Ascertaining the Environmental Advantages of Pavement Designs Incorporating Recycled Content through a Parametric and...

Reclaimed asphalt pavement (RAP) is a widely used end-of-life (EoL) material in asphalt pavements to increase the material circularity. However, the performance loss due to using RAP in the asphalt binder layer often requires a thicker layer, leading to additional material usage, energy consumption, and transportation effort. In this study, we developed a parametric and probabilistic life cycle assessment (LCA) framework to robustly compare various pavement designs incorporating recycled materials. Our framework is built upon thermodynamic and physical principles to reveal the complex relationship among the parameters. Mechanistic-Empirical Pavement Design Guide (MEPDG) models and Highway Development Management (HDM4) models are integrated into the framework to estimate pavement roughness and vehicle fuel consumption during the use phase. The pedigree approach and Monte Carlo simulation are integrated into the framework to reflect data uncertainty at the parameter level. We applied the framework to evaluate 66 Flemish motorway segments, revealing that using RAP in the binder layer with increased thickness does not necessarily guarantee lower greenhouse gas (GHG) emissions for pavement construction. However, it may lead to lower GHG emissions due to fuel savings when considering the use phase, highlighting the vital role of the use phase in pavement LCA. Our global sensitivity analysis highlights several contributors (out of 87 parameters) to GHG emissions variance depending on the LCA scope: fuel consumption during the use phase, transport distances, mass of fine aggregate, and machine power and machine productivity during pavement construction. Reducing uncertainties in these parameters can decrease the variance by up to 60%, enhancing discernibility by up to 11%. In conclusion, our parametric and probabilistic LCA framework provides a nuanced understanding when comparing various pavement designs incorporating recycled content, enabling robust decision-making through improved data quality.