Natural resources and bioeconomy studies 48/2021 Sustainability Assessment of current and recommended methods TECH4EFFECT project report Diana Tuomasjukka, Michael den Herder, Janni Kunttu, Hernán Serrano León, Christophe Orazio, Venla Wallius, Mercedes Rois, Robert Prinz and Johanna Routa Natural Resources Institute Finland, Helsinki 2021 Natural resources and bioeconomy studies 48/2021 Sustainability Assessment of current and recommended methods TECH4EFFECT project report Diana Tuomasjukka, Michael den Herder, Janni Kunttu, Hernán Serrano León, Christophe Orazio, Venla Wallius, Mercedes Rois, Robert Prinz and Johanna Routa Contributors: Hans Verkerk, Benno Richard Eberhard, Thomas Holzfeind, Karsten Raae, Karol Bronisz, Raffaele Spinelli, Natachia Magagnotti, Giovanna Ottaviani Aalmo and Gernot Erber Recommended citation: Tuomasjukka, D., den Herder, M., Kunttu, J., Serrano León, H., Orazio, C., Wallius, V., Rois, M., Prinz, R. & Routa, J. 2021. Sustainability Assessment of current and recommended methods : TECH4EFFECT project report. Natural resources and bioeconomy studies 48/2021. Natural Re- sources Institute Finland. Helsinki. 112 p. ISBN: 978-952-380-238-4 (Print) ISBN: 978-952-380-239-1 (Online) ISSN 2342-7647 (Print) ISSN 2342-7639 (Online) URN: http://urn.fi/URN:ISBN:978-952-380-239-1 Copyright: Natural Resources Institute Finland (Luke) Authors: Diana Tuomasjukka, Michael den Herder, Janni Kunttu, Hernán Serrano León, Chris- tophe Orazio, Venla Wallius, Mercedes Rois, Robert Prinz and Johanna Routa Publisher: Natural Resources Institute Finland (Luke), Helsinki 2021 Year of publication: 2021 Cover photo: Johanna Routa/Luke Printing house and: publishing sales: Juvenes Print, http://luke.juvenesprint.fi http://urn.fi/URN:ISBN:978-952-380-239-1 Natural resources and bioeconomy studies 48/2021 3 Summary Diana Tuomasjukka1, Michael den Herder1, Janni Kunttu1, Hernán Serrano León1, Christophe Orazio1, Venla Wallius1, Mercedes Rois1, Robert Prinz2 and Johanna Routa2 1European Forest Institute (EFI), Joensuu, Finland 2Natural Resources Institute Finland (Luke), Joensuu, Finland The TECH4EFFECT project (http://www.tech4effect.eu/), funded by the "Bio Based Industries Joint Undertaking under the European Union's Horizon 2020 research and innovation pro- gram", is an international research collaboration of 20 partners from science and industry. The objective of the project is to enhance efficient wood production, by adapting the management of European forests to the requirements of a modern bioeconomy, and to meet new challenges such as climate change. Data and knowledge- based management will enable more efficient silviculture and harvesting, but also reduction of soil and environmental impact from forest operations with the TECH4EFFECT benchmarking system. Within the Tech4Effect project, the baseline reference of current and most common wood value chain practices in major EU regions (Northern, Central, Southern, Eastern EU) from stand re- generation to timber at road side was defined, building on the processes and supply chains gathered in Work Package (WP)5.This was done in a process-based approach, integrating the silvicultural and operational practices with current volumes of growing stock and fellings, cal- culating material flows along those wood value chains and quantifying via a set of indicators their environmental, social and economic performance. In a second step, the TECH4EFFECT scenarios of increased wood mobilization (link to WP2) and improved efficiency (link to WP3) was compared against the baseline. The analysis focused on the study cases analysed in WP2, WP3 & WP4, using the Tool for Sustainability Impact Assessment ToSIA (Lindner et al., 2010). The analysis of the environmental wood chain performance considered greenhouse gas emis- sions (consistent with LCA methodology), energy use, and soil impact indicators. Social impacts were studied in terms of employment effects and occupational safety. The economic perfor- mance of the alternative wood value chains was analysed with cost-benefit analysis. Indicators as well as data needs for calculating these pan-European indicator values was harmonised in close cooperation with WP5. This deliverable report consists of bottom-up upscaling from work studies and case studies to national level for selected representative countries, as well as top-down assessments at EU level and disaggregated impacts for four regions: Northern EU (NEU), Southern EU (SEU), Eastern EU (EEU) and Central EU (CEU). These impacts have been cross referenced to the Tech4Effect goals: Efficiency goals of 20% reduced production costs, 15% reductions in fuel consumptions, less environmental impacts (soil damage) and 2% increased forest (yield) productivity. These goals are discussed per impact category and technological solution. In addition, digital- isation and biofuels are assessed and discussed as options to mobilise timber at reduced en- vironmental impact. Keywords: Scenario, value-chain, material flow, wood harvesting, wood operations, digitaliza- tion Natural resources and bioeconomy studies 48/2021 4 Tiivistelmä Diana Tuomasjukka1, Michael den Herder1, Janni Kunttu1, Hernán Serrano León1, Christophe Orazio1, Venla Wallius1, Mercedes Rois1, Robert Prinz2 and Johanna Routa2 1European Forest Institute (EFI), Joensuu, Finland 2Luonnonvarakeskus (Luke), Joensuu, Finland TECH4EFFECT (http://www.tech4effect.eu/ on kansainvälinen tutkimusyhteistyöhanke, johon osallistuu 20 partneria sekä tutkimuslaitoksia että käytännön toimijoita. Hanketta rahoittaa Bio- based Industries Joint Undertaking (BBI JU). Hankkeen tavoitteena on tehostaa puuntuotantoa mukauttamalla Euroopan metsien hoito nykyaikaisen biotalouden vaatimuksiin ja vastaamaan uusiin haasteisiin, kuten ilmastomuutokseen. Tietoon ja tiedonsiirtoon perustuva metsänkäsit- tely mahdollistaa tehokkaammat metsänhoidon ja puunkorjuun menetelmät, mutta vähentää myös metsänhoitotoimien maaperä- ja ympäristövaikutuksia. TECH4EFFECT-hankkeessa määritettiin ensin nykyisten, yleisimpien hankintaketjujen nykytila keskeisillä EU-alueilla (Pohjois-, Keski-, Etelä- ja Itä-EU) metsänuudistamisesta tienvarteen kor- jattuun puuhun asti. Tämä tehtiin työpaketti 5:ssa kerättyjen prosessien ja arvoketjujen pohjalta hyödyntäen prosessikeskeistä lähestymistapaa, jossa metsänhoito- ja metsänkäsittelymenetel- mät yhdistetään nykyisiin puusto- ja hakkuumääriin. Näiden hankintaketjujen materiaalivirrat laskettiin ja niiden sosiaaliset sekä ympäristö- ja talousvaikutukset määritettiin valittujen indi- kaattorien avulla. Seuraavassa vaiheessa TECH4EFFECT:ssä luotuja skenaarioita puun mobili- soinnin lisäämisestä (Työpaketti 2 ja tehokkuuden kasvattamisesta (Työpaketti 3) verrattiin ny- kytilaan. Analyysi keskittyi työpaketeissa 2, 3 ja 4 tehtyihin case-tutkimuksiin ja siinä hyödyn- nettiin ToSIA-työkalua (Tool for Sustainability Impact Assessment, Lindner et al. 2010). Puuar- voketjujen ympäristövaikutusten analyysi huomioi kasvihuonekaasupäästöt (LCA-metodolo- gian mukaisesti), energiankulutuksen ja maaperävaikutukset. Sosiaalisten vaikutusten arvioin- nissa analysoitiin työllisyysvaikutuksia ja työturvallisuutta. Vaihtoehtoisten arvoketjujen talou- dellisia vaikutuksia selvitettiin kustannus-hyötyanalyysin avulla. Näiden paneurooppalaisten in- dikaattorien laskentaan tarvittava data ja indikaattorit yhdenmukaistettiin yhteistyössä työpa- ketti 5:n kanssa. Tämä raportti koostuu tapaustutkimusten skaalaamisesta kansalliselle tasolle valituissa maissa sekä EU-tason vaikutusten eriyttämisestä neljälle EU-alueelle: Pohjois-, Keski-, Etelä- ja Itä-EU. Vaikutuksia on peilattu TECH4EFFECT-projektin tavoitteisiin: 20 % alhaisemmat tuotantokus- tannukset, 15 % vähennys polttoaineen kulutuksessa, pienemmät ympäristövaikutukset (maa- perän vahingoittuminen) ja 2 % suurempi metsän tuottavuus (saanto). Tavoitteita pohditaan jokaisen vaikutusluokan ja teknologisen ratkaisun osalta. Raportissa käsitellään lisäksi digitali- saation ja biopolttoaineiden mahdollisuuksia puumateriaalin mobilisoimiseksi pienemmillä ympäristövaikutuksilla. Avainsanat: skenaario, arvoketju, materiaalivirta, puunkorjuu, metsänkäsittelymenetelmä, di- gitalisaatio Natural resources and bioeconomy studies 48/2021 5 Contents 1. Introduction ............................................................................................................. 7 2. Material and Methods ............................................................................................ 8 2.1. Bottom-up: Representative case studies integrated into national generic chains ................ 8 2.1.1. Geographic representation and upscaling ................................................................................... 8 2.1.2. ToSIA Method: Comparative value chains with indicators, material flow, baselines and scenarios per each country ........................................................................................................ 9 2.1.3. Scenarios ................................................................................................................................................. 10 2.1.4. Volumes and indicators .................................................................................................................... 11 2.2. Top-down: EU level current and increased mobilisation volumes calculated for predominant and potential volumes and changes in technology ............................................ 12 2.2.1. Geographic representation and upscaling ................................................................................ 13 2.2.2. Method: Comparative value chains with indicators, material flow, baselines and scenarios per each country top level figure .............................................................................. 13 3. Results .................................................................................................................... 15 3.1. Upscaling bottom-up: baseline versus innovations per country and elaboration of importance as representative of a specific ecoregion or management system .................. 15 3.1.1. Norway ..................................................................................................................................................... 15 3.1.2. Finland ..................................................................................................................................................... 21 3.1.3. France ....................................................................................................................................................... 28 3.1.4. Austria ...................................................................................................................................................... 41 3.1.5. Poland ...................................................................................................................................................... 49 3.1.6. Italy ............................................................................................................................................................ 58 3.1.7. Denmark .................................................................................................................................................. 67 3.2. Upscaling top-down: based on D7.2 volumes and D3.4 top performance figures for all of Europe ........................................................................................................................................................... 74 3.2.1. Bioeconomy in Europe and role of forestry .............................................................................. 74 3.2.2. Material flow results for Baseline and Scenarios ..................................................................... 77 3.2.3. Indicators for baseline and scenario ............................................................................................ 80 4. Discussion .............................................................................................................. 