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Time dependent greenhouse gas emission profiles for selected forest based bioenergy pathways relevant in Austria

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This paper investigates the timing of emissions from the use of woody bioenergy in an Austrian context using state of the art forest and wood product supply-chain models. It assesses the greenhouse gas implications of a variety of options for Austria to meet its renewable targets under the national Biomass Action Plan. Of the options assessed, increasing the intensity of forest management fulfils the target but also causes a short term increase in emissions as compared to business-as-usual (50 years). However, long-term this option reduces greenhouse gas emission. The period of time is less than previously estimated due to the inclusion of substitution of greenhouse gas intense materials by wood products. In particular, the role of paper in substituting plastic based is very important. For this reason allowing biomass to be simply diverted from paper production for energy may allow renewable energy targets to be reached, but never reduces greenhouse gas emissions
Conceptual design of the SMART FORESTS tool ......................................................... 7 Figure 2: Estimated biomass required to meet demand scenarios ......................................... 12 Figure 3: Actual versus modelled biomass required ................................................................ 13 Figure 4: Results for the Beech forest assuming BAU management strategy, BaU policy scenario, Baseline climate scenario ................................................................................. 19 Figure 5: Results for the Spruce-Larch forest assuming BAU management strategy, BaU policy scenario, Baseline climate scenario ................................................................................. 20 Figure 6: Beech forest: A comparison of results with and without climate change ................ 22 Figure 7: Spruce-larch forest: A comparison of results with and without climate change ..... 22 Figure 8: Beech forest: A comparison of results with BAU and intensive management (AM1 management strategy) ..................................................................................................... 23 Figure 9: Spruce-larch forest: A comparison of results with BAU and intensive management (AM1 management strategy) ........................................................................................... 24 Figure 10: Beech forest: A comparison of results with BAU and WfE policies ........................ 26 Figure 11: Spruce-larch forest: A comparison of results with BAU and WfE policies .............. 27 Figure 12: Spruce-larch forest: A comparison of results with normal recycling and diversion of all discarded paper to energy use by 2020 .................................................................. 32 Figure 13: Beech forest: A comparison of results with business-as-usual and higher efficiency residential combustion technologies phased-in by 2020 ................................................ 33 Figure 14: Spruce-larch forest: sensitivity of results to the heat substitution factor .............. 36 Figure 15: Spruce-larch forest: sensitivity of results to the paper substitution factor ............ 37 Figure 16: Spruce-larch forest assuming no wood product substitution ................................ 38 Figure 17: Spruce-larch forest: sensitivity to paper lifetime .................................................... 39
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Selecting Management Alternatives
Responding to Targets. Forest Optimization
for Renewable Energy and Sequestration
using Time-dependent Strategies
Time dependent greenhouse gas
emission profiles for selected forest
based bioenergy pathways relevant in
Austria
D. N. Bird, L. Canella, G. Lettmayer, H. Schwaiger
S. Perez, M. Lexer, W. Rammer
Deliverables D.2.3. D.3.2 and D.4.1
Date: 31 October 2013
Del. D.2.3, D.3.2. and D.4.1, October 2013
1
Time dependent greenhouse gas
emission profiles for selected forest
based bioenergy pathways relevant in
Austria
Deliverables D.2.3, D.3.2. and D.4.1
Date: 31 October 2013
Abstract
This paper investigates the timing of emissions from the use of woody
bioenergy in an Austrian context using state of the art forest and wood
product supply-chain models. It assesses the greenhouse gas implications of a
variety of options for Austria to meet its renewable targets under the national
Biomass Action Plan. Of the options assessed, increasing the intensity of forest
management fulfils the target but also causes a short term increase in
emissions as compared to business-as-usual (50 years). However, long-term
this option reduces greenhouse gas emission. The period of time is less than
previously estimated due to the inclusion of substitution of greenhouse gas
intense materials by wood products. In particular, the role of paper in
substituting plastic based is very important. For this reason allowing biomass
to be simply diverted from paper production for energy may allow renewable
energy targets to be reached, but never reduces greenhouse gas emissions
Del. D.2.3, D.3.2. and D.4.1, October 2013
2
Table of Contents
1 Purpose ........................................................................................................................ 5
2 Introduction ................................................................................................................. 5
3 Materials and methods ................................................................................................ 6
3.1 Methods ....................................................................................................................... 6
3.2 Materials .................................................................................................................... 10
Policy scenarios .......................................................................................................... 10
Forest types, management strategies and climate scenarios ................................... 15
4 Results ........................................................................................................................ 18
4.1 Business-as-usual policy scenario .............................................................................. 18
Variation with forest type .......................................................................................... 18
Variation with climate scenario ................................................................................. 21
Variation with management regime ......................................................................... 21
4.2 Wood-for-energy policy scenario .............................................................................. 28
4.3 Increasing use of discarded paper for energy ........................................................... 29
4.4 Increasing the efficiency of residential energy systems. ........................................... 31
5 Discussion and conclusions ....................................................................................... 34
5.1 Sensitivity to assumptions ......................................................................................... 34
Type of forest management intensification .............................................................. 34
Energy substitution factor ......................................................................................... 36
Material substitution factors ..................................................................................... 37
Lifetime of paper ....................................................................................................... 38
Shortcomings of the study ......................................................................................... 39
6 Acknowledgements ................................................................................................... 39
7 References ................................................................................................................. 40
Del. D.2.3, D.3.2. and D.4.1, October 2013
3
List of Tables
Table 1: Estimated parameters for the paper pool .................................................................... 8
Table 2: Effective displacement factors for electricity generation for the period 2000 to 2011
............................................................................................................................................ 9
Table 3: Modelled displacement matrix for electricity generation for the period 2000 to 2011
............................................................................................................................................ 9
Table 4: Effective displacement factors for heat for the period 2000 to 2010 ......................... 9
Table 5: Modelled displacement matrix for heat production for the period 2000 to 2010 ...... 9
Table 6: Effective displacement factors for materials for the period 2000 to 2010 ................ 10
Table 7: Modelled displacement matrix for material consumption for the period 2000 to
2010 .................................................................................................................................. 11
Table 8: Modelled combined displacement matrix for material consumption for the period
