorptive capacity of enterprises, promote the communication of organizational members, and reduce the costs of enterprises in knowledge identification, absorption and utilization [50,51]. On the other hand, intelligent manufacturing concerns can also increase the amount and type of information available to enterprises, reduce the cost of absorbing external knowledge, promote the integration of external knowledge, and improve the innovation ability of enterprises [52]. Second, intelligent manufacturing concerns can further optimize the structure of human capital and fully realize the efficient cooperation between human and machine. The large-scale installation and use of intelligent manufacturing concerns systems in the production field objectively improves the organic composition of capital and amplifies the output contribution of capital factors, which also means that intelligent manufacturing concerns crowds out the amount of labor factor inputs. Intelligent manufacturing concerns shortens the working time of individual tasks, and some routine and repetitive jobs will be replaced. At the same time, intelligent manufacturing concerns forces workers to improve their labor skills and focus on human capital accumulation to a certain extent. Traditional manual workers and low-skilled workers will be replaced, which will invariably change the work content and efficiency of workers, thus optimizing the human capital structure, reducing labor redundancy, and fully realizing efficient human–machine collaboration. Thirdly, intelligent manufacturing concerns can optimize the supply chain relationship and reduce the transaction costs of enterprises. On the one hand, intelligent manufacturing concerns can eliminate the uncertainty of complex systems and alleviate information asymmetry through data advantages. On the other hand, intelligent manufacturing concerns can accurately capture user needs, and thus achieve efficient and rapid response to various resource configurations. In addition, intelligent manufacturing concerns can change the resource utilization mode of manufacturing industry, help to promote the division of labor position of China’s manufacturing industry in the global value chain, increase the foreign market share of products, optimize the internal industrial structure of manufacturing industry, and realize the transformation of manufacturing industry from scale advantage to technology and management advantage.

Secondly, this study examines how intelligent manufacturing concerns on promoting sustainable development of enterprises by influencing green innovation. Intelligent manufacturing concerns are an important way to deeply integrate information technology with traditional production technology. It can not only promote the interaction between enterprises and the external world, but also help enterprises integrate external information and knowledge, thereby enhancing innovation capabilities. Through the transformation of intelligent manufacturing concerns, enterprises can achieve wider resource sharing, which not only helps them quickly break technological barriers, but also achieves resource conservation, implements environmental practices, and further promotes green product innovation and green technology innovation. Therefore, intelligent manufacturing concerns, due to its unique advantages, has widely attracted the attention of enterprises. Enterprises can achieve long-term sustainable development through green technology innovation. Therefore, the focus of enterprises on intelligent manufacturing concerns can effectively improve their green innovation capabilities, achieve energy-saving and emission reduction effects, and thus achieve sustainable development of enterprises.

Thirdly, this study tests that business environment is positively moderating the impact of intelligent manufacturing concerns on the corporate sustainability. Business environment is the basic condition for enterprises to achieve sustainable development. While creating value for enterprises, a good business environment also requires a high degree of compliance with environmental regulations and joint efforts to maintain and create a better business environment, which also increases the operating costs of enterprises to a certain extent. The efficiency improvement brought by intelligent manufacturing concerns can offset the negative impact of the rising cost on the enterprise side revenue due to the business environment, further raising the spatial profitability boundary of enterprises in the core region. Based on the reality of unbalanced development in China’s regions, the level of business environment varies greatly. Compared with regions with poor business environment, enterprises in regions with better business environment face greater cost pressure and are more eager to improve operational efficiency through intelligent manufacturing concerns, thus alleviating the pressure of high housing prices and high costs in core areas. Therefore, the impact of enterprises’ attention to intelligent manufacturing concerns on the corporate sustainability is more significant in the regions with better business environment.

Finally, this study discusses the impact of heterogeneity. In terms of ownership heterogeneity, on the one hand, the innovation ability of private enterprises is generally much higher than that of state-owned enterprises, which leads to a greater role and advantage of intelligent manufacturing concerns in private enterprises, and a stronger promoting effect on enterprise sustainability. On the other hand, compared to state-owned enterprises, private enterprises face a series of problems such as shortage of funds, lack of technology, and poor survival ability. Intelligent manufacturing concerns are a good choice to solve these problems and achieve sustainable development of enterprises. Therefore, private enterprises’ attention to intelligent manufacturing concerns plays a more significant role in promoting sustainable development of enterprises. In terms of regional heterogeneity, the development level of intelligent manufacturing concerns in China is much higher in the eastern and coastal regions than in the central and western regions. Combining the advantages of external economy and business environment, the manufacturing industry in the eastern region is more reliant on intelligent manufacturing concerns, which leads to a more significant promotion of sustainable development by enterprises in the eastern and coastal regions. In terms of scale heterogeneity, endowment based on financial and human resources, the focus of large enterprises on intelligent manufacturing has a more significant impact on the sustainable development of enterprises.

