This paper examines the impact of institutional pressure (IP), top management support (TMS), green supply chain management practices (GSCM), and supply chain competitive advantage (SCCA) on corporate environmental performance (EP). We also analyze the mediation effect of GSCM on the interplay between TMS and EP. Additionally, the paper also provides an analysis of the moderating role of SCCA between IP and EP. To attain the objective of this research, we assembled data from 710 business entities within the Shaanxi province of China utilizing a survey design approach. The structural equation model (SEM) was applied to test and assess the hypothetical outline. The study outcomes empirically show that TMS, GSCM, and SCCA positively and significantly impact EP. Interestingly, our study found an insignificant association between IP and EP. The study's results also demonstrate that IP directly relates to top management support. Moreover, the study's empirical findings reveal that GSCM positively mediates IP and EP. The study findings show that SCCA shapes IP and EP's connection. Accordingly, the practical implications of our study's findings suggest that business managers, investors, and government agencies must know the importance of adopting sustainable practices within the supply chain. Business managers must take action to integrate environmental criteria into supplier selection, evaluate suppliers' EP, and collaborate with eco‐friendly suppliers. Hence, government agencies, stakeholders, and business managers can use this information to shape regulations and policies that encourage businesses to adopt sustainable supply chain practices. Offering incentives such as tax benefits or grants for sustainability initiatives can also promote adoption. The study recommends that a business culture that targets improving EP due to IP and top management support is essential in achieving GSCM practices, thereby promising competitive advantage.

The effect of green supply chain management practices on corporate environmental performance: Does supply chain competitive advantage matter?

Fault detection in wind turbines is essential for ensuring their safety, reliability, and optimal performance. However, some of the existing approaches for sensor fault detection in wind turbines face several challenges that hinder their effectiveness. These challenges include handling multivariate and non-Gaussian data, low sensitivity to small changes, and setting an appropriate detection threshold to avoid false alarms. Additionally, constructing an analytical model for monitoring wind turbines becomes particularly challenging and time-consuming, especially for large-scale wind turbines. This paper proposes a semi-supervised data-driven approach for sensor fault detection in wind turbines using supervisory control and data acquisition (SCADA) data. The proposed approach combines the advantages of independent component analysis (ICA) and the Kantorovich Distance (KD)-based fault detection scheme. ICA enables efficient handling of multivariate non-Gaussian data, while the KD scheme provides a sensitive indicator for assessing the residuals obtained from ICA. The ICA-based KD scheme needs only fault-free data in training, making it more attractive for fault detection in practice. Kernel density estimation is employed to compute the detection threshold of the KD scheme, making it more flexible. Experimental evaluations using simulated sensor faults based on real wind turbine data demonstrate the superior detection performance of the proposed approach, achieving an average F1-score of approximately 0.96 and outperforming conventional approaches. 

Uncovering sensor faults in wind turbines: An improved multivariate statistical approach for condition monitoring using SCADA data

Real-time anomaly detection on time series of industrial furnaces: A comparison of autoencoder architectures

Agriculture is weather dependent, and changes in climate can have a drastic impact on our ability to feed, fuel, and clothe the world's population. Climate change is causing more frequent and unprecedented extreme weather events that are already negatively affecting agriculture. We need to assess the effects of extreme temperatures and rainfall on agriculture. Patterns of short‐term extreme weather events, such as elevated temperatures, flooding, and strong winds, are not predictable enough to design field experiments around. Process‐based crop and soil simulation models allow us to explore new management options and thus provide whole‐system‐based knowledge and management guides for different locations over variable climate conditions. By using crop simulation models, researchers can test different adaptation strategies and assess their effectiveness in reducing the impacts of climate change on agricultural production. In this paper, we discuss the development of crop models and how they have been used to assess the effects of a changing climate on agricultural productivity and propose methods for agriculture to adapt to those changes. We describe potential applications of crop models to assess regional issues such as irrigation demand, greenhouse gas emissions, and policy decisions. Better understanding of how weather and climate forecasts at various scales are provided and the reliability of these forecasts is important for using crop models as a planning tool. Different approaches for simulating long‐term climate change impacts on crop yield and seasonal yield forecasting are discussed. The use of ensemble models to better assess climate change impacts is also discussed.