2000 to 2010 .................................................................................................................... 11
Table 9: Calculated demand for wood products ...................................................................... 12
Table 10: The estimated proportion of harvested biomass of various diameter classes that
goes directly to specific wood industries ......................................................................... 14
Table 11: Shares by energy of technology used for combusting biomass for fuel wood ........ 15
Table 12: Shares by biomass input of technology used for combusting biomass for fuel wood
.......................................................................................................................................... 15
Table 13: Modelled output as a % of output in 2000 for BAU management, BAU energy and
baseline climate................................................................................................................ 18
Table 14: Modelled output from increased intensification as a % of output in 2000 with BAU
management .................................................................................................................... 25
Table 15: Modelled output from WfE policy scenarios as a % of output in 2000 with BAU
policy scenarios ................................................................................................................ 28
Table 16: The estimated proportion of harvested biomass of various diameter classes that
goes directly to specific wood industries under a modified Wood-for-Energy scenario. 30
Table 17: Proportion by biomass input and efficiencies of various biomass combustion
technologies in Austria in 2005. ....................................................................................... 31
Del. D.2.3, D.3.2. and D.4.1, October 2013
4
List of Figures
Figure 1: Conceptual design of the SMART FORESTS tool ......................................................... 7
Figure 2: Estimated biomass required to meet demand scenarios ......................................... 12
Figure 3: Actual versus modelled biomass required ................................................................ 13
Figure 4: Results for the Beech forest assuming BAU management strategy, BaU policy
scenario, Baseline climate scenario ................................................................................. 19
Figure 5: Results for the Spruce-Larch forest assuming BAU management strategy, BaU policy
scenario, Baseline climate scenario ................................................................................. 20
Figure 6: Beech forest: A comparison of results with and without climate change ................ 22
Figure 7: Spruce-larch forest: A comparison of results with and without climate change ..... 22
Figure 8: Beech forest: A comparison of results with BAU and intensive management (AM1
management strategy) ..................................................................................................... 23
Figure 9: Spruce-larch forest: A comparison of results with BAU and intensive management
(AM1 management strategy) ........................................................................................... 24
Figure 10: Beech forest: A comparison of results with BAU and WfE policies ........................ 26
Figure 11: Spruce-larch forest: A comparison of results with BAU and WfE policies .............. 27
Figure 12: Spruce-larch forest: A comparison of results with normal recycling and diversion
of all discarded paper to energy use by 2020 .................................................................. 32
Figure 13: Beech forest: A comparison of results with business-as-usual and higher efficiency
residential combustion technologies phased-in by 2020 ................................................ 33
Figure 14: Spruce-larch forest: sensitivity of results to the heat substitution factor .............. 36
Figure 15: Spruce-larch forest: sensitivity of results to the paper substitution factor ............ 37
Figure 16: Spruce-larch forest assuming no wood product substitution ................................ 38
Figure 17: Spruce-larch forest: sensitivity to paper lifetime .................................................... 39
Del. D.2.3, D.3.2. and D.4.1, October 2013
5
1 Purpose
Increased use of renewable energy is a key EU strategy for reducing its greenhouse gas
(GHG) emissions and meeting other goals such as those established in the EU Energy and
Climate Package. Woody biomass from forests will play a major role in meeting these targets
(Ragwitz et al. 2009). However, extraction of woody biomass from forests can, in some
cases, lead to near-term increases of GHG compared to continued use of fossil fuel, with
GHG benefits emerging later. Therefore, it is important for Austrian decision makers to
understand the timing of GHG benefits of wood-based bioenergy.
In 2006 Austria published its official national Biomass Action Plan (BMLFUW, 2006). In this
document, the Federal Ministry of Agriculture, Forestry, Environment and Water
Management (BMLFUW) set the following goals for biomass-based energy in Austria until
2020:
Increase the utilization of biomass for energy by 75% by 2010; and
Double the amount of energy from renewable resources to 45% of the total
consumption by 2020.
The purpose of this paper is to investigate options that the Austria government can use to
fulfil the goals of the Biomass Action Plan that minimize greenhouse gas emissions short
term while providing long term benefits.
2 Introduction
Increased use of renewable energy is a key EU strategy for reducing its greenhouse gas
(GHG) emissions and meeting other goals such as those established in the EU Energy and
Climate Package. Woody biomass from forests will play a major role in meeting these targets
(Ragwitz et al. 2009). However, extraction of woody biomass from forests can, in some
cases, lead to near-term increases of GHG compared to continued use of fossil fuel, with
GHG benefits emerging later. This apparent paradox was first discussed by Walker et al
(2010) and Zanchi et al (2010). These result were followed by many studies that showed
similar effects (Agostini et al 2013, Böttcher et al, 2012, Holtsmark, 2010, 2012 2013a,
2013b, Hudiburg et al 2011, Lippke et al 2012, McKechnie et 2011, Mitchell et al 2012, Repo
et al 2011, , Zanchi et al 2011). Namely, increasing the intensification of forest management
(i.e. removals) for bioenergy production causes short term an increase in emissions (aka the
“carbon-debt”).
In this context, the SMART FORESTS project was conceived with the goal to provide
stakeholders with currently unavailable information and tools to evaluate and improve
management of forest resources, taking a full range of forest services e.g., bioenergy,
other products, climate change, disturbances and environmental protection as well as
alternate policy scenarios into account. The project planned to
Del. D.2.3, D.3.2. and D.4.1, October 2013
6
1. Model responses in representative Austrian forest types over time;
2. Compare GHG emissions of wood-based bioenergy and fossil fuels over time;
3. Identify promising carbon storage/fossil fuel substitution combinations and
4. Develop a decision tool available for stakeholder use.
This report represents the culmination of the SMART FORESTS project.
3 Materials and methods
3.1 Methods
To calculate the time dependent greenhouse gas emission profiles for selected forest based
bioenergy pathways relevant in Austria, we use a tool designed in the SMART FORESTS
project specifically for this task. The tool models the flow of wood in Austria by combining
forest dynamics modelling using PICUS (Lexer and Hönninger, 2001, Seidl et al. 2005, Seidl et
al. 2011), done by BOKU with a life-cycle assessment of (LCA) product-chain emissions from
GEMIS LCA database (GEMIS, 2013), supplied by JOANNEUM RESEARCH. Figure 1 shows the
components of the SMART FORESTS tool superimposed on the flow of wood in Austria in
2009 (Österreichischer Biomasse-Verband, 2011).
The outputs from the PICUS model are the carbon stocks in the forest (including soil organic
carbon) and the wood extracted as a result of management. The wood is divided into eight
components:
1. bark;
2. tops;
3. stems in the following diameter ranges
i. 15 19 cm
ii. 20 29 cm
iii. 30 39 cm
iv. 40 49 cm; and
v. More than 50 cm
This biomass is then distributed to four processing streams:
a. Energy (directly);
b. Paper;
c. Particle boards
d. Sawn wood
Processing wastes are cascaded to processes. For example, waste from the sawn wood
stream is diverted to three other processing streams, but waste from paper and particle
board production is only used for energy.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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Figure 1: Conceptual design of the SMART FORESTS tool
Del. D.2.3, D.3.2. and D.4.1, October 2013
8
As wood products are discarded after use they can be recycled, downcycled
*
or used for
energy. Finally, energy can be generated in eight carriers and end-uses:
1. Logs for use in
a. stoves; and
b. central heating systems;
2. Chips for use in
a. Central heating systems
b. District heating systems; and
c. Combined heat and power systems;
3. Pellets for use in
a. Ovens
b. Central heating systems; and
c. District heating systems.
