Research into the Path and Mechanism by Which Intelligent Manufacturing Promotes Carbon Emission Reductions

Abstract

Utilizing a longitudinal dataset encompassing 30 Chinese provinces and municipalities (with the exception of Tibet, Hong Kong, Macao, and Taiwan) that spans the years 2011 through to 2021, this study adopts the spatial Durbin model to study the path and mechanism behind the promotion of carbon emission reductions through intelligent manufacturing. The results show the following: ① Intelligent manufacturing plays a crucial role in promoting the reduction of carbon emissions. ② Government interventions can amplify the positive influence of intelligent manufacturing in reducing carbon emissions, and intelligent manufacturing promotes carbon emission reductions by accelerating scientific and technological innovation. ③ There is temporal heterogeneity: upgrading intelligent manufacturing exerted a substantial influence in advancing the reduction of carbon emissions during the timeframe from 2011 to 2019, while it exerted a notable impeding impact on the reduction of carbon emissions during the timeframe from 2011 to 2019. ④ There is spatial heterogeneity: in the eastern region, upgrading intelligent manufacturing promoted carbon emission reductions in 2011–2015, but it inhibited carbon emission reductions in 2016–2021. Consequently, here are the insights we have distilled: ① Enhancing the overall advancement level of intelligent manufacturing can effectively promote carbon emission reductions in China; ② It can also play an important role in guiding governments in making these upgrades and actively promoting them in conjunction with technological innovations.

1. Introduction

1.1. Background and Significance of Topic

As the severity of global climate change has become increasingly evident, reducing greenhouse gas emissions has become a global goal. The report of the twentieth Party Congress emphasized that nature is a basic condition for human survival and development and that development should be planned from the perspective of harmonious coexistence between human beings and nature. As a large manufacturing country, China is entering a new period of high-quality development, and the swift growth of the manufacturing sector is coupled with the substantial consumption of fossil fuels, which also leads to high carbon dioxide emissions. China’s manufacturing industry contributes more than fifty percent of the nation’s carbon emissions, so the requirements for the “double carbon” goal put forward for the smart upgrading of the manufacturing sector industry have also increased. Intelligent manufacturing involves the industrial internet, big data, artificial intelligence, and other technologies, which provide new ideas and new methods for energy saving and emission reductions in the manufacturing industry. They also promote the optimization and upgrade of the industrial structure by directing the traditional manufacturing industry toward high-end, intelligent, and green development, a vital impetus for enhancing the caliber of economic development. Under the constraints of the “double carbon” target, China’s energy saving and emission reduction efforts are predominantly found within the manufacturing sector, and the key to emission reductions in the manufacturing industry lies in intelligent upgrading and transformation. Regional differences in China’s intelligent manufacturing level are affected by a variety of factors, including the industrial agglomeration, development level, infrastructure, policy support, enterprise distribution, integrated development level, and dynamic evolution trend. Therefore, exploring the path and mechanism behind the promotion of carbon emission reductions through intelligent manufacturing in China is an important task. The exploration of this issue can not only offer valuable insights for the development of strategies aimed at the intelligent enhancement of manufacturing, but it also has important reference significance for China in terms of formulating scientific manufacturing emission reduction strategies, setting comprehensive energy saving and emission reduction targets, and building an ecological and civilized society.

1.2. Intelligent Manufacturing and Carbon Emissions

Wright, P.K. introduced the notion of intelligent manufacturing, which was initially defined as large-scale robotics and synthetic technology production [1]. At present, scholars mainly focus on the following aspects of the definition: From the perspective of the role of function, intelligent manufacturing is committed to the use of computer technology to replace or expand humans’ mental and physical labor [2]. From the perspective of system integration, intelligent manufacturing is centered around the latest generation of information technology as its core and comprehensively applies advanced manufacturing processes, systems, and modes in order to achieve energy savings and emission reductions across the whole process as well as the optimization of product performance [3]. From the perspective of economic benefits, intelligent manufacturing provides full coverage of the entire manufacturing process, allowing systematic intelligence to improve the ability of manufacturing enterprises to create value [4]. At present, the assessment of intelligent manufacturing levels primarily relies on a set of multifaceted indicators. Zhao, Y.H. chose the investment in telecommunications infrastructure and the aggregate count of software developers as metrics to gauge the intelligent underpinning, the total number of software enterprises and the software business revenue to measure software applications, and the earnings of the electronics and telecommunications equipment sector and its total production value for the current year of the electronics and communication equipment manufacturing industry to measure the market practice [5]. Sun, N. chose the total trade of industrial robots, the total assets of the electronic equipment manufacturing industry, the fixed asset investments, the count of research and development staff, along with the volume of patent applications of the electronic and information industry to measure intra-industry inputs. Moreover, they selected the aggregate assets of the software and information technology services sector, as well as the headcount of research and development staff., the total revenue, and the number of smart manufacturing policies to measure the inputs of support from related parties [6]. Wang, Y.M. and Zhang, X.W. selected the number of manufacturing enterprises, the total output value, the total assets, the operating income, and the total profit to measure the economic production capacity. Then, they selected the tally of firms conducting R&D initiatives, along with the personnel count, the internal expenditure, the sum of patent requests, and the amount of inventions to measure the technological innovation capacity. Finally, they selected the total consumption of energy, electricity, and water resources and the volume of wastewater and the quantity of solid waste discharged from the manufacturing industry to measure the resource utilization rate [7].