89 4.1. What is the potential in environmental and socio-economic impacts for the suggested systems? .......................................................................................................................................................... 89 4.2. What practices promote or maintain forest yield while having less environmental impact? ........................................................................................................................................................... 91 4.3. Which regions have the highest innovation potential in wood harvesting operations? . 91 4.4. What is the potential role and impact of digitalisation? .............................................................. 92 Natural resources and bioeconomy studies 48/2021 6 5. Conclusions ............................................................................................................ 93 References .................................................................................................................... 94 Annex ......................................................................................................................... 104 Natural resources and bioeconomy studies 48/2021 7 1. Introduction The current society is strongly based on non-renewable fossil resources and materials. The extensive use of fossil resources has led and contributed to many complex and global environ- mental issues. These include environmental degradation, resource scarcity leading to e.g. prob- lems in food and energy security, the loss of biodiversity, and climate change. Thus, finding better and more sustainable alternatives for fossil materials and products based on them is crucial in combating climate change as well as other major environmental issues. The concept of bioeconomy is seen as a potential solution to these issues. Bioeconomy refers to moving on from our current economy based on fossil materials and resources into an econ- omy where renewable biomass is utilized for bio-based materials, products, energy and chem- icals (McCormick & Kautto 2013). Bioeconomy increases the sustainability of society, creates jobs, and enhances food and energy security as well as decreases the dependency on finite fossil fuels (McCormick & Kautto 2013). The transition towards bioeconomy requires multidis- ciplinary efforts from numerous sectors and actors (Bugge et al. 2016). Bioeconomy can utilize biomasses derived from multiple sources. With new technologies and innovations, wood has proven to be an especially versatile material that can substitute fossil- based resources (e.g. Näyhä 2019). Currently, a great number of different products and mate- rials can be derived from wood. The benefits of forest biomass include sequestering a signifi- cant amount of carbon dioxide and not competing with food production to the same extent as agricultural biomass (Ministry of Economic Affairs and Employment of Finland 2017). On the other hand, the increased use of forest resources for wood-based bioeconomy can have neg- ative environmental impacts, too, and in recent years there has been a lot of discussion about the optimization of carbon stocks in forests and the trade-off between the increase in forest biomass utilization and biodiversity conservation (Johansson 2018). One key issue to be tackled is the availability of forest biomass; for example, not every private forest owner might be inter- ested in harvesting trees from their forests (Kraxner et al. 2017). Depending on the site charac- teristics, harvesting can be expensive and sometimes inefficient. Thus, it is increasingly im- portant to optimize and enhance the forest biomass availability with new, innovative forest management and operations practices, including digital solutions and management software. However, changes in practices aiming at increased wood mobilization and enhanced efficiency have economic, environmental and social impacts that need to be taken into account in poli- cymaking. Moreover, it is also in the operators’ and forest owners’ interests to increase the efficiency and economic feasibility of their activities. This report focuses on the sustainability impacts of selected innovative forest management and operations practices using seven European countries as case examples. The aim is to examine the impacts on the environmental and socio-economic performance of the forest value chain. The sustainability indicators used in this study include employment (social sustainability), pro- duction costs (economic sustainability), and greenhouse gas emissions (environmental sustain- ability). Different scenarios for forest management and operations are analyzed using ToSIA, the Tool for Sustainability Impact Assessment (Lindner et al. 2010). Focus is on finding innova- tions that can maintain or improve forest yield while having less environmental impacts. Natural resources and bioeconomy studies 48/2021 8 2. Material and Methods 2.1. Bottom-up: Representative case studies integrated into national generic chains 2.1.1. Geographic representation and upscaling This report combines individual case studies from seven European countries: Finland, Norway, Denmark, Poland, France, Austria, and Italy (Figure 1). The availability of data for the creation of sensible scenarios affected the choice of countries included in this study. Furthermore, the countries for case studies were chosen to represent the multiple biogeographical regions (so- called ecoregions) in Europe as well as the main EU regions (Northern, Central, Eastern, South- ern) (Table 1), so that the variety of geographical conditions, tree species, and the most com- mon forest management and operations practices in Europe were taken into consideration. Some countries included in the case studies represent more than one biogeographical region. Figure 1. Countries selected for case studies. Map created with mapchart.net. Natural resources and bioeconomy studies 48/2021 9 Table 1. Case studies and the ecoregions that the countries represent (dominant ecoregion bolded). Country Ecoregion(s) Area Finland Boreal NEU (Northern Europe) Norway Boreal, Alpine NEU Denmark Continental, Atlantic CEU (Central Europe) Poland Continental EEU (Eastern Europe) France Atlantic, Continental CEU Austria Alpine CEU Italy Mediterranean, Alpine, Continental SEU (Southern Europe) 2.1.2. ToSIA Method: Comparative value chains with indicators, material flow, baselines and scenarios per each country The Tool for Sustainability Impact Assessment (ToSIA) was developed to assess the sustaina- bility of forest-wood chains (Figure 2). Forest-wood chains consist of processes that are needed in order to convert forest biomass into products or services (Päivinen et al. 2012). Process is the element during which “the wood material changes its appearance and/or moves to another location” as stated by Päivinen et al. (2012). With the help of ToSIA, it is possible to evaluate selected sustainability impacts of changes occurring in the forest-wood chains and their oper- ational processes (e.g. harvesting, transport, industrial processing) (Lindner et al. 2010). In ToSIA, indicators for sustainability impacts can be chosen freely. ToSIA then calculates the ab- solute indicator values based on the volume of material flowing to the system and its processes, making it possible to evaluate and compare sustainability impacts under different conditions in a consistent and transparent manner (Lindner et al. 2010). In this study, forest-wood value chains were created with ToSIA for each country included. Baselines were structured according to the current forest operations and management prac- tices. Then, indicators and scenarios were added for each country-specific value chain sepa- rately. Impacts are only direct impacts of the specific process. It should be noticed that the ‘baseline’ value chains are generalized to represent certain forest types, and depending on the country, there might be several different ‘current practice’ value chains depending on the climate and surface conditions, tree species, and soil types, for in- stance. For the same reason, the baseline indicator values in the forest-wood value chains should only be used for scenario comparison in a relative scale. The indicator values in each country may vary heavily depending on the regional factors, thus they should not be consid- ered accurate statistical representatives. The aim of the ToSIA analysis is to show the relative impact of the scenario, meaning changing a process, its indicator value, or material flow in the baseline selected to represent the reference situation. Natural resources and bioeconomy studies 48/2021 10 Figure 2. ToSIA analyses sustainability impacts of forest-wood-chains using economic, social and environmental indicators (Lindner et al. 2010). 2.1.3. Scenarios Scenarios were chosen separately for each country-specific case study. Relevant literature in- cluding experimental studies as well as expert opinions were used as a reference. Scenarios included different changes in forest management and operations practices that could increase the wood biomass availability in the area. The number of scenarios per country varied from 1 to 3 depending on the availability of data for suitable scenarios (Table 2). A list of rejected scenarios can be found in Annex III. Natural resources and bioeconomy studies 48/2021 11 Table 2. Scenarios included in the study. Country Scenario Reference Finland, Norway Adjusting harvester settings Prinz et al. 2018 Finland, Norway Corridor thinning Nuutinen 2017 Finland N fertilization combined with improved breed- ing material Routa et al. 2013 Norway N and nutrient mix fertilization combined with early thinning Holt Hanssen & Kvaalen 2018 Austria Tree selection by harvester Eberhard 2019 Austria Traction winch-supported harvesting and for- warding in steep terrain Holzfeind et al. 2018 Poland Increase mechanical harvesting Gruchała et al. 2019; Karol Bronisz (pers. comm.) Denmark Filling in empty space: Planting spruce on skid- ding trails Strange & Raae 2019 Denmark Filling in empty space: Planting fast growing hybrid larch on skidding trails Strange & Raae 2019 France Stump harvesting for combined risk control and bioenergy recovery. Serrano-León et al. (unpublished)a France Improved breeding regeneration material Serrano-León et al. (unpublished)b Italy Increase mechanical harvesting Spinelli et al. 2020 (under preparation) 2.1.4. Volumes and indicators For each case, a baseline was created to describe the business as usual system for forest man- agement and operations in each country. Typically, the baseline was created for one forest stand the size of one hectare and upscaled to national level after finishing the baseline. Litera- ture review (using scientific studies, reports, official statistics etc.) was conducted in order to determine the average volumes of material flowing into and out of the system, e.g. the share of trees to be removed in thinnings. The volumes were then modified according to the scenar- ios to examine the impacts of chosen scenarios. The impacts were examined by using sustainability indicators to present the three pillars of sustainability: social, economic, and environmental. The indicators were the same for all country specific case studies and are presented in Table 3. Table 3. Sustainability indicators used for the country case studies. Aspect of sustainability Indicator Unit Social Employment Full-time equivalent FTE Occupational accidents (only in Poland, Austria, Italy) Number of non-fatal and fa- tal accidents Economic Operational costs € Environmental Greenhouse gas emissions CO2 equivalent Energy use MJ Natural resources and bioeconomy studies 48/2021 12 2.2. Top-down: EU level current and increased mobilisation volumes calculated for predominant and potential volumes and changes in technology The European Union (EU) accounts for approximately 5% of the world’s forests, and contrary to what is happening in many other parts of the world, the forested area of the EU is slowly increasing. The concept of forest used here is as defined in Eurostat (2018a), ‘land with tree crown cover or equivalent stocking level, of more than 10% and with an area of more than 0.5 hectares (ha). The trees should be able to reach a minimum height of 5 meters at maturity in situ’. For commercial timber production strict guidelines, certification and legislation exists to ensure sustainable and legal forest management, while maintaining diverse ecosystem service and natural capital functions (CICES). Forests are one of the major natural resources in Europe, covering about 42% of the land area. With an active forest industry, most forests in the EU are managed according to principles of sustainability (Forest Europe 2015). Felling rates are at 66% of the increment and forest areas are increasing by 44000 km2 per year (Forest Europe 2015). 