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Sustainability Assessment of current and recommended methods. TECH4EFFECT project report.

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Abstract

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 program", 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 regeneration 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, calculating 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 emissions (consistent with LCA methodology), energy use, and soil impact indicators. Social impacts were studied in terms of employment effects and occupational safety. The economic performance 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, digitalisation and biofuels are assessed and discussed as options to mobilise timber at reduced environmental impact.
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 O
razio, Venla Wallius, Mercedes Rois, Robert Prinz and Johanna Routa
Publisher: Natural Resources Institute Finland (Luke), Hel
sinki 2021
Year of publication: 2021
Cover photo: Johanna Routa/Luke
Printing house and: publishing sales: Juvenes Print, http://l
uke.juvenesprint.fi
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
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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
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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
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5. Conclusions ............................................................................................................ 93
References .................................................................................................................... 94
Annex ......................................................................................................................... 104
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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
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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
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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
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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
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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
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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
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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
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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
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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 3235% 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 913 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 814 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 12m 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 250300 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
210240 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)
Millions
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)
Person years
FTE
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)
1000 MWh
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)
Tons of CO2 equivalents
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 20162026, 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 23 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 mechanical harvesting (winch-supported harvester-forwarder) reduced production
costs by 58% (Figures 21 and 22). Compared to the winch-supported harvesting scenario, tree
Natural resources and bioeconomy studies 48/2021
45
selection by the harvester him/herself only led to a further cost reduction of 11 cents (0.2%)
per harvested cubic meter. Changing from motor-manual/cable yarding to winch-supported
harvesting mechanical harvesting would reduce energy use by 40% (Figures 23 and 24) and
the emissions from the machinery decreased by 42% (Figures 25 and 26). Tree marking had no
significant impact on energy use and emissions. Changing from motor-manual/cable yarding
to winch-supported mechanical harvesting reduced employment by 69% because of its im-
proved efficiency. Because of a small increase in forest yield if we would omit tree marking by
a forester and tree selection is done by the harvester, employment increased by about 3%
(Figure 27). However, the biggest advantage of changing to winch-supported mechanical har-
vesting is in improving occupational safety, mainly because workers are now protected by the
harvester/forwarder cabin and do not need to work with handheld chainsaws and cables used
in cable yarding. Changing to mechanical harvesting systems would reduce the risk for both
fatal and non-fatal accidents by about 92% (Figures 28 and 29).
Figure 21. Production costs (€ m-3) estimated over a full rotation in spruce forests in Austria.
Figure 22. Mean annual production costs (mill. € yr-1) estimated over a full rotation in spruce
forests in Austria.
63,19
26,20 26,09
0,00
10,00
20,00
30,00
40,00
50,00
60,00
70,00
Baseline Winch-supported
harvesting
Tree selection by
harvester
Production cost (€/m
3)
910,20
377,39 387,46
0,00
100,00
200,00
300,00
400,00
500,00
600,00
700,00
800,00
900,00
1000,00
Baseline Winch-supported
harvesting
Tree selection by
harvester
Production cost (mill. €/yr)
Natural resources and bioeconomy studies 48/2021
46
Figure 23. Energy use (MJ m-3) by machinery used in forest operations estimated over a full
rotation in spruce forests in Austria.
Figure 24. Mean annual energy use (PJ yr-1) by machinery used in forest operations estimated
over a full rotation in spruce forests in Austria.
157
95 95
0
20
40
60
80
100
120
140
160
180
Baseline Winch-supported
harvesting
Tree selection by
harvester
Energy use
-fossil fuel use
(MJ/m3)
2267,80
1372,14 1414,88
0,00
500,00
1000,00
1500,00
2000,00
2500,00
Baseline Winch-supported
harvesting
Tree selection by
harvester
Energy use -fossil fuel use
(TJ/yr)
Natural resources and bioeconomy studies 48/2021
47
Figure 25. Greenhouse gas emissions (kg CO2 eq. m-3) from machinery used in forest opera-
tions estimated over a full rotation in spruce forests in Austria.
Figure 26. Mean annual greenhouse gas emissions (x 1000 t CO2 eq. yr-1) from machinery used
in forest operations estimated over a full rotation in spruce forests in Austria.
36,8
21,4 21,4
0
5
10
15
20
25
30
35
40
Baseline Winch-supported
harvesting
Tree selection by
harvester
GHG emissions kg CO
2eq./m3
531
308 318
0
100
200
300
400
500
600
Baseline Winch-supported
harvesting
Tree selection by
harvester
GHG emissions x 1000 t CO
2
eq./yr
Natural resources and bioeconomy studies 48/2021
48
Figure 27. Mean annual employment (full-time equivalents) estimated over a full rotation in
spruce forests in Austria.
Figure 28. Risk for non-fatal accidents per year in forest operations estimated over a full rota-
tion in spruce forests in Austria.
7474
2315 2375
0
1000
2000
3000
4000
5000
6000
7000
8000
Baseline Winch-supported
harvesting
Tree selection by
harvester
Employment (full
-time
equivalent)
2212
171 176
0
500
1000
1500
2000
2500
Baseline Winch-supported
harvesting
Tree selection by
harvester
Accident risk (nr. non
-fatal)
Natural resources and bioeconomy studies 48/2021
49
Figure 29. Risk for fatal accidents per year in forest operations estimated over a full rotation
in spruce forests in Austria.
When comparing the selected management alternatives to the objectives of the TECH4EFFECT
project, we can conclude that changing from motor-manual harvesting to winch-supported
mechanical harvesting would reach the objective as production costs are markedly reduced
(Table 16). More important, the risk for occupational accidents was greatly reduced. Another
advantage was that fuel consumption and greenhouse gas emissions were reduced. Tree se-
lection by the harvester instead of marking trees by a forester only had a very minor or no
impact on costs and energy use but it did increase forest yield by about 3%.
Table 16. T4E goals and achievements in the Austrian scenarios at country level in spruce
dominated forest areas.
T4E goal / Scenario
Winch-supported harves-
ting/forwarding
Tree selection by harvester
20% decrease in
production costs
Production costs decreased
by 58%
Accident risk (non-fatal and
fatal) decreased by 92%
Production costs decreased by 0.2%
Accident risk remained the same
15% decrease in
fuel consumption
Decreased by 40% No impact
2% in forest (yield)
productivity
No impact Increased by 3%
3.1.5. Poland
Bioeconomy in Poland and role of forestry
Poland does not have a dedicated national bioeconomy strategy yet. Nevertheless, bio-based
industry elements feature prominently in the country’s Smart Specialization Strategy (2016) (EC
2015, BBIC 2018), which is built along five axes: i) healthy society, ii) agro-food, forestry-timber
23
22
0
5
10
15
20
25
Baseline Winch-supported
harvesting
Tree selection by
harvester
Accident risk (nr. fatal)
Natural resources and bioeconomy studies 48/2021
50
and environmental bioeconomy, iii) sustainable energy, iv) natural resources and waste man-
agement, and v) innovative technologies and industrial processes.
