Industrial robots application and collaborative governance of pollution reduction and carbon reduction

Abstract

The green and intelligent transformation of the manufacturing industry has become the focus of attention in various countries. Through the analysis of China’s empirical data, this paper found that the application of industrial robots can effectively reduce the emission intensity and total emission of industrial carbon dioxide and sulfur dioxide to promote green development, so as to realize the collaborative governance of reducing environmental pollution and carbon emission. In terms of mechanism, industrial robot applications play a role by improving capacity utilization, realizing economies of scale, attracting foreign investment and promoting biased technological progress, so as to realize the collaborative governance of reducing environmental pollution and carbon emission. Further analyses find that the application of industrial robots made technological progress tend to use capital factor, save energy and labor factors input, and the industrial structure optimization plays a positive moderating role in it. Through LMDI decomposition, it is found that the current production mode of “simple machine replacing human” can’t reduce pollutant emissions, and it is necessary to give full play to the productivity improvement, technological progress, factor substitution, and foreign capital introduction brought by robot applications to promote the collaborative governance of reducing environmental pollution and carbon emission.

Introduction

With the acceleration of global industrialization, environmental pollution and carbon emissions have become increasingly serious, and become a major challenge threatening human sustainable development. Therefore, how to promote coordinated industrial restructuring, pollution control, ecological protection, coping with climate change, and jointly promoting carbon reduction, pollution reduction, green expansion and growth, has become the key to achieving green and sustainable development. Moreover, environmental pollutants and carbon dioxide emissions are highly rooted in the same source. In view of this, it is an inevitable choice to promote the green transformation of the economy and society to strengthen the collaborative governance of pollutants and carbon dioxide emissions, achieve both pollution intensity and total pollution control, and take into account energy conservation and emission reduction and economic growth¹.

The first and second industrial revolutions brought large-scale and standardized production methods to mankind. With the development of information technology such as big data and cloud computing, industrial intelligence with intelligent manufacturing as the core has become the core of the third industrial revolution². In this context, as a key force in the transformation of industrial intelligence, industrial robots play an increasingly prominent role in promoting industrial transformation and upgrading and achieving green production. Therefore, how to play the role of industrial robots to help solve the problems of high energy consumption and high pollution in the traditional industrial production mode, and promote the green transformation of industrial enterprises has become a key measure to realize the collaborative governance of pollution and carbon emissions. Industrial robot is an important equipment of modern industrial intelligence. According to the definition of IFR and ISO, industrial robot is a kind of mechanical equipment with high degree of freedom and multiple functions that can independently complete a certain task. It is developing in the direction of self-awareness, self-adaptation, self-learning and intelligent cooperation, which integrates a variety of advanced technologies, leads the development route of advanced technologies, and becomes a key force to promote the transformation of production and lifestyle. According to CRIA and IFR data, from 2007 to 2022, the number of industrial robots installed in China has increased from less than 10,000 to 290,000. With the continuous popularity of industrial robots, scholars began to pay attention to the impact of industrial robot applications on energy efficiency³, carbon emission⁴ and green development⁵, but there were some differences in the conclusions^3,5. From the perspective of robot application status, although the number of industrial robots installed in China has increased rapidly, most of them are used in the fields of handling, loading and unloading, welding and assembly. Most robots are simply replacing workers’ labor, hence, whether the production mode of simple “machines replacing humans” ¹ at the current stage can reduce pollutant emissions is still in doubt.

The application of industrial robots has brought about a significant improvement in production efficiency, which will reduce the energy and material consumption of unit product production, thereby reducing the emissions of pollutants and carbon dioxide, but it may also lead to the “energy rebound” effect. Thus, enabling the popularity of industrial robots has provided new opportunities and challenges for China’s environmental governance and comprehensive green transformation of economy and society. Under the goal of controlling both the total amount and intensity of emissions, the control of emission intensity means to achieve carbon emissions and pollution governance in the process of promoting economic growth, which is the focus of this paper. It means the coordinated promotion of economic growth and environmental protection, which is different from the green development path of pollution first and then governance; And the reduction of emissions means the fundamental improvement of environmental quality, which is also the key to green development, so it is necessary to discuss the two separately. So, what impact will the application of industrial robots have on the collaborative governance of reducing environmental pollution and carbon emission? Will it promote the collaborative governance of reducing environmental pollution and carbon emission or aggravate pollution? Is this impact limited to the intensity of emissions, or does it include total emissions? What is the impact mechanism? These problems need further study. Therefore, this paper collected the panel data of 284 prefecture level cities in China from 2013 to 2021 to verify whether the application of industrial robots can promote the collaborative governance of reducing environmental pollution and carbon emission. Compared with the existing research, the possible contributions of this paper are as follows: Firstly, the emission intensity, total emission and the degree of carbon reduction synergy of carbon dioxide, sulfur dioxide, industrial dust and industrial wastewater are taken as the research objects to analyze the impact of industrial robot application on the collaborative governance of reducing environmental pollution and carbon emission. It is proved that industrial robot application can promote the collaborative governance of reducing environmental pollution and carbon emission, specifically, the application of industrial robots can reduce the emission intensity and total emission of carbon dioxide and sulfur dioxide. In a word, the difference between this paper and other related literature is that, this paper not only links the mechanism and principle of two important pollutants treatment and emission reduction, but also further discusses the significance of industrial intelligent transformation for “green expansion and growth”. In addition, this paper not only verifies whether the application of industrial robots can promote the collaborative governance of pollution and carbon reduction, but also further discusses the conduction mechanism, which expands the research content in this field, thus, in the subsequent mechanism analysis, it is found that the role of increased capacity utilization, economies of scale, foreign investment and biased technological progress in industrial robots to promote carbon pollution collaborative governance. Secondly, Existing studies have found that informatization will significantly promote green technology innovation. Therefore, based on the perspective of technological innovation, this paper focuses on the role of biased technological progress in it, therefore, further analysis found that the technological progress caused by the application of industrial robots tended to use capital factors and save energy and labor factors to promote the sustainable development of economy and society, and the industrial structure optimization plays a positive moderating role in it. Finally, in order to more clearly show the difference of driving factors of industrial robot application for emission reduction and treatment of two different pollutants, through LMDI decomposition of the factors affecting carbon dioxide and sulfur dioxide emissions, this paper found that the current simple machine replacement production mode can’t achieve energy conservation and emission reduction, and it is necessary to give full play to the productivity improvement, technological progress, factor substitution, and the introduction of foreign capital brought by robot applications, so as to promote the collaborative governance of reducing environmental pollution and carbon emission.

