Will advancement in technologies bring fear and damage human employment? Evidence from China's manufacturing industry

Advancement in technologies such as robotic industries and artificial intelligence bring fear among human being that jobs will be substituted by robots. Base on the panel data of 28 China's manufacturing industries, this research analyzed the impact of technical progress bias on employment. First, we calculate the technical progress bias index of 28 industries base on the stochastic frontier model with transcendental logarithm function found 16 industries were toward the skilled labor while the remaining 12 industries were toward the unskilled labor. Second, the empirical results show that technical progress bias has a positive impact on the total manufacturing employment and significant positive effect on the unskilled labor, while no significant impact on skilled labor employment. Third, the threshold effect test proves that if taking industry value-added per capita or R&D capital stock as threshold variable, the threshold about the impact exist, making the impact on skilled labor was insignificant.

Does the opening of producer services promote the wage growth of the downstream manufacturing industry?-Empirical evidence from Chinese manufacturing listed companies

Based on the vertical connection between upstream and downstream industries, a unique theoretical model is constructed to analyse the impact mechanism of the opening of producer services on downstream manufacturing wage growth. The empirical tests are carried out using the data of China's manufacturing listed companies from 1999 to 2020. Our findings indicate that the opening of producer services has an inverted-U-shaped impact on downstream manufacturing wage growth, and the average level of the opening of producer services in the sample period is lower than the corresponding threshold. Overall, it is in the stage of promoting the wage growth of the downstream manufacturing industry. The opening of producer services mainly affects the wage growth of the downstream manufacturing industry through two channels: labour productivity and labour income share. The results of heterogeneity analysis show that the wages of capital and technology-intensive and low-competitive manufacturing industries are relatively strongly promoted by the opening of producer services. Therefore, promoting the orderly opening of producer services and strengthening the technological links between industries will help promote the wage growth of downstream manufacturing industries.

Intelligence level evaluation and influencing factors analysis of equipment manufacturing industry in the Yangtze River Delta

The Yangtze River Delta (YRD) bears the vital task of driving the growth of China's equipment manufacturing industry (EMI) intelligence as an advanced region. Fostering the transformation and upgrading of the EMI in the YRD and constructing a modern production mode is vital to developing and reforming China's manufacturing industry. This paper uses industrial robot data to assess the level of intelligence (LoI) in the EMI from 2016 to 2019. The OLS (ordinary least squares) model is used for the measurements, and the MQ (the modified contribution index) is used to estimate the degree of contribution from a host of variables. It is identified that the LoI is on the rise. However, excluding railways, aerospace, shipbuilding, and other transportation equipment manufacturing, the LoI is significantly higher than in other subsectors. It is also identified that technological innovation ability, human capital density, and enterprise cost pressure govern the industry's LoI. Moreover, while there is a difference in the main influencing factors in LoI within different industries, R&D investment, technological innovation ability, and enterprise cost pressure have the most significant impact across most equipment manufacturing sub-industries.

A tripartite evolutionary game study of low-carbon innovation system from the perspective of dynamic subsidies and taxes

Traditional manufacturing industry is in the early stages of transition to low-carbon innovative production, and is in urgent need of a low-carbon innovation system to achieve the goal of carbon neutrality. In order to realize the effective supervision of enterprise carbon emissions, this paper constructs a tripartite evolutionary game model among the corporate, government and public from the perspective of dynamic subsidies and taxes. The main results are as follows. First, the increase in government subsidies to a certain extent will help encourage companies to choose low-carbon innovative production strategies, but more subsidies are not always better. Excessive subsidies will increase the cost of government regulation and reduce the probability of government regulation. Second, the tripartite evolutionary game system does not converge under the static subsidies and taxes mechanism. But the system could quickly converges to the stable condition under dynamic subsidies and taxes. The stable point is the situation of corporate low-carbon innovation, government regulation, and public supervision. Third, the public intervention and supervision can effectively prevent the phenomenon of government misconduct and enterprises over-emission production. And the influence of public reward and punishment is more effective for the government than for enterprises.

Characterization of VOC emissions and health risk assessment in the plastic manufacturing industry

Volatile organic compounds (VOCs) significantly contribute to ozone pollution formation, and many VOCs are known to be harmful to human health. Plastic has become an indispensable material in various industries and daily use scenarios, yet the VOC emissions and associated health risks in the plastic manufacturing industry have received limited attention. In this study, we conducted sampling in three typical plastic manufacturing factories to analyze the emission characteristics of VOCs, ozone formation potential (OFP), and health risks for workers. Isopropanol was detected at relatively high concentrations in all three factories, with concentrations in organized emissions reaching 322.3 μg/m3, 344.8 μg/m3, and 22.6 μg/m3, respectively. Alkanes are the most emitted category of VOCs in plastic factories. However, alkenes and oxygenated volatile organic compounds (OVOCs) exhibit higher OFP. In organized emissions of different types of VOCs in the three factories, alkenes and OVOCs contributed 22.8%, 67%, and 37.8% to the OFP, respectively, highlighting the necessity of controlling them. The hazard index (HI) for all three factories was less than 1, indicating a low non-carcinogenic toxic risk; however, there is still a possibility of non-cancerous health risks in two of the factories, and a potential lifetime cancer risk in all of the three factories. For workers with job tenures exceeding 5 years, there may be potential health risks, hence wearing masks with protective capabilities is necessary. This study provides evidence for reducing VOC emissions and improving management measures to ensure the health protection of workers in the plastic manufacturing industry.

Can digital finance development drive green transformation in manufacturing? Evidence from China

Digital finance is a product of emerging technology-enabled innovation in financial services and has a critical impact on the green transformation of the manufacturing industry. We propose a new efficiency measurement model based on the slacks-based measure (SBM) to measure the efficiency of green transformation on regional manufacturing. Chinese interprovincial data from 2010 to 2019 were obtained for the study. In addition, we estimated the effect of digital finance on green transformation of manufacturing using a benchmark panel model. Finally, considering the regional heterogeneity and spatial effects of green transformation efficiency in the manufacturing industry, we constructed a spatial Durbin model based on an economic-geographic nested spatial weight matrix to analyze the spatial influence of digital finance on green transformation in the manufacturing industry. The results show that the green transformation of the manufacturing industry has significant positive spatial spillover effects owing to the existence of competition, demonstration, and economic correlation effects among regions.

Prediction of digital transformation of manufacturing industry based on interpretable machine learning

The enhancement of digital transformation is of paramount importance for business development. This study employs machine learning to establish a predictive model for digital transformation, investigates crucial factors that influence digital transformation, and proposes corresponding improvement strategies. Initially, four commonly used machine learning algorithms are compared, revealing that the Extreme tree classification (ETC) algorithm exhibits the most accurate prediction. Subsequently, through correlation analysis and recursive elimination, key features that impact digital transformation are selected resulting in the corresponding feature subset. Shapley Additive Explanation (SHAP) values are then employed to perform an interpretable analysis on the predictive model, elucidating the effects of each key feature on digital transformation and obtaining critical feature values. Lastly, informed by practical considerations, we propose a quantitative adjustment strategy to enhance the degree of digital transformation in enterprises, which provides guidance for digital development.

A pathway towards high-quality development of the manufacturing industry: Does scientific and technological talent matter?