Other strategies related to bioeconomy are the Strategy for Innovation and Efficiency of the
Economy, the Strategy of Energy Safety and Environment, and the Strategy for Sustainable
Development of Agriculture, Rural Areas and Fisheries (EC 2015). Another relevant links are the
National Programme for the Development of a Low Emission Economy (2015), and the Strategy
for Development of the country 2020, which defines developmental goals for Poland up to
2020 (2012) (BBIC 2018), and the BIOSTRATEG Strategic and Research program ‘Environment,
Agriculture and Forestry’ (2013) (EC 2018a). Poland is also developing a roadmap towards Cir-
cular Economy, where bioeconomy has an own dedicated chapter.
Poland’s bioeconomy sector is focused on agriculture, forestry and food processing, areas
which are already central to the country’s economy (Woźniaka and Twardowski 2018). Other
sectors with relevance for the bioeconomy are fisheries, chemical, biotechnology and energy
industries.
In addition to national efforts to develop the bioeconomy there are also efforts taking place at
the regional level (Winther 2016). The Lodzkie Region was established as the first Bioregion in
Republic of Poland, aiming at converting the Lodzkie Region into one of the most innovative
regions in Europe and Poland in the area of sustainable bioeconomy. There are several other
regional bioeconomy strategies as part of the RIS (2014) (EC 2018a).
Poland participates in the Central-Eastern European Initiative for Knowledge-based Agricul-
ture, Aquaculture and Forestry in the Bioeconomy (BIOEAST), a macro-regional bioeconomy
initiative, developed by Central and Eastern European countries, aiming at establishing a com-
mon strategy on bioeconomy and at strengthening the links between the involved sectors
across the borders (EC 2018a; BBIC 2018). It envisions: i) sustainable increase of biomass pro-
duction, ii) the need for circular (“zero waste”) processing of available biomass, iii) the viability
of rural areas (EC 2015).
Baseline with processes, volumes and indicators
The baseline value chain is built to represent average Polish coniferous growth conditions. The
baseline consists of a rotation of around 100 years for one hectare even-aged Scots (Pinus
sylvestris) stand. Rotation start from soil scarification and natural regeneration and ends with
final felling.
The forest management practices (Table 17) timing, and intensity are based on data by
Cardellini et al. (2018). Value chains for Scots pine start from scarification to improve seedling
establishment. For Scots pine, both planting and natural regeneration can be applied. In the
baseline scenario, we assumed that natural regeneration is successful with about 10,000 natu-
rally regenerating seedlings per hectare.
Although mechanical harvesting is becoming more popular in Poland since the last decades,
the use of less productive and less effective manual harvesting is still very common. According
to previous published material, around the year 2010 only about 5% of the total volume was
harvested mechanically while about 95% of the volume was still harvested manually by chain-
saw (Szewczyk and Wojtala 2010; Kingsbury and Zochowska 2011). More recent estimates on
the level of mechanized harvesting in Poland were lacking at the time of this assessment. Skid-
ding and forwarding are the most common ways of timber extraction to the roadside (Tuomas-
jukka et al. 2015).
Natural resources and bioeconomy studies 48/2021
51
In the baseline, tending of the stand is carried out in year 12 followed by pre-commercial thin-
ning in year 17. Hereafter, the stand is thinned six times in year 25, 32, 40, 50, 65 and 80 fol-
lowed by a final felling in year 100 (Cardellini et al. 2018).
The baseline is presented in Table 17 on a hectare level and then upscaled to whole Poland. In
Poland, forest land covers 9.3 million hectares and stocked forest land covers 9.1 million hec-
tares (Gerasimov 2013). About 71% of the stocked forest area is covered by coniferous species
such as pine (60%, 5,476,000 ha), spruce (6%) and fir (3%). A substantial area is covered by
deciduous species such as birch (7%), alder (5%), aspen and poplar. The share of broadleaved
species such as oak, beech and hornbeam is about 13% of the stocked forest area (Gerasimov
2013).
Table 17. Baseline including processes and their hour productivity. The outflows are pre-
sented in a hectare level and scaled-up to represent pine-dominated forests in Poland.
Unit
Process
Productivity
(process
units/hour)
Outflow per
one hectare
Outflow for Scots
pine stands in Po-
land
ha
Site preparation
(scarification)
0.19 1 5,476,000
ha
Tending (brush saw)
0.05
1
5,476,000
ha
Precommercial thinning
(chainsaw)
1 1 5,476,000
m3
Thinning 1 (chainsaw)
0.96
17
93,092,000
m3
Thinning 2 (chainsaw)
0.96
19
104,044,000
m3
Thinning 3 (chainsaw)
0.96
23
125,948,000
m3
Thinning 4 (chainsaw)
0.96
45
246,420,000
m3
Thinning 5 (chainsaw)
0.96
47
257,372,000
m3
Thinning 6 (chainsaw)
0.96
52
284,752,000
m3
Final harvest (chainsaw)
0.96
470
2,573,720,000
The sustainability indicator values used in the baseline scenario are presented in Table 18. The
employment is estimated using annual full-time employment of 1,776 hours and process-
based hour productivities according to Cardellini 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 occupational accidents from
Jänich (2009).
Natural resources and bioeconomy studies 48/2021
52
Table 18. Baseline indicator values per process unit.
Unit Process
Employ-
ment
(FTE/pro
cess
unit)
Emis-
sions
from
machin-
ery (kg
CO2-
eq./pro-
cess
unit)
Energy
use
(kWh/
pro-
cess
unit)
Produc-
tion
costs
(€/pro-
cess
unit)
Occupa-
tional ac-
cidents
(non-fa-
tal) per
unit
Occupa-
tional acci-
dents (fa-
tal) per
unit
ha
Site prepara-
tion (scarifi-
cation)
0.00302 352 413 317 - -
ha
Tending
(brush saw)
0.0113 65 40 246 - -
ha
Precommer-
cial thinning
(chainsaw)
0.000563 3.92 2.5 12.29 - -
m3
Thinning 1
(chainsaw)
0.000587 4.09 2.6 11.80 0.000112 0.00000116
m3
Thinning 2
(chainsaw)
0.000587 4.09 2.6 11.80 0.000112 0.00000116
m3
Thinning 3
(chainsaw)
0.000587 4.09 2.6 11.80 0.000112 0.00000116
m3
Thinning 4
(chainsaw)
0.000587 4.09 2.6 11.80 0.000112 0.00000116
m3
Thinning 5
(chainsaw)
0.000587 4.09 2.6 11.80 0.000112 0.00000116
m3
Thinning 6
(chainsaw)
0.000587 4.09 2.6 11.80 0.000112 0.00000116
m3
Forwarding
(skidder or
tractor)
0.000587 6.72 7.89 3.10 0.0000119 0.00000012
m3
Final harvest
(chainsaw)
0.000587 4.09 2.6 11.80 0.000112 0.00000116
Scenarios with processes, volumes and indicators
Mechanization of harvesting in coniferous forests
Since mechanized harvesting is in general safer and more efficient, it is interesting to evaluate
the implications on a national scale if motor-manual harvesting would be replaced with me-
chanical harvesting. In this assessment, we replaced motor-manual harvesting processes using
chainsaw (2.5 kW) by harvesting with a medium harvester (149 kW). Forwarding processes with
a skidder or tractor (77 kW) were replaced by a forwarder (105 kW). Forest management prac-
tices, timing of thinning and final harvest, and amount of wood harvested remained the same
as in the baseline.