The role of crop simulation modeling in assessing potential climate change impacts

This study investigated the combined effects of inventory, green, supply and lean manufacturing practices on the sustainable performance (environmental [EP], social [SP] and economic [EcP] performances) of food manufacturing enterprises in emerging economies like Ghana. The study adopted the quantitative approach and explanatory research design. Primary data was obtained from 247 owners and managers of food manufacturing enterprises in Ghana through structured questionnaires. Twelve hypotheses were developed and tested using the partial least square-structural equation modelling (PLS-SEM) analytical tool. The study's results revealed that inventory and green manufacturing practices directly influenced sustainable performance (SP, EP, and EcP), while lean manufacturing practices had an insignificant influence on economic performance. Supply management, on the other hand, significantly improved only economic performance. The outcome of the study provides valuable information for food manufacturers in Ghana and sub-Saharan Africa. The outcome will provide more robust support for food manufacturers to invest heavily into sustainable supply chain management practices such as inventory, green and lean while establishing stronger relationships with key suppliers to guarantee a consistent supply of raw materials; thereby, promoting sustainable performance. This study contributes to the current literature on sustainable supply chain management and sustainable performance with concrete evidence from food manufacturing enterprises within an emerging economy. 

Sustainable manufacturing practices and sustainable performance: Evidence from Ghana’s food manufacturing sector

Sustainable supply chain management (SSCM) is a new area of interest to scientists and industrial practitioners through which to maintain productivity, reduce costs, and enhance agility. SSCM is especially important to protect the environment and reduce pollution by heavy industries. It considers the environment the main stakeholder in minimizing the carbon footprint during production, lowering emissions of dangerous gases, and reducing industrial pollution. Considering the aforementioned purposes, the aim of this study was to explore the relationships between top management support, perceived justice, supplier management, and SSCM and assess the moderating role of supply chain agility. This quantitative study was conducted in the vast textile sector in Pakistan. We collected data through a questionnaire and found that top management support, perceived justice, and supplier management are positively and significantly associated with SSCM. However, there was no significant moderating effect of supply chain agility on the independent variables and SSCM. These findings have practical implications for production managers and top management in enhancement of their roles in promoting environmental wellbeing. By developing rules at the organizational and governmental levels that consider the role of top management, perceived justice, and improved supplier management, the sustainability of the supply chain can be improved. This analysis provides academics who study the supply chain a practical prescription and adds to the body of knowledge about the validity of top SSCM pillars.

/pdf/the-impact-of-top-management-support-perceived-justice-2lnlybdc.pdf

The impact of top management support, perceived justice, supplier management, and sustainable supply chain management on moderating the role of supply chain agility

Anomaly detection and early warning of the blast furnace ironmaking process is an important research direction of blast furnace production. In this paper, through the analysis and optimization of the multivariate statistical process control (MSPC) model, MSPC based on TOPSIS and the grey (GT-MSPC) model is established based on the problems of missing alarms, false alarms and untimely prediction in the abnormal detection process of the traditional blast furnace ironmaking production process. First, a composite volatility (CV) that consists of the squared prediction error and T2 is computed to form the MSPC model. The parameters of different training set sizes and different alarm conditions are optimized by using the technique for order preference by similarity to an ideal solution model to optimize the effect of anomaly detection and fault diagnosis. Second, according to the results of the contribution plot, the fault diagnosis of abnormal occurrence is carried out, the main influencing factors of abnormal occurrence are analysed, and the monitoring is strengthened. Finally, the grey model is used to predict CV to realize anomaly early warning and form the GT-MSPC model. By collecting the field data of blast furnace production and performing empirical analysis, the results show that the GT-MSPC model has higher anomaly detectability. The detection rate is 50 % higher than that of the manual observation method, and the GT-MSPC model alarms 16.4861 min earlier than manual detection method. Fault diagnosis can accurately locate the main influencing factors of blast furnace anomalies, such as the furnace body static pressure and differential pressure. In summary, the GT-MSPC model realizes the accurate identification and prediction of anomalies and greatly reduces the occurrence of missing and false alarms and untimely prediction in the blast furnace system, thus reducing the risk of blast furnaces. It can be used to assist field engineers in dealing with abnormal faults and improve the product quality and production safety of blast furnace ironmaking. 

Anomaly detection of the blast furnace smelting process using an improved multivariate statistical process control model

A comprehensive meta-analysis of the impacts of intensified drought and elevated CO2 on forage growth.

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The impact of top management support, perceived justice, supplier management, and sustainable supply chain management on moderating the role of supply chain agility

Anomaly detection of the blast furnace smelting process using an improved multivariate statistical process control model

A comprehensive meta-analysis of the impacts of intensified drought and elevated CO2 on forage growth.