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Our study examines how artificial intelligence (AI) can enhance decision-making processes to promote circular economy practices within the utility sector.

A unique case study of Alia Servizi Ambientali Spa, an Italian multi-utility company using AI for waste management, is analyzed using the Gioia method and semi-structured interviews.

Our study discovers the proactive role of the user in waste management processes, the importance of economic incentives to increase the usefulness of the technology and the role of AI in waste management transformation processes (e.g. glass waste).

The present study enhances the circular economy model (transformation, distribution and recovery), uncovering AI’s role in waste management. Finally, we inspire managers with algorithms used for data-driven decisions.

Construction and Demolition (C&D) Waste Management (WM) poses significant challenges in Sri Lanka, contributing to environmental degradation and resource depletion. To address these issues, this study explores the application of Circular Economy (CE) strategies in minimising waste generation and optimising resource utilisation in Sri Lankan construction industry. The research focuses on the construction and building renovation and use and operate stages of the building project life cycle, recognising their significance in waste generation and resource consumption.

The research employed a qualitative approach, utilising the Delphi technique through three rounds of expert interviews. Seventeen experts were involved in the first round, followed by fifteen in the second round, and twelve in the final round. The collected data was analysed using manual content analysis methods.

The research findings revealed fifteen C&D WM issues in the construction and building renovation stage in Sri Lanka, along with suitable strategies to overcome each of them. Similarly, eight C&D WM issues were identified for the use and operate stage of the building, and corresponding strategies were provided to address each issue. By adopting CE strategies such as modular design and material reuse, construction projects can optimise the project's timeline, cost, and quality factors. These strategies enable efficient resource allocation, reduce waste generation, and contribute to the overall sustainability of the project. The impact of CE strategies on mitigating these issues within the project management iron triangle was also discussed.

This paper entails delving into how construction, building renovation, and operation stages of a building's life cycle intersect with CE strategies, which profoundly influence operational efficiency and long-term sustainability. By incorporating principles such as energy efficiency, water conservation, and circular product design, the paper illuminates how these strategies facilitate decreased energy usage, enhanced resource management, and diminished waste production throughout the building's lifespan.

This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model.

In dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts.

The research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management.

This study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management.

This research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.

The purpose of the article is to identify relevant criteria for decision support in the implementation of waste-to-energy (WtE)-based systems.

The methodology is a simple case study with a qualitative approach. Five experts involved in the project of a thermoelectric power plant qualitatively evaluated, on a Likert scale, a decision model with 15 indicators derived from recent studies. The research object was the first stage of a project to implement a thermoelectric plant employing municipal solid waste (MSW) in southern Brazil.

The study identified 15 criteria supporting the decision-making process regarding WtE implementation for MSW in a mid-sized city in southern Brazil. The study identified that compliance with MSW legislation, compliance with energy legislation, initial investment and public health impact are the most influential criteria. The study offered two models for decision processes: a simplified one and a complete one, with ten and fifteen indicators, respectively.

The study concerns mid-sized municipalities in southern Brazil.

Municipal public managers have now a methodology based on qualitative evaluation that admits multiple perspectives, such as technical, economic, environmental and social, to support decision-making processes on WtE technologies for MSW.

MSW management initiatives can yield jobs and revenues for vulnerable populations and provide a correct destination for MSW, mainly in developing countries.

The main originality is that now municipal public decision-makers have a structured model based on four constructs (technical, economic, environmental and social) deployed in 15 indicators to support decision-making processes involving WtE and MSW management.

The apparel and textile sector poses a significant environmental challenge due to its substantial contribution to pollution in the form of air, water and soil pollution. To combat these issues, the adoption of sustainable practices is essential. This study aims to identify and analyse the barriers that hinder the progress of sustainability in the apparel and textile industry. By consulting experts in the field, critical barriers were identified and given special attention.

To achieve the research objective, an integrated approach involving Interpretive Structural Modelling (ISM) and fuzzy MICMAC decision-making techniques was employed. The results were further validated through the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method.

The findings highlight that barrier related to clothing disposal, inadequate adaptation to modern technology, challenges affecting sector efficiency and issues related to fashion design are crucial in influencing the remaining six barriers. Based on the outcomes of the DEMATEL method, a comprehensive cause-and-effect diagram was constructed to gain a deeper understanding of these challenges.

This research provides valuable insights for policymakers and stakeholders in the apparel and textile industry. It offers a strategic framework to address and overcome sustainability barriers, promoting the development of a more environmentally responsible and resilient sector.

The purpose of this research is to conduct an in-depth investigation of the barriers apparel and textile sectors. It is feasible that both the management team and the medical experts who provide direct patient care could benefit from this research.

The purpose of this paper is to develop the theory of environmentally responsible behavior by extending the theory of planned behavior to empirically examine waste reduction behaviors among young coastal tourists in Bangladesh.

Through review of literature, in-depth interviews and survey, the study is conducted. Initial survey collects 355 data, which is used for exploratory factor analysis to develop constructs and its measurement items. After preparing the formal survey questionnaires, another survey is conducted and collected 403 usable responses. The subsequent valid responses are used for confirmatory factor analysis through structural equation modeling, using Smart PLS 3.3.3.

