Digitalization is impacting almost every aspect of people’s life and is greatly affecting supply chain processes. At the same time, supply chains are becoming progressively more aware of their impacts on people, the planet and profits (triple bottom line perspective) and the concept of sustainable supply chain has received increasing attention among scholars and practitioners. In this context, this article aims to analyze scientific research on supply chain management in the field of sustainability and digitalization using a bibliometric approach. To this end, this article analyses 484 papers from Scopus database according to the following parameters: chronology, discipline, source, center, country and citations. The dataset consisted of articles from scientific journals published over the period 2002–2018. In addition, VOSviewer software is used to map the bibliographic material using the co-occurrence of keywords and most influential journals. The analysis reveals that: sustainability and digitalization are important emerging fields within supply chains that have increased significantly during the last ten years; the USA is the most influential country; and the topic is structured around 2 clusters (1) Supply chain management, concepts and practices and (2) energy related technologies. The results may facilitate the planning, designing, running and publishing of future research on this topic.

Designing sustainable and scalable educational systems is a challenge. Artificial Intelligence (AI) offers promising solutions to enhance the effectiveness and sustainability of blended learning systems. This research paper focuses on the integration of the Chat Generative Pre-trained Transformer (ChatGPT), with a blended learning system. The objectives of this study are to investigate the potential of AI techniques in enhancing the sustainability of educational systems, explore the use of ChatGPT to personalize the learning experience and improve engagement, and propose a model for sustainable learning that incorporates AI. The study aims to contribute to the body of knowledge on AI applications for sustainable education, identify best practices for integrating AI in education, and provide insights for policymakers and educators on the benefits of AI in education delivery. The study emphasizes the significance of AI in sustainable education by addressing personalized learning and educational accessibility. By automating administrative tasks and optimizing content delivery, AI can enhance educational accessibility and promote inclusive and equitable education. The study’s findings highlight the potential benefits of integrating AI chatbots like ChatGPT into education. Such benefits include promoting student engagement, motivation, and self-directed learning through immediate feedback and assistance. The research provides valuable guidance for educators, policymakers, and instructional designers who seek to effectively leverage AI technology in education. In conclusion, the study recommends directions for future research in order to maximize the benefits of integrating ChatGPT into learning systems. Positive results have been observed, including improved learning outcomes, enhanced student engagement, and personalized learning experiences. Through advancing the utilization of AI tools like ChatGPT, blended learning systems can be made more sustainable, efficient, and accessible for learners worldwide.

The separation at the source of Municipal Solid Waste (MSW) is an initiative that facilitates the subsequent recycling work and contributes to palliate the negative impacts of the traditional unsorted collection system. This paper presents a multi-objective integer linear programming model of the determination of the optimal location of assorted waste bins in an urban area. We consider, jointly, the objectives of minimizing the investment cost and the average distance from the dwellings to the bins. The model was applied in simulated instances of an Argentinian medium-size city, contributing to the transition from the current door-to-door based system to a community bins system. To solve this problem, we apply both the weighting method, which has been used to solve similar problems in the literature, and a novel version of the augmented ε-constraint method (AUGMECON2). The results over simulated scenarios show that, in general, AUGMECON2 has a better performance, yielding a larger number of efficient solutions at lower computation times.

Today, urban areas have been considerably expanded, which has caused numerous problems due to the large population in metropolitan areas. City logistics models can be effective in solving complicated problems such as traffic congestion, air pollution, accidents, and high energy consumption rate. The present paper proposes a bi-objective mathematical model for designing a city logistics distribution network in order to minimize the economic and environmental costs as well as the response time. The proposed model would make decisions on the location and allocation problems in the city logistics distribution network. In fact, the objective of the problem is to select some fixed sites in order to construct the city distribution centers. The demand for commodities is considered probabilistic and the network is modeled based on the queuing theory. Furthermore, in the proposed model, the policy of putting tax on carbon and applying the low-carbon emission resources are used for establishment at the city distribution centers. Afterwards, in order to validate the proposed model, a numerical example is generated and, then, the results obtained from solving the model via epsilon constraint method are presented. Analysis of the results in the present study indicates that the proposed model was highly efficient in achieving its objectives. Finally, there are some suggestions for future studies that can be taken into account.

This research explores the significance of sustainability development through the role of environmental disclosure in Jordanian ministries and government departments by focusing on ministries and government departments that are related with the environment. This research presents the relationship between the environmental disclosure in annual reports and its impact on the overall environmental performance in Jordan, with the use of a mixed approach that will provide better quality of research results. The quantitative approach was used to examine the budgets of 22 ministries and government departments. The study was conducted using the time series from 2006 to 2015. The qualitative approach was conducted to raise the issues related to the subject that are not looked at in the quantitative study and to understand the perception of respondents in the ministries and government departments on the factors and items included in annual reports affecting of environmental disclosure and environmental performance in Jordanian governments. The results show a significant relationship between environmental disclosure and environmental performance in Jordanian government. The findings of this paper present new implications for theorists and practitioners of sustainability development by presenting the importance of the impact of environmental disclosure in the ministries and departments and its relationship on environmental performance of the Jordanian government.