Against the background of the accelerated evolution of the new round of scientific and technological revolution and industrial change, scientific and technical talents, as essential innovation resources, play an important role in promoting the high-quality development of the manufacturing industry. Based on the panel data of 30 provinces in China from 2012 to 2021, the article constructs a fixed-effects model and systematically researches the impact of scientific and technological talents on the high-quality development of the manufacturing industry. The results show that scientific and technical talents play a significant role in promoting the high-quality development of the manufacturing industry, and the upgrading of the consumption structure and the accumulation of productive service industries play a mediating role. Heterogeneity analysis found that the promotion effect of scientific and technical talents is more favorable in the eastern region, medium-technology level manufacturing, and labor-intensive manufacturing. Among the three sub-dimensions of scientific and technological talents, the scale of scientific and technical talents has the most significant impact on the development of the manufacturing industry. The analysis of the spatial spillover effect finds that scientific and technological talents will have a positive spillover effect on the development of the manufacturing industry in neighboring areas. The study provides a basis for relevant departments to formulate effective strategies and policies.

Advancing the Production of Clinical Medical Devices Through ChatGPT

As a recently popular large language model, Chatbot Generative Pre-trained Transformer (ChatGPT) is highly valued in the field of clinical medicine. Due to the limited understanding of the potential impact of ChatGPT on the manufacturing side of clinical medical devices, we aim to fill this gap through this article. We elucidate the classification of medical devices and explore the positive contributions of ChatGPT in various aspects of medical device design, optimization, and improvement. However, limitations such as the potential for misinterpretation of user intent, lack of personal experience, and the need for human supervision should be taken into consideration. Striking a balance between ChatGPT and human expertise can ensure the safety, quality, and compliance of medical devices. This work contributes to the advancement of ChatGPT in the medical device manufacturing industry and highlights the synergistic relationship between artificial intelligence and human involvement in healthcare.

Evaluating the impact of the innovation efficiency of high-tech industry on carbon emissions: a case study of the manufacturing industry in China

Amid China's economic shift to high-quality development, addressing environmental challenges like greenhouse gas emissions and manufacturing pollution, there is a crucial demand for sustainable and eco-friendly development strategies. This study aims to investigate the impact of innovation efficiency in the high-tech industry on carbon emissions. It seeks to explore regional differences, mechanisms, and the influence of energy consumption structures in achieving sustainable development goals. Utilizing data from 30 provinces spanning 2009 to 2020, the study employs the DEA-Malmquist index model, spatial and temporal classification evaluation, and a panel measurement model to assess the efficiency of innovation and development in high-tech industries and their relationship with carbon emissions. The results indicate several key findings: (1) The overall operational efficiency of high-tech industry innovation and development in China is steadily increasing. However, there are distinct characteristics observed among provinces and cities, reflecting diverse input and output types. (2) High-tech industry innovation efficiency significantly contributes to carbon emission reduction, and there is regional heterogeneity in this impact. The central and western regions exhibit greater effects compared to other provinces and cities. (3) The optimization of the energy structure is identified as a mechanism through which high-tech industry innovation efficiency reduces carbon emissions. Moreover, different intervals of high-tech industry innovation efficiency yield varying effects on carbon emissions. This research underscores the importance of fostering high-tech industry innovation efficiency as a means to reduce carbon emissions. It also identifies key areas for future policy development and resource allocation, emphasizing the support needed for low-carbon technology research and development.

Investor attention and ESG performance: Lessons from China's manufacturing industry

In the context of the rapid growth of corporate green investment and the rapid dissemination of information brought about by Internet technology, it is important to explore the relationship between investor attention and corporate environmental responsibility. Unfortunately, an in-depth research on the relationship between investor attention and firms' environment, society, and governance (ESG) performance remains unexplored. The results show a mutual inhibition between investor attention and firms' ESG performance. Each 1% increase in investor attention decreases ESG performance by 0.252%, while each 1% increase in ESG performance decreases investor attention by 2.296%. Thus, ESG performance dominates this mutual influence. Moreover, ESG performance positively affects ESG performance and investor attention of neighboring firms. Each 1% increase in ESG performance increases ESG performance and investor attention of neighboring firms by 0.371% and 0.983%, respectively. Investor attention negatively affects investor attention and ESG performance of neighboring firms. Each 1% increase in investor attention decreases ESG performance and investor attention of neighboring firms by 0.04% and 0.104%, respectively. Further research reveals significant regional and organizational heterogeneity in the relationship between investor attention and ESG performance. The findings provide theoretical and empirical insights for further improvement of the ESG system and continued strengthening of investor guidance by regulators.

Managing carbon waste in a decarbonized industry: Assessing the potential of concrete mixing storage

The effort towards a greener future will entail a shift to more environmentally friendly alternatives of many human activities. Within this context, the path towards a decarbonized society in general, and industrial decarbonization in particular, will require using low carbon solutions and/or capturing carbon emissions at the source. This flux of captured carbon will then require management and one option is to store it in concrete. The incorporation of the captured CO2 can be done during the mixing and/or curing. While the latter is more efficient and effective in terms of the amount of CO2 incorporated, it is limited to concrete in elements that are compatible with chamber curing. In practice, this would be restricted to the concrete pre-fabrication industry and, most probably, only to small size elements. Despite the lower performance, incorporation of CO2 into concrete during the mixing stage is a relatively universal alternative. The present research effort reveals that the latter solution is beneficial from an environmental point of view, with an estimated yearly carbon storage of 23 million tonnes worldwide against emissions of 2.5 million tonnes to do it.

The impact of population aging on FDI: A panel data analysis based on 27 segments in China's manufacturing industry

Foreign Direct Investment (FDI) is an important part of China's new "double-cycle" development pattern. Among the many factors affecting FDI, will the aging population have an impact on manufacturing, the key industry for FDI? This paper examines the direct and indirect effects of an aging population on FDI using panel data from 27 manufacturing sub-industries in China between 2005 and 2020. It is found that (1) the deepening of the population's aging negatively affects FDI inflows and this result continues to hold after a series of robustness tests. (2) Using labor quantity and labor cost as mediating variables, it is found that the population's aging indirectly affects FDI by reducing labor quantity and increasing labor cost. (3) The heterogeneity analysis study finds that the deepening of the population's aging significantly inhibits FDI in labor-intensive and capital-intensive industries among manufacturing sub-industries, and the inhibitory effect on FDI in technology-intensive industries is not significant. This study provides meso-evidence to support the findings of existing studies and provides suggestions and insights for the government to formulate relevant policies to actively cope with aging.

Research on the identification and evolution of health industry policy instruments in China

The application of health industry policies could be discovered more quickly and comprehensively through the automated identification of policy tools, which could provide references for the formulation, implementation, and optimization of subsequent policies in each province. This study applies the Bidirectional Encoder Representation from Transformer (BERT) model to identify policy tools automatically, utilizes Focal Loss to reduce the unbalance of a dataset, and analyzes the evolution of policy tools in each province, which contains time, space, and topic. The research demonstrates that the BERT model can improve the accuracy of classification, that supply and environment policy tools are more prevalent than demand tools, and that policy instruments are organized similarly in four major economic regions. Moreover, the policy's attention to topics related to healthcare, medicine, and pollution has gradually shifted to other topics, and the extent of policy attention continues to be concentrated on the health service industry, with less attention paid to the manufacturing industry from the keywords of the various topics.

