Duration: 36 months
Space sustainability is an emerging area of research that combines the sustainable use of space with the sustainable use of resources on Earth to develop space systems. The latter can be enabled by the eco-design approach which evaluates the environmental impact of the whole life cycle of space systems. The evaluation of the environmental impact is currently performed using life cycle assessment (LCA) tools that heavily rely on databases collecting information on materials, processes and the associated generation of pollutants or exploitation of natural resources. The data is then processed with simple mathematical models and assessment tools that produce results across a number of indicators. Both data and models are affected by uncertainty, and the value of the indicators is, therefore, dependent on such uncertainty. Thus, a proper decision based on the value of the indicators needs to account for this uncertainty, which, in nature, is most often mixed with both epistemic and aleatory ones [U6 and 7]. However, although uncertainty analysis is a vital component of LCA, there is no detailed guidance on the concept under the ESA LCA guidelines [L1], whilst the suggested uncertainty parameter stated in the PEF guide [L2] is only based on qualitative expert judgement or relative standard deviation as a percentage if a Monte Carlo simulation is used. Something more robust is required, something that can quantify the intrinsic epistemic and aleatory uncertainty in data, expert judgment, and LCA models. This project aims at using modern Imprecise Probability Theories to define an uncertainty model for the Life Cycle Assessment of space systems. This uncertainty model will then be used to derive a quantification of the uncertainty in the LCA indicators with desirable efficiency and robustness. The ultimate goal of this project is to establish for the first time a proper framework for robust LCA (RLCA) that enables robust decision-making.
