A conceptual framework for uncertainty-informed system boundary expansion in life cycle assessment

Life cycle assessment (LCA) is the most comprehensive framework for evaluating the environmental impacts of products and services, yet defining system boundaries remains a subjective exercise and can lead to unknown truncation error and complications in comparing across studies. Here, we introduce an uncertainty-informed, iterative boundary-expansion algorithm that quantitatively guides boundary expansion by answering which processes in the foreground system should be included and expanded to upstream detail and which may be aggregated or omitted. The proposed algorithm starts with the foreground system and identifies hotspot processes by comparing predefined thresholds to a process's mean and upper-tail contribution share, computed from Monte Carlo results that propagate uncertainty in both a process's input amount and emission intensity. We developed a stochastic simulation framework and created synthetic production system trees to examine the behavior of the proposed algorithm under controlled conditions. Our results show that the cut-off threshold is the dominant factor in determining final model complexity, while uncertainty-driven expansion is most beneficial in low-data-quality scenarios. Foreground impact concentration could shape the distribution of effort required for system boundary expansion, suggesting that conventional LCA practices focused on primary supply chains may systematically miss significant impacts in diffuse systems. Applying the proposed approach could potentially enhance the documentation of system expansion and improve uncertainty analysis within LCA, ultimately increasing reproducibility and transparency in environmental assessments.