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A life cycle carbon balance for electricity produced from forest residues : a British Columbian case… Burke, Michael Andrew 2011

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A
LIFE
CYCLE
CARBON
BALANCE

 FOR
ELECTRICITY
PRODUCED

 FROM
FOREST
RESIDUES:
 A
BRITISH
COLUMBIAN
CASE
STUDY
 
 
 by
 
 
 Michael
Andrew
Burke
 
 B.Sc.W.,
The
University
of
British
Columbia,
2006
 
 
  
  A
THESIS
SUBMITTED
IN
PARTIAL
FULFILLMENT
OF
 THE
REQUIREMENTS
FOR
THE
DEGREE
OF
 
 MASTER
OF
SCIENCE
 
 
 in
 
 
 The
Faculty
of
Graduate
Studies
 
 (Forestry)
 
 
 THE
UNIVERSITY
OF
BRITISH
COLUMBIA
 
 (Vancouver)
 
 
 June
2011
 
 
 
 ©
Michael
Andrew
Burke,
2011 
  
  
  
  Abstract
 This
study
estimated
the
annual
life
cycle
carbon
emissions
for
a
hypothetical
bioenergy
 combustion
 process
 that
 generates
 electricity
 from
 forest
 residues
 in
 northern
 British
 Columbia.
 Net
 annual
 carbon
 dioxide
 emissions
 were
 calculated
 for
 the
 process
 life
 cycle,
 which
 identified
 and
 quantified
 each
 CO2
 emission
 source
 along
 the
 bioenergy
 supply
 chain.
 The
 sources
 identified
 included
 soil
 carbon,
 harvesting
 machinery,
 chipping,
 transportation,
 and
 biomass
 combustion.
 Emissions
 that
 were
 avoided
 as
 a
 result
of
this
process
were
also
calculated.

 
 This
 thesis
 highlighted
 the
 importance
 of
 utilizing
 complete
 accounting
 methodology
 when
 calculating
 emissions
 from
 energy
 processes
 and
 emphasized
 that
 the
 blanket
 assumption
 that
 all
 biomass
 is
 carbon
 neutral
 should
 not
 be
 made.
 While
 combustion
 emissions
from
the
biomass
source
utilized
in
this
study
can
be
considered
to
be
carbon
 neutral,
a
sensitivity
analysis
showed
that
emissions
could
increase
by
more
than
275%
 if
the
forest
residues
were
disposed
of
differently
in
the
business‐as‐usual
scenario.
In
 addition,
 net
 process
 emissions
 were
 reduced
 by
 over
 80%
 when
 it
was
assumed
 that
 there
 were
 no
 significant
 soil
 carbon
 emissions.
 Consequently,
 bioenergy
 typically
 appears
 more
 environmentally
 attractive
 than
 it
 actually
 is
 because
 combustion
 emissions
are
generally
treated
as
carbon
neutral
and
upstream
emissions,
such
as
from
 soil
carbon,
are
not
typically
attributed
to
residues.
 
 In
 a
 British
 Columbian
 context,
 utilizing
 forest
 residues
 for
 bioenergy
 may
 usefully
 increase
the
provincial
energy
supply
without
incurring
significant
 new
emissions.
This
 thesis
 showed
 that
 the
 emissions
 from
 harvesting,
 chipping
 and
 transporting
 residues
 are
only
a
fraction
of
the
emissions
already
produced
by
burning
the
forest
residues.
In
 addition,
 this
 thesis
 proposes
 that
 although
 utilizing
 forest
 residues
 for
 bioenergy
 will
 not
 result
 in
 any
 new
 soil
 carbon
 emissions,
 a
 portion
 of
 the
 soil
 emissions
 already
 incurred
should
be
allocated
to
forest
residues
if
they
are
used
for
bioenergy.
Using
this
  ii
 
  
  
  
  full
emission
accounting
methodology,
a
 bioenergy
combustion
 process
utilizing
forest
 residues
 from
 the
 Mackenzie
 area
 of
 northern
 British
 Columbia
 would
 produce
 approximately
164
kilograms
of
carbon
dioxide
per
megawatt‐hour
of
electricity,
which
 is
significantly
less
than
electricity
produced
from
both
coal
(721
–
996
kgCO2/MWh)
and
 natural
gas
electricity
(500
kgCO2e/MWh).
 