Natural resources and bioeconomy studies 48/2021
53
The modified indicator values used in the mechanization scenario are presented in Table 19.
Employment is estimated based on the process-based hour productivities according to
Tuomasjukka et al. (2015). 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 occupational accidents from Jänich (2009).
Table 19. Mechanized harvesting scenario - indicator values per process unit.
Unit
Process
Emplo-
yment
(FTE/pro-
cess unit)
Emissions
from ma-
chinery (kg
CO2-
eq./process
unit)
Energy use
(kWh/pro-
cess unit)
Produc-
tion
costs
(€/pro-
cess
unit)
Occupational
accidents
(non-fatal)
per unit
Occupational
accidents
(fatal) per
unit
ha
Precommer-
cial thinning
(harvester)
0.000563 119 149 70.40 - -
m3
Thinning 1
6(harvester)
0.0000647 13.75 17.13 6.8 0.00000594 0.00000006
m3
Forwarding
(forwarder)
0.000144 22.02 26.92 5.5 0.00000594 0.00000006
m3
Final
harvest
(harvester)
0.0000557 11.85 14.75 4 0.00000594 0.00000006
In our theoretical scenario, replacing motor-manual harvesting (chainsaw-skidder) with me-
chanical harvesting (harvester-forwarder) would results in a 31% production costs reduction
(Figure 31). Changing to mechanical harvesting would increase energy use by machinery used
in forest operations by 286% (Figures 32 and 33) and the emissions from the machinery used
increases by 207% (Figures 34 and 35). Because of increased productivity, employment in forest
operations would decrease by 66% if motor-manual harvesting is replaced with mechanical
harvesting (Figure 36). The biggest advantage of changing to a higher level of mechanical har-
vesting is in improving occupational safety. Changing to mechanical harvesting systems would
reduce the number of both fatal and non-fatal accidents by 90% (Figures 37 and 38).
Natural resources and bioeconomy studies 48/2021
54
Figure 30. Mean production costs (€ m-3) estimated over a full rotation in pine forests in Po-
land.
Figure 31. Mean annual production costs (mill. € yr-1) estimated over a full rotation in pine
forests in Poland.
Natural resources and bioeconomy studies 48/2021
55
Figure 32. Mean energy use (MJ m-3) by machinery used in forest operations estimated over a
full rotation in pine forests in Poland.
Figure 33. Mean annual energy use (PJ yr-1) by machinery used in forest operations estimated
over a full rotation in pine forests in Poland.
Natural resources and bioeconomy studies 48/2021
56
Figure 34. Mean greenhouse gas emissions (kg CO2 eq. m-3) from machinery used in forest
operations estimated over a full rotation in pine forests in Poland.
Figure 35. Mean annual greenhouse gas emissions (x 1000 t CO2 eq. yr-1) from machinery used
in forest operations estimated over a full rotation in pine forests in Poland.
Natural resources and bioeconomy studies 48/2021
57
Figure 36. Mean annual employment (full-time equivalents) estimated over a full rotation in
pine forests in Poland.
Figure 37. Risk for non-fatal accidents per year in forest operations estimated over a full rota-
tion in pine forests in Poland.
Natural resources and bioeconomy studies 48/2021
58
Figure 38. Risk for fatal accidents per year in forest operations estimated over a full rotation
in pine forests in Poland.
When comparing scenario performances in relation to objectives for TECH4EFFECT project, it
is clear that changing from motor-manual harvesting to mechanical harvesting would mean an
increase in fuel use and related emissions (Table 20). However, changing to more mechanized
harvesting has also some very important advantages. It has a large potential in reducing pro-
duction costs and most of all it can create a very large improvement in occupational safety.
Table 20. T4E goals and achievements in the Polish scenario at country level in pine-domi-
nated forest areas.
T4E goal / Scenario
Mechanized harvesting
20% decrease in
production costs
Production costs decreased by 31%. Both fatal and non-fatal ac-
cidents decreased by 90%
15% decrease in
fuel consumption
Energy use increased by 286%
2% in forest (yield)
productivity
No impact
3.1.6. Italy
Bioeconomy in Italy and role of forestry
The Italian Strategy for Bioeconomy has been launched in 2017, as part of the implementation
process of the National Smart Specialization Strategy, in synergy with the Italian Strategy for
Sustainable Development (APRE 2019; Il Bioeconomista 2019). The Bioeconomy Strategy in
Italy (BIT) has been updated in 2019. The strategy aims at interconnecting the main bioecon-
omy sectors, creating longer, more sustainable and locally routed value chains. It promotes the
integration of research and innovation needs and opportunities, policy, business, and cultural
attitude into a single systemic vision for the bioeconomy in line with the development model
Natural resources and bioeconomy studies 48/2021
59
of the circular economy (CNBBSV 2019). It is envisioned as: i) moving from sectors to systems,
ii) creating value from local biodiversity and circularity, iii) moving from economy to sustainable
bioeconomy, iv) promoting the bioeconomy in the Mediterranean area, and v) moving from
concept into reality.
The bioeconomy refers to the set of economic activities relating to the invention, development,
production and use of biological products, services and processes across four macro-sectors:
agrifood, forestry, biobased industry, and marine bioeconomy. It is important to remark that
marine bioeconomy has a strong relevance in the Italian bioeconomy, the second biggest Eu-
ropean fish industry (CNBBSV 2019). The Action Plan identifies the most significant challenges
and research priorities.
