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PREFERENCE FOR AND IMPACTS OF DIFFERENT UTILITY-SCALE ENERGY SITING DECISIONS FOR ELECTRICITY GENERATORS IN COASTAL REGIONS

dc.contributor.advisorHoward, Gregory
dc.contributor.authorQuainoo, Ruth
dc.contributor.departmentCoastal Studies
dc.date.accessioned2023-09-14T12:56:58Z
dc.date.available2023-09-14T12:56:58Z
dc.date.created2023-07
dc.date.issued2023-07-13
dc.date.submittedJuly 2023
dc.date.updated2023-09-12T17:47:43Z
dc.degree.departmentCoastal Studies
dc.degree.disciplinePHD-Coastal Resources Mgmt
dc.degree.grantorEast Carolina University
dc.degree.levelDoctoral
dc.degree.namePh.D.
dc.description.abstractBoth site selection and fuel choice decisions for utility-scale generators have important ramifications for the surrounding community. This is especially true in coastal regions, where land is often scarce due to high population densities. This study explores different aspects of site selection and fuel choice for large utility-scale energy generators in three different papers. Chapter 1 explores the public's preference for utility-scale solar energy siting in Rhode Island based on four current land types: agricultural, brownfield, commercial, and forest land. This chapter uses a discrete choice experiment survey to help evaluate how program attributes affect respondent preferences for large utility-scale solar energy siting in Rhode Island. Public's willingness to pay for a large set of solar siting decision attributes such as the size of a solar installation, visibility of solar panels, setback or a minimum distance of the solar panels from property lines, and the probability of residential development was estimated. Chapter 2 uses the hedonic pricing method and spatial difference-in-differences estimators to examine how multiple energy sources (that is, clean or dirty fuel types) impact property values in four East Coastal US states (GA, NC, RI, and SC) using Zillow ZTRAX housing transaction data and Energy Information Administration powerplant data. Geographic Information Systems (GIS) is used to measure the distance from each property to the closest energy generators within the region. The final chapter of this study uses data from Energy Information Administration (EIA) and spatially explicit data on flood risk with a variety of measures from First Street Foundation's Flood Lab to assess the resilience of coastal community energy infrastructure and the flood risk faced by renewable energy infrastructure.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10342/13119
dc.language.isoen
dc.publisherEast Carolina University
dc.subjectDiscrete Choice Experiment
dc.subjectMixed Logit Model
dc.subjectSpatial difference-in-difference
dc.subjectSpatial Merge
dc.subjectEnergy Infrastructure
dc.subjectHousing Transactions
dc.subjectConditional Logit Model
dc.subject.lcshSolar energy industries--Environmental aspects--Rhode Island
dc.subject.lcshSolar energy industries--Environmental aspects--Atlantic Coast (Middle Atlantic States)
dc.subject.lcshElectric generators--Rhode Island
dc.subject.lcshElectric generators--Atlantic Coast (Middle Atlantic States)
dc.subject.lcshFloods--Risk assessment.
dc.subject.lcshHedonic pricing
dc.titlePREFERENCE FOR AND IMPACTS OF DIFFERENT UTILITY-SCALE ENERGY SITING DECISIONS FOR ELECTRICITY GENERATORS IN COASTAL REGIONS
dc.typeDoctoral Dissertation
dc.type.materialtext

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