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This study aims to explore the impact of carbon neutral announcements on “stock market value” of publicly listed companies in China.

The event study approach is adopted. Market, market-adjusted, Carhart four-factor model and a cross-sectional regression model are employed to examine the impacts of carbon neutral announcements on “stock market value” of Chinese companies based on data from 188 carbon neutral announcements.

Carbon neutral announcements positively impact Chinese shareholder value. Carbon neutral announcements at the strategic level have a more positive and significant impact on Chinese stock market value. Innovative carbon neutral announcements do not significantly cause Chinese stock market reactions. Companies have more positive and significant stock market reactions when the companies make carbon neutral announcements that reflect high supply chain network resilience and heterogeneity and strong supply chain network relationships.

The findings uncover the business value of carbon neutral activities and provide operations managers in developing countries insights into how to improve enterprises' market value by actively implementing carbon neutral activities.

This paper is the first trial to apply an event study to examine the relationship between carbon neutral announcements and Chinese stock market value from the perspective of announcement level and type and supply chain networks. This paper introduces corporate reputation theory and enriches the application of corporate reputation theory in the field of low-carbon environmental protections and supply chains.

This study aims to empirically examine the effect of supply chain innovation (SCI) announcements on shareholder value within the context of Industry 4.0 and Industry 5.0.

This study uses an event study method to examine the effect of SCI announcements on shareholder value of the 156 listed companies in China.

First, SCI announcements have a positive effect on shareholder value. Second, SCI with an integrated form more positively affects shareholder value than SCI with an independent form. SCI at the strategy level more positively affects shareholder value than SCI at the operation level. Technology-type SCI more positively affects shareholder value than process-type SCI. Third, this study finds that investors pay more attention to the SCI of companies in the service industry than that of in the manufacturing industry. Finally, the post-hoc analysis finds that digital SCI more positively affects shareholder value than intelligent SCI.

First, most scholars use questionnaire data rather than second-hand data to conduct empirical research to explore the impact of SCI on performance. Second, although scholars focus on performance comprehensively, including operational, financial, relational and environmental performance, no scholars use an event study to explore the impact of SCI on the stock market. Third, no scholars have explored the differential impact of SCI in different industries. Forth, few scholars have classified SCI according to the characteristics to explore the differential impact of SCI. Finally, the differences between SCI of Industry 4.0 and SCI of Industry 5.0 have been described, but no scholars have used empirical research to explore the differences.

Using social network theory (SNT), this study empirically examines the impact of digital supply chain announcements disclosing corporate social responsibility (CSR) information on stock market value.

Based on 172 digital supply chain announcements disclosing CSR information from Chinese A-share listed companies, this study uses event study method to test the hypotheses.

First, digital supply chain announcements disclosing CSR information generate positive and significant market reactions, which is timely. Second, strategic CSR and value-based CSR disclosed in digital supply chain announcements have a more positive impact on stock market, however there is no significant difference when the CSR orientation is either towards internal or external stakeholders. Third, in terms of digital supply chain network characteristics, announcements reflecting higher relationship embeddedness and higher digital breadth and depth lead to more positive increases of stock value.

First, the authors consider the value of CSR information in digital supply chain announcements, using an event study approach to fill the gap in the related area. This study is the first examination of the joint impact of digital supply chain and CSR on market reactions. Second, compared to the previous studies on the single dimension of digital supply chain technology application, the authors innovatively consider supply chain network relationship and network structure based on social network theory and integrate several factors that may affect the market reaction. This study improves the understanding of the mechanism between digital supply chain announcements disclosing CSR information and stock market, and informs future research.

The purpose of this paper is to find the optimal power structure that drives green practices in the supply chain and coordinate the costs and benefits of green practices in supply chain under different power structures.

This paper developed a supply chain of one supplier and one manufacturer, in which the supplier and the manufacturer are responsible for the “greening” of products. Then, the game theory modeling method is used to explore the influence of different power structures on green practices in the supply chain. Finally, the authors developed a green cost-sharing contract made by the leader; regarding optimal supply chain profits and green performance, the proposed contracts and the non-coordination situation are compared and tested by a numerical simulation.

The increase of the green practice difficulty of any member in the supply chain will not only reduce the greenness of products at that stage but will also reduce the green investment of the supply chain partner. Becoming a channel leader does not necessarily mean being more profitable than being a follower, and when the green practice difficulty of the leader is less than a certain threshold, ceding dominant power to the follower may benefit both sides. A green cost-sharing contract made by the leader is not necessarily beneficial to all enterprises.

This paper helps to better understand the role of the power relation in realizing the industry's green goals and helps decision-makers to achieve win-win cooperation by adjusting power relations and optimizing green cost-sharing contracts.

Considering two types of subsidies for producers (supplier and manufacturer) and one for consumers based on product greenness and sales quantity, this study aims to formulate optimal supply chain green innovation and subsidy strategies, and to achieve this goal with the support of information systems.

This study introduces a composite green-product supply chain where suppliers focus on green innovation for component greenness and manufacturers focus on green innovation for manufacturing process greenness. Game theory modeling is applied to investigate the differences of product greenness, supply chain members’ profit and social welfare under different government subsidy strategies.

Increasing the unit greenness subsidy coefficient can boost product greenness and supply chain members’ profits, but does not always raise social welfare. When the government exclusively offers subsidies to producers, subsidies should be allocated to suppliers when there is a significant disparity in supply chain green innovation costs. Conversely, it is more beneficial to subsidize manufacturers. Consumer subsidies have the potential to enhance both environmental and economic performance in the supply chain compared with producer-exclusive subsidies, but may not always maximize social welfare when supply chain members have low unit costs associated with green innovation.