Amidst the growing challenges of industrial resource depletion and escalating environmental degradation, the integration of intelligent technology into economic development is a necessary path for the advancement of sustainable and eco-friendly growth of enterprises [8]. The application of intelligent technologies in the manufacturing sector leads to a different degree of energy efficiency [9] and exerts a wide-ranging influence on China’s carbon dioxide emissions. There is a significant spatial relationship between intelligent industry and carbon emission reductions [10], which varies in cities with different sizes and scales, different degrees of infrastructure completeness, and high and low levels of technology [11]. The enhancement of manufacturing intelligence exerts a substantial mitigating influence on the emissions per unit of economic activity. and its effect is particularly prominent in high-tech industries and capital-intensive industries, as well as being more significant in developing countries [12]. Taking into account the geographic differences, the efficacy of manufacturing intelligence in curbing carbon emissions within the industrial sectors of central and western China is considerably more pronounced compared with the eastern regions [13]. Taking into account the variation among industries, the influence of manufacturing intelligence on carbon emissions is far more substantial in industries with high carbon intensity than in those with a lower carbon profile [14]. Some scholars further note that in reducing the implied carbon intensity of the export trade, the inhibitory effect of smart manufacturing transformation in developing countries is more prominent than in developed countries, and it is stronger than the inhibitory effect on the export of most final goods [15].

The existing literature has analyzed and explored various aspects of the problems related to smart manufacturing and carbon emissions, establishing a robust theoretical basis for the current research. However, few scholars have considered the spatial correlation and explored the path and mechanism by which smart manufacturing promotes carbon emission reductions. For this reason, this study seeks to build a spatial panel econometric model based on the outcomes of prior studies to empirically test the path and mechanism behind the promotion of carbon emission reductions through smart manufacturing in China.

1.3. Research Hypothesis

The smart manufacturing industry utilizes robotic production to improve the production efficiency, promote green employment, upgrade the energy structure, and improve the environmental quality [16]. In addition, smart manufacturing transformation achieves an emission reduction effect by improving emission reduction technology, increasing the capital investment in emission reduction equipment, and reducing the unit energy consumption emissions, thus promoting carbon emission reductions [17]. As a result, Hypothesis 1 is proposed.

H1. 

An inverse relationship exists between the degree of smart manufacturing and carbon emissions.

Innovation serves as the foremost engine of advancement, and technological innovation is a key element for industrial transformation and upgrading and an important way to realize the “double carbon” goal. Intelligent manufacturing can catalyze enterprises’ technological innovation and promote enterprises’ high-quality development [18]. It can rely on enterprises’ technology integration abilities and promote enterprises’ green technology innovation [19]. At the same time, an improvement in an enterprise’s technology innovation level can reduce the carbon dioxide generation pathway, decelerate the pace of carbon dioxide emissions [20], and inhibit carbon emissions at the root. As a result, Hypothesis 2 is proposed.

H2. 

Technological innovation mediates the influence of the transition to smart manufacturing on carbon emissions.

The core of government intervention lies in its intervention in industry. Government intervention exploits the latecomer advantage, promotes industrial structure upgrading [21], and fosters the smart upgrading of the manufacturing sector. However, as the intelligent transformation of manufacturing progressively intensifies, a series of environmental problems have emerged. Thus, it is necessary for the government to comprehensively consider various factors and to intervene appropriately in the midst of the intelligent manufacturing industry’s evolution so as to enhance the effect of regional emission reductions by adjusting the industrial structure, ensuring that it is more advanced and rational [22]. As a result, Hypothesis 3 is proposed.

H3. 

Government intervention has a moderating role in the transmission of the influence of smart manufacturing transformation on levels of carbon discharge.

2. Variable Selection

2.1. Variable Selection

Explanatory variable: Levels of carbon discharge (CEs). The method of estimating the total regional carbon emissions is shown in Equation (1). Since China has not explicitly published data on carbon emissions, this study refers to the IPCC measurement method for estimation [23]. The specific formula is as follows:

$$C={\displaystyle \sum (E_i\times NC{V}_i\times C{C}_i\times CO{F}_i\times \frac{44}{12})}$$

(1)

Here, $i$ is the category of energy, $C$ is the carbon dioxide emission, $E_i$ is the energy consumption, $NC{V}_i$ is the average low-grade thermal energy emanation from energy source i, $C{C}_i$ is the carbon proportion in energy i, and $CO{F}_i$ is the rate of carbon oxidation for energy source i. The molecular weight ratio of 44 to 12 corresponds to that of carbon dioxide (CO₂) and carbon (C).

Core explanatory variable: Intelligent manufacturing level (IM). This study synthesizes existing research results and, based on the connotations and mechanism of intelligent manufacturing, divides intelligent manufacturing into three levels, namely basic inputs, software support, and market practices. The specific index system is presented in

Table 1. Assessment metrics for China’s intelligent manufacturing.

Level 1 Indicator	Level 2 Indicator	Description of Indicator (Unit)
Basic inputs $X_1$	Intelligent equipment fixed assets $X_{11}$	Investment in fixed assets in towns and cities for the manufacture of communication electronic equipment (units/tens of thousands of people)
	Intelligent equipment import inputs $X_{12}$	Imports in the electronics industry as a proportion of industrial main business income (CNY million)
	Staffing inputs $X_{13}$	Average number of workers in the electronics and communications manufacturing industry (persons)
	Intelligent infrastructure $X_{14}$	Internet broadband access port count (in units of 10,000)
	Investment in scientific research $X_{15}$	R&D personnel in electronics and communications manufacturing (persons)
Software support $X_2$	Popularization of software applications $X_{21}$	Revenue from basic software products as a share of industrial main business income (%)
	Revenue from software operations $X_{22}$	Revenue from software operations (million US dollars)
	Investment in scientific research $X_{23}$	Software developers (persons)
	Software business exports $X_{24}$	Export revenue from software business (million US dollars)
Market practices $X_3$	Economic efficiency $X_{31}$	Profit of the electronic and communication equipment manufacturing industry (CNY billion)
	Social efficiency $X_{32}$	Total assets of communication, computer, and other electronic equipment in the manufacturing industry (CNY billion/person)
	Innovation capacity $X_{33}$	Number of patent applications in the electronics and communications manufacturing industry (pieces)

.

