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Most European countries have signed the United Nations Framework Convention on climate change and its Kyoto Protocol. Because the European Union is a party to the convention just like the individual countries, there is a need for harmonizing emissions reporting. This specifically applies to the Land Use, Land-Use Change, and Forestry sector, for which harmonized reporting is complex and generally challenging. For example, parties use a variety of different methods for estimating emissions and removals, ranging from application of default factors to advanced methods adapted to national circumstances, such as ongoing field inventories. In this study, we demonstrate that without harmonization, national definitions and methods lead to inconsistent estimates. Based on case studies in Finland, Germany, Norway, Portugal, Slovenia, and Sweden, we conclude that common reference definitions and country-specific bridges are means to harmonize the estimates and make greenhouse gas reporting from forests comparable across countries

Under the United Nations Framework Convention for Climate Change all Parties have to report on carbon emissions and removals from the forestry sector. Each Party can use its own approach and country specific data for this. Independently, large-scale models exist (e.g. EFISCEN and G4M as used in this study) that assess emissions and removals from this sector by applying a unified approach to each country, still often based on country specific data. Differences exist between the national reported values and the calculations from the large scale models. This study compares these models with national reporting efforts for 24 EU countries for the period 2000–2008, and identifies the most likely causes for differences. There are no directly identifiable single input parameters that could be targeted to fully close the gap between country and model estimates. We found that the method applied by the country (i.e. stock-difference or gain-loss) contributes significantly to differences for EFISCEN and was the best explaining variable for G4M, although for the latter it was not significant. Other variables (biomass expansion factors, harvest volumes and the way harvest losses are treated) were not found to provide a conclusive explanation for the differences between the model estimations and the country submissions in an over-all.