Info-Gap Decision Theory for Assessing the Management of Catchments for Timber Production and Urban Water Supply
Date
2007
Authors
McCarthy, Michael
Lindenmayer, David B
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Volume Title
Publisher
Springer
Abstract
While previous studies have examined how forest management is influenced by the risk of fire, they rely on probabilistic estimates of the occurrence and impacts of fire. However, nonprobabilistic approaches are required for assessing the importance of fire risk when data are poor but risks are appreciable. We explore impacts of fire risk on forest management using as a case study a water catchment in the Australian Capital Territory (ACT) (southeastern Australia). In this forested area, urban water supply and timber yields from exotic plantations are potential joint but also competing land uses. Our analyses were stimulated by extensive wildfires in early 2003 that burned much of the existing exotic pine plantation estate in the water catchment and the resulting need to explore the relative economic benefits of revegetating the catchment with exotic plantations or native vegetation. The current mean fire interval in the ACT is approximately 40 years, making the establishment of a pine plantation economically marginal at a 4% discount rate. However, the relative impact on water yield of revegetation with native species and pines is very uncertain, as is the risk of fire under climate change. We use info-gap decision theory to account for these nonprobabilistic sources of uncertainty, demonstrating that the decision that is most robust to uncertainty is highly sensitive to the cost of native revegetation. If costs of native revegetation are sufficiently small, this option is more robust to uncertainty than revegetation with a commercial pine plantation.
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Keywords
Keywords: Economic return; Info gap decision theory; Nonprobabilistic approaches; Potential joint; Catchments; Data acquisition; Forestry; Revegetation; Stochastic models; Timber; Uncertainty analysis; Water supply; Decision theory; coniferous tree; forest manageme Economic return; Info-gap decision theory; Stochastic model; Uncertainty
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Source
Environmental Management (New York)
Type
Journal article
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2037-12-31