Control variables:

(a) Urbanization level (UL). The process of urban development is accompanied by the continuous accumulation of human capital, which, in turn, promotes green technology innovation and energy efficiency. However, it also raises residents’ incomes and stimulates their consumption, thus achieving high economic development while requiring substantial energy utilization. Therefore, the mechanism of urbanization with regard to carbon emissions is not yet clear. In this study, the urbanization rate is applied in order to measure the level of urbanization. The formula for the calculation of the urbanization rate is as follows:

$$UL={\displaystyle \frac{P}{TP}}\times 100\%$$

(2)

where P is the residential population of the province (municipality), and TP is the total residential population of the country.

(b) Urban greening (CG). Urban greening can directly increase carbon sinks through photosynthesis or indirectly affect carbon emissions by improving the urban microclimate. Forests can absorb greenhouse gases. Therefore, this study uses forest cover to characterize the level of urban greening. The formula for the calculation of forest cover is as follows:

$$CG={\displaystyle \frac{FA}{TFA}}\times 100\%$$

(3)

where FA is the area of land covered by trees in each province (city), and TFA is the total geographical area of the country.

(c) Energy intensity (EI). The energy intensity, or energy consumption intensity, is the amount of energy required per unit of GDP. A lower energy intensity is usually associated with higher energy efficiency and more advanced technology. Therefore, effectively reducing the energy intensity is an important path to reducing carbon emissions. The formula for the calculation of the energy intensity is as follows:

$$EI={\displaystyle \frac{TEI}{GDP}}$$

(4)

where TEI is the total energy consumption of each province (city), and GDP is the gross domestic product.

(d) Productivity level (PR). The level of productivity directly determines the amount of energy consumption and carbon emissions in the production process. Abundant labor resources are the basis for the development of productivity; therefore, this study uses the number of people employed at the end of the year to indicate the level of productivity.

Moderating variable: Government intervention (GI). In the early stage of intelligent transformation, most enterprises pursue their own development interests and ignore the harm to the environment caused by the large amount of carbon dioxide produced in the process of production transformation. Therefore, effective government intervention will regulate the contradiction between intelligent manufacturing transformation and carbon emissions. This study adopts the proportion of fiscal expenditure to fiscal revenue to measure government intervention; the specific formula is as follows:

$$GI={\displaystyle \frac{FI}{RI}}$$

(5)

where FI is the respective fiscal expenditure, and RI is the fiscal revenue.

Mediating variable: Technological innovation (TI). Technological innovation is vital in the process of smart manufacturing for carbon emission reductions. Technological innovation can improve the production efficiency, promote the use of green energy, optimize the production processes, etc., significantly mitigating carbon emissions and contributing to the ambition of achieving a carbon-neutral status. In this study, we rely on R&D investment intensity to evaluate the extent of technological innovation, with the specific formula for it provided subsequently:

$$TI={\displaystyle \frac{RD}{GDP}}$$

(6)

where RD is the R&D expenditure.

2.2. Data Sources

The research utilizes a panel dataset spanning from 2011 to 2021 that encompasses 30 provincial-level municipalities in China, excluding Tibet and Taiwan as well as the Special Administrative Regions of Hong Kong and Macao. The data used to measure the level of intelligent manufacturing come from the China Statistical Yearbook, China Electronic Information Industry Statistical Yearbook, China High-Tech Statistical Yearbook, and China Fixed Asset Investment Statistical Yearbook. The data for the measurement of carbon emissions come from the China Energy Statistical Yearbook. The data for the measurement of scientific and technological innovation come from the China Science and Technology Statistical Yearbook.

2.3. Descriptive Analysis of Sample

The definitions and descriptive statistics for the variables in this study are displayed in

Table 2. Descriptive statistics of the sample.

Variable	Designation	Sample	Unit	Average	Minimum	Maximum
CE	Carbon emission	330	10 million tons	31.153	3.495	89.278
IM	Intelligent manufacturing level	330	—	0.087	0.002	0.830
UL	Urbanization level	330	%	58.981	34.975	89.616
CG	Urban greenery	330	%	33.385	4.0201	66.802
EI	Energy intensity	330	Tons/million	1.224	0.376	4.453
PR	Productivity level	330	millions	2.619	0.097	7.072
GI	Government intervention	330	%	2.355	1.074	6.603
TI	Technological innovation	330	%	1.758	0.410	6.530

. The maximum value of the intelligent manufacturing level is 0.830, and the minimum value is 0.002. There is a significant disparity between the maximum and minimum values, indicating that a substantial range of differences is noted in the level of intelligent manufacturing in each province (city). In addition, a considerable gap is observed between the top and bottom values of the urbanization level, urban greening, energy intensity, productivity level, government intervention, and technological innovation, indicating that there is a large difference between these indicators in each province (city) in China. Therefore, the relationship between carbon emissions and the level of smart manufacturing, as well as the control variables, needs further empirical analysis.

3. Empirical Modeling

3.1. Spatial Correlation Analysis Model

Global spatial autocorrelation was employed to evaluate the potential self-dependence of certain variables in a study area and to analyze their spatial distribution characteristics. The premise of the establishment of a spatial measurement model is that there is a certain spatial correlation between the variables, so the correlation test between carbon emissions and the level of intelligent manufacturing was carried out first. In this study, it was measured using $Moran’s I$, whose specific formula is as follows:

$$I={\displaystyle \frac{n{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{w}_{ij}(x_i-\overline{x})(x_j-\overline{x})}}}{{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{w}_{ij}{\displaystyle \sum _{i=1}^n{(x_i-\overline{x})}^2}}}}}$$

(7)

where n is the number of samples in all 30 provinces (cities), $w_{ij}$ is the spatial weight, and $x_i$ and $x_j$ denote the attribute values of region $i$ and region j.

The value of Moran’s statistic belongs to $[-1,1]$. A positive result indicates a positive spatial interrelation, a negative result indicates a negative spatial interrelation, and a result of zero indicates no spatial interrelation autocorrelation and a random spatial distribution; the larger the absolute value is, the higher the degree of agglomeration among regions with similar attribute values.

