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Intelligent Manufacturing, Man-Machine Matching Degree and Urban Green Total Factor Productivity
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To obtain a consistent estimate of the impact of digital economy on the carbon total factor productivity (CTFP) is important not only in the academic field but also for the policymakers. However, the existence of endogeneity issue makes it challenging. This paper constructs a novel analysis framework that can measure the CTFP as well as the impact of digital economy simultaneously after addressing the endogeneity. By employing the endogenous stochastic frontier analysis method, the findings indicate that digital economy has a positive and significant effect on CTFP, which persists when different production functions and instrumental variables are employed. Mechanism analysis reveals that the positive impact is facilitated by changes in production methods and improvements in urban environmental regulatory governance. Moreover, the effect of digital economy on CTFP is more pronounced in regions with higher environmental awareness among residents, lower resource abundance, and higher levels of human capital. The results provide valuable policy insights into the utilization of the digital economy to achieve green and high-quality development in China, including to promote the regional balanced development of digital economy and so on.
Sustainable Development Goal 8 proposes the promotion of full and productive employment for all. Intelligent production factors, such as robots, the Internet of Things, and extensive data analysis, are reshaping the dynamics of labour supply and demand. In China, which is a developing country with a large population and labour force, analysing the impact of artificial intelligence technology on the labour market is of particular importance. Based on panel data from 30 provinces in China from 2006 to 2020, a two-way fixed-effect model and the two-stage least squares method are used to analyse the impact of AI on employment and to assess its heterogeneity. The introduction and installation of artificial intelligence technology as represented by industrial robots in Chinese enterprises has increased the number of jobs. The results of some mechanism studies show that the increase of labour productivity, the deepening of capital and the refinement of the division of labour that has been introduced into industrial enterprises through the introduction of robotics have successfully mitigated the damaging impact of the adoption of robot technology on employment. Rather than the traditional perceptions of robotics crowding out labour jobs, the overall impact on the labour market has exerted a promotional effect. The positive effect of artificial intelligence on employment exhibits an inevitable heterogeneity, and it serves to relatively improves the job share of women and workers in labour-intensive industries. Mechanism research has shown that virtual agglomeration, which evolved from traditional industrial agglomeration in the era of the digital economy, is an important channel for increasing employment. The findings of this study contribute to the understanding of the impact of modern digital technologies on the well-being of people in developing countries. To give full play to the positive role of artificial intelligence technology in employment, we should improve the social security system, accelerate the process of developing high-end domestic robots and deepen the reform of the education and training system.
China’s economic growth over the past two decades has led to severe environmental pollution in most of its cities. To address this issue, China has developed and implemented several strategies to increase its resource utilization efficiency and move towards a green production economy, such as the information infrastructure construction policy (IICP). To assess the extent to which green production is being achieved, this study first measured the green total factor productivity (GTFP) in Chinese prefecture-level cities using a combined super slacks-based measure and the global Malmquist-Luenberger index (SBM-GML) model. Then, to assess the effectiveness of the IICP, the paths affecting the GTFP were explored using a time-varying difference-in-difference (time-varying DID) model. It was found that the IICP had significantly contributed to urban GTFP through its green technology diffusion effects and improved employment structures, with its implementation resulting in an average increase of 0.201 in the pilot cities compared to the non-pilot cities. This research attempted to explain how IICP plays a role in improving green production, and policy makers should attach importance to the power of IICP.
This paper aims to empirically study the short-term relationship between robot adoption and the labor market across a diverse set of advanced and emerging economies. Additionally, it seeks to analyze the impact of macroeconomic and institutional factors on this relationship. This study reveals robot adoption promotes employment growth in advanced economies, while it has a negative effect on employment in emerging economies. This heterogeneity can be attributed to both direct and indirect linkages between robots and labor in production. Directly, robots can either substitute or complement human labor. Indirectly, robot adoption stimulates output growth, leading to increased labor demand. We also show that the robot–labor relationship is influenced by macroeconomic variables such as development stage, unemployment rate, and education level, as well as institutional variables such as business regulation and structural reforms. These findings suggest the need for a more inclusive and sustainable approach to the advancement of robot adoption and automation.
The synergy of the digital economy and green total factor productivity (TFP) is the foundation for achieving beneficial outcomes for both the economy and environment. This synergy is also the catalyst for high-quality development and sustainable economic growth in China. The study applied a modified Ellison-Glaeser (EG) index, super-efficiency slacks-based measure (SBM) with a Malmquist-Luenberger (ML) index, coupling coordination degree, and other models to explore the spatiotemporal heterogeneity of the coupling between the digital economy and green TFP from 2011 to 2020 and also analyzed the influencing factors of the coupling. The results show that the coupling between the digital economy and green TFP showed an overall upward trend from imbalance to synergy during the study period. The distribution of the synergistic coupling expanded from point-like to band-like, and there was a significant spreading pattern from the east to the center or west China. The number of cities in a transition state decreased significantly. Spatial jumps, a coupling linkage effect, and evolution in time were prominent. Additionally, the absolute difference among cities expanded. Although coupling in the west experienced the fastest growth rate, the coupling in the east and resource-based cities showed significant benefits. Coupling did not reach an ideal coordinated state, and a neutral interaction pattern remains to be formed. Industrial collaboration, industrial upgrading, government support, economic foundation, and spatial quality all positively impacted the coupling; technological innovation had a lagged effect; and environmental regulation has not reached its full potential. Further, the positive effects of government support and spatial quality performed better in the east and in non-resource-based cities. Due to the optimization of industrial structure, the coupling of the west and resource-based cities achieved better dividends; however, spatial quality needs further improvement. Therefore, the efficient coordination of China’s digital economy and green TFP requires a scientific, reasonable, localized, and distinctive approach.
