This assessment was part of project Baltic ForBio funded by the Interreg Baltic Sea Region Programme (https://www.slu.se/en/departments/forest-economics/forskning/research-projects/baltic-forbio/). The project was carried out in 2017-2020. The harvesting potentials in Finland were calculated for the following assortments: • Stemwood for energy from 1st thinnings, pine • Stemwood for energy from 1st thinnings, spruce • Stemwood for energy from 1st thinnings, broadleaved • Stemwood for energy from 1st thinnings (smaller than pulpwood-sized trees), pine • Stemwood for energy from 1st thinnings (smaller than pulpwood-sized trees), spruce • Stemwood for energy from 1st thinnings (smaller than pulpwood-sized trees), broadleaved • Logging residues, pine • Logging residues, spruce • Logging residues, deciduos • Stumps, pine • Stumps, spruce. 1.1 Decision support system used in assessment Regional energywood potentials were calculated with MELA forest planning tool (Siitonen et al. 1996; Hirvelä et al. 2017). 1.2 References and further reading Anttila P., Muinonen E., Laitila J. 2013. Nostoalueen kannoista jää viidennes maahan. [One fifth of the stumps on a stump harvesting area stays in the ground]. BioEnergia 3: 10–11. Anttila P., Nivala V., Salminen O., Hurskainen M., Kärki J., Lindroos T.J. & Asikainen A. 2018. Re-gional balance of forest chip supply and demand in Finland in 2030. Silva Fennica vol. 52 no. 2 article id 9902. 20 p. https://doi.org/10.14214/sf.9902 Hakkila, P. 1978. Pienpuun korjuu polttoaineeksi. Summary: Harvesting small-sized wood for fuel. Folia Forestalia 342. 38 p. Hirvelä, H., Härkönen, K., Lempinen, R., Salminen, O. 2017. MELA2016 Reference Manual. Natural Resources Institute Finland (Luke). 547 p. Hynynen, J., Ojansuu, R., Hökkä, H., Siipilehto, J., Salminen, H. & Haapala, P. 2002. Models for predicting stand development in MELA System. Metsäntutkimuslaitoksen tiedonantoja 835. 116 p. Koistinen A., Luiro J., Vanhatalo K. 2016. Metsänhoidon suositukset energiapuun korjuuseen, työopas. [Guidelines for sustainable harvesting of energy wood]. Metsäkustannus Oy, Helsinki. ISBN 978-952-5632-35-4. 74 p. Mäkisara, K., Katila, M., Peräsaari, J. 2019: The Multi-Source National Forest Inventory of Finland - methods and results 2015. Muinonen E., Anttila P., Heinonen J., Mustonen J. 2013. Estimating the bioenergy potential of forest chips from final fellings in Central Finland based on biomass maps and spatially explicit constraints. Silva Fennica 47(4) article 1022. https://doi.org/10.14214/sf.1022. Natural Resources Institute Finland. 2019. Industrial roundwood removals by region. Available at: http://stat.luke.fi/en/industrial-roundwood-removals-by-region. Accessed 22 Nov 2019. Ruotsalainen, M. 2007. Hyvän metsänhoidon suositukset turvemaille. Metsätalouden kehittämiskeskus Tapio julkaisusarja 26. Metsäkustannus Oy, Helsinki. 51 p. ISBN 978-952-5694-16-1, ISSN 1239-6117. Siitonen M, Härkönen K, Hirvelä H, Jämsä J, Kilpeläinen H, Salminen O et al. 1996. MELA Handbook. 622. 951-40-1543-6. Äijälä, O., Kuusinen, M. & Koistinen, A. (eds.). 2010. Hyvän metsänhoidon suositukset: energiapuun korjuu ja kasvatus. Metsätalouden kehittämiskeskus Tapion julkaisusarja 30. 56 p. ISBN 978-952-5694-59-8, ISSN 1239-6117. Äijälä, O., Koistinen, A., Sved, J., Vanhatalo, K. & Väisänen, P. (eds). 2014. Metsänhoidon suositukset. Metsätalouden kehittämiskeskus Tapion julkaisuja. 180 p. ISBN 978-952-6612-32-4. 2. Output considered in assessment Valid for scenario: Maximum sustainable removal Main output ☒Small-diameter trees ☒Stemwood for energy ☒Logging residues ☒Stumps ☐Bark ☐Pulpwood ☐Saw logs Additional information Stemwood for energy from 1st thinnings. Part of this potential consists of trees smaller than pulpwood size. This part is reported as Small-diameter trees. Forecast period for the biomass supply assessment Start year: 2015 End year: 2044 Results presented for period 2025-2034 3. Description of scenarios included in the assessments Maximum sustainable removal The maximum sustainable removal is defined by maximizing the net present value with 4% discount rate subject to non-declining periodic total roundwood removals, energy wood removals and net incomes, further the saw log removals have to remain at least at the level of the first period. There are no sustainability constraints concerning tree species, cutting methods, age classes or the growth/drain -ratio in order to efficiently utilize the dynamics of forest structure. Energy wood removal can consist of stems, cutting residues, stumps and roots. According to the scenario the total annual harvesting potential of industrial roundwood is 80.7 mill. m3 (over bark) for period 2025-2034. In 2018 removals of industrial roundwood in Finland totaled 68.9 mill. m3 (Natural Resources… 2019). 