An analysis of design strategies for circular economy through life cycle assessment

A systematic review of plastic recycling: technology, environmental impact and economic evaluation

In this systematic review, advancements in plastic recycling technologies, including mechanical, thermolysis, chemical and biological methods, are examined. Comparisons among recycling technologies have identified current research trends, including a focus on pretreatment technologies for waste materials and the development of new organic chemistry or biological techniques that enable recycling with minimal energy consumption. Existing environmental and economic studies are also compared. The findings highlight differences in the environmental characteristics of various recycling methods, including their ability to recover plastic resins, carbon footprint, electricity consumption and gas emissions. The comparisons also reveal the challenges associated with these methods: mechanical recycling often encounters economic barriers due to contamination and inefficiencies in sorting and cleaning processes; thermolysis is constrained by high energy demands and operational costs, whereas chemical and biological recycling faces limitations related to scalability and material costs. Additionally, current challenges, emerging research areas and future directions in plastic recycling are discussed. For example, the role of innovative techniques, such as artificial intelligence, in refining recycling processes is emphasized. The importance of incorporating circular economy principles in the integrated sustainable analysis of recycling processes is also highlighted. The innovative contribution of this review is to address both technological developments and their environmental and economic implications. The focus is placed on literature from the past 10 years to ensure coverage of the most recent advancements. Overall, the insights of this review article aim to guide researchers, policymakers and industry stakeholders in improving sustainable management practices for plastic waste.

Pharmaceuticals and personal care product modelling: Unleashing artificial intelligence and machine learning capabilities and impact on one health and sustainable development goals

The presence of pharmaceutical and personal care products (PPCPs) in the environment poses a significant threat to environmental resources, given their potential risks to ecosystems and human health, even in trace amounts. While mathematical modelling offers a comprehensive approach to understanding the fate and transport of PPCPs in the environment, such studies have garnered less attention compared to field and laboratory investigations. This review examines the current state of modelling PPCPs, focusing on their sources, fate and transport mechanisms, and interactions within the whole ecosystem. Emphasis is placed on critically evaluating and discussing the underlying principles, ongoing advancements, and applications of diverse multimedia models across geographically distinct regions. Furthermore, the review underscores the imperative of ensuring data quality, strategically planning monitoring initiatives, and leveraging cutting-edge modelling techniques in the quest for a more holistic understanding of PPCP dynamics. It also ventures into prospective developments, particularly the integration of Artificial Intelligence (AI) and Machine Learning (ML) methodologies, to enhance the precision and predictive capabilities of PPCP models. In addition, the broader implications of PPCP modelling on sustainability development goals (SDG) and the One Health approach are also discussed. GIS-based modelling offers a cost-effective approach for incorporating time-variable parameters, enabling a spatially explicit analysis of contaminant fate. Swin-Transformer model enhanced with Normalization Attention Modules demonstrated strong groundwater level estimation with an R2 of 82 %. Meanwhile, integrating Interferometric Synthetic Aperture Radar (InSAR) time-series with gravity recovery and climate experiment (GRACE) data has been pivotal for assessing water-mass changes in the Indo-Gangetic basin, enhancing PPCP fate and transport modelling accuracy, though ongoing refinement is necessary for a comprehensive understanding of PPCP dynamics. The review aims to establish a framework for the future development of a comprehensive PPCP modelling approach, aiding researchers and policymakers in effectively managing water resources impacted by increasing PPCP levels.

Analysis of Agro Alternatives to Boost Cameroon’s Socio-Environmental Resilience, Sustainable Development, and Conservation of Native Forests

Located in Central Africa, Cameroon is a country with strong social inequalities and fragile governance and institutions. This has a direct impact on the sustainable development of its territory, communities, and native forest, which are subject to constant socio-environmental and economic pressures due to overexploitation. This research has three purposes: (1) to conduct a comparative theoretical/empirical diagnosis on the quality of Cameroon’s institutional framework, governance, and public policies related to territorial sustainability; (2) to assess the impact of the three clusters identified among the 44 stakeholders interviewed (forestry companies/certifiers; NGOs/communities; and banks/public institutions) on each other; and (3) to analyze the contribution of the use of cassava (Manihot esculenta) as an agro alternative to Cameroon’s socio-ecological resilience, sustainable development, and conservation of native forests. The research found: (1) the need for mixed governance with joint accountability to find equitable and lasting sustainable solutions for the parties involved, making communities/ethnic groups visible in the decision-making process; and (2) the agro use of cassava has a positive impact on socio-ecological resilience by contributing to employment, the protection of devastated soils, and the provision of quality food, and by reducing pollution from the cement industry through using cassava waste as an input.