3.2. Spatial Weighting Matrix Setting

A spatial weight matrix can be used to visualize complex geographic environments based on spatial correlation. In this study, we mainly used a 0–1 adjacency matrix with a spatial economic distance matrix.

(i) 0–1 adjacency matrix:

$$w_{ij}=\left\{\begin{array}{c}1\\ 0\end{array}\right. \begin{array}{c}where areas i and j are adjacent\\ other\end{array}$$

(8)

In this study, we define adjacency as having common edges or vertices.

(ii) Spatial economic distance matrix:

$$w_{ij}=\left\{\begin{array}{cc}{\displaystyle \frac{1}{\left|\overline{m_i}-\overline{m_j}\right|}}& i\ne j\\ 0& i=j\end{array}\right.$$

(9)

where $\overline{m_i}$ and $\overline{m_j}$ are the average real GDP per capita of $i$ and $j$ provinces (cities) in 2011–2021, respectively.

3.3. Spatial Panel Modeling Setup

(i) Baseline spatial panel model

A notable spatial correlation is present in carbon emissions, and their impacts on the region and neighboring regions are different [24]. In order to comprehensively study the spatial correlation between the level of intelligent manufacturing and carbon emissions, this study established an econometric model of the impact of the intelligent transformation of the manufacturing industry on carbon emissions from a spatial perspective.

$$C{E}_{it}={\alpha }_0+\rho {\displaystyle \sum _{j=1}^n{W}_{ij}C{E}_{it}}+\beta X_{it}+\theta {\displaystyle \sum _{j=1}^n{W}_{ij}{X}_{it}}+{\mu }_i+{\sigma }_t+{\epsilon }_{it}$$

(10)

$${\epsilon }_{it}=\delta {\displaystyle \sum _{j=1}^n{W}_{ij}{\epsilon }_{it}}+{\vartheta }_{it}$$

(11)

where $W_{ij}$ is the spatial weight matrix; ${\alpha }_0$ is a constant term; $\rho$ and $\delta$ are the spatial autoregressive and autocorrelation coefficients, respectively; $X_{it}$ is the explanatory variable for province (city) $i$ in year $t$, which mainly consists of the primary term of the core explanatory variable of smart manufacturing as well as other control variables, namely the level of urbanization, urban greening, the energy intensity, and the level of productivity; ${\epsilon }_{it}$ is the autocorrelation term of the spatial error; and ${\vartheta }_{it}$ is the random disturbance term.

If $\rho$ is not zero, and $\theta$ and $\delta$ are zero, the model is the $SAR$; if $\rho$ and $\theta$ are zero, and $\delta$ is not zero, the model is the $SEM$; and if $\rho$ and $\theta$ are not zero, and $\delta$ is zero, the model is the $SDM$. After all of the tests, the $SDM$ was finally chosen, and this part will be explained in detail in the empirical section.

(ii) Mediated effects model

Progress in technology is the principal engine propelling the field of intelligent manufacturing, and green technological innovation can promote the greening and upgrading of industries and further reduce carbon emissions [25]. Based on this, this study takes technological innovation as a mediating variable and draws on Baron and Kenny’s stepwise regression mediation test to examine the transmission mechanism of the intelligent transformation of manufacturing that affects carbon emissions [26].

$$C{E}_{it}={\alpha }_1+{\gamma }_{1}M{I}_{it}+{\rho }_1{\displaystyle \sum _{j=1}^n{W}_{ij}C{E}_{it}}+{\beta }_1{X}_{it}+{\theta }_1{\displaystyle \sum _{j=1}^n{W}_{ij}{X}_{it}}+{\mu }_{1i}+{\sigma }_{1t}+{\epsilon }_{1it}$$

(12)

$$T{I}_{it}={\alpha }_2+{\gamma }_{2}M{I}_{it}+{\rho }_2{\displaystyle \sum _{j=1}^n{W}_{ij}T{I}_{it}}+{\beta }_2{X}_{it}+{\theta }_2{\displaystyle \sum _{j=1}^n{W}_{ij}{X}_{it}}+{\mu }_{2i}+{\sigma }_{2t}+{\epsilon }_{2it}$$

(13)

$$C{E}_{it}={\alpha }_3+{\gamma }_{3}M{I}_{it}+{\rho }_3{\displaystyle \sum _{j=1}^n{W}_{ij}C{E}_{it}}+{\lambda }_{3}T{I}_{it}+{\beta }_3{X}_{it}+{\theta }_3{\displaystyle \sum _{j=1}^n{W}_{ij}(X_{it}+T{I}_{it})}+{\mu }_{3i}+{\sigma }_{3t}+{\epsilon }_{3it}$$

(14)

where $T{I}_{it}$ is the mediating variable.

In the first step, spatial econometric estimation is carried out on the impact of the smart manufacturing level on carbon emissions; see Equation (12). In the second step, spatial econometric regression is carried out with technological innovation as the explanatory variable and the smart manufacturing level as the core explanatory variable; see Equation (13). In the third step, a spatial econometric test is carried out with carbon emissions as the explanatory variable, the smart manufacturing level as the core explanatory variable, and technological innovation as the mediator variable; see Formula (14).

If ${\gamma }_1$,${\gamma }_2$,${\gamma }_3$, and ${\lambda }_3$ are significant, and the coefficient is $\left|{\alpha }_3\right|<\left|{\alpha }_1\right|$, it indicates that the intermediary variable exerts a partial mediating influence with regard to smart manufacturing transformation affecting carbon emissions. If ${\gamma }_3$ is not significant, and ${\gamma }_1$,${\gamma }_2$, and ${\lambda }_3$ are significant, it indicates that the mediating variable has a full mediating effect with regard to smart manufacturing transformation affecting carbon emissions.

