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Forecasting Colloquium - Rasoul Yousefpour

Dr. Rasoul Yousefpour
Forstökonomie und Forstplanung
Universität Freiburg

Uncertainties in carbon forestry
Wann 19.11.2019
von 11:15 bis 12:30
Wo FRIAS, Albertstr. 19, Nordflügel
Teilnehmer universitätsoffen / open to university members
Termin übernehmen vCal

Uncertainties in carbon forestry

Forest growth predictions are used to build expectations about the future performance of management decisions. Faustmann land expectation value (LEV) is a widely used criterion in forestry to evaluate a diversity of decision parameters, such as rotation age and thinning regimes incorporating time preferences for money. Most of the predictions and, consequently, expectations are based on emperical knowledge, assuming a steady state in climate and a deterministic forest growth pattern. However, the climate may change to potentially different degrees in the coming decades, causing a dynamic and uncertain forest growth and carbon allocation. Moreover, ”climate smart forestry”, aims for efficient allocation of forest mitigation actions, and can hardly be analysed using empirical models and calls for process-based forest biomass production models. These models include numerous parameters and processes that embody some degree of uncertainty. The uncertainty of these parameters and climate state propagates over time to the final decision about carbon budget and optimal management solutions. Here we quantify this uncertainty using Bayesian inference and apply different climate change scenarios and discount rates in the contexts of European forestry. The results show a strong influence of the model’s parameters uncertainty on the final decisions about timber and carbon economy. The uncertainty triples if different climate change scenarios are applied as a source of deep uncertainty where no probability can be assigned to any scenario. To deal with deep uncertainty, a robust decison-making approach has been applied to find solutions with minimum regret or maximum value at risk regarding all scenarios.