4. Forest data characteristics Level of detail on forest description ☒High ☐Medium ☐Low NFI data with many and detailed variables down to tree parts. Sample plot based ☒Yes ☐No NFI sample plot data from 2013-2017. Stand based ☐Yes ☒No Grid based ☒Yes ☐No Multi-Source NFI data from 2015 (Mäkisara et al. 2019) utilized when distributing regional potentials to 1 km2 resolution. 5. Forest available for wood supply: Total forest area defined as in: FAO. 2012. FRA 2015, Terms and Definitions. Forest Resources Assessment Working Paper 180. 36 p. Available at: http://www.fao.org/3/ap862e/ap862e00.pdf. Forest and scrub land 22 812 000 ha Forest land 20 278 000 ha and scrub land 2 534 000 ha Forest area not available for wood supply Forest and scrub land 2 979 000 ha Forest land 1 849 000 ha and scrub land 1 130 000 ha Partly available for wood supply Forest and scrub land 2 553 000 ha (includes in FAWS, below) Forest land 1 149 000 ha and scrub land 1 404 000 ha. Forest Available for wood supply (FAWS) Forest and scrub land 19 833 000 ha Forest land 18 429 000 ha and scrub land 1 404 000 ha In MELA calculations all the scrub land belonging to the FAWS belongs to the category “Partly available for wood supply”, but there are no logging events on scrub land regardless or the category. 6. Temporal allocation of fellings Valid for scenario: Maximum sustainable removal Allocation method ☐Optimization based without even flow constraints ☒Optimization based with even flow constraints ☐Rule based with no harvest target ☐Rule based with static harvest target ☐Rule based with dynamic harvest target See item 3 above (max NPV with 4 % discount rate). 7. Forest management Valid for scenario: Maximum sustainable removal Representation of forest management ☐Rule based ☒Optimization ☐Implicit Treatments, among of the optimization makes the selections, are based on management guidelines (e.g. Äijälä etc 2014) 7.2 General assumptions on forest management Valid for scenario: Maximum sustainable removal ☒Complies with current legal requirements ☐Complies with certification ☒Represents current practices ☐None of the above ☐ No information available Forest management follows science-based guidelines of sustainable forest management (Ruotsalainen 2007, Äijälä et al. 2010, Äijälä et al. 2014). 7.3 Detailed assumptions on natural processes and forest management Valid for scenario: Maximum sustainable removal Natural processes ☒Tree growth ☒Tree decay ☒Tree death ☐Other? Tree-level models (e.g. Hynynen et al., 2002). Silvicultural system ☒Even-aged ☐Uneven-aged Click here to enter text. Regeneration method ☒Artificial ☒Natural Regeneration species ☐Current distribution ☒Changed distribution Optimal distribution may differ from the current one. Genetically improved plant material ☐Yes ☒No Cleaning ☒Yes ☐No Thinning ☒Yes ☐No Fertilization ☐Yes ☒No 7.4 Detailed constraints on biomass supply Volume or area left on site at final felling ☒Yes ☐No 5 m3/ha retained trees are left in final fellings. Final fellings can be carried out only on FAWS with no restrictions for wood supply. Constraints for residues extraction ☒Yes ☐No ☐N/A Retention of 30% of logging residues onsite (Koistinen et al. 2016) Constraints for stump extraction ☒Yes ☐No ☐N/A Retention of 16–18% of stump biomass (Muinonen et al. 2013; Anttila et al. 2013) 8. External factors Valid for scenario: Maximum sustainable removal External factors besides forest management having effect on outcomes Economy ☐Yes ☒No Climate change ☐Yes ☒No Calamities ☐Yes ☒No Other external ☐Yes ☒No

