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Article

Does It Help Carbon Reduction in China? A Research Paper about the Mediating Role of Production Automation Based on the Carbon Kuznets Curve

by
Panda Su
1,* and
Yu Wang
2
1
School of Economics and Management, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China
2
School of Public Administration, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(23), 16000; https://doi.org/10.3390/su142316000
Submission received: 2 November 2022 / Revised: 29 November 2022 / Accepted: 29 November 2022 / Published: 30 November 2022
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Abstract

:
As China puts forward its “carbon emissions peak and carbon neutrality” goals, how to achieve carbon reductions has become a key for China’s goal. The manufacturing industry is a significant source of carbon dioxide emissions. For a manufacturing country such as China, adjustments in various aspects of the industry would have a huge impact on its carbon emissions. As an important reform of the contemporary production mode, the process of production automation in China will inevitably affect China’s carbon emissions; therefore, the analysis of the impact of that production automation on the carbon dioxide emissions is an important basis for judging the future carbon reductions in China. Referring to the traditional study of the carbon Kuznets curve, this paper analyzes the impact of an average wage on production automation and the role of production automation in the carbon Kuznets curve (CKC). This paper proposes that production automation plays a mediating role in the process of carbon emissions, and gives a verification model of that mediating role. By analyzing the relationship between average wages and the production automation process, the U-shaped curve relationship between them was verified. By examining the relationship between carbon dioxide emissions data and the production automation industry in China, we verified that production automation plays a partial mediating role in the change of the carbon Kuznets curve. Combined with the analysis of the two parts, this paper believes that with the continuous development of China’s intelligent manufacturing industry, China’s carbon reduction prospects are more optimistic, and that there is a good industrial foundation to achieve the “carbon peaking and carbon neutrality” goals. Finally, this paper proposes policy suggestions so as to increase research investment in production automation, to help promote the application of production automation, encourage the research and application development of low-carbon technology, especially encouraging modular design, and to give full play to the role of production automation in the process of carbon neutrality in China.

1. Introduction

With the continuous development of China’s economy, the scale of the environmental costs of the economic growth is increasing day by day. As the world’s second largest economy and the world’s largest real economy, how to control the environmental impact of development while ensuring economic development has become a problem that China must deal with at the present stage. In September 2020, Chinese President Xi Jinping, in his seventy-fifth United Nations general assembly speech, announced that China will strive for a carbon emissions peak by 2030 and become carbon neutral by 2060. This means that China will take an overall low-carbon development as an important goal of its national development strategy in the medium- and long-term development plan.
To achieve this goal, China has made corresponding planning arrangements for energy conservation and carbon reduction at different levels, including in industrial development and social management. The goal of sustainable development requires development that meets the needs of the present without jeopardizing the ability of future generations to meet their needs. It can be seen that the key to ensuring sustainable development under the carbon peaking and carbon neutrality goals lies in how to effectively control the regional carbon emissions, and that the control of carbon emissions cannot be separated from the background of regional development. At present, China is in the stage of industrial development and transformation, and the intelligent manufacturing industry has become the key to China’s industrial transformation, and while in this process, the promotion of production automation is particularly obvious. From the aspect of the labor force, the application of industrial robots has greatly changed China’s manufacturing production mode, and the industrial automation represented by them will become a medium and long-term trend of China’s industrial development. Analyzing the impact of this trend on carbon emissions will help in judging the development prospects of China’s carbon emissions peak and carbon neutrality goals.
Therefore, the research focus of this paper is on the role of automation industry development while in the process of a carbon emissions reduction in China. This study can help us to have a better understanding of the role of production automation in the process of carbon reduction, and to understand the path of carbon emissions reduction. Based on this analysis, we can provide a basis for the prediction of the prospect of the carbon neutrality goals, and it can also provide a new perspective on the construction of low-carbon development industry prospects for regions with similar goals and conditions. The paper’s structure arrangement is as follows: Section 2 reviews the relevant research on carbon emissions and production automation, and explains the basic viewpoints of this research; Section 3 analyzes the relationship between automation and a carbon reduction, and gives the analytical framework of this paper; Section 4 establishes an empirical model, and analyzes the relationship between production automation and carbon emissions; Section 5 discusses some conclusions of this paper and the relevant research results; Section 6 states the main conclusions of this paper and gives some policy recommendations.