In Italy, the entire bioeconomy sector (including agriculture, forestry, fisheries, food and bev-
erages production, paper and pulp industries, textiles from natural fibers, leather, bio-pharma-
ceuticals, green chemistry, biochemicals and bioenergy) accounted for a total turnover of EUR
330 billion in 2017, and around 2 million employees. The main objective of the Strategy is to
increase the current turnover and jobs by 15% by 2030, while increasing the level of circularity
in the economy (Gyekye 2019; CNBBSV 2019). This will be done by:
a) improving the sustainable production and quality of products in each of the sectors and
interconnecting and leveraging the sectors more efficiently; allowing an effective valorization
of national terrestrial/marine biodiversity, ecosystem services and circularity by creating longer
and more locally routed value chains, where the actions of public and private stakeholders
integrate across the board at the regional, national and EU level; regenerating abandoned/mar-
ginal lands and former industrial sites;
b) creating: i) more investments in R&I, spin offs/ start-ups, education, training, and commu-
nication, ii) better coordination between regional, national and EU stakeholders/policies, iii)
better engagement with the public, and iv) tailored market development actions.
The availability of local competitive biological feedstocks is an important requirement for bio-
economy industries. Italian regions, at an individual level, have a high level of agricultural and
natural landscape specificity linked to the biodiversity of cultivated plants, animals, related eco-
system services and their diverse cultural heritage. Each territory, with its own specificity, can
play an important role in the national bioeconomy, due to the different geographical allocation
of biological resources, technologies, skills and expertises (CNBBSV 2019).
Baseline with processes, volumes and indicators
Coppice management is a widespread forestry practice whereby stand regeneration is ob-
tained from the re-sprouting of cut stumps, rather than from the establishment of new trees
from seed. For this reason, coppice management is only suited to tree species that can sprout
new shoots from their stumps after cutting, that is true for most hardwoods, especially if the
interval between cuts does not exceed 50 or 60 years. Coppice management offers the benefits
of simplified care, prompt regeneration and short waiting time; its main drawback is in offering
relatively small trees, which are only suitable for the production of pulpwood, fencing assort-
ments and energy wood (Buckley 1992). As a main source of firewood, coppice stands were
very common in the European countryside until the advent of fossil fuels (Hédl et al. 2010).
After that, interest in coppice management has faced a steady decline, leading to abandon-
ment and conversion into high forest. Nevertheless, coppice management still persists on large
forest areas: estimates range from over 26 million hectares in the EU and its neighbours in the
Balkans and in the Anatolian plateau (Nicolescu et al. 2015) to ca 16% of all productive forests
Natural resources and bioeconomy studies 48/2021
60
in Europe, covering a total area of ca. 23 million ha (see COST FP1301). These are mainly located
in the far west, south and south-eastern parts of the continent. Over half of European coppice
forests are situated in industrialized countries, such as France, Italy and Spain.
Coppice management is the most common silvicultural system in Italy. Within the approxi-
mately 8,500,000 ha of Italian forests, the forest land classified as coppice currently includes
almost 35% of the national forest cover (approximately 3,663,100 ha) (INFC 2007), yet its dis-
tribution varies between administrative units (INFC 2007). The most important species tradi-
tionally managed as coppice are deciduous oaks (Quercus spp., 33%), European hop hornbeam
(Ostrya carpinifolia Scop., 17%), beech (Fagus sylvatica L., 13%), sweet chestnut (Castanea sativa
Miller, 16%), which are usually grown as pure stands, and the evergreen holly oak (Quercus ilex
L., 10%), which frequently grows in mixed stands (Nicolescu et al. 2015).
Coppice forests are normally harvested for firewood, using manual methods. Unfortunately,
the mechanization of coppice harvesting is especially challenging, because coppice forests
produce relatively small trees, which grow in clumps and have a marked basal sweep (Cacot
2015). That hinders mechanical felling and may result in increased time consumption and oc-
casional stump damage (McEwan et al. 2016). On top of that, broadleaf trees often present
heavy branching, which makes mechanized delimbing and bucking especially difficult (Su-
chomel et al. 2012) . Taken together, the characteristics of coppice trees severely restrict har-
vester productivity, compared with the levels achieved in softwood stands (Labelle et al. 2018).
The strongest limitation to the mechanization of coppice harvesting operations is in the rela-
tively poor quality of the cut surface. In order to assure prompt regeneration, forest regulations
prescribe that cuts must be as clean as possible and cut height may not exceed 10 cm from
the ground surface. Since mechanical felling cannot guarantee that these requirements are
met, forest managers often forbid mechanized felling in their coppice forests and accept the
higher cost of motor-manual felling. However, a proper estimate of the regeneration deficit
possibly caused by mechanized felling, which is far from ascertained. the association between
cut quality and stump resprouting is part of traditional knowledge, which has been questioned
in several scientific studies. It is therefore quite possible that mechanical cutting has no signif-
icant effect on stump regeneration, or that such effect is minimal. This issue is the subject of a
separate line of experiments, still in progress and soon to offer objective evidence of one or
the other outcomes (Spinelli et al. 2017).
Systems and system boundaries
The study (Spinelli et al. 2020; in preparation) considered two alternative coppice-based supply
chains, and namely: 1) a traditional supply chain, using semi-mechanized work systems to pro-
duce split firewood for residential stoves and 2) an innovative supply chain, using fully-mech-
anized work systems to produce chips for a modern district-heating network. the transport,
chipping and heating processes were excluded to only focus on the harvesting operation as
done in other countries in this deliverable.
Stand characteristics were taken from the study by Schweier et al. (2015), and represented
stand 1, i.e. a Mediterranean mixwood coppice with Turkey oak (Quercus cerris L.), downy oak
(Quercus pubescens L.), common maple (Acer campestris L.) and small-leaf ash (Fraxinus oxyph-
illus L.) as the main species. This stand was selected because it represents well the hillside
coppice stands that are most easily accessible to modern machinery, due to their relatively flat,
even and firm soil conditions. Beech and chestnut coppice often grow on steep terrain (mini-
yarders), where the introduction of mechanical felling is technically more difficult to achieve
and may only be considered when cable-assist machine technology will become widespread
Natural resources and bioeconomy studies 48/2021
61
in these regions (Visser and Stampfer 2015). Stands of this type are harvested at the age of 20,
leaving behind ca. 100 standards per hectare, or 20% of the standing mass. Total harvest was
estimated at 120 m3 ha-1 for the traditional system and 150 m3 ha-1 for the innovative one.
The traditional supply chain represented the case described by Picchio et al. (2009) and in-
cluded the following steps: motor-manual felling and processing into 1-m logs with chainsaws;
forwarding by farm tractors with front and rear boxes on a mean distance of 400 m.
Table 21. Baseline including processes and their hour productivity. The outflows are pre-
sented in a hectare level and scaled-up to represent coppice systems with beech and chest-
nut forests in Italy.
Unit
Process
Productivity
(process
units/hour)
Outflow per
one hectare
Outflow for coppice
stands in Italy
ha
Stumps after
coppicing
1 1 3663100
m3
Felling (Motorsaw)
1.3
120
439572000
m3
Extraction
(mechanised)
3 120 439572000
The productivity, production cost and fuel consumption of felling and extraction were obtained
from a large review study recently published by Spinelli et al. (2016a). Fuel use was calculated
as 0.8 l/m3 for felling, 2.7 l/m3 for extraction as input to Energy use and greenhouse gas emis-
sion calculation. Average annual employment for a fulltime equivalent (FTE) was 1608 h (EU-
STAT, 2016).