Each process and conversion technology carries with it emission factors per unit biomass
(dry) input and efficiency factors (biomass output/biomass input). Each combustion
technology has an associated efficiency (MJ/kg biomass dry). These values have been taken
from the GEMIS database and augmented with analyses of energy requirements for paper
production in Austria.
Estimates of the life-time of harvested wood products, with the exception of paper, are
based on IPCC default values. For paper, the life-time and recycled fractions have been
calculated using annual reports from Austropapier. The methodology and analysis is
presented in Bird (2013a). The results of this study are summarised in Table 1
Table 1: Estimated parameters for the paper pool
Estimated Value
5,291,326
6.7
4.6
0.94
Substitution factors for wood-based energy displacing other forms of energy and harvested
wood products displacing other materials have included. The methodology and analysis is
presented in Bird (2013b). The results for electricity are shown in Table 2 and Table 3.
Combining these two tables, 89.6% of biomass for electricity substituted natural gas.
*
Downcycle: To convert (waste materials etc.) into new materials or products of lesser quality and reduced
functionality.
R2 = 1- SSE / SSTot
Del. D.2.3, D.3.2. and D.4.1, October 2013
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Table 2: Effective displacement factors for electricity generation for the period 2000 to 2011
Type of generation
Average effective
displacement factor
Occurrence
(% of years)
Biomass
89.6%
92%
Coal
96.9%
50%
Hydro
52.4%
58%
Natural gas
82.4%
58%
Oil
93.6%
25%
Renewables
90.9%
83%
Table 3: Modelled displacement matrix for electricity generation for the period 2000 to 2011
Type Displacing
Biomass
Coal
Hydro
Natural
gas
Oil
Renewables
Type being
displaced
Biomass
0%
0%
0%
0%
0%
0%
Coal
0%
0%
56%
21%
0%
18%
Hydro
0%
92%
0%
79%
100%
12%
Natural gas
100%
5%
29%
0%
0%
62%
Oil
0%
3%
16%
0%
0%
8%
Renewables
0%
0%
0%
0%
0%
0%
Table 4: Effective displacement factors for heat for the period 2000 to 2010
Source of heat
Average effective
displacement factor
Occurrence
(% of years)
Biomass
48.9%
64%
Coal
73.4%
27%
Natural gas
59.0%
55%
Oil
38.8%
36%
Renewables
79.0%
82%
Table 5: Modelled displacement matrix for heat production for the period 2000 to 2010
Type Displacing
Biomass
Coal
Natural
gas
Oil
Renewables
Type being
displaced
Biomass
0%
52%
22%
44%
3%
Coal
12%
0%
58%
0%
19%
Natural gas
12%
0%
0%
56%
45%
Oil
74%
48%
20%
0%
33%
Renewables
2%
0%
0%
0%
0%
The results for biomass for heat are shown in Table 4 and Table 5. Combining the effective
displacement with the displacement vector result in an increase in heat from biomass
Del. D.2.3, D.3.2. and D.4.1, October 2013
10
displacing coal (6%), natural gas (6%), oil (36%) and renewables (1%). Some 50% of new heat
from biomass does not displace any other source (i.e. expanded consumption).
Table 6: Effective displacement factors for materials for the period 2000 to 2010
Material
Average effective
displacement factor
Occurrence
(% of years)
Construction materials
100.0%
18%
Fossil derived
81.7%
73%
Iron ore
88.5%
55%
Metals
97.0%
55%
Non-metals
87.8%
64%
Panels
79.4%
73%
Paper
73.4%
73%
Sawnwood
68.1%
36%
The results for wood products substituting non-wood products are shown in Table 6, Table 7
and Table 8. In summary, panels and sawnwood displaced construction materials 79% and
68% of the time respectively. Paper substituted a wide- range of materials, but mainly fossil
derived materials (plastics) with minor amounts of construction and other materials.
3.2 Materials
Policy scenarios
In 2006 Austria published its official national Biomass Action Plan (BMLFUW, 2006). In this
document, the Federal Ministry of Agriculture, Forestry, Environment and Water
Management (BMLFUW) set goals for biomass-based energy in Austria until 2020. The most
significant of these for the purpose of this study are the goal to
Increase the utilization of biomass for energy by 75% by 2010; and
Double the amount of energy from renewable resources to 45% of the total
consumption by 2020.
In a seminal paper studying the effects of these policies on the forest-based sector in
Austria, Schwarzbauer and Stern (2010), using an economic model, have estimated the
amount of biomass used in Austria in two scenarios: Business-as-usual (BaU) and Wood-for-
energy (WfE). The latter scenario assumes that the goals of the Biomass Action Plan are
fulfilled.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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Table 7: Modelled displacement matrix for material consumption for the period 2000 to 2010
Material displacing
Material being
displaced
Construction
Fossil
Iron ore
Metals
Non-metal
Panels
Paper
Sawnwood
Construction
0%
12%
77%
1%
88%
100%
14%
100%
Fossil
24%
0%
0%
37%
12%
0%
62%
0%
Iron ore
69%
0%
0%
50%
0%
0%
3%
0%
Metals
0%
0%
0%
0%
0%
0%
6%
0%
Non-metal
0%
81%
23%
7%
0%
0%
4%
0%
Panels
0%
0%
0%
2%
0%
0%
5%
0%
Paper
0%
5%
0%
1%
0%
0%
0%
0%
Sawnwood
7%
2%
0%
2%
0%
0%
6%
0%
The displacement fractions have been calculated using equation 8. The fields shaded have been constrained to be near zero using a weighted cost function. The
weight, 2.17E+06, has been chosen the sum of the penalty terms equals the sum of the misfit error prior to solving for the displacement fractions.
Table 8: Modelled combined displacement matrix for material consumption for the period 2000 to 2010
Material displacing
Material being
displaced
Construction
Fossil
Iron ore
Metals
Non-metal
Panels
Paper
Sawnwood
Construction
0%
10%
68%
1%
77%
79%
10%
68%
Fossil
24%
0%
0%
36%
11%
0%
46%
0%
Iron ore
69%
0%
0%
49%
0%
0%
3%
0%
Metals
0%
0%
0%
0%
0%
0%
4%
0%
Non-metal
0%
66%
20%
6%
0%
0%
3%
0%
Panels
0%
0%
0%
2%
0%
0%
4%
0%
Paper
0%
4%
0%
1%
0%
0%
0%
0%
Sawnwood
7%
2%
0%
2%
0%
0%
4%
0%
Del. D.2.3, D.3.2. and D.4.1, October 2013
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Their calculated demand for wood products under these two scenarios is repeated here in
Table 9.
