Effect of increased wood harvesting and utilization on required greenhouse gas displacement factors of wood-based products and fuels.

A displacement factor (DF) may be used to describe the efficiency of using wood-based products or fuels instead of fossil-based ones to reduce net greenhouse gas (GHG) emissions. However, the DFs of individual products and their production volumes could not be used alone to evaluate the climate impacts of forest utilization. For this reason, in this study we have developed a methodology to assess a required displacement factor (RDF) for all wood products and bioenergy manufactured and harvested in a certain country in order to achieve zero CO2 equivalent emissions from increased forest utilization over time in comparison with a selected baseline harvesting scenario. Input data for calculations were produced with the simulation model, Monsu, capable of predicting the carbon stocks of forests and wood-based products. We tested the calculations in Finnish conditions in a 100-year time horizon and estimated the current average DF of manufactured wood-based products and fuels in Finland for the interpretation of RDF results. The results showed that if domestic wood harvesting will be increased by 17-33% compared to the basic scenario, the RDF will be 2.0 to 2.4 tC tC-1 for increased wood use in 2017-2116. However, the estimated average DF of manufactured wood-based products and fuels currently in Finland was less than 1.1 tC tC-1. The results indicate strongly that the increased harvesting intensity from the current situation would represent a challenge for the Finnish forest-based bioeconomy from the viewpoint of climate change mitigation. For this reason, there is an immediate need to improve reliability and applicability of the RDF approach by repeating corresponding calculations in different circumstances and by improving estimations of DFs on country levels.

A decision support tool to prioritize risk management options for contaminated sites.

The decisions on risk management (RM) of contaminated sites in Finland have typically been driven by practical factors such as time and money. However, RM is a multifaceted task that generally involves several additional determinants, e.g. performance and environmental effects of remediation methods, psychological and social factors. Therefore, we adopted a multi-criteria decision analysis approach and developed a decision support tool (DST) that is viable in decision-making in such a complex situation. The basic components of the DST are based on the Dutch REC system. However, our DST is more case-specific and allows the consideration of the type, magnitude and scale of contamination, land use, environmental conditions and socio-cultural aspects (e.g. loss of cultural heritage, image aspects). The construction of the DST was started by structuring the decision problem using a value tree. Based on this work we adopted the Multi-Attribute Value Theory (MAVT) for data aggregation. The final DST was demonstrated by two model sites for which the RM alternatives and site-specific data were created on the basis of factual remediation projects and by interviewing experts. The demonstration of the DST was carried out in a workshop where representatives of different stakeholders were requested to rank and weight the decision criteria involved. To get information on the consistency of the ranking of the RM alternatives, we used different weighting techniques (ratio estimation and pair-wise weighting) and alternative ways to treat individual respondents' weights in calculating the preference scores for each RM alternative. These dissimilar approaches resulted in some differences in the preference order of the RM alternatives. The demonstration showed that attention has to be paid to the proper description of the site, the principles of the procedure and the decision criteria. Nevertheless, the procedure proved to enable efficient communication between different stakeholders and the identification of the preferred RM option.