Natural resources and bioeconomy studies 48/2021
62
Table 22. Baseline indicator values per process unit.
Unit
Process
Employ-
ment
(FTE/pro-
cess unit)
Greenhouse
gas emissions
from machin-
ery (kg CO2
eq./process
unit)
Energy use
(MJ/process
unit)
Production
costs
(€/process
unit)
ha
Stumps after
coppicing
- - - -
m3
Felling
(Motorsaw)
0.000478 2.034 27 11.5
m3
Extraction
(mechanised)
0.000207 7.050 95 15.0
m3
Total
0.000686
9.084
123
26.5
Scenarios with processes, volumes and indicators
Innovation could target harvesting productivity and work safety, as well as assortments with a
shift from firewood to chips, and from manual shortwood harvesting to mechanized whole tree
harvesting. While the demand in bioenergy for energy generation is increasing and coppice
forest could satisfy this demand very well in producing bulk material without form require-
ments fast, also other product categories with similar requirements emerge in the wake of the
EU Circular Bioeconomy strategy (2018), namely as raw material for chemicals, pharmaceuticals,
textiles, even fuels. Coppice forests are ideally suited for supplying this market with significant
amounts of wood, if production can be achieved at competitive conditions (Jansen and Kuiper
2004). In particular, harvesting cost must be reduced, while increasing operator safety and
comfort (Picchio et al. 2009). A dramatic improvement in this direction is only obtained through
mechanization, which has a multiplier effect on operator productivity and offers a much safer
and comfortable workstation than can ever be found for the motor-manual work techniques
that characterize traditional coppice harvesting (Spinelli et al. 2016).
The innovative supply chain included the following steps: mechanical felling and bunching with
excavator-mounted feller shears; extraction by purpose-built forwarder, on a mean distance of
400 m.
Table 23. Baseline including processes and their hour productivity. The outflows are pre-
sented in a hectare level and scaled-up to represent coppice systems with beech and chest-
nut forests in Italy.
Unit
Process
Productivity
(process
units/hour)
Outflow per
one hectare
Outflow for coppice
stands in Italy
ha
Stumps after
coppicing
1 1 3,663,100
m3
Felling (mechanised)
7
150
549,465,000
m3
Extraction
(mechanised)
11 150 549,465,000
Natural resources and bioeconomy studies 48/2021
63
As to the 120 m3 ha-1 vs. 150 m3 ha-1 that is because the conventional system produces tradi-
tional firewood and thus limits the use wood to a minimum diameter of 56 cm, whereas the
chipping of whole trees allows to recover smaller branches as well. Fuel use was calculated as
1.7 l/m3 for felling, 1.1 l/m3 for extraction.
Table 24. Scenario indicator values per process unit
Unit
Process
Employ-
ment
(FTE/pro-
cess unit)
Greenhouse
gas emissions
from machin-
ery (kg CO2
eq./process
unit)
Energy use
(MJ/pro-
cess unit)
Production
costs
(€/process
unit)
ha
Stumps after coppic-
ing
- - - -
m3
Felling (mechanised)
0.000089
4.532
61
9.3
m3
Extraction (mecha-
nised)
0.000057 2.884 39 7.7
m3
Total
0.000145
7.050
100
17.0
Environmental indicators
Figure 39. Environmental indicators. Energy use and GHG emission per m3.
0
20
40
60
80
100
120
140
Energy use (MJ/process unit) GHG emission (kg CO2 equivalent/process
unit)
Environmental indicators per m3
Traditional (per m3) Innovative (per m3)
Natural resources and bioeconomy studies 48/2021
64
Figure 40. Environmental indicators. Energy use and GHG emission per ha.
Clear reduction in Energy use and Greenhouse gas emission of 18% per m3, and slight increase
of 2% per ha. Note that in the comparison per ha the extraction rate is higher 150 m3 ha-1
instead of 120 m3 ha-1.
Social indicators
Employment numbers decline from 0.000686 FTE/m3 to 0.000145 FTE/m3 due to mechaniza-
tion. While particularly in rural areas a decrease in employment potential is negative, the
work itself becomes a lot safer and thus attractive. Simultaneously, as harvesting potentials and
volumes are expected to increase, and increase in efficiency and decrease in workload will po-
tentially reduce pressure on the workforce and make it possible to meet the demands.
Multiplied with the potential extraction of 120 m3 ha-1 for the traditional system on all of Italy’s
coppice forests would require 10051 FTE of workers, assuming 30-year rotation periods. For
the innovative system, extracting 150 m3 ha-1 2656 FTE would be required annually, again as-
suming 30-year rotation periods. The reduction in needed, qualified workers while still con-
siderable makes it more feasible that timber is mobilized from these coppice stands.
0,0
2000,0
4000,0
6000,0
8000,0
10000,0
12000,0
14000,0
16000,0
Energy use (MJ/process unit) GHG emission (kg CO2
equivalent/process unit)
Environmental indicators per ha
Traditional (per ha) Innovative (per ha)
Natural resources and bioeconomy studies 48/2021
65
Figure 41. Social indicator employment per m3.
Figure 42. Social indicator employment per ha.
The study also considered two main types of social impacts: 1) employment potential and re-
lated potential for wages and salaries, and 2) work safety. The former was estimated by invert-
ing worker productivity for each work step: this was especially easy because operators were
considered to work independently from each other, even when multiple operators were en-
gaged on the same worksite, as it is common with motor-manual felling and processing. The
Natural resources and bioeconomy studies 48/2021
66
latter was estimated using existing scientific literature on the subject. In particular, it was as-
sumed that mechanization would allow reducing non-fatal accident by a factor 3, that is from
15 to 5 claims per 100 workers (Bell 2002, Laflamme and Cloutier 1998), and fatal accidents by
a factor 5, from 0.5 to 0.1 fatalities per million m3 (Klun and Medved 2007).
Economic indicators
Figure 43. Economic indicator. Production cost per m3.
Figure 44. Economic indicator. Production cost per ha.
0,0
5,0
10,0
15,0
20,0
25,0
30,0
Traditional (per m3) Innovative (per m3)
Production cost (€/process unit)
Production cost (€/process unit)
Natural resources and bioeconomy studies 48/2021
67
Lower costs of 36% per m3 for innovation, and 20% per ha despite increased volume per ha.
Quantified T4E goals and achieved results with these scenarios are reflected in Table 25.
Table 25. T4E goals and achievements in the Italian scenario in coppice stands.
T4E goal / Scenario
Mechanized harvesting
20% decrease in
production costs
Production costs decreased by 36%. Fatal accidents decreased by
80% and non-fatal by 66%.