Table 9: Calculated demand for wood products
Business-as-Usual (BaU) Scenario
Units
2000
2005
2010
2020
Sawn wood
Mm3
10.4
10.9
11.1
11.0
Board products
Mm3
3.1
3.2
3.5
3.8
Paper products
Mt
4.4
4.9
5.5
6.3
Fuel wood
Mm3
4.6
4.8
4.8
4.4
Wood-for-Energy (WfE) Scenario
Units
2000
2005
2010
2020
Sawn wood
Mm3
10.4
10.6
10.4
9.9
Board products
Mm3
3.1
3.1
3.3
3.6
Paper products
Mt
4.4
4.9
5.4
5.8
Fuel wood
Mm3
4.6
7.4
10.1
14.7
Source: Schwarzbauer and Stern (2010). Note: Paper products include paper from recycled materials.
The Austrian Biomass Association (Österreichischer Biomasse-Verband) estimated that in
2009
We calculated the amount of biomass required to meet this demand using these data and
values from GEMIS and Schwarzbauer (personal communication) for sawn wood, board and
paper production chains for:
1) the amount of finished product from biomass input; and
2) the amount of biomass used for energy internally.
The estimate is shown in Figure 2.
Figure 2: Estimated biomass required to meet demand scenarios
Del. D.2.3, D.3.2. and D.4.1, October 2013
13
The modelled demand in 2010 from Schwarzbauer is very close to the actual demand as
estimated by the ÖBV (Figure 3).
Figure 3: Actual versus modelled biomass required
From the estimate of biomass required and knowledge of the typical distribution of bark and
wood in various diameter classes, we can estimated the percentage of wood in various
diameter classes that must go directly to the different wood industries. These are listed in
Table 10. As shown in this table, under WfE there is a significant change in the distribution of
residues and stems with diameters < 20 cm as compared to BaU. In WfE, all this material
must be used for energy directly. Whereas, in BaU, only 30% of these material is used
directly for energy. The remainder is roughly split evenly between biomass for particle
boards and paper. This reduction in biomass for particle board and paper means that these
two sectors start increasing their share of larger diameter biomass classes at the expense of
the sawn wood sector. The conflict between these competing uses of forest resources
predicted by Schwarzbauer and Stern (2010) and has already made its way into the public
eye (Austropapier 2013a, 2013b, Kleine Zeitung 2012, 2013a, 2013b, ÖBMV 2013).
The proportions of harvested biomass in various diameter classes that goes directly to
specific industries are important input parameters in the SMART FOREST tool. Of lesser
importance is the share and type of systems that combust the fuel wood. ÖBMV (2011)
estimated the shares from 2005 and 2009 (Table 11). They show that there has been a slight
change in technology from lower efficiency chopped wood in single stoves and central
heating systems to higher efficiency systems that use wood chips in central heating, district
heating and combined heat and power (CHP) systems.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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Table 10: The estimated proportion of harvested biomass of various diameter classes that goes directly to specific wood industries
Business as Usual Scenario
Wood for Energy Scenario
Bark
Bark
All to energy
All to energy
Residues and stems < 20cm
Residues and stems < 20cm
Year
Energy
Sawn wood
Particle
board
Paper
Year
Energy
Sawn wood
Particle
board
Paper
2000
41.0%
0.0%
27.0%
32.0%
2000
41.0%
0.0%
27.0%
32.0%
2010
38.2%
0.0%
28.0%
33.8%
2010
72.0%
0.0%
23.0%
5.0%
2020
32.0%
0.0%
31.0%
37.0%
2020
100.0%
0.0%
0.0%
0.0%
2100
32.0%
0.0%
31.0%
37.0%
2100
100.0%
0.0%
0.0%
0.0%
Stems > 20 cm
Stems > 20 cm
Year
Energy
Sawn wood
Particle
board
Paper
Year
Energy
Sawn wood
Particle
board
Paper
2000
0.0%
77.0%
0.0%
23.0%
2000
0.0%
77.0%
0.0%
23.0%
2010
0.0%
73.9%
0.0%
26.1%
2010
0.0%
69.0%
0.0%
31.0%
2020
0.0%
71.0%
0.0%
29.0%
2020
0.0%
55.0%
11.0%
34.0%
2100
0.0%
71.0%
0.0%
29.0%
2100
0.0%
55.0%
11.0%
34.0%
Note: Schwarzbauer and Stern (2010) make estimates until 2020. However, the SMART FORESTS tool includes effects until 2010. For simplicity, we have assumed that there is no
change after 2020 in the share of biomass that is directly used by the various sectors.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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Table 11: Shares by energy of technology used for combusting biomass for fuel wood
Year
Chop
single
stove
Chop
central
heating
Chip
central
heating
Chip
district
heating
Chip
CHP
Pellets
single
stove
Pellets
central
heating
Pellets
district
heating
Total
2000
29%
10%
23%
4%
23%
3%
7%
0%
100%
2010
24%
8%
19%
7%
34%
3%
6%
0%
100%
2020
23%
8%
19%
8%
34%
3%
6%
0%
100%
2100
23%
8%
18%
9%
34%
3%
5%
0%
100%
Source for 2000 to 2020: ÖBMV (2011). For simplicity, we have assumed no change from 2020 to 2100
Table 12: Shares by biomass input of technology used for combusting biomass for fuel wood
Year
Chop
single
stove
Chop
central
heating
Chip
central
heating
Chip
district
heating
Chip
CHP
Pellets
single
stove
Pellets
central
heating
Pellets
district
heating
Total
2000
36%
11%
20%
4%
20%
3%
6%
0%
100%
2010
30%
9%
17%
6%
30%
2%
5%
0%
100%
2020
29%
9%
17%
7%
31%
2%
5%
0%
100%
2100
29%
9%
17%
8%
31%
2%
5%
0%
100%
Source for 2000 to 2020: ÖBMV (2011) plus average efficiency factors. For simplicity, we have assumed no
change from 2020 to 2100
Forest types, management strategies and climate scenarios
The idea of the simulation study is twofold: (1) to evaluate major forest types in Austria with
regard to biomass production and in situ carbon storage under several management and
climate scenarios, and (2) to deliver customized data sets on harvested timber assortments
per age class and stand type further use in carbon accounting modules.
With this purposes, two generic forest types based on real data were generated:
1. beech-dominated broadleaved forests of the submontane elevation zone
2. montane spruce-larch forests
These generic stands were used as the initial state of forest stands in a later simulation with
PICUS v1.5. The two study cases were chosen due to their broad distribution and importance
in Austrian forests.
The data base used for creating realistic initial stand conditions is a combination of inventory
data and data from related management plans. The stands have an area of 1 hectare and
single trees information is provided. At least 6 different age classes were considered,
covering each one a period of 20 years and for every age class an initial stand was generated.
For each forest type, suitable site and climate conditions were defined. Climate data for the
simulation consist of a 100-year time series and two climate scenarios were applied in the
simulations: the current climate or baseline climate, based on the climate in the period
1961-1990 and a climate change scenario corresponding to the A1B storyline of the Special
Report on Emissions Scenarios (SRES) of the IPCC (2000).
Del. D.2.3, D.3.2. and D.4.1, October 2013
16
Various alternative management strategies were defined for the time horizon of the
simulation (100 years). For the first one - the current practice or business as usual (BAU) -
the definition of the management is based on the real management systems applied in
Austria. The data were collected from real cases, from literature or consulting also experts if
needed.