Literature review

The early research on industrial robots mainly focused on the impact of industrial robot applications or industrial intelligence on the labor market^7,8,9,10,11. Acemoglu^7,8 analyzed the impact of industrial intelligence transformation on the labor market structure through endogenous labor skill distribution, and described the role of various input factors in production, laying a theoretical foundation for subsequent research. Sun and Hou⁹ has proved from theoretical model and experience that industrial intelligence will promote advanced equipment replaces the workforce with middle and high school education, but will increase the demand for highly skilled talents and make employment structure present the phenomenon of “polarization”. The empirical evidence of Autor¹⁰ and Frey¹¹ have also confirmed the existence of this phenomenon, and it is still very stable after considering the self-adjusting behavior of the labor market, indicating that the application of robots may lead to the upgrading of the labor structure in the industrial sector, but will not affect the wage level of enterprises¹². The above research shows that industrial robots will promote the upgrading of human capital structure, but will impact the labor market, which is specifically reflected in the imbalance of labor employment structure and the aggravation of unemployment of low skilled labor. With the growing call for environmental protection, the academic community has gradually recognized the role of industrial robots or industrial intelligence in promoting pollution governance or green transformation^{3,4,5,13,14,15,16,17}. Huang³ found through analysis that the promotion effect of industrial robot application on energy efficiency is reflected in the promotion effect on economic growth, and the impact on energy consumption structure is not significant; In contrast, the other study has found that robot applications not only improve production technology and reduce energy intensity, but also reduce emissions per unit of energy consumption through the effect of emission reduction technology⁵, which is somewhat different from Huang’s research conclusion. Jiang et al.⁴ introduced the robot elements into the LMDI analysis framework and found that the technology emission reduction effect of robots was higher than the scale emission increase effect on average, and the carbon emission reduction effect was gradually significant. In detail, the robot investment effect and capital automation effect showed significant emission increase contributions, while the labor robot element structure effect and robot energy element structure effect had significant emission reduction contributions. In terms of the impact mechanism of industrial robot application on different pollutants, existing studies believe that industrial robot application can reduce urban carbon emission intensity through green technology innovation incentive mechanism and man-machine matching effect¹³. Yu et al.¹⁴ took urban PM2.5 concentration as the research object and found that robot application can reduce urban pollution level by promoting the upgrading of industrial structure and the improvement of scientific and technological level. In addition to the above-mentioned literature using provincial and city level panel data to study the impact of robot applications on green development, there are also some literatures that analyze the impact of industrial robot applications on energy conservation and emission reduction based on enterprise level data from a micro perspective. Li et al.¹⁵ found that industrial robot applications can significantly improve regional green technological innovation capabilities, and the effect is heterogeneous among different industries. Wei et al.¹⁶ found that “green dividends” do exist in enterprise intelligent transformation through micro data test, and environmental investment and green technology innovation play a key role in it, in addition to the emission reduction effect brought by the technology dividend, the existing research also found that industrial robot applications can reduce the intensity of sulfur dioxide emissions through front-end and end-of-end processing¹⁷. It can be found that although the existing research specifically analyzed the relationship between intelligent transformation and environmental protection, it did not pay attention to the relationship between intelligent transformation and collaborative governance, and the mechanism part needs to be further discussed.

The existing research on the collaborative governance of reducing environmental pollution and carbon emission is mainly based on the “accompanying effect”¹⁸. Po Kou et al.¹⁹ found that the synergistic effect of sulfur dioxide emission reduction of carbon emissions trading is mainly achieved by reducing the consumption of fossil energy, while Shindell²⁰ believed that if the elimination of fossil energy sources is not global, the emission reduction effect of sulfur dioxide will be limited to regions using clean energy, but the emission reduction effect of carbon dioxide will spread to the world, emphasizing the key to the synchronous transformation of energy structure in various regions. Gao²¹ found that although carbon dioxide was not included in the tax target in the EPT law, it still significantly improved the collaborative emission reduction of “sulfur dioxide (SO2) -co2” and “particulate matter (PM) -co2”, indicating that the control of emissions of other pollutants will also reduce sulfur dioxide emissions. With the development of the era of digital economy, the dual control of emission intensity and total amount through digital elements and digital technologies has attracted scholars’ attention, in particular, the role of digital transformation in promoting the substitution of green energy has been confirmed in the research sample of Malaysia²². Therefore, from the perspective of China, whether the transformation of industrial intelligence can promote the substitution of energy factors still needs some practical basis.

The above literature shows that, on the one hand, the existing research has a certain understanding of the role of industrial robots in promoting pollution reduction or green transformation, but the analysis of the green development effect brought by the application of industrial robots is not comprehensive enough, the impact of industrial robots on the collaborative governance of reducing environmental pollution and carbon emission was not further discussed. On the other hand, although many mathematicians have highlighted the role of technological progress in promoting pollution reduction by industrial robots, the mechanism and path of the role of biased technological progress are not clear enough and still need to be investigated.

Theoretical background

The implementation plan for the synergy of pollution and carbon reduction issued by the Ministry of ecological environment and other seven ministries and commissions require strengthening the prevention and control of the source of pollution and carbon emissions. In economic operation, the production side is the main source of pollutants and carbon dioxide emissions. From the perspective of production, environmental pollution is determined by three factors at the same time: economic aggregate, industrial structure and technical level²³. Therefore, influencing factors of environmental governance can be divided into aggregate effect, structural effect and technical effect^23,24, so, industrial robots are mainly used in the field of production and manufacturing, which affect the emissions of pollutants and carbon dioxide through the impact on the economic aggregate, industrial structure and technical level. First, from the perspective of aggregate effect, industrial robots have the advantages of high precision and efficiency, which can promote the reduction of production costs and significantly improve the productivity of each enterprise^3,25, so that each enterprise can obtain more output from the input of production factors and increase the profits of enterprises. Profit growth enables enterprises to invest more money in emission reduction, so as to improve the pollution control level of manufacturing enterprises²². At the same time, the improvement of enterprise production efficiency will also promote the improvement of overall economic efficiency, significantly promote the growth of economic aggregate, reduce the energy consumption and material consumption per unit of GDP, and thus reduce the emission intensity of pollutants and carbon dioxide. Moreover, the technology emission reduction effect brought by robot application is higher than the scale emission increase effect on average⁴, which is generally conducive to reducing the total amount of pollutants and carbon dioxide emissions. Second, from the perspective of structural effect, IFR estimates that the value of industries driven by industrial robots is more than three times the volume of its own manufacturing industry. The use and manufacturing of industrial robots need the support of many cutting-edge technologies. With the increase of enterprises’ demand for industrial robots, it will promote upstream and downstream enterprises to improve production processes, promote product and technological innovation, and attract new materials, artificial intelligence, information technology and other industries to gather in the region, so that the industrial chain continues to develop in a clean and high value-added direction and accelerate the intelligent transformation of various industries¹⁴, thus promoting the upgrading of industrial structure, making high pollution, high energy consumption industries gradually withdraw from the market, and green and low-carbon emerging industries will continue to grow. In addition, with the popularity of industrial robots, China’s employment structure shows a phenomenon of “polarization“⁹. Low skilled labor is replaced while increasing the demand for high skilled labor, thus promoting the upgrading of regional human capital structure. The optimization and adjustment of economic structure, such as industrial structure and factor structure, has an overall “structural dividend” effect on regional carbon emission reduction²⁶. Third, from the perspective of technology effect, the robot industry is a high-tech emerging industry. With the popularity of industrial robots, it can effectively promote the improvement of regional scientific and technological level and innovation ability, so as to improve the level of environmental protection technology and energy utilization efficiency¹⁴. Robot applications not only have the “production technology” effect of reducing energy intensity by improving production technology, but also can achieve pollution reduction by improving emission reduction technology, with the “emission reduction technology” effect⁵. At the same time, due to the upgrading of regional human capital structure brought about by the application of industrial robots, the continuous accumulation of high-quality employees will help to give full play to the advantages of “learning by doing”, further promote the production process and technological innovation, strengthen the effect of “production technology” and “emission reduction technology”, and promote the collaborative governance of reducing environmental pollution and carbon emission.

