Carbon emissions in China's steel industry from a life cycle perspective: Carbon footprint insights.

China is the most important steel producer in the world, and its steel industry is one of the most carbon-intensive industries in China. Consequently, research on carbon emissions from the steel industry is crucial for China to achieve carbon neutrality and meet its sustainable global development goals. We constructed a carbon dioxide (CO2) emission model for China's iron and steel industry from a life cycle perspective, conducted an empirical analysis based on data from 2019, and calculated the CO2 emissions of the industry throughout its life cycle. Key emission reduction factors were identified using sensitivity analysis. The results demonstrated that the CO2 emission intensity of the steel industry was 2.33 ton CO2/ton, and the production and manufacturing stages were the main sources of CO2 emissions, accounting for 89.84% of the total steel life-cycle emissions. Notably, fossil fuel combustion had the highest sensitivity to steel CO2 emissions, with a sensitivity coefficient of 0.68, reducing the amount of fossil fuel combustion by 20% and carbon emissions by 13.60%. The sensitivities of power structure optimization and scrap consumption were similar, while that of the transportation structure adjustment was the lowest, with a sensitivity coefficient of less than 0.1. Given the current strategic goals of peak carbon and carbon neutrality, it is in the best interest of the Chinese government to actively promote energy-saving and low-carbon technologies, increase the ratio of scrap steel to steelmaking, and build a new power system.

Workplace Well-Being in Industry 5.0: A Worker-Centered Systematic Review.

The paradigm of Industry 5.0 pushes the transition from the traditional to a novel, smart, digital, and connected industry, where well-being is key to enhance productivity, optimize man-machine interaction and guarantee workers' safety. This work aims to conduct a systematic review of current methodologies for monitoring and analyzing physical and cognitive ergonomics. Three research questions are addressed: (1) which technologies are used to assess the physical and cognitive well-being of workers in the workplace, (2) how the acquired data are processed, and (3) what purpose this well-being is evaluated for. This way, individual factors within the holistic assessment of worker well-being are highlighted, and information is provided synthetically. The analysis was conducted following the PRISMA 2020 statement guidelines. From the sixty-five articles collected, the most adopted (1) technological solutions, (2) parameters, and (3) data analysis and processing were identified. Wearable inertial measurement units and RGB-D cameras are the most prevalent devices used for physical monitoring; in the cognitive ergonomics, and cardiac activity is the most adopted physiological parameter. Furthermore, insights on practical issues and future developments are provided. Future research should focus on developing multi-modal systems that combine these aspects with particular emphasis on their practical application in real industrial settings.

Industry 4.0-Compliant Occupational Chronic Obstructive Pulmonary Disease Prevention: Literature Review and Future Directions.

Chronic obstructive pulmonary disease (COPD) is among prevalent occupational diseases, causing early retirement and disabilities. This paper looks into occupational-related COPD prevention and intervention in the workplace for Industry 4.0-compliant occupation health and safety management. The economic burden and other severe problems caused by COPD are introduced. Subsequently, seminal research in relevant areas is reviewed. The prospects and challenges are introduced and discussed based on critical management approaches. An initial design of an Industry 4.0-compliant occupational COPD prevention system is presented at the end.

International operations and corporate R&D investment: Evidence from China.

This paper presents an examination of the relationship between international operations and corporate R&D investment. Using a large sample of Chinese listed firms for the 2009-2022 period and the ordinary least squares method, we find that international operations have a positive effect on corporate R&D investment. The finding remains valid after a battery of robustness tests. Mechanism tests show that international operations increase corporate R&D investment by diversifying product demand instead of increasing firms' international knowledge acquisition. This paper provides new evidence on the role of international operations in innovation activities.

Sexual violence and associated factors among female large-scale industry workers in Bahir Dar city administration, Amhara region, Ethiopia.