The alternative management regimes were established based on BAU management and
focusing on a higher production of biomass that could be used for energy, with shorter
rotation periods and applying heavier thinnings. In addition, an increased removal of logging
residues from the forest and use of biomass for bioenergy differs in the alternative regimes
in comparison to BAU.
Case study 1: Beech forest
Initial stand conditions
In this study case, pure stands of Fagus sylvatica, as well as stands which are dominated by
Fagus sylvatica but have admixed components (approximately 80% Fagus and 20% the other
species) are generated. Three different kinds of tree composition forests were considered:
- Only European beech (Fagus sylvatica)
- European beech and Sycamore maple (Acer pseudoplatanus)
- European beech and English oak (Quercus robur)
Different age classes were generated for every forest tree composition: six 20-years age
classes (A1: 0-20 years; A2:21-40 years; A3: 41-60 years; A4: 61-80 years; A5: 81-100 years;
A6: 101-120 years) and another age class including regeneration (A6v: 101-120 years with
regeneration).
Site and climate
The chosen site for the beech forest is a very productive soil at an altitude of 500 m a.s.l.
consisting of rich cambisol with a pH of 5.46 and a water holding capacity of 276 mm.
Mean annual temperature of the climate scenario baseline is 7.7°C and the mean annual
precipitation sum is 1050 mm (more than 50% occurs during the vegetation period, from
May to September). In the climate change scenario A1B, the mean annual temperature is
9.5°C and the mean annual precipitation sum is 1109 mm (approximately 50% of the
precipitation is during the vegetation period, from May to September).
Forest management strategies
Four management strategies were defined. The first strategy, business as usual (BAU),
reflects the practice that is currently being used by the forest owners in Austria. BAU is an
age class system with a rotation age of 120 years. It has natural regeneration achieved by a
shelterwood system with a regeneration period of 10 years, a pre-commercial thinning to
control the density of the stand and selective thinnings from below and from above.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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In the alternative managements (AM) the rotation period was reduced to 110 years and 2 to
3 heavier thinnings were planned. In AM1 only thinnings from below occurred, in AM2
thinnings from above (crown thinning) and in AM3 a combination of thinnings from above
and from below. All managements were specific for every simulated stand and changed
depending on the stand conditions, considering a minimum diameter and a minimum
density. The thinning intensity was specified both as a maximum removed volume and as a
percentage of the standing volume, considering five relative diameter classes.
In BAU regime, only “commercial wood” – timber with a diameter larger than 7 cm - was
harvested and the rest was left in the site. In the alternative managements, besides the
conventional harvest, a higher extraction of biomass from the forest was applied, taking this
“extra biomass” from wood from stem and branches also with diameter smaller than 7 cm.
Study case 2: Spruce Larch forest
Initial stand conditions
Initial conditions of stands of Picea abies and Larix sp. were generated, containing
approximately 80% spruce and 20% larch. In this case, seven 20-years age classes were
generated: A1: 0-20 years; A2:21-40 years; A3: 41-60 years; A4: 61-80 years; A5: 81-100
years; A6: 101-120 years; A7: 121-140 years.
Site and climate
The site for the spruce-larch forest is a subalpine site soil at an altitude of 1200 m a.s.l.
consisting of semipodsol on acid substrate with a pH of 3.95 and a water holding capacity of
237 mm.
Mean annual temperature of the climate scenario baseline is 5°C and the mean annual
precipitation sum is 1124 mm. In the climate change scenario A1B, the mean annual
temperature is 6.9°C and the mean annual precipitation sum is 1208 mm.
Forest management strategies
For the spruce-larch stands, three management strategies were defined. The business as
usual strategy, BAU, considers the currently used practice in Austria. BAU is a clear cut
system with a rotation age of 140 years and a thinning from above. The thinning intensity
was specified as a maximum removed volume and as a percentage of the standing volume in
five relative diameter classes. The regeneration is achieved by planting both species, being
the larch planted in groups.
In the first alternative management (AM1) the rotation period was shorted to 110 years.
Another thinning was added being these thinnings heavier than in the other management
strategies. Furthermore, a higher extraction of biomass from the forest was applied, taking
this “extra biomass” from wood from stem and branches. In the second alternative
management (AM2) the rotation period applied was 120 years, keeping the rest of the
activities like in BAU. In the harvests only timber with a larger diameter than 7 cm was
Del. D.2.3, D.3.2. and D.4.1, October 2013
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extracted from the site, leaving the rest in situ, except the extra biomass, which could have a
diameter smaller than 7 cm.
4 Results
4.1 Business-as-usual policy scenario
Variation with forest type
Figure 4 and Figure 5 shows the results for the Beech and Spruce-Larch forests respectively
under the business-as-usual policy scenario with business as usual management and no
effects of climate change. The significant difference between the two forest types is their
age class structure.
The Beech forest is roughly even-aged with an anomalous age class (20 39) that is only
slightly larger proportion than the average (Figure 4a). This means that the Beech forest has
a roughly constant forest carbon stock that has a slight carbon sequestration as this age class
matures. The roughly even age-class structure also means that on a per hectare basis, the
Beech forest has higher production of wood products and energy than the Spruce-larch
forest.
The Spruce-larch forest has an uneven age-class structure (Figure 5a). Almost 30% of the
forest has an age of 20 39 years; an indirect legacy of the 1970s energy crisis
*
. This age
class is of the age when the annual increment is very large and as a result the spruce-larch
forest shows an increase in carbon stocks until the stand matures. This uneven age-class
structure also explains the low amount of wood products and energy produced per hectare
forest
As shown in Figure 4 and Figure 5 d, e and f there is an increase in wood products and
energy production from both forest types in 2010 and 2020 as compared to 2000.
Table 13: Modelled output as a % of output in 2000 for BAU management, BAU energy and
baseline climate
Year
Beach Forest
Spruce-larch Forest
Combined
Products
Energy
Products
Energy
Products
Energy
2010
22%
8%
48%
24%
47%
23%
2020
62%
31%
93%
46%
91%
45%
The Beech forest creates 8% and 31% more energy in 2010 and 2020 respectively than in
2000, while the Spruce-larch forest produces 24% and 46% more (Table 13). However this is
well short of the increase required in the Wood-for-energy policy scenario. The analysis by
*
During the energy crisis, the price of sawlogs almost tripled. However the price of fuel wood stayed roughly
constant (P. Schwarzbauer, personal communication)
Del. D.2.3, D.3.2. and D.4.1, October 2013
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a. b. c.
d. e. f.
Figure 4: Results for the Beech forest assuming BAU management strategy, BaU policy scenario, Baseline climate scenario
a. Forest inventory, b. Carbon stocks in the forest, c. Combined cumulative greenhouse gas emissions from changes in carbon stocks and fossil fuel use in
production chains, d. Wood products, e. Fuel wood, and f. Total energy and electricity produced
Del. D.2.3, D.3.2. and D.4.1, October 2013
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a. b. c.
d. e. f.
