Renewable Resource Management with Environmental Prediction
By Christopher Costello, Stephen Polasky and Andrew Solow
ABSTRACT
Variations in environmental conditions affect renewable resource growth. The ability to predict such variations is improving, providing scope for improved management. We generalize a common stochastic stock recruitment model to explore how optimal management changes with environmental prediction. We obtain three main results. First, while it might seem that a prediction of adverse future conditions should lead to more conservative management, the opposite may be true. Second, optimal management may only require a one period ahead forecast, suggesting forecast accuracy is more important than forecast lead time. Finally, we derive conditions on environmental fluctuations guaranteeing positive optimal harvest in every period.
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