The study aim is to examine the impact of industry 4.0 on sustainability of industrial organizations in Jordan. The population of the study consists of employees at various administrative levels of industrial organizations in Jordan. Due to the large population and the spatial and temporal limitations of the research, it was difficult to collect data using the comprehensive method. Therefore, the random sampling method was applied to collect data from the research population. The structural equation modeling (SEM) technique was used to test the impact of industry 4.0 dimensions on sustainability. The study results confirmed that all dimensions of industry 4.0 had an impact on sustainability. The greatest effect was for cyber-physical systems. Based on this result, researchers recommend the management of industrial organizations to invest in information technology to provide a large variety of data in a very short time and providing appropriate programs for analyzing big data and producing accurate and reliable information that can be used by employees.

Increasing in attentions to the environment, city legislative and social problems make companies change their prospects towards supply chain management and design sustainable transportation networks. In this paper, two-stage problem have been investigated in which the transportation stage is considered before Location-Routing Problem, so we call it Transportation-Location-Routing Problem (TLRP). It is an extension of the two-echelon Location-Routing Problem. In the first stage, there is a transportation problem with truck capacity limitation. Furthermore, customers’ time windows should be met in the second stage to make the mode more realistic. Minimization of distribution cost, fuel consumption, and carbon dioxide emission along with balancing the workloads for city drivers are considered as the objective functions of the mathematical model to design a sustainable distribution network. To tackle these conflicting objectives, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are applied to solve the problem. A new customized chromosome based on a priority based technique is presented for the problem. Due to the three comparison metrics for multi-objective problems, with tolerating a little more computational time, MOPSO has the better performance in this problem than NSGA-II.

There is an increase in awareness about sustainable manufacturing process. Manufacturing industries are backbone of a country’s economy. Although it is important but there is a great concern about consumption of resources and waste creation. The primary aim of this study was to explore sustainability concern in turning process in an Indian machining industry. The effect of cutting parameters, Speed/Feed/Depth of Cut, the machining environment, Dry/MQL/Wet, and the type of cutting tool on sustainability factors under study were observed. Analysis of Variance (ANOVA) was used to analyse the data obtained from experimentation in a small scale machining industry. The process is modelled mathematically using response surface methodology (RSM).The economic and environmental aspect like surface roughness, material removal rate and energy consumption were considered as sustainability factors. The model helps to understand the effect of the cutting parameters and conditions on surface finish, energy consumption, and material removal rate. The process was optimized for minimum power consumption considering environmental concern as prime importance. Studies suggest that the cutting environment and tool type influenced on the power consumption during turning process. Extended form of the proposed model could be useful to predict the environmental impact due to machining process, which would bring environmental concern into conventional machining.

As part of their corporate social responsibility (CSR) and sustainable products, many big players in the industry have now reduced plastic through the design, manufacturing and packaging of products and their ultimate disposal. This paper investigates the direct and indirect relationships between awareness, trust, and brand preference on purchase intention in green marketing. Based on a review of the literature, a series of hypotheses are derived and tested using regression analysis. The research employs an online survey-based method to test a theoretically grounded set of proposed hypotheses. The data were collected from 348 young adults living in Jakarta, Indonesia. The results show that green awareness does not influence purchase intention directly. On the other hand, the indirect effect of green awareness through green brand preference on purchase intention was greater compared with the indirect effect through green trust. Therefore, this study draws attention to the importance of green brand preference and green trust on purchase intention. Given that the consumption of organic products has the potential to elicit awareness, trust, and preference of eco-friendly consumers, these findings have significant management implications for corporate managers when considering the production of organic commodities.

Sustainability is getting preference now-a-days in most of the countries in the world. This implies industrial and other developments for economic growth, and of course civilization, but certainly not at the cost of environmental degradation. Environmental conservation is a must. One of the major components of industrial sectors is their electrical energy inputs through electrical substation. Numerous research addresses the issue of energy pollution, cantering on fossil fuel. A very few research can be found which address the issue of pollution generated from electrical energy. This research addresses this issue, with special attention to electrical substations. In the last two or three decades, several electrical equipment manufacturers have started producing this equipment in Bangladesh, which were previously import-oriented. This equipment use various types of oils, acids, zinc and other metals, which are all environmentally hazardous. Thus, any industrial company, buying such equipment from substation manufacturers, should consider green aspects. As such, now it is of prime importance to select green suppliers of substation equipment. Since selection of right or green supplier involves multi-criteria evaluation, Multi-criteria Decision Making (MCDM) technique is essential. Additionally, both qualitative and quantitative criteria require fuzzy analysis to make it more precise and realistic. This research addressed the issue of environmental pollution, its sources, nature and severity, and their mitigation through selecting green suppliers.