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Climate change, the emission of greenhouse gases, and air pollution are some of the most important and challenging environmental issues. One of the main sources of such problems is the field of transportation, which leads to the emission of greenhouse gases. An efficient way to deal with such problems is carrying out sustainable transportation to reduce the amount of air pollution in an efficient way. The evaluation of sustainable vehicles can be considered a multi-criteria decision-making (MCDM) method due to the existence of several criteria. In this paper, we aim to provide an approach based on MCDM methods and the spherical fuzzy set (SFS) concept to evaluate and prioritize sustainable vehicles for a transportation system in Tehran, Iran. Therefore, we have developed a new integrated approach based on the stepwise weight assessment ratio analysis (SWARA) and the measurement of alternatives and ranking according to the compromise solution (MARCOS) methods in SFS to assess the sustainable vehicles based on the criteria identified by experts. The evaluation results show that the main criterion of the environment has a high degree of importance compared to other criteria. Moreover, autonomous vehicles are the best and most sustainable vehicles to reduce greenhouse gas emissions. Finally, by comparing the ranking results with other decision-making methods, it was found that the proposed approach has high validity and efficiency.

Coal has been Malaysia’s primary energy source for electricity generation for the past few decades, resulting in increased greenhouse gas emissions and irreversible environmental damage. Solid Oxide Fuel Cells (SOFCs) have emerged as a viable clean-energy alternative to mitigate these environmental effects. There has been significant emphasis on developing pollution-free technology, with limited attention given to the environmental impact of SOFC. Research and development efforts have primarily focused on the design and technical aspects of SOFC. Prior to the introduction of SOFC to market, quantifying the environmental footprint of SOFC manufacturing is necessary to support a sustainable energy transition. This study conducts a comprehensive Life Cycle Assessment (LCA) of SOFC manufacturing in accordance with ISO 14040 and 14044 standards. The analysis focuses on a planar electrolyte-supported SOFC with a lifespan of 4.57 years, using a functional unit of 1 kWh electrical output. The Environmental Footprint (EF) 3.1 method implemented in GaBi Software was used for the impact assessment. Key environmental impact categories considered in the LCA include Climate Change (CC), Acidification Potential (AP), Eutrophication Potential (EP), Ozone Depletion Potential (ODP), Photochemical Ozone Formation (POF), and Human Toxicity Potential (HTP). The total climate change impact is approximately 19.674 kg CO2 eq./kWh, with the Balance of Plant (BoP) phase contributing 91% of this impact, while the fuel cell stack phase contributes 1.25%. The study identifies key areas for improvement, primarily related to BoP and other high-impact processes, and emphasizes the importance of targeted measures to effectively reduce the environmental impacts associated with SOFC manufacturing.

Research has often overlooked examination of circular consumption practices from the consumer’s perspective by primarily focusing on specific consumption activities, hindering researchers from obtaining comprehensive insights into consumers’ upstream and downstream roles. Addressing this gap would highlight their role as simultaneous product users and resource suppliers. The framework draws from the concepts of the circular economy, attitude–behavior–context theory, and practice theory to develop a model that explores circular consumption behavior based on 8R-strategies for addressing and reducing product demand and disposal. These strategies comprise refuse, rethink, reduce, reuse, repair, refurbish, repurpose, and recycle. The proposed model was empirically tested using partial least squares structural equation modeling with data collected from 528 consumers. The results show that the antecedents positively impacted circular consumption behavior, with environmental concern and consumer social responsibility acting as partial mediators. Habits moderated the relationship between these variables, though they showed insignificant moderation between circular economy knowledge and circular consumption behavior. The findings underscore the importance of consumers’ role as both product users and resource suppliers in circular consumption practices.

5G deployment in Malaysia is increasing the need for safe and efficient radio-frequency (RF) scanning at telecommunication towers, but service providers lack a clear, structured way to choose among available methods. This study develops a decision framework using a hybrid Delphi–Analytic Hierarchy Process (AHP) approach. A literature review identified criteria, sub-criteria, and six RF scanning alternatives. Ten experts then participated in three Delphi rounds: Rounds 1 and 2 confirmed five criteria and twenty-five sub-criteria, while Round 3 produced an expert ranking of the six alternatives, with drone-based and human-based scanning as the top priorities. Thirty practitioners subsequently completed AHP pairwise comparisons based on the Delphi-validated hierarchy. The AHP results show that Safety and Environment are the most important criteria, with ‘Fall’ and ‘Thunderstorm’ having the highest global weights. Drone-based scanning ranks highest, followed by human-based and ground-based methods, and the AHP ranking closely matches the expert ranking. The study provides a clear decision method for industry and policymakers to improve worker safety, guide inspection decisions, and strengthen telecommunication infrastructure in line with SDG 8 (Decent Work), SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities), and SDG 13 (Climate Action).

