Stochastic analysis for comparing life cycle carbon emissions of hot and cold mix asphalt pavement systems

Uncertainties cause inconsistency in comparing the carbon emissions of hot mix asphalt (HMA) and cold mix asphalt (CMA) pavement systems and misinform the choice of more sustainable pavement recycling techniques. This research integrates sensitivity analysis, Pedigree Matrix, and Monte Carlo simulation into a robust stochastic Life cycle Assessment (LCA) for comparing carbon emissions of HMA and CMA pavement systems. First, influential inputs are identified by a sensitivity analysis and assessed by the Pedigree Matrix to obtain data quality scores. Second, the transformative relationship between quality scores and Beta distribution quantifies the uncertainty of influential inputs. Third, the Monte Carlo simulation propagates uncertainty from influential inputs to stochastic results. Stochastic simulations demonstrate that the probability of life cycle carbon emissions of the HMA exceeding the CMA pavement system is 99.74 % in the investigated case. This study enables the evaluation of confidence intervals to determine more sustainable alternatives in comparative pavement LCA.