(iii) Moderated effects model

In order to further study the mechanism of smart manufacturing for carbon emission reductions, this study takes government intervention as a moderating variable to investigate whether government intervention can modulate the intensity of the effect of smart manufacturing transformation on carbon emissions.

$$C{E}_{it}={\alpha }_0+{\alpha }_{1}M{I}_{it}+{\alpha }_{2}G{I}_{it}+{\alpha }_{3}Interac{t}_{it}+\beta X_{it}+\rho {\displaystyle \sum _{j=1}^n{W}_{ij}C{E}_{it}}+\theta {\displaystyle \sum _{j=1}^n{W}_{ij}{X}_{it}}+{\mu }_i+{\sigma }_t+{\epsilon }_{it}$$

(15)

where $M{I}_{it}$ is the core explanatory variable level of smart manufacturing and $G{I}_{it}$ is the moderator variable of government intervention; $Interac{t}_{it}$ is the interaction term, and $Interac{t}_{it}=c\_M{I}_{it}\times c\_G{I}_{it}$ ($c\_M{I}_{it}$ and $c\_G{I}_{it}$ denote the centering on $M{I}_{it}$ and $G{I}_{it}$).

If both ${\alpha }_1$ and ${\alpha }_3$ are significant and have a common sign, the moderating variable enhances the impact of smart manufacturing transformation on carbon emissions. If both ${\alpha }_1$ and ${\alpha }_3$ are significant but have opposite signs, the moderating variable weakens the effect of transitioning to smart manufacturing on CO₂ emissions.

4. Model Testing and Analysis of Results

4.1. Spatial Correlation Test

To delve into the spatial connections within carbon emissions and the smart manufacturing level, this study calculated the global Moran’s index of carbon emissions and the smart manufacturing level in 30 provinces (cities) from 2011 to 2021, and the results are shown in

Table 3. Global Moran’s index of CO₂ emissions and the smart manufacturing level.

Year	CE		IM
	$\mathit{M}\mathit{o}\mathit{r}\mathit{a}\mathit{n}’\mathit{s} \mathit{I}$	p-Value	$\mathit{M}\mathit{o}\mathit{r}\mathit{a}\mathit{n}’\mathit{s} \mathit{I}$	p-Value
2011	0.1164	0.024	0.0368	0.081
2012	0.0982	0.030	0.0433	0.065
2013	0.0902	0.038	0.0466	0.059
2014	0.0823	0.045	0.0446	0.060
2015	0.0723	0.053	0.0611	0.040
2016	0.0626	0.075	0.0531	0.037
2017	0.0507	0.080	0.0403	0.047
2018	0.0551	0.089	0.0447	0.044
2019	0.0502	0.088	0.0532	0.038
2020	0.0416	0.080	0.0539	0.042
2021	0.0236	0.086	0.0534	0.041

. The Moran indices were all significant at the 10% confidence level, suggesting the presence of a general positive spatial correlation concerning carbon emissions and the level of smart manufacturing, which exhibits significant spatial dependence; therefore, this study took the spatial factors into account and used the spatial econometric model to execute the upcoming empirical research.

4.2. Spatial Econometric Modeling Tests

In this study, the LM test, Hausman test, Wald test, and LR test were conducted in turn, and the test results are shown in

Table 4. Results of the LM, Hausman, Wald, and LR tests.

Test Method	Statistic	p-Value
LM—spatial error	160.965	0.000
LM—spatial error (robust)	159.136	0.000
LM—spatial lag	6.443	0.011
LM—spatial lag (robust)	4.613	0.032
Hausman test	11.560	0.041
Wald—spatial lag	47.780	0.000
Wald—spatial error	47.640	0.000
LR—spatial lag	68.680	0.000
LR—spatial error	63.700	0.000

. The LM test was significant, which indicated that spatial error and the spatial lag effect coexisted, so it was initially determined that the spatial Durbin model with general significance should be selected. The spatial Durbin fixed-effects model was further selected through the Hausman test. Finally, the Wald and LR tests were applied to hypothesize the degeneration of the spatial Durbin model into the spatial error model, and the spatial lag model was rejected. Individual and time-fixed effects were considered simultaneously, and two-way fixed effects were selected. Therefore, the spatial Durbin fixed-effects model was selected in this study.

4.3. Analysis of Empirical Results of the Benchmark Model

Table 5. Model test results.

Variable	Hybrid OLS	SDM
IM	−40,396.817 ***	−18,244.542 ***
	(10,282.619)	(4078.715)
UL	597.175 ***	425.622 ***
	(102.180)	(85.328)
CG	−181.862 ***	−197.164 *
	(54.690)	(107.820)
EI	8928.837 ***	15,222.401 ***
	(1515.935)	(1264.532)
PR	11.215 ***	3.917 ***
	(0.720)	(0.694)
W × IM		59,666.469 ***
		(7737.912)
W × PR		−4.469 ***
		(1.704)
Province effect		YES
Time effect		YES
Rho	−34,784.065 ***	−0.103
	(9091.494)	(0.063)
N	330	330
R-squared	0.5375	0.3550
Log-likelihood		−3002.9006

Note: * and *** represent significance tests at the 10% and 1% level, respectively; standard errors are given in parentheses.

displays the findings from the model test. The two-way fixed-effects smart manufacturing level coefficient demonstrated a markedly negative impact at a 1% significance level, corroborating the results from the spatial Durbin model estimations, indicating that smart manufacturing and emission levels of carbon display a significant negative correlation. In particular, the higher the level of manufacturing intelligence, the lower the emission levels of carbon. The main reason is that intelligent manufacturing technology can not only optimize the manufacturing production process but also enhance innovation capabilities to steer industries toward green development and the achievement of the reduction of carbon emissions.

For the control variables, the urbanization level, energy intensity, and productivity level had a significant positive effect on carbon emissions. In the process of urbanization, the aggregation of the population and economic activities will increase energy consumption and thus increase carbon emissions [27]. The higher the energy intensity of the region, the greater the energy consumption per unit of GDP, and the more carbon emissions will increase [28]. Urban greening and carbon emissions were negatively correlated at the 5% significance level; i.e., the higher the level of urban greening, the higher the efficiency of carbon dioxide absorption, which can suppress carbon dioxide emissions more effectively.

