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- All Subjects: Sustainability
- All Subjects: Machine Learning
- Genre: Doctoral Dissertation
- Creators: Arizona State University
Description
ABSTRACT Water resources in many parts of the world are subject to increasing stress because of (a) the growth in demand caused by population increase and economic development, (b) threats to supply caused by climate and land cover change, and (c) a heightened awareness of the importance of maintaining water supplies to other parts of the ecosystem. An additional factor is the quality of water management. The United States-Mexican border provides an example of poor water management combined with increasing demand for water resources that are both scarce and uncertain. This dissertation focuses on the problem of water management in the border city of Ciudad Juarez, Chihuahua. The city has attracted foreign investment during the last few decades, largely due to relatively low environmental and labor costs, and to a range of tax incentives and concessions. This has led to economic and population growth, but also to higher demand for public services such as water which leads to congestion and scarcity. In particular, as water resources have become scarce, the cost of water supply has increased. The dissertation analyzes the conditions that allow for the efficient use of water resources at sustainable levels of economic activity--i.e., employment and investment. In particular, it analyzes the water management strategies that lead to an efficient and sustainable use of water when the source of water is either an aquifer, or there is conjunctive use of ground and imported water. The first part of the dissertation constructs a model of the interactive effects of water supply, wage rates, inward migration of labor and inward investment of capital. It shows how growing water scarcity affects population growth through the impact it has on real wage rates, and how this erodes the comparative advantage of Ciudad Juarez--low wages--to the point where foreign investment stops. This reveals the very close connection between water management and the level of economic activity in Ciudad Juarez. The second part of the dissertation examines the effect of sustainable and efficient water management strategies on population and economic activity levels under two different settings. In the first Ciudad Juarez relies exclusively on ground water to meet demand--this reflects the current situation of Ciudad Juarez. In the second Ciudad Juarez is able both to import water and to draw on aquifers to meet demand. This situation is motivated by the fact that Ciudad Juarez is considering importing water from elsewhere to maintain its economic growth and mitigate the overdraft of the Bolson del Hueco aquifer. Both models were calibrated on data for Ciudad Juarez, and then used to run experiments with respect to different environmental and economic conditions, and different water management options. It is shown that for a given set of technological, institutional and environmental conditions, the way water is managed in a desert environment determines the long run equilibrium levels of employment, investment and output. It is also shown that the efficiency of water management is consistent with the sustainability of water use and economic activity. Importing water could allow the economy to operate at higher levels of activity than where it relies solely on local aquifers. However, at some scale, water availability will limit the level of economic activity, and the disposable income of the residents of Ciudad Juarez.
ContributorsGarduno Angeles, Gustavo Leopoldo (Author) / Perrings, Charles (Thesis advisor) / Holway, Jim (Thesis advisor) / Aggarwal, Rimjhim (Committee member) / Arizona State University (Publisher)
Created2011
Description
The greatest challenge facing humanity in the twenty-first century is our ability to reconcile the capacity of natural systems to support continued improvement in human welfare around the globe. Over the last decade, the scientific community has attempted to formulate research agendas in response to what they view as the problems of sustainability. Perhaps the most prominent and wide-ranging of these efforts has been sustainability science, an interdisciplinary, problem-driven field that seeks to address fundamental questions on human-environment interactions. This project examines how sustainability scientists grapple with and bound the deeply social, political and normative dimensions of both characterizing and pursuing sustainability. Based on in-depth interviews with leading researchers and a content analysis of the relevant literature, this project first addresses three core questions: (1) how sustainability scientists define and bound sustainability; (2) how and why various research agendas are being constructed to address these notions of sustainability; (3) and how scientists see their research contributing to societal efforts to move towards sustainability. Based on these results, the project explores the tensions between scientific efforts to study and inform sustainability and social action. It discusses the implications of transforming sustainability into the subject of scientific analysis with a focus on the power of science to constrain discourse and the institutional and epistemological contexts that link knowledge to societal outcomes. Following this analysis, sustainability science is repositioned, borrowing Herbert Simon's concept, as a "science of design." Sustainability science has thus far been too focused on understanding the "problem-space"--addressing fundamental questions about coupled human-natural systems. A new set objectives and design principles are proposed that would move the field toward a more solutions-oriented approach and the enrichment of public reasoning and deliberation. Four new research streams that would situate sustainability science as a science of design are then discussed: creating desirable futures, socio-technical change, sustainability values, and social learning. The results serve as a foundation for a sustainability science that is evaluated on its ability to frame sustainability problems and solutions in ways that make them amenable to democratic and pragmatic social action.