Surveillance of noise exposure level in industrial enterprises-Jiangsu Province, China, 2022

Occupational noise exposure is the most prominent problem in industrial enterprises in Jiangsu Province. Since 2019, China has established the National Surveillance System for Occupational Hazards in the workplace to grasp the current occupational hazards in critical industries, including occupational noise. According to the Work Plan for Surveillance of Occupational Hazards in the Workplace (2022) issued by the National Health Commission of the People's Republic of China, the noise exposure level of 3,142 enterprises in our province was monitored, the median and interquartile range (IQR) were calculated, and the distribution of noise exposure level was described by industry classification, enterprise-scale and ownership type of the enterprise. The railway, shipping, aerospace, and other transportation equipment manufacturing industries exhibited the highest proportion (42.9%) of individual noise exposure levels exceeding 85 dB(A), followed by the motor vehicles manufacturing industry (36.4%). The proportion of individual noise exposure levels exceeding 85 dB(A) was higher in medium and small enterprises, with rates of 28.1 and 28.6%, respectively. The highest proportion of personal noise exposure levels exceeding 85 dB(A) was observed in Hong Kong, Macao and Taiwan investment enterprises (37.5%), followed by incorporated companies (34.6%) and limited liability companies (28.1%), the lowest was state-owned enterprises(15.5%). The areas with excessive noise are primarily concentrated in grinding, welding, machining, cutting, and other related operations, accounting for 61.2% of the total. Among these operations, grinding accounts for 29.8%. The highest environmental noise and individual noise intensity were found in sandblasting and grinding positions, with individual noise intensities of 115.5 dB(A) and 108.4 dB(A), respectively. The noise exposure risk is so high that cannot be ignored in the manufacturing industry, especially in Hong Kong, Macao, and Taiwan investment enterprises, incorporated companies and medium and small enterprises.

Linking green supply chain management practices and environmental performance in the manufacturing industry: a hybrid SEM-ANN approach

This research determines the influence of green supply chain management practices (GSCM) on environmental performance. It also investigates the moderating role of supply chain environmental cooperation on GSCM practices and environmental performance relationships. A total of 370 employees of several Bangladeshi manufacturing companies were conveniently chosen as respondents. To verify the data validity and reliability and to test the hypotheses, we used SmartPLS. Finally, we employed an artificial neural network (ANN) to examine the relationship. Green design and green manufacturing have significant positive impacts on environmental performance, while green procurement and green distribution do not. Moreover, environmental cooperation moderates the relationships of green design and green distribution with environmental performance. The moderating effect of supply chain environmental cooperation in the relationship between GSCM practices and environmental performance in the manufacturing industry adds knowledge to the existing literature by incorporating a hybrid model combining PLS-SEM and ANN. Our study adds to the current body of knowledge by delving into the literature on GSCM from the perspective of Bangladesh's industrial sector. This study fills a knowledge gap by shedding light on the interactions of GSCM and environmental performance. Indeed, this study represents a step forward from classic linear regression-based models to an ANN-based nonlinear model. It also demonstrates new contributions to the literature on green supply chain management and environmental performance.

Research on the impact of China's reform to delegate power, streamline administration, and optimize government services on the technology innovation efficiency of the pharmaceutical manufacturing industry

Objective

The government has recently implemented reforms aimed at delegating power, streamlining administration, and optimizing government services. This reform has eliminated barriers that impede the growth of various industries, thereby unleashing innovative potential. Additionally, there have been several medical policies, including changes to medical insurance and centralized volume-based procurement. China's pharmaceutical market has undergone significant changes, leading to increased demands for innovation technology efficiency in pharmaceutical manufacturing.

Methods

The three-stage BCC theory was employed to assess the effectiveness of technology innovation in the industry under this reform. Calculate precise comprehensive technical efficiency values, pure technical efficiency values, and scale efficiency values for technological innovation in the pharmaceutical industry across 30 provinces from 2018 to 2020, after removing environmental factors.

Results

In 2020, Jiangsu and Shandong and nine other provinces reached the comprehensive technical efficiency frontier surface, joining Tianjin, Zhejiang, and Guangdong provinces. However, Gansu, Qinghai, Ningxia, and Xinjiang still need to catch up due to their smaller industrial scale and lack of technology.

Discussion

To ensure the effectiveness of reforms, it is crucial to fully consider provincial differences. Articulating national and provincial policies is necessary to allow efficient provinces to continue and allocate resources toward less efficient provinces to improve overall efficiency.

Determinants of exports performance: Evidence from Indonesian low-, medium-, and high-technology manufacturing industries

This study explores the determinants of the export performance of Indonesia's low-, medium-, and high-technology manufacturing industries by focusing on the role of raw-material imports and technical efficiency. Micro firm-level data from 2010-2015 were utilized for the analysis in this study. The stochastic frontier analysis was employed to measure technical inefficiency and to determine its effect on export performance. Our findings indicate that in all categories of industry technical efficiency, raw materials imports, foreign direct investment (FDI), location, firm size, labour productivity, and concentration of industries were significant determinants of export performance. While high efficiency increases exports in low- and medium-technology firms, exports decrease in firms with high efficiency accompanied by high imports, FDI, size, and labour productivity. Furthermore, in high-technology industries, efficiency reduces exports and again increases them when mediated by a concentration of industries and location. The empirical strategy also supports the positive effect of imports on export performance in both industries, which also aligns with decreased exports in firms with high imports accompanied by high FDI, efficiency, labour productivity, the concentration of industries, and size. To this end, the study has implications for low-, medium-, and high-technology manufacturing that are mainly concerned with increasing exports.

A fuzzy decision support system for evaluating Lean-Green and company-specific production system

Lean production is regarded as systematic solution for identifying and eliminating waste through continuous improvement of manufacturing processes to meet customers' requirements. Due to rising environmental concerns and the significant environmental impact of companies operations, issues such as environmental management and green production are on board agendas. In essence, green production seeks to reduce the environmental impacts of business processes. In order to examine green production, an original equipment manufacturer in the supply chain of tractor manufacturing industry is selected for this work to enable a focus on lean production aspects and its specific requirements to evaluate a company-specific production system (XPS). The results suggest that the company utilizes six lean-green production criteria including suitable and green packaging, quality improvement, waste reduction, environmental pollution reduction, transport management and additional transportation reduction, and cost reduction. Furthermore, continuous improvement and integrated auditing are selected as two critical and fundamental elements with the highest ranks, regarding the relationships between the XPS elements and lean-green production criteria.

Forecasting the energy demand and CO2 emissions of industrial sectors in China's Beijing-Tianjin-Hebei region under energy transition

As the world's greatest energy consumer, China's energy consumption and transition have become a focus of attention. The most significant location for regional integration in the north of China is the Beijing-Tianjin-Hebei region, where the industrial sector dominates its energy consumption. Forecasting the energy demand and structure of industrial sectors in China's Beijing-Tianjin-Hebei region may help to promote the energy transition and CO2 emission mitigation. This study conducts a model based on the year 2020 using the Long-Range Energy Alternatives Planning System (LEAP) software and sets two scenarios (baseline scenario and emission peak scenario) to forecast the future energy demand and CO2 emissions of industrial sectors in China's Beijing-Tianjin-Hebei region until the year 2035. Moreover, the industrial sectors are classified into traditional high-energy-consuming industries, emerging manufacturing industries, daily-related light industries, and other industries. The forecasting results show that (1) The industrial energy demand of the entire Beijing-Tianjin-Hebei region will grow from 234 Mtce in 2020 to 317 Mtce in 2035, and the corresponding energy structure will shift from coal-based to electricity-based; (2) at the provincial level, all three provinces will experience an increase in industrial energy demand between 2020 and 2035, with Hebei experiencing the fastest average annual growth rate of 2.18% and the largest share of over 80%, and Beijing experiencing the highest average annual electrification rate of 70%; (3) at the industrial sector level, the electricity and natural gas will gradually replace other energy sources as the main energy source for industry. The most representative industrial sub-sector in Beijing, Tianjin, and Hebei provinces are all traditional high-energy-consuming industries, which will account for more than 90% of the total energy demand in both Tianjin and Hebei by 2035.

