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Description
Life cycle assessment (LCA) is a powerful framework for environmental decision making because the broad boundaries called for prevent shifting of burden from one life-cycle phase to another. Numerous experts and policy setting organizations call for the application of LCA to developing nanotechnologies. Early application of LCA to nanotechnology may

Life cycle assessment (LCA) is a powerful framework for environmental decision making because the broad boundaries called for prevent shifting of burden from one life-cycle phase to another. Numerous experts and policy setting organizations call for the application of LCA to developing nanotechnologies. Early application of LCA to nanotechnology may identify environmentally problematic processes and supply chain components before large investments contribute to technology lock in, and thereby promote integration of environmental concerns into technology development and scale-up (enviro-technical integration). However, application of LCA to nanotechnology is problematic due to limitations in LCA methods (e.g., reliance on data from existing industries at scale, ambiguity regarding proper boundary selection), and because social drivers of technology development and environmental preservation are not identified in LCA. This thesis proposes two methodological advances that augment current capabilities of LCA by incorporating knowledge from technical and social domains. Specifically, this thesis advances the capacity for LCA to yield enviro-technical integration through inclusion of scenario development, thermodynamic modeling, and use-phase performance bounding to overcome the paucity of data describing emerging nanotechnologies. With regard to socio-technical integration, this thesis demonstrates that social values are implicit in LCA, and explores the extent to which these values impact LCA practice and results. There are numerous paths of entry through which social values are contained in LCA, for example functional unit selection, impact category selection, and system boundary definition - decisions which embody particular values and determine LCA results. Explicit identification of how social values are embedded in LCA promotes integration of social and environmental concerns into technology development (socio-enviro-technical integration), and may contribute to the development of socially-responsive and environmentally preferable nanotechnologies. In this way, tailoring LCA to promote socio-enviro-technical integration is a tangible and meaningful step towards responsible innovation processes.
ContributorsWender, Ben A. (Author) / Seager, Thomas P (Thesis advisor) / Crozier, Peter (Committee member) / Fraser, Matthew (Committee member) / Guston, David (Committee member) / Arizona State University (Publisher)
Created2013
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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

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
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Description
Comparative life cycle assessment (LCA) evaluates the relative performance of multiple products, services, or technologies with the purpose of selecting the least impactful alternative. Nevertheless, characterized results are seldom conclusive. When one alternative performs best in some aspects, it may also performs worse in others. These tradeoffs among different impact

Comparative life cycle assessment (LCA) evaluates the relative performance of multiple products, services, or technologies with the purpose of selecting the least impactful alternative. Nevertheless, characterized results are seldom conclusive. When one alternative performs best in some aspects, it may also performs worse in others. These tradeoffs among different impact categories make it difficult to identify environmentally preferable alternatives. To help reconcile this dilemma, LCA analysts have the option to apply normalization and weighting to generate comparisons based upon a single score. However, these approaches can be misleading because they suffer from problems of reference dataset incompletion, linear and fully compensatory aggregation, masking of salient tradeoffs, weight insensitivity and difficulties incorporating uncertainty in performance assessment and weights. Consequently, most LCA studies truncate impacts assessment at characterization, which leaves decision-makers to confront highly uncertain multi-criteria problems without the aid of analytic guideposts. This study introduces Stochastic Multi attribute Analysis (SMAA), a novel approach to normalization and weighting of characterized life-cycle inventory data for use in comparative Life Cycle Assessment (LCA). The proposed method avoids the bias introduced by external normalization references, and is capable of exploring high uncertainty in both the input parameters and weights.
ContributorsPrado, Valentina (Author) / Seager, Thomas P (Thesis advisor) / Chester, Mikhail V (Committee member) / Kullapa Soratana (Committee member) / Tervonen, Tommi (Committee member) / Arizona State University (Publisher)
Created2015
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Description
Advancing sustainable food systems requires holistic understanding and solutions-oriented approaches that transcend disciplines, so expertise in a variety of subjects is necessary. Proposed solutions are usually technically or socially oriented, but disagreement over the best approach to the future of food dominates the dialogue. Technological optimists argue that scientific advances

Advancing sustainable food systems requires holistic understanding and solutions-oriented approaches that transcend disciplines, so expertise in a variety of subjects is necessary. Proposed solutions are usually technically or socially oriented, but disagreement over the best approach to the future of food dominates the dialogue. Technological optimists argue that scientific advances are necessary to feed the world, but environmental purists believe that reductions in consumption and waste are sufficient and less risky. Life cycle assessment (LCA) helps resolve debates through quantitative analysis of environmental impacts from products which serve the same function. LCA used to compare dietary choices reveals that simple plant-based diets are better for the environment than diets that include animal products. However, analysis of soy protein isolate (SPI) demonstrates that certain plant-based proteins may be less preferable for the environment than some unprocessed meats in several categories due to additional impacts that come from industrial processing. LCAs' focus on production risks ignoring consumers, but the food system exists to serve consumers, who can be major drivers of change. Therefore, the path to a sustainable food system requires addressing consumption issues as well. Existing methods for advancing sustainable food systems that equate more information with better behavior or performance are insufficient to create change. Addressing food system issues requires sufficient tacit knowledge to understand how arguments are framed, what the supporting content is, the findings of primary sources, and complex and controversial dialogue surrounding innovations and interventions for food system sustainability. This level of expertise is called interactional competence and it is necessary to drive and maintain holistic progress towards sustainability. Development strategies for interactional competence are informed by studying the motivations and strategies utilized by vegans. A new methodology helps advance understanding of expertise development by assessing levels of expertise and reveals insights into how vegans maintain commitment to a principle that influences their daily lives. The study of veganism and expertise reveals that while providing information to debunk fallacies is important, the development of tacit knowledge is fundamental to advance to a stage of competence.
ContributorsBerardy, Andrew (Author) / Seager, Thomas P (Thesis advisor) / Hannah, Mark (Committee member) / Costello, Christine (Committee member) / Landis, Amy (Committee member) / Wharton, Christopher (Christopher Mack), 1977- (Committee member) / Arizona State University (Publisher)
Created2015