ContributorsMiller, Thaddeus R. (Author) / Minteer, Ben A (Thesis advisor) / Redman, Charles L. (Committee member) / Sarewitz, Daniel (Committee member) / Wiek, Arnim (Committee member) / Arizona State University (Publisher)
Created2011
Description
The uncertainty of change inherent in issues such as climate change and regional growth has created a significant challenge for public decision makers trying to decide what adaptation actions are needed to respond to these possible changes. This challenge threatens the resiliency and thus the long term sustainability of our social-ecological systems. Using an empirical embedded case study approach to explore the application of advanced scenario analysis methods to regional growth visioning projects in two regions, this dissertation provides empirical evidence that for issues with high uncertainty, advanced scenario planning (ASP) methods are effective tools for helping decision makers to anticipate and prepare to adapt to change.
ContributorsQuay, Ray (Author) / Pijawka, David (Thesis advisor) / Shangraw, Ralph (Committee member) / Holway, James (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms.
ContributorsSun, Liang (Author) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Liu, Huan (Committee member) / Mittelmann, Hans D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Consumer goods supply chains have gradually incorporated lean manufacturing principles to identify and reduce non-value-added activities. Companies implementing lean practices have experienced improvements in cost, quality, and demand responsiveness. However certain elements of these practices, especially those related to transportation and distribution may have detrimental impact on the environment. This study asks: What impact do current best practices in lean logistics and retailing have on environmental performance? The research hypothesis of this dissertation establishes that lean distribution of durable and consumable goods can result in an increased amount of carbon dioxide emissions, leading to climate change and natural resource depletion impacts, while lean retailing operations can reduce carbon emissions. Distribution and retailing phases of the life cycle are characterized in a two-echelon supply chain discrete-event simulation modeled after current operations from leading organizations based in the U.S. Southwest. By conducting an overview of critical sustainability issues and their relationship with consumer products, it is possible to address the environmental implications of lean logistics and retailing operations. Provided the waste reduction nature from lean manufacturing, four lean best practices are examined in detail in order to formulate specific research propositions. These propositions are integrated into an experimental design linking annual carbon dioxide equivalent emissions to: (1) shipment frequency between supply chain partners, (2) proximity between decoupling point of products and final customers, (3) inventory turns at the warehousing level, and (4) degree of supplier integration. All propositions are tested through the use of the simulation model. Results confirmed the four research propositions. Furthermore, they suggest synergy between product shipment frequency among supply chain partners and product management due to lean retailing practices. In addition, the study confirms prior research speculations about the potential carbon intensity from transportation operations subject to lean principles.
ContributorsUgarte Irizarri, Gustavo Marco Antonio (Author) / Golden, Jay S. (Thesis advisor) / Dooley, Kevin J. (Thesis advisor) / Boone, Christopher G. (Committee member) / Basile, George M. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Over the past century in the southwestern United States human actions have altered hydrological processes that shape riparian ecosystems. One change, release of treated wastewater into waterways, has created perennial base flows and increased nutrient availability in ephemeral or intermittent channels. While there are benefits to utilizing treated wastewater for environmental flows, there are numerous unresolved ecohydrological issues regarding the efficacy of effluent to sustain groundwater-dependent riparian ecosystems. This research examined how nutrient-rich effluent, released into waterways with varying depths to groundwater, influences riparian plant community development. Statewide analysis of spatial and temporal patterns of effluent generation and release revealed that hydrogeomorphic setting significantly influences downstream riparian response. Approximately 70% of effluent released is into deep groundwater systems, which produced the lowest riparian development. A greenhouse study assessed how varying concentrations of nitrogen and phosphorus, emulating levels in effluent, influenced plant community response. With increasing nitrogen concentrations, vegetation emerging from riparian seed banks had greater biomass, reduced species richness, and greater abundance of nitrophilic species. The effluent-dominated Santa Cruz River in southern Arizona, with a shallow groundwater upper reach and deep groundwater lower reach, served as a study river while the San Pedro River provided a control. Analysis revealed that woody species richness and composition were similar between the two systems. Hydric pioneers (Populus fremontii, Salix gooddingii) were dominant at perennial sites on both rivers. Nitrophilic species (Conium maculatum, Polygonum lapathifolium) dominated herbaceous plant communities and plant heights were greatest in effluent-dominated reaches. Riparian vegetation declined with increasing downstream distance in the upper Santa Cruz, while patterns in the lower Santa Cruz were confounded by additional downstream agricultural input and a channelized floodplain. There were distinct longitudinal and lateral shifts toward more xeric species with increasing downstream distance and increasing lateral distance from the low-flow channel. Patterns in the upper and lower Santa Cruz reaches indicate that water availability drives riparian vegetation outcomes below treatment facilities. Ultimately, this research informs decision processes and increases adaptive capacity for water resources policy and management through the integration of ecological data in decision frameworks regarding the release of effluent for environmental flows.