2. Literature Review

For the analysis of carbon emissions, the most representative research is carried out around the carbon Kuznets curve (CKC). The theory of the CKC originates from the hypothesis of the environmental Kuznets curve, and its basic point of view is that at the low income level, the regional environmental impact will increase with an increase in per capita output value, while at the high income level, it will decrease with an increase in per capita output value [1]. This hypothesis has been confirmed in many studies [2]. From the empirical analysis of different regions, the CKC hypothesis is generally applicable to explain the carbon emissions process in different regions [3,4], which shows that this relationship also widely exists in the relationship between various human activities and carbon emissions [5]. What is certain is that the existence of the CKC as a phenomenon has been recognized by most studies. The current research results mainly include three types of explanations for the causes of inflection points in the process of a carbon emissions change: institutional adjustment in the process of development [6], technological change that drives development [7], and the impact of related events [8]. In addition, there are explanations for the phased changes in the environmental impact on economic development from the perspective of civic awareness and other cultural aspects [9]. However, some scholars question the accuracy of the carbon Kuznets curve from the perspective of model characteristics due to an ambiguity of the detail description [10,11]. In order to supplement and improve the theory of the CKC, Some scholars supplement the theory of the carbon Kuznets curve by exploring the intermediary channels between economic growth and carbon emissions [12]. In general, to explain the relationship between socioeconomic development and carbon emissions, the carbon Kuznets curve is still applicable to explain the process of carbon emissions in most regions. At the same time, the extension model based on this theory has a good extensibility. However, as pointed out by Webber and Allen, the phenomenon of the environmental Kuznets curve generally exists in the process of changes of various environmental indicators, but the inflection points of changes caused by human activities on the environment are different in different cases [13]. Therefore, according to the development characteristics of different regions, the influencing factors of the change process of carbon emissions are analyzed from the key development characteristics. Such analyses are important for projecting regional carbon emission prospects.
For different regions, researchers have conducted a large number of analyses on their carbon emissions according to the important factors of regional development, and these analyses all show that the key development trends of the region have an important impact on the regional carbon emissions. For example, in the cases of developed countries or regions, by analyzing the impact of various socio-economic factors on the carbon emissions in Central and Eastern Europe, Atici believes that the per capita energy consumption in central and Eastern Europe is the main cause of local carbon emissions [14]. Vieira et al. analyzed the carbon emissions of European manufacturing and energy industries, and they pointed out that the main difficulty for EU countries to achieve the goal of net zero emissions lies in the emissions control of large emitters [15]. Miura et al. analyzed the carbon emissions contributions of the economic sectors in different regions of Japan and pointed out that there are regional differences in the carbon emissions contributions of different economic sectors, depicting the impact of carbon emissions in the modernization of Japanese industries [16]. These studies all show that the dominant factors of carbon emissions are different within the cases of the same developed countries. In contrast, the economic structure of developing countries is more diverse, and the factors for carbon emissions are more complex. For example, India’s international tourism industry has grown rapidly in recent years, and Jayasinghe and Selvanathan’s study points out that international tourist spending is also an important factor in India’s carbon emissions [17]. As a manufacturing power, China’s carbon emissions have attracted extensive attention from scholars around the world. Jalil and Mahmud analyzed China’s carbon emissions and pointed out that the per capita income and energy consumption are the long-term determinants of China’s carbon emissions [18]. Even within China, there are still differences in the carbon emissions of different regions. The study by Lu et al. points out that most of China’s eight economic zones have already crossed the turning point of the CKC and have entered the ranks of low-carbon development; however, the northwest region still needs to be improved, and the reason for this regional difference is China’s differentiated regional development strategy [19]. Therefore, the analysis of the impact of China’s key development trends on carbon emissions is of great significance for the analysis of China’s carbon emission prospects.
At present, the world is in the stage of a rapid development of emerging industrial technology, and at the production level, production automation is undoubtedly one of the most eye-catching trends [20]. Compared with the traditional production mode, the most significant advantage of production automation lies in its improvement of production efficiency [21,22], while at the same time, this process will bring about a reduction in the total share of the labor-added value [23]. In the early stages of production automation, its application has mainly been in the automobile manufacturing industry, machinery manufacturing industry and other industries that are easier to promote already using Fordism [24]. Obviously, there is a technical basis for promoting production automation in a wider range of fields, such as the planting industry [25], breeding industry [26], and food production industry [27]. The increase in global risks represented by the global COVID-19 epidemic in recent years has become an opportunity for the further promotion of production automation technology [28,29]. These changes in production technology will undoubtedly have an impact on a carbon emissions reduction. Shin et al.‘s study on the relationship between production automation and economic growth also shows that production automation contributes to the sustainable development of society [30]. Moreover, Van and Morlet’s study also points out that the traditional environmental costs are gradually changing with the development of production automation [31]. As the world’s largest manufacturing economy, production automation is an important trend in China’s manufacturing transformation; therefore, analyzing the impact of the development of production automation on carbon emissions is of great significance for judging the prospect of China’s dual carbon strategy.
In order to analyze the details of the environmental external effects generated by a development trend, an important perspective is to start from the representative industry of this transformation. Focusing on the impact of key polluting industries [32] and the change of carbon emissions in industries with high energy consumption [33], or the change of carbon emissions in the process of service industry development [34,35], can reflect the impact of the carbon emissions caused by an industrial structural transformation. For example, the research on the carbon emissions of the construction industry can reflect the details of the changes of carbon emissions in the process of urbanization [36,37]. Meanwhile, an analysis of the carbon emissions changes in the transportation industry can be used to describe the changes in carbon emissions during the development of the trade circulation network [38,39]. In general, to analyze the environmental externality of a development trend, it is necessary to observe the environmental impact of the industry that can represent the trend.
Considering the research results of these literatures, we propose three basic understandings as follows: firstly, although there are differences in the understanding of the causes and development process, most studies agree that the CKC exists as a phenomenon; secondly, on the basis of recognizing the CKC as a universal law, most studies agree that the main development trend of a region greatly affects the local carbon emissions process; thirdly, as an important form of the modern manufacturing revolution, production automation is an important trend that will affect the development of China’s future productivity. To sum up, the contribution of this paper is to add variables about production automation to the traditional CKC model, to analyze the impact of production automation on carbon emissions changes, and to provide a new viewpoint for the expectation of China’s carbon emission reduction prospects.

3. Methodology and Data

To analyze the role of production automation in the process of carbon emissions reduction, it is necessary to first clarify the relationship between the two. By reviewing the existing studies, we can confirm that there is a functional relationship between the per capita income and the carbon emissions in line with the CKC hypothesis [40], and the per capita income has a strong impact on the carbon emissions [41]. Previous studies have usually associated it with changes in the consumption levels and consumption preferences, and have believed that changes in the per capita income achieve a carbon emissions reduction through changes in consumer behavior [42]. However, when the research perspective is narrowed to the level of individual behavior, it may be difficult to explain the mechanism of the CKC [43]. Therefore, from the perspective of income changes for future industrial development trends, this paper attempts to put forward an analytical framework for the relationship between the per capita income and the total carbon emissions mediated by production automation, so as to discuss how an increase in the per capita income affects the total carbon emissions. The analytical framework is shown in Figure 1.
In this analysis framework, the effect of the per capita income on the carbon emissions is divided into two paths: (1) an increase in the per capita income leads to an increase in the total carbon emissions. The logic of this path is relatively simple, that is, an increase in the per capita income will lead to an increase in personal consumption, which in turn will affect the production scale of consumer goods and, thus, lead to the growth of carbon emissions; (2) the increase in per capita income will push up the labor cost in the production of products, and the increasing consumer demand will put forward new demands for improving production efficiency, thus, promoting the development of production automation. This will promote the application of more carbon reduction technologies in the production process, thus. helping to achieve a carbon emissions reduction. There are many related studies on the former path [44]; therefore, that path is not the focus of this paper. The second path is the key of this paper because it is able to explain the role played by production automation in the carbon reduction process, and can explain the cause of formation of the CKC from the perspective of production mode reform.
Therefore, the problem to be demonstrated in this paper consists of two parts: firstly, whether an increase in income levels can promote production automation; secondly, with production automation as the intermediary, how does the change of per capita income affect the total amount of carbon emissions? Since production automation contains more specific market behaviors, this paper will also observe the effects of different market developments on carbon emissions from different market behaviors.