15% decrease is fuel
consumption
Energy use and Greenhouse gas emission decreased by 18%.
2% in forest (yield)
productivity
There are no results on the de facto sump regeneration available
yet (Ongoing study), however, from initial observations it seems
that stumps are healthy and no loss in growth potential is expected.
Thus, the forest productivity is likely to be maintained (0%), while at
the same time increasing the outtake of materials by 25%.
3.1.7. Denmark
Bioeconomy in Denmark and role of forestry
Denmark does not yet have a dedicated national bioeconomy strategy. However, the govern-
ment’s commitment to bioeconomy is framed by the “Growth Plan for Foods” and the “Growth
Plan for Water, Bio and Environmental Solutions” (2013). In addition, Denmark has appointed
a National Bioeconomy Panel, composed of experts from universities, industry and non-gov-
ernmental organisations as well as policymakers. In the meantime, there are strategical papers
for almost all industrial sectors stressing the meaning of a sustainable economy and encour-
aging respective developments. This concerns not only agriculture and forestry, but also the
aquaculture, food and energy sector (German Bioeconomy Council 2015).
Denmark joined the European Union Strategy for the Baltic Sea Region (EUSBSR), which is the
first Macro-regional Strategy in Europe. The Strategy is divided into three objectives, repre-
senting the three key challenges of the Strategy: saving the sea, connecting the region and
increasing prosperity. Each objective relates to a wide range of policies and has an impact on
the other objectives. Bioeconomy is one of 13 Policy Areas of the EUSBSR Action Plan (EUSBSR
2019; Nordic Council of Ministers 2019a).
Baseline with processes, volumes and indicators
The baseline value chain is built to represent average Danish coniferous growth conditions. The
baseline consists of a rotation of around 50 years for one hectare even-aged Norway spruce
(Picea abies) stand. Rotation starts from soil scarification and planting and ends with final
felling.
The forest management practices (Table 26), timing, and intensity are based on simulation data
by Cardellini et al. (2018) and a report by Raae & Strange (In Routa et al. 2020b). Value chains
for spruce start from scarification to improve seedling establishment. For spruce, planting is
the preferred regeneration method. In the baseline scenario, Norway spruce is planted in the
whole area of a hectare except on those areas where skidding roads are planned. Skidding
Natural resources and bioeconomy studies 48/2021
68
roads take up about 20% of the area. Planting distance is 1.5 x 1.65m which results in about
3,200 seedlings per hectare.
In year 23, when the mean tree height is about 9.4m, a first thinning of the stand takes place
removing approximately 20% of the standing volume. In year 25, a second thinning takes place
removing approximately 25% of the standing volume.
To estimate biomass production for Norway spruce, we applied modelled results by Raae &
Strange (In Routa et al. 2020b) from planting experiments at four different locations in Den-
mark: Løvenholm, Store Hareskov, Harager Hegn and Nørlund. Raae and Strange assumed a
yield class 16.4 m3 ha-1 yr-1 for Norway spruce at a planting distance of 1.75 x 1.75 m (initial
planting density of 3,265 plants per ha) and a dbh of 10.6 cm at age 23 and simulated the dbh
of various planting distances. To estimate biomass production for Norway spruce after year 23,
we used the modelled results from even-aged uniform spruce clear-cut systems in Denmark
from Cardellini et al. (2018).
The baseline is presented in Table 26 on a hectare level and then upscaled to whole Denmark.
Total productive forest area in Denmark is 625,603 hectares of which 38% is dominated by
coniferous forests (Nord-Larsen et al. 2018). Norway spruce (Picea abies) is the most common
species and covers 17.1% of the forest area (about 106,978 ha), followed by beech (Fagus syl-
vatica, 12.9%), pine (Pinus ssp, 12%), oak (Quercus robur, 9.6%), Sitka spruce (Picea sitchensis,
6.1%), abies (Abies normania 4.5%), maple (Acer pseudoplatanus, 3.5%), and ash (Fraxinus ex-
celsior, 3.4%).
Table 26. Baseline including processes and their hour productivity. The outflows are pre-
sented in a hectare level and scaled-up to represent spruce-dominated forests in Denmark.
Unit
Process
Productivity
(process
units/hour)
Outflow per
one hectare
Outflow for Norway
spruce stands in
Denmark
ha
Site preparation (removals
of residuals, chipping,
scarification)
1 1 106,978
ha
Planting
0.08
1
106,978
m3
Thinning 1 (harvester)
6.5
20
2,139,560
m3
Thinning 2 (harvester)
6.5
37.5
4,011,675
m3
Final felling (harvester)
19
215
23,000,270
The sustainability indicator values used in the baseline scenario are presented in Table 26. The
employment is estimated using annual full-time employment of 1,411 hours and process-
based hour productivities according to Cardellini et al. (2018), Tuomasjukka et al. (2015) and
Laine et al. (2016). Fuel consumption and related emissions of the machinery were taken from
Finnish statistics database Lipasto (VTT 2016). Productions costs were taken from Aaronen
(2011), Tuomasjukka et al. (2015) and Routa et al. (2019).
Natural resources and bioeconomy studies 48/2021
69
Table 27. Baseline indicator values per process unit.
Unit
Process
Employ-
ment
(FTE/pro-
cess unit)
Greenhouse gas
emissions from
machinery (kg
CO2 eq./process
unit)
Energy use
(kWh/
process
unit)
Production
costs
(€/process
unit)
ha
Site preparation (remov-
als of residuals, chip-
ping, scarification)
0.000709 66 77 264
ha
Planting
0.00845
-
-
384.12
m3
Thinning 1
(harvester)
0.000109 18.4 22.9 6
m3
Thinning 2
(harvester)
0.000109 18.4 22.9 6
m3
Forwarding
(forwarder)
0.0000601 7.3 8.9 3.16
m3
Final felling
(harvester)
0.0000373 6.3 7.8 7.09
Scenarios with processes, volumes and indicators
A considerable share of green energy production in Denmark relies on wood energy. A large
part of the energy wood used in Denmark is imported. It has been debated if these imports
are sustainable and should be restricted. This would imply an increased demand for energy
wood from Danish forests. Although, the forest area has increased and still is increasing, the
supply would in the long run be insufficient under the present conditions. It has been sug-
gested to increase forest production in existing forests by silvicultural measures. One option is
to replace trees with faster growing tree species.
This assessment evaluates the implications for biomass production if a fast-growing species, in
this case hybrid larch, is mixed when establishing stands of Norway spruce. The impacts for
biomass production are simulated if hybrid larch is planted on skidding roads.
