Adaptive economic and ecological forest management under risk

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.

A climate change adaptive dynamic programming approach to optimize eucalypt stand management scheduling: a Portuguese application

“…Addressing risk and uncertainty in forest management has also been object of numerous previous studies (e.g., Spring et al 2008, Zhou and Buongiorno 2011, Couture and Reynaud 2011Ferreira et al 2012;Buongiorno and Zhou 2015). Segura et al (2014) highlighted the use of DP models to optimize forest management planning.…”

Risk aversion and risk seeking in multicriteria forest management: a Markov decision process approach

“…MDPs have been effective in modeling forest ecosystems subject to the uncertainty of forest growth and D r a f t wood markets. In particular, linear programming solutions of MDPs allow for multiple economic and ecological goals with the objective function and/or constraints (Buongiorno and Zhou, 2015). However, most of the published applications consider only the expected value of criteria and ignore their variance.…”

Optimal forest management under financial risk aversion with discounted Markov decision process models

“…Management consistent with risk aversion that reduced the variance of the financial NPV by half led to a near 60% lower expected NPV of financial returns. Previous studies have shown that the current actual management generates approximately 25% of the maximum possible NPV (Buongiorno and Zhou 2015). Zhou (2017) attributes this difference between actual and potential revenues to the amenity value of the standing trees.…”

“…There is still no consensus on whether the current and future environment and nature should have different weights and what should be the discount factor, if any (Arrow et al 1996(Arrow et al , 2014Portney and Weyant 1999;Goulder and Stavins 2002;Hardisty and Weber 2009;Harrison 2010). Here, as in some previous studies (e.g., Buongiorno and Zhou 2015), each ecological criterion such as tree species diversity was measured with its undiscounted expected value over an infinite horizon, a value directly comparable with its current value. Consequently, only the long-term expected value of ecological criteria was considered in this study, in view of tracing the consequences of financial objectives and financial risk aversion on the long-term ecological condition of the forest.…”