3.1. How Does the Demand for Labor Substitution Due to Rising Incomes Affect the Development of Production Automation

In the analytical framework proposed in this paper, the first relationship to be tested is whether an increase in the per capita income will lead to the development of production automation. In this analysis framework, the impact of the per capita income on the production automation industry has two paths: (1) an increase in the per capita income will become the labor cost in the production process, which will stimulate the demand of manufacturing enterprises for labor substitution in the production process, and then promote the development of the production automation industry [45]; (2) an increase in the per capita income can stimulate the consumption willingness of residents, resulting in an increase in market consumption demand, and then an expansion in the production scale of consumer goods [23]. These theories are based on studies in regions with more developed productivity. When we look at the longer term development, this unilateral promotion may have its limits. Consequently, we attempt to take a longer view of the impact of increasing income on the development of production automation.
Production automation is essentially a reform of the production mode of the manufacturing industry. In this trend, the influence of wage income (the price of labor) on the process of production automation is different at different stages. The basis of this judgment is as follows: firstly, the scale of production is proportional to the wage income. The larger the scale of production, the higher the wage income [46]; secondly, according to the theory of a marginal effect of scale, there is an intersection between the marginal revenue and wage income. On the basis of these two conclusions, we can see that with an expansion of the scale of production (with the rise of the price of labor), the process of the change can be depicted as in Figure 2. In the diagram we can see two important points where the first is the apex of the marginal benefits of scale (X1), and the second is the intersection of the labor costs and marginal benefits (X2). These points, especially X2, can be helpful to understand the relationship between the labor price and production automation. The impact of labor price on production automation is manifested in two directions, which can be regarded as the characteristics of two development stages. In the early stage of large-scale development, the price of labor is relatively low, and the boundary effect generated by an expansion of the production scale makes it profitable for enterprises to expand production by increasing the labor;, therefore, there will not be a strong demand for the realization of production automation at this stage (in the left range of X2). With an increase in the labor price, the scale effect of expanding production will be gradually exceeded by the increase of the labor cost. In contrast, production automation becomes one of the options for cost control; therefore, there will be an increasing demand for production automation (in the right range of X2).
To sum up the above analysis, we can make a basic inference: at the stage of a low labor price, with an increase in the labor price, the demand for production automation will decrease; at the stage of a high labor price, the demand for production automation will increase with an increase in the labor price. Therefore, in terms of function form, there should be a U-shaped curve between the labor price and the development of production automation. In order to verify this relationship, this study uses a static panel model for preliminary test:
P A = β 1 · G D P + β 2 · E C + β 3 · A v e W a g e + ∑ β i · X + γ
P A = β 1 · G D P + β 2 · E C + β 3 · A v e W a g e + β 4 · A v e W a g e 2 + ∑ β i · X + γ
where, PA represents the development of production automation, and the explanatory variables are GDP, EC and AveWage, which, respectively, represent the regional gross product, the total regional energy consumption and the average salary of employees. X is the control variable, including the population urbanization rate, total bank loans, manufacturing output value and the population’s education level. By comparing the results of the two equations, we can test the relationship between the number of market players in the industrial robot industry and the average wage level. If both β3 and β4 of Equation (2) are significant, and the goodness of fit of Equation (2) is better than that of Equation (1), then the relationship between the labor price and production automation can be considered as a U-shaped curve. The turning point of the curve can be regarded as the position of X2 in Figure 2. When the per capita income moves to the right range of the turning point, the increase in per capita income will promote the progress of production automation. In other words, the need for automation due to an increase in the per capita income does not occur until the turning point is crossed.
Based on this model, we can further observe the details of the impact of economic development on production automation. Because of the highly detailed division of labor in the production automation industry, enterprises engaged in related work may be affected differently by changes in the per capita income according to their own work in the industrial chain. From the perspective of the process of industrial demand, the market activities related to production automation can be divided into the research on production automation, the manufacturing of equipment, and all kinds of related services, such as promotion, marketing, after-sales, maintenance, etc. These demands should occur in a sequential order: before the actual application, there will be a demand for research and development of related technology, and only after that, will there be a demand for manufacturing and other services. In the early stages of development, when the technology is not yet perfect and the market demand is still relatively small, the manufacturing enterprises of relevant equipment both need and have the ability to provide the services, such as the marketing, maintenance, and installation, but when the wage further increases, the market demand expands, and it will then produce the demand of a specialized service. This inference can be expressed in the form shown in Figure 3.
Continuing the prediction, it can be believed that with an increase in average wages, the demand for production automation will continually increase, and the process of production automation will persistently accelerate. This prediction can be verified by the inflection point of Equation (4). The demand for production automation can be divided into research and development, manufacturing and service, which are respectively, substituted into Equation (4) for testing, when the difference in their turning points can be analyzed. Thus, the demand generated in the different stages can then be verified.

3.2. The Mediating Role of Production Automation in Carbon Reduction

In the analytical framework of this paper, another relationship that needs to be verified is the mediating role of production automation in a carbon emissions reduction. Of the reasons for believing that production automation contributes to carbon emissions reductions, the direct one is that the essence of production automation is to further standardize the production process, and to cut more randomness from the subjective judgment of working human labor. Production automation has the characteristic of being modular, so that technical updates can be integrated into the production module, and are easier to be applied to the production process. Another context is that China’s energy structure has changed significantly over the past twenty years. As a production mode that relies on electricity as the main energy, automated production means an increase in the electricity energy consumption. From 2000 to 2018, China’s energy structure changed significantly. Among them, the share of coal energy decreased from 68.5% to 59.0%, and that of petroleum energy decreased from 22.0% to 18.9%, which are major sources of carbon emissions. In contrast, in terms of clean energy, the share of natural gas increased from 2.2% to 8.4%, and that of other clean electricity increased from 7.3% to 14.5%. In the context of this changing energy structure, we can be sure that production automation means that more consumer goods are produced by using cleaner energy.
By this analysis, we made a basic inference that production automation plays a partial mediating role in the process of carbon emissions reduction, that is, production automation is a means to assist the realization of a carbon emissions reduction. Before building the model, we first needed to select the variables to be modeled. First of all, the core issue to be explained in this study was the role of production automation in the CKC phenomenon; therefore, it contained the variable, PA, that represented the production automation. It also needed to include the AveWage, a core explanatory variable representing labor prices [47]. In addition, the impact of the production scale on carbon emissions has been confirmed in a large number of empirical studies [48], while the energy consumption is a more direct factor affecting carbon emissions [49]; therefore, both were included as explanatory variables. The above four variables were the explanatory variables of this study, while in order to eliminate the influence from other factors (which are some common influencing factors in CKC-related research), four control variables were selected [2,50,51,52]. To verify this inference, refer to the research on the mediating role [53]. Here, we designed Equations (3) and (4) to verify the partial mediating role:
C E = β 1 · G D P + β 2 · E C + β 3 · A v e W a g e + β 4 · A v e W a g e 2 + ∑ β i · X + γ
C E = β 1 · G D P + β 2 · E C + β 3 · A v e W a g e + β 4 · A v e W a g e 2 + β 5 · P A + ∑ β i · X + γ
where, CE is the regional carbon emissions, and the explanatory variables are GDP, EC, AveWage, and PA, which, respectively, represent the regional gross product, energy consumption, average wage, and production automation. X is the control variable, including the population urbanization rate, total bank loans, manufacturing output value and the population’s education level. By comparing the difference between the correlation coefficient in Equations (3) and (4), we can verify the role of production automation in the process of carbon emissions reduction. According to our analysis, if β3 and β4 are significant in Equation (3), this indicates that the hypothesized phenomenon of the CKC exists, there will be a turning point in China’s carbon emissions, when the average wage will be higher than the turning point, and that the carbon emissions will reach a peak and start to decline gradually as the average wage increases. In Equation (4), if β3 and β4 are still significant and β5 is also significant, this indicates that production automation is one of the pathways in the CKC hypothesis processes to reduce the carbon emissions, and that it has a partial mediating role.