Increasing production using ”power-cultures”
The term “power culture” reflects regeneration systems which make use of fast-growing tree
species mixed with a main species, for instance Norway spruce. The main species is meant for
high quality timber production. It is the idea that the fast-growing species both supports the
main species in the stand during the first years and after 1025 years it delivers a profitable
output in terms of biomass (Figure 39). The power culture model can in principle be applied in
any planted forest stand following a final felling. The fast-growing tree species can be planted
in rows, equally distributed over the whole area or in rows or where future skidding roads are
planned. In this case study, quality timber production of Norway spruce is assessed in combi-
nation with hybrid larch as the fast-growing supportive species.
For Norway spruce, a yield class 16.4 (m3 ha-1 yr-1) was assumed (Raae & Strange In Routa et
al. 2020b). Growth and yield of Norway spruce in Danish forests is very well documented. Less
documentation is found for hybrid larch. Lately, a number of yield tables for hybrid larch have
been elaborated in Denmark and Southern Sweden. The first 3540 years of growth is relatively
well supported by measurements in existing stands of hybrid larch. Inventories of older stands
are much less documented. However, in a Danish context the growth of hybrid Larch seems to
Natural resources and bioeconomy studies 48/2021
70
be relatively unaffected by soil conditions. For the simulations presented in this study, a yield
class of 17 m3 ha-1 yr-1 was assumed (Raae & Strange In Routa et al. 2020b). In their study, Raae
and Strange were interested in modelling the growth of the first 23 year of the growth of the
hybrid larch. At this point the increment of the larch peaks and it is at the same time important
to have the larch removed in order not to reduce production of quality timber of Norway
spruce.
In the assessment, two scenarios were compared with the baseline. In the baseline, as stated
above, Norway spruce is planted in the whole area of a hectare except in those areas where
skidding roads are planned (Figure 45).
In the second scenario Norway spruce on skidding roads, Norway spruce is planted over the
whole area of a hectare, including the skidding roads. Spacing is 1.5 x 1.65 m which results in
approximately 4,000 seedlings per hectare.
In the third scenario Hybrid larch on skidding roads, Norway spruce is planted over the whole
area of a hectare except where skidding roads are planned. Hybrid larch is planted on the
skidding roads. Skidding roads take up about 20% of the area. Spacing is 1.5 x 1.65 m resulting in
approximately 3,200 Norway spruce and 220 hybrid larch seedlings per hectare (spacing 3 x 3 m).
In the second and third scenario, spruce or hybrid large are harvested in year 23 in order to
establish skidding roads and about 20% of the standing volume will be removed in thinning.
Hybrid larch is estimated to have reached a total production usable for wood chips after 23
years at a height of 16,7m. After the first thinning in year 23, stands are managed the same
way and develop along the same pattern in all three scenarios. In year 25, a second thinning
of the stand takes place removing about 25% of the standing volume.
Figure 45. Power cultures for increased biomass and energy production.
Planting spruce on skidding roads resulted in a 3.3% production costs reduction (Figure 46.
Planting fast growing hybrid larch on the same skidding roads resulted in a 19.6% cost reduc-
tion. However, energy use and its related greenhouse gas emissions from machinery increased
Natural resources and bioeconomy studies 48/2021
71
by 6.4% for planting spruce and 20.9% and for planting hybrid larch (Figure 47, Figure 48,
Figure 49, Figure 50). This increase in energy use and emissions was mainly due to that har-
vesting small-diameter trees requires more energy and consequently also causes more emis-
sions compared to harvesting larger diameter wood in final felling. Since both planting spruce
and larch on skidding roads improves forest yield (by about respectively 10% or 44%), employ-
ment increased by 12% for planting spruce and by 50% for planting hybrid larch. For the whole
country this would mean annually the creation of 11 additional jobs for planting spruce and 44
additional jobs for planting hybrid larch (Figure 51).
Figure 46. Production costs (€/m3) estimated over a full rotation in spruce forests in Denmark.
Figure 47. Mean energy use (MJ m-3) by machinery used in forest operations estimated over a
full rotation in spruce forests in Denmark.
Natural resources and bioeconomy studies 48/2021
72
Figure 48. Mean annual energy use (TJ yr-1) by machinery used in forest operations estimated
over a full rotation in spruce forests in Denmark.
Figure 49. Mean greenhouse gas emissions (kg CO2 eq. m-3) from machinery used in forest
operations estimated over a full rotation in spruce forests in Denmark.
Natural resources and bioeconomy studies 48/2021
73
Figure 50. Mean annual greenhouse gas emissions (x 1000 t CO2 eq. yr-1) from machinery used
in forest operations estimated over a full rotation in spruce forests in Denmark.
Figure 51. Mean annual employment (full-time equivalents) estimated over a full rotation in
spruce forests in Denmark.
When comparing scenario performances in relation to objectives for TECH4EFFECT project, it
is obvious that planting spruce on skidding roads would result in a small cost reduction, but
planting hybrid larch would reach the T4E goal of reducing production costs by the target of
20%. However, both management options would at the same time result in an increase in fuel
use and related emissions (Table 28). Nevertheless, another advantage was that forest yield
increased as previously unused skidding roads are now also used for wood production, leading
to a more efficient use of land, which is a scarce resource.
Natural resources and bioeconomy studies 48/2021
74
Table 28. T4E goals and achievements in the Danish scenarios at country level in spruce
dominated forest areas.
T4E goal / Scenario
Plant spruce on
skidding roads
Plant hybrid larch on
skidding roads
20% decrease in production costs
Decreased by 3.3%
Decreased by 19.6%
15% decrease in fuel consumption
Increases by 6.4%
Increases by 20.9%
2% in forest (yield) productivity
Increased by 10%
Increased by 44%
3.2. Upscaling top-down: based on D7.2 volumes and D3.4 top
performance figures for all of Europe
3.2.1. Bioeconomy in Europe and role of forestry
The EU Bioeconomy Strategy was launched back in 2012, addressing the production of renew-
able biological resources and their conversion into bio-products and bioenergy. The 2018 up-
date aims for the deployment of a sustainable European bioeconomy to maximize its contri-
bution towards the 2030 Agenda and its Sustainable Development Goals (SDGs), as well as the
Paris Agreement (EC 2019). The renewed bioeconomy strategy supports the transition to a
sustainable and circular bioeconomy, fitting wider EU priorities and policies (climate, circular,
innovation, food, energy, trade, industry, agriculture, fisheries and marine, etc.). The purpose
of the updated European Bioeconomy Strategy is therefore to further develop a bioeconomy
that valorizes and preserves ecosystems and biological resources, drives the renewal of our
industries and the modernization of our primary production systems through bio-based inno-
vation, involves local stakeholders, protects the environment and enhances biodiversity (EC
2018b).
The objectives of the Bioeconomy Strategy (EC 2018c) are:
1. Ensuring food and nutrition security
2. Managing natural resources sustainably
3. Reducing dependence on non-renewable resources
4. Mitigating and adapting to climate change
5. Strengthening European competitiveness and creating jobs
The action plan focuses on: (i) strengthening and scaling up the bio-based sectors, unlocking
investments and markets; (ii) deployment of local bioeconomies rapidly across the whole of
Europe; and (iii) understanding the ecological limitations of the bioeconomy (EC 2018d, EC
2019).
