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- Creators: School of Politics and Global Studies
- Status: Published
Description
This thesis examines how the wording of proposed government policies can affect the level of public support that a given policy generates. By surveying 158 Phoenix residents, I tested the differing degrees of support that voters would have for a proposed city ordinance, which would stop Homeowners' Associations from restricting the use of native desert plants in residential landscaping. The ordinance was framed in the survey as a self-governance issue or a water conservation issue. I found that the message frames had little effect on the overall level of support for the ordinance, since most residents had moderate support for the policy. However, participants who were either residents of Homeowners' Associations that did not have native plant restrictions, or native residents of Arizona, demonstrated greater levels of support for the self-determination frame of the proposed ordinance. These findings have implications for policy makers who use targeted messages to establish pro-environmental policies at the local level.
ContributorsSmith, Mary Hannah (Author) / Darnall, Nicole (Thesis director) / Ramirez, Mark (Committee member) / Tetreault, Colin (Committee member) / Barrett, The Honors College (Contributor) / School of Sustainability (Contributor) / School of Politics and Global Studies (Contributor)
Created2013-05
Description
The Gulf of Mexico (or “Gulf”) is of critical significance to the oil and gas industries’ offshore production, but the potential for accidental petrochemical influx into the Gulf due to such processes is high; two of the largest marine oil spills in history, Pemex's Ixtoc I spill (1979) and British Petroleum's (BP) Deepwater Horizon (2010), have occurred in the region. However, the Gulf is also of critical significance to thousands of unique species, many of which may be irreparably harmed by accidental petrochemical exposure. To better manage the conservation and recovery of marine species in the Gulf ecosystem, a Petrochemical Vulnerability Index was developed to determine the potential impact of a petrochemical influx on Gulf marine fishes, therein providing an objective framework with which to determine the best immediate and long term management strategies for resource managers and decision-makers. The resulting Petrochemical Vulnerability Index (PVI) was developed and applied to all bony fishes and shark/ray species in the Gulf of Mexico (1,670 spp), based on a theoretical petrochemical vulnerability framework developed by peer review. The PVI for fishes embodies three key facets of species vulnerability: likelihood of exposure, individual sensitivity, and population resilience, and comprised of 11 total metrics (Distribution, Longevity, Mobility, Habitat, Pre-Adult Stage Length, Pre-Adult Exposure; Increased Adult Sensitivity Due to UV Light, Increased Pre-Adult Sensitivity Due to UV Light; and Abundance, Reproductive Turnover Rate, Diet/Habitat Specialization). The resulting PVI can be used to guide attention to the species potentially most in need of immediate attention in the event of an oil spill or other petrochemical influx, as well as those species that may require intensive long-term recovery. The scored relative vulnerability rankings can also provide information on species that ought to be the focus of future toxicological research, by indicating which species lack toxicological data, and may potentially experience significant impacts.
ContributorsWoodyard, Megan (Author) / Polidoro, Beth (Thesis advisor) / Saul, Steven (Thesis advisor) / Matson, Cole (Committee member) / Arizona State University (Publisher)
Created2020
Description
Spatial regression is one of the central topics in spatial statistics. Based on the goals, interpretation or prediction, spatial regression models can be classified into two categories, linear mixed regression models and nonlinear regression models. This dissertation explored these models and their real world applications. New methods and models were proposed to overcome the challenges in practice. There are three major parts in the dissertation.
In the first part, nonlinear regression models were embedded into a multistage workflow to predict the spatial abundance of reef fish species in the Gulf of Mexico. There were two challenges, zero-inflated data and out of sample prediction. The methods and models in the workflow could effectively handle the zero-inflated sampling data without strong assumptions. Three strategies were proposed to solve the out of sample prediction problem. The results and discussions showed that the nonlinear prediction had the advantages of high accuracy, low bias and well-performed in multi-resolution.
In the second part, a two-stage spatial regression model was proposed for analyzing soil carbon stock (SOC) data. In the first stage, there was a spatial linear mixed model that captured the linear and stationary effects. In the second stage, a generalized additive model was used to explain the nonlinear and nonstationary effects. The results illustrated that the two-stage model had good interpretability in understanding the effect of covariates, meanwhile, it kept high prediction accuracy which is competitive to the popular machine learning models, like, random forest, xgboost and support vector machine.
A new nonlinear regression model, Gaussian process BART (Bayesian additive regression tree), was proposed in the third part. Combining advantages in both BART and Gaussian process, the model could capture the nonlinear effects of both observed and latent covariates. To develop the model, first, the traditional BART was generalized to accommodate correlated errors. Then, the failure of likelihood based Markov chain Monte Carlo (MCMC) in parameter estimating was discussed. Based on the idea of analysis of variation, back comparing and tuning range, were proposed to tackle this failure. Finally, effectiveness of the new model was examined by experiments on both simulation and real data.