ContributorsWhite, Margaret Susan (Author) / Stromberg, Juliet C. (Thesis advisor) / Fisher, Stuart G. (Committee member) / White, Dave (Committee member) / Holway, James (Committee member) / Wu, Jianguo (Committee member) / Arizona State University (Publisher)
Created2011
Description
Economic development over the last century has driven a tripling of the world's population, a twenty-fold increase in fossil fuel consumption, and a tripling of traditional biomass consumption. The associated broad income and wealth inequities are retaining over 2 billion people in poverty. Adding to this, fossil fuel combustion is impacting the environment across spatial and temporal scales and the cost of energy is outpacing all other variable costs for most industries. With 60% of world energy delivered in 2008 consumed by the commercial and industrial sector, the fragmented and disparate energy-related decision making within organizations are largely responsible for the inefficient and impacting use of energy resources. The global transition towards sustainable development will require the collective efforts of national, regional, and local governments, institutions, the private sector, and a well-informed public. The leadership role in this transition could be provided by private and public sector organizations, by way of sustainability-oriented organizations, cultures, and infrastructure. The diversity in literature exemplifies the developing nature of sustainability science, with most sustainability assessment approaches and frameworks lacking transformational characteristics, tending to focus on analytical methods. In general, some shortfalls in sustainability assessment processes include lack of: * thorough stakeholder participation in systems and stakeholder mapping, * participatory envisioning of future sustainable states, * normative aggregation of results to provide an overall measure of sustainability, and * influence within strategic decision-making processes. Specific to energy sustainability assessments, while some authors aggregate results to provide overall sustainability scores, assessments have focused solely on energy supply scenarios, while including the deficits discussed above. This paper presents a framework for supporting organizational transition processes towards sustainable energy systems, using systems and stakeholder mapping, participatory envisioning, and sustainability assessment to prepare the development of transition strategies towards realizing long-term energy sustainability. The energy system at Arizona State University's Tempe campus (ASU) in 2008 was used as a baseline to evaluate the sustainability of the current system. From interviews and participatory workshops, energy system stakeholders provided information to map the current system and measure its performance. Utilizing operationalized principles of energy sustainability, stakeholders envisioned a future sustainable state of the energy system, and then developed strategies to begin transition of the current system to its potential future sustainable state. Key findings include stakeholders recognizing that the current energy system is unsustainable as measured against principles of energy sustainability and an envisioned future sustainable state of the energy system. Also, insufficient governmental stakeholder engagement upstream within the current system could lead to added risk as regulations affect energy supply. Energy demand behavior and consumption patterns are insufficiently understood by current stakeholders, limiting participation and accountability from consumers. In conclusion, although this research study focused on the Tempe campus, ASU could apply this process to other campuses thereby improving overall ASU energy system sustainability. Expanding stakeholder engagement upstream within the energy system and better understanding energy consumption behavior can also improve long-term energy sustainability. Finally, benchmarking ASU's performance against its peer universities could expand the current climate commitment of participants to broader sustainability goals.
ContributorsBuch, Rajesh (Author) / Wiek, Arnim (Thesis advisor) / Basile, George (Thesis advisor) / Williams, Eric (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task. Modeling the relationship of multiple tasks is critical to the generalization performance of the MTL algorithms. In this dissertation, I propose a series of MTL approaches which assume that multiple tasks are intrinsically related via a shared low-dimensional feature space. The proposed MTL approaches are developed to deal with different scenarios and settings; they are respectively formulated as mathematical optimization problems of minimizing the empirical loss regularized by different structures. For all proposed MTL formulations, I develop the associated optimization algorithms to find their globally optimal solution efficiently. I also conduct theoretical analysis for certain MTL approaches by deriving the globally optimal solution recovery condition and the performance bound. To demonstrate the practical performance, I apply the proposed MTL approaches on different real-world applications: (1) Automated annotation of the Drosophila gene expression pattern images; (2) Categorization of the Yahoo web pages. Our experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.
