Abstract: Between now and 2060, the world needs to add about 6 billion square meters of new floor area annually to keep pace with population projections. This is equivalent to constructing one New York City every month. The carbon emissions associated with this construction are a major driver of climate change. This dissertation develops a probabilistic Life Cycle Analysis (pLCA) framework to quantify how industrialized construction (IC) influences both the mean and standard deviation of total life-cycle impacts of buildings. Using SIPMath for Monte Carlo simulation, this work analyzes and compares buildings with varying industrialization levels. Probabilistic LCA results demonstrate that increased industrialization reduces both life-cycle carbon impacts and their uncertainty, and identifies which structural markers of industrialized construction are driving these improvements. Additional analysis illustrates that deterministic methods systematically overstate designers’ confidence in meeting sustainability targets. When uncertainty is incorporated into sustainable target value (STV) design, the probability of successfully meeting a target can be explicitly quantified, a significant advantage over the binary pass/fail approach associated with deterministic results. The findings advance uncertainty quantification and visualization methods in sustainability assessment and provide evidence-based insights into IC's environmental benefits, enabling more risk-aware approaches to sustainable building design.