ANALYSES OF FUNCTIONAL UNIT AND NORMALIZATION OPTIONS FOR PRESENTATION OF TRANSPORTATION LIFE CYCLE ASSESSMENT RESULTS

Abstract

Thesis (Ph.D.), Civil Engineering, Washington State UniversityLife cycle assessment (LCA) is a methodology for analyzing the potential environmental impacts of products, processes, and systems. This dissertation develops methods for novel forms of normalization, and preliminarily analyzes options for selecting functional units (fUns) in LCA. These methods are intended to reduce bias from analyst-selected elements and increase interpretation context. The normalization research centers on using an entity’s (e.g., company, agency, etc.) impacts, rather than a geographic region’s, as a normalization reference, providing decision-makers with additional perspective on results. The section begins with the development of an environmental impact database for Washington State Ferries. Results of published studies were compared when normalized to the entity versus a geographic region, with a correlation test between entity and regional product scores showing a moderate impact from entity normalization (r^2=0.53). This effect varied widely by case study. Novel forms of normalization using multiple reference areas (hybrid normalization) were developed to provide multiple decision-context support, avoid biases introduced by singular reference selection, and mitigate inverse proportionality in the entity normalized results. These used series and parallel combinations of entity or regional normalization, paired with a ratio factor of entity/regional references to reveal entity hotspots. A survey testing the use of hybrid normalization distributed to Washington state agency employees found hybrid normalization could impact environmental favorability selections in an LCA case study (chi-square p<0.001 in cross-tabulation test). Further, respondents found hybrid normalization to be a useful approach that adds multi-perspective information relevant to decision-making. For the fUn section, a rubric was developed for assessing fUn quality and was applied to vehicle LCAs to examine strengths and weaknesses in published studies. Additionally, an approach for presenting results in multiple fUns was described and examined through a survey, which suggested that most decision-makers can effectively use multiple fUns and find the approach to be informative. The advances made herein provide additional perspective on LCA results, and benefit methodological bias reduction through consistency analysis. This research contributes to making LCA a more reliable, trustworthy, and objective method, thereby increasing its usefulness and the likelihood that environmental aspects are considered in transportation decision-making.Washington State University, Civil Engineerin