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Harvesting in a Fishery with Stochastic Growth and a Mean-Reverting Price

Author

Listed:
  • Sturla Kvamsdal
  • Diwakar Poudel
  • Leif Sandal

Abstract

We analyze a continuous, nonlinear bioeconomic model to demonstrate how stochasticity in the growth of fish stocks affects the optimal exploitation policy when prices are stochastic, mean-reverting and possibly harvest dependent. Optimal exploitation has nonlinear responses to the price signal and should be conservative at low levels of biological stochasticity and aggressive at high levels. Price stochasticity induces conservative exploitation with little or no biological uncertainty, but has no strong effect when the biological uncertainty is larger. We further observe that resource exploitation should be conservative when the price reverts slowly to the mean. Simulations show that, in the long run, both the stock level and the exploitation rate are lower than in the deterministic solution. With a harvest-dependent price, the long-run price is higher in the stochastic system. The price mean reversion rate has no influence on the long-run solutions. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • Sturla Kvamsdal & Diwakar Poudel & Leif 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.
  • Handle: RePEc:kap:enreec:v:63:y:2016:i:3:p:643-663
    DOI: 10.1007/s10640-014-9857-x
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    Citations

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    Cited by:

    1. Muhammad Mohsin & Yin Hengbin & Zhang Luyao & Li Rui & Qian Chong & Ana Mehak, 2022. "An Application of Multiple-Criteria Decision Analysis for Risk Prioritization and Management: A Case Study of the Fisheries Sector in Pakistan," Sustainability, MDPI, vol. 14(14), pages 1-21, July.
    2. Nævdal, Eric, 2023. "Biological productivity and optimal harvesting of a biological resource — An uncomplicated exercise in comparative dynamics," Economics Letters, Elsevier, vol. 226(C).
    3. Jose Pizarro & Eduardo Schwartz, 2021. "Fisheries Optimal Harvest Under Price and Biomass Uncertainty," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 78(1), pages 147-175, January.
    4. Zhao, Yu & Yuan, Sanling, 2017. "Optimal harvesting policy of a stochastic two-species competitive model with Lévy noise in a polluted environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 477(C), pages 20-33.
    5. Ana Mehak & Yongtong Mu & Muhammad Mohsin & Xing-Can Zhang, 2023. "MCDM-Based Ranking and Prioritization of Fisheries’ Risks: A Case Study of Sindh, Pakistan," Sustainability, MDPI, vol. 15(11), pages 1-21, May.
    6. Jules Selles, 2018. "Fisheries management: what uncertainties matter?," Working Papers hal-01824238, HAL.
    7. Gaston Clément Nyassoke Titi & Jules Sadefo-Kamdem & Louis Aimé Fono, 2020. "Fishery Management in a Regime Switching Environment: Utility Based Approach," Working Papers hal-02433395, HAL.

    More about this item

    Keywords

    Feedback policy; Fisheries management; Hamilton–Jacobi–Bellman approach; Mean-reversion; Stochastic optimization; C61; Q22; Q57;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q22 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Fishery
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics

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