3.3. Data

According to the validation model built by the above analysis, we selected 9 variables for analysis. The research time range was from 2000 to 2019, and the research sample was 289 cities with districts in China. The description of each variable is shown in Table 1.
(1)
Regional carbon emissions. This paper chose the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) Fossil Fuel Emission Dataset (ODIAC2020b) as an indicator of the regional carbon emissions, and this dataset draws a 1 × 1 km distribution image of the global fossil fuel carbon dioxide emissions through emissions modeling methods [54]. The impact at this resolution can effectively reflect the carbon dioxide emissions at the spatial scale of prefecture-level cities. Using the carbon emissions raster data of the ODIAC superimposed on the administrative boundaries of prefecture-level cities, the carbon emissions of 289 sample cities from 2000 to 2019 could be extracted.
(2)
Production automation. For the observation of production automation, this paper chose the perspective of enterprises engaged in production automation-related market activities, and took the number of relevant enterprises in the region as the index. The number of enterprises engaged in an industry can reflect the market demand for that industry; therefore, the number of relevant enterprises engaged in providing production automation services in a region can well reflect the development of production automation in that region. Through the online data platform of Qcc.com (https://www.qcc.com/), the national industrial and commercial registration data was retrieved according to the standard that the business scope included the business content related to production automation. The industrial and commercial registration data was used as the basis for judging the existence of enterprises to determine the number of production automation enterprises in the different years and different regions. After filtering, there were 77,484 enterprises in 289 sample areas. Then, the number of enterprises engaged in production automation-related work in each region from 2000 to 2019 was obtained. According to the different types of enterprises engaged in production automation service work, the research enterprises, production enterprises and service enterprises were classified, denoted by PAR, PAM and PAS, respectively..
(3)
The explanatory variables. The core explanatory variables of this article was the urban worker’s average wage, and the reason the urban worker’s average wage was selected rather than the manufacturing worker’s average wage was that due to the lack of obvious industry barriers in the modern labor force, the flow of the labor force between industries is more and more frequent. Therefore, the average wage of urban workers, which can reflect the overall level of labor income, could better reflect the impact of wage changes on production automation. Other explanatory variables also included the regional gross product and energy consumption, reflecting the impact of the regional economic size and energy use, respectively. These data were acquired from the EPS data platform (https://www.epsnet.com.cn).
(4)
Control variables. The control variables selected in this paper included the population’s urbanization rate, financial industry activity, manufacturing output value and the population’s education level. The urbanization rate of the population was expressed by the population with an urban household registration divided by the total population of the region. The financial industry activity was represented by the total social loans. The education level of the population was represented by the proportion of the population with a college education or above in the total population in the sample population survey of the concept area.

4. Results

4.1. Unit Root Test

In order to avoid errors in the regression results caused by data problems, unit root tests were conducted on all the variables. If there was a unit root, the panel data was unstable and it was necessary to differentiate the data. After the first-order difference processing, all the variables passed the unit root test, with the results of the unit root test shown in Table 2. Consequently, all the variables in this study were first-order single-integer sequences.

4.2. Verification of the Relationship between the Average Wage and Production Automation

Based on Equations (1) and (2), the relationship between the average wage and production automation was verified. The Hausman test was first used to select the type of model effect, and the test results are shown in Table 3. The test results show that the chi-square value was 341.63 and that the Prob. was less than 0.05; therefore, the fixed-effect model was selected. The regression results are shown in Table 4.
It can be seen from the results expressed that, the goodness of fit in Equation (2) was better than that of Equation (1), indicating that the regional average wage income level and the number of industrial robot enterprises present a quadratic function relationship, and the correlation coefficient shows that the relationship between the two was a U-shaped curve. Therefore, the following analysis was based on the regression results of Equation (2). The results of the core explanatory variables show that the relationship between the regional average wage income and industrial robot enterprises was significant, which verifies the previous conjecture in this paper that there is a U-shaped function relationship between the per capita wage income and production automation. According to the coefficient, the turning point of the relationship between the per capita wage income and production automation was 6.9748 (X2), indicating that when the regional average income level is lower than CNY 69,748, the scale effect of a labor agglomeration caused by wage growth is greater than the increase in the labor cost caused by the wage growth, and that the increase in the wage level will inhibit the demand for robot substitution in regional industries. When the average income level is higher than CNY 69,748, the increase in the labor cost exceeds the marginal productivity. In terms of the other explanatory variables, in Equation (2), the relationship between the regional economic scale and production automation was not significant, while the relationship between the energy consumption and production automation was significant.
According to the analysis in Section 3.2, replacing PA with the number of different types of production automation enterprises into Equation (2), verifies the sequence in which the different need for production automation arises. In Table 5, the test results are shown for the PAR on behalf of research enterprises, the PAM on behalf of manufacturing enterprises, and the PAS on behalf of service enterprises.
From the perspective of the different types of enterprises, in terms of the core explanatory variables, there was a positive U-shaped relationship between the average wage level and the three types of production automation enterprises. Among them, the inflection point of the research enterprises was 6.2789, that of the production enterprises was 7.0252, and that of the service enterprises was 7.1373. This verifies that in the different stages of development, the demand for production automation will change differently. This will be elaborated in the discussion section of this paper. In terms of the other explanatory variables, the GDP was only significant in the model of the service-oriented enterprises, indicating that the regional economic scale is conducive to the development of service-oriented enterprises. The regional energy consumption had a significant positive impact on the three types of enterprises, with the largest impact on the production enterprises and the smallest impact on the research enterprises.

4.3. Verification of the Mediating Role of Production Automation

Based on Equations (3) and (4), the mediating role of production automation in the carbon emissions process was verified. The Hausman test was used to select the model, and the test results show that the chi-square value of Equation (3) was 111.22 and the Prob. was less than 0.05, and that the chi-square value of Equation (4) was 116.82 and the Prob. was less than 0.05. Consequently, fixed effects were used in both models and the regression results are shown in Table 6.
Judging from the test results, firstly, the correlation coefficient of the average wage in Equations (3) and (4) showed that there was an inverted U-shaped curve between the regional average wage and regional carbon emissions, which also verifies the existence of the CKC curve [55]. This means, that with an increase in the wage income, more and more regions will enter the stage of carbon emissions reduction. Secondly, before and after the production automation index was added, the correlation coefficient between the average wage and the carbon emissions was significant, and the correlation coefficient of production automation in Equation (4) was also significant, indicating that production automation has a partial mediating effect in the process of carbon emissions reduction. The correlation coefficient of production automation in Equation (4) was negative, indicating that production automation played a role in inhibiting the carbon emissions in the process of a regional carbon emissions change. This also verifies the conjectures made during the model construction phase.