4.4. Mechanical Testing

(i) Intermediary effect

Technology innovation (IT) was introduced as a mediating variable to explore whether the smart upgrading of the manufacturing sector could indirectly affect carbon emissions through technology innovation (shown in

Table 6. Results of intermediary effects.

Variable	Model (1)	Model (2)	Model (3)
	CE	TI	CE
IM	−18,244.542 ***	2.249 ***	−12,750.950 ***
	(4078.715)	(0.309)	(4242.909)
UL	425.622 ***	0.020 ***	488.835 ***
	(85.328)	(0.007)	(85.163)
CG	−197.164 *	0.017 **	−161.168
	(107.820)	(0.008)	(105.998)
EI	15,222.401 ***	0.235 **	15,940.086 ***
	(1264.532)	(0.100)	(1252.181)
PR	3.917 ***	−0.000	3.735 ***
	(0.694)	(0.000)	(0.681)
TI			−2651.235 ***
			(690.298)
W × IM	59,666.469 ***	−1.708 ***	53,953.508 ***
	(7737.912)	(0.623)	(7722.046)
W × PR	−4.469 ***	−0.000	−5.027 ***
	(1.704)	(0.000)	(1.674)
Province effect	YES	YES	YES
Time effect	YES	YES	YES
Rho	−0.103	−0.086	−0.072
	(0.063)	(0.073)	(0.063)
N	330	330	330
R-squared	0.3550	0.4278	0.3144
Log-likelihood	−3002.9006	116.2295	−2995.6414

Note: *, **, and *** represent significance tests at the 10%, 5%, and 1% levels, respectively; standard errors are given in parentheses.

). In model (1), the total impact coefficient of the level of intelligent manufacturing on the total carbon emissions was negative and passed the 1% significance test. In model (2), the impact coefficient of the degree of intelligent manufacturing on technological innovation was positive. In model (3), the impact coefficient of the degree of intelligent manufacturing on the carbon emissions was negative and passed the 1% significance assumption, and the impact coefficient was reduced in absolute value compared with that in model (1). This indicates that the mediating effect of technological innovation exists and that it is a partial mediating effect, and intelligent manufacturing can enhance carbon emissions by promoting technological innovation. Through its existence and partial mediating effect, smart manufacturing can enhance the inhibition of carbon emissions by promoting technological innovation. The main reason is that smart manufacturing transformation, through the introduction of smart technology, changes the original manufacturing system, promoting in-depth technological innovation in all aspects of the industry and contributing to the development of a beneficial atmosphere. Thus, it promotes R&D, green innovation practices, and efficient technology in line with the national requirements and the needs of the community. This further enhances industrial production efficiency and energy utilization, resulting in a reduction in carbon emissions. It is evident that technological innovation is crucial for the process of conserving energy and reducing emissions, and it is necessary to effectively strengthen the green technology innovation capacity of enterprises [29].

(ii) Moderating effect

In this study, government intervention (GI) was used as a moderating variable to study the role and path of government intervention in terms of the impact of smart manufacturing transformation on carbon emissions. The interaction term between smart manufacturing transformation and carbon emissions was introduced into the spatial Durbin model (shown in

Table 7. Results of moderating effects.

Variable	Regression Results
IM	−54,032.181 ***
	(7599.355)
GI	−2546.790 ***
	(821.541)
Interact	58,412.464 ***
	(7425.177)
UL	574.310 ***
	(86.744)
CG	−204.305 **
	(103.485)
EI	15,372.953 ***
	(1225.720)
PR	3.393 ***
	(0.671)
W × IM	−3.449 **
	(1.672)
W × PR	−40,903.755 ***
	(7461.820)
Province effect	YES
Time effect	YES
rho	−0.078
	(0.062)
N	330
R-squared	0.4148
Log-likelihood	−2988.3796

Note: ** and *** represent significance tests at the 5% and 1% levels.

). The interaction term’s coefficient was notably negative at the 1% significance level, which means that government intervention enhanced the repercussions of smart manufacturing advancements on CO₂ outputs. The government can encourage and advance the growth of intelligent manufacturing by devising and executing a range of policies aimed at reducing carbon emissions; it can also stipulate the environmental standards that enterprises must comply with through regulations to force them to reduce their carbon emissions. Moreover, it can raise the public’s awareness of smart manufacturing and environmental protection through cultivation programs to further promote the development of smart manufacturing.

4.5. Heterogeneity Analysis

(i) Analysis of temporal heterogeneity

In this study, taking into account the time of the promulgation of the Intelligent Manufacturing Development Plan (2016–2020), the sample interval was divided into three stages, namely 2011–2016, 2017–2019, and 2020–2021.

Table 8. Heterogeneity analysis by time point.

Variable	2011–2016	2017–2019	2020–2021
IM	−27,779.705 ***	−28,924.355 **	29,684.736 ***
	(5978.821)	(12,372.989)	(9914.710)
UL	440.918 ***	−365.855	−250.917
	(112.576)	(386.334)	(546.807)
CG	−259.117 **	25.553	−11,260.292 *
	(115.664)	(138.715)	(6506.012)
EI	12,350.680 ***	24,359.267 ***	6445.511 **
	(1339.240)	(2970.673)	(2557.839)
PR	2.044 **	4.180	36.685 ***
	(0.983)	(4.163)	(10.325)
W × IM	52,240.808 ***	107,751.445 ***	40,093.032 **
	(11,146.701)	(26,455.721)	(18,289.626)
W × PR	−5.847 ***	−12.787	10.807
	(2.195)	(8.602)	(20.246)
Province effect	YES	YES	YES
Time effect	YES	YES	YES
rho	−0.140	−0.046	0.048
	(0.101)	(0.096)	(0.183)
N	180	90	60
R-squared	0.3452	0.4444	0.4552
Log-likelihood	−1529.8810	−755.7205	−473.6559

Note: *, **, and *** represent significance tests at the 10%, 5%, and 1% levels, respectively; standard errors are given in parentheses.

