In an era where sustainability and efficient resource utilization are paramount, the closed-loop supply chain (CLSC) emerges as a critical approach, particularly in the context of perishable goods. The perishability of products adds a layer of complexity to supply chain management, necessitating innovative strategies for maximizing product life and minimizing waste. This comprehensive review article delves into the integration of perishable products within the framework of CLSC. The study thoroughly examines existing research to identify gaps and outline future research directions. It emphasizes the unique challenges and complexities of managing perishable goods, a crucial but often overlooked component in sustainable supply chain practices. The review highlights the balance between efficiency and sustainability, underscoring the importance of reverse logistics and circular economy principles in enhancing supply chain resilience. By synthesizing various methodologies and findings, the article presents a holistic view of the current state of perishable product management in CLSCs, offering valuable insights for academia and industry practitioners. The study not only contributes to the theoretical understanding of CLSCs, but also proposes practical approaches for their optimization, aligning with broader sustainability goals.

Road transport is a major contributor to air pollution, necessitating sustainable solutions for urban logistics. This study presents a time-dependent vehicle routing problem (VRP) model aimed at minimizing fuel consumption and greenhouse gas emissions while addressing stochastic customer demands. By incorporating key environmental factors such as road gradients, vehicle load, temperature, wind direction, and asphalt type, the proposed model provides a comprehensive approach to reducing transportation-related pollutants. To solve the computationally complex problem, a hybrid algorithm combining the gray wolf optimizer (GWO) and the multilayer perceptron (MLP) neural network is introduced. The algorithm demonstrates superior performance, achieving an error rate of less than 2% for medium-scale problems and significantly reducing fuel and driver costs. Sensitivity analyses reveal the profound impact of environmental parameters, with wind speed and direction altering optimal routing in over 80% of cases for large-scale instances. This research advances green logistics by integrating dynamic environmental considerations into routing decisions, balancing economic objectives with sustainability. The proposed model and algorithm offer a scalable solution to real-world challenges, enabling policymakers and logistics planners to improve environmental outcomes while maintaining operational efficiency.

This study introduces the Multi-Objective Sustainable Vehicle Routing Problem (MOSVRP) with time windows, designed for congested urban networks. The model simultaneously addresses economic, environmental, and social sustainability by minimizing costs and emissions while maximizing customer satisfaction through enhanced service at pickup nodes. To manage the complexity of large-scale urban routing, we propose a novel Voronoi diagram-based network shrinking procedure that significantly reduces computational effort. More specifically, the model incorporates time-dependent traffic patterns to capture realistic urban conditions. For the solution, we propose a tailored metaheuristic, the enhanced Multi-Objective Volleyball Premier League (MOVPL) algorithm, which incorporates reference point guidance, disruption operators, and adaptive weight adjustment. This hybrid approach effectively balances conflicting objectives and improves solution diversity. Applied to Tehran’s urban freight network, the proposed method demonstrates superior performance across all metrics compared to benchmark algorithms and exact methods. Results show notable reductions in fuel consumption and travel distance, alongside improved service equity. Furthermore, the framework offers a scalable and transferable solution for sustainable logistics in other urban contexts.

This study maps how Natural Language Processing (NLP) contributes to sustainability. Using a PRISMA‐guided review of 131 English‐language articles from Web of Science (2018–2025), we combine bibliometric co‐word mapping with BERTopic to derive complementary structural and semantic views. Four themes emerge: Topic 0—Climate Change Discourse and Energy Innovation (SDG 13, SDG 7); Topic 1—Sustainability Policy and Public Engagement (SDG 11, SDG 16); Topic 2—Tech‐Driven Environmental Solutions (SDG 6, SDG 9, SDG 15); and Topic 3—Corporate Sustainability and Sustainable Infrastructure (SDG 8, SDG 9, SDG 12). Social equity remains comparatively under‐covered, with limited attention to SDG 5 and SDG 10. Theoretically, we position NLP as a bridge that supports sensemaking, coordination, and accountability across the environmental, social, and economic pillars. Practically, our synthesis highlights deployable uses of NLP for real‐time narrative monitoring, environmental, social, governance (ESG) claim assessment, and horizon scanning for emerging risks and innovation niches. For policy, coupling narrative analytics with policy‐text tracking can create feedback loops that enhance communication, legitimacy, and policy‐mix coherence for SDG implementation. By jointly leveraging network‐based and embedding‐based evidence, the review reveals thematic frontiers, makes SDG linkages explicit, and offers a topic‐based agenda with testable propositions.

Electromobility provides an effective solution for developing countries to reduce dependence on fossil fuels, enhance energy security, and increase environmental sustainability. The current study evaluates the feasibility of implementing electric vehicles (EVs) powered by renewable energy in developing countries. Based on qualitative methods, including expert interviews, it discusses existing transportation systems, the benefits of EVs, and significant constraints such as poor infrastructure, high initial investment, and ineffective policy structures. Evidence further suggests that EV adoption is likely to bring considerable benefits, particularly in cities with high population densities, adequate infrastructure, and supportive regulations that facilitate rapid adoption. Countries like India and Kenya have reduced their fuel import bills and created new jobs. At the same time, cities such as Bogota and Nairobi have seen improved air quality through the adoption of electric public transit. However, the transition requires investments in charging infrastructures and improvements in power grids. Central to this is government backing, whether through subsidy or partnership. Programs like India’s Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME) initiative and China’s subsidy program are prime examples of such support. The study draws on expert interviews to provide context-specific insights that are often absent in global EV discussions, while acknowledging the limitations of a small, regionally concentrated sample. These qualitative findings complement international data and offer grounded implications for electromobility planning in developing contexts. It concludes that while challenges remain, tailored interventions and multi-party public–private partnerships can make the economic and environmental promise of electromobility in emerging markets a reality.