This study examines the optimal decisions for green supply chain innovation and government subsidy strategies. Supply chain members and the government can use the information system to collect and evaluate the cost of upstream and downstream green innovation, and then develop reasonable collaborative green innovation and subsidy strategies.

This study aims to address the critical issue of machine breakdowns in industrial settings, which jeopardize operation economy, worker safety, productivity and environmental compliance. It explores the efficacy of a predictive maintenance program in mitigating these risks by proactively identifying and minimizing failures, thereby optimizing maintenance activities for higher efficiency.

The article implements Logical Analysis of Data (LAD) as a predictive maintenance approach on an industrial machine maintenance dataset. The aim is to (1) detect failure presence and (2) determine specific failure modes. Data resampling is applied to address asymmetrical class distribution.

LAD demonstrates its interpretability by extracting patterns facilitating the failure diagnosis. Results indicate that, in the first case study, LAD exhibits a high recall value for failure records within a balanced dataset. In the second case study involving smaller-scale datasets, enhancement across all evaluation metrics is observed when data is balanced and remains robust in the presence of imbalance, albeit with nuanced differences in between.

This research highlights the importance of transparency in predictive maintenance programs. The research shows the effectiveness of LAD in detecting failures and identifying specific failure modes from diagnostic sensor data. This maintenance strategy exhibits its distinction by offering explainable failure patterns for maintenance teams. The patterns facilitate the failure cause-effect analysis and serve as the core for failure prediction. Hence, this program has the potential to enhance machine reliability, availability and maintainability in industrial environments.

Utilizing the Marxist theory of unequal exchange to explain the terms of trade between nations, this paper elucidates one possible mechanism that gives rise to ecologically unequal exchange between developed and developing economies.

We propose a two-sector linear production model and demonstrate that a decrease in the organic composition of capital and an increase in the rate of surplus value in a sector will lead to a relative price decrease and value transfer out of that particular sector, as well as increasing the environmental costs of trade. Furthermore, we measure the levels of unequal exchange (value transfer) and ecologically unequal exchange of 40 economies and empirically validate their relationship.

The findings suggest that an important cause of the ecologically unequal exchange is the value transfer between economies caused by the international division of labor and real wage disparities. The inequality in international trade is a significant factor contributing to the gap in the ecological environment level between developed and developing economies.

By introducing the theory of unequal exchange or value transfer into the analysis of ecological unequal exchange, we provide a mathematical framework for analyzing ecological unequal exchange and a method for calculating the scale of ecological unequal exchange and value transfer, thereby enhancing the theoretical depth and practical significance of the ecological unequal exchange theory.

The purpose of this paper is to better understand and study the architecture and system characteristics of the underlying support platform for crowd system, by recognizing the characteristics of service internet is similar to the coordination characteristics between the massive units in the underlying platform of crowd system and studying the form, nature and guidelines of the service internet.

This paper points out the connection between the underlying support platform of crowd system and service internet, describes the framework and ideas for researching service internet and then proposes key technologies and solutions for service internet architecture and system characteristics.

The research unit in the underlying support platform of crowd system can be regarded as a service unit. Therefore, the platform can also be regarded as service internet to some extent. The ideas and technical approaches for the study of service internet’s form, criteria and characteristics are also provided.

According to this paper, relevant staff can be guided to better build the underlying support platform of crowd system. And it can provide a highly robust and sustainable platform for research studies of crowd science and engineering in the future.

The purpose of this paper is to propose a new tool for stock investment risk management through studying stocks with what kind of characteristics can be predicted by individual investor behavior.

Based on comment data of individual stock from the Snowball, a thermal optimal path method is employed to analyze the lead–lag relationship between investor attention (IA) and the stock price. And machine learning algorithms, including SVM and BP neural network, are used to predict the prices of certain kind of stock.

It turns out that the lead–lag relationships between IA and the stock price change dynamically. Forecasting based on investor behavior is more accurate only when the IA of the stock is stably leading its price change most of the time.

One limitation of this paper is that it studies China’s stock market only; however, different conclusions could be drawn for other financial markets or mature stock markets.

As for the implications, the new tool could improve the prediction accuracy of the model, thus have practical significance for stock selection and dynamic portfolio management.

This paper is one of the first few research works that introduce individual investor data into portfolio risk management. The new tool put forward in this study can capture the dynamic interplay between IA and stock price change, which help investors identify and control the risk of their portfolios.

Nowadays, designing environmentally compatible buildings with acceptable performance in terms of cost, materials, and energy efficiency is considered crucial for developing sustainable cities. This research aims to identify and rank the most influential factors in the application of Building Information Modeling (BIM) systems in the smartification of green and sustainable buildings.

The present research is applied and descriptive. In this study, we identified the most influential factors in the application of Building Information Modeling (BIM) systems through library studies and expert opinions. Data were collected using a questionnaire, and a combination of the one-sample t-test method with a 95% confidence level and the fuzzy VIKOR method was employed for analysis.

The results show that the most influential factors in the application of Building Information Modeling (BIM) systems in the Smartification of green and sustainable buildings, in order, are: “Energy saving and consumption reduction,” “Increased productivity and efficiency,” “Life-cycle assessment (LCA),” “Eco-friendly design,” “Integration with IoT and other technologies.”

In this study, while addressing the intersection of BIM technology, green building principles, and smart building objectives to optimize the performance of buildings during their life cycle, the most influential factors in the use of this system were ranked based on the criteria of “impact level,” “importance level,” and “availability of necessary tools” for implementation in Kerman. Moreover, solutions for more effectively utilizing this system in the smartification of green and intelligent buildings were proposed.