The logic of Marx’s machine mass production theory believes that the essential difference between machines and tools is that machines promote the reform of production mode by changing the relationship between workers and tools^27,6. According to CRIA and IFR statistics, nearly 90% of robots are currently used for material handling, welding and loading and unloading. At the current stage, enterprises only use industrial robots as power machines for production, which simply replace human labor and provide new kinetic energy for production, but cannot bring about changes in the mode of production⁶. With the popularity of industrial robots in the production process and the expansion of the production scale of enterprises, each production unit uses more energy in the production process³, thus generating more pollution emissions. Although the total amount of energy consumption and pollutant emissions is still growing, the production efficiency improvement effect caused by robot application promotes the growth of economic aggregate faster, and the emission intensity per unit output is gradually reduced, which promotes the reduction of regional pollutant and carbon dioxide emission intensity, and promotes the reduction of pollution and carbon. However, this effect is only reflected in the control of emission intensity, not the control of total emissions. Therefore, hypothesis 1 is proposed in this paper, namely:

Hypothesis 1A

The application of industrial robots can reduce the emission intensity and total emission of pollutants and carbon dioxide, and promote the collaborative governance of reducing environmental pollution and carbon emission.

Hypothesis 1B

The current industrial robot application can only reduce the emission intensity of pollutants and carbon dioxide, but cannot reduce the total emission, because it is only a simple replacement for human labor.

The reason why the application of industrial robots can reduce the emission of pollutants and carbon dioxide is mainly through the improvement of capacity utilization, the development of economies of scale, the attraction of foreign investment and the role of biased technological progress.

With the maturity of artificial intelligence, big data and cloud computing technology, the application of industrial robots makes industrial production move towards a higher level of intelligent production, and solves the problem of low capacity utilization at this stage to a certain extent by “combining production and marketing“²⁸; First of all, each production enterprise will optimize the allocation of its own production factors according to its own production management and market demand²⁹, which will help to give full play to the production capacity of production equipment and reduce non efficiency problems, so as to reduce overcapacity and even backward production capacity problems³⁰, and then improve capacity utilization. Secondly, the personalized customization of high-level intelligent production makes consumers gradually integrate into the production process of enterprises, realizing the “integration” of producers and consumers, supply and demand. Connecting the customer demand side and manufacturing production side through flexible production can not only improve the added value of products, but also avoid the mismatch between product supply and demand caused by incomplete market information, and solve the problems of backward production capacity and low capacity utilization from the perspective of product quality and product quantity²⁸. On the one hand, the improvement of capacity utilization can reduce ineffective output, thus reducing the waste of resources and energy, and thus reducing the emissions of pollutants and carbon dioxide; On the other hand, the improvement of capacity utilization can improve the efficiency of resource allocation, put factors into more efficient departments, and then improve the overall resource utilization and technological innovation ability of the industrial sector and reduce the emissions of pollutants and carbon dioxide. Therefore, this paper puts forward the following assumptions:

Hypothesis 2

Industrial robot applications can improve capacity utilization rate, improve resource allocation efficiency by reducing waste of resources and energy, and then reduce pollutants and carbon dioxide emissions, so as to realize the collaborative governance of reducing environmental pollution and carbon emission.

Economies of scale means that with the expansion of production scale, the unit cost of products decreases. Compared with the first and second industrial revolutions, the third and fourth industrial revolutions characterized by industrial intelligence showed a broader trend of “machine replacement”. Unlike human labor, industrial robots can independently complete production tasks according to program settings, and the continuous operation time is far longer than that of workers. Even if the production efficiency of workers and robots remain unchanged, as long as the cost of robots decreases (there is economies of scale in robot production, and the unit cost of robots will decrease), more enterprises and departments will use robots. Generally speaking, the production efficiency of enterprises and departments using robots is generally high. With the increase of enterprises choosing to use industrial robots for production, capital deepening can be accelerated to improve regional total factor productivity³¹. The improvement of regional overall productivity will save the input of regional production factors, reduce energy and material consumption, reduce the emissions of pollutants and carbon dioxide, and promote the collaborative governance of reducing environmental pollution and carbon emission. Therefore, this paper puts forward the following assumptions:

Hypothesis 3

Industrial robot application has scale effect, which can improve the regional total production efficiency, thereby reducing the emissions of pollutants and carbon dioxide, so as to realize the collaborative governance of reducing environmental pollution and carbon emission.