44% of EU territory is under Natura 2000 protection (EEA 2016), more than 60% of forests are certified. Forests and wood products – both from virgin and recycled uses – feature heavily in the circular Bioeconomy strategy (2018). To be sustainable, this demands resilient management of the European forests, while increasing material supply. The overall level of EU-28 roundwood production reached an estimated 458 million m3 in 2016. Among the EU Member States, Sweden produced the most roundwood (81 million m3) in 2016, followed by Finland, Germany and France (each producing between 51 and 61 million m3). Slightly more than one fifth (21.6%) of the EU-28’s roundwood production in 2016 was used as fuelwood, while the remainder was industrial roundwood used for sawn wood and veneers, or for pulp and paper production. The total output of sawn wood across the EU-28 was ap- proximately 100 (106 in 2016) million m3 per year from 2010 to 2016 (Forest Europe 2015, Eurostat 2018b). These actual fellings are contrasted by the sustainable potential of wood supply. The potential to increase wood supply is given according to calculations by Verkerk et al. 2019: forests in 39 European countries could currently provide 401 million tonnes dry matter yr-1 of biomass. The total potential availability of woody biomass for all uses from forest resources in the 28 EU member states is estimated at 335 million tonnes dry matter yr-1 overbark in 2020 and 319 million tonnes dry matter yr-1 overbark in 2050. By 2050, this potential could increase to 321 and 406 million tonnes dry matter yr-1 overbark for the Enhanced production and Improved supply scenarios, respectively. The minimum basis for these scenario calculations stipulates that the felling levels never exceed the annual increment and excludes environmentally fragile areas. Natural resources and bioeconomy studies 48/2021 13 2.2.1. Geographic representation and upscaling Typical value chains for harvesting primary domestic biomass production have been modelled for four EU regions: • Northern EU (NEU): Denmark, Estonia, Finland, Ireland, Latvia, Lithuania, Sweden, UK • Central EU (CEU): Austria, Benelux, France, Germany, Netherlands • Southern EU (SEU): Bulgaria, Cyprus, Spain, Greece, Italy, Portugal, Spain (no data available for Malta) • Eastern EU (EEU): Czech Republic, Hungary, Croatia, Poland, Romania, Slovak Republic, Slovenia 2.2.2. Method: Comparative value chains with indicators, material flow, baselines and scenarios per each country top level figure Basic value chains (processes): The most common harvesting identified in this study systems were based on earlier work, such as INFRES project and improved for TECH4EFFECT: 1. Harvester and forwarder in cut-to-length method (CTL): This fully mechanized harvesting system originates from Scandinavia and represents the currently highest level of mechanization. Today, it is used across the whole Europe mainly in coniferous forests and on flat or slightly sloping terrain. 2. Recent advances in mechanization, led to winch-supported fully mechanized harvesting operations (both for harvester and forwarder). Primarily to reduce slip and associated soil disturbance by attaching a traction aid winch to fully mechanized harvesting and to increase safety during timber harvesting on slopes by mechanization. From early on, it has been used on slopes not traversable with standard harvester and forwarders without excessive soil disturbance. The number of machines in operation has increased exponentially in recent years and is expected to increase even more. 3. Chainsaw and cable yarder: This highly mechanized harvesting system is considered the most efficient system for timber harvesting on steep terrain not traversable by ground-based machinery, not even for winch-supported systems. Furthermore, it is regarded superior to ground-based harvesting systems as regards to soil disturbance. In our calculations we assumed 50:50 split between WTS and CTL system. For the scenario only CTL system was considered. 4. Chainsaw and skidder in whole-tree system (WTS): This partially mechanized harvesting system has been widespread in Central, Eastern and Southern Europe in the past and continues to do so especially in Eastern and Southern Europe. Further, it is a very common harvesting system in management of forest owned by farmers, where a winch is attached to a tractor primarily used for agricultural purposes. While harvesting by chainsaw can be either done in a whole-tree or cut-to-length system, we assumed a combination of chainsaw + skidding in WTS, while chainsaw CTL is combined with extraction by forwarder or cable yarder. For the scenario all motor manual WTS were considered to be replaced by CTL systems, and high levels of mechanization. Natural resources and bioeconomy studies 48/2021 14 Figure 3. Baseline (black and grey processes) and scenario (black processes only) value chains assessed in this report. Natural resources and bioeconomy studies 48/2021 15 3. Results 3.1. Upscaling bottom-up: baseline versus innovations per country and elaboration of importance as representative of a specific ecoregion or management system 3.1.1. Norway Bioeconomy in Norway and role of forestry Norway has signed and ratified the Paris Agreement in 2016 and is aiming at reducing green- house gas emissions 40% below 1990 levels by 2030 (Norway in the UN 2019). The target is to transform to a low-emission society by 2050. To support this, the Government of Norway has created a National Bioeconomy Strategy that was first published in 2016. The Government’s Bioeconomy Strategy of Norway has three overarching objectives: increased value creation and employment, reduction in greenhouse gas emissions, and more efficient and sustainable use of resources. Consequently, four focus areas are discussed in the strategy: i) supporting cross-cutting cooperation across sectors, ii) promoting the markets for renewable bio-based products, iii) using and processing biological resources in an efficient and profitable manner, and iv) producing and extracting bioresources sustainably (Norwegian Ministry of Ag- riculture and Food 2018). The strategy highlights the importance of education, research, and industry involvement. It focuses also on enabling new technologies, such as nanotechnology and ICT, to boost the modern bioeconomy. The natural resources of Norway are extensive. During the last century, Norway has made ef- forts to improve the state of its forest resources after intensive logging in the 19th Century. Currently, approximately 37% of land in Norway has forest cover, with a total volume of 960 million m3 and productive forests covering 8,144,200 hectares (Statistics Norway 2018). Norway has strong aquaculture and large fisheries. Moreover, it has relevant knowledge to support the research and development of various bioeconomy opportunities. Norway has numerous na- tional institutes with expertise in bioeconomy, such as multiple universities, Norwegian Insti- tute for Nature Research, and Norwegian Institute of Bioeconomy Research established in 2015 (Norwegian Ministry of Agriculture and Food 2018). Norway has stable funding programmes for bio-based industrial sector alongside with bioeconomy research and innovations (Norwe- gian Ministry of Agriculture and Food 2018). The turnover of Norwegian bioeconomy is approximately 5% of total economy turnover and equals to about 36 billion Euros or 350 billion NOK (Norwegian Ministry of Agriculture and Food 2018). Food industry has the biggest value creation, followed by aquaculture and fisher- ies, agriculture and forestry, and the wood products industry. Traditional bio-based industries (including agriculture, forestry, fisheries and aquaculture) employ 5% of total labour force in Norway. However, this does not include the labour force working with bioeconomy in the smaller sectors of e.g. construction, textiles, and chemicals (Norwegian Ministry of Agriculture and Food 2018). Natural resources and bioeconomy studies 48/2021 16 Baseline with processes, volumes and indicators The baseline value chain is built to represent average Norwegian coniferous growth conditions. The baseline consists of full rotation of around 85 years for even-aged Norway spruce (Picea abies) stand and 100 years for Scots pine (Pinus sylvestris) stand, sized one hectare each. The site is assumed to be locating in the southern part of Norway where most of the productive forests in Norway are located. Norway uses the so-called H40 system to indicate the quality of site. In this study, the site quality is assumed to be 14, indicating that the largest trees in the site are 14m high DBH at the age of 40 years. This is categorized to represent the average, good quality stand in Norway (Statistics Norway 2018). The forest management practices (Table 4), timing, and intensity are based on official statistics as well as guidelines for forest operators, mainly by Skogbrukets Kursinstitutt, and simulation data by Cardellini et al. (2018). Value chains for both spruce and pine start from scarification to improve seeding conditions. For pine, the typical regeneration method is seed tree regenera- tion where high-quality pines (on average, 60 trees per hectare) are left to grow and regenerate the area naturally. For spruce, planting is preferred. Approximately 2,000 seedlings are planted per one hectare. Pre-commercial thinning is conducted approximately 10 years after regeneration. This is done to avoid unwanted tree species and vegetation and leave room for growth for the trees that are not thinned. Approximately 50% of the growing stock is removed in pre-commercial thin- ning, thus, around 2,000 trees/ha for spruce and 2,500 trees/ha for pine are left to grow. The first commercial thinning is performed when the mean height of trees is around 14m. Second thinning takes place when the mean height is around 17m. Around 32–35% of growing stock is removed in both thinnings. The baseline is presented in Table 4 on a hectare level and then upscaled to Norwegian level. Total productive forest area in Norway is 8,144,200 hectares, however, in this study, the regions of Nordland, Troms and Finnmark in northern Norway are excluded due to their deviant to- pography. Protected forests were also excluded. Hence, the total productive forest area used in further calculations is 6,691,000 hectares of which 1,949,800 hectares are spruce dominant forests and 1,685,700 hectares are pine dominant forests. The rest is mixed or broadleaf dom- inant forests, thus excluded from scenario analysis. Natural resources and bioeconomy studies 48/2021 17 Table 4. Baseline including processes and their hour productivity. The outflows are presented in a hectare level and scaled up to represent spruce and pine dominated forests in Norway. Unit Process Productivity (process units/hour) Outflow per one hectare (Norway spruce) Outflow per one hectare (Scots pine) Outflow for Norway spruce stands in Norway (ha) Outflow for Scots pine stands in Norway (ha) ha Scarification (for- warder and rip- per) 0.5 1 1 1.949.800 1.685.700 ha Planting 0.06 1 n/a 1.949.800 n/a ha Pre-commercial thinning (brush saw) 0.1 1 1 1.949.800 1.685.700 m3 Thinning 1 (har- vester) 5.8 71 56 138.435.800 94.399.200 m3 Thinning 2 (har- vester) 7.3 85 75 165.733.000 126.427.500 m3 Final felling (har- vester) 20.5 260 199 506.948.000 335.454.300 m3 Seed tree felling (chain saw) 10 n/a 11 n/a 