Anticipating uncertainty in infrastructure LCC, LCA, and S-LCA: A systematic, context-aware early identification framework

Abstract

Uncertainty undermines the reliability of Life Cycle Costing (LCC), Life Cycle Assessment (LCA), and Social Life Cycle Assessment (S-LCA) in Infrastructure Asset Management (IAM). Many methods for uncertainty analysis exist, but practitioners often lack systematic guidance to anticipate how uncertainties will unfold in specific assessments and thereby how to manage them. We propose a pre-emptive framework that anchors uncertainty analysis in the shared modelling structure of product systems, processes, and flows, making it transferable across the three methodologies. The framework links assessment context to uncertainty through three profiling indicators—instance count, intensity level, and prospective needs—and eleven infrastructure-specific dimensions that shape them. Mapping these dimensions across IAM decision-making levels illustrates how uncertainty escalates in the assessment contexts in which individual studies are embedded. A practitioner's checklist translates the framework into an early uncertainty profiling tool, guiding analysts to target rigorous modelling and quantification where it matters most. The discussion highlights the critical interdependencies between dimensions and identifies prospective needs as the dominant driver of uncertainty. Ultimately, by making uncertainty profiles explicit up front, the framework fosters proportionate, transparent, and context-responsive uncertainty analysis practices. The paper concludes by underscoring the need for future research into methodology-specific uncertainty modelling and quantification methods—especially for S-LCA—and how to formally and explicitly link their use to different uncertainty profiles to support designing LCT studies that account for individual uncertainty needs from the start.