The EMODnet (European Marine Observation and Data network) Geology project (http://www.emodnet-geology.eu/) collects and harmonizes marine geological data from the European sea areas to support decision- making and sustainable marine spatial planning. The partnership includes 36 marine organizations from 30 countries. The partners, mainly from the marine departments of the geological surveys of Europe (through the Association of European Geological Surveys-EuroGeoSurveys), have assembled marine geological information at a scale of 1:1 000 000 from all European sea areas (e.g. the White Sea, Baltic Sea, Barents Sea, the Iberian Coast, and the Mediterranean Sea within EU waters). This data includes the EMODnet seabed substrate map at a scale of 1:1 000 000 from the European marine areas. Traditionally, European countries have conducted their marine geological surveys according to their own national standards and classified substrates on the grounds of their national classification schemes. These national classifications are harmonized into a shared EMODnet schema using Folk's sediment triangle with a hierarchy of 16, 7 and 5 substrate classes. The data describes the seabed substrate from the uppermost 30 cm of the sediment column. The data has been generalized into a target scale (1:1 000 000). The smallest cartographic unit within the data is 4 km2. Further information about the EMODnet- Geology project is available on the portal (http://www.emodnet- geology.eu/).

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 Finnish Forest Research Institute (Metla) developed a method called multi-source national forest inventory (MS-NFI). The first operative results were calculated in 1990. The first country level estimates correspond to years 1990-1994. Small area forest resource estimates, in here municipality level estimates, and estimates of variables in map form are calculated using field data from the Finnish national forest inventory, satellite images and other digital georeferenced data, such as topographic database of the National Land Survey of Finland. Nine sets of estimates have been produced for the most part of the country until now and eight sets for Lapland. These three themes have been produced for production of the CORINE2006. The products cover the combined land categories forest land, poorly productive forest land and unproductive land. The other land categories as well as water bodies have been delineated out using the elements of the topographic database of the Land Survey of Finland. The original map data can be downloaded from http://kartta.luke.fi/

The Finnish Forest Research Institute (Metla) developed a method called multi-source national forest inventory (MS-NFI). The first operative results were calculated in 1990. Small area forest resource estimates, in here municipality level estimates, and estimates of variables in map form are calculated using field data from the Finnish national forest inventory, satellite images and other digital georeferenced data, such as topographic database of the National Land Survey of Finland. Nine sets of estimates have been produced for the most part of the country until now and eight sets for Lapland. The number of the map form themes in the most recent version, from year 2019, is 45. In addition to the volumes by tree species and timber assortments, the biomass by tree species groups and tree compartments have been estimated. The first country level estimates correspond to years 1990-1994. The most recent versions are from years 2005, 2007, 2009, 2011, 2013, 2015, 2017 and 2019. The maps from 2019 is the fifth set of products freely available. It is also the fourth set produced by the Natural Resources Institute Finland. A new set of the products will be produced annually or biannually in the future. The maps are in a raster format with a pixel size of 16m x 16m (from 2013) and in the ETRS-TM35FIN coordinate system. The products cover the combined land categories forest land, poorly productive forest land and unproductive land. The other land categories as well as water bodies have been delineated out using the elements of the topographic database of the Land Survey of Finland.

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.