5. Discussion

Some speculations in the third part of this paper have been verified by empirical tests and the relationship between the results of this paper and some related studies are further discussed below.

5.1. The Development Prospect of Production Automation

Production automation, as a revolution in production technology that has been underway for decades, has already surpassed the technical limitations of its initial development. From the initial application scenarios that can only be applied to some standardized assembly lines, such as in the automobile industry, to today, it has gradually entered a more extensive production process, and production automation will also undoubtedly greatly change the future manufacturing production mode [56]. Most of the previous studies on production automation have focused on the external effects of production automation, regard it as representing the technological factors that drive other changes in the economy and, to a large extent, regard production automation as a simple technological innovation, while there are few discussions on the economic laws of the development of production automation. From our analysis in this paper, it can be basically determined that with an increase in the average wage, labor costs will promote the further popularization of production automation.
From the turning points of the per capita wage for different types of enterprises, we can find that the demand for production automation did not evolve naturally from the beginning, but gradually occurred at different stages. As wages rose, the demand to understand the technology of production automation occurred first, creating a need for research firms and as with other production changes in history, this was a technological preparation for a new production change. What followed was the demand for the production of related equipment, that is, in some regions with the highest levels of wage income, the demand for the use of automation equipment was generated. Firms in these regions were the first to feel the pressure from rising wages and, therefore, they were the first to demand to use automated production for replacing human labor. At that stage, production automation began to develop in a few developed areas, and then entered the field of scenario application. This can be mutually proved with the development history of production automation [57]. Presently, with the continued development of the economy, the average wages in most areas are higher than the turning point, and the demand for production automation will occur in these regions. Comparing the average wages of the sample cities in 2019, 203 of the 289 sample cities had crossed the last inflection point and entered the stage of a widespread popularization of production automation. It can be expected that with the further innovation of production automation technology, the application scenarios of production automation will be more extensive, and the research on the impact of production automation on various aspects will be more realistic.
In addition, the conclusion in this paper about the sequence of the turning point of the different demands on production automation can also show that the first-move regions will generate the demand for production automation earlier, and will lay the framework of the industries related to production automation. This phenomenon reflects that, despite the demand for production automation rising in most regions and related enterprises covering most of the sample cities, firms engaged in research and manufacturing are still concentrated in a few large, urban agglomeration centers.

5.2. The Effect of Production Automation on Regional Carbon Emissions

The environmental impact of production automation has also been discussed in relevant studies [58,59]. In general, the most prominent significance of production automation for the environment is that it can further reduce the randomness in the traditional human-dominated process and can further standardize the whole production process. At the same time, in the application of production automation, a standardized and modular production mode is more conducive to the application of new energy-saving technology. Compared with human labor, with new technologies and standards that staff need to be trained for, it can be directly nested into existing modules in the production processes of production automation, which is obviously more conducive to the promotion and application of environment-friendly technologies. Just as the results verified in this paper, production automation plays a partial mediating role in the process of carbon emissions reduction, that is to say, production automation itself is only one of the paths in the process of carbon emissions reduction. To make production automation work, it still requires the research and development of environmental technologies at the source. In the case of the development of environmental protection technology, production automation can help accelerate the process of technology application; thus, playing a positive role in carbon emissions reduction.
From this perspective, we can predict that in the future, the application of production automation will be more widely used. As the world’s largest industrialized country, the process of production automation in China will also greatly change the production mode of China, and more and more production scenarios will apply the related technologies of production automation. This also means that under the background of vigorously promoting environmental protection technology innovation, relevant technologies will be more convenient to be applied in the production process, helping China to achieve its strategic goal of “carbon emission peak and carbon neutrality”. We can be optimistic about China’s prospects for carbon neutrality.

6. Conclusions

This paper attempts to discuss the role of production automation in the process of carbon reduction under the framework of CKC theory based on the economic characteristics of production automation and the observation point of relevant market activities. Based on the panel data from 289 sample cities in China from 2000 to 2019, this paper analyzes the development law of production automation and its significance for carbon emissions reduction. The main findings of this paper are as follows:
(1)
The relationship between the average wages and production automation shows a U-shaped curve, indicating that in the early stage of economic development, the marginal cost of expanding production is low, so that an increase in wages does not promote the demand for production automation; when the average wages are higher than the turning point, the increase in wages will promote the development of production automation.
(2)
The influence of the average wage is different in various types of production automation, while the whole results show as the form of a u-shaped curve, but the three types of automated production enterprises have a different turning point, reflecting the average wage change in the process, and that the demand for production automation occurs sequentially. The industry will first begin to prepare the technology to automate production, and then create specialized research units, then, it will start to develop equipment manufacturing enterprises in a few pioneer areas, before finally spreading to more regions through related services enterprises.
(3)
From the verification results of the mediating effect, production automation plays a partial mediating role in the process of carbon emissions reduction. This indicates that production automation is an intermediate medium that can promote a carbon emissions reduction. The promotion of production automation will help achieve China’s goal of “carbon neutrality”.
With these conclusions, this paper has a relatively optimistic expectation for the realization of China’s strategic goal of “carbon peak and carbon neutrality”. China’s development will enable production automation to be more widely used in various economic scenarios, and the promotion of production automation will also make it easier to apply various low-carbon technologies in the production process. At the same time, based on the findings of this paper, two suggestions are put forward to promote carbon emissions reduction:
(1)
Increase research investment in production automation and promote the application of production automation. Presently, most regions in China have the economic conditions of the process of production automation, and relevant development plans for production automation and intelligent development have also been formulated. In further stages, the government needs to strengthen the supervision and guidance of production automation-related industries, to ensure the stability of the automation market development while promoting related technologies, and to specify the development direction of production automation. At the same time, the government also needs to encourage production enterprises to use production automation technology through fiscal and tax subsidies and other financial tools, to accelerate the promotion of production automation, and to prepare the industrial base for the application of low-carbon technology.
(2)
Encourage research and application of low-carbon technologies, especially via modular design. The conclusion of this paper shows that production automation is one path to promote a carbon reduction, and that carbon emissions, in essence, still need low-carbon technology innovation and promotion. Meanwhile, production automation will improve new technologies to promote the role of the environment; therefore, low-carbon technology innovation will still need to be encouraged. Additionally, the application of low-carbon technologies in production automation will provide favorable market conditions. In particular, this involves encouraging the development of modular designs that can be directly applied to automated production processes, to make low-carbon technology more convenient to apply in production links.

