A Decision-support Flowchart for Including Parameter Uncertainty in Prospective Life Cycle Inventory Modeling: An Application to a PEM Fuel Cell-based APU System for a Hydrogen-powered Aircraft

Emerging energy technologies offer significant opportunities for climate change mitigation. However, the assessment of their potential environmental impact through prospective life cycle assessment (pLCA) is challeng‑ ing owing to parameter uncertainties arising from data gaps, temporal variability, and evolving technological contexts when modeling their prospective life cycle inventories (pLCI). Existing methodologies lack standardized approaches for systematically integrating parameter uncertainty within pLCI frameworks, often initially overlooking it. In order to fill this gap, this study proposes a structured and transparent approach for incorporating parameter uncertainty directly into the pLCI modeling process. The goal is to enhance the robustness, transparency and reproducibility of pLCI models. A decision–support flowchart based on an adapted six-step framework was developed to help life cycle assessment (LCA) practitioners address parameter uncertainty during the “goal and scope definition” and“life cycle inventory” phases of pLCA. The flowchart guides users through the process of defining of the assessment’s goal, scope, as well as its temporal and geographical boundaries, and the technology’s maturity level (Step 1). Step 2 entails gathering data to depict the technology’s development. Steps 3 and 4 involve identifying parameters that are likely to change in the future, such as manufacturing processes, materials, equipment and component dimensions, as well as their respective uncertainties. Step 5 includes the learning effects required for industrial-scale production once the technology has reached maturity. Finally, step 6 identifies external developments impacting the technology, as well as contributing uncertainties. A case study of a fuel cell-based propulsion system for a hydrogen-powered aircraft in 2040 illustrates the applicability of the framework. This study introduces a structured flowchart to support decision making in cases when parameter uncertainty should be integrated into pLCI modeling. By supporting the selection of appropriate prospective meth‑ ods as well as uncertainty identification and characterization strategies, the proposed flowchart enhances the trans‑ parency, consistency, and representativeness of the pLCA results, facilitating their broader application in emerging technology assessment methods.