The intelligent city pilot policy is a major measure in China to promote urban development from factor driven and investment driven to innovation driven. Intelligent city construction can effectively coordinate specialized production factors and information sharing mechanism, promote digital information technology innovation, promote smart industry cluster, and expand ecological scenarios of clean industry application, so as to reduce carbon emissions. This paper reveals the internal mechanism of intelligent city construction to promote carbon emission reduction. Based on the quasi-natural experiments carried out in three batches of pilot construction of intelligent cities since 2012, the difference-in-difference model (DID) is used to identify its impact on urban carbon emissions. The research results show that the pilot construction of intelligent cities is conducive to reducing carbon emissions, which is still robust under multiple scenarios such as placebo test and endogenous test. Heterogeneity analysis shows that the pilot policies have a more significant carbon emission reduction effect on the Beijing-Tianjin-Hebei urban agglomeration, non-resource-based cities, and non-old industrial bases. After further quantitative analysis of 917 pilot policy texts based on Simhash algorithm, Jieba word segmentation, and word frequency statistics, it is found that intelligent industry policies reduce carbon emissions by driving data elements agglomeration and optimizing industrial structure, while intelligent government and intelligent people’s livelihood policies improve energy efficiency and reduce carbon emissions through green technological innovation. Counterfactual tests using machine learning algorithms show that the later the pilot batch, the better the sustainable carbon emission reduction effect of intelligent city pilot policies.
Green economic development is a worldwide concern. This paper not only contributes to the advancement of studies pertaining to green development but also offers policy recommendations for China to achieve a green and low-carbon economic transformation from the perspective of outward foreign direct investment (OFDI) and industrial structure. A mechanism for the effects of OFDI and industrial structure upgrading on green total factor productivity (GTFP) is proposed in this study. Based on measurement and analysis of the evolution characteristics of GTFP and industrial structure level of 30 provinces (municipalities and autonomous regions) in China from 2004 to 2019, the spatial Durbin model is applied to test their spatial effects. The findings demonstrate that (1) the overall trend of China’s GTFP and industrial structure level is upward, with obvious regional non-equilibrium and spatial dependence; (2) both OFDI and industrial structure upgrading can promote green development independently, with the spatial spillover effect of Industrial structure advancement being more evident; (3) the synergistic effect between OFDI and industrial structure advancement is greater than that between OFDI and industrial structure rationalization, and the spatial spillover effect on regions with comparable economic development is greater than that of surrounding regions; (4) in view of the different levels of openness between regions, the independent and synergistic effects in coastal regions and non-coastal regions are heterogeneous. Therefore, China should optimize OFDI, promote the efficiency of resource allocation, maximize the technology spillover, and strengthen interregional cooperation in order to transform towards a green economy.
This study investigates the moderating effect of financial development on the renewable energy–CO2 emissions nexus in OECD countries. We find that both composite financial development and banking sector development have an inverted U-shaped impact on CO2 emissions, while stock market development has a U-shaped impact on CO2 emissions. Further, an increase in renewable energy will reduce CO2 emissions, and this reducing impact is affected by different levels of financial development. When promoting financial development, policymakers should pay more attention to its role in enhancing renewable energy, which is related to emissions reduction.
Since China has put forward the Belt and Road Initiative in 2013, research on the BRI-related countries along the Belt and Road has sprung up. With the advent of the era of carbon peak and carbon neutralization, environmental regulation, as one of the important methods to control carbon emissions, is becoming increasingly prominent. Research on the impact pathway of environmental regulation of countries along the Belt and Road on carbon emissions has important implications for environmental protection and carbon emission reduction. Based on the panel data of 38 countries along the Belt and Road from 2005 to 2018, this research applied linear Tobit model and nonlinear dynamic panel regression model (PSTR) to evaluate the direct impacts on carbon emissions from environmental regulation of countries along the Belt and Road, and to analyze the different impacts of environmental regulation on carbon emissions in terms of technical innovation, industrial structure, and energy intensity. We found that (1) the direct impact of environmental regulation on carbon emissions in the countries along the Belt and Road is positive, with slight differences between the Silk Road Economic Belt and 21st Century Maritime Silk Road (2) when technical innovation is at a low level, environmental regulation promotes carbon emissions, while at a high level, environmental regulation significantly inhibits carbon emissions. (3) When industrial structure is at both a low and high level, environmental regulation inhibits carbon emissions, with a stronger degree of inhibition at a higher level. (4) When energy intensity is at a low level, environmental regulation promotes carbon emissions, while at a high level, environmental regulation inhibits carbon emissions. Accordingly, we suggest that countries along the Belt and Road follow the road of sustainable and low-carbon development, which should further enhance their focus on environment protection, improve their environmental awareness, and take environmental regulation measures rationally to reduce carbon emissions. Meanwhile, relevant adjustments should be done on technical innovation, industrial structure, and energy intensity to achieve carbon emission reduction.
China's economic development has entered a new historical stage, and it is crucial to coordinate the linkage between economic development, resource allocation and environmental protection in this new stage. In view of this, this paper selects the panel data of 30 provinces (municipalities and autonomous regions) in China from 2011 to 2020, and firstly measures the Green Total Factor Productivity (GTFP) by using Slack Based Measure -Malmquist Luenberger method (SBM-ML); Then, the relationship between the development of digital economy and regional GTFP is analyzed by using the two-way fixed effect model and threshold effect; Finally, relevant policy suggestions are put forward. This paper found that: firstly, the development of digital economy significantly improves China's GTFP, and the conclusion still valid after considering a series of robustness tests; Secondly, there are obvious disparities in the development level of digital economy among provinces, and the development level of coastal areas is generally higher than that of inland areas, and according to the sub-regional regression results, the positive effect of digital economy on GTFP has regional heterogeneity, and the development of digital economy in coastal areas has a more significant effect on the enhancement of GTFP, while this effect in inland areas does not pass the significance test; Thirdly, according to the threshold effect test results, there is also a single threshold effect with institutional environment and technological innovation as the threshold variables for the impact of digital economy on GTFP in China.
