Uncertainty Aversion and the Stochastic Forest Harvesting Problem
49 Pages Posted: 12 Aug 2024
Abstract
We examine uncertainty in a dynamic forestry model where a harvester is unsure about model specification, and where there are both benefits to standing forests and to harvesting. We extend the literature to allow for unknown drift and volatility under several uncertainties, including amenity flows and harvest prices, relaxing usual assumptions of risk aversion and known distributions to cases of uncertainty aversion consistent with pure uncertainty. Our results show that model uncertainty can influence harvesting paths in new ways, showing in some cases that the forest can be depleted or, alternatively, never harvested because of cautionary behavior. We also reexamine two basic arguments in the literature, first, that amenity values always result in less harvesting and higher forest stocks over time, and second, whether higher variances (volatility) in prices lead to lower harvesting as landowners wait for possibly higher future prices. Here we find that conventional wisdom may be flawed. Our new approach is important to the fate of forests considering that climate fluctuations may be associated with collapse of tropical forests.
Keywords: Climate change, pure uncertainty, robust control, stochastic dynamic programming
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- Abstract Views: 64
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