18.542,700 The sustainability indicator values used in the baseline scenario are presented in Table 5. The employment is estimated using annual full-time employment of 1,630 hours and process- based hour productivities according to Cardellini et al. (2018). Fuel consumption of the ma- chinery was taken from the data by Cardellini et al. (2018) and the CO2 equivalent of machinery from Finnish statistics database Lipasto (VTT 2016). Table 5. Baseline indicator values per process unit. Unit Process Employment (FTE/process unit) Greenhouse gas emissions from machinery (kg CO2-e/process unit) Energy use (kWh/pro- cess unit) Production costs (€/process unit) ha Scarification (forwar- der and ripper) 0.00116 389 476 340 ha Planting 0.00982 n/a n/a 1100 ha Pre-commercial thin- ning (brush saw) 0.00577 162 99.2 430 m3 Thinning 1 (harves- ter) 0.000108 12.6 15.7 24.2 m3 Thinning 2 (harves- ter) 0.0000847 9.9 12.3 21.3 m3 Final felling (harves- ter) 0.0000299 3.5 4.35 13.1 m3 Seed tree felling (chain saw) 0.000112 26 16.2 13.6 Natural resources and bioeconomy studies 48/2021 18 Scenarios with processes, volumes and indicators The alternative scenarios are formed by applying innovative forest management methods to the baseline. The scenarios aim at either i) increasing the growth of forests and biomass pro- duction (N and nutrient mix fertilization; increased thinnings), ii) increasing the production ef- ficiency (corridor thinning) or iii) decreasing GHG emissions from machinery (harvester set- tings). The alternative scenarios are based on data available from experimental studies con- ducted preferably in Norway, but in case the data was lacking, studies conducted in other Nor- dic countries (Sweden, Finland) were used. The differences in location, soil type, and other ge- ographical restriction could not, however, be taken into account. N and nutrient mix fertilization Repeated (3 times) fertilization of spruce stands was used together with early, so-called bio thinning carried out when trees are 9–13 meters high. Fertilization was carried out by spreading either only nitrogen (150 kg/ha) or a mixture of nitrogen (150 kg/ha) and other nutrients (K, Ca, Mg, P, S, Cl, B, Mn, Cu). The first fertilization and thinning took place when the average height of trees was between 8 and 12 m, second fertilization after (on average) 8 years, and third one from 8 to 14 years after the second. The trees were measures 8–14 years after the third fertilization. Second, ordinary thinning was carried when the top height reached 16 m (together with third fertilization in most test sites). Third thinning was not carried out. The increase in biomass production compared to no fertilization but thinnings according to above description was 13% when using only N fertilization, and 23% when N and nutrient mix fertili- zation was used (Holt Hanssen & Kvaalen 2018). As the yield increases due to fertilization in the mechanized processes (thinning 1 + 2 + final felling), the average processing efficiency per unit over the whole rotation time (m3-yr1) increases. In the motor manual processes it remains the same. Corridor thinning Instead of selective harvesting, thinning using 1–2m wide corridors is adopted as a method for first thinning in pine stands (Bergström 2009). Corridors used in this scenario were assumed to be perpendicular, even though fan-shaped corridors could also be used (Figure 4). Otherwise the value chains (spruce and pine) and the volumes are the same as in the baseline. Based on a Finnish field study, hour productivity in the first thinning in pine stands increased 31.6% com- pared to the baseline (Nuutinen et al. 2017). Natural resources and bioeconomy studies 48/2021 19 Figure 4. Corridor thinning methods (Bergström, 2009). Harvester settings In the mechanical thinnings (1st and 2nd thinnings) and final felling, forest harvester machine settings were switched from business-as-usual to ECO-mode. The ECO-mode was used in both spruce and pine stands. Based on a Finnish case study by Prinz et al. (2018), ECO-mode de- creased hour productivity by 5.5%, but still reduced the total GHG emissions and the energy consumption on average by 1%. The volumes are the same as in the baseline. The impact of scenarios on indicators were upscaled to country level. Annual averages are over rotations (85 years for spruce, 100 years for pine) are presented. 'Harvester settings’ scenario increased production costs by 2.4%. Corridor thinning decreased costs by 2.5% and fertilization scenario increased them by 7.8% (Figure 5). However, fertilization also increases the production volumes. Employment decreased by 2.7% in corridor thinning scenario but increased by 3% in harvester settings scenario and by 8% in fertilization scenario (Figure 8). Fertilization scenario did not take the actual fertilization work into account as ferti- lization is typically done by forest-owners themselves and the increase is mainly from the har- vesting of increased biomass, thus, the actual increase in employment might be even higher. Annual average energy use decreased by 0.4% in ‘harvester settings’ scenario and by 3.7% in corridor thinning (due to increased efficiency) (Figure 6). On the other hand, fertilization sce- nario increased annual energy use by 10%, however, fertilization also increases production vol- umes significantly. The relative energy use per cubic meter decreased by 3.4% in fertilization scenario. Subsequently, GHG emissions decreased by 3.5% in corridor thinning scenario and 0.4% in ‘harvester settings’ scenario. Fertilization scenario increased absolute emissions by 10% compared to the baseline (Figure 7). Natural resources and bioeconomy studies 48/2021 20 Figure 5. Annual average production costs over rotations in coniferous forests in Norway. Figure 6. Annual average energy use over rotations in coniferous forests in Norway. Figure 7. Annual average greenhouse gas emissions over rotations in coniferous forests in Norway. Natural resources and bioeconomy studies 48/2021 21 Figure 8. Annual average employment over rotation in coniferous forests in Norway. When comparing scenario performances in relation to objectives for TECH4EFFECT project, fertilization scenario seems to have the biggest potential (Table 6), decreasing both production costs and fuel consumption (when calculated as relative values per cubic meter) while increas- ing forest yield. Corridor thinning and adjusting harvester settings have no impact on forest yield but can have minor reductions in both production costs and fuel consumption. Table 6. T4E goals and achievements in the Norwegian scenarios (in relation to production volumes) in country level (in spruce and pine dominated forest areas). T4E goal / Scenario Corridor thinning Harvester settings Fertilization 20% decrease in production costs Decreased by 2.5% Increased by 2.4% Decreased by 7.2% 15% decrease in fuel consumption Energy use decreased by 3.7% Energy use decreased by 0.4% Energy use decreased by 3.4% 2% in forest (yield) productivity No impact No impact Increased yield by 12% 3.1.2. Finland Bioeconomy in Finland and role of forestry The forests cover 86% of the total land area in Finland, in which 77% are forestry lands (areas preserved for forest management) (Vaahtera et al. 2018). Only 7.6% of the land area are agri- cultural lands (Ministry of Agriculture and Forestry, 2014). As Finland is located in boreal con- ditions, around half of the growing stock is pine (Pinus sylvestris), 30% spruce (Picea abies), and the rest is mainly birch (Betula pendula or Betula pubescens) (Vaahtera et al. 2018). The soil is mainly mineral and around 33% is peat (Vaahtera et al. 2018). Forests are in the centre of the Finnish bioeconomy, as they are the biggest renewable resource in the country. The total domestic turnover of the forest industries was nearly 30 billion euros in 2017, repre- senting 22% of the total Finnish industrial turnover (Vaahtera et al. 2018). The pulp and paper industry is the biggest industry in Finland and contributed nearly 80% of the whole sector’s turnover (Vaahtera et al. 2018). Another major industry is sawmilling industry and continuing Natural resources and bioeconomy studies 48/2021 22 growing due to increasing Asian sawn wood demand. Whereas the turnover and overall prof- itability of forest industries continues increasing, forest sector’s employment has decreased since 2015 (Natural Resources Institute 2017a). In 2018 forest sector employed in total 63,000 persons, whereas in 2015 total employment was 65,000. However, in general the employment of forest industries has increased, and the decrease is mainly in forest management operations. Digitalization and mechanization generally improve the productivities; therefore, it is natural that the nature of jobs may change. In addition, mechanization and digitalization related ser- vice-based job classification is still difficult, therefore official statistics may not offer the full picture. It is estimated that the Finnish bioeconomy could grow to contribute in total of 100 billion euros by 2025 (Ministry of Employment and the Economy, 2014). The objective of the Finnish Bioeconomy Strategy is to ”generate new economic growth and new jobs from an increase in the bioeconomy business and from high added value products and services while securing the operating conditions for the nature’s ecosystems” (Ministry of Employment and the Economy, 2014). The strategy focuses on the diversification of wood-based products and new uses of wood, and forest resource mobilization and management technologies in that sense. One of the objectives is to create new business and employment through mechanical engineering sector and equipment manufacture and increase the expertise and digital solutions in forest management technologies (Ministry of Employment and the Economy, 2014). The harvest level was nearly 80 million cubic meters in 2018 and planned new pulp factory investments may increase the wood use even more if actualized (Natural Resources Institute Finland, 2018a). Thus, it is even more important to improve wood mobilization and resource efficiency and develop low-carbon solutions for forest sector in the future. Baseline with processes, volumes and indicators The baseline value chain is built to represent typical Finnish coniferous growth conditions and forest management. The baseline represents even-aged Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) full rotation periods of around 80 years, starting from soil scarification and planting (spruce) or sowing (pine), and ending to final felling. The site is assumed to be medium fertile locating in middle boreal conditions in Finland. The forest management prac- tices (Table 7), timing, and intensity is based on Tapio’s “Best Practice Guidelines for Sustainable Forest Management”, which is an official Finnish guideline for forest management (Äijälä et al. 2014), and INFRES simulation data (Cardellini et al. 2018). Both value chains (spruce and pine) start from scarification to improve seeding conditions. Usually in medium fertile soil types moulding is commonly used (Vaahtera et al. 2018; Äijälä et al., 2014), and forest owner can use for example excavator to implement the practise. Natural regeneration by using seed trees could be used for pine, but for spruce it is not recommended due to uncertain regeneration success (Äijälä et al. 2014). However, in this case manual sowing is used for pine, and planting for spruce with a manual tool called ‘pottiputki’. In normal envi- ronmental circumstances the recommendation for pine sowing is 250–300 g seeds/ ha (Äijälä et al. 2014). To date, mechanized sowing is used in large regeneration areas, whereas planting is still carried out by hand (Vaahtera et al. 2018). However, in this case we choose manual sowing so that the differences in costs between spruce and pine value chains are less radical and therefore will not distort the analysis. For spruce, around 2,000 seedlings per ha are planted (Äijälä et al. 2014). The tending of stand is performed to avoid unwanted tree species and vegetation, control the number of seedlings, and