A non-stochastic portfolio model for optimizing the transformation of an even-aged forest stand to continuous cover forestry when information about return fluctuation is incomplete
A non-stochastic portfolio model for optimizing the transformation of an even-aged forest stand to continuous cover forestry when information about return fluctuation is incomplete
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当收益波动信息不完整时，优化同龄林地向连续覆盖林地转变的非随机组合模型
DOI：
10.1007/s13595-017-0643-0
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发表时间：
2017
影响因子：
3
通讯作者：
Pretzsch
中科院分区：
文献类型：
--
作者：
Messerer;Pretzsch
关键信息当可用统计数据不完整时，林分的非随机组合优化为随机均值方差优化提供了良好的替代方案。所提出的方法具有鲁棒优化、连续多标准决策和模糊理论领域的理论背景。与随机优化的有效前沿相比，由此产生的稳健投资组合仅显示出轻微的经济损失。上下文解决混合不均匀年龄林分多样化的经济优化对于森林规划者来说是一个有用的工具。目的该研究旨在比较两种优化风险下轮作年龄组投资组合的方法。轮龄队列是根据对两种树种模拟的基于年龄的再生收获操作而产生的：冷杉和水青冈。方法第一种优化方法是随机均值方差方法。第二种是非随机优化方法，很少应用于优化树种组成和多个时期采伐木材的分布。它的目标是相对较好的解决方案，即使与最初假设的回报的偏差非常高。两种方法的目标函数对于林地不同部分的不同收获期的选择都很敏感。对于随机方法，目标函数通过将面积比例分配给采伐期和树种，最大化特定风险水平的年化净现值（经济回报）。在非随机方法中，面积比例的分配反而最大限度地减少了许多不确定情景（非随机方法）中与最大可能经济回报的最大偏差。结果两种方法的投资组合在轮换年龄组中是不同的。与显示相同标准差的有效前沿投资组合相比，非随机投资组合更加多样化。然而，P.冷杉显然在非随机投资组合中占主导地位，而随机投资组合也在更大程度上整合了山毛榉，但仅限于风险非常低的投资组合。与均值方差方法的有效边界相比，非随机投资组合的经济损失仅在不同的可接受风险水平下才在 1% 到 3% 之间。 结论 迄今为止，在大不确定性空间上的非随机投资组合优化在森林科学中并不常见，但却为随机优化提供了一种可行的替代方案，特别是在可用数据稀缺的情况下。然而，进一步的研究应考虑生态影响，例如混合林中针叶树对危害的抵抗力增强。
Key messageNon-stochastic portfolio optimization of forest stands provides a good alternative to stochastic mean-variance optimization when available statistical data is incomplete. The suggested approach has a theoretical background in the areas of robust optimization, continuous multicriteria decision-making, and fuzzy theory. Resulting robust portfolios only show slight economic losses compared to the efficient frontier of a stochastic optimization.ContextEconomic optimization addressing diversification in mixed uneven-aged forest stands is a useful tool for forest planners.AimsThe study aims to compare two approaches for optimizing rotation age cohort portfolios under risk. Rotation age cohorts emerge from age-based regeneration-harvesting operations simulated for two tree species:Picea abiesandFagus sylvatica.MethodsThe first optimization approach is a stochastic mean-variance approach. The second is a non-stochastic optimization approach, which has rarely been applied to optimize tree species composition and the distribution of harvested timber over many periods. It aims at relatively good solutions, even if the deviation from the initially assumed return is very high. The objective function for both approaches is sensitive to the selection of various harvesting periods for different parts of the stand. For the stochastic approach, the objective function maximizes the annuitized net present value (economic return) for specific levels of risk by allocating area proportions to harvesting periods and tree species. In the non-stochastic approach, the allocation of area proportions instead minimizes the maximum deviation from the greatest possible economic return among many uncertainty scenarios (non-stochastic approach).ResultsPortfolios from both approaches were diverse in rotation age cohorts. The non-stochastic portfolios were more diverse when compared with portfolios from the efficient frontier, which showed the same standard deviation. However,P. abiesclearly dominated the non-stochastic portfolios, while stochastic portfolios also integrated beech to a greater extent, but only in very low risk portfolios. The economic losses of the non-stochastic portfolios compared to the efficient frontier of the mean-variance approach lay between 1 and 3% only for different levels of accepted risk.ConclusionThe non-stochastic portfolio optimization over a large uncertainty space is so far uncommon in forest science, yet provides a viable alternative to stochastic optimization, particularly when available data is scarce. However, further research should consider ecological effects, such as increased resistance against hazards of conifers in mixed stands.
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影响因子：
2.2
作者：
C. Palma;J. Nelson
通讯作者：
C. Palma;J. Nelson
混交林和灵活的采伐政策：传统风险分析的问题吗？
影响因子：
2.8
作者：
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影响因子：
2.8
作者：
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通讯作者：
Griess
雅科夫·本·海姆 (Yakov Ben-Haim)，《信息差距经济学：操作介绍》，Palgrave Macmillan (2010) ISBN: 978-0-230-22804-7, 245 页。
DOI：
--
发表时间：
2011
期刊：
影响因子：
--
作者：
T. Knoke
通讯作者：
T. Knoke
严重不确定性和信息差距决策理论
DOI：
--
发表时间：
2013
期刊：
影响因子：
--
作者：
K. Hayes;S. Barry;G. Hosack;Gareth W. Peters
通讯作者：
Gareth W. Peters