 
 
 
 
 
 
 
  iii
 
  
  
  
  Table
of
Contents
 Abstract ....................................................................................................... ii Table
of
Contents........................................................................................ iv List
of
Tables .............................................................................................. vii List
of
Figures.............................................................................................. ix List
of
Abbreviations.................................................................................... x Acknowledgements ................................................................................... xii Dedication................................................................................................. xiii 1
Introduction ..............................................................................................1 1.1
Background ........................................................................................................... 1 1.2
Project
Description ................................................................................................ 5 1.2.1
Study
Area ...................................................................................................... 5 1.2.2
Process
Life
Cycle............................................................................................ 5 1.3
Literature
Review .................................................................................................. 9 1.3.1
Carbon
Neutrality
of
Biomass ......................................................................... 9 1.3.2
Land
Use
and
Land‐use
Change..................................................................... 13 1.3.3
Existing
Life
Cycle
Studies ............................................................................. 14 1.3.4
Soil
Carbon ................................................................................................... 27 1.3.5
Harvesting
and
Chipping
Emissions ............................................................... 33 1.3.6
Transportation
Emissions.............................................................................. 35 1.3.7
Facility
Emissions .......................................................................................... 36 1.4
Study
Objectives.................................................................................................. 43  2
Materials
and
Methods .......................................................................... 44 2.1
Biomass
Data
and
Calculations ............................................................................ 45 2.1.1
BiOS
Model ................................................................................................... 45 2.1.2
Stand
Data .................................................................................................... 46 2.1.3
Polygon
Selection.......................................................................................... 47 2.1.4
Annual
Allowable
Cut
Calculation ................................................................. 50 2.1.5
Operating
Areas
and
Transportation
Distances ............................................. 54 2.2
Carbon
Flow
Calculations..................................................................................... 57 2.2.1
Soil
Carbon
Emissions ................................................................................... 57 2.2.2
Harvesting
Emissions .................................................................................... 61 2.2.3
Piling
and
Chipping
Emissions ....................................................................... 63 2.2.4
Transportation
Emissions.............................................................................. 64 2.2.5
Facility
Emissions .......................................................................................... 66 2.2.6
Avoided
Emissions ........................................................................................ 66 2.3
Electrical
Output.................................................................................................. 67  3
Results
and
Discussion............................................................................ 69 3.1
BiOS
Results
and
Electrical
Output....................................................................... 69 3.1.1
BiOS
Values
and
Results................................................................................ 69 iv
 
  
  
