In today’s business environment, contributions made by the manufacturing sector to the economy and social development is evident. With a focus on long-term development, the manufacturing sector has adopted advanced operating strategies, such as lean manufacturing, industry 4.0, and green practices in an integrated manner. The integrated impact of circular economy, industry 4.0, and lean manufacturing on sustainability performance has not been adequately addressed and investigated. Therefore, the aim of this study is to investigate the integrated impact of circular economy, industry 4.0, and lean manufacturing on the sustainability performance of organizations in Saudi Arabia. Data were collected through a questionnaire-based survey as a primary data instrument. A total of 486 organizations have responded to the survey within the timeframe. Moreover, the structural equation modeling method is utilized for data analysis through SmartPLS tool for the developed hypotheses of the research. The findings highlight the positive impact of circular economy on the sustainability of the organizations. Furthermore, the results indicate that industry 4.0 and lean manufacturing have positive mediating impacts as enablers for the successful implementation of circular economy toward the sustainable performance of organizations in Saudi Arabia. The study finding confirms that lean manufacturing is a substantial mediating variable that is essential for the successful implementation of industry 4.0 technologies. Moreover, the study indicates the recognition and acknowledgment of companies on circular economy principles, industry 4.0 technologies, and lean manufacturing tools to achieve the desired sustainability.

Plastic and petrochemical industries are lagging behind in terms of environmental sustainability performance because of the nature of these industries. Although plastic and petrochemical industries have adopted lean manufacturing and/or Industry 4.0 technologies, more efforts are needed to enhance sustainable development. The purpose of this study is to explore the integrated impact of Industry 4.0 technologies and lean manufacturing on the sustainability performance of plastic and petrochemical industries in Saudi Arabia. Moreover, it investigates the casual relationship between Industry 4.0 technologies and sustainability performance as well as the direct linkage between Industry 4.0 and lean manufacturing. A questionnaire is used as the primary instrument for collecting data from 112 plastic and petrochemical organizations. A hypothesized relationship is formulated and then analyzed using the structural equation modeling (SEM) approach. The outcome of the analysis shows that Industry 4.0 and lean manufacturing have a positive impact on sustainability performance. The study also presents a list of valid constructs for Industry 4.0 technologies, lean manufacturing, and sustainability performance. Furthermore, the study shows that the plastic and petrochemical industries in Saudi Arabia acknowledge and recognize the contribution of Industry 4.0 technologies and lean manufacturing principles to the protection of the environment as a dimension of sustainability performance.

Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.