Browsing by Author "Borsuk, Mark Edward"

In recent years, NaTech disasters, which are defined as the technological accidents caused by natural events, have led to huge losses all over the world. To reduce these losses, assessments of the vulnerabilities of industrial facilities are necessary. In this study, an effort was made to locate aboveground storage tanks from remotely sensed imagery. A dataset that identifies different types of tanks was generated. The data were acquired from the National Agriculture Imagery Program (NAIP) and tanks were labeled as closed roof tank, external floating roof tank, spherical tank, water treatment tank, or water tower according to their shapes. After collecting these data, the Faster R-CNN algorithm, an object detection architecture, was applied to test the performance of this algorithm on the prelabeled dataset. Results of testing indicate that the algorithm could well achieve the goal that having a high recall rate for all the classes of tanks. The precision and recall rates were 82.92% and 90.03% for closed roof tanks, 85.85% and 91.68% for external floating roof tanks, 34.81% and 60.26% for spherical tanks, 49.63% and 89.33% water treatment tanks, 9.43% and 38.46% for water towers. For spherical tanks and water towers, although having low precision and recall, the percentage of missed tanks was extremely low, which is 2.08% and 0 respectively. These results suggest that this aboveground storage tank dataset and the pretrained model generated from Faster R-CNN could be further used in future work for tank detection and vulnerability assessment.

In the spirit of American individualism, the majority of the United States’ forested landscape is controlled by private landowners, who make autonomous decisions that impact a shared wealth of biodiversity and ecosystem services. It is important to understand not only the forest management decisions made by private landowners, but also the motivations that incentivize these consequential actions. Furthermore, it is useful to have the capacity to infer such insights using publicly available data, and by employing transparent, flexible, and scalable statistical frameworks. This dissertation seeks to elucidate the motivations and actions of private landowners in the United States using a variety of data sources, including Zillow home estimates, the American Community Survey, satellite remote sensing imagery, and the Forest Inventory and Analysis database, and by implementing interpretable modeling frameworks, such as the hedonic pricing method and structural equation modeling. I uncover nuanced insights about human-environmental systems, including (1) a positive feedback loop between affluence and tree-shading in metropolitan areas; (2) the dominance of normative pressures on forest owners’ harvest intentions; and (3) a causal link between invasive insects and the quantity and sizes of harvested trees. Understanding such relationships benefits policymakers, forest managers, and urban planners tasked with optimizing human-natural systems.

Integration of disparate research fields has become a major concern in recent years due to the increasing complexity of the issues that face policy makers and researchers. Concerted efforts have therefore been initiated to remove the traditional barriers between research fields to allow for greater cooperation between policy makers and researchers, particularly in the fields of health, the environment, and development. The Bridge Collaborative is one such organization dedicated to facilitating this process through the use of results chains. However, because of a lack of experimental data or observational datasets traditionally endemic to interdisciplinary policy research, they lack an effective mechanism for analyzing causal dependence among network variables. The purpose of this thesis is therefore to create a method of analyzing causal relationships using expert knowledge that can still pass the rigorous tests necessary to assert causality in the traditional experimental and observational data approaches. Building upon previous work of statisticians, philosophers, and computer scientists, I create a question template that will allow a researcher to easily check and refine a causal network and explore alternatives to that network based on experience and elicited expert judgement alone. I then perform a case study using this template based on the work of the Food-Energy-Water (FEW) Catalyst project, a group initiative within the Bridge Collaborative, to review a causal network based on a systematic literature search. I conclude that a causal network can indeed be constructed, explored, and adjusted using logical reasoning and expert judgement—a finding that has implications for researchers seeking to create reliable models using causal networks as their base.

This dissertation is primarily concerned with the development and application of statistical models for analyzing ecological and hydrological data. A key technical achievement contained within is the deployment of Markov chain Monte Carlo methods leveraging log posterior gradients for dramatically speeding up inference for hybrid empirical-mechanistic models as well as for analyses of large ecological datasets. In order to utilize such methods, the environmental models had to be implemented in automatic differentiation frameworks originally designed for optimization of deep neural networks. These models are highly challenging to use with previously existing methods such as the Gibbs sampler and random walk Metropolis sampling. These findings enable the enumeration and estimation of an enormous variety of new models spanning a range of process specificities from purely empirical to purely mechanistic forms, all within the same coherent joint parameter estimation framework. Additionally, these methods were employed in an analysis of ongoing changes in hydrology in the Prairie Pothole Region of North America (PPR). Key contributions from this analysis include the identification of a major structural shift in the number and geometry of ponds and wetlands in the PPR likely exacerbated by shifts in agricultural practices. Observational data from this region were used to develop and assess the performance of the first Bayesian model of upland-embedded wetland water volumes. The utility of this approach is shown by conducting inference of model parameters using biased and highly noisy calibration data derived from remote sensing.

This dissertation contributes to the fields of environmental, natural resource and development economics. It contains three essays, each tackling related but different sets of questions by developing theoretical, analytical and econometric methods for policy relevant analysis. In the first essay I develop theoretical models to discuss how fossil fuel firms may respond to anticipated climate friendly policies by intensifying resource extraction from existing reserve bases (green paradox) and/or by reducing investments in expansion of the pool of extractable reserves. In the second essay I construct theoretical models to discuss the design of institutions for regulation of novel climate altering geoengineering technologies by first exploring the dangers of a lack of carbon policy commitment and then suggesting institutional solutions that draw from the monetary policy literature. Finally in the third essay, I consider the design of a multiple cut-off regression discontinuity design and show how it can be used to answer policy relevant questions in development economics in situations involving multiple treatments and treatment conditions. Collectively, the studies involve theoretical ideas and concepts that help understand the impact of policy uncertainty, think about the design of institutions for policy governance and estimate the impacts of past implemented policies.

The process of translating fundamental environmental research into evidence-based policies meant to protect and manage our ecosystems has long fallen short of expectations. Policymakers’ desires for straightforward solutions with short-term, certain outcomes are constantly at odds with the nature of uncertainty in environmental research and decision modeling. The current methodologies of bridging the science-policy gap by encouraging researchers to communicate the uncertainties more clearly in their models and relate the results of individual studies to the entirety of complex decision-making frameworks are inadequate. Through this thesis, I explore the role of research within the boundary of science and policy and the ways in which we might increase the translation from research to evidence-based policy. Three different model frameworks employ three methodologies to reach this goal (1) Contextualize model outputs in previously existing policy frameworks; (2) Utilize environmental indicators as model outputs that are known to resonate with policy-makers and the public; and, (3) Collaboratively construct model structures to match to the pre-existing mental models of policy and decisionmakers. By understanding the balance between model complexity, uncertainty, and interpretability researchers can better understand how to integrate across research, social, and political boundaries to effectively inform policy.