Current Safe and Sustainable by Design (SSbD) frameworks, such as those proposed by the Joint Research Centre, support the development of safer, more sustainable substances, materials, and manufacturing processes. However, these were not designed for product developers or designers and therefore do not align with the iterative and creative nature of product development, nor with its specific needs and constraints. Broad adoption of SSbD requires tiered, resource-efficient assessments tailored to the specific needs of different professionals. The product developer’s perspective is essential for creating safer, more sustainable products, enabling context-specific evaluation of chemical applications, substances of concern (SoC), and product life cycles. In a circular economy, where products, parts, and materials circulate, holistic views become critical, as emissions and exposures may increase and accumulate. Therefore, we developed the Mapping Assessment for Product Substance Safety and Sustainability (MAPSSS) tool, which integrates principles of Life Cycle Assessment (LCA) and Risk Assessment (RA) into a product development context. Through iterative development based on real-life cases of products containing SoC, we combined essential elements of RA and LCA into a product development workflow. Expert input on environmental assessment and ecotoxicology informed each iteration of the process. MAPSSS supports product developers in identifying and assessing SoC risks, trade-offs, uncertainty levels, and environmental impacts across the product life cycle. It enables the identification of risk hotspots in reference products, supporting mitigation strategies and comparison with new solutions. This represents an initial step toward a product-specific SSbD assessment tool for early design stages.

Understanding how product design shapes recycling outcomes is becoming increasingly important as the demand for recyclable electronics grows. This study investigates how design choices influence recyclability through an analysis of four commercially available smart TVs from major brands. We conducted manual disassembly and controlled shredding tests to explore how materials and connections behave under realistic recycling conditions.Our findings show that recyclability is mainly limited by plastics that are seldom recovered in practice, even when recyclable in principle. Most connections broke down effectively during shredding, with limited material losses. Some design features, however, still hindered recovery, such as ductile materials that trap others. Based on these findings, we propose design guidelines and compare them with existing literature. Our work contributes to the growing body of research linking product design with current recycling processes and can inform design practice and future regulations that support a more circular electronics industry.

Electronics recycling aims at the recovery of materials from obsolete electronic products, containing valuable, harmful, and polluting materials. While product design can improve recyclability, a gap remains between design and recycling practices. This study assessed the quality of published Design for Recycling (DfR) methods for electronics to develop criteria for high-quality methods. A systematic review of 30 years of the literature identified 16 distinct methods, which were evaluated for completeness, practical applicability, and alignment with recycling practices. The review shows major gaps in method development and validation, with few methods being tested in design practice or validated by recycling experiments. Based on these findings, we propose criteria emphasizing aspects such as design practitioner involvement, integration with recycling practice, adaptability, and clear documentation. This study shows the need for practical and accessible methods that bridge the gap between design and recycling.

Non-professional repairs could contribute to an increase in repairs, supporting circular economy goals, yet they raise safety concerns. While recent regulatory actions promote repair, they focus mainly on advancing access to professional repair, rather than enabling repairs by non-professionals, such as consumers and volunteers. This study investigates how product design can mitigate repair safety risks and contribute to safe non-professional repairs. In this exploratory research, a washing machine and a washer-dryer combo were analyzed through repeated disassembly and reassembly to identify mechanical, electrical, thermal, and chemical risks occurring during and after repair, as well as risk-inducing and risk-preventing design features and product architecture. Identified risk-inducing features include exposed components, vulnerable electrical and water connections, lack of reassembly guidance, and differences between disassembly and reassembly sequences. Based on the analysis insights, we developed a first version of a method for identifying, assessing, and mitigating repair safety risks. The method provides elements to visualize repair safety risks as an additional layer on disassembly maps and preliminary design recommendations to mitigate risk. The repair-risk mapping method aims to support designers in analyzing and anticipating repair safety risks and identifying relevant risk-inducing design features. Our findings show that relatively small design interventions, such as visual guidance, reusable connectors, robust connections, and enforced disassembly and reassembly sequences, can reduce risks and make repairs more accessible to non-professionals. Our research suggests that product design plays a crucial role in expanding repair opportunities to a broader range of users while maintaining safety.