ContributorsChen, Jianhui (Author) / Ye, Jieping (Thesis advisor) / Kumar, Sudhir (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2011
Description
This dissertation studies routing in small-world networks such as grids plus long-range edges and real networks. Kleinberg showed that geography-based greedy routing in a grid-based network takes an expected number of steps polylogarithmic in the network size, thus justifying empirical efficiency observed beginning with Milgram. A counterpart for the grid-based model is provided; it creates all edges deterministically and shows an asymptotically matching upper bound on the route length. The main goal is to improve greedy routing through a decentralized machine learning process. Two considered methods are based on weighted majority and an algorithm of de Farias and Megiddo, both learning from feedback using ensembles of experts. Tests are run on both artificial and real networks, with decentralized spectral graph embedding supplying geometric information for real networks where it is not intrinsically available. An important measure analyzed in this work is overpayment, the difference between the cost of the method and that of the shortest path. Adaptive routing overtakes greedy after about a hundred or fewer searches per node, consistently across different network sizes and types. Learning stabilizes, typically at overpayment of a third to a half of that by greedy. The problem is made more difficult by eliminating the knowledge of neighbors' locations or by introducing uncooperative nodes. Even under these conditions, the learned routes are usually better than the greedy routes. The second part of the dissertation is related to the community structure of unannotated networks. A modularity-based algorithm of Newman is extended to work with overlapping communities (including considerably overlapping communities), where each node locally makes decisions to which potential communities it belongs. To measure quality of a cover of overlapping communities, a notion of a node contribution to modularity is introduced, and subsequently the notion of modularity is extended from partitions to covers. The final part considers a problem of network anonymization, mostly by the means of edge deletion. The point of interest is utility preservation. It is shown that a concentration on the preservation of routing abilities might damage the preservation of community structure, and vice versa.
ContributorsBakun, Oleg (Author) / Konjevod, Goran (Thesis advisor) / Richa, Andrea (Thesis advisor) / Syrotiuk, Violet R. (Committee member) / Czygrinow, Andrzej (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sustainability visioning (i.e. the construction of sustainable future states) is considered an important component of sustainability research, for instance, in transformational sustainability science or in planning for urban sustainability. Visioning frees sustainability research from the dominant focus on analyzing problem constellations and opens it towards positive contributions to social innovation and transformation. Calls are repeatedly made for visions that can guide us towards sustainable futures. Scattered across a broad range of fields (i.e. business, non-government organization, land-use management, natural resource management, sustainability science, urban and regional planning) are an abundance of visioning studies. However, among the few evaluative studies in the literature there are apparent deficits in both the research and practice of visioning that curtails our expectations and prospects of realizing process-based and product-derived outcomes. These deficits suggests that calls instead should focus on the development of applied and theoretical understanding of crafting sustainability visions, enhancing the rigor and robustness of visioning methodology, and on integrating practice, research, and education for collaborative sustainability visioning. From an analysis of prominent visioning and sustainability visioning studies in the literature, this dissertation articulates what is sustainability visioning and synthesizes a conceptual framework for criteria-based design and evaluation of sustainability visioning studies. While current visioning methodologies comply with some of these guidelines, none adhere to all of them. From this research, a novel sustainability visioning methodology is designed to address this gap to craft visions that are shared, systemic, principles-based, action-oriented, relevant, and creative (i.e. SPARC visioning methodology) and evaluated across all quality criteria. Empirical studies were conducted to test and apply the conceptual and methodological frameworks -- with an emphasis on enhancing the rigor and robustness in real world visioning processes for urban planning and teaching sustainability competencies. In-depth descriptions of the collaborative visioning studies demonstrate tangible outcomes for: (a) implementing the above sustainability visioning methodology, including evaluative procedures; (b) adopting meaningful interactive engagement procedures; (c) integrating advanced analytical modeling, sustainability appraisal, and creativity enhancing procedures; and (d) developing perspective and methodological capacity for long-range sustainability planning.
ContributorsIwaniec, David (Author) / Wiek, Arnim (Thesis advisor) / Childers, Daniel L. (Committee member) / Lant, Timothy (Committee member) / Arizona State University (Publisher)
Created2013