Stochastic-dynamic modelling of farm-level investments under uncertainty. (May 2020)

• Record Type: Journal Article
• Authors: Spiegel, Alisa; Britz, Wolfgang; Djanibekov, Utkur; Finger, Robert
• Is Part Of: Environmental modelling & software. Volume 127(2020)
• Publication Date: 2020-05
• Subjects: Investment decision -- Real options -- Monte Carlo simulation -- Stochastic programming -- Perennial crop; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104656
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: We offer a new method of spatial and temporal optimization of natural resource use. We use Monte Carlo simulation, scenario tree reduction and stochastic programming. The method allows for multiple uncertainties, non-linearity, various restrictions. The method ensures computational efficiency while being tractable and intuitive. The method produces comprehensive results and allows risk analyses.