prevent plant pathogens such as twist rust (Melampsora pinitorqua) Natural resources and bioeconomy studies 48/2021 23 spreading to pines through aspen (Populus tremula). Based on recommendations, 2,000 trees/ha for pine stands and around 1,800 trees/ha for spruce stands are left to grow (Äijälä et al., 2014). Tending is traditionally made by using brush saw, although some piloting studies have already implemented on mechanized tending (e.g. Routa et al. 2020). Currently, mecha- nized tending still requires more piloting and development to achieve higher hour productivity. The pre-commercial harvesting is also traditionally carried out motor-manually (Vaahtera et al. 2018). In the baseline, pre-commercial harvesting is implemented, when the volume is around 20 m3, based on Finnish recommendations on pre-commercial harvesting limit, and the esti- mated removal of trees is around 25% of the growing stock (Äijälä et al. 2014). The first commercial thinning is performed when the mean height of trees is around 12m. The share of removals is calculated based on recommendations to leave 87m3 ha-1 for pine and spruce stands (Äijälä et al. 2014). The second thinning is performed, when the stock exceeds 210–240 m3/m2 depending on the species (adjusted estimate based on INFRES results and recommendations (Äijälä et al. 2014). Trees left to grow is 167 m3 ha-1 for pine and spruce stands. The final felling is performed when the pre-intervention volume is around 243 m3 based on INFRES results and recommendations (Äijälä et al. 2014). The total timber yield in the final felling is around 220 m3 (Table 7). The total yield of saw log and pulp wood over the whole rotation period for spruce is 332 m3, and for pine 314 m3, respectively. The results are upscaled to spruce-dominated and pine-dominated areas in Finland (Natural Resources Institute 2017b), when the annual yields (total yield divided by the rotation time of 80 years) would be 21 million m3 for spruce and 51 million m3 for pine, respectively (Table 7). Table 7. Baseline hour productivities and material outflows based on a process in Norway spruce and Scots pine stands. The outflows are presented in a hectare level and scaled up to represent spruce- and pine dominated forests in Finland. Unit Process Hour produc- tivity (process unit/hour) Outflow per one hectare (Spruce) Outflow per one hectare (Spruce) Outflow per one hectare (Spruce) Outflow pine forests in Finland (ha) ha Scarification 1 1 1 5,062,000 12,973,000 ha Planting/sowing 0.06 1 1 5,062,000 12,973,000 ha Tending with brushsaw 0.11 1 1 5,062,000 12,973,000 ha Precommercial harvesting (motor manual) 0.08 1 1 5,062,000 12,973,000 m3 Thinning 1 with harvester 5 50 40 253,100,000 518,920,000 m3 Thinning 2 with harvester 8 65 56 329,030,000 726,488,000 m3 Final felling with harvester 20.5 217 218 1,097,361,784 2,829,748,394 Natural resources and bioeconomy studies 48/2021 24 The sustainability indicator values used in the baseline scenario are presented in Table 8. The harvesting costs are based on official statistics of mechanical harvesting in Finland (Natural Resources Institute Finland 2018b). In Finland, regeneration and young forest management are traditionally non-commercial and profitless operations, and there is national funding available (KEMERA) for these activities (the Finnish Forest Centre 2019). Thus, private forest owners often implement these operations by themselves. The costs for these processes are estimated by using several sources: pre-commercial thinning based on available funding per one hectare (the Finnish Forest Centre 2019), pine sowing based on seed price (Suomen 4H-liitto 2007), spruce planting based on seedling price (Fin Forelia Oy 2019), and scarification based on unit cost presented in the study of Routa et al. (2013). The employment is simply estimated by using annual full-time employment 1,732 hours and process-based hour productivities which are es- timated based on INFRES data, and the energy consumption and GHG emissions (Co2, N2O,CH4) are taken from statistic database Lipasto (VTT 2016). Table 8. Baseline indicator values per unit. Unit Process Emplo- yment FTE/unit Greenhouse gas emissions from machin- ery CO2 kg equiv./unit Energy use - Di- rect fossil fuel use kWh/unit Production costs €/unit ha Scarification (Spruce, Pine) (Excavator/ Forest machine) 0.000577 183.70 218.17 264 ha Planting (Spruce) - - - 720 ha Sowing (Pine) - - - 140 ha Cleaning with brushsaw 6.42E-05 1310.90 803.25 - ha Pre-commercial thinning (motor manual) 4.62E-05 1820.70 1115.63 160 m3 Thinning 1 with harvester 0.000115 12.74 15.87 16.71 m3 Thinning 2 with harvester 7.22E-05 9.95 12.40 13.91 m3 Final felling with harvester 2.82E-05 5.24 6.53 8.18 Scenarios with processes, volumes and indicators The alternative scenarios are formed by applying innovative forest management methods to the baseline, which aim at either i) increasing the growth of forests (N fertilization and clone trees), ii) increasing the production efficiency (Corridor thinning), or decrease GHG emissions from machinery (Harvester settings ECO-mode). The alternative scenarios utilize WP2 data gathered from field- and simulation studies. To assess the potential total impact of alternative management scenarios, the value chains are the same as in the baseline. It should also be noticed that the findings from original field- and simulation studies are generalized to a coun- try level (spruce- and pine dominated areas), and no restrictions e.g. soil type, geographical location, etc. are considered. The differences between the alternative scenarios and baseline are presented in the below descriptions. Natural resources and bioeconomy studies 48/2021 25 Corridor thinning instead of selective harvesting a straight corridor thinning was adopted as a method for first thinning in pine stands. Otherwise the value chains (spruce and pine) and the volumes are the same as in the baseline. Based on field results, hour productivity in the first thinning in pine stands increased 31.6% compared to the baseline (Nuutinen 2017). Harvester settings ECO-mode In the mechanical thinnings, meaning 1st and 2nd thinnings, and final felling, forest harvester machine settings were switched from BaU to ECO-mode. The ECO-mode was used in both, spruce and pine stands. Based on the study of Prinz et al. (2018), ECO-mode decreased the GHG emissions and the energy use on average by 1%. At the same time, the study indicated that ECO-mode may decrease the hour productivity by 5.5%, but as the empirical study settings varied (tree size etc.), this was not included to country level scenario. Therefore, also impact on production costs is left out (assumed the same as in the baseline). The volumes are the same as in the baseline. N fertilization and clone trees Traditional Norway spruce seedlings were replaced with cloned ones, and Nitrogen fertilization (150 kg/ha) was applied after first and second thinning on spruce stands in a rotation time of 80 years. The cost of clone trees was 1,5-fold compared to traditional ones, and the cost N fertilization was 124€/ha. The annual timber yield (from the 1st, 2nd, and final felling) increased by 34%. The data was based on a simulation study of Routa et al. (2013). *Note: The yield increases due to fertilization in the mechanized processes Thinning 1 + 2 + Final Felling, the average processing efficiency per unit over the whole rotation time (m3 yr-1) increases. In the motor manual processes it remains the same. The annual average production volume in coniferous areas in Finland, meaning the total timber yield from commercial harvesting, increased by 10% in the ‘N fertilization and clone trees’ sce- nario compared to the baseline. The spruce timber yield per year was around 28 million m3, whereas in the baseline it was 21 million m3. In other scenarios the production volumes re- mained the same, but the economic, social, and environmental sustainability impacts varied. The annual average production costs were the highest in the ‘N fertilization and clone trees’, and 13.7% higher compared to the baseline (Figure 9). However, in relation to the production volumes, the unit costs per harvested cubic meter were 14% lower in the ‘N fertilization and clone trees’ compared to the baseline, indicating higher economic profitability. In the ‘Corridor thinning’ scenario the annual average production costs decreased 6.2% compared to the base- line, whereas the ‘Harvester settings ECO-mode’ did not have an impact at all. Similarly, in the ‘Harvester settings ECO-mode’ the employment impacts remained the same as in the baseline (Figure 10). Corridor thinning decreased the annual average of full-time employment by 5.1%, whereas ‘N fertilization and clone trees’ increased it by 9.9%. Even though in this case, the unit level employment was again lower compared to the baseline, the impacts are still positive as the total employment rate still increases due to higher production volumes. Should also be noticed that the employment of fertilization is not included to the assessment, thus the actual rate can be slightly higher. The annual average energy use and GHG emissions increased 5.8% and 5.2% in the ‘N fertili- zation and clone trees’ compared to the baseline (Figure 11, Figure 12). However, the energy use and GHG emissions per harvested cubic meter were again approximately 21% lower due Natural resources and bioeconomy studies 48/2021 26 to higher average processing efficiency, compared to the baseline. The N fertilization could affect the soil emissions, but they are not included to this assessment as we are focusing on the management operations. The ‘Corridor thinning’ decreased both annual average energy use and GHG emissions approximately by 2%, and ‘Harvester settings ECO-mode’ by approxi- mately 0.5%, respectively. Figure 9. The average production costs per year over total rotation time (80 years) in Finland. Figure 10. The average employment impact in FTEs per year over total rotation time (80 years) in Finland. 295 295 295 420 616 560 616 616 0 200 400 600 800 1000 1200 Baseline Corridor thinning (pine stands) Harvester settings ECO- mode N fertilization and clone trees (spruce) M ill io ns € Annual average production costs over rotation (80 years) in coniferous forests in Finland Spruce Pine 1092 1092 1092 1448 2512 2331 2512 2512 0 500 1000 1500 2000 2500 3000 3500 4000 4500 Baseline Corridor thinning (pine stands) Harvester settings ECO-mode N fertilization and clone trees (spruce) Pe rs on y ea rs FT E Annual average employment impact over rotation (80 years) in coniferous forests in Finland Spruce Pine Natural resources and bioeconomy studies 48/2021 27 Figure 11. The average energy use (MWh) per year over total rotation time (80 years) in Fin- land. Figure 12. The average GHG emissions per year over total rotation time (80 years) in Finland. 