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Description
Current policies subsidizing or accelerating deployment of photovoltaics (PV) are typically motivated by claims of environmental benefit, such as the reduction of CO2 emissions generated by the fossil-fuel fired power plants that PV is intended to displace. Existing practice is to assess these environmental benefits on a net life-cycle basis,

Current policies subsidizing or accelerating deployment of photovoltaics (PV) are typically motivated by claims of environmental benefit, such as the reduction of CO2 emissions generated by the fossil-fuel fired power plants that PV is intended to displace. Existing practice is to assess these environmental benefits on a net life-cycle basis, where CO2 benefits occurring during use of the PV panels is found to exceed emissions generated during the PV manufacturing phase including materials extraction and manufacture of the PV panels prior to installation. However, this approach neglects to recognize that the environmental costs of CO2 release during manufacture are incurred early, while environmental benefits accrue later. Thus, where specific policy targets suggest meeting CO2 reduction targets established by a certain date, rapid PV deployment may have counter-intuitive, albeit temporary, undesired consequences. Thus, on a cumulative radiative forcing (CRF) basis, the environmental improvements attributable to PV might be realized much later than is currently understood. This phenomenon is particularly acute when PV manufacture occurs in areas using CO2 intensive energy sources (e.g., coal), but deployment occurs in areas with less CO2 intensive electricity sources (e.g., hydro). This thesis builds a dynamic Cumulative Radiative Forcing (CRF) model to examine the inter-temporal warming impacts of PV deployments in three locations: California, Wyoming and Arizona. The model includes the following factors that impact CRF: PV deployment rate, choice of PV technology, pace of PV technology improvements, and CO2 intensity in the electricity mix at manufacturing and deployment locations. Wyoming and California show the highest and lowest CRF benefits as they have the most and least CO2 intensive grids, respectively. CRF payback times are longer than CO2 payback times in all cases. Thin film, CdTe PV technologies have the lowest manufacturing CO2 emissions and therefore the shortest CRF payback times. This model can inform policies intended to fulfill time-sensitive CO2 mitigation goals while minimizing short term radiative forcing.
ContributorsTriplican Ravikumar, Dwarakanath (Author) / Seager, Thomas P (Thesis advisor) / Fraser, Matthew P (Thesis advisor) / Chester, Mikhail V (Committee member) / Sinha, Parikhit (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
Traditional infrastructure design approaches were born with industrialization. During this time the relatively stable environments allowed infrastructure systems to reliably provide service with networks designed to precise parameters and organizations fixated on maximizing efficiency. Now, infrastructure systems face the challenge of operating in the Anthropocene, an era of complexity. The

Traditional infrastructure design approaches were born with industrialization. During this time the relatively stable environments allowed infrastructure systems to reliably provide service with networks designed to precise parameters and organizations fixated on maximizing efficiency. Now, infrastructure systems face the challenge of operating in the Anthropocene, an era of complexity. The environments in which infrastructure systems operate are changing more rapidly than the technologies and governance systems of infrastructure. Infrastructure systems will need to be resilient to navigate stability and instability and avoid obsolescence. This dissertation addresses how infrastructure systems could be designed for the Anthropocene, assessing technologies able to operate with uncertainty, rethinking the principles of technology design, and restructuring infrastructure governance. Resilience, in engineering, has often been defined as resistance to known disturbances with a focus on infrastructure assets. Resilience, more broadly reviewed, includes resistance, adaptation, and transformation across physical and governance domains. This dissertation constructs a foundation for resilient infrastructure through an assessment of resilience paradigms in engineering, complexity and deep uncertainty (Chapter 2), ecology (Chapter 3), and organizational change and leadership (Chapter 4). The second chapter reconciles frameworks of complexity and deep uncertainty to help infrastructure managers navigate the instability infrastructure systems face, with a focus on climate change. The third chapter identifies competencies of resilience in infrastructure theory and practice and compares those competencies with ‘Life’s Principles’ in ecology, presenting opportunities for growth and innovation in infrastructure resilience and highlighting the need for satisficed (to satisfy and suffice) solutions. The fourth chapter navigates pressures of exploitation and exploration that infrastructure institutions face during periods of stability and instability, proposing leadership capabilities to enhance institutional resilience. Finally, the dissertation is concluded with a chapter synthesizing the previous chapters, providing guidance for alternative design approaches for advancing resilient infrastructure. Combined, the work challenges the basic mental models used by engineers when approaching infrastructure design and recommends new ways of doing and thinking for the accelerating and increasingly uncertain conditions of the future.
ContributorsHelmrich, Alysha Marie (Author) / Chester, Mikhail V (Thesis advisor) / Grimm, Nancy B (Committee member) / Garcia, Margaret (Committee member) / Meerow, Sara (Committee member) / Arizona State University (Publisher)
Created2021