Aviation presents unique challenges for conventional Life Cycle Assessments (LCAs) due to its system complexity, long service lifetimes of aircraft, and highly globalised operations. While methodological approaches exist to account for temporal and spatial variations in Life Cycle Inventories (LCIs), they have been applied to limited extent or low spatio-temporal resolution. In this study, we demonstrate how spatio-temporally resolved datasets and life cycle simulation can be systematically integrated into LCAs for aircraft. For this purpose, we combine discrete-event modelling with the LCA framework, dividing the complex aircraft life cycle into individual events, such as single flights or maintenance activities. Each event is characterised by time and location attributes. In this study, maintenance activities are used as a representative use case to demonstrate the approach by allowing dynamic adaptation based on temporal and spatial factors. Our results show, that the implementation of dynamic LCIs in a discrete-event life cycle simulation framework has the potential to increase the accuracy of environmental assessments of aircraft and other complex technical systems. Such dynamic modelling could be particularly relevant for the application of novel technologies, as their benefits may vary substantially over time and space.

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.

Increasing calls for safer and more sustainable approaches to innovation in the chemical sector necessitate adapted methods for the environmental assessment of emerging chemical technologies. While these technologies are still in the research and development phase, gaining an early understanding of their potential implications is crucial for their eventual introduction into markets worldwide. Life Cycle Assessment (LCA) is a core tool which has been recently adapted for such purpose. Prospective LCA approaches aim to develop plausible future-oriented models which account for the evolution of factors both intrinsic and extrinsic to the technologies assessed. Such future-oriented models introduce many indeterminacies, which could, to some extent, be addressed by Machine Learning techniques. Recent demonstrations of such techniques in the context of prospective LCA, as well as promising avenues for further research, are critically discussed.