Incorporating risk in forest sector modeling - state of the art and promising paths for future research

The use of numerical forest sector models (FSM) for economic and policy analyses has strongly increased in the last decades. Nearly all of these models are deterministic; however, long-term market projections are inevitably uncertain. The main objective of this article is to explore the possibilities of introducing risk in such models. For that we (i) review how risk has been incorporated in FSM, forestry and equilibrium models in adjacent sectors (agriculture, fishery, energy) and in macroeconomic models, and (ii) based on the review, identify and discuss promising approaches for including risk in FSM. Rather few large-scale model applications where risks were explicitly included beyond scenario and sensitivity analyses were identified. For incorporating risk in FSM, fuzzy set theory and robust optimization techniques seem promising new approaches, alongside methods that already are in use, like Monte Carlo simulation and, in particular, scenario and sensitivity analysis.