Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- All Subjects: Environmental engineering
- Creators: Seager, Thomas
Description
Several prominent research strategy organizations recommend applying life cycle assessment (LCA) early in the development of emerging technologies. For example, the US Environmental Protection Agency, the National Research Council, the Department of Energy, and the National Nanotechnology Initiative identify the potential for LCA to inform research and development (R&D) of photovoltaics and products containing engineered nanomaterials (ENMs). In this capacity, application of LCA to emerging technologies may contribute to the growing movement for responsible research and innovation (RRI). However, existing LCA practices are largely retrospective and ill-suited to support the objectives of RRI. For example, barriers related to data availability, rapid technology change, and isolation of environmental from technical research inhibit application of LCA to developing technologies. This dissertation focuses on development of anticipatory LCA tools that incorporate elements of technology forecasting, provide robust explorations of uncertainty, and engage diverse innovation actors in overcoming retrospective approaches to environmental assessment and improvement of emerging technologies. Chapter one contextualizes current LCA practices within the growing literature articulating RRI and identifies the optimal place in the stage gate innovation model to apply LCA. Chapter one concludes with a call to develop anticipatory LCA – building on the theory of anticipatory governance – as a series of methodological improvements that seek to align LCA practices with the objectives of RRI.
Chapter two provides a framework for anticipatory LCA, identifies where research from multiple disciplines informs LCA practice, and builds off the recommendations presented in the preceding chapter. Chapter two focuses on crystalline and thin film photovoltaics (PV) to illustrate the novel framework, in part because PV is an environmentally motivated technology undergoing extensive R&D efforts and rapid increases in scale of deployment. The chapter concludes with a series of research recommendations that seek to direct PV research agenda towards pathways with the greatest potential for environmental improvement.
Similar to PV, engineered nanomaterials (ENMs) are an emerging technology with numerous potential applications, are the subject of active R&D efforts, and are characterized by high uncertainty regarding potential environmental implications. Chapter three introduces a Monte Carlo impact assessment tool based on the toxicity impact assessment model USEtox and demonstrates stochastic characterization factor (CF) development to prioritize risk research with the greatest potential to improve certainty in CFs. The case study explores a hypothetical decision in which personal care product developers are interested in replacing the conventional antioxidant niacinamide with the novel ENM C60, but face high data uncertainty, are unsure regarding potential ecotoxicity impacts associated with this substitution, and do not know what future risk-relevant experiments to invest in that most efficiently improve certainty in the comparison. Results suggest experiments that elucidate C60 partitioning to suspended solids should be prioritized over parameters with little influence on results. This dissertation demonstrates a novel anticipatory approach to exploration of uncertainty in environmental models that can create new, actionable knowledge with potential to guide future research and development decisions.
Chapter two provides a framework for anticipatory LCA, identifies where research from multiple disciplines informs LCA practice, and builds off the recommendations presented in the preceding chapter. Chapter two focuses on crystalline and thin film photovoltaics (PV) to illustrate the novel framework, in part because PV is an environmentally motivated technology undergoing extensive R&D efforts and rapid increases in scale of deployment. The chapter concludes with a series of research recommendations that seek to direct PV research agenda towards pathways with the greatest potential for environmental improvement.
Similar to PV, engineered nanomaterials (ENMs) are an emerging technology with numerous potential applications, are the subject of active R&D efforts, and are characterized by high uncertainty regarding potential environmental implications. Chapter three introduces a Monte Carlo impact assessment tool based on the toxicity impact assessment model USEtox and demonstrates stochastic characterization factor (CF) development to prioritize risk research with the greatest potential to improve certainty in CFs. The case study explores a hypothetical decision in which personal care product developers are interested in replacing the conventional antioxidant niacinamide with the novel ENM C60, but face high data uncertainty, are unsure regarding potential ecotoxicity impacts associated with this substitution, and do not know what future risk-relevant experiments to invest in that most efficiently improve certainty in the comparison. Results suggest experiments that elucidate C60 partitioning to suspended solids should be prioritized over parameters with little influence on results. This dissertation demonstrates a novel anticipatory approach to exploration of uncertainty in environmental models that can create new, actionable knowledge with potential to guide future research and development decisions.
ContributorsWender, Ben A. (Author) / Seager, Thomas (Thesis advisor) / Guston, David (Committee member) / Westerhoff, Paul (Committee member) / Arizona State University (Publisher)
Created2016
Description
Life Cycle Assessment (LCA) results are typically presented using default visualization and communication approaches without acknowledging: the goals of the end-user, the end-user’s level of knowledge in LCA, the qualitative explanation supporting the visual, and the uncertainty in the process. The motivating hypothesis of this research is that the way practitioners communicate and visualize LCA results poses a risk to the interpretations of the end-users, especially when the goal of the study is not of focus when designing the visuals. Different LCA goals, whether it is for comparisons, hotspot identifications, or environmental declarations, require different visualization designs. To test this, studies were conducted with a variety of participants by giving them several visual representations of LCA results and asking them to share their interpretations of them. The participants’ interpretations of each visual were compared to the opinions of a panel of LCA experts and to the author’s intended use of it. This research gives insight on where misalignments or enhancements in the interpretation of results can occur based on the visual representations used in a certain goal category and the other factors previously mentioned. The results also provided three more key findings: 1) The majority of visuals that accurately presented and communicated the results were in the same goal category that the authors intended the visuals to be used for, suggesting that visuals are more effective when designed with the goal of the study in mind. 2) Several visuals suggested misconceptions in the presentation of results which included a misconception of the participants, a misconception of the authors, or a misconception between all groups. 3) None of the visuals in the environmental declarations category received a consensus from the panel of experts that they were well-suited for that purpose which suggests a significant research gap in accurately visualizing results for these purposes. These results aided the development of guidance documents to suggest both what to consider and what to avoid based on the goal of the study. The findings from this study can assist in bridging the gap in communication between the practitioner and the end-user.
ContributorsGuglielmi, Giovanni (Author) / Seager, Thomas (Thesis advisor) / Chester, Mikhail (Committee member) / Prado, Valentina (Committee member) / Arizona State University (Publisher)
Created2023