Spatial interactions and optimal forest management on a fire-threatened landscape
Christopher J. Lauer,
Claire A. Montgomery and
Thomas G. Dietterich
Forest Policy and Economics, 2017, vol. 83, issue C, 107-120
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.
Keywords: Wildland fire; Spatial; Ecological disturbance; Risk; Approximate dynamic programming; Reinforcement learning; Forestry (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:forpol:v:83:y:2017:i:c:p:107-120
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