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.

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.

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.