Scaling up the transition: The role of corporate governance mechanisms in promoting circular economy strategies

This study examines the role of corporate governance (CG) mechanisms in promoting Circular Economy (CE) strategies among non-financial listed companies in manufacturing industries, which is still a relatively unexplored topic in the CE literature. Our findings indicate that the presence of stakeholder engagement practices, sustainability reporting, and environment management teams have a direct impact on the adoption of CE strategies, while the presence of a CSR committee, adherence to the United Nations' Global Compact, and executives' compensation linked to environmental, social, and corporate governance performance do not have a direct effect but support CE strategies through other mechanisms. Overall, this study provides valuable insights for policymakers and managers as it shows that CG mechanisms can be used to promote the adoption of CE business models, thus contributing to climate risk mitigation objectives.

Corporate ESG performance when neighboring the Environmental Protection Agency

Practicing ESG concept is a necessary way for sustainable development of enterprises and an important hand in realizing high-quality economic growth. This paper introduces the hypothesis of "distance attenuation effect" of geo-economics into the research field of corporate ESG performance, and systematically examines the impact of neighboring the Environmental Protection Agency (EPA) on corporate ESG performance by using the data of China's listed companies in the manufacturing industry from 2011 to 2021. It is found that being a neighbor to the EPA has a significant inhibitory effect on corporate ESG performance, which was reduced by 0.196% when the distance between the two was reduced by every 1 km. And the inhibitory effect has a stepwise decreasing characteristic, i.e., it is the strongest at the level of county EPA, followed by municipal EPA, and the smallest at the level of provincial EPA. The mechanism analysis shows that the rent-seeking behavior caused by neighboring with EPA is an important reason for the decline of ESG performance, which verifies the "distance attenuation effect" of rent-seeking difficulty. Although neighboring the EPA improves firms' financial performance, it greatly harms firms' environmental, social and governance performance, which confirms the profit-seeking motive of firms' rent-seeking. In addition, the deterrent effect generated by third-party supervision and digital supervision can inhibit corporate rent-seeking to a certain extent, which helps weaken the negative impact of neighboring the EPA on corporate ESG performance.

How to systematically reduce the carbon emissions of the manufacturing industry? Evidence from four-party evolutionary game analysis

To effectively reduce carbon emissions from the manufacturing industry and promote green and sustainable developments evolutionary game theory is widely used. This study has constructed a four-party evolutionary game model, in which the government, civil environmental protection organisations, manufacturing enterprises, and consumers participate. A local robustness analysis and numerical simulation were used to assess the stability conditions under which the strategic behaviour of the four parties reaches an ideal state, and the influence of government parameter changes on the game system were further analysed. The results show that when the government's penalty is greater, the decision-making time of civil environmental protection organisations and manufacturing enterprises is shortened to varying degrees. When the subsidy coefficient provided by the government increases, civil environmental protection organisations, manufacturing enterprises, and consumers can reduce the time required for the system to stabilise to varying degrees. As the subsidy coefficient increases, the government's strategic choice evolves and strict regulations are loosened. The government should thus actively establish a reward and penalty mechanism, according to its own actual situation, set reasonable punishment and subsidy coefficients, and actively guide the subjective initiative of civil environmental protection organisations to reduce carbon emissions in the manufacturing industry.

Outward foreign direct investment and GVC position of manufacturing industry: A perspective on China's general trade and processing trade structure

Depending on the trading modes, the effect of Outward Foreign Direct Investment (OFDI) on the manufacturing industry's position within the global value chain (GVC) may differ considerably. This paper examines the GVC position of China's manufacturing industry from 2003 to 2018, specifically focusing on the general trade and processing trade. Drawing upon this premise, this paper analyzes the effect and mechanism by which OFDI influences the GVC position of China's manufacturing industry. The result demonstrates that: (1) China's processing trade manufacturing industry has a much lower GVC position than general trade manufacturing industry. The GVC position of China's general trade manufacturing industry rose from 2.76 to 2.90 from 2003 to 2018, while processing trade manufacturing industry remained around 1.93. (2) OFDI boosts the GVC position of general trade manufacturing industry through facilitating reverse technology spillover, inducing industry structure upgrading, and enabling export scale expansion. (3) OFDI hinders the GVC position growth of processing trade manufacturing industry. The research findings offer theoretical backing for China to develop OFDI strategies that are tailored to different trading modes within the new framework of dual circulation. These strategies aim to facilitate the transformation and advancement of the manufacturing industry, as well as the growth of the GVC position.

Research on the dynamic changes of China's agro-processing industry agglomeration and spatial impact of production factors on agglomeration

Agglomeration is an important phenomenon that accompanies with a large concentration of various production factors. Since the agro-processing industry has become a vital bridge connecting the primary and secondary industries, agglomeration and distribution within this industry are arousing wide concern. Based on provincial panel data of China from 2001 to 2020, this study described the dynamic changes of China's agro-processing industry agglomeration using spatial Gini coefficient and average concentration ratio. A theoretical analysis framework was established and a dynamic spatial Durbin model was used to quantitatively explore the spatial effects of production factors on the agro-processing industry agglomeration, results showed that: first, the agglomerations of agro-processing industry and its sub-industries all have exhibited a fluctuating trend of "up-down-up-down", meanwhile the agro-processing industry shifted from the Eastern coastal region to the central and western regions. Second, in the short term, the capital, labor, and technology factors respectively have remarkably promoted the agro-processing industries agglomeration both in local and neighboring areas. Third, in the long term, these three production factors all had a crowding effect on industrial agglomeration. Therefore, it is necessary to continue optimizing the agro-processing industry spatial layout through adjusting production factor inputs to promote high-quality development of the agro-processing industry.

Research on the synergetic development of producer services and manufacturing in northwest China's inland central cities-A case study of Lanzhou

Under the post Ford flexible production organization system, the existence of specialized division of labor has made the forward and backward connections between the producer services as an intermediate input and the manufacturing increasingly close. With the proposal of the "Belt and Road" and the deepening of "The development of the western region in China", the northwest region plays an increasingly important role in China's regional development strategy, and the economic transformation, especially the transformation and upgrading of the manufacturing, and it is crucial to its completion of the strategic mission entrusted by the state. Taking Lanzhou City as a case area, based on the measurement of the cooperative development level of producer services and manufacturing, this paper explores the internal law of the synergetic development of producer services and manufacturing in the northwest inland central city. The study found: The overall synergy development level of producer services and manufacturing in Lanzhou has been significantly improved in the dynamic evolution process, and two industries are in the transition stage from the primary coordinated development to the intermediate coordinated development at present. The spatial layout of producer services and manufacturing in Lanzhou City has certain spatial proximity, and the overall presents a "center-periphery" spatial pattern with the main urban area as the main component. From the perspective of the synergistic development of producer services and manufacturing in Lanzhou, the two industries have initially formed an interactive relationship of two-way promotion at present, but there is a significant asymmetry in the two-way promoting effect of the two industries.

The influence of human resource management practices on occupational health and safety in the manufacturing industry

This study provides an overview of the influence of applying two human resource management (HRM) practices on workers' occupational health and safety (OHS). These practices are selection and recruitment, and development and training. Furthermore, this study highlights the observed activities to define hazards, processes and OHS measures in the manufacturing industries. It also assesses the importance of workers' knowledge and awareness of OHS programs. A structured questionnaire was administered to those working in the manufacturing sector in several countries. The responses were analyzed statistically. The results showed that the demographic variables had a significant impact on workers' knowledge and awareness of OHS programs. In addition, HRM practices played a major role in OHS. Therefore, it is recommended to top management to highly consider OHS practices by integrating them into the workplace, starting from worker selection, all the way to production, operations and management, by establishing a strong HRM system.