Introduction: sexual violence is currently a serious public health problem affecting women´s health. Globally, 1 in 3 women faces sexual violence in their lifetime. Female industry workers are at an increased risk of sexual violence. Assessing the magnitude and factors associated with sexual violence among female industrial workers is important for interventions. The objective was to assess the prevalence and factors associated with sexual violence among female large-scale industries workers in Bahir Dar, Ethiopia, 2021. Methods: institution-based cross-sectional study was conducted on 807 female industry workers from September to October 2021. Participants were selected by systematic random sampling. The data were collected by a structured questionnaire. Data entry and analysis were done by Epi data v.3.1 and SPSS v.23, respectively. Multivariable logistic regression analysis was done to identify factors. Adjusted odds ratios were computed at 95%CI. A P-value below 0.05 was used to declare association. Results: the prevalence of sexual violence were 59.4% (95% CI; 56.0%-62.6%). The significantly associated factors include; age less than twenty-five (AOR=4.01, 95%CI; 2.81, 10.83), never-married women (AOR=3.07, 95%CI; 1.11, 8.46), being secondary education (AOR=2.65, 95%CI; 1.51, 4.66), being contract employee (AOR=4.65, 95%CI; 1.92, 11.22), drinking alcohol (AOR=3.01, 95%CI; 1.49, 6.09), and night work shift (AOR=9.01, 95%CI; 4.53, 17.93). Conclusion: high rate (59.4%) of sexual violence was reported. Age, marital status, educational status, contract type of work agreement, drinking alcohol, and working night work shift were risk factors. Hence, emphasis on creating safe working environment & transportation, education on reproductive rights and reporting of sexual violence.

Unveiling the economic potential of sports industry in China: A data driven analysis.

The article explains the economic dynamics of the sports industry with adoption of deep learning algorithms and data mining methodology. Despite outstanding improvements in research of sports industry, a significant gap prevails with regard to proper quantification of economic benefits of this industry. Therefore, the current research is an attempt to filling this gap by proposing a specific economic model for the sports sector. This paper examines the data of sports industry covering the time span of 2012 to 2022 by using data mining technology for quantitative analyses. Deep learning algorithms and data mining techniques transform the gained information from sports industry databases into sophisticated economic models. The developed model then makes the efficient analysis of diverse datasets for underlying patterns and insights, crucial in realizing the economic trajectory of the industry. The findings of the study reveal the importance of sports industry for economic growth of China. Moreover, the application of deep learning algorithm highlights the importance of continuous learning and training on the economic data from the sports industry. It is, therefore, an entirely novel approach to build up an economic simulation framework using deep learning and data mining, tailored to the intricate dynamics of the sports industry.

Sickness absence after work accidents and post-traumatic stress among white-collar workers in the retail and wholesale industry; a longitudinal Swedish cohort study.

BACKGROUND: Most studies about accidents and about PTSD, respectively, have been conducted either on blue-collar workers, or on the entire working population. There are very few such studies on white-collar workers. AIM: To examine diagnosis-specific sickness absence (SA) and disability pension (DP) after a work accident or PTSD, respectively, among white-collar workers in the private retail and wholesale industry. METHODS: A prospective population-based cohort study of all 192,077 such workers aged 18-67 (44% women) in Sweden in 2012, using linked microdata from nationwide registers. We identified individuals who had secondary healthcare due to work-related accidents (n = 1114; 31% women) or to PTSD (n = 216; 79% women) in 2012-2016. Their average number of net days of diagnosis-specific SA (in SA spells > 14 days) and DP were calculated for 365 days before and 365 days after the healthcare visit. RESULTS: 35% of the women and 24% of the men had at least one new SA spell during the 365 days after healthcare due to work accidents. Among women, the average number of SA/DP days increased from 14 in the year before the visit to 31 days the year after; among men from 9 to 21 days. SA days due to fractures and other injuries increased most, while SA days due to mental diagnoses increased somewhat. 73% of women and 64% of men who had healthcare due to PTSD had at least one new SA spell in the next year. Women increased from 121 to 157 SA/DP days and men from 112 to 174. SA due to stress-related disorders and other mental diagnoses increased the most, while DP due to stress-related diagnoses and SA due to musculoskeletal diagnoses increased slightly. CONCLUSIONS: About a quarter of those who had secondary healthcare due to work accidents, and the majority of those with such healthcare due PTSD, had new SA in the following year. SA due to injury and mental diagnoses, respectively, increased most, however, SA/DP due to other diagnoses also increased slightly. More knowledge is needed on factors associated with having or not having SA/DP in different diagnoses after work accidents and among people with PTSD.