Using data from 30 provinces and cities in China from 2005–2018, panel regression models, mediation models, quantile regressions and threshold regressions were used to examine the relationship between graduate student size expansion, innovative human capital and green total factor productivity (GTFP) and analyze the influence mechanisms and heterogeneity among them. The results of the study are as follows: First, graduate student expansion and innovative human capital are the driving force for GTFP growth in China, and graduate student size expansion indirectly boosts GTFP by promoting the supply of innovative human capital, with a 73% mediating effect. Second, the effects of graduate student expansion and innovative human capital on GTFP show a trend of diminishing marginal benefits, and the estimated coefficient of the effect of graduate student scale share on GTFP has an inverted U-shaped relationship. Third, there is a significant threshold feature of industrial structure upgrading in the influence of graduate student scale expansion on GTFP. The study makes suggestions in terms of expanding of the graduate students, rationalizing the enrollment structure of universities, optimizing the regional industrial structure and creating a social innovation environment.
China is confronted with the dual constraints of economic transformation and carbon emission reduction. As the digital economy is a key force in promoting economic transformation and optimizing industrial structure, it is crucial to analyze the digital economy’s impact on carbon emission reduction from the perspective of energy consumption and industrial value chain implications. We selected data from 251 prefecture-level cities and above in China from 2011 to 2019 as research samples, measured the development level of the digital economy using the entropy value method, and constructed relevant regression models based on two-way fixed effects, intermediary analysis, and moderation analysis. The research reveals that: (1) The digital economy has a significant contribution to carbon emission efficiency, and there are significant regional heterogeneity and city size differences; (2) The digital economy can improve carbon emission efficiency by reducing energy consumption. (3) From a value chain perspective, industrial structure rationalization weakens the carbon emission efficiency improvement effect of the digital economy to a certain extent, whereas industrial structure upgrading obviously enhances the carbon efficiency improvement effect of the digital economy. The above findings enrich the research in the field of digital economy and environmental governance, contribute to a more comprehensive understanding of the mechanisms by which the digital economy affects the carbon emission efficiency, as well as provide policy implications for enhancing the use of the digital economy in the regional energy consumption and industrial value chain.
As an integration of artificial intelligence and advanced manufacturing technology, intelligent manufacturing has realized the innovation of manufacturing mode and created conditions for the green development of industry. After constructing a theoretical framework between intelligent manufacturing and industrial green total factor productivity, this paper uses panel data of 30 provinces in China from 2006 to 2020, and expresses the level of intelligent manufacturing with industrial robot density, to discuss the economic effects and mechanisms of intelligent manufacturing. The results show that intelligent manufacturing has a positive effect on industrial green total factor productivity, and the panel quantile regression model indicates that there is an increasing marginal effect. With the quantile points going from low to high, the coefficient and statistical significance become larger. Human capital is the mechanism for intelligent manufacturing to improve industrial green total factor productivity. Green technology innovation and producer service industry agglomeration have strengthened the positive effect. There is also heterogeneity in the effect, and the stronger the effect in regions launched local pilot schemes for carbon emissions trading and industrial green transformation development policy. In order to give full play to the technological dividend and empower sustainable industrial development, the paper argues that we need to accelerate the integration of artificial intelligence and manufacturing technology, thus improving the level of industrial intelligence and empowering green development.
In the context of carbon peak and carbon neutrality, digital green innovation development is becoming more and more important for enterprises. In order to effectively improve green competitiveness and increase profits, photovoltaic building materials enterprises must choose digital green innovation projects for investment. The purpose of this study is to build a reasonable investment project selection framework system and propose appropriate methods for photovoltaic building materials enterprises to help them correctly choose digital green innovation investment projects. This study firstly combines relevant theories and digital green innovation characteristics of target investment projects to build a framework system for photovoltaic building materials enterprises to select investment projects. Secondly, this study innovatively proposes a dynamic intuitionistic fuzzy multi-attribute group decision-making method considering the interaction between attributes. Finally, this study takes Yingli Group as the research object and conducts an empirical study on it to verify the scientific nature and reliability of the framework system and method selection. The results show that the framework system includes four aspects: external support system, commercialization expectation, project operation ability and project operation resources. Yingli Group should choose project A3 for cooperation. The framework system and method proposed in this study are feasible and can help Yingli Group correctly choose digital green innovation investment projects. At the same time, this study also brings positive enlightenment to other photovoltaic building materials enterprises in the world when choosing digital green innovation investment projects.
In the context of the “double cycle,” promoting the development of a green economy is an important goal for China’s high-quality economic development in the digital age. This paper uses data from 30 provinces (municipalities and autonomous regions) in China during the 2006–2019 period using the Compiled Green Finance Index (GF) and Digital Economy Index (DE). The interrelationship between green finance, digital economy and green total factor productivity (GTFP) is empirically tested by conducting multiple regressions on panel data from 2006–2019 to perform an empirical analysis. Based on this, further analysis was performed with the threshold model. This study found that green finance and digital economy can contribute well to green total factor productivity, but the combination of the two does not have a good effect on green total factor productivity. Further study found that the green finance and digital economy’s contribution to green total factor productivity is mainly derived from technological progress. The regression results based on the panel threshold model show that the more underdeveloped the digital economy is in certain regions, the stronger the role of green finance in promoting efficiency improvement. Therefore, policymakers should formulate differentiated green financial policies according to the level of development of the digital economy and give play to the role of green finance and the digital economy in promoting green total factor productivity.
