Sustainable yields in fisheries: uncertainty, risk-aversion and mean-variance analysis

Ewald, C.-O. and Wang, W.-K. (2010) Sustainable yields in fisheries: uncertainty, risk-aversion and mean-variance analysis. Natural Resource Modeling, 23(3), pp. 303-323. (doi:10.1111/j.1939-7445.2010.00065.x)

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Abstract

We consider a model of a fishery in which the dynamics of the unharvested fish population are given by the stochastic logistic growth equation: [EQUATION] Similar to the classical deterministic analogon, we assume that the fishery harvests the fish population following a constant effort strategy. In the first step, we derive the effort level that leads to maximum expected sustainable yield, which is understood as the expectation of the equilibrium distribution of the stochastic dynamics. This replaces the nonzero fixed point in the classical deterministic setup. In the second step, we assume that the fishery is risk averse and that there is a tradeoff between expected sustainable yield and uncertainty measured in terms of the variance of the equilibrium distribution. We derive the optimal constant effort harvesting strategy for this problem. In the final step, we consider an approach that we call the mean-variance analysis to sustainable fisheries. Similar as in the now classical mean-variance analysis in finance, going back to Markowitz [1952], we study the problem of maximizing expected sustainable yields under variance constraints, and with this, minimizing the variance, e.g., risk, under guaranteed minimum expected sustainable yields. We derive explicit formulas for the optimal fishing effort in all four problems considered and study the effects of uncertainty, risk aversion, and mean reversion speed on fishing efforts.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Ewald, Professor Christian
Authors: Ewald, C.-O., and Wang, W.-K.
Subjects:H Social Sciences > HB Economic Theory
S Agriculture > SH Aquaculture. Fisheries. Angling
College/School:College of Social Sciences > Adam Smith Business School > Economics
Journal Name:Natural Resource Modeling
ISSN:0890-8575
ISSN (Online):1939-7445
Published Online:26 May 2010

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