  
  3.1.2
Base
Case
Electrical
Output........................................................................... 70 3.2
Carbon
Flows
and
Emissions ................................................................................ 71 3.2.1
Soil
Carbon
Emissions ................................................................................... 72 3.2.2
Harvesting
and
Chipping
Results ................................................................... 72 3.2.3
Transportation
Results.................................................................................. 73 3.2.4
Facility
and
Avoided
Emission
Results ........................................................... 74 3.2.5
Net
Emissions ............................................................................................... 74 3.3
Sensitivity
Analysis
&
Discussion.......................................................................... 76 3.3.1
Forest
Floor
and
Mineral
Soil
Carbon
Emissions............................................ 76 3.3.2
Harvesting
Emissions .................................................................................... 78 3.3.3
Chipping
Emissions ....................................................................................... 78 3.3.4
Transportation
Emissions.............................................................................. 79 3.3.5
Facility
Emissions .......................................................................................... 81 3.3.6
Residue
Decomposition ................................................................................ 82 3.3.7
Emission
Attribution ..................................................................................... 84 3.3.8
Best
Case
Scenario ........................................................................................ 84 3.3.9
Sensitivity
Analysis
Results............................................................................ 85  4
Conclusion...............................................................................................94 4.1
Summary ............................................................................................................. 94 4.2
Conclusions ......................................................................................................... 95 4.3
Potential
Applications
and
Future
Research......................................................... 99  References ...............................................................................................101 Appendices ..............................................................................................109 Appendix
A:
Sample
Calculations............................................................................. 109 Equation
1.
Harvest
Volume ................................................................................ 109 Equation
2.
Mass
of
Recovered
Timber................................................................ 109 Equation
3.
Net
Life
Cycle
Emissions.................................................................... 110 Equation
4.
Soil
Carbon
Emissions ....................................................................... 110 Equation
5.
Area
Disturbed.................................................................................. 110 Equation
6.
Forest
Floor
Emissions ...................................................................... 111 Equation
7.
Mineral
Soil
Emissions ...................................................................... 111 Equation
8.
Harvesting
Emissions ........................................................................ 112 Equation
9.
Chipping
Emissions ........................................................................... 112 Equation
10.
Transportation
Emissions................................................................ 113 Equation
11.
Facility
Emissions ............................................................................ 113 Equation
12.
Avoided
Emissions .......................................................................... 114 Equation
13.
Electrical
Output ............................................................................. 114 Appendix
B:
BiOS
Species
Abbreviations.................................................................. 115 Appendix
 C:
 Polygon
 Area,
 Diameter
 At
 Breast
 Height
 (DBH),
 Basal
 Area,
 Live
 Stems
 Per
Hectare
And
Dead
Stems
Per
Hectare
Of
The
100
Representative
Polygons...... 116 Appendix
 D:
 Leading,
 Second,
 Third
 And
 Fourth
 Species
 Types,
 Species
 Percent
 Of
 Stand,
Stand
Densities
And
Heights
Of
The
100
Representative
Polygons................ 119  v
 
  
  
 
 
  
  
  Appendix
 E:
 BiOS
 Results
 Showing
 Harvest
 Volume
 And
 Volume
 Of
 Roundwood
 Removed
Per
Hectare.............................................................................................. 122 Appendix
 F:
 Roundwood,
 Total
 Residues
 And
 Recovered
 Residues
 As
 A
 Percent
 Of
 Total
Polygon
Biomass............................................................................................. 125  vi
 
  
  
  
  List
of
Tables
 Table
1.
Conversion
efficiencies
and
CO2
emissions
from
three
coal‐powered
electricity
 plants
(Spath
et
al.,
1999) ...................................................................................... 17 Table
2.
Comparison
of
conversion
efficiencies
and
life
cycle
emission
(Spath
et
al.,
 2009;
Carpentieri
et
al.,
2004) ............................................................................... 23 Table
3.
Net
life
cycle
emissions
for
various
energy
systems
and
their
reduction
against
a
 coal
reference
case
(Spath
&
Mann,
2004) ............................................................ 24 Table
4.
Life
cycle
greenhouse
gas
emissions
from
direct‐fired
biomass
combustion
and
 integrated
biomass
gasification
combined
cycle
(Spath
&
Mann,
2004)................. 25 Table
5.
Collated
net
life
cycle
emission
results
from
Heller
et
al.
(2004),
Carpentieri
et
 al.
(2004)
and
Spath
&
Mann
(2004) ...................................................................... 25 Table
6.
Canadian
and
American
soil
classifications
(Steila,
2008) ................................. 31 Table
7.
Mean
(±SD)
mineral
soil
and
forest
floor
carbon
stocks
in
tonnes
carbon
per
 hectare
by
forest
stand
age
and
soil
type
at
the
Aleza
Lake
Research
Forest
 (Fredeen
et
al.,
2005) ............................................................................................ 32 Table
8.
Collated
results
from
soil
carbon
studies.......................................................... 32 Table
9.
Fuel
use
from
harvesting
operations
(Johnson
et
al.,
2006).............................. 34 Table
10.