Existing material selection methods seem to offer limited support for addressing substance safety in practice, as the focus remains on intrinsic material properties and less on exposure risk. This hinders Safe and Sustainable by Design (SSbD) efforts that can prevent use and accumulation of substances of concern (SoCs) across product lifecycles in a circular economy. This study reviews 29 sustainable material selection methods to evaluate how they do support substance safety. Results show that substance safety is generally embedded within the broader sustainability realm without explicit risk or lifecycle-based assessment. Of the four steps that can be distinguished in material selection, most methods support the steps ‘Establishing a set of candidates’ and ‘Comparing candidates’ but the steps ‘Formulating selection criteria’ and ‘Choosing suitable candidates’ are often unsupported, leaving critical substance safety trade-offs unaddressed. The importance of mindsets such as systemic thinking and iterative reflection is recognized but underrepresented. The findings highlight the need to adapt existing methods with better guidance and risk integration to advance SSbD in material selection.

Repair plays a pivotal role in the circular economy, but evaluating repairability factors such as disassembly times can be complex and time consuming. Existing tools listing proxy times to avoid such measurements had limited libraries of activities, only relevant to ICT product fasteners, and reassembly times were neglected or assumed to be equal to disassembly times, which is not always true. This study introduces the Disassembly and Reassembly Time (DaRT) model: a simplified, time-based tool developed from over 10,000 data points on many connection types from several household products, generating proxy times for many more activities applicable to many more product types. It also compared median times for disassembly and reassembly, showing which tasks are similar enough to use the same proxy time, while highlighting occasional differences, such as part-handling steps. Validated through comparisons with independent disassembly data for vacuum cleaners, washing machines, and smart TVs, DaRT demonstrates strong correlation and minimal bias. Balancing ease of use with reliability, it outperforms simpler step-count methods and offers greater practicality than the complex eDiM. Overall, DaRT enhances repair scoring systems, guides design for repair, and fosters a more circular electronics industry, ultimately fueling more sustainable innovation.

Recent research has emphasized the need to adapt life cycle inventories and life cycle impact assessments to account for changes in future scenarios. This is mostly achieved by using Integrated Assessment Models (IAMs), which combine environmental and economic data to develop prospective life cycle inventories (LCIs). However, running complex IAMs to simulate scenarios is computationally expensive, and the resulting data is not always aligned with the geographical scope of background inventories. This study explores the potential of machine learning (ML) to create prospective water-scarcity characterisation factors by forecasting the AWARE factor for ~ 9700 watersheds globally. Historical time series of water-scarcity characterisation factors are generated using the global freshwater model WaterGAP v2.2d and the AWARE method. Several ML models are trained on these historical datasets and benchmarked using symmetric mean absolute percentage error (sMAPE). The best-performing model, N-Beats, is then used to forecast AWARE values through 2032. The results demonstrate that ML can produce spatially and temporally explicit forecasts with reasonable accuracy (median sMAPE ~ 28%). However, the models primarily capture seasonal patterns rather than long-term structural trends and the results are sensitive to the quality and representativeness of the training data. This study highlights both the potential and the limitations of time series forecasting for developing prospective characterisation factors in life cycle assessment.

The increasing digitisation that we have witnessed in the past few years has resulted in increased information and communications technology (ICT) hardware manufacturing, which is not sustainable due to the growing demand for critical materials and the greenhouse emissions associated with it. A solution is transitioning to a circular economy (CE). To facilitate this, boost the data economy and digital innovation, the European Union has introduced digital product passports (DPPs), which should provide information about a product’s lifetime to bring more transparency into supply chains. However, several challenges, namely the lack of findable, accessible, interoperable, reusable ICT and materials data and tools to support its interpretation for decision-making, are present. Utilising ontologies and knowledge graphs is a possible solution. Although the ontology work in the ICT and materials domains has been on the rise, there is a lack of a unified semantic model that can capture the complex, heterogeneous cross-domain data needed for building DPPs of ICT devices such as laptops and data servers. Motivated by this, we present the RePlanIT ontology for ICT DPPs, which captures knowledge on several levels – ICT device, hardware components, materials and the CE itself. RePlanIT’s specification is based on a literature survey, interviews and inputs from domain experts from both industry and academia. The ontology, its utilisation for building a knowledge graph of DPPs of laptops and data servers and its application have been successfully validated in a real-world case focusing on supporting more sustainable ICT procurement in government.