The European bioeconomy is one of the EU’s largest and most important sectors encompassing
agriculture, forestry, fisheries, food, bioenergy and bio-based products with an annual turnover
of around 2.3 trillion euro and employing around 18 million people (EC 2018d). It is estimated
that bio-based industries could create up to one million green jobs by 2030, especially in rural
and coastal areas (ENRD, 2019). Currently, bioeconomy accounts for 7% of the European econ-
omy.
The bioeconomy, while it benefits the whole society, has a special resonance for rural areas,
were most of the biological resources (plants, animals, micro-organisms and derived biomass,
Natural resources and bioeconomy studies 48/2021
75
including organic waste) are produced. The mainstreaming of the bioeconomy is being accel-
erated by Rural Development Programmes (RDPs) around Europe, leading to the production
of sustainable food and feed, innovative bio-based products, renewable energy and other ser-
vices. The processing and distribution of bio-based products from food and feed to fuels and
materials creates new opportunities for processors, retailers and consumers particularly in
rural areas, but also beyond (ENRD 2019).
Apart from the forests' ecological value and impact on the EU landscape, the forest sector is
also an economic resource. The overall level of EU-28 roundwood production reached an esti-
mated 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). Figure 52 shows the forest share of the land area in Europe.
Figure 52. Forest map of Europe (Gunia et al. 2011).
The EU’s wood-based industries cover a range of downstream activities, including woodwork-
ing industries, large parts of the furniture industry, pulp and paper manufacturing and convert-
ing industries, and the printing industry. Together, some 420,000 enterprises were active in
wood-based industries across the EU-28; they represented one in five (20 ) manufacturing en-
terprises across the EU-28, highlighting that - with the exception of pulp and paper manufac-
turing that is characterized by economies of scale - many wood-based industries had a rela-
tively high number of small or medium-sized enterprises.
The economic weight of the wood-based industries in the EU-28 as measured by gross value
added was equivalent to EUR 139 billion or 7.3% of the manufacturing total in 2015. Within the
EU-28’s wood-based industries, the highest share was recorded for pulp, paper and paper
Natural resources and bioeconomy studies 48/2021
76
products manufacturing (32.9% or EUR 46 billion), while the other three sectors had nearly
equal shares - printing and service activities related to printing and the manufacture of furni-
ture each amounted to 2122% of the gross value added of wood based industries, while the
manufacturing of wood and wood products made up 24%. The wood-based industries em-
ployed 3.3 million persons across the EU-28 in 2015 or 11% of the manufacturing total. There
were 2 million persons employed within both the manufacture of wood and wood products
and the manufacture of furniture, 644,000 persons were recorded for the activity of pulp, paper
and paper products manufacturing, the lowest employment of the four activities (Eurostat
2018b).
Bio-Innovation in forest sector
The EU supports the bioeconomy with research and innovation funding. It has already invested
€3.85 billion under Horizon 2020 (20142020) and proposed €10 billion for food and natural
resources, including the bioeconomy, under Horizon Europe (20212027) (EC 2018b, EC 2018c).
Forests provide biomass that can have a wide range of uses as recent advances have demon-
strated. Basically any item made from fossil fuels can be made out of trees, ranging from energy
production, construction material, furniture, paper and many different bio-based products,
such as wood-based textiles, packaging material, carbon fibre, bio-based plastics and compo-
sites, etc.
Figure 53 shows the EU Member States and regions having research and innovation priorities
in the field of forest-based bioeconomy for the 20142020 period.
Natural resources and bioeconomy studies 48/2021
77
Figure 53. EU-28 regions/Member States with Bioeconomy Research & Innovation priorities
(20142020) related to forest-based bioeconomy. Source: Spatial Foresight et al. 2017.
3.2.2. Material flow results for Baseline and Scenarios
Tree harvesting systems are either motor-manual, often with tree-length method, or fully
mechanized, with cut-to-length method. In Europe these are the most common systems:
1. Harvester and forwarder in cut-to-length method (CTL)
2. Chainsaw and cable yarder (WTS/CTL)
3. Chainsaw and skidder in whole-tree system (WTS)
Natural resources and bioeconomy studies 48/2021
78
For the scenario, all motormanual WTS were considered to be replaced by CTL systems, and
high levels of mechanization. The baseline data of shares for most common harvesting opera-
tion systems was based on Tuomasjukka et al. (2018) and WP2-4 communication, and thus
adjusting for Tech4Effect scenario calculations to reflect all harvesting while excluding stump
harvesting. Stump harvesting is practiced only in selected areas in Europe (mainly Sweden,
Finland) and highly controversial while yielding only low volumes under high energy input. For
this reason, they were excluded from the calculations in this study, while still using Verkerk et
al. 2019 as input to the calculation.
Table 29. Percentage volumes and operational scenarios.
Country
*chain-
saw
CTL
[%]
*chain-
saw
WTS
[%]
*har-
vester
CTL
[%]
#for-
warder
CTL [%]
#skid-
ding
WTS
[%]
#ca-
bleyard-
ing [%]
BASELINE
EU 28 2020
12
32
55
62
35
3
CEU
7
52
41
45
47
8
SEU
42
34
24
38
55
8
EEU
15
75
9
13
85
1
NEU
7
1
91
100
0
0
SCENARIO
EU 2050
Removal
46
54 97
3
CEU
59
41
92
8
SEU
91
9
92
8
EEU
91
9
99
1
NEU
9
91
100
0
Verkerk et al. 2019 estimated the potential availability of forest biomass. According to the cal-
culations, forests in 39 European countries could currently provide 401 million tonnes dry mat-
ter yr-1 of biomass. The potential availability of woody biomass in the 28 investigated countries
in 2020 is estimated at 335 million tonnes dry matter yr-1 overbark (or 732 million m3 yr-1 over-
bark), or 330 million tonnes dry matter yr-1 overbark (or 722 million m3 yr-1 overbark) excluding
stumps for T4E impact upscaling calculations, according to the Base scenario.
The potential was projected to decrease to 319 million tonnes dry matter yr-1 overbark by 2050,
but in general, the potential was rather stable over time. This is mainly because the potential
for each year is based on the average maximum harvest level that can be maintained through-
out the next 50-year period. By 2050, this potential could increase to 409 million tonnes dry
matter yr-1 overbark for the combined scenario of Enhanced production and Improved supply;
without stumps this potential is 365 million tonnes dry matter yr-1 overbark (or 797 million m3 yr-1
overbark) as used for the T4E impact upscaling calculations.
The potential removal volumes used for the material flow and indicator calculation for the
Baseli