Author Contributions

Conceptualization, P.S. and Y.W.; methodology, P.S.; software, P.S.; validation, P.S.; formal analysis, P.S. and Y.W.; investigation, Y.W.; resources, P.S.; data curation, P.S.; writing—original draft preparation, P.S.; writing—review and editing, P.S. and Y.W.; visualization, P.S.; supervision, P.S.; project administration, P.S.; funding acquisition, P.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors are grateful to the editors and reviewers for their patient work.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Müller-Fürstenberger, G.; Wagner, M. Exploring the environmental Kuznets hypothesis: Theoretical and econometric problems. Ecol. Econ. 2009, 62, 648–660. [Google Scholar] [CrossRef] [Green Version]
  2. Shuai, C.; Chen, X.; Shen, L.; Jiao, L.; Wu, Y.; Tan, Y. The turning points of carbon Kuznets curve: Evidences from panel and time-series data of 164 countries. J. Clean. Prod. 2017, 162, 1031–1047. [Google Scholar] [CrossRef]
  3. Apergis, N.; Ozturk, I. Testing environmental Kuznets curve hypothesis in Asian countries. Ecol. Indic. 2015, 52, 16–22. [Google Scholar] [CrossRef]
  4. Zhang, X.P.; Cheng, X.M. Energy consumption, carbon emissions, and economic growth in China. Ecol. Econ. 2009, 68, 2706–2712. [Google Scholar] [CrossRef]
  5. Xu, Q.; Dong, Y.X.; Yang, R. Urbanization impact on carbon emissions in the Pearl River Delta region: Kuznets curve relationships. J. Clean. Prod. 2018, 180, 514–523. [Google Scholar] [CrossRef]
  6. Burke, P.J. Climbing the electricity ladder generates carbon Kuznets curve downturns. Aust. J. Agric. Resour. Econ. 2012, 56, 260–279. [Google Scholar] [CrossRef]
  7. Burnett, J.W.; Bergstrom, J.C.; Wetzstein, M.E. Carbon dioxide emissions and economic growth in the US. J. Policy Model. 2013, 35, 1014–1028. [Google Scholar] [CrossRef]
  8. Colenbrander, S.; Gouldson, A.; Sudmant, A.H.; Papargyropoulou, E.; Chau, L.W.; Ho, C.S. Exploring the economic case for early investment in climate change mitigation in middle-income countries: A case study of Johor Bahru, Malaysia. Clim. Dev. 2016, 8, 351–364. [Google Scholar] [CrossRef]
  9. Nazarov, Z.; Obydenkova, A. Environmental challenges and political regime transition: The role of historical legacies and the European Union in Eurasia. Probl. Post-Commun. 2022, 69, 396–409. [Google Scholar] [CrossRef]
  10. Itkonen, J.V.A. Problems estimating the carbon Kuznets curve. Energy 2012, 39, 274–280. [Google Scholar] [CrossRef]
  11. Zhou, C.S.; Wang, S.J.; Wang, J.Y. Examining the influences of urbanization on carbon dioxide emissions in the Yangtze River Delta, China: Kuznets curve relationship. Sci. Total Environ. 2019, 675, 472–482. [Google Scholar] [CrossRef] [PubMed]
  12. Yao, S.J.; Zhang, S.; Zhang, X.M. Renewable energy, carbon emission and economic growth: A revised environmental Kuznets Curve perspective. J. Clean. Prod. 2019, 235, 1338–1352. [Google Scholar] [CrossRef]
  13. Webber, D.J.; Allen, D.O. Environmental Kuznets curves: Mess or meaning? Int. J. Sustain. Dev. World Ecol. 2010, 17, 198–207. [Google Scholar] [CrossRef]
  14. Atici, C. Carbon emissions in Central and Eastern Europe: Environmental Kuznets curve and implications for sustainable development. Sustain. Dev. 2009, 17, 155–160. [Google Scholar] [CrossRef]
  15. Vieira, L.C.; Longo, M.; Mura, M. Are the European manufacturing and energy sectors on track for achieving net-zero emissions in 2050? An empirical analysis. Energy Policy 2021, 156, 112464. [Google Scholar] [CrossRef]
  16. Miura, T.; Tamaki, T.; Kii, M.; Kajitani, Y. Efficiency by sectors in areas considering CO2 emissions: The case of Japan. Econ. Anal. Policy 2021, 70, 514–528. [Google Scholar] [CrossRef]
  17. Jayasinghe, M.; Selvanathan, E.A. Energy consumption, tourism, economic growth and CO2 emissions nexus in India. J. Asia Pac. Econ. 2021, 26, 361–380. [Google Scholar] [CrossRef]
  18. Jalil, A.; Mahmud, S.F. Environment Kuznets curve for CO2 emissions: A cointegration analysis for China. Energy Policy 2009, 37, 5167–5172. [Google Scholar] [CrossRef] [Green Version]
  19. Lu, C.X.; Venevsky, S.; Shi, X.L.; Wang, L.Y.; Wright, J.S.; Wu, C. Econometrics of the environmental Kuznets curve: Testing advancement to carbon intensity-oriented sustainability for eight economic zones in China. J. Clean. Prod. 2021, 283, 124561. [Google Scholar] [CrossRef]
  20. Paula, A.P.P.D.; Paes, K.D. Fordism, post-fordism, and cyberfordism: The paths and detours of Industry 4.0. Cadernos Ebape BR 2022, 19, 1047–1058. [Google Scholar] [CrossRef]
  21. Saliba, M.A.; Zammit, D.; Azzopardi, S. A study on the use of advanced manufacturing technologies by manufacturing firms in a small, geographically isolated, developed economy: The case of Malta. Int. J. Adv. Manuf. Technol. 2017, 89, 3691–3707. [Google Scholar] [CrossRef]
  22. Aristova, N.I.; Chadeev, V.M. A Methodology for Estimating the Benefits of Mass Production Automation. Autom. Remote Control 2018, 79, 366–371. [Google Scholar] [CrossRef]
  23. Acemoglu, D.; Restrepo, P. Automation and new tasks: How technology displaces and reinstates labor. J. Econ. Perspect. 2019, 33, 3–30. [Google Scholar] [CrossRef] [Green Version]
  24. Kim, D.H. Two Opposing Perspectives on Technological Progress and Future Education: Arts of Living in a Free Society. J. Soc. Thoughts Cult. 2019, 22, 103–138. [Google Scholar] [CrossRef]
  25. Park, J.E.; Nakamura, K. Automatization, labor-saving and employment in a plant factory. Environ. Control Biol. 2015, 53, 89–92. [Google Scholar] [CrossRef] [Green Version]
  26. Pyeon, Y.B.; Kim, Y.G.; Kim, D.H.; Oh, W.; Han, I.; Lee, K.H. Development of an Automatic Assembly Machine for Oyster Farm Lines. J. Inst. Control Robot. Syst. 2018, 24, 111–115. [Google Scholar] [CrossRef]
