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

Abstract

Uncertainty is a persistent challenge in Life Cycle Costing (LCC), Life Cycle Assessment (LCA), and Social Life Cycle Assessment (S-LCA), particularly in Infrastructure Asset Management (IAM), where unique object-related and spatiotemporal characteristics exacerbate uncertainty concerns and undermine their reliability. Despite the availability of rigorous methods for uncertainty modelling and quantification, practitioners often lack systematic guidance to anticipate how uncertainties will unfold in individual assessments. This paper addresses this gap by introducing a framework that: 1) explicitly links scenario and parameter uncertainties to structural modelling elements—product system, processes, and flows across LCC, LCA, and S-LCA, offering a practical lens for understanding uncertainties within and across methodologies; 2) introduces profiling indicators—uncertainty cardinality, level, and temporality—that describe how uncertainties scale with assessment context and identifies 11 uncertainty dimensions that affect these indicators.; and 3) examines how these dimensions vary across IAM decision-making levels—portfolio, network and object—to demonstrates its potential to systematically link assessment contexts with uncertainty profiles in practice. The discussion highlights the interdependencies between dimensions, emphasizing the critical interactions among those influencing temporality, and recognizes that different asset types—e.g., roads, bridges—may warrant dedicated research. Further, we underline the need to research uncertainty analysis methods tailored to methodology-specific needs, with particular attention to S-LCA. Ultimately, this work lays the groundwork to formally and pre-emptively link context-specific uncertainty profiles to rigorous uncertainty modelling and quantification methods in life cycle thinking studies, supporting the formulation of approaches that systematically account for individual uncertainty needs from the start.

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