Comprehensive technological assessment for different treatment methods of leather tannery wastewater

The leather-making process necessitates large amounts of water and consequently generates tons of liquid waste as leather tannery wastewater (TWW) is disposed of directly in the open environment. Open disposal of untreated TWW into the natural environment causes an accumulation of various polluting compounds, including heavy metals, dyes, suspended solids inorganic matter, biocides, oils, tannins, and other toxic chemicals. It thus poses potential hazards to the environment and human health. This study primarily focuses on providing in-depth insight into the characteristics, treatment strategies, and regulatory frameworks for managing TWW in leather processing industries. Different technologies of conventional physico-chemical (equalization, coagulation, and adsorption), advanced approaches (Fenton oxidation, ozonation, cavitation), thermo-catalytic and biological treatments available to treat TWW, and their integrative approaches were also highlighted. This review also sheds light on the most frequently applied technologies to reduce contaminant load from TWW though there are several limitations associated with it such as being ineffective for large quantities of TWW, waste generation during treatment, and high operational and maintenance (O&M) costs. It is concluded that the sustainable alternatives applied in the current TWW technologies can minimize O&M costs and recirculate the treated water in the environment. The exhaustive observations and recommendations presented in this article are helpful in the industry to manage TWW and recirculate the water in a sustainable manner.

Impact of carbon border adjustment mechanism on China's manufacturing sector: A dynamic recursive CGE model based on an evolutionary game

As the global carbon emission problem is getting more and more serious, the European Union has proposed the Carbon Border Adjustment Mechanism (CBAM) to prevent carbon leakage, which will have an impact on China's foreign trade. Based on this background and the problems of current research that ignores factors such as commodity prices, long-term impacts, intertemporal substitution analysis, and changes in national strategies for foreign exports, this paper propose a Computable General Equilibrium (CGE) model based on an evolutionary game as a dynamic recursive basis to study the impact of the CBAM on the Chinese manufacturing industry. For the industries mentioned in the CBAM, 18 affected sectors are collated, and a CGE model containing multiple blocks such as production, trade, and carbon emissions is built. Meanwhile, we establish an evolutionary game model with EU suppliers, manufacturers, and the government, allowing Chinese suppliers to compete with EU suppliers. Based on the above model, the impact of carbon tariff policies from 2020 to 2050 is studied. The results indicate that under the intra-EU competition condition, carbon tariffs will reduce the price of Chinese exports and slightly decrease China's real GDP, as well as the carbon emission intensity of 18 sectors and fossil energy. However, the impact of carbon tariffs on overall carbon emissions is small and is essentially a protective policy. In addition, the evolutionary game-based CGE model takes into account the dynamic strategies of both sides and is therefore less affected by carbon tariff shocks and recovers more quickly and more realistically. Finally, the article argues that the advantage of carbon tariffs for local suppliers is unsustainable and Chinese suppliers will still dominate, and local suppliers need more subsidies from the EU government. On the other hand, the Chinese government needs to implement a stricter carbon tax regime and export subsidies to improve the competitiveness of Chinese products in terms of green levels and price advantages. The purpose of this paper is to examine the quantitative impacts of CBAM on China's related manufacturing industries and strategies to maximize the benefits to both governments.

Has the consistency evaluation policy of generic drugs promoted the innovation quality of Chinese pharmaceutical manufacturing industry? An empirical study based on the difference-in-differences model

Introduction

In March 2016, the Chinese government officially launched a nationwide consistency evaluation of the quality and efficacy of generic drugs.

Methods

This paper conducted an empirical study using the Difference-in-Differences method to explore the effect of this policy on the innovation quality of China's pharmaceutical manufacturing industry and further analyzed the underlying mechanism of action.

Results

The results of the study show that the generic consistency evaluation policy has a significant promotion effect on the innovation quality of China's pharmaceutical manufacturing industry, and the promotion effect is the largest for non-state-owned enterprises and enterprises in the central region; in addition, the intensity of R&D capital investment and R&D personnel investment which play a mediating role.

Discussion

Therefore, we should fully recognize the positive effect of generic drug consistency evaluation policy on improving the innovation quality of the pharmaceutical manufacturing industry and pay attention to the necessity of regional coordination and unification in policy implementation and the formulation of supporting policy tools. This study provides empirical evidence for the implementation effect of the generic drug consistency evaluation policy, which can provide an essential reference for the further improvement of the procedure and the R&D decision-making of pharmaceutical enterprises.

Influencing factors and improvement paths of manufacturing innovation performance: Configuration analysis based on TOE framework

Innovation is the first driving force to lead development, how to improve manufacturing innovation performance has become a hot topic. Based on 47 listed companies in the computer, communication and other electronic equipment manufacturing industry in the A-share market, this paper adopted the Fuzzy set qualitative comparative analysis (fsQCA) to explore the influencing factors of technology, organization and environment on the innovation performance of manufacturing industry and the improvement path. The findings are as follows: (1) A single condition is not a necessary condition for high innovation performance in manufacturing industry, but government support plays a key role in improving innovation performance in manufacturing industry. (2) There are two improvement paths for high innovation performance in manufacturing industry, which are specifically explained as "technology-environment dual improvement path" and "technology-organization-environment collaborative improvement path". (3) The improvement of innovation performance in the manufacturing industry is the result of multiple factors, showing the characteristics of "all paths lead to the same destination". Different manufacturing enterprises have different paths to improve innovation performance based on their actual conditions. Based on these findings, this study may provide some implications for the effective improvement of manufacturing innovation performance.

Innovation network, knowledge absorption ability, and technology innovation performance--An empirical analysis of China's intelligent manufacturing industry

Based on the social network theory, this study utilizes knowledge absorption capacity as the mediating variable and technology turbulence as the moderating variable; furthermore, it focuses on China's intelligent manufacturing industry data to explore the effect of the intelligent manufacturing enterprise innovation network on technology innovation performance and the regulating mechanism of technology turbulence. Based on the patent data obtained from Derwent Database (survey period: 2016-2020), the empirical analysis indicates the following: (1) Network relationship, network location, and network density are significantly and positively correlated with technology innovation performance; however, network size exerts no significant effect on technology innovation performance. (2) Network relationship strength, network location, and network density exert significantly positive effects on the two dimensions of knowledge absorption capacity, namely the In-degree and the Out-degree. Network size exerts no significant effect on knowledge absorption capacity. (3) Knowledge absorption capacity exerts a partial mediating effect on the relationship between innovation network and technology innovation performance. (4) The three dimensions of innovation network that exert a significant effect on technology innovation performance are positively correlated with the interaction terms of technology turbulence, which indicates that the interaction terms, namely innovation network and technology turbulence, exert a positive impact on technology innovation performance through knowledge absorption capacity, and that the moderating effect of technology turbulence exerts a role through knowledge absorption capacity. Finally, this study postulates implementations and policy proposals for enhancing the innovation performance of intelligent manufacturing enterprises.

Collaborative agglomeration level and spatial correlation of intercity manufacturing industry: An empirical study based on the cities of the Yangtze River Delta

Taking 27 cities in the Yangtze River Delta as an example, the time section data from 2009 to 2019 are selected, and the location entropy index and the modified E-G index are introduced to measure the collaborative agglomeration level of intercity manufacturing industry in the Yangtze River Delta. The spatial weight matrix is constructed based on the highway mileage between cities. Using Moran's index and Local Moran's index, this article analyzes the spatial correlation of the collaborative agglomeration level of intercity manufacturing industry in the Yangtze River Delta. The results show that: Firstly, the overall agglomeration degree of manufacturing industry of cities in the Yangtze River Delta shows a fluctuating downward trend. The agglomeration degree of manufacturing industry in Jiangsu and Zhejiang Provinces has decreased, that of most cities in Anhui Province has increased steadily. Secondly, the collaborative agglomeration level of manufacturing industry between Shanghai, Nanjing, Hangzhou and other cities in Jiangsu and Zhejiang Provinces showed a downward trend, while the collaborative agglomeration level with Anhui cities increased steadily. The collaborative agglomeration level of manufacturing industry between Wuxi, Suzhou, Nanjing and cities with obvious advantages in manufacturing industry agglomeration degree shows an upward trend, while the collaborative agglomeration level with other cities has declined. The collaborative agglomeration level of intercity manufacturing industry in Hefei has generally improved, especially the collaborative agglomeration quality and depth of manufacturing industry between Hefei and other cities in Anhui Province have significantly improved. Thirdly, the regions with high collaborative agglomeration level of intercity manufacturing industry in the Yangtze River Delta are still mainly concentrated in Jiangsu and Zhejiang Provinces. Although there has been a trend of westward transfer in the past decade, the overall change range is small, and the spatial correlation of the collaborative agglomeration level of intercity manufacturing industry shows a weakening trend as a whole.