This paper investigates the impact of two types of environmental regulations (ERs), command-and-control environmental regulation (CACER) and market-incentive environmental regulation (MIER), on green total factor productivity (GTFP) through outward foreign direct investment (OFDI) in 30 provinces in China for the period of 2006–2019. The Global Malmquist–Luenberger (GML) Index based on non-radial directional distance function (NDDF) considering undesired outputs is used to measure GTFP growth at the provincial level. To explore the mediating effect of OFDI, the two-step econometric model and the non-linear mediating effect model are employed. The empirical results show that CACER has an inverted U-shaped impact on OFDI and a U-shaped impact on GTFP, while MIER has a linearly positive effect on OFDI and GTFP. The current intensity of CACER lies on the left side of the inflection point of the U-shaped curve. OFDI significantly positively influences the increase in GTFP and is a significant mediating variable in the relationship between ERs and GTFP. Moreover, the introduction of OFDI delays the appearance of the inflection point. Further analysis, taking into account the regional heterogeneity, indicates that the inverted U-shaped and U-shaped curve is still valid in the eastern and western area and that the mediating effect of OFDI on ERs in the western area is stronger than that in the eastern area. Based on these conclusions, policy implications are provided to improve GTFP in China.
With the advent of the digital age and urbanization in China, the digital economy (DE) has gradually become a new engine for city sustainability (SUS). DE and SUS rely on human capital (HC) accumulation and development. It is necessary to study the linear and non-linear impact of DE on SUS, and the moderating effect of HC on the relationship between DE and SUS. The data of 278 prefecture-level cities from 2011 to 2019 were collected. The fixed-effect model and panel threshold regression model were adopted. The results show that DE can significantly promote SUS, and there is a single threshold of HC. In areas with a low level of HC, DE significantly inhibits SUS, and in areas with a high level of HC, DE particularly promotes SUS. There is a regional heterogeneity about the role of DE on SUS. The innovation is that DE, HC, and SUS are first brought into the same research framework. Furthermore, the impact of specific stages of HC development on the relationship between DE and SUS was quantitatively investigated.
Exploring the determinants of green total factor productivity (green TFP) is of great importance to economic performance and ecological sustainability. Based on the data of 30 provincial units in China from 2004 to 2016, this study first analyzes the mechanism of land transfer marketization (LTM) affecting green TFP through resource allocation, then the regional resource allocation level is measured using the indicator of factor market distortion, and regional green TFP is estimated by the slack-based measure (SBM) directional distance function and Malmquist–Luenberger (ML) index. On the basis of that, a panel threshold regression model is used to empirically examine the theoretical mechanism of LTM affecting green TFP through the intermediate variable of resource allocation. We find that there is one single-threshold effect between LTM, resource allocation, and green TFP taking resource allocation as the threshold variable. Specifically, while the degree of resource mismatch is lower than 0.1371, the coefficient of LTM on green TFP is 0.1553; otherwise, the coefficient changes to −0.2776. This study concludes that LTM would significantly increase green TFP when the degree of regional resource mismatch is below the threshold; otherwise, it would have an inhibitory effect on the development of green TFP. In addition, the economic development level, R&D investment, and infrastructure level can, to a certain extent, contribute to the improvement of green TFP. The findings have three important policy implications for the land transfer policy of local governments, investment strategies of enterprises, and differentiated policy services.
Driven by the information technology revolution, using artificial intelligence to promote intelligent manufacturing while achieving carbon emissions reduction is increasingly the focus of international attention. Given this, based on the fact that China’s industrial manufacturing is more intelligent, this paper uses industrial sector data and robot data from 2000 to 2017 to examine the impact of intelligent manufacturing on industrial carbon dioxide emissions and to discuss its internal mechanism. The research found that intelligent manufacturing significantly inhibits carbon dioxide emissions in the industrial sectors. The emission reduction effect is more obvious in industries with higher carbon emissions and intelligence. The mechanism test shows that intelligent manufacturing mainly achieves industrial emission reduction by reducing fossil energy consumption in the production process and improving energy use efficiency. The research findings of this paper provide favorable evidence for using new technologies, such as artificial intelligence, to achieve carbon emissions reduction, and validate the importance of intelligent manufacturing in tackling climate change in the future. It provides an essential reference for developing countries to use artificial intelligence for their carbon emissions reduction goals.
Industrial intelligence is gaining more prominence in the new era of the technical revolution. This paper conducts an empirical test based on the panel data of 30 Chinese provinces (municipalities and autonomous regions) from 2006 to 2017. Firstly, the stochastic frontier analysis developed from the transcendental logarithmic production function is applied to calculate the total factor productivity of 30 provinces in China. The fluctuation of the total factor productivity is employed to reflect the quality of economic development. Secondly, the multilevel mediation model is applied to conduct the empirical test. Then, the robustness and endogeny of the conclusions are tested, and a further discussion is finally made, respectively, for eastern, central and western China. The results show that: (1) Industrial intelligence has a promoting effect on the improvement of total factor productivity. (2) Industrial intelligence can increase the demand for highly skilled labor and reduce the demand for low-skilled labor, but it has no significant impact on the demand for medium-skilled labor. (3) Industrial intelligence influences the improvement of total factor productivity through labor force structure.
The relationship between environmental regulation and green economic growth has become a focal issue in China. This study utilizes the environmental information disclosure (EID) policy as a quasi-natural experiment in the Chinese context. Using a sample of 280 Chinese cities from 2003–2019 and measuring urban green total factor productivity (GTFP), the propensity score matching and difference-in-difference methods are applied to assess the impact mechanism of EID on urban GTFP in China. The results show that, first, the urban GTFP showed a decreasing trend from 2003 to 2008 and a general increasing trend from 2009 to 2019. The EID policy had a significantly positive impact on GTFP, and this finding remained robust after a series of tests. Second, the policy effect of EID was more pronounced in large and medium-sized cities than in small cities and eastern and central regions. The mechanism analysis shows that a positive effect from EID on GTFP in cities can be achieved through green technological innovation and industrial agglomeration.