displays the outcomes of the heterogeneity analysis test by time point. The coefficients of the core explanatory variable and smart manufacturing transformation were all significant, with a negative correlation at the 1% significance level in 2011–2016 and a negative correlation at the 5% significance level in 2017–2019. This indicates that smart manufacturing transformation had a suppressive effect on carbon emissions in 2011–2019. In the incipient stage of smart manufacturing transformation, the fusion of the traditional manufacturing industry and smart technology optimized some of the high-energy, low-efficiency production processes, which led to a sharp reduction in carbon dioxide emissions, indicating that smart manufacturing transformation in the early stage was more effective in suppressing carbon emissions. During the epidemic prevention and control period, many factories shut down and suspended production, while production and life resumed rapidly as the epidemic eased. Moreover, the stimulus policies launched by the government to boost the economy encouraged enterprises to increase their production, which led to a sharp increase in carbon dioxide emissions. From the perspective of the spatial lag term, the coefficients of the lag term for the three stages of smart manufacturing transformation on carbon emissions are significantly positive, indicating that the smart manufacturing transformation in neighboring regions had a facilitating effect on local carbon emissions. This illustrates the existence of the transfer of highly polluting enterprises caused by the upgrading of smart manufacturing in the manufacturing industry, further increasing the effect of earlier stages on CO₂ emissions during 2016–2019 and 2020–2021. The reasons for this effect should be considered.

(ii) Analysis of spatial heterogeneity

China is a vast country, and there are considerable disparities in the economic development level, the industrial structure, and the geographic environment of each region. In this study, China was divided into three regions.

Table 9. Heterogeneity analysis by region.

Variable	Eastern Region	Central Region	Western Region
IM	7698.916	−64,783.024 **	−68,432.924 ***
	(4784.663)	(25,446.517)	(16,090.736)
UL	720.963 ***	−494.132 *	12.352
	(95.031)	(290.326)	(246.343)
CG	−163.425	−1676.370 ***	36.441
	(104.387)	(544.801)	(141.455)
EI	13,345.405 ***	26,038.359 ***	14,160.557 ***
	(2716.112)	(3186.138)	(1697.012)
PR	−3.819 ***	1.155	7.682 ***
	(1.394)	(1.142)	(1.666)
W × IM	−30,459.057	68,274.565	60,007.379
	(21,107.463)	(69,169.727)	(52,418.906)
W × PR	−7.316	−7.570 ***	−12.018 *
	(6.164)	(2.874)	(7.280)
Province effect	YES	YES	YES
Time effect	YES	YES	YES
Rho	−0.312	−0.312 *	−0.485 ***
	(0.215)	(0.163)	(0.187)
N	121	99	110
R-squared	0.2044	0.0211	0.1744
Log-likelihood	−1032.3224	−885.6298	−970.6475

Note: *, **, and *** represent significance tests at the 10%, 5%, and 1% levels, respectively; standard errors are given in parentheses.

shows the results of the heterogeneity analysis test by region. The coefficient of smart manufacturing transformation was insignificant in the eastern region of China, and the coefficients of smart manufacturing in the central and western regions were significantly negative at the 5% and 1% significance levels, respectively. This indicates that the transformation of smart manufacturing in the eastern region did not exert a significant influence on carbon emissions; however, the central and western regions demonstrated a notable mitigating impact on carbon emissions. A possible reason for this is that the economic development of the eastern region has reached a higher level compared with that of the central and western regions; with the increase in the level of intelligent manufacturing, the marginal utility of carbon emissions diminishes.

4.6. Robustness Check

(i) Endogeneity test

To validate the stability of the benchmark model’s regression results, this study employed the dynamic generalized method of moments (GMM) for the regression analysis to control for possible endogeneity issues. The dynamic GMM uses appropriate instrumental variables from the lagged terms of the explanatory variables without adding any unnecessary instrumental variables [30].

Table 10. Dynamic GMM analysis.

Variable	Regression Results
L.CE	1.009 ***
	(0.033)
IM	−25,898.667 ***
	(4797.717)
UL	412.176 ***
	(139.981)
CG	59.948
	(111.433)
EI	4968.172 **
	(2124.822)
PR	1.982 ***
	(0.568)
W × IM	33,592.655 ***
	(11,265.414)
W × PR	−1.352
	(1.048)
rho	−35,402.912 **
	(14,078.943)
AR(1)	0.087
AR(2)	0.298
Hansen test	0.713

Note: ** and *** represent significance tests at the 5% and 1% levels, respectively.

shows the regression results of the dynamic GMM analysis. As can be seen from Table 10, the p-value of the AR(2) test is 0.298, which is greater than the significance level of 0.1, so there is no second-order serial autocorrelation, and the problem of endogeneity has been overcome. The value of Hansen’s test is 0.713, which indicates that the original hypothesis that “all the instrumental variables are valid” can be accepted, and the dynamic GMM is reasonable. The lagged first-order term of carbon emissions is significantly positive at the 1% significance level; the core explanatory variables of the smart manufacturing level and its lagged terms are significant, and the sign of the coefficients remains unchanged. It is thus proven that the central findings of this research are consistently reliable, and the underlying regression results also have a certain degree of stability.

(ii) Replacement matrix

In order to verify the reliability of the measurement results, this study used the spatial economic distance matrix instead of the 0–1 adjacency matrix utilizing the spatial Durbin model for the regression assessment; the outcomes are presented in

Table 11. Spatial Durbin model results after replacing the spatial weight matrix.

Variable	Spatial Economic Distance Matrix
IM	−9381.465 **
	(4178.922)
UL	373.380 ***
	(84.033)
CG	−275.556 ***
	(102.273)
EI	15,250.201 ***
	(1255.503)
PR	2.897 ***
	(0.734)
W × IM	254,596.632 ***
	(34,503.413)
W × PR	−15.974 ***
	(4.421)
Province effect	YES
Time effect	YES
Rho	−0.801 ***
	(0.229)
N	330
R-squared	0.4100
Log-likelihood	−3000.6099

Note: ** and *** represent significance tests at the 5% and 1% levels, respectively; standard errors are given in parentheses.