From Covid-19 to conflict: Does environmental regulation and green innovation improve industrial sector decarbonization efforts and environmental management?

During the COVID-19 period, industrial production was slowed or halted due to COVID restrictions and lockdowns, followed by global tensions and conflicts, which created uncertainties for industrial production. Similarly, during this period, the growth in carbon emissions was seen shrinking. However, it is greatly important to explore whether this shrinking trend will continue or whether a new growth pattern could emerge. Considering this, the research was carried out to investigate the relationship between green innovation initiatives and environmental regulations in the process of environmental management and environmental performance in the industrial sector. To do so, the survey-based research methodology using PLS-SEM was adopted, and data was gathered from 279 managers working in the equipment manufacturing industry. Our empirical findings revealed that environmental regulation and green innovation efforts strengthen decarbonization efforts, which further improve environmental management and environmental performance. The mediating role of decarbonization efforts was found to be prominent among green process innovation, environmental management and environmental performance. Moreover, decarbonization serves as a mediator between green product innovation and environmental management. In contrast, we could not verify that decarbonization mediates the relationship between green product innovation and environmental performance. The key findings are greatly important and provide a fresh roadmap for environmental management in the post-COVID era.

Micro-perspective of listed companies in China: Digital development promotes the green transformation of the manufacturing industry

In the context of the rapid development of the global digital economy, it is of great significance to explore the greening transformation of the manufacturing industry from the micro-perspective of enterprise digital development. This paper empirically examines the impact and mechanism of enterprise digital development on the greening transformation of the manufacturing industry using the 2010-2020 data of Chinese A-share listed companies in the manufacturing industry as a sample. The study shows that enterprise digital development can significantly promote the greening transformation of China's manufacturing industry, and this conclusion still holds after a series of robustness tests. Technological innovation and financing constraints are important mediating mechanisms. Further research found that the impact of enterprise digital development on the greening transformation of China's manufacturing industry has a positive nonlinear effect, and its marginal effect shows a weakening trend. Heterogeneity analysis shows that, from the perspective of micro characteristics, digital development is more able to promote the green transformation of state-owned and large enterprises. From a macro-regional perspective, digital development can better promote the green transformation of the manufacturing industry in eastern cities, key city clusters, and high-level cities. The findings of this paper can provide corresponding insights for "revitalizing the manufacturing industry", and also provide decision-making references for countries aiming to make the manufacturing industry bigger and stronger.

Inducement factor of talent agglomeration in the manufacturing industrial sector: A survey on the readiness of Industry 4.0 adoption

China's economy has progressed from a rapid growth phase to one of high-quality development and innovation. Industry 4.0 manufacturing technology and processes include cyber-physical systems (CPS), Industrial Internet of Things (IIOT), Cognitive Computing and Artificial Intelligence (CCAI) as advancements in computerization and information exchange the relevant variables data, and a survey questionnaire are used to accumulate three-year data from 2017 to 2019. The Structured Equation Modeling (SEM), analytic hierarchy process (AHP), and mediating variable in a SOBEL test are applied. The results show that Industry 4.0 is the primary practical corridor to official and familiar in sequence substitute policy and collaboration for talent agglomeration on research projects. It lowers the fixed price of human capital and significant factors active long-term innovation and profit at the end of the inferential test results. Hypotheses findings show that the associations between dependent and independent variables are essential, and latent variables GFI, CFI, TLI, and IFI have acceptable values. CMINDF and RMR fulfill the fit criteria and results will assist managers and policymakers in spotting talent agglomeration activities implemented to increase manufacturing businesses' readiness to reap the most benefits from Industry 4.0 adoption.

Employee green behavior: a study on the impact of corporate social responsibility (CSR) on employee green behavior, green culture: the moderating role of green innovation

This study adopts the natural resource-based view (NRBV) of the firm to investigate the relationships between corporate social responsibility (CSR), employees' green behavior (EGB), green culture, environmental performance (EP), and green innovation (GI) in China's automobile manufacturing industry. A survey of 465 employees was conducted. The data were analyzed using partial least squares structural equation modeling. The results showed that CSR positively influences EGB and GC, which in turn improves EP. CSR also directly impacts EP. Furthermore, GI enhances the positive effect of CSR on EP through the mediators EGB and GC. The findings contribute to the literature by elucidating the mechanisms linking CSR and EP. They provide an integrated framework incorporating direct, mediated, and moderated effects. For practice, the study highlights the importance of CSR, EGB, and GC for manufacturers aiming to improve environmental sustainability. It suggests managers should promote green values, foster eco-innovation, and encourage employees' discretionary green actions. The moderating effect of GI implies embedding sustainability in corporate culture and operations to magnify the gains from CSR. Overall, the research offers insights into translating green strategic intent into performance outcomes through cultural and behavioral transformation.

Screening of White-Rot Fungi Isolates for Decolorization of Pulp and Paper Mill Effluent and Assessment of Biodegradation and Biosorption Processes

Ten white-rot fungal isolates were evaluated for the decolorization potential of pulp and paper mill effluent. Trametes elegans PP17-06, Pseudolagarobasidium sp. PP17-33, and Microporus sp.2 PP17-20 showed the highest decolorization efficiencies between 42 and 54% in 5 d. To reveal the mechanisms involved in decolorization and assess the long-term performance, PP17-06, which showed the highest decolorization efficiency, was further investigated. It could reduce the ADMI color scale by 63.6% in 10 d. However, extending the treatment period for more than 10 d did not significantly enhance the decolorization efficiencies. The maximum MnP activity of 3.27 U L-1 was observed on the 6 d during the biodegradation. In comparison, laccase activities were low with the maximum activity of 0.38 U L-1 (24 d). No significant LiP activities were monitored during the experiment. Dead fungal biomass showed an optimum decolorization efficiency of 44.18% in 8 d employing the biosorption mechanism. No significant changes in the decolorization efficiency were observed after that, suggesting the equilibrium status was reached. These results revealed that PP17-06 has the potential to decolorize pulp and paper mill effluent by employing both biodegradation and biosorption processes.

How does digital trade promote and reallocate the export technology complexity of the manufacturing industry? Evidence from 30 Chinese provinces, 2011-2020

It is important for China to break the "low-end lock" of the manufacturing value chain worldwide by revealing how digital trade promotes and reallocates the export technology complexity of the manufacturing industry. Panel data for 30 provinces in China from 2011 to 2020 were employed to measure the digital trade development and export technology complexity of the manufacturing industry. Benchmark regression, intermediary effect regression, panel threshold and other models were used to test the promotion and reallocation of digital trade on the export technology complexity of the manufacturing industry. The findings are as follows: (1) Digital trade promotes the export technology complexity of the manufacturing industry, with significant regional heterogeneity (eastern, central and western regions), and the most obvious promotion in technology-intensive manufacturing. (2) Technological innovation and human capital play a reallocation role in the process of digital trade, affecting the technological complexity of manufacturing exports, with mediating effects of 14.19% and 8.61%, respectively. (3) Digital trade promotes and reallocates the export technology complexity of the manufacturing industry through industrial structure upgrading, and a nonlinear relationship was found. These results provide empirical support and a decision-making basis for digital trade in promoting the export technology complexity of the manufacturing industry. The development of digital trade should be encouraged; the differential development of digital trade in the eastern, central, and western regions should be boosted; importance should be attached to the intermediary incentive role of technological innovation and human capital; and the upgrading of the industrial structure should be promoted scientifically.

