No abstract

Life-cycle cost analysis (LCCA) is an important tool for evaluating the merits of alternative investments. Inevitably, inputs for an LCCA are subject to a high level of uncertainty in both the short and long term. However, the way LCCA is currently implemented in the field treats LCCA inputs as static, deterministic values. Conducting an analysis in this way, though computationally simpler, hides the underlying uncertainty of the inputs by only considering a few possible permutations. The results of such an analysis could potentially lead a decision maker to the incorrect pavement selection. One methodology that has gained traction in the past decade is describing uncertain parameters probabilistically and allowing the analysis to consider a range of possible outcomes. Although this methodology is recommended by FHWA, practitioners still generally conduct deterministic LCCAs. One of the major reasons is that further work must be conducted to characterize uncertainty statistically for input parameters. This research attempted to build on previous work by probabilistically characterizing several input parameters for which empirical data were and were not readily available. This paper characterizes uncertainty and variability in the LCCA of pavements and then applies the methodology presented to two case studies to understand the implications of a probabilistic LCCA for the pavement selection process.

High-albedo materials reflect more solar radiation and, thereby, alter the earth's radiative balance. Increasing pavement albedo, therefore, has been considered as a technological strategy to mitigate global warming. Previous studies have evaluated this strategy using global average models. To factor this effect into life cycle assessments, location-specific models of the albedo effect for pavements are required. A parametric analytical model is developed to estimate the radiative forcing (RF) using a novel model form and an iterative solution approach. The new model is extended to estimate the corresponding global warming potential (GWP) over an analysis period of 50 years for an albedo change in a pavement surface. This was applied to quantify the GWP impacts of increasing pavement albedo in 14 cities across various climate zones in the US. For the United States, the GWP in kg CO 2 equivalent per square meter of altered surface ranges from 0.8 to 1.6 per 0.01 change in albedo, a range of more than 40%. Analysis of a hypothetical albedo change to all darker pavements in the US would produce a negative RF of a magnitude equivalent to that associated with a reduction in CO 2 emissions of more than 17 Mton per year.