In addition, the focus on intelligent manufacturing has increased companies’ understanding of the high-tech industry, enabling them to fully understand the benefits of intelligent manufacturing and increase their investment in high-tech fields, especially green technology innovation. The intervention of intelligent manufacturing requires a large amount of one-time investment from enterprises, which will inevitably affect their production costs and pricing (especially for small- and medium-sized enterprises). However, in the long run, the introduction of intelligent manufacturing can not only reduce the environmental penalties suffered by enterprises due to pollution, but also inevitably reduce the manufacturing costs of the entire industry. Therefore, the transformation of enterprises to intelligent manufacturing is also a trend. In addition, intelligent manufacturing has to some extent improved the production efficiency of enterprises, resulting in significant improvements in environmental protection. This also alleviates the pressure of the government and regulatory authorities on environmental protection in enterprise production.

7. Concluding Remarks

7.1. Conclusions

According to the panel data of A-share listed companies in China’s manufacturing industry from 2010 to 2021, this study examines the relationship between intelligent manufacturing concerns and corporate sustainability, deeply discusses its transmission mechanism, and further studies several heterogeneity characteristics of enterprises. The results are as follows:

Firstly, intelligent manufacturing concerns have promoted corporate sustainability; secondly, intelligent manufacturing concerns can promote corporate sustainability by influencing green innovation; thirdly, the impact of intelligent manufacturing concerns on corporate sustainability is positively moderated by the business environment; finally, based on the heterogeneity perspective, the promotion of intelligent manufacturing concerns on corporate sustainability is stronger among non-state listed companies; the promotion of intelligent manufacturing concerns on corporate sustainability is stronger in the eastern and coastal regions; the promotion of intelligent manufacturing concerns on corporate sustainability is stronger in large companies.

7.2. Recommendations

Firstly, strengthen the research on the core technology of intelligent manufacturing concerns. At present, China’s scientific and green innovation capability in the field of intelligent manufacturing concerns is still not strong, and core technologies and software systems such as chips, sensors, and industrial robots still rely on imports. The “technological weaknesses” seriously restrict the development of China’s intelligent manufacturing concerns. The government should further focus on the key technologies urgently needed in major projects and key fields, carry out “organized scientific research” in the face of the national major scientific and technological needs, gather forces to tackle key core technologies, and strive to achieve breakthroughs in “bottleneck” technology in key fields.

Secondly, accelerate the process of intelligent infrastructure construction. The industrial internet platform construction cycle is long, the degree of technical integration and coordination is high, and the investment cost is huge. It is difficult to transform the accumulated knowledge into patents and production processes by relying on enterprises themselves. At this time, the government should give full play to its coordination function and accelerate the construction of new infrastructures such as national integrated big data center to provide guarantee for the construction of industrial Internet.

Thirdly, promote the coordinated development of regional intelligent manufacturing concerns. At present, the development of intelligent manufacturing concerns regions in China is still unbalanced, and the distribution of intelligent manufacturing concerns pilot demonstration projects is mainly concentrated in the eastern regions such as the Yangtze River Delta and the Pearl River Delta. Explore the development path of regional intelligent manufacturing concerns with different characteristics according to local conditions, formulate differentiated digital transformation plans, encourage local innovation and improve the policy system, and guide the gathering of various resources. Cross-regional collaborative development, promote cross-regional cooperation in key technology innovation of intelligent manufacturing concerns, supply and demand docking, talent training, and encourage local, industrial organizations, and leading enterprises to jointly promote advanced technologies, equipment, standards and solutions.

In addition to macroeconomic policy adjustments, enterprises should further strengthen their own environmental management. Based on increasingly strict environmental regulations, enterprises should effectively achieve sustainable development. On the one hand, with the government’s environmental policy regulation, enterprises should increase their past rough development model, increase their own environmental investment, timely address their own pollution problems, and reduce negative impacts on the environment. On the other hand, enterprises should increase their investment in high-tech areas (such as intelligent manufacturing), achieve energy conservation and emission reduction through breakthroughs in green innovation technology, and achieve coordinated development of financial and environmental performance.

Limitations and future research: On the one hand, this study only explores the impact of intelligent manufacturing on enterprise sustainability, and does not involve too much at the macro level, such as the coordinated development of socio-economic and environmental factors. On the other hand, due to limited data, this study only covers samples of Chinese enterprises, rather than samples from developed countries (such as the United States and the United Kingdom) or poor and underdeveloped countries (such as Africa). It is worth discussing whether environmental appeals have an impact on eco-investing of enterprises in other countries.