We propose a multicriteria decision-making framework to support strategic decisions in forest management, taking into account uncertainty due to climate change and sustainability goals. In our setting, uncertainty is modeled by means of climate change scenarios. The decision task is to define a harvest scheduling that addresses, simultaneously, conflicting objectives: the economic value of the strategy, the carbon sequestration, the water use efficiency for biomass production and the runoff water, during the whole planning horizon. While the first objective is a classical managerial one, the later tree objectives aim at ensuring the environmental sustainability of the forest management plan.

The proposed framework is a combination of Goal Programming and Stochastic Programming. Depending on the decision-maker preferences, the model produces harvest scheduling policies that yield different trade-offs among the conflicting criteria. Furthermore, we propose the incorporation of a risk-averse component in order to improve the performance of the obtained policies with respect to their economical value.

This novel approach is tested on a real forest, located in central Portugal, which is comprised of a large number of stands (aggregated into 21 strata), climate change is modeled by 32 scenarios, and a planning horizon of 15 years is considered. The obtained results show the capacity of the designed framework to provide a pool of diverse solutions with different trade-offs among the four criteria, giving to the manager the possibility of choosing a harvesting policy that meets her/his requirements.