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Description
Life Cycle Assessment (LCA) quantifies environmental impacts of products in raw material extraction, processing, manufacturing, distribution, use and final disposal. The findings of an LCA can be used to improve industry practices, to aid in product development, and guide public policy. Unfortunately, existing approaches to LCA are unreliable in the cases of emerging technologies, where data is unavailable and rapid technological advances outstrip environmental knowledge. Previous studies have demonstrated several shortcomings to existing practices, including the masking of environmental impacts, the difficulty of selecting appropriate weight sets for multi-stakeholder problems, and difficulties in exploration of variability and uncertainty. In particular, there is an acute need for decision-driven interpretation methods that can guide decision makers towards making balanced, environmentally sound decisions in instances of high uncertainty. We propose the first major methodological innovation in LCA since early establishment of LCA as the analytical perspective of choice in problems of environmental management. We propose to couple stochastic multi-criteria decision analytic tools with existing approaches to inventory building and characterization to create a robust approach to comparative technology assessment in the context of high uncertainty, rapid technological change, and evolving stakeholder values. Namely, this study introduces a novel method known as Stochastic Multi-attribute Analysis for Life Cycle Impact Assessment (SMAA-LCIA) that uses internal normalization by means of outranking and exploration of feasible weight spaces.
ContributorsPrado, Valentina (Author) / Seager, Thomas P (Thesis advisor) / Landis, Amy E. (Committee member) / Chester, Mikhail (Committee member) / White, Philip (Committee member) / Arizona State University (Publisher)
Created2013
Description
The first task faced by many teams endeavoring to solve complex scientific problems is to seek funding for their research venture. Often, this necessitates forming new, geographically dispersed teams of researchers from multiple disciplines. While the team science and organizational management fields have studied project teams extensively, nascent teams are underrepresented in the literature. Nonetheless, understanding proposal team dynamics is important because if left unaddressed, obstacles may persist beyond the funding decision and undermine the possibility of team successes adjunctive to funding. Participant observation of more than 100 multi-investigator proposal teams and semi-structured interviews with six leaders of multidisciplinary proposal teams identified investigator motivations for collaboration, obstacles to collaboration, and indicators of proposal team success. The motivations ranged from technical interests in the research question to a desire to have impact beyond oneself. The obstacles included inconsistent or non-existent communication protocols, unclear processes for producing and reviewing documents, ad hoc file and citation management systems, short and stressful time horizons, ambiguous decision-making procedures, and uncertainty in establishing a shared vision. While funding outcome was the most objective indicator of a proposal team’s success, other success indicators emerged, including whether the needs of the team member(s) had been met and the willingness of team members to continue collaborating. This multi-dimensional definition of success makes it possible for teams to simultaneously be considered successes and failures. As a framework to analyze and overcome obstacles, this work turned to the United States military’s command and control (C2) approach, which relies on specifying the following elements to increase an organization’s agility: patterns of interaction, distribution of information, and allocation of decision rights. To address disciplinary differences and varied motivations for collaboration, this work added a fourth element: shared meaning-making. The broader impact of this work is that by implementing a C2 framework to uncover and address obstacles, the proposal experience—from team creation, to idea generation, to document creation, to final submittal—becomes more rewarding for faculty, leading to greater job satisfaction. This in turn will change how university research enterprises create, organize, and share knowledge to solve complex problems in the post-industrial information age.
ContributorsPassantino, Laurel (Author) / Seager, Thomas P (Thesis advisor) / Cantwell, Elizabeth R (Committee member) / Johnston, Erik (Committee member) / Arizona State University (Publisher)
Created2021