326 326 324 391 793 768 789 793 0 200 400 600 800 1000 1200 1400 Baseline Corridor thinning (pine stands) Harvester settings ECO-mode N fertilization and clone trees (spruce) 10 00 M W h Annual average energy use MWh over rotation (80 years) in coniferous areas in Finland Spruce Pine 350 350 348 415 896 876 893 896 0 200 400 600 800 1000 1200 1400 Baseline Corridor thinning (pine stands) Harvester settings ECO-mode N fertilization and clone trees (spruce) To ns o f C O 2 eq ui va le nt s Annual average GHG emissions over rotation (80 years) in coniferous forests in Finland Spruce Pine Natural resources and bioeconomy studies 48/2021 28 Table 9. T4E goals and achievements in the Finnish scenarios (in relation to production vol- umes) in a country-level (in spruce and pine dominated forest areas). The unit-level impacts can be found in the original studies. T4E goal / Scenario Corridor thinning Harvester settings ECO-mode N fertilization and clone trees 20% decrease in production costs Decreased by 6.2% No impact Decreased by 14% 15% decrease is fuel consumption Decreased by 2% Decreased by 0.5% Decreased by 21% 2% increase in for- est (yield) produc- tivity No impact No impact Increased by 10% 3.1.3. France Bioeconomy in France and role of forestry France endorsed its National Bioeconomy Strategy in 2017 as an official framework for the production and valorization of renewable resources (MAAF 2016). The priority of this strategy will focus on: i) an increased and sustainable mobilization of local biomass, which preserves the ecosystems producing raw materials (respect for biodiversity, landscapes, soil organic matter), and ii) optimizing the use and valorization of biomass produced to ensure capacity to meet food and non-food needs. This strategy aims at strengthening all value chains from multiple sectors: agriculture, forest, marine biomass, new materials, biofuels, biomolecules, bio-based materials, bioenergy, etc. It aims to raise social awareness about bioeconomy to make con- sumers and users more aware of these products. Their quality must be guaranteed through certification and normative standards, and their positive externalities highlighted, especially for the environment. The National Bioeconomy Strategy is complemented by the Action Plan 2018-2020 outlining the concrete actions to be implemented (MAAF 2018). The Action Plan represents the outcome of strategic committee – the Bioeconomy Council – and a broad-based consultation process bringing together public authorities, industries, NGOs, academics and research institutes as well as local, regional and national decision makers. The Action Plan is focused on the national framework and tools likely to encourage the deployment of the bioeconomy in the regions. The French National Bioeconomy Strategy is also consistent with the objectives of the National Forest and Wood Programme (PNFB) envisaged for the period 2016–2026, which considers the forest-wood sector as one of the bioeconomy pillars as provider of materials, chemicals, and energy derived from biological renewable resources (MAAF 2017). In addition, an ambitious National Forest-Wood Plan: Research, Development and Innovation 2025 (FBRI) aims at pro- moting new products and processes valorizing the French forest resource with priorities to put in place the future industry (chemicals, green industry, digital) in the context of the bio-econ- omy (Amecourt et al. 2016). The total turnover of the bioeconomy in France in 2015 summed up to 333 billion € (repre- senting 15% of the total EU28 bioeconomy) and 1.56 million employed in bioeconomy (9% of the total EU28) (Ronzon and M’Barek 2018). The bioeconomy profile of France is leaded by the agriculture and food industry sectors, which generate more than the three quarters of their Natural resources and bioeconomy studies 48/2021 29 bioeconomy turnover. The contribution of the entire forest sector accounts for 11.8% of the total French bioeconomy turnover and 11.1% of the bioeconomy employment in 2015. The forestry sector contributes with 6.8 million € (2.1%) and 31,900 employees (2%), while the wood industry generates 14.2 million € (4.2%) and 78,400 employees (5%), and pulp-paper industry 18.17 million € (5.5%) and 54,700 employees (4.1%) (JRC-EC 2018; Ronzon and M’Barek 2018). The forest contribution to bioeconomy is supported by the 16.9 million ha of forest covering more than 29% of the metropolitan France territory (ADEME 2018; MAAF 2016). The forest area of France includes 15.5 million ha forests for wood production, with 10.4 million ha of private forests supporting economically more than 3.3 million forest owners (MAAF-IGN 2016). Nearly half of the harvested volume is subject to sustainable management certification. From 62 mil- lion m3 of harvested wood in 2014, 38 million m3 were marketed for a value of 1.8 billion euros; while the energy wood consumed as solid biocombustible generated nearly 10 megatonnes of oil equivalent consumed (MAAF, 2016). Nevertheless, the felling rate in France is only around 50% of the biological production due to several factors limiting or discouraging logging of the available resource, such as forest fragmentation, growing stock in less accessible areas, increase of logging costs, and international market competition (MAAF-IGN 2016). As a result, the har- vest pressure is increased on the most productive areas and conifer stands for which the re- moval rate is close to 100%. The most representative of these productive areas where the processing capacity is high com- pared with the available resource is the Landes Massif forest in the Nouvelle-Aquitaine region. The Landes Massif consists of a large forest area of approximately 1 million ha dominated by maritime pine plantations that produces 7.6 million m3 harvested annually, representing 24% of the national wood harvest (MAAF-IGN 2016). This large afforestation effort was originally planted in previously low productive marshes since 18th century. Decades of continuous in- tensification of silviculture and progress of breeding programmes has resulted on an increase of the average productivity up to 11 m³/ha/year (Arbez et al. 2017; Bouffier et al. 2013; Mullin et al. 2011). Despite the large area and productivity, the available resource supply is under high pressure as a result of the large catastrophic windthrows from the storms “Martin” in 1999 and “Klaus” in 2009, which cleared more than 100,000 ha and 223,000 ha respectively. In a post-storm context of limited wood production, the pressure on the wood resource is exacerbated by an increasing local demand of wood biomass for energy from 0.5 to 2 M m3 in just 10 years (Brahic and Deuffic 2017; Emeyriat 2016; Landmann and Nivet 2014). Concretely, the wood energy demand has radically changed the industrial fabric organized around the forest of the Landes with the establishment of new players on the energy wood market, with a strong stake in struc- turing the players among themselves and promoting co-products in a circular logic. For exam- ple, the Regional Energy Commission (CRE) in Aquitaine have concluded contracts with almost all the paper mills and bio-refineries located in the region to develop high-power cogeneration plants, aiming to support their competitiveness with the bio-economy and energy transition. To diversify the mobilization of the wood resource and reduce the wood-energy tension on the Landes forests, different initiatives aim to valorize underused wood resources (broadleaves, harvest residues, stump biomass) contributing to the supply of bioresources to the bio-econ- omy sectors (Colin et al. 2009). Given the large biomass demand of these high-power cogen- eration plants (consuming more than 500,000 t of biomass per year), up to 250,000 t of biomass could be covered from stumps and forest residues (Demolis and Roman-Amat 2016). As a re- sult, the wood energy sector has found on the stump biomass a high-quality fuel resource at competitive price that is not in conflict with other uses of wood (timber, pulp), thus allowing Natural resources and bioeconomy studies 48/2021 30 to reduce the pressure on the wood resources for energy use while boosting the maritime pine silviculture (Demolis and Roman-Amat 2016). This extractive practice is a territorial innovation in a context of intensive forestry in large-scale planted forests with favorable conditions for stump extraction (flat terrain and sandy soils that limit the risk of erosion) (Banos and Dehez 2015; Landmann and Nivet 2014). Baseline with processes, volumes and indicators The baseline value chain was built to represent the standard forest management in maritime pine plantations in the Atlantic region of France, by focusing on the example of the Landes Massif. Stand-level development, harvest volumes and biomass were modelled using the forest growth model PP3 integrated in the simulation platform CAPSIS (Lemoine 1991; Meredieu 2002). The model allows analyzing the effects of alternative silvicultural scenarios on stand growth for pure even-aged stands of maritime pine depending on the site index and planting density (Salas-González et al. 2001). Growth simulations were conducted for an average fertility stand (dry mesophilic, site index 23.5m at 40 years) regenerated by seedling planting. The standard silvicultural itinerary was simulated according to the recommended site-specific management guidelines (Sardin and Canteloup 2003). Standard silviculture consists on manual planting with a density of 1,250 seedlings/ha and four thinning operations from above with machine harvester, leading the stand to a final density of 300 trees/ha for final harvest at 45 years. Conventional practice between clearcut and stand preparation consist on leaving the stand for a fallow period of 2–3 years before reforestation. This fallow period aims to reduce the risk and damage rates of pest (Hylobius abietis) and root rot pathogens (Heterobasidion annosum and Armillaria ostoyae) that develops on stump, by taking advantage of progressive decomposition of the stump substrate (Brunette and Caurla 2016; Jactel et al. 2009). The baseline productivity and outflow units by silvicultural process are presented in Table 10 on a hectare level and upscaled to the total productive area of maritime pine in the Atlantic regions of France. Baseline process-based hour productivities were taken from the average productivities of silvicultural regimes from the largest forest cooperative group of France (Alli- ance Forêts Bois, expert communication). Total productive area was estimated 943,000 ha, cal- culated as the total area from the large ecological regions of the National Forest Inventory (GRECO A - Grand Ouest, GRECO B – CentreNord, GRECO F – SudOuest) where maritime pine is the main species (IGN 2014). It should be noticed that the results from the growth simulation studies were generalized to extrapolate at a country level, and no restrictions nor stand varia- bility (e.g. soil type, fertility, geographical location, climate, etc.) were considered. Natural resources and bioeconomy studies 48/2021 31 Table 10. Baseline process hour productivities and material outflows in maritime pine stands in French Landes. The outflows are presented in a hectare level and scaled up to represent maritime pine dominated forests in Atlantic France (IGN 2014). Unit Process Hour produc- tivity (process unit/hour) Outflow (process units / ha) Outflow Mari- time pine in France (process units) ha Site preparation after fallow pe- riod (Mechanical brushing, Full ploughing + Fertilization) 0.25 1 943,000 ha Seedling planting 0.21 1 943,000 ha Mechanical clearing (1,3 years) 0.06 1 943,000 m3 Thinning 1 with harvester (including clearing of interlines) 5.39 24.8 23,358,110 m3 Thinning 2 with harvester (including clearing of interlines) 7.90 42.0 39,643,720 m3 Thinning 3 with harvester (including clearing of interlines) 9.70 47.4 44,735,920 m3 Thinning 4 with harvester (including clearing of interlines) 11.18 65.9 62,181,420 m3 Final felling with harvester 25.00 393.4 370,929,050 The sustainability indicator values used in the baseline scenario are presented in Table 11. The employment is estimated from the process-based hour productivities by considering an annual full-time employment of 1690 hours for France (INSEE 2018). Fuel consumption and costs of each silvicultural process were taken as average values per process unit from the largest forest cooperative group of France (Alliance Forêts Bois, expert communication). The energy con- sumption and GHG emissions were based on the average fuel consumption by machinery. En- ergy use of machinery was calculated from the fuel use in diesel liters per process unit, consid- ering 35.7 MJ per liter of direct fuel use and an equivalence of 1 kWh per 3.6 MJ (Berg 2011). GHG emissions from machinery were calculated considering 73.01 g CO2-equivalent per liter of direct used diesel fuel (Myhre et al. 2013, Tuomasjukka et al. 2017). Natural resources and bioeconomy studies 48/2021 32 Table 11. Baseline indicator values per unit. Unit Process Employment FTE/unit Greenhouse gas emis- sions from machinery kg CO2 equiv./unit Energy use - Di- rect fossil fuel use kWh/unit Production costs €/unit ha Site preparation after fallow period (Mechanical brushing, Full ploughing + Fertili- zation) 0.002367 199.39 758.63 532.5 ha Seedling planting 0.002774 0.00 0.00 275.0 ha Mechanical