In the first part, nonlinear regression models were embedded into a multistage workflow to predict the spatial abundance of reef fish species in the Gulf of Mexico. There were two challenges, zero-inflated data and out of sample prediction. The methods and models in the workflow could effectively handle the zero-inflated sampling data without strong assumptions. Three strategies were proposed to solve the out of sample prediction problem. The results and discussions showed that the nonlinear prediction had the advantages of high accuracy, low bias and well-performed in multi-resolution.
In the second part, a two-stage spatial regression model was proposed for analyzing soil carbon stock (SOC) data. In the first stage, there was a spatial linear mixed model that captured the linear and stationary effects. In the second stage, a generalized additive model was used to explain the nonlinear and nonstationary effects. The results illustrated that the two-stage model had good interpretability in understanding the effect of covariates, meanwhile, it kept high prediction accuracy which is competitive to the popular machine learning models, like, random forest, xgboost and support vector machine.
A new nonlinear regression model, Gaussian process BART (Bayesian additive regression tree), was proposed in the third part. Combining advantages in both BART and Gaussian process, the model could capture the nonlinear effects of both observed and latent covariates. To develop the model, first, the traditional BART was generalized to accommodate correlated errors. Then, the failure of likelihood based Markov chain Monte Carlo (MCMC) in parameter estimating was discussed. Based on the idea of analysis of variation, back comparing and tuning range, were proposed to tackle this failure. Finally, effectiveness of the new model was examined by experiments on both simulation and real data.
ContributorsLu, Xuetao (Author) / McCulloch, Robert (Thesis advisor) / Hahn, Paul (Committee member) / Lan, Shiwei (Committee member) / Zhou, Shuang (Committee member) / Saul, Steven (Committee member) / Arizona State University (Publisher)
Created2020
Description
Climate change, as it becomes more prevalent, is putting a much harsher strain on the resources of the world, specifically food, energy, and water. With this in mind, now is the time to make a change and begin working towards a more sustainable future for everyone. Arizona is an especially susceptible location that has the opportunity to be the leader of this change. In order to effectively manage this movement through governance, a food-energy-water nexus approach is required. This approach recognizes and accounts for the intricate relationships between these industries in order to promote more resilience and balance throughout the nexus. While the main focus in Arizona tends to be on water, and rightfully so, it is important to understand the intricacies of the food, energy, and water systems together. Right now, the system is fragile and needs a new, more complex approach. Ultimately, legislation that intertwines water rights with agriculture regulation and energy production goals, while also including equity and justice measures, have the capacity to work towards limiting the effects of climate change that Arizona will see. Arizona has the opportunity here to either provide a cautionary tale to other regions of how mismanagement can lead to destruction or can showcase the legislative success that the nexus governance approach can provide.
ContributorsKonopka, Violet (Author) / York, Abigail (Thesis director) / Richter, Jennifer (Committee member) / School of Sustainability (Contributor) / School of Life Sciences (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
This paper explores how the United States federal government has historically supported animal agribusiness to the detriment of native ecosystems and wildlife. It focuses on agricultural subsidies and state-funded predator persecution, with an emphasis on the historical conflict between gray wolves and ranchers in America. This paper examines the ecological consequences of agricultural policy in the U.S. and potential solutions to these issues that account for the needs of humans, wildlife, and the environment.
ContributorsPoe, Katherine (Author) / Barca, Lisa (Thesis director) / Schultz, Judith (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of Politics and Global Studies (Contributor)
Created2022-05
Description
Political boundaries often divide ecosystems and create the challenge of conserving the ecosystem across borders. Through transboundary ecosystem management multiple groups can come together and manage the ecosystem that spans their borders collaboratively. In the United States there are several examples of ecosystems that span borders, such as the Sonoran Desert along the US-Mexico frontier and the Rocky Mountains running through the US and Canada. To gain insight into what leads to effective transboundary resource management I compared two case studies that manage resources over borders with multiple collaborators: Glacier National Park and Organ Pipe Cactus National Monument. These two cases offer contrasting ecosystems and backgrounds in transboundary resource management in the United States. To compare the cases I coded them using a collaborative governance codebook (Schoon et al. 2020). The codebook uses a Context-Mechanisms-Outcomes framework to identify aspects of collaborative governance and contextual factors present in each park (Pawson & Tilley 1997; Salter & Kothari, 2014). Once coded, the cases were compared to identify what aspects were similar and different in the parks to help potentially explain what features did or did not lead to effective transboundary resource management.
ContributorsTaetle, Noah (Author) / Schoon, Michael (Thesis director) / Carr Kelman, Candice (Committee member) / Barrett, The Honors College (Contributor) / School of Sustainability (Contributor) / School of Politics and Global Studies (Contributor)
Created2022-05