Improving Life Cycle Assessment as a Decision Support Tool Using Empirical Data for Multicriteria Prioritization
Energy systems are essential to society and play major roles in two of the greatest global challenges of our time: climate change and social inequity. Advancing environmentally sustainable and socially responsible energy transitions around the world requires interdisciplinary and multicriteria approaches. My dissertation addresses life cycle assessment (LCA) as an engineering decision support tool, using rigorous social science methods to derive and implement an approach to multicriteria optimization founded on empirical data about societal priorities. I argue that rigorous, transparent approaches to prioritizing across noncomparable impact categories in LCA is fundamental to enabling LCA as an engineering method that is useful and robust enough to play a role in policy processes where life cycle evaluation is demanded by decision makers. Further, I argue that prioritization is ultimately a question of values and, as such, should privilege society's values over those of individual decision makers or the expert community. My research uses case studies of communities experiencing energy development to elicit a range of societal value systems matched to expert-identified impact categories in LCA, derive a set of empirical archetypes or patterns of prioritization for use as values-based weighting factors in LCA, and demonstrate the value of using these priority archetypes for sensitivity analysis during an LCA-based decision process.