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Renewable resource management with environmental prediction: the importance of structural specification

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  • Chris J. Kennedy
  • Edward B. Barbier

Abstract

Environmental variability can substantially influence renewable resource growth, and as the ability to forecast environmental conditions improves, opportunities for adaptive management emerge. Using a stochastic stock‐recruitment model, Costello, et al. show the optimal management response to a prediction of favourable growth conditions is to reduce current harvests. We find this result may be reversed when environmental variability and stock are substitutes in growth, a possibility that has been ignored by resource economists. As an example, we analyze the South Carolina white shrimp fishery, finding the optimal response to a prediction of favourable overwinter conditions is to increase fall harvests. Gestion d'une ressource renouvelable quand il y prévision environnementale : importance des spécifications structurelles. La variabilité de l'environnement peut affecter de manière significative la croissance d'une ressource renouvelable, et, à proportion que l'habileté à prévoir les conditions environnementales, des occasions de gestion qui s'y adaptent émergent. Utilisant un modèle stochastique du type stock‐recrutement, Costello et al. montrent que la gestion optimale face à une prédiction de conditions de croissance favourables serait de réduire les récoltes présentes. On montre que ce résultat peut être inversé quand la variabilité de l'environnement et les stocks sont des substituts dans la croissance – une possibilité que les économistes spécialisés en ressources ont ignoré. On examine, en tant qu'exemple, les pêches de crevettes blanches de la Caroline du Sud, et on montre que la réponse optimale à une prévision de conditions favourables au cours de l'hiver qui vient est d'accroître les récoltes d'automne.

Suggested Citation

  • Chris J. Kennedy & Edward B. Barbier, 2013. "Renewable resource management with environmental prediction: the importance of structural specification," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 46(3), pages 1110-1122, August.
  • Handle: RePEc:wly:canjec:v:46:y:2013:i:3:p:1110-1122
    DOI: 10.1111/caje.12038
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    References listed on IDEAS

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    1. Dudley, Norman & Waugh, Geoffrey, 1980. "Exploitation of a single-cohort fishery under risk: A simulation-optimization approach," Journal of Environmental Economics and Management, Elsevier, vol. 7(3), pages 234-255, September.
    2. Christopher Costello & Stephen Polasky & Andrew Solow, 2001. "Renewable resource management with environmental prediction," Canadian Journal of Economics, Canadian Economics Association, vol. 34(1), pages 196-211, February.
    3. Huang, Ling & Smith, Martin D., 2011. "Management of an annual fishery in the presence of ecological stress: The case of shrimp and hypoxia," Ecological Economics, Elsevier, vol. 70(4), pages 688-697, February.
    4. Reed, William J., 1979. "Optimal escapement levels in stochastic and deterministic harvesting models," Journal of Environmental Economics and Management, Elsevier, vol. 6(4), pages 350-363, December.
    5. Richard M. Adams & Stephen Polasky, 1998. "The Value of El Niño Forecasts in the Management of Salmon: A Stochastic Dynamic Assessment," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(4), pages 765-777.
    6. Ferdinand D. Vinuya, 2010. "Technical efficiency of shrimp fishery in South Carolina, USA," Applied Economics Letters, Taylor & Francis Journals, vol. 17(1), pages 1-5, January.
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    Cited by:

    1. Christopher Costello & Bruno Nkuiya & Nicolas Querou, 2017. "Extracting spatial resources under possible regime shift," Working Papers hal-01615939, HAL.
    2. Strand,Jon & Siddiqui,Sauleh, 2015. "Value of improved information about forest protection values, with application to rainforest valuation," Policy Research Working Paper Series 7423, The World Bank.

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    More about this item

    JEL classification:

    • Q20 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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