In the light of uncertainties, high initial costs, and temporal managerial flexibility, the real options approach has gained interest as a valuation tool for different types of natural resources management problems. Yet, neither real options valuation method excels under consideration of variability of resource endowments, returns-to-scale and predefined sizes of options. We fill the methodological gap by developing a method based on Monte Carlo simulation, scenario tree reduction, and stochastic programming that is advantageous for valuing real options where timing, scale and interactions among constraints and alternatives matter. The method advances in straightforward conversion of deterministic programming applications based on the classical net present value approach into a real options framework, and in introducing complexity into existing real options models. We illustrate the method with a case study featuring investment options regarding the adoption, coppicing, and conversion of perennial biomass energy production systems.

鉴于不确定性，高昂的初始成本和临时管理的灵活性，实物期权方法作为一种针对不同类型自然资源管理问题的评估工具而引起了人们的兴趣。但是，考虑到资源end赋的可变性，规模收益和预定规模的期权，两种实物期权评估方法都无法胜任。我们通过开发基于蒙特卡洛模拟，方案树还原和随机编程的方法来填补方法论上的空白，这对于评估实物期权非常有利，因为实物期权的时机，规模以及约束与替代方案之间的相互作用至关重要。该方法可以将基于经典净现值方法的确定性编程应用程序直接转换为实物期权框架，并将复杂性引入现有的实物期权模型中。我们以一个案例研究为例来说明该方法，该案例研究包含有关多年生生物质能源生产系统的采用，复制和转换的投资选择。