Enterprises usually choose the production form with low cost. According to the CRIA questionnaire, nearly 80% of enterprises apply industrial robots to reduce the cost of using labor force, and machine replacement can give full play to its cost advantage⁶. In addition, the efficiency advantage brought by the application of industrial robots promotes the gradual improvement of the production efficiency and business performance of China’s industrial enterprises³. Driven by the pursuit of profit, international capital will give priority to regions or industries with high level of industrial robots in China. At the same time, with the establishment of complete robot industrial parks in various parts of the country by the leading domestic robot enterprises such as Xinsong, EFT intelligence, Kaiyuan, Changtai, etc., a large number of parts and productive service providers are gathered in the industrial parks, which promote the continuous improvement of infrastructure, and constantly optimize the scale and manufacturing capacity of China’s industrial robot industry. The expansion of market scale and the gradual improvement of industrial infrastructure attract internationally renowned robot manufacturers and related manufacturing industries to settle in China^32,33. The entry of FDI in the above regions and industries can bring advanced production technology, management experience and pollution governance technology. On the one hand, it can improve the energy utilization rate. On the other hand, it can enhance the environmental protection awareness of domestic enterprises and the use of clean energy through mutual exchange and learning, so as to improve the innovation ability of green technology³⁴. Moreover, intelligent technology and automated production can also spread foreign advanced environmental protection concepts and technologies brought by FDI to other enterprises or regions³⁴, especially for foreign direct investment related to intelligence, which is conducive to reducing the emissions of pollutants and carbon dioxide and promoting the collaborative governance of reducing environmental pollution and carbon emission. Therefore, this paper puts forward the following assumptions:

Hypothesis 4

The application of industrial robots is conducive to attracting foreign investment, and reducing the emission of pollutants and carbon dioxide by acquiring foreign advanced technology, management experience and environmental awareness and spreading them to other enterprises or regions, so as to realize the collaborative governance of reducing environmental pollution and carbon emission.

Since Hicks proposed biased technological progress, there have been many controversies about this theory in academic circles due to its lack of a certain micro basis. However, since this century, the research on this theory has been deepening, and more and more scholars have found that the main technological progress has shown obvious element bias, and compared with neutral technological progress, biased technological progress can more truly reflect the regional technological level. With the change of the degree of surplus of factor input, there are different biases in technological progress. The application of industrial robots reflects the continuous deepening of capital in the production field and accelerates the replacement of robots for people in the labor market¹², through calculation, it is found that the substitution elasticity between capital and labor in China and the United States is less than 1. Therefore, robot application is capital biased technological progress³⁵. The capital embodied technological progress brought by robots has improved total factor productivity and reduced energy consumption. By using robots with relatively low marginal cost and high efficiency, the factor input required by the unit output of enterprises has been reduced, which can reduce pollution discharge⁴, and energy saving technological progress can effectively reduce energy intensity³⁶. In addition, for the same industry or enterprise, capital biased technological progress can improve the marginal productivity of capital, so it can play the market scale effect of biased technological progress to maintain higher R&D investment³⁷. It can also improve the level of pollution discharge treatment by regional enterprises or industries through technology spillover, thus generating “R&D growth effect” and further promoting green technology innovation to reduce industrial pollution discharge^37,38. In general, the capital biased technological progress embodied in industrial robot applications can improve total factor productivity, reduce energy and other factor inputs, and produce “R&D growth effect”, which helps to reduce pollutants and carbon dioxide emissions. Therefore, this paper puts forward the following assumptions:

Hypothesis 5

The application of industrial robots makes technological progress tend to use capital, improve total factor productivity, save energy and other factor inputs and generate R&D growth effect, thereby reducing pollutants and carbon dioxide emissions, so as to realize the collaborative governance of reducing environmental pollution and carbon emission.

Data and methods

Model selection

In order to verify whether the application of industrial robots can promote the collaborative governance of reducing environmental pollution and carbon emission, this paper selects the double fixed effect model for analysis, and the specific model is as follows:

$$\,\,\,\,\,\,\,\,\frac{{Pollutant}_{it}}{{GDP}_{it}}={\alpha\,}_{0}+{\alpha\,}_{1}{Bot}_{it}+{\alpha\,}_{3}{Control}_{it}+\lambda\,+\gamma\,+{\mu\,}_{it}\,$$

(1)

Among them, Pollutant_it/GDP_it is the explained variable, which represents the intensity of pollutant emission, and is intended to measure whether industrial robot applications can promote green development and contribute to the collaborative governance of reducing environmental pollution and carbon emission. In addition, Pollutant_it and Gov_it are also included in the model (1) as explanatory variables for regression analysis. Pollutant_it/GDP_it represents the industrial pollution emission intensity of each city, α₀ is the intercept term, α₁ is the coefficient of the core explanatory variable, and Bot_it is the core explanatory variable, that is, the installation density of industrial robots in each city; Control_it is the control variable, λ and γ are the individual and time fixed effects, µ_it is the random error term. In addition, Pollutant_it is used as the explained variable to verify the impact of industrial robot applications on the total amount of industrial carbon emissions in each city. Through the above analysis, it can be tested whether industrial robot applications can control the emission intensity or amount of carbon dioxide and other pollutants at the same time. If α₁ is significantly negative for different explained variables, it means that industrial robot applications can control the emission of carbon dioxide and other pollutants at the same time, so as to realize the collaborative governance of reducing environmental pollution and carbon emission.

In order to test how industrial robot applications promote the collaborative governance of reducing environmental pollution and carbon emission, this paper constructs the following model to test:

$$\,{Med}_{it}={\beta\,}_{0}+{\beta\,}_{1}{Bot}_{it}+{\beta\,}_{3}{Control}_{it}+\lambda\,+\gamma\,+{\mu\,}_{it}\,$$

(2)

Among them, Med_it is the mediating variable, and other variables are the same as model (1). If β₁ is significant, it indicates that the mediating effect exists.

Variables selection

This paper selects the panel data of 284 prefecture level cities in China from 2013 to 2021 (excluding Tibet, Hong Kong, Macao and Taiwan) as the research sample, and the specific variables are as follows.

Dependent variables Collaborative governance of reducing environmental pollution and carbon emission. As a typical air pollutant, sulfur dioxide is one of the two major pollutants in the Chinese government’s pollution reduction targets, while carbon dioxide is the primary target of green emission reduction. Existing studies have confirmed that a single emission reduction treatment for sulfur dioxide or carbon dioxide emissions will drive the emission reduction treatment of another pollutant¹⁹. Therefore, if the industrial robot application can promote the emission reduction treatment of both at the same time, it indicates that there is a synergistic treatment effect. Moreover, In order to verify the viewpoint put forward in this paper, that is, whether the application of industrial robots can promote the coordinated evolution of “pollution reduction, carbon reduction, green expansion and growth”, this paper makes regression analysis on the emission intensity and total emission respectively. In dependent variables, GDP_it is the actual gross national product of each city, which is deflated based on the price in 2007. Pollutant_it is the amounts of pollutants emitted by each city in each year. Pollutant_it/GDP_it represents the intensity of pollutant emissions in each region. Since carbon dioxide and sulfur dioxide are highly homologous, and sulfur dioxide is a landmark indicator to measure the degree of industrial pollution, this paper first takes the industrial carbon dioxide (CO2) and sulfur dioxide (SO2) emissions by each city as the research samples to verify whether the application of industrial robots can reduce the emission intensity and amount of the above pollutants, and uses Pollutant_it to measure the total amount of industrial environmental pollution carbon emissions in each city. Among them, the data of carbon dioxide emissions comes from the energy statistical yearbook of each region, and calculated according to the carbon emission coefficient and conversion factor of each energy. The emission data of sulfur dioxide is provided by CSMAR database and EPS database.

