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Modeling in Natural Resource Economics
Forecasting Electricity Demand, and SimulatingLandowner Response to Wildfire Risk
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This work presents three modeling techniques applied§to natural resource economics. Chapter I makes use of§ordinary least-squares regression analysis (OLS) to§investigate the effects of climate warming on§electricity demand. Using hourly models and data for§the U.S. mid-Atlantic, the model predicts a 4.6%§§Chapter II offers an introduction to modeling with§artificial neural networks (ANNs) for researchers§familiar with OLS techniques. The general form of an§ANN is shown to be equivalent to a nested series of§OLS models. The forecasting exercise from Chapter I§is recast in the ANN framework to provide an example§of applying ANNs to economic studies, including an§analysis of the forecast residuals. §§Chapter III presents a model of forest-landowner§behavior in the context of managing timberlandsubject to wildfire. The model is used to examine the§impacts of externalities associated with fire risk:§(1) the public-good aspect of privately funded fire§prevention, and (2) moral hazard associated with§publicly funded fire suppression. Chapter III§concludes with a §consideration of policy options for§addressing these externalities.
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