In the face of resource and environmental problems caused by extensive economic development, China has put forward a green development strategy. Scientific measurement and analysis of green total factor productivity (GTFP) is of great significance for achieving high-quality economic development. By introducing the human capital composition, including education, health, scientific research, and training, this paper study adopts the Slack Based Measure-Global Malmquist-Luenberger (SBM-GML) index to re-measure the GTFP and its decomposition of 30 provinces, municipalities, and autonomous regions in China from 2000 to 2019. The results show that: (1) China’s GTFP has a fluctuating growth trend, with an average annual growth rate of 2.31%. (2) In terms of its decomposition, technical progress is the main force driving GTFP growth, with a contribution rate of 1.59%; the improvement of technical efficiency is a secondary driving force, with a contribution rate of 0.71%. (3) The measurement results of GTFP, considering the human capital composition, are generally higher than those without consideration, and the GTFP growth under the two modes shows a trend of “high in the east and low in the west”. The conclusions have enlightening significance for improving GTFP and the growth potential of the economy in China.
The Digital Transformation (DX) megatrend is fundamentally disrupting and changing the nature of work, business, and industry at a rapid pace. Although the notion of DX has garnered much research interest from practitioners, scholarship on this topic is somehow lagging behind, possibly because of the lack of theoretical frameworks on DX. Recently, most Japanese firms have begun to use diverse digital technologies to sustain their competitive advantages. However, the return of investment on digital technologies has not been as high as expected for some firms. Furthermore, as the visions of Industry 5.0 describe sustainable, resilient, and human-centered future factories that will require smart and resilient capabilities both from next-generation manufacturing systems and human operators, it is necessary to design resilient human–machine collaborations within factories. To this end, this paper presents a research model between DX technologies and scientific problem-solving in terms of deduction, induction, and abduction inference structures as an approach to resilient human–machine collaborations. The purpose of this research is to analyze the difference in the utilization pattern of the digital technology of American, German, and Japanese firms based on three types of decision-making methods. Next, we apply this framework in a comparative case study of two Japanese firms and one German firm, where we find that there is a difference in DX technologies utilization among the Japanese and German firms. We assert that the utilization of IoT technology in the United States and Germany is pursuing IoT with the aim of autonomous control, whereas Japanese firms prioritize robot–human collaboration. Finally, we discuss how our findings contribute to the burgeoning field of resilient human–machine collaborations by showing the distinct roles of deduction, induction, and abduction inference structures. Furthermore, our research contributes to international comparative studies to identify the difference in national IT utilization. Lessons and implications are discussed.
Background: Inclusive green development aims to combine economic inclusiveness with greenness, which is an important goal of current economic development for China to achieve common prosperity. Measuring inclusive green total factor productivity (IGTFP) is of great significance for evaluating the quality of inclusive economic growth and accelerating inclusive economic growth.
Method: This study establishes an index evaluation system of IGTFP from four aspects of high quality, efficient, fair, and sustainable development, using super-SBM to measure IGTFP based on panel data of 276 cities in China from 2006 to 2019, and conducts empirical comparative analysis from national central cities, provincial capitals, and ordinary prefecture.
Result: 1) The IGTFP and technical progress rate in the central city and the provincial capital are significantly higher than that of the ordinary prefecture, but there is no significant difference in technical efficiency; the growth rate of IGTFP in the most central city remains around 10%, which is significantly higher in the south than that in the north. 2) According to the index decomposition result, all cities have basically realized the double-line improvement of technological efficiency and technological progress rate, but the technological efficiency is mostly lower than the technological progress rate. 3) From the perspective of economic convergence, only the IGTFP of provincial capitals shows the σ convergence feature, that is, the phenomenon of intra-group convergence; the IGTFP of all cities in three levels shows the β convergence feature, indicating that there is an obvious catch-up phenomenon within the group.
Conclusion: The integration of “technology” and “efficiency” is the main driving force and path to realize the sustainable improvement of IGTFP in cities. Inclusive green growth needs to break “regional boundaries,” including north–south boundaries and urban boundaries.
The digital economy is an important way to relieve current pressure on resources and the environment. Based on ecological modernization theory and global value chain theory, this paper adopts panel data of 17 manufacturing industries from 2000 to 2014, uses the non-radial directional distance function and the meta-frontier method to measure China’s green total factor productivity (GTFP) and its sub-indices, and takes the embedding degree and embedding position as moderating variables and threshold variables to construct an econometric model to explore the relationship among the digital economy, global value chain, and GTFP. The results show that (1) the digital economy has a positive effect on GTFP in both time and space. And in terms of time, the digital economy mainly enhances GTFP by improving technical efficiency and narrowing the technology gap. (2) Global value chain embedding position positively moderates the relationship between the digital economy and GTFP; In terms of sub-samples, labor-intensive and capital-intensive industries show the same impact characteristics as the total sample. (3) Further, it is found that the digital economy and GTFP have nonlinear characteristics. There is a single threshold effect on the embedding position. When the embedding position is low, the digital economy is positively correlated with GTFP but not significant; when the threshold value is crossed, the digital economy can significantly promote GTFP. The conclusions of this paper are helpful to realize the digital economy to promote the green development of the manufacturing industry, and provide an effective reference for the realization mechanism of China’s green economy transformation and ecological civilization construction in the post-pandemic era.