Independent variable (industrial robot application) referring to the method of Acemoglu²⁵, this paper collects the installation volume of industrial robots in various industries in China published by IFR, according to the idea of Bartik’s instrumental variable method, the penetration of industrial robots in each city is calculated by using the employment share of each city’s industry and the penetration of industrial robots in the base year, the specific formula is as follows:

$$\,{Robot}_{it}=\sum\,_{j=1}^{J}\frac{{labor}_{i,j,t=2004}}{{labor}_{i,t=2004}}\times\,\frac{{Robot}_{jt}}{{labor}_{j,t=2004}}$$

(3)

where i, j, t respectively refers to city, industry and year, Labor_{i, j,t=2004} represents the labor force scale of industry j in i City in 2004, and Robot_jt represents the number of industrial robots installed in industry j in t (year).

Control variables Since carbon dioxide is highly consistent with the emission sources of sulfur dioxide, in order to control the relevant factors affecting the emission of various pollutants, refer to the existing research^4,15,37, and the control variables are selected as follows: (1) Financial deepening degree (Fin): the degree of financial development may have an impact on regional economic growth and pollution control, so the loan balance of each city as a proportion of urban GDP is selected for measurement. (2) Government expenditure on science and Technology (Gst): the proportion of government expenditure on science and education in GDP is selected to measure the impact of government investment in scientific and technological innovation on the collaborative governance of reducing environmental pollution and carbon emission. (3) Energy consumption structure (Ecs): considering that China’s energy consumption structure is dominated by coal, and the combustion of coal will produce a large amount of sulfur dioxide, the annual consumption of natural gas, liquefied petroleum gas and electricity in various regions and cities is selected to measure the change of China’s energy consumption structure, so as to control the impact of the change of energy consumption structure on the coordinated governance of reducing environmental pollution and carbon emission. (4) Rationalization level of industrial structure (Ris): Given that the Theil index can reflect the degree of concentration of various industries in total output, comparing the income or output ratios of different industries can intuitively understand the proportion of different industries in the overall industry, and thus evaluate the rationality and health level of industrial structure. Therefore, the Theil index is selected to measure the rationalization level of industrial structure in various prefecture level cities in China. (5) Environmental pollution level (Pol): The unit output of pollutants in each city is calculated and standardized, and then the standardized results are summed up to control the impact of urban pollution level on the collaborative governance of reducing environmental pollution and carbon emission. (6) Digital economy development level (Dig): This paper selects the number of Internet broadband access households with 100 people in each city, the number of telecommunication industry businesses per capital, the proportion of computer and software industry employees, and the digital finance index of Peking University as the sub indicators to measure the development of the digital economy, and uses the entropy method to measure the above indicators. (7) Number of employees (Peo): The number of urban employees in each year of each prefecture level city is selected to measure the impact of labor force size on the level of urban green development. The above raw data are all from CNRDS, CSMAR, EPS data platforms, as well as manually organized and calculated by individuals. Table 1 shows the descriptive statistics of each variable:

Table 1 Descriptive statistics of variables.

Empirical results and analysis

Benchmark effect regression

According to our assumptions, due to the high degree of homology between environmental pollutants and carbon dioxide, the governance of carbon dioxide is bound to be accompanied by the reduction of emission intensity of sulfur dioxide, and similarly, the governance of sulfur dioxide is bound to be accompanied by the reduction of carbon emission intensity; In this paper, the variables are brought into the model (1) for analysis to verify whether the application of industrial robots can simultaneously promote the reduction of the emission intensity of environmental pollutants such as carbon dioxide and sulfur dioxide, so as to promote the collaborative governance of reducing environmental pollution and carbon emission. See Table 2 for the specific results. It can be seen from the results in Table 2 that the application of industrial robots can significantly reduce the emission intensity of carbon dioxide and sulfur dioxide. The possible reasons are as follows: the emissions of sulfur dioxide and carbon dioxide are mainly from the combustion of fossil fuels. On the one hand, the application of industrial robots will promote the growth of economic aggregate, on the other hand, promote the upgrading of industrial structure and enhance green technology innovation capability, which will reduce the consumption of fossil energy, thereby reducing the emissions of sulfur dioxide and carbon dioxide, making the increase of economic aggregate accompanied by the decline of emissions, and ultimately reducing the intensity of sulfur dioxide and carbon dioxide emissions. In order to further judge whether the industrial robot application can control the emission intensity and reduce the emission at the same time, this paper takes the emissions of carbon dioxide and sulfur dioxide as the explained variables again into the model (1) for analysis, and the results are shown in Table 2.

Table 2 Benchmark effect regression.

According to Table 2, the application of industrial robots can effectively reduce the emissions of carbon dioxide and sulfur dioxide, indicating that industrial robots can promote the simultaneous reduction of the emission intensity and total amount of carbon dioxide and sulfur dioxide, and realize the dual control of the governance intensity and total amount, so as to realize the collaborative governance of reducing environmental pollution and carbon emission. To sum up, the application of industrial robots can reduce the emission intensity and amount of carbon dioxide and sulfur dioxide at the same time, but it can’t improve the pollution of industrial dust and industrial wastewater. Hypothesis 1A is proven.

Robustness check

Replace the explained variable. The collaborative governance of pollution carbon reflects China’s demand for green and low-carbon development. In this paper, the explained variable is replaced by the result of SBM super efficiency model, see the mechanism analysis section for the specific calculation method. After variable replacement, the core explanatory variable is still significant.

Add control variables. In order to avoid the endogenous problems caused by the omission of control variables. In this paper, green finance (GF), industrial structure upgrading (Ind) and marketization level (Mar) are added into model (1) for regression analysis. The test found that the core explanatory variables were still significant after adding additional control variables.

Replacement regression model. In this paper, the dynamic panel regression model (GMM) is selected to re regress model 1. The test shows that the core explanatory variables are still significant after the first-order lag term of the explanatory variables is included.

