The importance of incorporating uncertainty into pavement life cycle cost and environmental impact analyses

We present an approach for conducting probabilistic life cycle cost analyses (LCCA) and life cycle assessments (LCA) and demonstrate its value with case study results. We define uncertainty quantities and methods for characterizing uncertainty for different types of parameters. The approach includes leveraging outputs from Pavement-ME to characterize uncertainty in pavement performance over time. Uncertainty in the input data and scenarios is used in a Monte Carlo analysis to quantify the uncertainty in life cycle costs and environmental impacts. The probabilistic results are then used to calculate several comparative metrics, including the statistical confidence that one alternative has a lower cost or environmental impact than another alternative, and to determine the parameters that contribute most to the variance of the results. The approach enables a wide analysis of the scenario space to determine which scenarios are most relevant to the comparison of alternatives, and iterative analyses that feature refined data selected in the influential parameter analysis. We demonstrate the value of the approach and the benefits of incorporating uncertainty into LCCAs and LCAs via results from cases in the literature. 2 UNCERTAINTY QUANTITIES, CHARACTERIZATION, AND ANALYSIS 2.1 Uncertainty quantities The LCA literature (see (Lloyd & Ries 2007) for a summary) has coalesced around three types of uncertainty for both life cycle inventories (LCI) and life cycle impact assessment (LCIA) methods: parameter (uncertainty in input data), scenario (uncertainty in choices), and model (uncertainty in mathematical relationships) uncertainty. Differentiating these types of uncertainty can be challenging because of the overlap among them. All forms of uncertainty are expressed as uncertainty in a parameter value, even if there is an aggregate of multiple types of uncertainty. We found guidance on uncertainty quantities from the work of Morgan and Henrion (1990), which defines quantities used in uncertainty analyses for risk and policy analysis. They define eight types of uncertainty quantities. The five that are of most relevance to LCA and LCCA are listed in Table 1. There will likely be a single decision variable and only a few outcome criteria for each analysis. However, there will almost certainly be numerous empirical, model domain, and value parameters. Some empirical quantities will be used directly in life cycle inventories, such as quantities of material inputs or emission outputs; these are inventory parameters. However, other empirical quantities are actually model parameters, such as pavement thickness or vehicle fuel efficiency, which are used to calculate inventory parameters. Table 1. Summary of types of quantities in LCAs and LCCAs. Content adapted from (Morgan & Henrion