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Economics of Controlling Invasive Species: A Stochastic Optimization Model for a Spatial-dynamic Process

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  • Morteza Chalak
  • Maksym Polyakov
  • David J. Pannell

Abstract

We analyze the dynamic process of invasive-species control in a spatially explicit and stochastic setting. An integer optimization model is applied to identify optimal strategies to deal with invasive species at a steady state. Optimal strategies depend on the spatial location of invasion as well as on stochastic characteristics of spread and control. Previous studies of invasive-species control have been stochastic or spatial, but not both. We model a landscape as consisting of multiple cells, each of which may be subject to border control or eradication within the cell. Optimal strategies from the model are characterized as eradication, containment, or abandonment of control. Representing the rate of species spread as stochastic rather than deterministic results in less-intensive control becoming optimal at equilibrium. The optimal strategy may switch from eradication to containment or from containment to abandonment. If an infestation occurs at the boundary of the region within which it may spread, it is more likely to be optimal to eradicate or contain the species, compared to an infestation in the interior of the region. If the effectiveness of border control is stochastic, then containment is not feasible in the long term, but it is still optimal as a temporary measure in some scenarios.

Suggested Citation

  • Morteza Chalak & Maksym Polyakov & David J. Pannell, 2017. "Economics of Controlling Invasive Species: A Stochastic Optimization Model for a Spatial-dynamic Process," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 99(1), pages 123-139.
    • Handle: RePEc:oup:ajagec:v:99:y:2017:i:1:p:123-139.
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    File URL: http://hdl.handle.net/10.1093/ajae/aaw043
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    Cited by:

    1. Courtois, Pierre & Figuieres, Charles & Mulier, Chloe & Weill, Joakim, 2018. "A Cost–Benefit Approach for Prioritizing Invasive Species," Ecological Economics, Elsevier, vol. 146(C), pages 607-620.
    2. Billionnet, Alain, 2013. "Mathematical optimization ideas for biodiversity conservation," European Journal of Operational Research, Elsevier, vol. 231(3), pages 514-534.
    3. Atallah, Shady S. & Gómez, Miguel I. & Jaramillo, Juliana, 2018. "A Bioeconomic Model of Ecosystem Services Provision: Coffee Berry Borer and Shade-grown Coffee in Colombia," Ecological Economics, Elsevier, vol. 144(C), pages 129-138.
    4. Dai, Bingyan & Gomez, Miguel I. & Fan, Xiaoli & Loeb, Gregory & Shrestha, Binita, 2024. "Cost-effectiveness and Risk Assessment in Integrated Pest Management: The Case of Spotted Wing Drosophila," 2024 Annual Meeting, July 28-30, New Orleans, LA 343531, Agricultural and Applied Economics Association.
    5. Tom Kompas & Pham Van Ha & Hoa-Thi-Minh Nguyen & Graeme Garner & Sharon Roche & Iain East, 2020. "Optimal surveillance against foot-and-mouth disease: A sample average approximation approach," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-21, July.
    6. Sean F. Ellis & Mark Masters & Kent D. Messer & Collin Weigel & Paul J. Ferraro, 2021. "The Problem of Feral Hogs and the Challenges of Providing a Weak‐Link Public Good," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(3), pages 985-1002, September.
    7. repec:ags:aaea22:343531 is not listed on IDEAS

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    JEL classification:

    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics

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