Endogenous problems. This paper refers to the idea of Bartik’s instrumental variables, the product of the lag one period of the penetration of industrial robots in prefecture level cities and the first-order difference in the penetration time of industrial robots is used to construct the tool variable, and the endogenous test is carried out through the tool variable method. The test shows that the core explanatory variables in the first and second stage regression are still significant. The original hypothesis is still true.

See Supplementary Appendix for the above robustness test results.

Mechanism analysis

In order to verify whether industrial robot applications can reduce pollutants and carbon dioxide emissions through improving capacity utilization, economies of scale, attracting foreign investment and facilitating biased technological progress, so as to promote the collaborative governance of reducing environmental pollution and carbon emission, this paper analyzes according to model (2), and the specific intermediary variables are selected as follows.

The calculation of capacity utilization rate (CU_it) refers to Dong et al.³⁰, DEA method is used for measurement. Constant input is industrial fixed capital input, variable input is industrial energy and average labor quantity, and output is the actual total industrial output value, the regional industrial capacity utilization rate is obtained by multiplying the technical efficiency and the equipment utilization rate. (2) FDI is measured by the actual amount of foreign investment in each city in each year. (3) This paper chooses Malmquist index to measure scale efficiency and biased technological progress. This paper chooses input-output analysis to measure the efficiency of scale (Secit) and biased technological progress (IBTC_it). The input variables are the actual capital stock of each region (calculated according to the sustainable inventory method), energy consumption and the number of employees of each city in each year. The expected output is the industrial production added value of each city calculated at constant prices in each year, and the undesirable output is the annual industrial sulfur dioxide and carbon dioxide emissions of each city; Because the super efficiency SBM model can avoid the situation that there is no feasible solution to a certain extent, this paper selects the super efficiency SBM model to measure the green development efficiency of various regions as the robustness test, and calculates the SBM-GML index, which decomposes the efficiency of scale (Secit) and biased technological progress (IBTC_it),The measurement of scale efficiency (Secit) depends on the decomposition of Malmquist index, Malmquist = EC×TC, while the EC index can be decomposed into pure technical efficiency index (PEC) and scale efficiency index (SEC). The scale efficiency index (SEC) reflects the impact of the expansion of production scale on TFP. Therefore, in this paper, the scale efficiency index of each year is multiplied to measure the scale efficiency. The TC index can be decomposed into IBTC×MATC×OBTC. IBTC represents the impact of biased technological progress on total factor productivity. If IBTC > 1, it indicates that biased technological progress can promote the improvement of production efficiency. Therefore, the higher the IBTC index, the more conducive it is to promote the improvement of total factor productivity. Since the decomposition items of Malmquist index represent the change rate at the beginning of the base period, this paper takes the base period as one, and obtains the real value of each year by multiplying the annual scale efficiency and biased technological progress index, and the calculation of the above indicators depends on Maxdea.

According to column 1 of Table 3, the application of industrial robots can effectively improve the utilization rate of production capacity, avoid the production interruption or efficiency fluctuation caused by human factors, ensure the environmental friendliness of the production process, reduce the waste of resources and improve the utilization rate of resources, so as to promote the improvement of economic efficiency, not only improve the economic benefits of industrial enterprises, but also reduce the emissions of pollutants and carbon dioxide, which is helpful to realize the collaborative governance of reducing environmental pollution and carbon emission. It can be seen from column 2 of Table 3 that the application of industrial robots can attract foreign investment, so as to make use of the advanced technology, management experience and environmental protection awareness brought by foreign investment and spread them to other enterprises or regions³⁴ and reduce the energy intensity of each region³⁹, so as to reduce the emissions of pollutants and carbon dioxide and promote the collaborative governance of reducing environmental pollution and carbon emission. It can be seen from column 3 of Table 3 that the application of industrial robots can give full play to its advantages of large-scale production through accurate control and automation of production lines, improve the overall production efficiency of the region, reduce energy consumption and raw material waste, optimize the production process, reduce the emissions of pollutants and carbon dioxide, and help to realize the collaborative governance of reducing environmental pollution and carbon emission. It can be seen from column 4 of Table 3 that the application of industrial robots can effectively play the role of biased technological progress by improving factor productivity, factor substitution effect, saving total factor investment and generating R&D growth effect, so as to promote green technology progress and promote energy factor substitution, and ultimately reduce pollutants and carbon dioxide emissions, and help to realize the collaborative governance of reducing environmental pollution and carbon emission. To sum up, hypotheses 2, 3, 4, 5 are proved.

Table 3 Mechanism analysis.

Heterogeneity analysis

Biased technological progress

IBTC index represents the impact of biased technological progress on TFP, but this index alone cannot determine which factor of production is more preferred by technological progress and which factor of production is saved. According to the classification and definition of Acemoglu⁴⁰ and Wang and Qi³⁶, this paper judges the factor use bias of technological progress according to the input ratio between two factors and the size of IBTC index. See Table 4 for specific discrimination methods:

Table 4 Discrimination method of factor input tendency.

In order to further verify whether the application of industrial robots will promote technological progress in favor of the use of capital factors, thereby reducing the input of labor and energy factors, promoting the improvement of enterprise production efficiency and reducing energy consumption intensity, thereby reducing pollution emissions, and ultimately realizing the collaborative governance of reducing environmental pollution and carbon emission, this paper compares the size changes of capital factor/energy factor and capital factor/labor factor in each year with IBTC index, so as to determine the technological progress bias in each year, and set the dummy variable, that is, the technological progress in the current year is biased towards the use of capital, so it is assigned as 1, otherwise it is 0, so as to obtain the explained variables EBK (capital and energy factors are biased towards the use of capital) and LBK (capital and labor factors are biased towards the use of capital). Since the explained variables are dummy variables, probit and logit models are selected for regression analysis. See Table 6 for the specific results. Columns 3 and 4 of Table 5 are the regression results of Logit model, and columns 5 and 6 are the regression results of probit model. The coefficient in the table is its marginal effect, and the goodness of fit in R2 has also been recalculated; It can be seen from the results that the coefficient of the core explanatory variable is positive and significant in the results of both probit model and logit model, that is, the technological progress caused by the application of industrial robots tends to use capital factors, save energy and input of labor production factors, which is conducive to energy conservation and emission reduction, which confirms the conclusion of Lin et al.⁵ from the perspective of technological progress.

Table 5 Heterogeneity analysis.

