Modeling in Natural Resource Economics
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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 timberland
subject 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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