Low carbon and digitalization are the general trends of manufacturing upgrading and transformation. Digital technology enables the whole process of green manufacturing and breaks down the spatial barrier. To achieve the dual carbon goals, the pressure-state-response (PSR) model, in which digital technology enables the green innovation of the manufacturing industry, was theoretically analyzed in this study. The measurement system of the digital green innovation (DGI) in the manufacturing industry was constructed according to the PSR framework. An evaluation model based on the analytic hierarchy process and the deviation maximization technique for order preference by similarity to an ideal solution method was constructed to measure the level of DGI. The results of this study from Chinese manufacturing are as follows. (i) The measurement system of the level of DGI in manufacturing industry includes a pressure system, state system and response system. (ii) In the past five years, the comprehensive index of the DGI in manufacturing industry has generally shown a trend of fluctuating rise. There are overall low and unbalanced phenomena in all regions. The gap decreased from 0.1320 to 0.1187, showing a gradually narrowing trend. (iii) Compared with other regions, the composite index of DGI is generally higher in the regions with a better ecological environment in the east and a more developed economy in the north. State parameters are higher than pressure and response parameters in most areas. (iv) Compared with other regions, the composite index of DGI in western and southern regions is lower, and the parameters of pressure, status and response are basically coordinated. (v) The application degree of digital technology, the emission intensity of waste water/exhaust gas of output value of one hundred million yuan and the expenditure intensity of digital technology adopted by enterprises are the key influencing factors of DGI in the manufacturing industry. This study not only proposed an evaluation index system of the digital green innovation level, but also puts forward policy guidance and practical guidance of digital technology to accelerate the green and intelligent manufacturing industry.
In the context of global climate changes, the green transformation of tourism industry has become a general trend. This study constructed a comprehensive evaluation model (Super SBM-GML) to measure the tourism green productivity (TGP) and used methods including kernel density estimation, spatiotemporal transition, and quantile regression to analyze the evolutionary characteristics and driving factors of province-level TGP in China between 2009 and 2019. The results showed the following: (1) China’s TGP exhibited a two-stage growth trend, maintaining steady growth before 2014 and accelerating thereafter. (2) Regarding the dynamic evolutionary characteristics, the local spatial structure of provincial TGP remains unstable, and the provinces themselves can change their relative positions relatively easily, showing certain characteristics of spatial agglomeration and dynamic interaction. (3) The spatiotemporal network pattern of provincial tourism green productivity was dominated by positive correlations, indicating that the overall collaborative dynamics between provinces were stronger than the competition. However, a certain degree of spatiotemporal competition could be found between some neighboring provinces. (4) The TGP is affected by various driving factors such as tourism economic scale, industrial structure, scientific and technological innovation and environmental regulations, but the driving intensity and direction of each factor differ significantly across the TGP developmental stages.
How to break the labor gap caused by population aging has triggered an excellent concern for sustainable development in various countries. Meanwhile, Industry 4.0 is leading the world to explore the artificial intelligence field. Therefore, a deeper analysis of the effects of labor substitution of artificial intelligence on carbon emissions in the context of population aging is urgent. We constructed fixed effect, moderating effect, and panel threshold models using data from the World Federation of Robotics (IFR) and 30 Chinese provinces from 2006 to 2019 to examine the critical relationships between population aging, industrial robots, and carbon emissions. The findings of the paper are: (1) Industrial robots have a significant impact on reducing carbon emissions. (2) Population aging also leads to a decrease in carbon emissions, and industrial robots serve as a mediating factor for this effect. (3) Threshold regression shows that the inhibiting effect on carbon emissions decreases as industrial robots surpass the threshold and increases as population aging crosses the threshold. The conclusions of this paper provide countermeasures for countries and regions in the throes of population aging to achieve sustainable development; that is, seizing the opportunity of industrial robots' development is extremely imperative.
In the era of widespread internet access, green development has also entered an era of big data. Using panel data of 282 prefecture-level cities in China from 2003 to 2019, this study adopts slacks-based measure (SBM) directional distance function and Global Malmquist-Luenberger (GML) index to calculate the green total factor productivity (GTFP) and its decomposition (i.e. technological progress, scale efficiency, and resource allocation efficiency). On this basis, econometric models are used to study the internal-structural effects of internet development on China’s urban GTFP. It is found that: (1) Internet development plays a positive role in promoting urban GTFP; (2) specifically, internet development is proven to achieve this effect simultaneously by inducing technological innovations, optimizing economic scale, and improving resource allocation efficiency. Among these, inducing technological innovations is the most important path; (3) there are distinct regional heterogeneous effects of internet development on China’s urban GTFP. Based on the above results, corresponding policy suggestions are provided.
Factor mismatch is considered to be an important restriction on the growth of total factor productivity. Based on the panel data of 30 Chinese provinces from 2013 to 2019, this work first measures the digital economy development index of each Chinese province by using a particle swarm optimization projection pursuit model, followed by a panel econometric model, to verify the effect of the digital economy and artificial intelligence manufacturing on the labor-resource mismatch. The results show that, from 2013 to 2019, China’s digital economy generally showed a trend of steady progress, with an average annual growth rate of 12.10%. The mismatch index of the labor force dropped by 1.46% every year, and the situation of insufficient or surplus allocation of labor force resources in China was alleviated. The fitting results of the spatial econometric model show that the digital economy can reduce the labor mismatch index, and this conclusion has remained valid after a series of robustness tests. The intermediary mechanism shows that intelligent manufacturing plays a masking role in the process of alleviating labor misallocation in the digital economy. Artificial intelligence cannot alleviate labor mismatches, but it strengthens the corrective function of the digital economy.
Aiming to seek for the key factors of urban building carbon emissions under the background of new-type urbanization, this study attempts to establish an integrated analysis framework to explore the impact of new-type urbanization on urban building carbon emissions from scale, average and structure dimensions, the carbon emission coefficient method and panel data model were adopted to analysis the panel data of 31 provinces in China from 2009 to 2020. Spatial characteristics indicate that the internal difference of urban building carbon emissions is insignificant in northwest and significant in southeast of China. The empirical results indicated that the new-type urbanization construction has reduced urban building carbon emissions, but there was still a significant carbon emission effect from the dimensions of scale, average, and structure. Industrial upgrading and urban green space have a significant positive effect on urban building carbon emissions reduction, but the added value of the tertiary industry still has a significant carbon emission effect. Additionally, urbanization rate, population density, and total population have increased urban building carbon emissions under the new-type urbanization construction. This study provides a deeper perception for the policy design of carbon emission reduction based on the background of new-type urbanization.
