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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 stockrecruitment 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.

Suggested Citation

  • Chris J. Kennedy & Edward B. Barbier, 2013. "Renewable resource management with environmental prediction: the importance of structural specification," Canadian Journal of Economics, Canadian Economics Association, vol. 46(3), pages 1110-1122, August.
    • Handle: RePEc:cje:issued: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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    Citations

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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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