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Spatial interactions and optimal forest management on a fire-threatened landscape

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  • Lauer, Christopher J.
  • Montgomery, Claire A.
  • Dietterich, Thomas G.

Abstract

Forest management in the face of fire risk is a challenging problem because fire spreads across a landscape and because its occurrence is unpredictable. Accounting for the existence of stochastic events that generate spatial interactions in the context of a dynamic decision process is crucial for determining optimal management. This paper demonstrates a method for incorporating spatial information and interactions into management decisions made over time. A machine learning technique called approximate dynamic programming is applied to determine the optimal timing and location of fuel treatments and timber harvests for a fire-threatened landscape. Larger net present values can be achieved using policies that explicitly consider evolving spatial interactions created by fire spread, compared to policies that ignore the spatial dimension of the inter-temporal optimization problem.

Suggested Citation

  • Lauer, Christopher J. & Montgomery, Claire A. & Dietterich, Thomas G., 2017. "Spatial interactions and optimal forest management on a fire-threatened landscape," Forest Policy and Economics, Elsevier, vol. 83(C), pages 107-120.
    • Handle: RePEc:eee:forpol:v:83:y:2017:i:c:p:107-120
    DOI: 10.1016/j.forpol.2017.07.006
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    References listed on IDEAS

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    Cited by:

    1. Kanieski da Silva, Bruno & Tanger, Shaun & Marufuzzaman, Mohammad & Cubbage, Frederick, 2022. "Perfect assumptions in an imperfect world: Managing timberland in an oligopoly market," Forest Policy and Economics, Elsevier, vol. 137(C).
    2. Massimiliano Agovino & Massimiliano Cerciello & Aniello Ferraro & Antonio Garofalo, 2021. "Spatial analysis of wildfire incidence in the USA: the role of climatic spillovers," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 6084-6105, April.

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