Technological advancements have played a key role in improving energy efficiency and reducing emissions, and industrial robots are important carriers of intelligent manufacturing and industrial upgrading. Although various countries and regions are under pressure to reduce their carbon emissions, a consensus has not been reached on whether industrial robots can help. This study investigates how industrial robots affect carbon emissions by categorizing industry data from the International Federation of Robotics (IFR, 2010-2018) into city-level variables. The empirical finding revealed that cities' carbon emissions have been significantly reduced by the application of industrial robots. By using the penetration of robots in Chinese cities as an instrumental variable constructed through the combination of employment level and robot imports, the beneficial role of robots is further verified by a plausibly exogenous test. The mechanism analysis revealed that industrial robots contribute to cities' decarbonization by enhancing energy efficiency and green technology efficiency. The heterogeneity analysis showed that the effect of industrial robots on decarbonization is more pronounced in megacities, advanced manufacturing bases, and low-carbon pilot cities. This study empirically confirms the positive role of industrial robots in carbon emission reduction, provides evidence for industrial robots' technical characteristics of decarbonization, and proposes novel ideas for achieving net-zero carbon emissions.
Green total factor productivity (GTFP) growth is an essential route to realize the target of carbon peak and carbon neutrality. Chinese government has implemented several environmental regulations (ER) to accelerate GTFP growth. However, the influencing path of ER on China’s GTFP growth is still puzzled. This paper based on Porter’s and the Compliant Hypotheses, employs super-efficiency Epsilon-based measure (EBM) model and Malmquist-Lunberger (ML) index under meta-frontier to estimate the provincial GTFP growth from 1998 to 2018. A Systematic Generalized Method of Moments (SYS-GMM) model was applied to probe the nexus between ER and GTFP growth from the perspective of industrial structure upgrading and technological innovation. The results reveal that the average ML index is 0.955, while efficiency change (EC) and technology change (TC) index are 1.003 and 0.953. The Porter’s hypothesis was confirmed refuting the Compliance Cost Hypothesis. Environmental regulation directly promotes China’s GTFP growth. ER can also facilitate industrial structure rationalization (ISR) and industrial structure advancement (ISA), which further promote China’s GTFP growth. Green technological innovation (GTI) and production technological innovation (PTI) mediates between ER and GTFP growth in China. Hence, the Chinese government should strengthen the ER for industrial structure upgrading, technological innovation, and GTFP growth in China.
Promoting coordinated industrialization and environmental development is an objective set forth by the United Nations for Sustainable Development and shared by different nations worldwide. Environmental regulation (ER), green finance (GF), and increased investment in green technologies (IGT) are major initiatives in this regard. These fields have become prominent in achieving green development and climate recovery objectives. Several factors affecting green productivity growth have been analyzed in the literature; nevertheless, quantitative studies focusing on ER, GF, IGT, and green productivity are scarce. This research investigates the role of ER, GF, foreign direct investment (FDI), and IGT on GTFP in 27 Chinese provinces from 2010 to 2021. The findings reveal that strict ER significantly increases green productivity in China with a 1.826 beta value. In addition, other factors such as GF, FDI, and IGT contribute substantially to green manufacturing. This study is one of the first to integrate ER, GF, FDI, and IGT into a coherent framework of green productivity and considers the negative yield in GTFP as ignored in the previous ones. Based on empirical findings, policy implications for environmental planning in China are made.
Cities are both the primary cause of global climate change and the key to the mitigation agenda. China's unprecedented urbanization has paralleled a growth in energy demand and urban areas have emerged as the crux of CO2 emissions reduction in China. There is a crucial need for policymakers to understand how CO2 emissions scale with city size and adopt economies of scale (cost savings) for mitigation, particularly through a multidimensional lens of city size. This study reveals a set of scaling relations between urban scope 1 CO2 emissions and five dimensions of city size in 340 Chinese cities, including population (POP), built-up area (BA), building height (BH), specific built-up area (SBA), and built-up volume (BV). The findings show that CO2 emissions in Chinese cities scale linearly with POP and BA but sublinearly with BA, SBA, and BV, and more diverse regimes exist across various geographic zones, population hierarchies, administrative hierarchies, and governance contexts. The prevalent sublinear scaling regime between CO2 emissions and SBA and BV demonstrates the potential importance of optimizing the vertical built-up landscapes for establishing a zero‑carbon society. Furthermore, the top 10 % and bottom 10 % performance of individual cities in emissions identified by the Scale-Adjusted Metropolitan Indicator (SAMI) (the smaller the better) highlights the imprints of the socioeconomic context (e.g., Low Carbon City Initiative) on the scaling of CO2 emissions in Chinese cities, which is critical for developing decarbonization strategies. Our multidimensional analysis can assist in the local-tailored low-carbon development of Chinese cities.
As one of the most important economic regions in China, the improvement of green total-factor productivity (GTFP) in Yangtze River economic belt has vital significance for economic transformation in China. Using 109 cities’ panel data from 2004-2018, slacks-based global data envelopment analysis was first applied to construct GTFP. On the basis, dynamic GMM and mediation effect models were used to investigate the role of internet development on GTFP. The empirical results illustrated that GTFP lacked endogenous driving force. However, internet development not only has significant direct positive effect on GTFP, but also indirectly promote GTFP through technology innovation and industrial structure upgrade. And the mediation variables also have two-way positive impacts on internet development. After accounting robustness test, the empirical conclusions were still valid. Based on the analysis, our study provided scientific and reasonable policies to improve GTFP in Yangtze River economic belt.