. The influence of the smart manufacturing level on the emissions of carbon dioxide was essentially the same with regard to the direction and significance of the impact coefficients. Each control variable showed only a change in the coefficient size.

(iii) Replacement of the dependent variable

The dependent variable, total carbon emissions, was replaced with carbon emissions per capita (TCE); i.e., the indicator of the ratio of the total local population to its total carbon emissions was employed as the explanatory variable for the re-regression. The regression findings are outlined in

Table 12. Explanatory results of the spatial Durbin model after replacing the dependent variable.

Variable	TCE
IM	−14.272 ***
	(1.811)
UL	0.221 ***
	(0.040)
CG	−0.130 ***
	(0.049)
EI	7.716 ***
	(0.580)
PR	0.001 ***
	(0.000)
W × IM	0.872
	(3.504)
W × PR	0.001
	(0.001)
Province effect	YES
Time effect	YES
Rho	−0.224 ***
	(0.070)
N	330
R-squared	0.1392
Log-likelihood	−467.4802

Note: *** represent significance tests at the 1% levels.

, where the coefficient of smart manufacturing still satisfies the 1% significance assumption and is negatively correlated, which further verifies the credibility of the core findings of this study.

5. Results

Firstly, the main-effect regression coefficient of the effect of the manufacturing industry’s intelligent transformation on carbon emissions was notably adverse at the 1% significance level, indicating that a transition to smart manufacturing practices played a facilitating role in carbon emission reductions. The urbanization level, energy intensity, and productivity level played a significant inhibitory role in carbon emission reductions, but the inhibitory role of the productivity level was relatively less obvious. Urban greening had a significant promotional role in carbon emission reductions. From the lag term, the shift toward intelligent manufacturing processes in adjacent areas had an inhibitory effect on local carbon emission reductions. Productivity development in adjacent areas had a promotional effect on local carbon emission reductions.

Secondly, the influence of the smart manufacturing level on technological innovation was significantly positive, while the impact of technological innovation on carbon emissions was significantly negative, and the impact of smart manufacturing on carbon emissions was also significantly negative; i.e., technological innovation had a partially mediating effect, suggesting that smart manufacturing can promote carbon emission reductions by facilitating technological innovation and then promoting carbon emission reductions.

Thirdly, under the regulation of government intervention, both smart manufacturing transformation and government intervention had a significant negative impact at the 0.01 significance threshold, indicating a significant contribution to carbon emission reductions. The interaction term’s coefficient was likewise notably negative; i.e., government intervention enhanced the contribution of smart manufacturing transformation to carbon emission reductions.

Finally, based on the temporal and spatial heterogeneity of smart manufacturing transformation with regard to carbon emissions, an empirical analysis was conducted. Temporally, smart manufacturing transformation made a significant contribution to carbon emission reductions in 2011–2016 and 2017–2019, and the contribution was larger in 2011–2016. Meanwhile, smart manufacturing transformation had a notable suppressive effect on carbon emission reductions in 2020–2021. From a spatial point of view, intelligent manufacturing transformation had a pronounced function in the central and western regions in promoting carbon emission reductions, while in the eastern region, it had no significant role in carbon emission reductions. Dividing the eastern region into time periods, the intelligent manufacturing transformation in the eastern region in 2011–2015 had a significant role in promoting carbon emission reductions, while the intelligent manufacturing transformation in the eastern region in 2016–2021 had a significant inhibitory effect.

6. Conclusions

Intelligent manufacturing effectively promotes carbon emission reductions, with technological innovation playing an intermediary role, and government interventions strengthen the effect of emission reductions.

Firstly, it is necessary to elevate the overall maturity of intelligent manufacturing. On the one hand, we should increase infrastructure investment, pay attention to the development and utilization of smart machinery, and promote the development of intelligent manufacturing, as intelligent infrastructure is fundamental to the evolution of intelligent manufacturing. On the other hand, we should improve the treatment of scientific research personnel and actively introduce and cultivate talented business personnel to support the advancement of manufacturing intelligence. By persistently merging manufacturing with intelligent technologies, industrial productivity has been improved, energy consumption has been reduced, and carbon emissions have been effectively reduced.

Secondly, it is crucial to proactively facilitate the integration of intelligent manufacturing transformation and technological innovation. On the one hand, research and the development of key technologies and breakthroughs is the key to technological innovation. We should actively carry out green technological innovation and strengthen the whole manufacturing production process to promote its intelligent green transformation. On the other hand, intelligent manufacturing and green transformations and upgrades are the driving forces of technological innovation; we should improve the green requirements of intelligent manufacturing so as to stimulate green technological innovations.

Thirdly, it is important to exploit the role of government interventions in directing the green upgrade of smart manufacturing. From one perspective, the government can encourage and promote the green transformation of smart manufacturing by formulating relevant policies and providing financial support. From another perspective, the government can promote the overall transformation of regional intelligence by guiding the cooperation between enterprises.

Finally, the implementation of differentiated smart manufacturing transformation strategies is necessary. In terms of time, the government should reasonably regulate the pace of the growth of smart manufacturing transformation according to the actual situation so as to prevent the further deterioration of the environment due to excessive CO₂ emissions. On the other hand, in the eastern region, the pace of manufacturing intelligence should be appropriately adjusted to actively integrate economic development with green development. As for the central and western regions, they should combine their advantages and utilize the experience of intelligent manufacturing development in the western region to promote intelligent manufacturing and green transformation.

7. Research Weaknesses and Limitations

Intelligent manufacturing is evolving rapidly, and research may fail to capture the latest trends.

There is still room for improvement in methods for evaluating intelligent manufacturing levels. This study did not delve into city-level details while exploring the regional differences, and there are opportunities in the future to further refine the analysis to more fully understand specific influencing factors across cities. Measurement indicators will also be further improved to more accurately quantify the level of intelligent manufacturing.