Supporting Information for, ‘Climate change impacts due to biogenic carbon: Addressing the issue of attribution using two
metrics with very different outcomes’
Process/
parameter
Supply and use
of woody
biomass
between
industries
Desired Data for analysis
Data and assumptions used in analysis
References
HWP/residual flows to and from all
relevant domestic industries
disaggregated to at least 5 digit
resolution SIC codes, and preferably
with a list of associated companies that
reside within each SIC industry at the
give digit resolution. Imported flows at
the same SIC resolution is also desired.
Accurate account of residuals lost to
decomposition or utilized for energy is
also desired. Dry mass flow with known
carbon content (CC) of each commodity
is also needed.
Ideally data that describes which input
of wood flow goes where for all
products with impacts and exports
included.
We took a material flow analysis of the Norwegian HWP
industry (in 2006) which disaggregated between the major
HWP industries. The level of resolution is as seen in figure 1.
Flows were in solid m3 and tonnes so we converted these
flows with representative densities and CC from literature.
Regionalized
Imports
Ideally we would like to know (to 5 digit
SIC code resolution) what products are
being imported to the domestic country.
We used trade statistics which were reported by the Food and
Agriculture Organization (FAO) of the United Nations. We then
grouped these imports at the nation to nation level into nine
continental regions (see tab.2).
(FAO 2006)
Species type
and associated
rotation period of
each imported
product
Alongside the desired regionalized
imports it would also be ideal to know
the specific species and their
associated rotation periods for each of
the product flows.
We used statistics from an FAO survey on nine globally
aggregated regions. This survey provided for each region an
area distribution of the most prevalent productive woody
species alongside data on rotation period and yield per
hectare. We took the average rotation period (note only
minimum and maximum rotation data) for each species/region
and weighted these values by [area*yield/rotation] to arrive to
an overall weighted rotation period for each region.
(FAO 2006)
Dividing flows to
and from each
process
We assume that imported and domestic woody biomass that
enters an industry/process has a proportionately (to mass)
equal opportunity to end up in each of the output flows. Such a
balance was done across each process.
Material flow analysis: (Grinde 2006)
Densities: Domestic species woody
materials: (Korhonen, Wihersaari et al.
2001).
Panels: (IPCC 2006).
Imported wood species: (Ketterings,
Coe et al. 2001)
CC: Domestic Species: (Korhonen,
Wihersaari et al. 2001)
Imported species: (Thomas and Martin
2012);
Paper, pulp, panels. (IPCC 2006)
Own assumption.
Supporting Information
Process/
parameter
Species type
and associated
Net Ecosystem
Profiles of each
imported
product
Desired Data for analysis
As with the rotation periods, it would be
ideal to have access to the net
ecosystem production (NEP) profile of
the biomass resource pool that every
imported product was derived from.
Species type
and associated
rotation period of
each
domestically
sourced product
Similarly to the imported products we
would like to connect all product flows
to their species source along with
associated rotation period.
Species type
and associated
NEP profiles of
each
domestically
sourced product
As with the rotation periods, it would be
ideal to have access to the net
ecosystem production (NEP) profile of
each biomass resource pool that every
domestically sourced product was
derived from.
Data and assumptions used in analysis
For all globalized regions (except Europe) we assumed forest
residues (FR) left in the forest upon harvest to only be below
ground biomass. Standardized allometric factors were used.
For Europe we followed the same approach as we used for
Norwegian species. However, for all regions we used the
Yasso07 model with assumed average annual regionalized
temperature and precipitation satellite based data from NASA.
Chemical compositions of important regional species were
used as Yasso07 inputs.
All global regions were assumed to have net primary
productivity profiles (NPP) following log normal distribution
curves. The resulting FR and NPP profiles were used to
calculate the NEP profiles.
We used Norwegian specific statistics which reported species
dominated area distributions and their site quality classes.
From this data we calculated the area weighted average site
quality for each of the three main species, Norway Spruce,
Scots Pine and Birch. We then used national guidelines that
relate the suitable minimum age (age class V) when a stand
can be cut for each site quality index. With these two data
items we were able to deduce an average rotation period for
each forest species. Both Norway Spruce and Scots pine
worked out to be about 100 years while Birch was around 70
years rotation period. The overall weighted average rotation
period remained to be about 99 years since there was not a lot
of Birch harvested relative to the other dominating species
Scots Pine and Norway Spruce.