Spatial distribution characteristics and analysis of influencing factors on different manufacturing types in Shandong Province

Investigating the spatial distribution characteristics and influencing factors of various industry types is critical for promoting the high-quality transformation and development of China's industry. This study combined the Getis-Ord Gi* statistic method, the random forest-based importance assessment method, and the geographically weighted regression method to determine the spatial distribution characteristics of four industry types and their influencing factors. The results revealed that the raw material industry was primarily concentrated in the surrounding districts and counties of Linyi and Qingdao. The food and light textile industry was mainly concentrated in the surrounding districts and counties of Qingdao, and a few were concentrated in some counties of Linyi. The processing and manufacturing industry was also concentrated in the surrounding districts and counties of Qingdao, and a few were concentrated in the belt regions connecting Jinan, Zibo, and Weifang. The high-tech industry was mainly concentrated in the surrounding districts and counties of Jinan and Qingdao. The key spatial influencing factors of the four industry types were different. The number of employees in the secondary industry and road density were most important in determining the spatial distribution of the raw material industry. The financial environment and number of research institutions were most important to the spatial distribution of the food and light textile industry. The gross domestic product and number of medical facilities were most important to the spatial distribution of the processing and manufacturing industry. Urbanization rate, number of research institutions, and gross domestic product were most important to the spatial distribution of the high-tech industry. Geographically weighted regression analysis revealed that the impact intensity of these key factors on the industry exhibits significant spatial heterogeneity. Taken together, these results are useful for formulating the development strategy for each industrial type in different regions.

Are green HRM Practices enough to build successful green ventures interlinked with innovation and knowledge-sharing behavior? Case of China

Green practices are now treated as an essential component of organizational element and firms are now exploring ways to incorporate new growth strategies that ensure environmentally friendly methods. The present study focuses on manufacturing industry in China and identify that green HRM practices influence eco-innovation and organization's knowledge-sharing culture. The study also aims to identify whether eco-innovation and knowledge-sharing culture help to build successful green venture and provide indirect path to green HRM and green ventures. An adopted survey was used to collect data from manufacturing employees and SPSS-AMOS is employed to assess the model reliability and proposed hypotheses. Study outcomes reveal that green HRM practices increase knowledge-sharing behavior and promote green innovation. Findings also expose that eco-innovation and knowledge-sharing behavior are potential mediator, hence provide an indirect path between green HRM practices and green ventures. Results confirm that essentiality of green HRM in order to promote knowledge-sharing behavior among employees through which environmental commitment can be fulfilled by organizations, further leading to successful green venture.

Advanced exergoenvironmental and thermo-sustainability evaluation of cement plant, splitting the environmental impact into endogenous and exogenous parts: a case study

The paper presents an advanced exergoenvironmental and thermo-sustainability analysis of a dry process, 5-million-ton capacity cement plant with real-time operational data. The analysis was based on component-wise modelling by separating the exergy destruction (ED) into endogenous, exogenous, avoidable and unavoidable parts for the two production lines, L1 and L2. The result shows an exergy efficiency ([Formula: see text]) of 55.58% for L1 and 58.22% for L2. Additionally, the overall exergoenvironmental factor (EEF) was calculated at 6.47% for (L1) and 5.89% for (L2), with the rotary kiln (RK) having the highest (EEF) of 53.7%. Conversely, the advanced exergy analysis showed that the plant's mainstream ED rates are endogenous and unavoidable. The component sustainability index (SI) for L1 and L2 ranged between [Formula: see text] and [Formula: see text], respectively. Similarly, the sustainability results show that the environmental destruction coefficient (EDC) and the environmental destruction index (EDI) were high in L1. Nonetheless, the study identified components with high potential for improvement, making system design and optimization suggestions possible.

Does environmental governance matter to the relationship between digital transformation and high-quality development? Evidence from manufacturing sector in China

Environmental governance has emerged as a crucial tactic to support the sustainable development of human civilization in light of the serious environmental issues. Meanwhile, during the process of promoting the high-quality development of China's economy, digital transformation plays a significant role in improving the total factor productivity of the manufacturing sector. We seek to find out whether urban environmental governance has an impact on micro-enterprises, and therefore, the study selects the data of A-share listed manufacturing companies from 2012 to 2020 to study the effect of digital transformation on the total factor productivity of the manufacturing industry and how the effect of environmental governance affects the relationship between digital transformation and total factor productivity. The results unveil that digital transformation can significantly contribute to the total factor productivity of the manufacturing industry. At the same time, digital transformation can promote the high-quality development of enterprises by promoting the fulfillment of corporate social responsibility. Additionally, it is shown that poor environmental governance will weaken the promoting effect of digital transformation on total factor productivity. Furthermore, in state-owned companies and non-heavy polluting industries, environmental governance has a more significant moderating influence on digital transformation and total factor productivity. This study enriches the literature on urban environmental governance and micro-enterprise development, and they support the notion that, from the standpoints of environmental protection and economic development, the level of environmental governance should be continuously optimized and the development of ecological civilization should be strengthened.

Determinants of the duration of sick leave due to occupational injuries: Evidence from Spanish manufacturing

INTRODUCTION

Despite the significant economic impact of occupational injuries on companies and society, studies focused on analyzing the determinants of workdays lost due to sick leave remain scarce and incomplete. This paper contributes to this issue by (a) analyzing the drivers of sick leave duration, distinguishing factors that explain the health recovery time from those that could lead workers to a voluntary extension of the absence period, and (b) formulating and empirically testing the effect of gender, citizenship, temporary work, job tenure, amount of disability benefit, and size of the injured worker's firm on the number of days the employee is off work after the injury.

METHOD

Hypotheses are tested on a comprehensive dataset that includes all nonfatal occupational injuries causing sick leave that occurred in the manufacturing sector in Spain during 2015-2019, with more than 400,000 injuries. We conduct ordinary least squares and count data regression models in which the number of days off work is regressed on employees and work characteristics while accounting for a set of variables to control the injury's nature and severity.

RESULTS

The results show that after considering the intrinsic characteristics of the injury and the severity of the worker's injuries, women, native workers, workers with more seniority, workers with higher salaries, and those working in larger companies have longer periods of sick leave. The results suggest that moral hazard considerations significantly impact the time to return to work after an occupational injury.

PRACTICAL APPLICATIONS

Based on the findings, several insights for company managers and public decision-makers are discussed. Specifically, interventions aimed at improving the organization of work and the working conditions of workers in manufacturing industries are highlighted, as well as the need to improve control and supervision mechanisms during the recovery process of injured workers.