The impact of Industry 4.0 on occupational health and safety: A systematic literature review.

INTRODUCTION: Industry 4.0 has brought new paradigms to businesses based on high levels of automation and interconnectivity and the use of technologies. This new context has an impact on the work environment and workers. Nevertheless, these impacts are still inconclusive and controversial, requiring new investigative perspectives. This study aimed to investigate the requirements sought, the risk factors identified, and the adverse effects on workers caused by the characteristics of I4.0. METHOD: The methodology was based on a systematic literature review utilizing the PRISMA protocol, and 30 articles were found eligible. A descriptive and bibliometric analysis of these studies was performed. RESULTS: The results identified the main topics that emerged and have implications for workers' Occupational Health and Safety (OHS) and divided them into categories. The requirements are related mainly to cognitive, organizational, and technological demands. The most significant risk factors generated were associated with the psychosocial ones, but organizational, technological, and occupational factors were also identified. The adverse effects cited were categorized as psychic, cognitive, physical, and organizational; stress was the most cited effect. An explanatory theoretical model of interaction was proposed to represent the pathway of causal relations between the requirements and risk factors for the effects caused by I4.0. CONCLUSIONS AND PRACTICAL APPLICATIONS: This review has found just how complex the relationships between the principles of Industry 4.0 are (e.g., requirements, risk factors, and effects) and the human factors. It also suggests a pathway for how these relationships occur, bridging the gap left by the limited studies focused on connecting these topics. These results can help organizational managers understand the impacts of I4.0 on workers' safety and health.

MDL: Industrial carbon emission prediction method based on meta-learning and diff long short-term memory networks.

Greenhouse gas emissions, as one of the primary contributors to global warming, present an urgent environmental challenge that requires attention. Accurate prediction of carbon dioxide (CO2) emissions from the industrial sector is crucial for the development of low-carbon industries. However, existing time series models often suffer from severe overfitting when data volume is insufficient. In this paper, we propose a carbon emission prediction method based on meta-learning and differential long- and short-term memory (MDL) to address this issue. Specifically, MDL leverages Long Short-Term Memory (LSTM) to capture long-term dependencies in time series data and employs a meta-learning framework to transfer knowledge from multiple source task datasets for initializing the carbon emission prediction model for the target task. Additionally, the combination of differential LSTM and the meta-learning framework reduces the dependency of the differential long- and short-term memory network on data volume. The smoothed difference method, included in this approach, mitigates the randomness of carbon emission sequences, consequently benefiting the fit of the LSTM model to the data. To evaluate the effectiveness of our proposed method, we validate it using carbon emission datasets from 30 provinces in China and the industrial sector in Xinjiang. The results show that the average absolute error (MAE), Coefficient of Determination (R2) and root mean square error (RMSE) of the method have been reduced by 61.8% and 63.8% on average compared with the current mainstream algorithms. The method provides an efficient and accurate solution to the task of industrial carbon emission prediction, and helps environmental policy makers to formulate environmental policies and energy consumption plans.

Impacts of industrial agglomeration on the energy consumption structure's low-carbon transition process: A spatial and nonlinear perspective.