Figure 5: Results for the Spruce-Larch forest assuming BAU management strategy, BaU policy scenario, Baseline climate scenario
a. Forest inventory, b. Carbon stocks in the forest, c. Combined cumulative greenhouse gas emissions from changes in carbon stocks and fossil fuel use in
production chains, d. Wood products, e. Fuel wood, and f. Total energy and electricity produced
Del. D.2.3, D.3.2. and D.4.1, October 2013
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Schwarzbauer and Stern (2010) indicates that energy from wood needs to increase by 66%
and 117% in 2010 and 2020 respectively as compared to 2000. Most of the increase will
occur in the residential heating sector.
Variation with climate scenario
The previous models assume that there is no climate change. However, with climate change
increased growth due to CO2 fertilization is expected. Studies find that on average across
several species and under unstressed conditions, compared to current atmospheric CO2,
increases in above-ground biomass at for trees are in the range 0-30%, with the higher
values observed in young trees and little to no response observed in mature natural forests.
For commercial forestry, slow growing trees may respond little to elevated CO2, and fast-
growing trees more strongly, with harvestable wood increases of 15-25% expected (IPCC,
2007 page 282).
We will make the assumption that climate change occurs and use the PICUS model results
from climate scenario A1B. The differences between these models and the results assuming
no climate change are shown in Figure 6 and Figure 7. The increased growth causes an
increase in sequestration and hence more negative total cumulative greenhouse gas
emissions (Figure 6a and Figure 7a). This is very apparent after 2030. However over the time
span of policy interest, climate change does not make any difference to the amount of wood
products created or energy from biomass produced (Figure 6 b&c, and Figure 7 b&c)
Variation with management regime
One of the ways to increase removals, specifically for energy, from the forest is to intensify
the forest management. In Figure 8 and Figure 9 we investigate the impacts of increasing
intensification (AM1 management). In the top row of both diagrams shows the cumulative
greenhouse gas emissions from the two management systems. In both forest types,
increasing intensification of management increases emissions due to decreases in carbon
stocks and an increase in emissions for processing the biomass removed. The Beach forest
becomes a source of emissions with intensification whereas the Spruce-larch forest becomes
roughly “carbon neutral”. Nevertheless, the spruce larch-forest would be more of a sink
without intensification!
Both forests create more wood products and energy with increased management (Figure 8
b, c and Figure 9 b, c). As shown in Table 14, increasing intensification is modelled to
increase the amount of wood products and energy that comes from the forest significantly
enough to fulfil the Wood-for-fuel policy scenario. In fact, the biomass demand for energy
required by the Wood-for-fuel policy scenario can be achieved if only 15% of the forest is
subject to intensive management.
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a. b. c.
Figure 6: Beech forest: A comparison of results with and without climate change
a. Combined cumulative greenhouse gas emissions from changes in carbon stocks and fossil fuel use in production chains b. Wood products, and c. Total energy
and electricity produced
a. b. c.
Figure 7: Spruce-larch forest: A comparison of results with and without climate change
a. Combined cumulative greenhouse gas emissions from changes in carbon stocks and fossil fuel use in production chains b. Wood products, and c. Total energy
and electricity produced
Del. D.2.3, D.3.2. and D.4.1, October 2013
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a. b. c.
d. e. f.
Figure 8: Beech forest: A comparison of results with BAU and intensive management (AM1 management strategy)
a. Combined cumulative greenhouse gas emissions from changes in carbon stocks and fossil fuel use in production chains b. Wood products, and c. Total energy
and electricity produced, d. Cumulative emissions including emissions from product and energy substituted under BAU management, e. Cumulative emissions
including emissions from product and energy substituted under AM1 management, f. The difference in LCA emissions between BAU and intensive management
strategies.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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a. b. c.
d. e. f.
Figure 9: Spruce-larch forest: A comparison of results with BAU and intensive management (AM1 management strategy)
a. Combined cumulative greenhouse gas emissions from changes in carbon stocks and fossil fuel use in production chains b. Wood products, and c. Total energy
and electricity produced, d. Cumulative emissions including emissions from product and energy substituted under BAU management, e. Cumulative emissions
including emissions from product and energy substituted under AM1 management, f. The difference in LCA emissions between BAU and intensive management
strategies.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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Table 14: Modelled output from increased intensification as a % of output in 2000 with BAU
management
Year
Beach Forest
Spruce-larch Forest
Combined
Products
Energy
Products
Energy
Products
Energy
2010
102%
118%
279%
357%
267%
343%
2020
77%
92%
399%
500%
376%
476%
Figure 8a and Figure 9a show the emissions from the forest system only. However, in both
forest types there is an increase in wood products and energy output. To be properly
comparable, the emissions both management systems should provide the same services.
Hence emissions from the BAU management strategy should include emissions due to
products and energy substituted by more energy-intense products and fossil energy. These
are the environmental benefits created by intensification. Nevertheless, for the first 40 or 50
years (depending on forest type), intensification creates more greenhouse gas emissions
than does the BAU forest management strategy (Figure 8c Figure 9c). After this period,
intensification produces a greenhouse gas benefit that increases with time.
This result is very similar to those from many studies (Agostini et al 2013, Böttcher et al,
2012, Holtsmark, 2010, 2012 2013a, 2013b, Hudiburg et al 2011, Lippke et al 2012,
McKechnie et 2011, Mitchell et al 2012, Repo et al 2011, Walker et al 2010, Zanchi et al
2010, Zanchi et al 2011). Namely, increasing the intensification of forest management (i.e.
removals) for bioenergy production causes short term an increase in emissions (aka the
“carbon-debt”). However, intensifying forest management is a good strategy for reducing
greenhouse gas emissions long term. The period when there are more emissions in the high
intensity scenario than the BAU scenario is a result of the time required by the forest before
it stabilizes to a new dynamic equilibrium and the emission intensity of the energy and
products substituted by the extra biomass. The quicker the forest adapts, and the larger the
emission intensity of the energy and products substituted the shorter is the period of time
(aka the “carbon payback time”).
The current study is the first to include the environmental benefits of wood product
substitution. In this example, the inclusion of these benefits shortens the pay-back time.
Figure 8d and Figure 9d indicate that, in particular, the environmental benefits from paper
substituting fossil-based materials (i.e. plastics) are extremely important.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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a. b. c.
d. e. f.
Figure 10: Beech forest: A comparison of results with BAU and WfE policies
a. Combined cumulative greenhouse gas emissions from changes in carbon stocks and fossil fuel use in production chains b. Wood products, and c. Total energy
and electricity produced, d. Cumulative emissions including emissions from product and energy substituted under BAU policy, e. Cumulative emissions including
emissions from product and energy substituted under WfE policy, f. The difference in LCA emissions between BAU and WfE policies.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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a. b. c.
d. e. f.