Moderating effect

The rational evolution of industrial structure means to promote the optimal allocation of resources by adjusting the unreasonable industrial structure, so as to realize the adaptation of industrial reform to economic development, which can significantly affect technological innovation, economic benefits and environmental benefits; In order to verify the regulatory effect of the rationalization level of industrial structure on the relationship between industrial robot application and the collaborative governance of reducing environmental pollution and carbon emission, this paper adds the cross product term (BOT × IS) between the installation density of industrial robots and the rationalization level of industrial structure for regression, and the data is standardized. See Table 5 for the specific results. It can be seen from Table 6 that the multiplicative term is significantly negative at the 1% level, indicating that the more reasonable the industrial structure is, the more conducive it is to reducing the emission of sulfur dioxide and carbon dioxide through the application of industrial robots, thus contributing to the realization of collaborative governance of reducing environmental pollution and carbon emission.

Table 6 Analysis of impact factors of CO₂ emission.

LMDI decomposition

In order to further understand the contribution of robot applications to the collaborative emission reduction of sulfur dioxide and carbon dioxide, this paper selects LMDI decomposition framework to analyze the contribution of industrial robots to SO₂ and CO₂ reduction. Since the provincial capitals and central cities can represent the economic development and environmental protection status of the region, the research sample is the provincial capitals and central cities of provinces, cities and autonomous regions across the country. The data of 34 cities from 2013 to 2021 are broken down. See formula 4 for the decomposition formula:

$$\begin{aligned} \,\text{P}\text{o}\text{l}\text{l}\text{u}\text{t}\text{a}\text{n}\text{t} &=\sum\,_{\text{i}=1}^{34}{\text{P}\text{o}\text{l}\text{l}\text{u}\text{t}\text{a}\text{n}\text{t}}_{\text{i}}=\sum\,_{\text{i}=1}^{34}{\text{G}}_{\text{i}}\times\,\frac{{\text{P}\text{o}\text{l}\text{l}\text{u}\text{t}\text{a}\text{n}\text{t}}_{\text{i}}}{{\text{G}}_{\text{i}}}=\sum\,_{\text{i}=1}^{34}{\text{F}\text{D}\text{I}}_{\text{i}}\times\,\frac{{\text{G}}_{\text{i}}}{{\text{F}\text{D}\text{I}}_{\text{i}}}\times\,\frac{{\text{L}}_{\text{i}}}{{\text{G}}_{\text{i}}}\times\,\frac{{\text{R}}_{\text{i}}}{{\text{L}}_{\text{i}}}\times\,\frac{{\text{G}}_{\text{i}}}{{\text{R}}_{\text{i}}}\times\,\frac{{\text{K}}_{\text{i}}}{{\text{G}}_{\text{i}}}\times\,\frac{{\text{E}}_{\text{i}}}{{\text{K}}_{\text{i}}}\times\,\frac{{\text{P}\text{o}\text{l}\text{l}\text{u}\text{t}\text{a}\text{n}\text{t}}_{\text{i}}}{{\text{E}}_{\text{i}}}\\ &=\sum\,_{\text{i}=1}^{34}{\text{P}}_{\text{F},\text{i}}{\text{P}}_{\text{G}/\text{F},\text{i}}{\text{P}}_{\text{L}/\text{G},\text{i}}{\text{P}}_{\text{R}/\text{L},\text{i}}{\text{P}}_{\text{G}/\text{R},\text{i}}{\text{P}}_{\text{K}/\text{G},\text{i}}{\text{P}}_{\text{E}/\text{K},\text{i}}{\text{P}}_{\text{P}/\text{E},\text{i}}\,\end{aligned}$$

(4)

According to the assumption of this paper, robot application can improve productivity and promote green technology innovation, but also increase the use of capital factors, save labor and energy factors. In addition, robot applications can attract the entry of foreign-funded enterprises, so as to absorb the advanced technology and management experience of foreign capital, and promote the reduction of carbon dioxide or sulfur dioxide emissions. Therefore, the factors affecting the emissions of the two are decomposed according to the formula as follows: the scale of foreign investment, foreign capital productivity, labor share, man-machine substitution, robot productivity, capital deepening, energy and capital element substitution, and emission intensity. FDI represents the amount of foreign investment, G is the regional gross domestic product, L is the average number of employees of regional industrial enterprises, R is the installation density of robots, K is the regional capital stock, E is the energy consumption of regional industrial enterprises, and Pollutant stands for pollutant discharge. Referring to the research of Ang⁴¹, the method of addition decomposition is adopted: ∆Pollutant = Pollutant^t−Pollutant⁰=∆P_{F, i}+∆P_{G/F, i}+∆P_{L/G, i}+∆P_{R/L, i}+∆P_{G/R, i}+∆P_{K/G, i}+∆P_{E/K, i}+∆P_{P/E, I}, taking the first impact factor ∆P_{F, i} as an example, the calculation formula of each factor is shown in Eq. 5:

$$\,\varDelta\,{\text{P}}_{\text{F},\text{i}}=\sum\,_{\text{i}=1}^{34}\frac{\left({\text{P}}_{\text{i}}^{\text{t}}-{\text{P}}_{\text{i}}^{0}\right)}{\left({\text{l}\text{n}\text{P}}_{\text{i}}^{\text{t}}-{\text{l}\text{n}\text{P}}_{\text{i}}^{0}\right)}\times\,\text{l}\text{n}\left(\frac{{\text{P}}_{\text{F},\text{i}}^{\text{t}}}{{\text{P}}_{\text{F},\text{i}}^{0}}\right)\,$$

(5)

The calculation method of other impact factors is the same as that of Eq. (5), and the specific decomposition results are shown in Tables 6 and 7:

Table 7 Analysis of impact factors of SO₂ emission.

It can be seen from Table 6 that the main driving factors of carbon dioxide emission reduction are emission intensity factor, energy capital factor substitution factor, robot productivity factor and labor force share factor, indicating that the improvement of production efficiency and energy factor saving brought by robot application contribute to the reduction of carbon emissions in cities and cities, and the carbon emission reduction effect of emission intensity factor mainly comes from the energy conservation of industrial enterprises, the progress of cleaner production technology and the improvement of energy utilization efficiency. The man-machine substitution factor will lead to an increase in carbon emissions, which may be due to the substantial increase in productivity caused by the substitution of industrial robots for labor, which will accelerate the expansion of the production scale of industrial enterprises and increase the demand for energy factors, and cause the “energy rebound” effect without considering the technical effects brought by robot applications and other factors; From the man-machine substitution effect, it can be found that the replacement of robots will increase carbon emissions. At this stage, the simple “machine replacing human” production mode⁶ can’t lead to the reduction of carbon dioxide emissions. We should pay attention to the productivity improvement, technological innovation and the optimization of investment structure brought about by the application of robots, relying on the change of production mode brought by the application of industrial robots to promote energy conservation and emission reduction. Foreign investment and foreign capital productivity have only reduced regional carbon dioxide emissions in part of the years, which shows that only relying on FDI can’t stably reduce urban carbon dioxide emissions. Moreover, due to the preference of FDI for urban infrastructure³³, local governments will continue to strengthen local infrastructure in order to introduce foreign investment, which will lead to an increase in carbon dioxide emissions. Therefore, local governments should further play the positive role of green fiscal policy and guide capital flow to the field of green production and consumption⁴².