Based on panel data collected from 2003 to 2020 across 30 provinces in China, the paper employs the spatial vector angle method and spatial Durbin model to investigate industrial agglomeration's nonlinear and spatial spillover effects on the energy consumption structure's low-carbon transition process (Lct). The results indicate the following: First, the influence of industrial agglomeration on Lct exhibits an inverted U-shaped pattern. As the degree of industrial agglomeration expands, its effect on Lct shifts from positive to negative. Second, industrial agglomeration demonstrates spatial spillover effects. It promotes the improvement of Lct in neighboring provinces through agglomeration effects. However, the continuous expansion of industrial agglomeration inhibits the improvement of Lct in neighboring provinces through congestion effects. Third, the heterogeneity test finds that industrial agglomeration has a significant role in promoting Lct in the samples of eastern region, but this effect is not significant in the samples of western and middle regions.

Effects of producer service industry co-agglomeration and manufacturing on the green and high-end transformation of manufacturing: An empirical study from Hunan Province.

In the context of the "dual carbon goals" and intensified international manufacturing competition, the green and high-end transformation of manufacturing is the direction for the industry's future growth in China. The study discusses the effect of producer service industry co-agglomeration and manufacturing on the transformation of manufacturing into being green and high-end. Firstly, we systematically elaborate on the mechanism of the collaborative promotion of high-end manufacturing by the service and manufacturing industries and propose research hypotheses. Based on the 2010 to 2020 Hunan Provincial Statistical Yearbook data, we used the coupling coordination model and entropy method to calculate the level of collaborative development between the manufacturing and service industry, as well as the level of green high-end development in the manufacturing industry. Lastly, the specific impact of the synergistic effect of the two industries on the green high-end transformation of the manufacturing industry was analyzed using the dynamic panel regression model. Results found that service industry manufacturing synergy has a noteworthy positive driving effect on the green and high-end transformation of manufacturing. However, the impact varies across different service industries and manufacturing sectors with different technological levels. We also provide some implications for improving transformation efficiency in the green and high-end manufacturing industry.

The relationship between entrepreneurial personality patterns linked to risk, innovation and gender across industrial sectors.

This study examines the personality patterns of solo founders in both high-tech and non-high-tech sectors during the first seven years of their entrepreneurial journey to emphasize the patterns' implications during policymaking, investment decisions, and self-assessments. IAB/ZEW startup panel microdata for the sector classification of 4470 solo entrepreneurs in Germany were analyzed to identify Big Five trait patterns influenced by risk propensities, innovation inclination, and gender. The entrepreneurial profiles indicate positive openness, emotional resilience, and sector-specific clusters. Conscientiousness suggests flexibility, and while variations in extraversion and agreeableness exist, negative neuroticism was predominantly found, except for gender-related differences and multidimensional service innovators. Big Five traits provide information about important foundational profile patterns to describe unique solo entrepreneur types influenced by risk, innovation, and gender. Originality and value: Risk propensity characterizes 'Adaptive Services,' 'Dynamic Knowledge Innovators,' and 'Strategic Risk Navigators.' Additionally, 'Multidimensional Service Innovators' and 'Focused Tech Innovators' signify innovation understanding. The Big Five profiles show openness and emotional stability across sectors, providing crucial insights for effective entrepreneurial support and investment strategies.

Multi-model methods for structural analysis of China's green economy network based on input-output method.

The green economy has been advocated globally as a solution to environmental issues. In China, it is considered a national strategy for future economic development. This study utilizes methods such as Industry Network, Maximum Spanning Tree (MST) method, Leiden Community Clustering (LCC) algorithm, and Weaver-Thomas (WT) model to explore the contribution and position of the green economy and industries in China's economic development. The findings are as follows: (1) The density of China's green industry network has experienced a process of initially tightening and then loosening, ultimately tending towards stability. (2) The trunk structure of China's industrial network remains relatively stable, forming an industrial structure with electricity, heat production and supply as the core. (3) China's industrial and green industry communities continue to improve and become more cohesive, but some green industries are still on the periphery of communities. (4) The ability of green industries to pull other industries is weak, and the subsequent promotion momentum needs to be improved. However, the green industry still has enormous room for growth and potential to unleash its long-term positive multiplier effects. More attention and support need to be given by managers and decision-makers, so that it can make better contributions to society and the economy.

Can digital technology innovation contribute to firms' market value?

