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Optimal fishery harvesting rules under uncertainty

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  • Sarkar, Sudipto

Abstract

This paper derives the optimal fishery harvest policy in a real-option model with a stochastic logistic growth process, harvest-sensitive output price, and both fixed and variable harvesting costs. The policy specifies the harvest trigger and harvest size, while outputs from the model include the value of the fishery and the risk of extinction. The optimal policy is illustrated with data from the Pacific Halibut Fishery. For this particular case, the optimal policy recommends harvesting when the fish stock rises to about three-quarters the environmental carrying capacity, and the amount harvested should be approximately a quarter of the prevailing stock. This harvesting policy maximizes the value of the fishery, and importantly, the resulting risk of extinction is negligible. We also carry out some sensitivity analysis to see how the optimal policy (and the resulting fishery value and risk of extinction) change when the input parameters are varied, particularly the ecological parameters intrinsic growth rate and volatility of the stock, and also the economic parameters that have been ignored in previous papers (price sensitivity and fixed cost). If the optimal policy is followed, the risk of extinction will be negligible, except for very low growth rate and high volatility.

Suggested Citation

  • Sarkar, Sudipto, 2009. "Optimal fishery harvesting rules under uncertainty," Resource and Energy Economics, Elsevier, vol. 31(4), pages 272-286, November.
  • Handle: RePEc:eee:resene:v:31:y:2009:i:4:p:272-286
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    References listed on IDEAS

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    1. Arantza Murillas & José Manuel Chamorro, 2006. "Valuation and Management of Fishing Resources Under Price Uncertainty," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 33(1), pages 39-71, January.
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    Cited by:

    1. Sturla Furunes Kvamsdal & Diwakar Poudel & Leif Kristoffer Sandal, 2016. "Harvesting in a Fishery with Stochastic Growth and a Mean-Reverting Price," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 63(3), pages 643-663, March.
    2. Poudel, Diwakar & Sandal, Leif K., 2014. "Stochastic Optimization for Multispecies Fisheries in the Barents Sea," Discussion Papers 2014/2, Norwegian School of Economics, Department of Business and Management Science.
    3. Engelen, Peter-Jan & Kool, Clemens & Li, Ye, 2016. "A barrier options approach to modeling project failure: The case of hydrogen fuel infrastructure," Resource and Energy Economics, Elsevier, vol. 43(C), pages 33-56.
    4. Poudel, Diwakar & Sandal, Leif K. & Kvamsdal, Sturla F., 2012. "Analyzing Risk of Stock Collapse in a Fishery under Stochastic Profit Maximization," Discussion Papers 2012/4, Norwegian School of Economics, Department of Business and Management Science.
    5. C.E. Dangerfield & A.E. Whalley & Nick Hanley & C.A. Gilligan, 2016. "What a difference a stochastic process makes: epidemiological-based real options models of optimal treatment of disease," Discussion Papers in Environment and Development Economics 2016-03, University of St. Andrews, School of Geography and Sustainable Development.
    6. repec:eee:eneeco:v:67:y:2017:i:c:p:1-16 is not listed on IDEAS
    7. Y. Li & C.J.M. Kool & P.J. Engelen, 2016. "Hydrogen-Fuel Infrastructure Investment with Endogenous Demand : A Real Options Approach," Working Papers 16-12, Utrecht School of Economics.

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