We made assumptions about the forest residues left in the
forest along with using allometric equations and in addition to
this we assumed annual turnover of the tree components and
natural deaths of whole trees. This was coupled with an
assumed NPP profile. For Norway Spruce and Scots pine we
assumed a Schnute function whereas for Birch we assumed a
log normal distribution. We used Yasso07 where monthly
average temperature and precipitation data was used and this
data was representative of the region of Norway where the
majority of forestry takes place.
2
References
Allometric factors: IPPC 2006
Europe FR decomposition: (Guest,
Cherubini et al. 2012) .
Yasso07 model: (Tuomi, Thum et al.
2009); (Pettersen 1984).
Temperature and Precipitation data:
(NASA 2013)
Area distribution of species specific
quality classes: (Larsson and Hylen
2007) and (Landskap 2007).
See Guest et al. (2012) for all
assumptions with regards to the
methods of attaining these NEP
profiles.
Supporting Information
Process/
parameter
Decomposition
curves of HWPs
Desired Data for analysis
Data and assumptions used in analysis
References
Ideally we would have liked to know an
estimated mean life time of HWPs
disaggregated to 5-digit resolution SIC
codes. In practice it would also be ideal
to have perfect knowledge of the
lifetime of each product at its end-oflife.
We take estimated half-lives for 15 products based on data
from the United States forest product economy. We adopt
these values by converting them to mean life-times (dividing
half-life by ln(2)). We then applied a chi-square distribution to
represent the probability distribution profile of each HWP
coming to its end-of-life.
HWP life times: (Smith, Heath et al.
2006)
Chi-Square Distribution: Cherubini et
al.2012.
Determining how
primary HWPs
are being used
in final HWPs
As stated earlier it would be ideal to
know the HWP flow through all
industries that deal with HWPs (with at
least 5-digit SIC resolution).
(Smith, Heath et al. 2006)
Glue laminated
case example
This was an arbitrary example of a
HWP value chain that fit within our
study. Similar value chains should be
constructed for all HWPs using primary
data from the industries that actually
produce the given HWPs.
Since the material flow analysis stopped only at primary HWP
products (i.e. panels, and sawn wood) we needed to determine
how these resources will likely be used. Again we used
available data from the United States forest product economy
where a table provides percentage breakdowns of where
primary HWPs end up into final HWPs.
We devised our bio-C balance across this value chain based
on a life cycle inventory study. Construction wastes were
based on a US report.
Life cycle inventory: (Werner, Althaus et
al. 2007).
Construction waste fraction: (Smith,
Heath et al. 2006).
3
Supporting Information
<Figure S1: Standard Industrial Codes at 5-digit resolution categorized into the several party groups (PG) that constitutes the HWP value chain>
When any of the SIC codes has an x it means that the industry is not defined under the SIC standard but was found to be an important industry to
disaggregate at this resolution for the HWP value chain system.
4
Supporting Information
Examples of attribution tables for the other value chain combinations as according to the above PG categorization.
When using the PG categories as depicted in fig.S1, then there can be a total of 5 value chain combinations to consider. The following are examples
of what the attribution tables could look like for each of these value chains. By applying these or similar tables, then it can be quite straight forward
to understand how much of the climate change impact should be attributed to each PG, and these impacts can be further disaggregated across
companies from within the given PG. Depending on the given value chain, there could be several companies within a PG that contribute to the
production of a product. Also, there may not necessarily be third party brokerage companies involved at each step of the value chain. Also, local
scale projects, that are for the most part privately undertaken, would involve a simpler accounting framework. The numbers presented in each table
below are solely based on the judgment of the authors. In practice they would need to be negotiated amongst those parties that contribute to the
given HWP value chain.