The results of this study reveal that waste reduction behavior is significantly affected by waste reduction intention, and then subsequently waste reduction intention is significantly affected by environmental concern, climate change concern and healthy environmental norms of young coastal tourists in Bangladesh.

The findings of the study will benefit industry operators and policymakers by understanding the factors that are crucial for influencing young costal tourists’ environmentally responsible behaviors (i.e. waste reduction behaviors). Eventually, the findings may assist industry operators and policymakers to develop strategies to attract more young tourists to the coastal tourist destinations in Bangladesh.

The importance of environmentally responsible behavior has received a significant attention in present decade. Consistent with this phenomenon, this study uniquely develops the theory of environmentally responsible behavior and establishes the link between environmental concern, climate change concern, healthy environmental norms, waste reduction intention and waste reduction behavior in coastal tourism domain in Bangladesh.

The number of young people using e-cigarettes (commonly referred to as vaping) has grown at an alarming rate, creating the need for urgent action. This paper demonstrates rapid step-by-step iterative application of the Co-create, Build and Engage (CBE) framework to showcase how marketing was applied in response to emerging trends that have negative health and environmental consequences. This paper aims to demonstrate how CBE is applied iteratively, ensuring student feedback leads module development.

In Study 1, a pure seven-step co-design implementation, 19 high school students were invited to co-design a vaping prevention approach that would work for them and other people like them. During the sensitisation phase of co-design, students completed one Blurred Minds Vaping module. Feedback grids were provided, with students identifying likes, dislikes and ideas. This likeability data was used, together with input from technical experts and pedagogical best practice, in a 12-week research and development project that aimed to develop a new online learning module focused on vaping and their environmental impacts for the Blurred Minds Academy. The new module was tested with 20 high school students. Feedback grids were provided once again, allowing a comparison of results.

Examination of feedback grid data demonstrates that the newly developed Vaping and the Environment module was improved. Considerations taken on board in the new module design (e.g. increased variability within the module) overcame criticisms expressed previously (e.g. it was boring and too long). Other criticisms remained evident, albeit at a much lower proportion suggesting the new Vaping and the Environment module, and future Blurred Minds module development, would benefit from iterative CBE application.

Conduit et al. (2022) note that marketing academia has been criticised for having an increasingly less relevant managerial agenda. This paper outlines a rapid step-by-step application of marketing in response to one of society’s most pressing health challenges – vaping. The iterative application of CBE is outlined, demonstrating that the student experience can be enhanced when marketing’s continual improvement mindset is used. This is the first vaping prevention programme that has included substantive information around the negative impacts of vaping on the environment.

Municipalities, as the front lines of service delivery, use websites as one of the tools to communicate information to the public. While it is considered a record, many organisations, including municipalities, do not manage websites as such. This study aims to explore the archiving of websites as records in the municipalities of KwaZulu-Natal (KZN) Province in South Africa by using the web archiving life cycle model.

This study used a mixed-methods research with an explanatory design, with quantitative data collected first through content analysis of websites and qualitative data collected through interviews. Researchers used multilevel sampling, first quantitatively analysing all available websites of the municipalities (52) in KZN, and then qualitatively selecting only records managers, information managers, web administrators, communication managers and website managers or designers from municipalities because of their understanding and involvement with websites in some way.

This study established that some records on municipal websites are often in paper format in record-keeping systems, whereas others are born digital and are not captured in the systems. Municipalities lack a dedicated web online harvesting tool as well as an archiving policy or strategy to guide website archiving. Furthermore, municipalities placed a high reliance on service providers to keep their websites operational.

It became clear during the interviews that most of the participants were unfamiliar with web archiving. As a result, only 12 of the 56 selected participants from the municipalities provided the required information in relation to the current study as others could not provide answers. Data for other participants were not analysed.

Due to a lack of infrastructure for ingesting digital records into archival custody, a framework for harvesting web content of value is proposed both internally in municipalities and externally to an archive repository.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

This study aims to develop a comprehensive insolvency model tailored to Islamic banks, ensuring alignment with Shariah principles throughout pre-insolvency, bankruptcy and post-bankruptcy stages.

The research adopts a qualitative research method, using a desktop research approach. Primary sources and secondary sources are examined to gather information and draw conclusions.

This study presents a comprehensive insolvency model designed for Islamic banks, rooted in Shariah principles. The model covers pre-insolvency, bankruptcy (taflis) and post-bankruptcy stages, incorporating key Shariah parameters to ensure adherence to Islamic finance principles. It addresses challenges such as adapting to dynamic financial landscapes and varying interpretations of Shariah principles. Notably, the model recognizes the separate legal personality of Islamic banks and emphasizes transparency, fairness and compliance with religious obligations. In the post-bankruptcy stage, directors are urged to voluntarily settle remaining debts, aligning with ethical and Shariah-compliant standards.

The study contributes to the stability and growth of Shariah-compliant financial systems by extending insolvency principles to Islamic banks, providing a foundation for future research and policymaking specific to this context.