The growing penetration of digitalization has caused profound changes in economy and energy, and then may affect green total factor productivity (GTFP). This study uses the World Input-Output Database (WIOD) to verify the impact and mechanism of input digitalization on green total factor productivity under the constraint of carbon emissions (low-carbon GTFP). The results indicate that input digitalization significantly improves low-carbon GTFP. Heterogeneity analysis demonstrates that input digitalization has a greater positive effect on low-carbon GTFP in middle-income countries than high-income ones. Input digitalization has a positive effect on low-carbon GTFP in manufacturing and service industry, with no significant effect in agriculture. The relationship for samples after the 2008 financial crisis is consistent with benchmark results while it is not for samples before 2008. Moreover, mechanism analysis shows that the impact of input digitalization on low-carbon GTFP works through two channels: energy efficiency and labor productivity. Further, from the perspective of “dual circulation”, the “localization” tendency of input digitalization under the domestic circulation and the “globalization” tendency of input digitalization under the international circulation can significantly improve low-carbon GTFP. This study provides empirical evidence and decision-making basis for promoting input digitalization as well as the green and high-quality economic development.
Improving green total factor productivity (GTFP) is an important way to promote high-quality economic development. The “innovation dividend” based on green innovation is the key to enhancing green TFP. In this study, the effect of green innovation on GTFP in cities was empirically investigated. The results show that: (1) after including year and city-fixed effects and clustering over time and individuals, green innovation has a significant positive effect on GTFP. Financial development, population density, and environmental regulation correlate significantly negatively with GTFP, whereas urbanization promotes GTFP; (2) the effect of green innovation is more pronounced in the eastern region. The effect of green innovation on GTFP is more significant in non-resource-based cities or cities with higher levels of economic development or more government intervention; (3) the effect of green innovation on GTFP may be affected by the upgrading of industrial structure and innovation factor agglomeration. Cities where GTFP increases first have a first-mover advantage. After adding spatial factors to the model, a notable “positive spillover effect” of GTFP improvement can be observed in neighboring cities.
In the context of the outbreak of the COVID‐19 pandemic and China's “digital power” strategy, the realization of a green shift of manufacturing has become a necessary condition to promote the economy, and the digital factor has increasingly become a new driving force. The DEA‐Malmquist index and entropy method were used to measure the manufacturing green total factor productivity (GTFP) and the level of digital economy level from 2011 to 2018, respectively. This study then explored the impact of digital economy on manufacturing GTFP based on the system generalized method of moments (GMM) model, as well as the adjustment effects of talent aggregation and financial scale according to the moderating model. This research came to four conclusions. (1) The digital economy can significantly improve the manufacturing GTFP of China, and the influence shows the characteristic of a “marginal increase”; (2) notably, the perspective of manufacturing GTFP decomposition indicates that the digital economy exerts a significant positive effect on manufacturing technical efficiency during the current period but obviously hinders technical progress; (3) interestingly, a mechanistic test showed that the two dimensions of innovation environment—talent aggregation (0.385) and financial scale (0.359)—play critical moderating roles in the influencing process; and (4) the influence has evident regional heterogeneity—it is significantly positive in the east and negative in the central region and west. Finally, corresponding policy suggestions are suggested.
Manufacturing green technology innovation is important in achieving climate goals and is the key in promoting sustainable economic development. Using the industrial robot data and manufacturing green technology innovation data from 34 countries from 1993 to 2019, this paper reveals the mechanism and heterogeneity of the application of industrial robots (IRA) affecting green technology innovation (GTI) in the global manufacturing sector. The results indicate the following: (1) The IRA significantly promotes GTI, and the endogenous and robustness tests show that the results are robust. (2) The IRA promotes GTI with a dual-channel mechanism—the mediating effect of green R&D investment and the moderating effect of environmental regulation. (3) There is two-dimensional heterogeneity in terms of the application industries and regions in terms of the green technology innovation effects of industrial robot applications. (4) In addition, the implementation of Industry 4.0 is in favor of the stimulating effects of industrial robots on green technology innovation. Finally, valuable policy advices are proposed based on the empirical results.
The new industrial revolution featuring artificial intelligence (AI) as its core is flourishing globally. However, there are many controversies surrounding the impact of AI on productivity owing to the different understandings of its development. Thus, this study adopts a text mining method to construct indicators for measuring the intelligent development of enterprises based on the information obtained from the annual reports of listed Chinese manufacturing companies from 2009 to 2019. To explore the impact of intelligent development on the total factor productivity (TFP) of enterprises, fixed-effect regression and panel threshold models are employed to empirically prove its overall and threshold effects. The result reveals that the impact of intelligent development on TFP of enterprises is significantly positive at the aggregate level. Regarding the stage characteristics, “Solow’s paradox” exists in the development of intelligence. The effect of intelligence development on TFP is not significant at its early stage; moreover, the rapid development of intelligence exerts a “promotion effect.” However, at the extreme stage (when intelligent development crosses the critical value), it exerts a negative effect on the TFP of enterprises.
Purpose
Intelligent manufacturing has attracted extensive attention from national strategy, academic research and enterprises' practices. The purpose of this study is to investigate the influence of intelligent manufacturing on performance in manufacturing firms. Moreover, how intelligent manufacturing technology affects enterprise performance, this study provided a practice that can be replicated by other businesses.
Design/methodology/approach
This study uses text mining to collect the intelligence level of Chinese listed companies. It uses quantitative analysis to test the proposed model based on samples of 2,091 manufacturers.
Findings
Intelligent manufacturing has positive effect on short-term performance and long-term performance. Intelligent manufacturing can empower firms with ambidextrous capabilities, including exploit capability and explore capability. Exploit capability has positive effects on short-term performance and long-term performance. Explore capability has negative effects on short-term performance, but has positive effects on long-term performance.
Originality/value
On the theoretical side, it enriches the research framework between intelligent manufacturing and enterprise performance. This study explains the preconditions and results of ambidextrous capabilities. Moreover, based on the practice-based view (PBV), this study proposes that technologies can be used as strategies, filling a gap in the existing research on strategic management. On the practical side, how to quantify the intelligent manufacturing level of enterprises provides a certain reference. Also, this study provides an easy to imitate practice that can serve as a model for under-performing enterprises.