With the rapid development of digital technology, digital technology innovation has become a core driver of China's economic development. Thus, this study uses A-share listed companies from 2003 to 2021 as the research sample. The digital patents of firms are identified to portray the level of digital technology innovation by matching the digital economy industry classification code, national economy industry classification code, and IPC number. Considers the economic effect of digital technology innovation from the perspective of firm market value. It is found that digital technology innovation significantly contributes to the increase in firm market value, and this finding still holds when robustness tests are performed. Mechanistic tests have shown that digital technology innovation affects firm market value by driving digital transformation, promoting productivity, and enhancing market profitability. Further analysis reveals that digital technology innovation has a more significant effect on increasing firm market value for large, non-state, capital-intensive, technology-intensive and low internal control costs firms. This study verifies the enabling effect of digital technology innovation on the development of the real economy at the micro level, and provides insights for the optimization of China's digital technology innovation policies and the formulation of firms' digital development strategies.

Industry visions of technology for older adults: A futures anthropology perspective.

The negative portrayal of ageing as a human decline burdening society has prompted Ageing Technology industries (AgeTech) to foresee solutions rooted in the Ageing in Place paradigm. These ostensibly neutral future interventions are intertwined with socio-technical dynamics. While Science and Technology Studies (STS) and anthropology scholars have questioned these AgeTech practices, limited literature explores industry's predictions of future AgeTech. Drawing on STS and futures-anthropology literature, I interrogate AgeTech industry visions of future assemblages involving older people, smart home technology, and socio-material discourses rooted in their own discrepancies and dilemmas. To unpack AgeTech futures, my methods include a review of 49 industry reports and 29 interviews with industry experts. Based on the reports, I designed comics to be used in interviews with experts spanning CEOs and managers of companies designing technology for older people, consultants, and aged-care workers based in 12 countries. Ageing futures are far from being neutral or a chronological process, instead they are non-consensual and fragmented. In the review and interviews, I captured future assemblages of a fragmented AgeTech industry in relationships with governments and industry giants. The fragmentation continues unfolding in participants from diverse countries and professions contesting dominant AgeTech narratives. In dissecting future assemblages, I also unpack non-consensual futures based on diverging experts' values (e.g. safety versus activity) and humans' values like control and improvisation challenging predictive and surveillance technology. AgeTech Futures transcend physical matters or assemblages of technologies and humans. They encompass future normativities, tensions, divergent values, and ideological concepts. I propose not only alternatives to the visions found in industry narratives, but also encourage scholars to understand the AgeTech industry's dilemmas.

Sustainable Digital Transformation: Its Impact on Perceived Value and Adoption Intention of Industry 4.0 in Moderating Effects of Uncertainty Avoidance.

Background: Industry 4.0 is a significant technical revolution that combines big data analytics, the Internet of Things (IoT), and cyber-physical systems to improve manufacturing productivity. This study investigates the impact of digital trust and sustainable attitude on perceived value and the intention to adopt Industry 4.0 technologies. It also examines the moderating role of uncertainty avoidance in these relationships. Methods: Data were collected from 189 employees of leading manufacturing companies in Indonesia that are recognized for their Industry 4.0 practices. The data were analyzed using Partial Least Squares (PLS) methodology with SmartPLS software to test the proposed hypotheses and explore the moderating effects. Results: The findings reveal that both digital trust and sustainable attitude significantly influence perceived value. However, these factors do not directly affect the intention to adopt Industry 4.0 technologies. Uncertainty avoidance moderates the relationship between digital trust and adoption intention. Specifically, in environments with high uncertainty avoidance, digital trust becomes a critical factor influencing the decision to adopt Industry 4.0 technologies. Conclusions: The study provides valuable insights for organizations aiming to implement Industry 4.0 initiatives. It highlights the importance of fostering digital trust and considering cultural dimensions, such as uncertainty avoidance, in their technology adoption strategies.

Racial and ethnic disparities in health risk from industrial surface water pollution in the United States, 2011-2021.

