Abstract
BACKGROUND: Finland's national Climate Act contains a target for carbon neutrality by 2035. Achieving this target not only depends on the effective implementation of emission reductions, but to a large part on the forest carbon sink. A recent publication of the Government's analysis, assessment, and research activities highlights a potential disparity in forest land greenhouse gas (GHG) balance estimates by the ex-ante scenario model used in the National Energy and Climate Plan (NECP), and the ex-post GHG inventory methodology used for creating an official record of emissions and removals. Better methodological compatibility is needed to answer a key question: How large will the forest carbon sink be in different scenarios? This study is a first attempt to show the usefulness of applying the GHG inventory calculation approach to predict the forest carbon sink. RESULTS: In this study, we introduce a tool that can be used to estimate the GHG balance for forest land, what we call a "synthetic inventory", and validate it by comparing outputs against historical data reported in Finland's GHG inventory. Second, we use it to predict GHG balances in year leading up to 2035 at various roundwood and forest residue harvest rates. The tool can replicate forest GHG balances for forest land with an average annual error of 1.0 Mt CO(2), representing 4% of the average annual forest carbon sink. We estimate the forest GHG balance in 2035 to be around 3, -15, -32 Mt CO(2)eq at levels of total annual drain 92, 80, 70 Mm(3) respectively. CONCLUSIONS: According to our calculations the forest land net GHG balance in 2035 is approximately 12 Mt CO(2)eq higher than what is presented in Finland's NECP. Conceptual differences between how GHGI methodologies and scenario models estimate living biomass gains and losses contribute to this outcome, in addition to uncertainties associated with both approaches. The tool presented here shows agreement with the National Inventory Report 2023 approach for forest land, and it can be quickly updated to fit new data.