Implementing Faustmann–Marshall–Pressler at Scale: Stochastic Dynamic Programing in Space

The authors construct an intertemporal model of rent-maximizing behavior on the part of a timber harvester under potentially multidimensional risk as well as geographical heterogeneity. Subsequently, the authors use recursive methods (specifically, the method of stochastic dynamic programing) to characterize the optimal policy function – the rent-maximizing timber-harvesting profile. One noteworthy feature of their application to forestry in the province of British Columbia, Canada is the unique and detailed information the authors have organized in the form of a dynamic geographic information system to account for site-specific cost heterogeneity in harvesting and transportation, as well as uneven-aged stand dynamics in timber growth and yield across space and time in the presence of stochastic lumber prices. Their framework is a powerful tool with which to conduct policy analysis at scale.