Document Type
Article
Publication Date
2015
College/Unit
Davis College of Agriculture, Natural Resources and Design
Department/Program/Center
Division of Resource Economics & Management
Abstract
Background
Forest managers must deal with inherently stochastic ecological and economic processes. The future growth of trees is uncertain, and so is their value. The randomness of low-impact, high frequency or rare catastrophic shocks in forest growth has significant implications in shaping the mix of tree species and the forest landscape. In addition, the fluctuations of wood prices influence greatly forest revenues.
Methods
Markov decision process models (MDPs) offer a rigorous and practical way of developing optimum management strategies, given these multiple sources of risk.
Results
Examples illustrate how such management guidelines are obtained with MDPs for combined ecological and economic objectives, including diversity of tree species and size, landscape diversity, old growth preservation, and carbon sequestration.
Conclusions
The findings illustrate the power of the MDP approach to deal with risk in forest resource management. They recognize that the future is best viewed in terms of probabilities. Given these probabilities, MDPs tie optimum adaptive actions strictly to the state of the forest and timber prices at decision time. The methods are theoretically rigorous, numerically efficient, and practical for field implementation.
Digital Commons Citation
Buongiorno, Joseph and Zhou, Mo, "Adaptive economic and ecological forest management under risk" (2015). Faculty & Staff Scholarship. 2252.
https://researchrepository.wvu.edu/faculty_publications/2252
Source Citation
Buongiorno, J., Zhou, M. Adaptive economic and ecological forest management under risk. For. Ecosyst. 2, 4 (2015). https://doi.org/10.1186/s40663-015-0030-y
Comments
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0), which permits use, duplication, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.