It can be seen from Table 7 that the contribution of some impact factors to the reduction of sulfur dioxide emissions differs from that shown in Table 6. Among them, FDI can consistently reduce urban sulfur dioxide emissions over many years. This may be attributed to the fact that the primary sources of sulfur dioxide emissions are sulfur-related fossil fuel combustion and industrial processes, whereas the main source of carbon dioxide emissions is from fossil fuel combustion, as well as production and transportation activities in sectors such as transportation and construction, including some indirect emission routes. Although the causes of these two gases are highly homologous, there are also some specific differences. Comparatively speaking, sulfur dioxide emissions are more influenced by industrial production, while foreign investment is primarily focused on China’s industrial manufacturing sector. The influx of foreign capital brings advanced cleaner production technology and management efficiency, which is more conducive to reducing regional industrial enterprise sulfur dioxide emissions. Additionally, emission intensity factors, capital-energy factor substitution factors, robot productivity factors, and labor share factors can still reduce the sulfur dioxide emissions, while man-machine substitution factors will lead to an increase in sulfur dioxide emissions. In summary, at this stage, the simple production model of machine substitution⁶ can’t reduce pollutant and carbon dioxide emissions. It is necessary to rely on productivity improvements, technological advancements, factor substitution, and the introduction of foreign capital brought about by industrial robot applications to achieve energy conservation and emission reduction, so as to truly play the role of industrial robots in promoting the reform of production mode.

Conclusions and suggestions

This article analyzes the panel data of 284 prefecture-level cities in China from 2013 to 2021 and finds that the application of industrial robots can help achieve collaborative governance of reducing environmental pollution and carbon emission. Among them, the application of industrial robots can effectively reduce the emission intensity of industrial carbon dioxide and sulfur dioxide, moreover, it can also reduce the total emissions of carbon dioxide and sulfur dioxide, so as to achieve the goal of double control of intensity and total amount. Through mechanism analysis, it is found that the application of industrial robots plays a role by improving capacity utilization, realizing economies of scale, attracting foreign investment, and promoting biased technological progress, thereby reducing pollutant and carbon dioxide emissions and contributing to the collaborative governance of reducing environmental pollution and carbon emission. The above results have undergone robustness testing and the conclusion is relatively reliable. Further analysis reveals that the improvement of industrial structure rationalization can promote industrial robots to play their role in reducing pollution and carbon emissions, especially, the biased technological progress triggered by the application of industrial robots can save energy and labor factors input. Through LMDI analysis, it was found that emission intensity, substitution of energy capital factors, robot productivity effect, and labor share are the main factors driving the reduction of carbon dioxide and sulfur dioxide emission. Through our research, we found that the production mode that simple “machine replacing human” cannot reduce pollutant emissions, we should pay attention to the change of production mode brought by the application of industrial robots to promote energy conservation and emission reduction, and FDI can sustainably reduce the intensity of regional sulfur dioxide emissions, but cannot steadily reduce regional carbon dioxide emissions. Based on the above analysis conclusions, this article proposes the following suggestions:

Accelerate the deep application of industrial robots in production, thereby better promoting collaborative governance of reducing environmental pollution and carbon emission. First of all, we should clarify the promotion focus and objectives in the manufacturing field, further promote the application of robots in the manufacturing field, focus on the automotive manufacturing, electronic equipment manufacturing, machining and other industries, further give full play to the advantages of high automation of industrial robots, improve the utilization rate of production capacity, and realize the intelligent scheduling and collaborative operation of equipment through the introduction of intelligent production management system, so as to improve the utilization rate of production capacity. Secondly, refine the application path and standard of energy system, and increase the application of robots in energy system, especially in new energy fields such as photovoltaic power generation and wind power generation. Formulate the application standards and specifications of industrial robots in new energy systems, and encourage enterprises to adopt robots that meet the standards for power production, operation and maintenance. Finally, focus on the precise implementation and supervision of key industries, increase the application of industrial robots in key industries of industrial dust and wastewater discharge, such as steel, chemical industry, printing and dyeing, encourage enterprises to adopt industrial robots with dust collection and treatment functions in sintering, iron making, steel making and other processes, and accurately control dyeing, printing and other processes, reduce dye waste and wastewater discharge, and also encourage industry associations and third-party institutions to evaluate and certify the application effect of industrial robots, so as to provide technical support and services for enterprises.

Fully leverage the positive impact of industrial robot applications on production methods, industrial chains, and value chains, avoid excessive investment in low-end fields, and truly unleash the role of industrial robots as “tool machines”. From the perspective of enterprises, firstly, they should explore the application of industrial robots in production processes, optimize production line layout, achieve flexible production, improve production efficiency and product quality, and fully utilize technologies such as artificial intelligence and big data to collect and analyze production data, optimize production decisions, and reduce operating costs. Secondly, enterprises should strengthen the application of robots in precision manufacturing, personalized customization and other fields, expand new business areas and market space, extend the industrial chain and increase product added value. In order to promote the breadth and depth of the application of industrial robots, it is better to expand the scope of application of the preferential tax policies. The current preferential tax policies may have a narrow scope of application. We can consider including more types of industrial robots in the preferential scope, including not only robots in the production and manufacturing process, but also robots in the fields of logistics, warehousing and so on. At the same time, it is also necessary to expand the scope of enterprises. In addition to large manufacturing enterprises, small, medium-sized and micro enterprises also face financial pressure in the application of industrial robots. Therefore, special tax preferential policies can be formulated for small, medium-sized and micro enterprises to reduce the cost of applying industrial robots, and the proportion of accelerated depreciation of fixed assets can also be increased, so that enterprises can quickly deduct the cost of purchasing industrial robots before tax and reduce the current tax burden of enterprises.

Finally, at present, we still need to find a more appropriate index to measure the level of industrial intelligent transformation. At the same time, we need to give you more consideration not to consider the impact of industrial intelligent transformation on the biased technological progress of enterprises from the micro enterprise level, so as to further deepen the conclusion of this paper. We look forward to further research on these issues in the future.