Economics at your fingertips  

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

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.

Keywords: Wildland fire; Spatial; Ecological disturbance; Risk; Approximate dynamic programming; Reinforcement learning; Forestry (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Forest Policy and Economics is currently edited by M. Krott

More articles in Forest Policy and Economics from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2018-12-07
Handle: RePEc:eee:forpol:v:83:y:2017:i:c:p:107-120