  27. Lipton, J.I. Printable food: The technology and its application in human health. Curr. Opin. Biotechnol. 2017, 44, 198–201. [Google Scholar] [CrossRef]
  28. Sato, H. Industrialization of Developing Economies in the Global Economy with an Infectious Disease. Dev. Econ. 2021, 59, 126–153. [Google Scholar] [CrossRef]
  29. Shim, S.Y.; Kang, Y.S.; Myungki, N. A Study on RPA Approach for Office and Administration Automation in the Era of Post COVID-19: Evaluation and Suggestion. Inf. Syst. Rev. 2021, 50, 93–118. [Google Scholar]
  30. Shin, J.K.; Jung, Y.H.; Lee, S.H. The Role of Production Automation in Sustainable Economic Growth in South Korea. J. Korean Data Anal. Soc. 2022, 24, 1099–1111. [Google Scholar] [CrossRef]
  31. Van, A.T.; Morlet, T. Turning Activity into a Lever for Integrating Humans into the Workplace: A Transversal Approach for Innovative Projects. In Proceedings of the 20th Congress of the International-Ergonomics-Association (IEA), Florence, Italy, 16 July 2019. [Google Scholar]
  32. Jiang, J.J.; Ye, B.; Ji, J.P.; Ma, X.M. Research on contribution decomposition by industry to China’s carbon intensity reduction and carbon emission growth. Huanjing Kexue 2014, 35, 4378–4386. [Google Scholar] [PubMed]
  33. Chen, Y.; Jing, W. Changes in carbon emission performance of energy-intensive industries in China. Environ. Sci. Pollut. Res. 2022, 29, 43913–43927. [Google Scholar] [CrossRef]
  34. Sun, Y.; Qian, L.; Liu, Z. The carbon emissions level of China’s service industry: An analysis of characteristics and influencing factors. Environ. Dev. Sustain. 2021, 12, 1–26. [Google Scholar] [CrossRef]
  35. Zhang, T.L.; Su, P.D.; Deng, H.B. Does the agglomeration of producer services and the market entry of enterprises promote carbon reduction? An empirical analysis of the Yangtze river economic belt. Sustainability 2021, 13, 13821. [Google Scholar] [CrossRef]
  36. Zhou, Y.X.; Liu, W.L.; Lv, X.Y.; Chen, X.H.; Shen, M.H. Investigating interior driving factors and cross-industrial linkages of carbon emission efficiency in China’s construction industry: Based on Super-SBM DEA and GVAR model. J. Clean. Prod. 2019, 241, 118322. [Google Scholar] [CrossRef]
  37. Lu, N.; Feng, S.Y.; Liu, Z.M.; Wang, W.D.; Lu, H.L.; Wang, M. The determinants of carbon emissions in the Chinese construction industry: A spatial analysis. Sustainability 2020, 12, 1428. [Google Scholar] [CrossRef] [Green Version]
  38. Li, S.J.; Liu, J.G.; Wu, J.J.; Hu, X.Y. Spatial spillover effect of carbon emission trading policy on carbon emission reduction: Empirical data from transport industry in China. J. Clean. Prod. 2022, 371, 133529. [Google Scholar] [CrossRef]
  39. Zhao, X.Y.; Wang, J.W.; Fu, X.; Zheng, W.L.; Li, X.P.; Gao, C. Spatial–temporal characteristics and regional differences of the freight transport industry’s carbon emission efficiency in China. Environ. Sci. Pollut. Res. 2022, 29, 75851–75869. [Google Scholar] [CrossRef]
  40. Aldy, J.E. Energy and carbon dynamics at advanced stages of development: An analysis of the US states, 1960–1999. Energy J. 2007, 28, 91–111. [Google Scholar] [CrossRef]
  41. Parker, S.; Bhatti, M.I. Dynamics and drivers of per capita CO2 emissions in Asia. Energy Econ. 2020, 89, 104798. [Google Scholar] [CrossRef]
  42. Greening, L.A. Effects of human behavior on aggregate carbon intensity of personal transportation: Comparison of 10 OECD countries for the period 1970–1993. Energy Econ. 2004, 26, 1–30. [Google Scholar] [CrossRef]
  43. Bassetti, T.; Benos, N.; Karagiannis, S. CO2 emissions and income dynamics: What does the global evidence tell us? Environ. Resour. Econ. 2013, 54, 101–125. [Google Scholar] [CrossRef]
  44. Stuart, D.; Gunderson, R.; Petersen, B. Overconsumption as ideology: Implications for addressing global climate change. Nat. Cult. 2020, 15, 199–223. [Google Scholar] [CrossRef]
  45. Hedvicakova, M.; Kral, M. Level of industry automation 4.0 in the Czech Republic and impact on unemployment. In Proceedings of the European Financial Systems 2018: Proceedings of the 15th International Scientific Conference, Brno, Czech Republic, 25–26 June 2018. [Google Scholar]
  46. Yang, Y.; Yang, B.O. Empirical Analysis on Relation between Scale and Wage Rate of Listed Company. In Proceedings of the International Conference of Asia-Pacific Low Carbon Economy/9th Northeast Asia Academic Network, Changsha, China, 26 November 2010. [Google Scholar]
  47. Schröder, E.; Storm, S. Economic growth and carbon emissions: The road to “hothouse earth” is paved with good intentions. Int. J. Political Econ. 2020, 49, 153–173. [Google Scholar] [CrossRef]
  48. Pao, H.T.; Chen, C.C. Decoupling of environmental pressure and economic growth: Evidence from high-income and nuclear-dependent countries. Environ. Sci. Pollut. Res. 2020, 27, 5192–5210. [Google Scholar] [CrossRef]
  49. Shobande, O.; Asongu, S. The rise and fall of the energy-carbon Kuznets curve: Evidence from Africa. Manag. Environ. Qual. 2022, 33, 390–405. [Google Scholar] [CrossRef]
  50. Ma, M.; Cai, W.; Cai, W.G.; Dong, L. Whether carbon intensity in the commercial building sector decouples from economic development in the service industry? Empirical evidence from the top five urban agglomerations in China. J. Clean. Prod. 2019, 222, 193–205. [Google Scholar] [CrossRef]
  51. Ye, Y.; Khan, Y.A.; Wu, C.; Shah, E.A.; Abbas, S.Z. The impact of financial development on environmental quality: Evidence from Malaysia. Air Qual. Atmos. Health 2021, 14, 1233–1246. [Google Scholar] [CrossRef]
  52. Chen, X.; Shuai, C.; Wu, Y.; Zhang, Y. Analysis on the carbon emission peaks of China’s industrial, building, transport, and agricultural sectors. Sci. Total Environ. 2020, 709, 135768. [Google Scholar] [CrossRef]
