IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-03169348.html

Optimal Renewable Resource Harvesting model using price and biomass stochastic variations: A Utility Based Approach

Author

Listed:
  • Gaston Clément Nyassoke Titi

    (Université de Douala)

  • Jules Sadefo-Kamdem

    (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier)

  • Louis Aimé Fono

    (Université de Douala)

Abstract

In this article, we provide a general framework for analyzing the optimal harvest of a renewable resource(i.e. fish, shrimp) assuming that the price and biomass evolve stochastically and harvesters have a constantrelative risk aversion (CRRA) . In order to take into account the impact of a sudden change in the environ-ment linked to the ecosystem, we assume that the biomass are governed by a stochastic differential equationof the ‘Gilpin-Ayala' type, with regime change in the parameters of the drift and variance. Under the aboveassumptions, we find the optimal effort to be deployed by the collector (fishery for example) in order tomaximize the expected utility of its profit function. To do this, we give the proof of the existence anduniqueness of the value function, which is derived from the Hamilton-Jacobi-Bellman equations associatedwith this problem, by resorting to a definition of the viscosity solution.

Suggested Citation

  • Gaston Clément Nyassoke Titi & Jules Sadefo-Kamdem & Louis Aimé Fono, 2022. "Optimal Renewable Resource Harvesting model using price and biomass stochastic variations: A Utility Based Approach," Post-Print hal-03169348, HAL.
    • Handle: RePEc:hal:journl:hal-03169348
    DOI: 10.1007/s00186-022-00782-0
    Note: View the original document on HAL open archive server: https://hal.science/hal-03169348v3
    as

    Download full text from publisher

    File URL: https://hal.science/hal-03169348v3/document
    Download Restriction: no
    File URL: https://libkey.io/10.1007/s00186-022-00782-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. is not listed on IDEAS
    2. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-03169348. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.