clearing 0.009231 31.58 120.14 650.0 m3 Thinning 1 with harvester 0.000110 6.68 25.41 13.8 m3 Thinning 2 with harvester 0.000075 4.52 17.21 10.3 m3 Thinning 3 with harvester 0.000061 4.44 16.91 8.2 m3 Thinning 4 with harvester 0.000053 4.05 15.40 7.2 m3 Final felling with harvester 0.000024 2.09 7.93 4.5 Scenarios with processes, volumes and indicators The alternative scenarios are formed by applying innovative forest management methods to the baseline. The alternative scenarios aim at either: i) increasing the production efficiency for early treatment and stand establishment (Stump extraction), or ii) increasing the growth of forests and biomass production (Improved breeding regeneration material). The alternative scenarios are based on data available from literature and simulations of silvicultural manage- ment. It should be noticed that the results from the growth simulation studies were generalized to extrapolate at a country level, and no restrictions nor stand variability (e.g. soil type, fertility, geographical location, climate, etc.) were considered. Stump harvesting for combined risk control and bioenergy recovery The standard practice of a fallow period between final felling and reforestation delays the re- forestation actions and its effectiveness for control is limited, given that significant infestation from stumps may still occur years after felling and root rots can be maintained in the post- harvest stumps for several decades (Heritage and Moore 2001). A promising technique for effective risk prevention consists on the extraction of the stumps and coarse roots from the clearcut after final felling (Augusto et al. 2018; Cleary et al. 2013; Landmann and Nivet 2014; Vasaitis et al. 2008). In addition to its application as a management tool for health risk control, stump extraction has further technical-economic benefits in terms of forest management. It allows for faster regeneration actions, reduces the reforestation cost due to work productivity improvement and efficiency gains in site preparation operations, and allows to recover the Natural resources and bioeconomy studies 48/2021 33 stump biomass as a new woodfuel resource for fossil fuel substitution (Colin et al. 2009; Walmsley and Godbold 2010). Total stump biomass was calculated from the simulated stand characteristics at rotation age using the allometric relationships estimated by Bert and Danjon (2006). Mobilizable stump bi- omass was assumed as 50% of available underground biomass (Colin et al. 2009) and was added to the total scenario production outflow in m3 ha-1. For the costs of stump extraction, two scenarios were considered: average direct extraction costs assumed by the forest owner (Alliance Forêts Bois, expert communication); or assuming free-of-charge stump extraction covered by the stump market contractors as a transaction for the stump biomass recovery for the energy wood industry (Banos and Dehez 2017). Both stump cost scenarios considered the cost reduction compared to the baseline as the opportunity cost from the fallow period. This opportunity cost was calculated as the difference in NPV (€/ha) between the baseline scenario considering the 2-year fallow period and the scenario with free-of-charge stump extraction. The subsequent facilitation of soil preparation operations after stump extraction was consid- ered as a reduction of 5% of site preparation ploughing costs corresponding to the efficiency gains estimations (GIS GPMF 2013). Control effectivity against Heterobasidion annosum and Hylobes sp. risk was considered comparable to the standard fallow practice, assuming no dam- age levels or mortality due to future infestation during the rotation in both baseline and stump extraction scenario. Other processes and harvest volumes of the stump extraction value chains were considered the same as in the baseline. Improved breeding regeneration material The maritime pine French breeding programme is one of the most advanced European pro- grammes of tree genetic improvement, with three generations of genetically improved seed orchards based on the local Landes provenance population that provide all the currently avail- able regeneration material in the French market (Bouffier et al. 2013; Mullin et al. 2011). While currently most of the harvested stands correspond to regeneration material from the first gen- eration of seed orchards Landes Vigor-Forme VF1, a second generation VF2 with greater ge- netic gain have been recently used for large regeneration areas, especially since the massive windthrown damages caused by the storms “Martin” in 1999 and “Klaus” in 2009 (Mullin et al. 2011). The genetic gains in volume (estimated from realized gains in progeny trials at age 13 years) are expected to be 30% higher for VF2 orchards compared to the standard VF1 regen- eration material (GIS PMF 2014; Mullin et al. 2011). We considered an alternative genetic material scenario that represented the expected gains reported for the developing maritime pine breeding programme in the Landes region. The development of improved stands was simulated in PP3 by modifying the site index correspond- ing to the expected 30% genetic gain in mean annual increment (MAI) at the end of rotation, while applying the same timing of thinnings and rotation age as in the reference scenario. Process-based hour productivities, fuel consumption and costs for the breeding scenario were estimated as a logarithmic extrapolation of the baseline average values per process unit as a function of each process outflow in m3 ha-1. The figures (Figures 13, 14, 15, 16) show the scenario results comparison of the sustainable indicators values per production volume unit (m3). Employment in FTE/m3 was only slightly increased (+0.3%) in the stump extraction scenario compared to the baseline, given the slight changes in hourly productivity (-0.3%). In contrast, the large increase in stand volume produc- tivity per ha in the breeding scenario, which lead to a notably higher hourly productivity (+25.4%), resulted in a large reduction of the employment per volume unit (-20.3%). Natural resources and bioeconomy studies 48/2021 34 Energy use and GHG emissions per volume unit were considerably reduced in the breeding scenario (-13.3%), while these indicators were slightly higher (+1.2%) in the stump extraction scenario. However, the stump harvesting scenario did not consider the fossil fuel substitution effects from the use of stump biomass energy. Production costs per m3 was also reduced in the breeding scenario (-13.1%), while they varied in the stump extraction scenarios depending on the assumptions. Considering that the extrac- tion costs are covered by the forest owner, the extra unitary production costs will be compen- sated with the opportunity cost from reduced silvicultural rotation without fallow period, re- sulting on a limited change of the unitary production costs (+0.1%). In contrast, if the extraction costs are not assumed by the forest owner, the unitary production costs are lower (-1.7%) than in the baseline scenario due to the opportunity costs of the fallow period. Nevertheless, it is important to note that none of the scenarios considered the additional costs (hourly, fuel, €) from timber and stump forwarding and loading onto trucks after harvest. Figure 13. Full-time employment per unit of production volume (person year FTE / m3) for different maritime pine silviculture scenarios in the French Landes. Natural resources and bioeconomy studies 48/2021 35 Figure 14. Energy use per unit of production volume (kWh / m3) for different maritime pine silviculture scenarios in the French Landes. Figure 15. GHG emissions from machinery per unit of production volume (kg of CO2 equiva- lents / m3) for different maritime pine silviculture scenarios in the French Landes. Natural resources and bioeconomy studies 48/2021 36 Figure 16. Production costs per unit of production volume (€ / m3) for different maritime pine silviculture scenarios in the French Landes. Figures 17, 18, 19 and 20 show the scenario comparison of the annual average indicators over rotation extrapolated to country level. Annual average indicators were calculated from the uni- tary indicator multiplied by the total scenario outflow per ha (573.5 m3 ha-1 in the baseline scenario, 663.4 m3 ha-1 including the stump extraction, and 745.4 m3/ha for breeding scenario) and the total forest area of maritime pine in the Atlantic regions of France (943 000 ha), and divided by the rotation years (47 years for baseline and breeding scenarios, 45 year for stump harvest scenario). Compared to the baseline scenario, annual average employment extrapolated to country level was considerable increased (+21.2%) for the stump scenario, compared with a moderate in- crease (+3.6%) in the breeding scenario. The higher indicator values resulted from the higher volume outflows of the alternative scenarios, which augmented the slight employment differ- ence in volume units for the stump scenario and compensated the large reduction of unitary employment values for the most efficient breeding scenario. Annual average energy use and GHG emissions were considerably higher (+22.3%) in the stump scenario (without considering the fossil fuel substitution from the use of stump biomass energy), while these indicators were also increased in the breeding scenario (+12.7%) due to the higher annual average productions. Annual average production costs were also increased compared with the baseline for the breeding (+12.7%) and for the stump extraction scenarios, for which total production costs differed between +20.9% when the stump extraction costs are covered by the forest owner and +18.7% if they are free-of-charge to the forest owner and covered by the stump market contractors. Natural resources and bioeconomy studies 48/2021 37 Figure 17. Annual average full-time employment (person year FTE / yr) over rotation time for different maritime pine silviculture scenarios extrapolated to France. Figure 18. Annual average energy use (MWh / yr) over rotation time for different maritime pine silviculture scenarios extrapolated to France. Natural resources and bioeconomy studies 48/2021 38 Figure 19. Annual average GHG emissions from machinery (tons of CO2 equivalents / yr) over rotation time for different maritime pine silviculture scenarios extrapolated to France. Figure 20. Annual average production costs (€/yr) over rotation time for different maritime pine silviculture scenarios extrapolated to France. Natural resources and bioeconomy studies 48/2021 39 The extrapolation of the indicators resulted on higher values for the baseline scenario than those reported in national statistics. For example, the extrapolation of the baseline scenario of maritime pine silviculture to the entire species areas in the Atlantic regions of France was esti- mated to provide employment for 3996 FTE person year. These values will result from an esti- mated annual average production of 11.5 M m3 yr-1, of which 9.9 M m3 yr-1 will correspond to the 813 000 ha of the Nouvelle Aquitaine region. This result is larger than the reported statistics for the Nouvelle Aquitaine region, which generates 2813 FTE of forest work for an average annual production of 5.4 M m3 yr-1 (IGN 2014, Agreste 2017). However, the lower production values in the statistics compared to the baseline can be related to the post-storm context of limited wood production. In addition, the sustainable indicators were calculated on the base of volume production ex- trapolated from a virtual stand hectare, as well as optimized hour productivities and costs es- timated from process productivities on the stand without considering machinery transport be- tween stands. The extrapolation of the theoretical stand to the total forest area does not con- sider the reduction in available productive stand area dedicated to, e.g., forest roads, wildfire breaks, ditches, etc. In real forest conditions, this will result in reduced volume production per stand surface, reduced machinery hour productivity and higher costs from increasing machin- ery distances. Finally, the stand-level