  53. Yang, Y.; Wei, X.; Wei, J.; Gao, X. Industrial Structure Upgrading, Green Total Factor Productivity and Carbon Emissions. Sustainability 2022, 14, 1009. [Google Scholar] [CrossRef]
  54. Oda, T.; Maksyutov, S. ODIAC Fossil Fuel CO2 Emissions Dataset (ODIAC2020b); Center for Global Environmental Research, National Institute for Environmental Studies: Tsukuba, Japan, 2015. [Google Scholar] [CrossRef]
  55. Han, J.W.; Miao, J.J.; Du, G.; Yan, D.; Miao, Z. Can market-oriented reform inhibit carbon dioxide emissions in China? A new perspective from factor market distortion. Sustain. Prod. Consum. 2021, 27, 1498–1513. [Google Scholar] [CrossRef]
  56. Zhang, R.S.; Zhang, C.; Zheng, W.G. The status and development of industrial robots. In Proceedings of the 4th International Conference on Applied Materials and Manufacturing Technology (ICAMMT), Nanchang, China, 25–27 May 2018. [Google Scholar]
  57. Kurosz, J.; Milecki, A. The idea of “Industry 4.0” in car production factories. In Proceedings of the Intelligent Systems in Production Engineering and Maintenance, Wrolclaw, Poland, 17–18 September 2018. [Google Scholar]
  58. Grift, T.; Zhang, Q.; Kondo, N.; Ting, K.C. A review of automation and robotics for the bio-industry. J. Biomechatron. Eng. 2008, 1, 37–54. [Google Scholar]
  59. Kang, H.; Sung, S.; Hong, J.; Jung, S.; Hong, T.; Park, H.S.; Lee, D.-E. Development of a real-time automated monitoring system for managing the hazardous environmental pollutants at the construction site. J. Hazard. Mater. 2021, 402, 123483. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. The role of production automation in carbon reduction.
Figure 1. The role of production automation in carbon reduction.
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Figure 2. Labor cost and marginal benefit in the process of scale expansion.
Figure 2. Labor cost and marginal benefit in the process of scale expansion.
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Figure 3. The turning point of different demand.
Figure 3. The turning point of different demand.
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Table
Table 1. Variable descriptions.
Table 1. Variable descriptions.
VariableSymbol
Explained variableregional carbon emissionsCE
Key variableproduction automationPA
Core explanatory variableaverage wageAveWage
Other explanatory variablesregional gross productGDP
energy consumptionEC
Control variablespopulation urbanization rateUP
financial sector activityFA
manufacturing output valueMO
population education levelPE
Table
Table 2. Results of unit root test after first difference.
Table 2. Results of unit root test after first difference.
UnitsLLC TestIm, Pesaran and Shin W-StatADF-Fisher Chi-SquarePP-Fisher Chi-Square
CEmillion tons0.00000.22280.00000.0000
PAnum0.00000.00000.00000.0000
AveWageten thousand CNY0.00000.00000.00000.0000
GDPmillion CNY0.00000.00000.00000.0000
ECtons of standard coal0.00000.00000.00000.0000
UP%0.00000.00000.00000.0000
FAmillion CNY0.00000.00000.00000.0000
MOmillion CNY0.00000.00000.00000.0000
PE%0.00000.00000.00000.0000
Table
Table 3. Hausman test results.
Table 3. Hausman test results.
Test SummaryChi-Sq. StatisticProb.
Cross-section random341.6337130.0000
Table
Table 4. Fixed effects regression results.
Table 4. Fixed effects regression results.
Coefficient
Variable(1)(2)
AveWage0.3554 ***−2.1465 ***
AveWage2-0.1539 ***
GDP0.4869 ***−0.0604
EC1.2268 ***1.4278 ***
UP−3.1834 ***−2.6002 ***
FA−0.0231 ***−0.0158 ***
MO0.4480 ***0.4419 ***
PE1.5358 ***1.0871 ***
C−9.5880 ***0.9070 ***
R-squared0.90050.9070
Adjusted R-squared0.89490.9017
Note: *** represents significant at the 1% level.
Table
Table 5. Fixed effects regression results of the different types of enterprises.
Table 5. Fixed effects regression results of the different types of enterprises.
Coefficient
VariablePARPAMPAS
AveWage−1.8435 ***−2.0829 ***−2.0313 ***
AveWage20.1468 ***0.1482 ***0.1423 ***
GDP−0.0503−0.0578−0.0596 **
EC1.3281 ***1.2307 ***1.3451 ***
UP−2.3800 ***−2.5978 ***−2.4861 ***
FA−0.0155 ***−0.0154 ***−0.0145 ***
MO0.4020 ***0.4229 ***0.4217 ***
PE1.0228 ***0.9220 ***1.0117 ***
C5.4727 ***5.3819 ***5.8273 ***
R-squared0.87600.87950.8654
Adjusted *** R-squared0.80520.87000.8432
Note: ** and ***, respectively, represent significant differences at the 5% and 1% levels.
Table
Table 6. Verification of the mediating effect of production automation.
Table 6. Verification of the mediating effect of production automation.
Coefficient
Variable(3)(4)
AveWage0.2379 ***0.2042 ***
AveWage2−0.0113 ***−0.0089 ***
PA-−0.0157 ***
GDP0.4853 ***0.4844 ***
EC−0.0896 ***−0.0672 **
UP0.06260.0218
FA0.0049 ***0.0046 ***
MO−0.0528 ***−0.0458 ***
PE−0.0762 ***−0.0591 ***
C6.9468 ***7.0464 ***
R-squared0.98320.9832
Adjusted R-squared0.98220.9823
Note: ** and ***, respectively, represent significant differences at the 5% and 1% levels.
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Su, P.; Wang, Y. Does It Help Carbon Reduction in China? A Research Paper about the Mediating Role of Production Automation Based on the Carbon Kuznets Curve. Sustainability 2022, 14, 16000. https://doi.org/10.3390/su142316000

AMA Style

Su P, Wang Y. Does It Help Carbon Reduction in China? A Research Paper about the Mediating Role of Production Automation Based on the Carbon Kuznets Curve. Sustainability. 2022; 14(23):16000. https://doi.org/10.3390/su142316000

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Su, Panda, and Yu Wang. 2022. "Does It Help Carbon Reduction in China? A Research Paper about the Mediating Role of Production Automation Based on the Carbon Kuznets Curve" Sustainability 14, no. 23: 16000. https://doi.org/10.3390/su142316000

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