The growing number of end-of-life vehicles (ELVs) engenders a genuine concern for achieving sustainable development. Properly recycling ELV is paramount to checking pollution, reducing landfills, and conserving natural resources. The present study evaluates the sustainability of India's ELV recycling system from techno-socio-economic and environmental aspects as an instrumental step for assessing performance and progress. This investigation has performed the Strength-Weakness-Opportunity-Threat (SWOT) analysis to evaluate ELV recycling in the long-term viability and examine the critical factors and potential. This research makes practical recommendations for effectively encountering persistent challenges in the ELV recycling system based on Indian values. This research adopts an explorative and Integrated bottom-up mixed approach; it interfaces qualitative and quantitative data and secondary research. This study reveals that the social, economic, technological, and environmental aspects of the sustainability of India's ELV recycling system are comparatively limited. The SWOT analysis demonstrates that potential market size and resource recovery are more significant strengths, whereas lack of an appropriate framework and limited technology are major challenges in the recycling of ELVs in India. Sustainable development and economic viability have emerged as great opportunities, while informality and environmental impact have surfaced as primary potential threats to ELV recycling in India. This paper offers insights and yields critical real-world data that may assist in rational decision-making and developing and implementing any subsequent framework.

This study assesses the performance impact of lean construction practices in Engineering, Procurement, and Construction (EPC) projects in China. While lean methods have demonstrated substantial benefits in conventional construction, their implementation in the EPC context—characterized by higher complexity and integration—remains underexplored, particularly within the Chinese infrastructure sector. This research develops a structured framework that classifies lean practices into five functional categories: planning and scheduling (PS), process and workflow optimization (PWO), quality and safety enhancement (QSE), resource and maintenance (RM), and visualization and communication (VC). This study evaluates the influence of these practices on four key performance indicators: efficiency and resource management, quality and safety, stakeholder satisfaction, and organizational and market impact. Data were collected from 456 EPC stakeholders via a structured questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that PS, PWO, and QSE exert the strongest positive effects on performance, while RM and VC contribute in more context-specific ways. The results highlight the value of lean practices for improving operational performance, stakeholder engagement, and sustainability in complex project delivery systems and underscore the need for policy support and digital integration to enhance lean adoption in Chinese EPC sector.

The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy c-means clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8±0.3 mm, and volume overlap error (VOE) of 5.6±1.2%.

The maintenance strategy used in an electric power plant plays a crucial role in its overall performance and operational efficiency. An effective maintenance strategy describes the approach to exploiting various forms of maintenance (corrective, preventive, predictive, proactive, etc.) in an electric power plant. In this paper, the effective and ineffective impacts of maintenance strategies on power plants were examined. Also, the distinction between corrective, preventive, and aggressive maintenance was considered. In terms of effective impacts, a well-designed and executed maintenance strategy enhances the reliability and availability of the electric power plant by minimising unplanned downtime. It extends the lifespan of critical equipment, improves safety measures, increases energy efficiency, and contributes to long-term cost savings. However, in terms of ineffective impacts, poorly planned or executed maintenance strategies can result in increased downtime, higher repair costs, safety risks, decreased efficiency, and regulatory compliance issues. Neglecting maintenance can lead to equipment failures, reduced productivity, and potential environmental incidents.

Firms are finding it increasingly important to leverage social media to facilitate knowledge access, get valuable feedback, and improve innovations to cater for emerging markets. However, using social media without integrating other key factors does not seem to add value to innovation efforts. Therefore, this study investigates the potential of social media usage (SMU) in enhancing green product innovation (GPI) and how two types of environmental collaboration may affect that relationship, which is a subject that has been under-explored. First, the literature on the expansion of the use of social media in enhancing GPI was reviewed to develop the theoretical framework and hypotheses. Then, data collected from 211 manufacturing firms were analysed using structural equation modelling to examine the proposed relationship. The results revealed that SMU does not directly influence GPI. Rather, internal environmental collaboration (IEC) and environmental collaboration with suppliers (ECS) fully mediate the relationship between SMU and GPI. The results further disclosed a positive relationship between IEC and ECS, where both types of environmental collaboration seem to be key factors in improving GPI. Hence, this study highlights the importance of knowledge sharing through environmental collaborations for the generation of ideas to improve products in order to remain competitive in the market.

The current global market forces compel most companies to create an international supply chain. Most of these companies have examined the problems and issues that they encounter during the implementation of this international supply chain. These problems are related to the attitude and culture of people around the world or rooted in the nature of the supply chain. Thus, this study compares the implementation of supply chain management (SCM) in two different countries with different cultures and attitudes. This study highlights the similarities of the problems and benefits of SCM implementation in the two countries. This study identifies the characteristics that are not related to attitude and culture and are rooted in the nature of SCM. A questionnaire was designed and distributed to 600 automotive companies in Malaysia and Iran. Several interviews were conducted to find solutions to the problems. Limited information and the lack of expert employees were identified as the most serious problems, whereas the improvement of warehouse management was identified as an important benefit of SCM implementation. Benchmarking and training courses were the important solutions to these problems. These results reveal that most issues in SCM implementation are rooted in its nature.