Environmental taxes, enterprise innovation, and environmental total factor productivity-effect test based on Porter's hypothesis

Under the increasingly severe environmental constraints, improving environmental total factor productivity (ETFP) is the fundamental way for the sustainable development of heavily polluting enterprises. Based on 3463 panel data of A-share listed companies in China from 2011 to 2019, this paper employs Porter's hypothesis (PH) framework to explore the impact of environmental tax (EN_T) on enterprise innovation and environmental total factor productivity for the heavily polluting manufacturing industry using the propensity score matching (PSM) method. The empirical results show the following. (i) Environmental taxes positively affect enterprise innovation (EI) and environmental total factor productivity (ETFP). (ii) Mechanism analysis verifies a partial mediating effect for EI between EN_T and ETFP. (iii) Regional heterogeneity analysis illustrates the differences in the impact of environmental taxes on innovation quality. (iv) Individual heterogeneity analysis shows that the "strong Porter hypothesis" is only valid for large-scale enterprises. The results are of great importance for both government and enterprises to improve the EN_T system and optimize the allocation of resources in realistic practice.

The impact of green finance on the optimization of industrial structure: Evidence from China

Green finance promotes the optimization of industrial structure and continuous improvement of ecological environment by supporting the development of green industries. Based on the panel data of 30 provinces in China from 2012 to 2020, this paper uses the entropy weight TOPSIS method to measure the development level of green finance and the level of industrial structure optimization in China, and constructs a panel data model to empirically test the impact of green finance on the upgrading of China's industrial structure. The study finds that there is still an imbalance and insufficiency in the development of green finance and industrial structure optimization in China. From 2012 to 2020, the development level of green finance and the level of industrial structure optimization in China have been continuously rising, but there is obvious heterogeneity, showing an eastern>central>western spatial pattern. Empirical analysis results show that at the significance level of 1‰, the development of green finance has a significant promoting effect on the rationalization and upgrading of the industrial structure. However, there is significant heterogeneity in the impact of green finance on industrial structure optimization. In terms of regional heterogeneity, at the significance level of 1‰, the role of green finance in promoting the optimization of industrial structure in central and western China is higher than that in eastern China, and the impact of green finance on China's industrial structure shows a spatial pattern of western>central>eastern China. In terms of industry heterogeneity, at the significance level of 1‰, green finance has a significant promoting effect on the development of green industries, and a significant inhibiting effect on the development of high-energy-consuming industries. Specifically, in the green industry, green finance has the greatest promoting effect on the communication and other electronic equipment manufacturing industry; in the high-energy-consuming industry, green finance has the greatest inhibiting effect on the black metal smelting and rolling processing industry, and the smallest impact on the petroleum, coal and other fuel processing industry. Finally, based on this, policy suggestions for green finance to support the optimization of industrial structure are proposed from two dimensions: government and financial institutions.

Towards sustainability: the impact of industrial synergistic agglomeration on the efficiency of regional green development

Guided by the concept related to sustainable development, we investigate the effects related to the synergistic agglomeration development of productive service and manufacturing industries on regional green development, which is also an important path for promoting the global sustainable development process and achieving carbon neutrality goals. Using the panel data of 285 prefecture-level cities in China from 2011 to 2020 as the basis of our study, we focus on the impact of industrial synergistic agglomeration on the efficiency of regional green development and the mediating influence of technological innovation. Results show that (1) industrial synergistic agglomeration positively contributes to the improvement of regional green development efficiency level and is significantly positive at the 5% level, (2) technological innovation plays a mediating role in the process of promoting regional green development efficiency through industrial synergistic agglomeration and can better realise the green development effect of industrial synergistic agglomeration, (3) results of the threshold effect test show the nonlinear effect of industrial synergistic agglomeration on regional green development efficiency with a single threshold value of 3.2397, and (4) the effect of industrial synergistic agglomeration on regional green development efficiency shows significant variability under different geographical locations, city scales, and resource endowment conditions. On the basis of these findings, we propose corresponding policy recommendations for improving the quality of inter-regional industrial synergistic agglomeration and formulating differentiated policy guidelines to help regions achieve long-term sustainable development.

Adoption of green supply chain management in developing countries: role of consumer cooperation, eco-design, and green marketing

This study investigates the influence of consumer cooperation, eco-design, and green marketing on the adoption of green supply chain management in developing countries. The mediating role of innovation in this relationship is also examined. A survey method was employed, using a questionnaire adapted from previous studies. The sample comprised 250 respondents who were employees of small and medium size and multinational manufacturing industries in Bangladesh. Smart partial least squares (PLS) are currently being used for data analysis, while PLS-structural equation modeling is being employed to assess measurement and structural models. The findings reveal that consumer cooperation, eco-design, and green marketing significantly affect innovation. Furthermore, innovation acts as a mediator between these variables and the adoption of green supply chain management. This study identifies green supply chain management practices that have the potential to enhance organizational performance and motivate companies to implement strategic and operational changes, leading to significant economic, social, and environmental impacts. The research holds significant importance for emerging economies and green supply chain adoption considering the constraints at both organizational and government levels. It provides a framework for a synergistic combination of asset-based elements, innovation, and green supply chain management, benefiting small and medium size organization, multinational corporations, and the supply chain sector in achieving sustainable development goals. The implications of this study extend to supervisors and managers in the corporate world, assisting them in making informed decisions. By expanding the existing literature on the consumer cooperation, eco-design, and green marketing model to include green supply chain management, this study contributes to the field. However, it should be noted that the findings and recommendations may be influenced by contextual factors, and therefore, future research should explore other countries to identify regional and specific sectors, enabling a broader perspective and comparisons as well as green related aspects and performances.

Efficient removal of NaOl from mineral processing wastewater using Al-electrocoagulation

Sodium oleate (NaOl) is widely used as collector for oxidised ore flotation, and residual NaOl in mineral processing wastewater is a serious threat to mine environment. In this work, the feasibility of electrocoagulation (EC) as an alternative for chemical oxygen demand (COD) removal from NaOl-containing wastewater was demonstrated. Major variables were evaluated to optimise EC, and related mechanisms were proposed to interpret the observations in EC experiments. The initial pH of the wastewater greatly affected the COD removal efficiency, which was likely to be related to the variation of predominant species. When the pH was below 8.93 (original pH), liquid HOl_(l) was the predominant specie, which could be rapidly removed by EC thought charge neutralisation and adsorption. At original pH or higher, Ol^- could react with dissolved Al³⁺ to form insoluble Al(Ol)₃, which was subsequently removed through charge neutralisation and adsorption. The presence of fine mineral particles could reduce repulsion force of the suspended solids and promote flocculation, whereas the presence of water glass had an opposite effect. These results demonstrated that EC can be employed as an effective process to purify NaOl-containing wastewater. This study will contribute to deepening our understanding of EC technology for NaOl removal and provide useful information to researchers in mineral processing industry.

Supply chain finance and innovation efficiency: An empirical analysis based on manufacturing SMEs

This paper firstly demonstrates the positive and negative effects of supply chain finance on the innovation efficiency of China's small and medium-sized enterprises (SMEs) in the manufacturing industry from the theoretical point of view. Based on the data of 267 manufacturing companies in China Growth Enterprise Market from 2015 to 2019, the DEA-SBM method was used to measure the comprehensive innovation efficiency of different companies, and it was further decomposed into technological innovation efficiency and organizational innovation efficiency. Afterwards, it conducts an empirical analysis through the double fixed effect model, and explores the difference in the impact of supply chain finance on innovation efficiency in enterprises with different industries and different property rights. The results show that supply chain financial services have a strong positive impact on the comprehensive innovation efficiency, technological innovation efficiency and organizational innovation efficiency of manufacturing SMEs. Further, supply chain finance has the most significant improvement on the technological innovation efficiency of the sample of private traditional enterprises, but it has a significant inhibitory effect on the organizational innovation efficiency of the sample of state-owned high-tech enterprises. Therefore, this paper suggests that the development of supply chain financial services should increase support for traditional manufacturing industries; appropriately tilt resources to private enterprises; improve relevant supply chain financial laws and regulations, establish and improve corresponding institutional arrangements, and encourage state-owned enterprises to participate in market competition.