Value chain a: forest >tier 1 HWPs>tier 2 HWPs >HWP end use & construction/manufacturing waste bioenergy
PGs involved in the processing, manufacturing and other non-brokerage services
PGs invovled in the brokerage of production
PG 1: Timber PG 2: Tier 1 HWP PG 3: Tier 2 HWP PG 6: End use PG 7: End use PG 8: Timber PG 9: Tier 1 HWP PG 10: Tier 2 PG 13: End
producers
producers
producers
collectors
producers
brokers
brokers
HWP brokers use (waste)
c1: Forest biomass production
1
0
0
0
0
0
0
0
0
c2: Brokering tree product
0
0
0
0
0
1
0
0
0
c3: Primary product
0,1
0,4
0,5
0
0
0
0
0
0
c4: Brokering primary product
0
0
0
0
0
0
0,4
0,6
0
c5: Enduse product
0
0
0
0,2
0,8
0
0
0
0
c6: Brokering end use proudct
0
0
0
0
0
0
0
0
1
Attribution Fraction
0,24
0,16
0,2
0,02
0,08
0,1
0,04
0,06
0,1
Σc1,j
U
1
1
1
1
1
1
1
0,2
0,1
0,4
0,1
0,1
0,1
1
Value chain b: forest > tier 1 paper production>tier 2 paper production> bioenergy
PG involved in the processing, manufacturing and other non-brokerage services
c1: Forest biomass production
c2: Brokering tree product
c3: Primary product
c4: Brokering primary product
c5: Enduse product
c6: Brokering end use proudct
Attribution Fraction
PG 1: Timber PG 2: Tier 1 HWP
PG 4: Tier 1
producers
producers
paper producers
1
0
0
0
0
0
0
0,2
0,3
0
0
0
0
0
0
0
0
0
0,20
0,08
0,12
PG 5: Tier 2
paper
0
0
0,4
0
0
0
0,16
PG 6: End-use PG 7: End use
collectors
producers
0
0
0
0
0,05
0,05
0
0
0,2
0,8
0
0
0,04
0,10
5
PGs invovled in the brokerage of production
PG 8: Timber
brokers
0
1
0
0
0
0
0,10
PG 9: Tier 1 PG 11: Tier 1 PG 12: Tier 2 PG 13: End use
HWP brokers paper brokers paper brokers (waste) brokers
0
0
0
0
0
0
0
0
0
0
0
0
0,1
0,3
0,6
0
0
0
0
0
0
0
0
1
0,01
0,03
0,06
0,10
Σc1,j
U
1
1
1
1
1
1
1
0,2
0,1
0,4
0,1
0,1
0,1
1
Supporting Information
Value chain c: forest > bioenergy
PGs involved in the processing,
manufacturing and other nonbrokerage services
PG 1: Timber PG 7: End use
producers
producers
c1: Forest biomass production
1
0
c2: Brokering tree product
0
0
c3: Primary product
0,2
0,8
c4: Brokering primary product
0
0
Attribution Fraction
0,35
0,4
Parties invovled in the
brokerage of production
PG 8: Timber
brokers
0
1
0
0,4
0,175
PG 13: End
use (waste)
0
0
0
0,6
0,075
Σc1,j
U
1
1
1
1
1
0,25
0,13
0,50
0,13
1
Value chain d: forest > tier 1 HWP residues > bioenergy
PGs involved in the processing, manufacturing and other nonbrokerage services
PGs invovled in the brokerage of production
PG 1: Timber PG 2: Tier 1 HWP PG 6: End use PG 7: End use PG 8: Timber PG 9: Tier 1 PG 13: End use
producers
producers
collectors
producers
brokers
HWP brokers (waste) brokers
c1: Forest biomass production
1
0
0
0
0
0
0
c2: Brokering tree product
0
0
0
0
1
0
0
c3: Primary product
0,3
0,7
0
0
0
0
0
c4: Brokering primary product
0
0
0
0
0
1
0
c5: Enduse product
0
0
0,2
0,8
0
0
0
c6: Brokering end use proudct
0
0
0
0
0
0
1
Attribution Fraction
0,32
0,28
0,02
0,08
0,1
0,1
0,1
6
Σc1,j
U
1
1
1
1
1
1
1
0,2
0,1
0,4
0,1
0,1
0,1
1
Supporting Information
Value chain e: forest > tier 1 paper production> bioenergy
PG involved in the processing, manufacturing and other nonPGs invovled in the brokerage of production
brokerage services
PG 1: Timber
PG 4: Tier 1
PG 6: End-use PG 7: End use PG 8: Timber PG 11: Tier 1 PG 13: End use
producers paper producers
collectors
producers
brokers
paper brokers (waste) brokers
c1: Forest biomass production
1
0
0
0
0
0
0
c2: Brokering tree product
0
0
0
0
1
0
0
c3: Primary product
0,1
0,2
0,2
0,5
0
0
0
c4: Brokering primary product
0
0
0
0
0
1
0
c5: Enduse product
c6: Brokering end use proudct
0
0
0
0
0
0
1
Attribution Fraction
0,27
0,09
0,09
0,22
0,11
0,11
0,11
7
Σc1,j
U
1
1
1
1
0,2
0,1
0,4
0,1
1
1
0,1
1
Supporting Information
3
BRP FR decomposition
8
Energy/losses fast
Paper
6
HWPs (u=9-144 yrs)
Global Ocean/Terrestrial
Atmosphere
4
BRP regrowth of bio CO2 emitted
BRP regrowth other CO2
2
0
0
2
Time, years
x 10
7.5
Instantaneous Radiative Forcing (RF)
Instantaneous Surface Temperature Change (AGTP)