Fuel
use
of
chipping
equipment
(MacDonald,
2010[a]) ................................. 35 Table
11.
Transportation
fuel
consumption
values
(National
Renewable
Energy
 Laboratory,
2010;
Martensson,
2008).................................................................... 36 Table
12.
Typical
composition
of
low
CV
producer
gas
from
wood
biomass
gasified
in
air
 (Sivasamy
et
al.,
2008)........................................................................................... 39 Table
13.
Names,
types,
power
ratings
and
conversion
efficiencies
of
some
existing
 biomass
combustion
facilities
(Wiltsee,
2000) ....................................................... 41 Table
14.
Gasification
systems
and
conversion
efficiencies
(Dornburg
&
Faaij,
2001) .... 42 Table
15.
Breast
height
diameter
and
stand
density
frequency
distributions
from
both
 the
harvestable
polygons
and
the
selected
polygons
(adapted
from
Province
of
 British
Columbia,
2010[b]) ..................................................................................... 48 Table
16.
Average
one‐way
haul
distances
from
operating
areas
to
Mackenzie............. 56 Table
17.
Soil
carbon
density
per
hectare
and
emission
factors
from
harvesting
(adapted
 from
Fredeen
et
al.,
2005;
Nave
et
al.,
2010)......................................................... 60 Table
18.
Harvesting
fuel
usage
(Johnson
et
al.,
2006)................................................... 62 Table
19.
Harvesting
and
chipping
fuel
usage
(MacDonald,
2010[a]) ............................. 64 Table
20.
The
effect
of
moisture
content
on
wood
heat
content
(U.S.
Department
of
 Energy,
2010) ........................................................................................................ 68 Table
21.
Timber
harvest
estimation ............................................................................. 69 Table
22.
Biomass
quantity,
number
of
trips
and
average
one‐way
transportation
 distances
from
operating
areas
to
Mackenzie
(results
from
BiOS
Model) .............. 70 Table
23.
Recovered
biomass
and
timber
recovery
rate
(results
from
BiOS
model) ....... 70 Table
24.
Life
cycle
emission
results .............................................................................. 71 Table
25.
Soil
carbon
emission
results ........................................................................... 72 Table
26.
Tonne‐kilometres
traveled
and
transportation
emissions .............................. 74 vii
 
  
  
  
  Table
27.
Net
Global
Warming
Potential
(kgCO2e/MWh)
from
various
energy
systems
 and
their
difference
from
this
study ...................................................................... 76 Table
28.
Average
fuel
economy
over
time
for
a
European
tractor
and
semi‐trailer
 (Martensson,
2008) ............................................................................................... 79 Table
29.
Biomass
power
plant
information
and
potential
electricity
production
using
all
 recoverable
biomass
(Dornburg
&
Faaij,
2001) ...................................................... 82 Table
30.
Sensitivity
Analysis
results.............................................................................. 86 Table
31.
The
effect
of
technology
choice
on
biomass
requirements,
facility
emissions
 and
net
emissions.................................................................................................. 91 Table
32.
The
effect
of
technology
choice
on
emissions
from
each
life
cycle
stage........ 91 Table
33.
The
effect
of
technology
choice
on
emissions
considering
the
combustion
of
 residues
not
utilized
for
bioenergy ........................................................................ 92  
  viii
 
  
  
