Downscaling of Long-Term Global Scenarios to Regions with a Forest Sector Model

Eriksson, L.O., Forsell, N., Eggers, J., & Snäll, T. (2020). Downscaling of Long-Term Global Scenarios to Regions with a Forest Sector Model. Forests 11 (5) e500. 10.3390/f11050500.

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Abstract

Research Highlights:
Long-term global scenarios give insights on how social and economic developments and international agreements may impact land use, trade, product markets, and carbon balances. They form a valuable basis for forming national forest policies. Many aspects related to long-term management of forests and consequences for biodiversity and ecosystem services can only be addressed at regional and landscape levels. In order to be attended to in the policy process, there is a need for a method that downscales national scenarios to these finer levels.

Background and Objectives:
Regional framework conditions depend on management activities in the country as a whole. The aim of this study is to evaluate the use of a forest sector model (FSM) as a method for downscaling national scenarios results to regional level. The national FSM takes the global scenario data (e.g., harvest level and market prices over time) and solves the national problem. The result for the region of interest is taken as framework conditions for the regional study.

Materials and Methods:
Two different specifications are tested. One lets product volumes and prices represent endogenous variables in the FSM model. The other takes volumes and prices from the global scenario as exogenous parameters. The first specification attains a maximum net social payoff whereas the second specification means that net present value is maximized under a harvest constraint.

Results:
The maximum net social payoff specification conforms better to economic factors than the maximum net present value specification but could give national harvest volume trajectories that deviates from what is derived from the global model. This means that regional harvest activity can deviate considerably from the national average, attesting to the benefit of the use of the FSM-based method

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