Integrated Radiative Forcing (iRF)
100
150
5
1
2.5
0
0
-1
-2
0
50
-6
2
Biogenic C balance [tonnes C]
10
x 10
100
200
300
Time, years
400
-5
2
x 10
iAGWP [Watt-years/m ]
12
b)
5
AGWP [Watts/m ] or AGTP [Kelvin]
a)
-2.5
500
Figure S2: a) Dynamic profile of the bio-C balance; b) Instantaneous and integrated radiative forcing and surface temperature change over time for Norwegian HWP 2006
imported HWPst used domestically; all biomass in HWP residues and HWP at end of life is assumed to be utilized for bioenergy or otherwise oxidized as CO2 to the
atmosphere>
Since the weighted average rotation period was calculated to be about 70 years for the imported biomass, the CO2 emitted to the atmosphere is
sequestered significantly faster than the Norwegian biomass/HWP case with a weighted average rotation period of 99 years as is shown in fig.S2
above. In the import system for domestic use (as depicted in fig.1 of the manuscript (blue values)) we apply the mirrored economy assumption to
describe the produced HWPs that are then imported to Norway. Since a great majority of the biomass is imported from within Europe, it is
reasonable to assume that biomass losses and residual energy demands across the different processing stages are quite similar to those found for
Norway.
8
Supporting Information
References:
FAO. (2006). "FAOSTAT " Retrieved 6.6.2010, 2010, from http://faostat.fao.org/site/406/default.aspx.
FAO (2006). Global planted forests thematic study: results and analysis. Planted Forests and Trees Working Paper 38. J. B. by A. Del Lungo, J.Carle. . Rome.
Grinde, M. (2006). Environmental Assessment of Scenarios for Products and Services based on Forest Resources in Norway. Department of Energy and Process
Engineering. Trondheim Norwegian University of Science and Technology. Masters: 88.
Guest, G., F. Cherubini, et al. (2012). "Climate impact potential of utilizing forest residues for bioenergy in Norway." Mitigation and Adaptation Strategies for
Global Change: 1-20.
IPCC (2006). Ch.12 Harvested Wood Products in IPCC Guidelines for National Greenhouse Gas Iventories, Prepared by the National
Greenhouse Gas Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). Japan, IGES. Volume 4: Agriculture, Forestry and
Other Land Use: 33.
Ketterings, Q. M., R. Coe, et al. (2001). "Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed
secondary forests." Forest Ecology and Management 146(1–3): 199-209.
Korhonen, J., M. Wihersaari, et al. (2001). "Industrial ecosystem in the Finnish forest industry: using the material and energy flow model of a forest ecosystem
in a forest industry system." Ecological Economics 39(1): 145-161.
Landskap, S. o. (2007). (Handbook) Landsskogtakseringens Feltinstruks Ås, Norwegian Institute of Forest and Landscape (Skog and Landskap).
Larsson, J. and G. Hylen (2007). Statistics of Forest Conditions and Forest Resources in Norway. Ås, Norwegian Instittute of Forest and Landscape (Skog og
Landskap).
NASA. (2013). "Surface meteorology and Solar Energy." Retrieved 11.04.2013, 2013, from https://eosweb.larc.nasa.gov/.
Pettersen, R. (1984). 2. The Chemical Composition of Wood. Rowell, RM (ed), The chemistry of solid wood. USDA. Madison, WI, Forest Products Laboratory.
Advances in chemistry series.
Smith, J., L. Heath, et al. (2006). Methods for calculating forest ecosystem and harvested carbon with standard estimates for forest types of the United States. .
Newton Square, PA, US Department of Agriculture, Forest Service, Northeastern Research Station.
Thomas, S. and A. Martin (2012). "Carbon Content of Tree Tissues: A Synthesis." Forests 3: 21.
Tuomi, M., T. Thum, et al. (2009). "Leaf litter decomposition—Estimates of global variability based on Yasso07 model." Ecological Modelling 220(23): 33623371.
Werner, F., H. Althaus, et al. (2007). Life Cycle Inventories of Wood as Fuel and Construction Material. Final report ecoinvent 2000 Dubendorf, Swiss Centre for
Life Cycle Inventories. No.9.
9