The greenhouse effect is one of our most severe current environmental problems. Forests make up large ecosystems and can play an important role in mitigating the emissions of CO 2 , the most important greenhouse gas. Different management regimes affect the ability of forests to sequester carbon. It is important to investigate in what way we best can use forests to mitigate the greenhouse effect. It is also important to study what effect different actions, done to increase carbon sequestration, have on other offsets from forestry, such as the harvest level, the availability of forest biofuel and economic factors.In this study, we present an optimization model for analysis of carbon sequestration in forest biomass and forest products at a local or regional scale. The model consists of an optimizing stand-level simulator, and the solution is found using linear programming. Carbon sequestration was accounted for in terms of carbon price and its value computed as a function of carbon price and the net carbon storage in the forest. The same price was used as a cost for carbon emission originating from deterioration of wood products.We carried out a case study for a 3.2 million hectare boreal forest region in northern Sweden. The result showed that 1.48-2.05 million tonnes of carbon per year was sequestered in the area, depending on what carbon price was used. We conclude that assigning carbon storage a monetary value and removal of carbon in forest products as a cost, increases carbon sequestration in the forest and decreases harvest levels. The effect was largest in areas with low site-quality classes.

Highlights• Growth models based on historical growth data gave reliable growth predictions up to the century shift.• Detailed single tree growth models had lower precision for estimation of total growth than one single stand-based model. • The prediction error was in average about 15% and did not increase with extended prediction period. AbstractThe performance of growth models implemented in the Swedish Forest Planning System Heureka was evaluated. Four basal area growth models were evaluated by comparing their predictions to data from five-year growth records for 1711 permanent sample plots of the National Forest Inventory (NFI-data). Also, two alternative implementations of Heureka, including a combined stand-and tree-level basal area growth model and a single tree-level model, respectively, were evaluated using data from 57 blocks in a thinning experiment (GG-data) involving Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst) in which the trees were monitored for 30 years after the first thinning. The predicted volume growth was also compared to observed values. Growth models based on data from 1970's and 1980's overestimated growth in the NFI test plots from the early 2000's by about 3%. Stand-level models had larger precision than tree-level models. Basal area growth was underestimated in dense NFI-plots and overestimated in non-thinned GG-plots, illustrating an un-solved modelling problem. Basal area growth was overestimated by 2-5% also in the GG-plots over the whole observation period. Volume growth was however accurately predicted for pine and underestimated by 2% for spruce. The relative prediction error did not increase with increasing length of prediction period. Thinning response models calibrated with GG-data worked well in the total application and produced growth levels for different thinning alternatives in line with observations.

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Forests make up large ecosystems and by the uptake of carbon dioxide can play an important role in mitigating the greenhouse effect. In this study, mitigation of carbon emissions through carbon uptake and storage in forest biomass and the use of forest biofuel for fossil fuel substitution were considered. The analysis was performed for a 3.2 million hectare region in northern Sweden. The objective was to maximize net present value for harvested timber, biofuel production and carbon sequestration. A carbon price for build-up of carbon storage and for emissions from harvested forest products was introduced to achieve an economic value for carbon sequestration. Forest development was simulated using an optimizing stand-level planning model, and the solution for the whole region was found using linear programming. A range of carbon prices was used to study the effect on harvest levels and carbon sequestration. At a zero carbon price, the mean annual harvest level was 5.4 million m 3 , the mean annual carbon sequestration in forest biomass was 1.48 million tonnes and the mean annual replacement of carbon from fossil fuel with forest biofuel was 61 000 tonnes. Increasing the carbon price led to decreasing harvest levels of timber and decreasing harvest levels of forest biofuel. Also, thinning activities decreased more than clear-cut activities when the carbon prices increased. The level of carbon sequestration was governed by the harvest level and the site productivity. This led to varying results for different parts of the region.

This study deals with the problem of including the risk of wind damage in longterm forestry management. A model based on Graph-Based Markov Decision Processes (GMDP) is suggested for development of silvicultural management policies. The model can both take stochastic wind events into account and be applied to forest estates containing a large number of stands. The model is demonstrated for a forest estate in southern Sweden. Treatment of the stands according to the management policy specified by the GMDP model increased the expected net present value (NPV) of the whole forest only slightly, less than 2%, under different wind-risk assumptions. Most of the stands were managed in the same manner as when the risk of wind damage was not considered. For the stands that were treated differently, however, the expected NPV increased by 3% to 8%.