The GTK’s Mineral Deposit database contains all mineral deposits, occurrences and prospects in Finland. Structure of the new database was created in 2012 and it is based on global geostan-dards (GeoSciML and EarthResourceML) and classifications related to them. The database is in Oracle, data products are extracted from the primary database. During 2013 GTK’s separate mineral deposit databases (Au, Zn, Ni, PGE, U, Cu, Industrial minerals, FODD, old ore deposit database) were combined into a single entity. New database contains extensive amount of information about mineral occurrence feature along with its associated commodities, exploration activities, holding history, mineral resource and re-serve estimates, mining activity, production and geology (genetic type, host and wall rocks, min-erals, metamorphism, alteration, age, texture, structure etc.) Database will be updated whenever new data (e.g. resource estimate) is available or new deposit is found. Entries contain references to all published literature and other primary sources of data. Also figures (maps, cross sections, photographs etc.) can be linked to mineral deposit data. Data is based on all public information on the deposits available including published literature, archive reports, press releases, companies’ web pages, and interviews of exploration geologists. Database contains 33 linked tables with 216 data fields. Detailed description of the tables and fields can be found in separate document. (http://tupa/metaviite/MDD_FieldDescription.pdf) The data products extracted from the database are available on Mineral Deposits and Exploration map service (http://gtkdata.gtk.fi/MDaE/index.html) and from Hakku -service (http://hakku.gtk.fi).

Sedimentation rates are part of EMODnet (European Marine Observation and Data network) Geology, Work Package 3 (WP3) Seabed substrate. The objective of WP3 is to compile all available seabed substrate information on a scale of 1:100 000 or finer from all European marine areas, and to update sedimentation rate data collected in the previous phases. WP3 has compiled and harmonized available information on the rate of sedimentation on the seafloor. The information on sedimentation rates for recent sediments is presented as point-source information. Estimations of modern sedimentation rates (centimetres/year) can be based e.g. on established historical records of anthropogenic radionuclides (e.g. 137Cs and 241Am), polychlorinated biphenyls (PCBs), lead (Pb) and stable lead isotope (206/207Pb ratios). Sedimentation rate estimations can be based also on varve/laminae counting, radionuclide 210Pb and 14C decay dating methods. In addition stratigraphic marker horizons, like in the Baltic Sea, horizons formed by documented Major Baltic Inflow (MBIs) events (Moros et al. 2017), can be used in the estimations. Project partners have delivered information on sedimentation rates available in their national waters including their EEZ. The focus is on the present-day sedimentation rates. That means sediment accumulation to the seabed over the past decades, since AD 1900 or so. Further information about the EMODnet-Geology project is available on the portal (http://www.emodnet-geology.eu/).

Bedrock of Finland 1:200 000 is a unified bedrock map dataset covering the whole Finland. It has been compiled by generalising the scale-free bedrock map feature dataset. The dataset consists of a lithological/stratigraphic geological unit polygon layer and linear layers, in which faults, diverse overprinting lines and dykes are represented. The dataset also includes an origin of the data and a quality estimation of the data polygon layers. The stratigraphic geological unit polygon layer includes lithological coding, geological time period and hierarchical lithostratigraphical or lithodemic classification as attributes in accordance with the Finnish database for stratigrafic geological units (Finstrati). The line layers have their own hierarchical classification. The data are at 1:200 000 scale, which indicates that the main part of the scale-free data have been generalised to correspond to a product at a scale of 1:200 000. Those areas where the source data is coarser than 1:200 000 have not been generalised. Coordinate reference system of the dataset was transformed in March 2013. The transformation from Finnish National Grid Coordinate System (Kartastokoordinaattijärjestelmä, KKJ) Uniform Coordinate Frame to ETRS-TM35FIN projection was done by using the three-dimensional transformation in accordance with the recommendations for the public administration JHS154.