The circular bioeconomy (CBE) is an increasingly popular method used to add value to bio-based products. However, these products entail impacts on sustainability that can be assessed by life cycle thinking (LCT). Therefore, this manuscript aims to describe the current development of using LCT for a CBE and to identify challenges and future perspectives with regard to this topic. A systematic literature review was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), analyzing a final portfolio of 57 documents. The results indicated that the use of LCT for a CBE is very recent and has been increasing over the years, is concentrated in Europe, and mainly assesses the conversion of biowastes in biofuels through biorefinery processes, considering mostly environmental concerns, by applying life cycle assessment; thus neglecting economic and social issues. The use of system boundaries, software, databases, and impact assessment methods was reviewed. Five challenges were revealed (the expansion of system boundaries, the consideration of more endpoints, the development and use of regional databases, the development of policies to encourage CBE, and the addition of economic and social issues. Future perspectives will be aimed at considering microalgae, wastewater, and animal biomass in CBE processes, developing more value-added bioproducts and biofuels, and adding cost assessment and more circularity to CBE processes.

The safe and sustainable management of healthcare waste (HCW) is essential for minimizing environmental impacts and protecting public health, particularly in developing countries with limited logistical infrastructure. Despite the growing adoption of routing optimization in HCW logistics, few studies integrate waste generator segmentation with algorithmic planning. This study proposes an optimization approach based on the Periodic Vehicle Routing Problem (PVRP), incorporating a segmentation of waste generators by volume. Two solution methods, the Clarke and Wright (CW) heuristic and Particle Swarm Optimization (PSO), are applied and compared through a real-world case study in Paraná, Brazil. Results show that PSO significantly outperforms CW in reducing travel distance and CO₂ emissions. For small generators, PSO achieves reductions of up to 41% in distance and 41.37% in emissions, compared to CW’s 35.42%. For large generators, PSO was reduced by 22% and 21.81%, respectively. The proposed method demonstrates the potential for scalable, data-efficient waste management strategies. This research contributes to sustainable urban logistics by bridging segmentation and routing optimization in resource-constrained settings, offering actionable insights for policymakers and planners.

The selection of equipment is a fundamental decision for the business’s future. Several methodologies have been proposed to assist in this kind of decision, one of them being the Multiple Criteria Decision-Making (MCDM) methods. However, over the years, new themes have gained strength such as Environmental, Social, and Governance (ESG), and Circular Economy (CE). This study’s objective is twofold: to identify the main multicriteria methods that are applied in equipment selection, as well as their methods variations and dissemination, and to identify how researchers incorporate sustainability concepts are incorporated in decision-making regarding equipment selection. A systematic review of the literature was conducted using the Methodi Ordinatio to rank the articles. The Bibliometrix tool was used for bibliometric analysis. The results demonstrate no concern with sustainability in the decision-making process in equipment selection. To better integrate MCDM decisions, the authors intend to study a multicriteria model through the sustainable approach.

The agricultural sector faces an escalating strategic challenge: reconciling the imperative to increase food production with the need to mitigate environmental impacts amid accelerating climate change. Although Digital Twin (DT) technology is a promising tool for agricultural management, existing models remain anchored in operational optimization, treating sustainability merely as a constrained parameter rather than a core strategic principle. This paper proposes the Sustainable Agriculture Digital Twin Framework (SADTF), a conceptual architecture advancing three original contributions to the smart agriculture literature. First, it models the sustainability-productivity dynamic as a formally defined strategic paradox, operationalizing this inherent tension as a bi-criteria optimization problem with a Pareto-optimal solution space rather than a conventional single-objective trade-off. Second, it introduces a dedicated Sustainability Layer designed exclusively to generate trade-off matrices across competing objectives aligned with the United Nations Sustainable Development Goals, marking a fundamental architectural departure from prior agricultural DT models. Third, it advocates a paradigm shift in the DT's role, moving it from an operational instrument to a strategic governance tool. It embeds Life Cycle Management (LCM) as the central mechanism, translating simulation outputs into strategic directives within a continuous PDCA improvement cycle. Developed through a systematic literature review and formally specified using a mathematical data flow pipeline, the framework is demonstrated concretely through a hypothetical soybean production scenario. Ultimately, the SADTF establishes a structured foundation for next-generation agricultural systems, moving beyond short-term optimization to govern the strategic transition toward practices that reconcile high productivity with long-term environmental sustainability.