Figure 11: Spruce-larch forest: A comparison of results with BAU and WfE policies
a. Combined cumulative greenhouse gas emissions from changes in carbon stocks and fossil fuel use in production chains b. Wood products, and c. Total energy
and electricity produced, d. Cumulative emissions including emissions from product and energy substituted under BAU policy, e. Cumulative emissions including
emissions from product and energy substituted under WfE policy, f. The difference in LCA emissions between BAU and WfE policies.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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4.2 Wood-for-energy policy scenario
A second way to increase the amount of energy from forest-based biomass is to change the
share of biomass going to the various forest-based industries without increasing the forest
management intensity. This is exactly the situation foreseen in the WfE policy scenario.
Figure 10 and Figure 11 shows the results of this analysis. For both forests types, there is a
slight increase in total cumulative emissions from the WfE as compared to BAU policy (Figure
10a and Figure 11a). The reason for this is that there is a slight decrease in carbon stored in
wood products as a result of switch in share of biomass from wood products to energy. This
is shown in the diagrams showing wood product and energy production (Figure 10 b&c and
Figure 11 b&c). The BAU policy produces more wood products and less energy as compared
to the WfE policy.
Table 15: Modelled output from WfE policy scenarios as a % of output in 2000 with BAU policy
scenarios
Year
Beach Forest
Spruce-larch Forest
Combined
Products
Energy
Products
Energy
Products
Energy
2010
12%
25%
31%
52%
30%
50%
2020
40%
64%
46%
118%
46%
115%
4.3 As shown in Wood-for-energy policy scenario
A second way to increase the amount of energy from forest-based biomass is to change the
share of biomass going to the various forest-based industries without increasing the forest
management intensity. This is exactly the situation foreseen in the WfE policy scenario.
Figure 10 and Figure 11 shows the results of this analysis. For both forests types, there is a
slight increase in total cumulative emissions from the WfE as compared to BAU policy (Figure
10a and Figure 11a). The reason for this is that there is a slight decrease in carbon stored in
wood products as a result of switch in share of biomass from wood products to energy. This
is shown in the diagrams showing wood product and energy production (Figure 10 b&c and
Figure 11 b&c). The BAU policy produces more wood products and less energy as compared
to the WfE policy.
Table 15, the WfE policy scenario creates 50% and 115% more energy in 2010 and 2020 as
compared to the energy production in 2000 in the BaU policy scenario. This is more than is
required under the analysis by Schwarzbauer and Stern (2010). They suggested that energy
from wood needs to increase by 66% and 117% in 2010 and 2020 respectively as compared
to 2000.
However, the increase in energy production comes at the expense of a decrease in creation
of wood product. In Table 13, the modelled increase in wood products in 2010 and 2020 was
47% and 91% respectively. In the WfE policy scenario, this has decreased to 30% and 46%. As
a result, the WfE policy requires more wood products to be substituted by high intensity
Del. D.2.3, D.3.2. and D.4.1, October 2013
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products than does the BaU policy (the pink and blue portions in Figure 10e and Figure 11e).
Of course, the BaU policy requires more energy from fossil energy (the red portion in Figure
10d and Figure 11d) than does the WfE policy. From an LCA perspective, the BaU policy has
less emissions in both forest types than does the WfE policy. The environmental benefits of
products, specifically paper, outweigh the benefits of fossil energy substitution and that
does not change for the next 100 years (Figure 10f and Figure 11f). Rueter et al (2011) also
found that substitution effects of forest-based fibre for construction materials, furniture and
paper create more reductions in greenhouse gas emissions than just biomass for energy.
4.4 Increasing use of discarded paper for energy
Another method to reach Austria’s bioenergy goals could be to increase the amount of
discarded paper being used for energy. Currently, some 85% of paper is recycled and the
remainder (15%) is incinerated for energy. In this simulation, we assume that by 2020, the
amount of paper recycled is 0% and all discarded paper is incinerated for energy. In addition,
I use a more realistic biomass distribution model than the “business-as-usual” energy policy
model, since as indicated previously, we are already following the “Wood-for-energy” policy
scenario (Table 16). This scenario assumes that from 2010-2020 all forest-based products
increase at an annual growth rate of 1.3%.
As shown in Figure 12, using all discarded paper for energy by 2020,
1) causes a slight decrease in the total amount of wood products created with time
(Figure 12b);
2) creates an insignificant increase in energy production (Figure 12c); but
3) the decrease in the amount of paper products causes a significant increase in
emissions due to the need to substitute reduced paper production with GHG intense
materials (Figure 12 d, e & f).
Del. D.2.3, D.3.2. and D.4.1, October 2013
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Table 16: The estimated proportion of harvested biomass of various diameter classes that goes
directly to specific wood industries under a modified Wood-for-Energy scenario.
Modified Wood-for-Energy Scenario
Bark
All to energy
Residues and stems < 20cm
Year
Energy
Sawn wood
Particle board
Paper
2000
41.0%
0.0%
27.0%
32.0%
2010
72.0%
0.0%
23.0%
5.0%
2020
94.0%
0.0%
0.0%
6.0%
2100
94.0%
0.0%
0.0%
6.0%
Stems > 20 cm
Year
Energy
Sawn wood
Particle board
Paper
2000
0.0%
77.0%
0.0%
23.0%
2010
0.0%
69.0%
0.0%
31.0%
2020
0.0%
62.0%
7.0%
31.0%
2100
0.0%
62.0%
7.0%
31.0%
The marginal increase in energy production is surprising, but can be explained because the
paper industry produces are large amount of energy already in its operation. We estimate
that the paper industry produces 0.9 kWh / kg input. Wood as fuel produces a bit more than
double this amount (2.4 kWh useful energy / kg). Hence, recycling is a better option for
discarded paper than burning it for energy.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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4.5 Increasing the efficiency of residential energy systems.
An option that should not be overlooked is improving the efficiency of the wood-burning
residential energy systems. Table 11 shows the percentage of different energy systems in
use in Austria. These systems have different energy efficiencies too (Table 17). The average
efficiency by biomass input of the Austria combustion systems is 74%. However, it is
composed mostly by combustion in lower efficiency single log-burning stoves (labelled Chop
single stove). If they were replaced by higher efficiency chip-fired central heating systems
the average efficiency of Austrian combustion systems would increase to 85%.
Table 17: Proportion by biomass input and efficiencies of various biomass combustion technologies
in Austria in 2005.
System
Proportion
Efficiency
Chop single stove
36%
60%
Chop central heating
11%
68%
Chip central heating
20%
85%
Chip district heating
4%
88%
Chip CHP
20%
85%
Pellets single stove
3%
80%
Pellets central heating
6%
85%
Pellets district heating
0%
90%
Average
74%
As shown in Figure 13, increasing residential energy efficiency has no effect on combined
cumulative greenhouse gas emissions or the amount of wood products created. It does
increase the total amount of energy and electricity produced (Figure 13c). However, the
total amount of energy increases by only 2% because the amount of energy produced by the
forest industries is quite high. Higher efficiency technologies also reduce greenhouse gas
emissions (Figure 13f) because less energy is required from fossil energy sources.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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a. b. c.
d. e. f.