indicators for the maritime pine silvicultural chain were based on the favorable conditions of the Landes region, with high stand productivity, easy forest accessibility and a strong forest sector structure. The extrapolation to the national level does not consider the differences in stand conditions, structure or access that would impact on the total indicator values. Nevertheless, the estimated figures allow us to compare the potential impact of the different scenarios in relation to the baseline reference. When comparing scenario performances in re- lation to the goals aimed by the TECH4EFFECT project (Table 12), the breeding scenario seems to have the biggest potential, decreasing both production costs (-13.1%) and fuel consumption (-13.3%) relative per cubic meter while increasing forest yield (+30.0%). Stump extraction had a positive impact in the forest yield (+15.7%) when considering the ad- ditional stump harvest on the total stand production. In contrast, the stump extraction slightly increased the unitary fuel consumption (+1.2%) over the rotation production. Regarding the unitary production costs, stump extraction has a minor impact (+0.1%) when the stump extrac- tion costs are considered in the silvicultural operations by the forest owner, although they can be considerably reduced (-1.7%) if they are covered by the stump market contractors. Natural resources and bioeconomy studies 48/2021 40 Table 12. T4E goals and achievements in the French scenarios in relation to production unit (m3). T4E goal / Scenario Stump harvesting Improved breeding re- generation material 20% decrease in production costs Increased by +0.1% / Decreased by -1.7% if ex- traction costs covered by stump market Decreased by -13.1% 15% decrease in fuel consumption Increased by 1.2% Decreased by -13.3% 2% in forest (yield) productivity Increased by +15.7% Increased by +30.0% Natural resources and bioeconomy studies 48/2021 41 3.1.4. Austria Bioeconomy in Austria and role of forestry The Austrian Bioeconomy Strategy was launched in 2019 and provides guidance for all bioe- conomy-relevant fields of action until 2030. It is complementary to the Integrated Climate and Energy Strategy on the decarbonisation efforts. The aim of the national bioeconomy strategy is to identify concrete measures for the further establishment of the bioeconomy in Austria in order to generate sustained growth spurts for bio-based products, bioenergy and related tech- nologies and services. It aims at providing a framework for: i) increasing efficiency at all levels, ii) promoting conscious consumer behaviour and sustainable product range, iii) exploitation of all renewable sources of raw materials by using residues, by-products, waste and the produc- tion of novel raw materials, and iv) using opportunities from innovation for the transformation in business and society (BMNT 2019). Agriculture, forestry and aquaculture are key sectors. Aquaculture becomes very relevant as it does not compete on the land use and offers a wide range of possibilities for the bioeconomy (BMNT et al. 2019). Also residuals, by-products, and waste are crucial resources for the Austrian bioeconomy (Gaugitsch 2019). As main products of the bioeconomy are highlighted: food and animal feed, materials (pulp and paper, fibres, chemicals, construction sector), and bioenergy (solid, liquid, gaseous) (Gaugitsch 2019). The strategy identifies the action fields to be translated into a National Action Plan, with re- sponsibilities, timeframe and budgetary requirements. A Center of Bioeconomy and a Bioecon- omy cluster will be created (Gaugitsch 2019). The operational goals according to BMNT et al. (2019) are: a) achieving the climate goals, b) reduction of dependence on non-renewable resources. This can be done by strengthening ex- isting sectors of the economy, by supporting innovative technologies and services, by better networking of knowledge, by raising awareness and by creating acceptance for bio-based products and services, c) promotion of innovation, increasing scientific publications, transdis- ciplinary projects and patents in the field of bioeconomy, d) promoting economic develop- ment, e) securing and creating jobs, and f) promoting sustainable social transformation. The strategy identifies an urgent need for behavioural and value changes, both by producers and consumers, to achieve all the goals of the bioeconomy strategy. Consumers decide on the choice of products and define the market demands, having a significant impact on the envi- ronmental impact of the Austrian economy (BMNT et al. 2019). Baseline with processes, volumes and indicators The baseline value chain represents average Austrian coniferous growth conditions. The base- line consists of a rotation of around 90 years for one hectare even-aged Norway spruce (Picea abies) stand. The forest management practices (Table 13), timing, and intensity are based on data by Cardellini et al. (2018). The value chain for Norway spruce starts from planting seedlings (about 2500 seedlings per hectare) and ends with final felling, cable yarding, debranching and cut-to-length at the roadside. In the baseline, the stand is thinned two times in year 40 and 55 followed by a final felling in year 90 (Cardellini et al. 2018). On steep slopes in the alpine areas in Austria, manual harvesting and hauling of the felled trees by cable yarding is a very common method, although winch-supported harvesting/forwarding is increasing in popularity mainly because of labour safety aspects. However, chainsaw and Natural resources and bioeconomy studies 48/2021 42 cable yarder in whole tree method is considered the most efficient system for timber harvesting on steep terrain not accessible by ground-based machinery. In addition, it is regarded superior to ground-based harvesting systems when minimizing soil disturbance. Current field tests have shown that apart from improved occupational safety, winch-supported harvesting/forwarding systems can also be an economically feasible alternative compared to the more commonly used cable yarding system. The baseline is presented in Table 13 on a hectare level and then upscaled to whole Austria. Almost half of the land area, 3.991 million ha, is covered with forest and this makes Austria one of the Central European countries with the highest share of forest (47,6%). About 60% of the country consists of mountainous areas (Quadt et al. 2013). According to the Austrian Forest Inventory 2007/09 (Austrian Forest Report 2015), coniferous forests cover 2.14 million hectares of land in Austria. This corresponds to 64% of the total forested area. In coniferous forests, spruce accounts for 81% of the trees. It covers 1.7 million hectares of land and thus 51% of the productive forest area in Austria. Table 13. Baseline including processes and their hour productivity. The outflows are pre- sented per hectare and scaled-up to represent spruce-dominated forests in Austria. Unit Process Productivity (process units/hour) Outflow per one hectare Outflow for Norway spruce stands in Aus- tria ha Planting 0.02 1 1,700,000 m3 Tree marking by forester 18.75 36/148 1,700,000 m3 Thinning 1 (chainsaw) 3 36 61,200,000 m3 Thinning 2 (chainsaw) 3 148 251,600,000 m3 Final harvest (chainsaw) 3 578 982,600,000 m3 Cable yarding whole trees to the roadside 3 578 982,600,000 m3 Debranching and cut to length by cable-yarding processing unit 10 578 982,600,000 Natural resources and bioeconomy studies 48/2021 43 Table 14. Baseline (motor-manual harvesting/cable yarding) indicator values per process unit. Unit Process Em- ploy- ment (FTE/pr ocess unit) Emis- sions from machin- ery (kg CO2- eq./pro- cess unit) Energy use (kWh/ process unit) Produc- tion costs (€/pro- cess unit) Occupa- tional ac- cidents (non-fa- tal) per unit Occupa- tional ac- cidents (fatal) per unit ha Planting 0.03 - - 3150 - - m3 Tree marking by forester 0.00003 - - 0.91 - - m3 Thinning 1 (chainsaw) 0.0002 1.31 0.83 23.67 0.000112 0.00000116 m3 Thinning 2 (chainsaw) 0.0002 1.31 0.83 23.67 0.000112 0.00000116 m3 Final harvest (chainsaw) 0.0002 1.31 0.83 23.67 0.000112 0.00000116 m3 Cable yarding whole trees to the roadside 0.0002 27.32 33 24.17 0.0000356 0.00000037 m3 Debranching and cut to length by cable-yarding processing unit 0.00006 8.2 9.9 11 0.00000594 0.00000006 Scenarios with processes, volumes and indicators Winch-supported harvester / forwarder Since mechanized harvesting using harvester / forwarder is in general safer compared to mo- tor-manual harvesting followed by cable yarding, it is interesting to evaluate the implications on a national scale if motor-manual harvesting / cable yarding would be replaced with me- chanical harvesting using winch-supported harvester / forwarder combination. In this scenario, we replaced motor-manual harvesting processes using chainsaw (2.5 kW) by harvesting with a winch-supported harvester (149 kW). Cable yarding (99 kW) was replaced by a winch-sup- ported forwarder (136 kW). Forest management practices, timing of thinning and final harvest, and amount of wood harvested remained the same as in the baseline. The modified indicator values used in the mechanization scenario are presented in Table 15. Employment is estimated based on the process-based hour productivities according to Tuomasjukka et al. (2015) and Holzfeind et al. (2018). Fuel consumption and related emissions of the machinery were taken from Finnish statistics database Lipasto (VTT 2016). Productions costs were taken from Tuomasjukka et al. (2015) and Holzfeind et al. (2018), and occupational accidents from Jänich (2009). Natural resources and bioeconomy studies 48/2021 44 Table 15. Winch-supported harvesting scenario - indicator values per process unit. Un it Process Emplo- yment (FTE/pro- cess unit) Emissions from ma- chinery (kg CO2- eq./pro- cess unit) En- ergy use (kWh /pro- cess unit) Produc- tion costs (€/pro- cess unit) Occupa- tional ac- cidents (non-fa- tal) per unit Occupa- tional ac- cidents (fatal) per unit ha Planting 0.03 - - 3150 - - m3 Tree marking by forester 0.0000329 - - 0.91 - - m3 Thinning 1 (winch-suppor- ted harvester) 0.0000685 13.29 16.56 12.75 0.00000594 0.00000006 m3 Thinning 2 (winch-suppor- ted harvester) 0.0000685 13.29 16.56 12.75 0.00000594 0.00000006 m3 Final harvest (winch-sup- ported har- vester) 0.0000685 13.29 16.56 12.75 0.00000594 0.00000006 m3 Forwarding (winch-sup- ported) to the roadside 0.0000449 8.1 9.91 9.1 0.00000594 0.00000006 Tree selection by harvester In Austria, trees to be felled in thinnings are usually marked by a forester before the harvesting takes place (Eberhard 2019). The general opinion in Austria is that tree marking by a forester is essential in managing forest stands. However, it is time and money consuming. For this reason, forest owners concentrate mainly on good stands and on older stands to make thinning oper- ations profitable. As a result, many stands where thinning is needed remain un-thinned, and the number of small dimension trees increases. According to Eberhard (2018), it would be pos- sible to harvest a considerable larger amount of wood in Austrian forests in thinning opera- tions. Therefore, Eberhard (2018) tested if tree marking by a forester was necessary at all. According to Eberhard (2018), the amount of wood removed during first thinning and second thinning together was almost equal when he compared the removal by a forester (206 m³) with the removal by a harvester (201 m³). The result in productivity after 50 years was also quite equal for forester (1004 m³) and harvester (1018 m³). The most significant result was a reduc- tion in production costs. Eberhard estimated that without tree marking, about 17,300 workdays per year would be saved which corresponds to approximately 2,350,000 € per year. In this scenario we compare tree selection by forester and harvester using our sustainability indicators. We used the costs of tree marking and the effect on wood production based on data by Eberhard 2018 and calculated the impact on a whole rotation cycle. In our analysis, replacing motor-manual harvesting (chainsaw-cable yarding) with winch-sup- ported