Life cycle assessment of greenhouse gas emissions with uncertainty analysis: A case study of asphaltic pavement in China

Abstract

There are a large number of complex uncertainties in the road life cycle assessment (LCA) analysis process and ignoring these uncertainties will lead to biased results and decision-making. To improve the accuracy of the quantification of carbon emissions (CEs) over the life cycle of pavements, a system for quantifying uncertainty was developed and a case study was conducted in conjunction with sensitivity analysis. The data quality index (DQI) combined with the beta distribution method was implemented to assess parameter uncertainty. The uncertainty of the model parameters was characterized using the results of slice sampling. The uncertainty in the shape of the model was described by the difference before and after the correction of the Hermite orthogonal polynomials. Various uncertainties are conveyed by the Monte Carlo simulation sampling method. To provide reliable emissions data, VISSIM and MOVES were used to calculate additional CEs from congestion. This study assesses the uncertainty of a specific road LCA case. The results show that uncertainty in model form has the greatest impact on results. The use and maintenance phases are key stages for improving the reliability of pavement life cycle CEs, with CEs and uncertainty contributions of around 90% and 94% respectively. The time-triggered conservation strategy has a 75% probability of dominance. This study provides a comprehensive theoretical basis for improving the quality of road LCA CE results, and also makes recommendations for the development of green and low-carbon road engineering.