Figure 12: Spruce-larch forest: A comparison of results with normal recycling and diversion of all discarded paper to energy use by 2020
a. Combined cumulative greenhouse gas emissions from changes in carbon stocks and fossil fuel use in production chains b. Wood products, and c. Total energy
and electricity produced, d. Cumulative emissions including emissions from product and energy substituted with normal recycling, e. Cumulative emissions
including emissions from product and energy substituted with all discarded paper to energy use by 2020, f. The difference in LCA emissions between normal and
no recycling options.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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a. b. c.
d. e. f.
Figure 13: Beech forest: A comparison of results with business-as-usual and higher efficiency residential combustion technologies phased-in by 2020
a. Combined cumulative greenhouse gas emissions from changes in carbon stocks and fossil fuel use in production chains b. Wood products, and c. Total energy
and electricity produced, d. Cumulative emissions including emissions from product and energy substituted with business as usual technologies, e. Cumulative
emissions including emissions from product and energy substituted with higher efficiency technologies, f. The difference in LCA emissions between business as
usual and high efficiency technologies.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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5 Discussion and conclusions
In this paper we have identified four methods that could be used in Austria for the country
to reach its goals under its official national Biomass Action Plan (BMLFUW, 2006). These
methods are:
1. intensifying forest management;
2. modifying the proportion of biomass going to energy from wood products;
3. increasing the amount of paper combustion at the expense of paper recycling; and
4. increasing residential energy efficiency.
Of these, intensifying forest management and modification of the proportion of biomass
going to energy can be used to meet the goals of the national Biomass Action Plan.
Increasing the combustion of paper did not increase the amount of bioenergy but increasing
residential energy efficiency only marginally.
The two options that meet the goals, however, also increase the amount of greenhouse gas
emissions. Intensifying forest management increased emissions over a 40-50 year period but
this strategy reducing greenhouse gas emissions. This result is consistent with many other
studies addressing the timing of greenhouse gas emissions from wood-based bioenergy
systems. Modifying the proportion of biomass use, however, always produced more
emissions and hence is a strategy that should be avoided.
5.1 Sensitivity to assumptions
The results are sensitive to the many assumptions that were made. In this section we
investigate the sensitivity to the most important factors:
a) type of forest management intensification;
b) energy substitution factor
c) wood product substitution factor; and
d) lifetime of paper.
The sensitivity of the results of the most significant comparison, Spruce-larch forest with and
without forest management intensification, will be investigated.
Type of forest management intensification
The comparison of results with and without intensification focused on only one
management option AM1, shortened rotation with thinning from below. Models for two
other options were made
a) AM2 longer rotation with thinning from above (crown thinning); and
b) AM3 a combination of AM1 and AM2.
Del. D.2.3, D.3.2. and D.4.1, October 2013
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a. Beech forest Forest management AM1 versus AM2
b. Beech forest Forest management AM1 versus AM3
c. Spruce-larch forest Forest management AM1 versus AM2
Figure 14: Sensitivity to types of intensified forest management
Del. D.2.3, D.3.2. and D.4.1, October 2013
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Figure 14 shows the sensitivity to types of intensified forest management. In general
shortening the rotation (AM1) creates a short-term decrease in carbon stocks and more
removals for wood products and energy. Increasing thinning causes an increase in biomass
for paper and energy at time of thinning, but a decrease of biomass at final felling. The
differences depend on the age-class structure of the forest. The Beech forest is roughly
even-aged while the Spruce-larch forest is skewed toward younger ages. This means that the
impacts of thinning on final felling are more apparent in the Beech forest than in the Spruce-
larch forest, and as a result the Beech forest shows more impacts due to thinning. For both
forests, management AM1 is a better option due to the increase in wood products that
result.
Energy substitution factor
Figure 15 shows the effect of changing the assumed heat substitution factor by ± 10%. Since
the AM1 management scenario creates more wood products than does the BAU
management scenario, changing the substitution factor effects only the emissions from the
latter. As shown the results are relatively insensitive to this parameter.
Figure 15: Spruce-larch forest: sensitivity of results to the heat substitution factor
Del. D.2.3, D.3.2. and D.4.1, October 2013
37
Material substitution factors
As seen in Figure 8 and Figure 9, the substitution of wood products, specifically paper, has a
significant emission in the BAU management model. As this substitution factor is very poorly
known, we will investigate the impact of changing the factor by ±25%. As shown in Figure 16
the results are sensitive to this factor. Changing it by ±25%, decreases or increases the time
at which the AM1 system causes fewer emissions than the BAU system by ±10 years.
Figure 16: Spruce-larch forest: sensitivity of results to the paper substitution factor
Out of interest, we created a simulation with no acknowledgement of wood product
substitution (Figure 17). These results indicate that in the first 100 years, intensifying forest
management causes more emissions than the business-as-usual management, assuming no
wood product substitution. This simulation is roughly similar to the original Birdlife study
(Zanchi et al 2010), which started the discussion of the timing of emissions from forest-based
bioenergy. In the Birdlife Study, the point at which thinning produced less emissions than
non-thinning occurred after 250 years (the so-called C/N = 0 point).
Del. D.2.3, D.3.2. and D.4.1, October 2013
38
Figure 17: Spruce-larch forest assuming no wood product substitution
Lifetime of paper
There is a rather large difference in the estimate of the average lifetime of paper made by
Bird (2013a) and the IPCC. The first study finds that paper has an average lifetime of 7 years
and the IPCC assumes 3 years. The results are not sensitive to this parameter (Figure 18).
There are slightly more emissions caused by a longer lifetime because there is less new
paper produced as a result of recycling because of fewer discards.
Del. D.2.3, D.3.2. and D.4.1, October 2013
39
Figure 18: Spruce-larch forest: sensitivity to paper lifetime
Shortcomings of the study
The study has potentially a significant short coming. We have included recycling and energy
production from discarded paper products, but have not included recycling and energy
production from discarded paper substitutes (i.e. plastics). In Austria, 35% of plastic is
recycled but no plastic is incinerated (EUROSTAT 2013). The inclusion of plastic recycling
could reduce the importance of paper.
6 Acknowledgements
This work has been undertaken as part of the project entitled: Selecting Management
Alternatives Responding to Targets. Forest Optimization for Renewable Energy and
Sequestration using Time-dependent Strategies (SMART-FORESTS) financed by the Austrian
Klima- und Energiefonds, Klimafonds (Project number: K10AC1K00023).
The authors would like to thank Dorian Frieden, Gerfried Jungmeier, Giuliana Zanchi, Hannes
Schwaiger and Peter Schwarzbauer for their input during the preparation of this document.
Del. D.2.3, D.3.2. and D.4.1, October 2013
40
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