Industrial chemical pollution is released into surface water at a large scale annually in the United States. However, geographic variation and racial disparities in potential exposure are poorly understood at a national scale. Using county-level Risk-Screening Environmental Indicators data for 2011-2021 and American Community Survey data, this study analyzes the spatial and temporal distribution of health risk from modeled water releases using a Gamma hurdle model. Several racial disparities in presence of risk and amount of risk were identified, particular for Black or African American and Asian populations. At least 200 million U.S. residents live in a county where health risk from this pollution is present. Exposure reduction in high-risk areas may improve health for the broader population while also reducing inequities.

Proportion of industry payment value by category in radiology relative to other specialties.

BACKGROUND: Industry payments to physicians are common, but it is unknown how the payments in different categories to radiologists compare to other specialties. OBJECTIVE: The aim of this study is to assess the proportion of industry payments to physicians in radiology in certain categories relative to other specialties. METHODS: The Open Payments Database was analyzed from January 1, 2017 to December 31, 2021 for industry payments to all allopathic & osteopathic physicians, and classified into distinct clinical specialties. Payments to physicians in three categories were calculated in relation to total payments in each specialty during the study period: consulting fees, research, and royalties/ownership (royalty, license, or current or prospective ownership or investment). RESULTS: The total value of industry payments to physicians across all specialties was just under $13 billion over the six-year period from 2017 to 2022. During this period, 51.4 million total payments were made to 791,746 physicians. US physicians in radiology received 452,027 payments for a total value of $357 million (2.8 % of total value). For radiologists, 32.8 % of industry payment value was attributed to royalties/ownership and 9.9 % to research, collectively adding up to 42.7 % of all industry payment. The only specialties with higher payments in these two categories considered reflective of innovation payments were the surgical specialties with higher royalty payments. CONCLUSION: The proportion of industry payments in radiology in categories reflecting innovation (royalty/ownership and research fees) is high and second only to surgical specialties.

Study on the impact of digital economy on industrial collaborative agglomeration: Evidence from manufacturing and productive service industries.

In the digital era, digital economy has a far-reaching impact on the collaborative agglomeration of manufacturing and service industries. This research aims to examine the economic relationship between digital economy and industrial collaborative agglomeration. Based on a panel data set of 286 Chinese cities, this research employs Tobit model, moderating effect model, and mediating effect model to conduct data analysis. It is found that digital economy has a nonlinear relationship with industrial collaborative agglomeration, and this relationship is a U-shape. Moderating effect analysis reveals that government intervention significantly regulates the role of digital economy in industrial collaborative agglomeration. Mediating effect analysis indicates that digital economy promotes industrial collaborative agglomeration through entrepreneurial activity. Heterogeneity analysis shows that the facilitating effect of digital economy on collaborative agglomeration in high-end industries comes earlier than in middle- and low-end industries. Moreover, this research finds that digital economy plays a significant role in industrial collaborative agglomeration in central and western regions of China but not in the eastern region. To enhance the impact of digital economy on industrial collaborative agglomeration, it is crucial to strengthen the engagement of the government and ensure the availability of digital technology.

Green Synergy: Interplay of corporate social responsibility, green intellectual capital, and green ambidextrous innovation for sustainable performance in the industry 4.0 era.

This study delves into the interconnections among corporate social responsibility, green intellectual capital, green ambidextrous innovation, and sustainable performance, particularly in the context of Industry 4.0 and sustainability. A questionnaire-based survey was conducted, and a sample of 317 small and medium enterprises was collected. Using Partial Least Squares Structural Equation Modeling in Smart-PLS v4, the findings reveal a significant relationship between corporate social responsibility and sustainable performance, with green intellectual capital and green ambidextrous innovation serving as mediating factors. Moreover, the study highlights the moderating role of Industry 4.0 among green intellectual capital and green ambidextrous innovation with sustainable performance. These findings may guide the managers in designing and implementing CSR strategies beyond compliance and contributing to competitive advantage through green intellectual capital and green ambidextrous innovation for business success in the era of Industry 4.0.