  
  List
of
Figures
 Figure
1.
Simple
diagram
of
bioenergy
processes
and
product
pathways
for
woody
 biomass
resources
(adapted
from
Sivasamy
et
al.,
2008)......................................... 2 Figure
2.
Diagram
outlining
the
average
proportion
of
residues
to
stem
wood
from
trees
 in
the
study
area
(adapted
from
Appendix
E)........................................................... 6 Figure
3.
Chipping
of
logging
residues
(MacDonald,
2009)............................................... 7 Figure
4.
Process
life
cycle
and
associated
carbon
flows.................................................. 8 Figure
5.
Decomposition
of
residues
in
a
landfill
(adapted
from
Mann
and
Spath,
2001) .............................................................................................................................. 12 Figure
6.
Global
net
electricity
production
by
energy
source,
2006
(Energy
Information
 Agency,
2009)........................................................................................................ 16 Figure
7.
Biomass
combustion
process
(adapted
from
DeMeo
&
Galdo,
1997).............. 37 Figure
8.
Flow
diagram
of
gasification
process
(Sivasamy
et
al.,
2008)........................... 39 Figure
9.
DBH
frequency
distributions
of
harvestable
polygons
(above)
versus
the
 selected
polygons
(below) ..................................................................................... 49 Figure
10.
Stand
density
frequency
distribution
of
the
harvestable
polygons
(above)
 versus
the
selected
polygons
(below) .................................................................... 50 Figure
11.
Timber
Supply
Review
process
(Pedersen,
2003) .......................................... 51 Figure
12.
Project
Area
and
TSA
boundaries
(adapted
from
Ministry
of
Forests,
Lands
 and
Natural
Resource
Operations,
2010) ............................................................... 52 Figure
13.
Operating
areas
and
major
roadways
within
the
study
area
(Adapted
from
 Google
Maps) ........................................................................................................ 56 Figure
14.
Life
cycle
carbon
flows .................................................................................. 57 Figure
15.
Simulation
of
soil
carbon
dynamics
following
harvest
of
standing
forest
 biomass
(adapted
from
Jandl
et
al.,
2006) ............................................................. 59 Figure
16.
Carbon
emissions
per
megawatt‐hour
electricity
generated ......................... 71 Figure
17.
Emissions
from
harvesting
and
chipping
activities......................................... 73 Figure
18.
Graphic
depiction
of
Soil
Carbon
Scenario
2
(adapted
from
Jandl
et
al.,
2006) .............................................................................................................................. 77 Figure
19.
B‐train
chip
hauler
(Hank's
Truck
Pictures,
2010) .......................................... 80 Figure
20.
Sensitivity
analysis
results
for
soil
carbon
emissions ..................................... 87 Figure
21.
Base
case,
attribution
scenario
and
best‐case
life
cycle
emissions
compared
to
 coal
energy
values
(Heller
et
al.,
2004;
Spath
&
Mann,
2004;
Carpentieri
et
al.,
 2004)..................................................................................................................... 89 Figure
22.
Impact
on
net
life
cycle
emissions
comparing
the
base
case
with
conversion
 efficiencies
for
grate‐fired
combustion
steam
turbine
(GFC/ST),
fluidized
bed
 combustion
steam
turbine
(FBC/ST),
fluidized
bed
gasification
gas
engine
(FBG/GE),
 biomass
integrated
gasification
combined
cycle
atmospheric
and
pressurized
 (BIG/CCA
&
BIG/CCP)............................................................................................. 90 Figure
23.
Sensitivity
analysis
results
for
transportation
emissions................................ 93 
 
 ix
 
  
  
  
  List
of
Abbreviations
 (A)
 
 (P)
 
 AAC
 
 AOI
 
 BAU
 
 BIG
 
 Btu
 
 CC
 
 CCF
 
 CCS
 
 CH4
 
 CN
 
 CO
 
 CO2
 
 CO2e
 
 CV
 
 CWD

 
 EIA
 
 EPRI
 
 FBC
 
 FBG
 
 GE
 
 GF
 
 GHG
 
 GJ
 
 H2
 
 HHV
 
 HRSG
 
 IBGCC
 
 ICGCC
 
 IGCC
 
 IPCC
 
 kWh
 
 LCA
 
 LCI
 
 LEBS
 
 LRDW
 
 LULUC
 
 MC
 
 MGEC
 
 MJ
 
 MW
 
  atmospheric
pressure
 pressurized
 annual
allowable
cut
 area
of
interest
 business‐as‐usual
 biomass
integrated
gasification
 British
thermal
unit
 combined
cycle
 hundred
cubic
feet
 carbon
capture
and
storage
 methane
 carbon
neutral
factor
 carbon
monoxide
 carbon
dioxide
 carbon
dioxide
equivalent
 calorific
value
 coarse
woody
debris
 Energy
Information
Agency
 Electric
Power
Research
Institute
 fluidized
bed
combustion
 fluidized
bed
gasification
 gas
engine
 grate‐fired
 greenhouse
gas
 gigajoule
 hydrogen
 higher
heating
value
 heat
recovery
steam
generator
 integrated
biomass
gasification
combined
cycle
 integrated
coal
gasification
combined
cycle
 integrated
gasification
combined
cycle
 Intergovernmental
Panel
on
Climate
Change
 kilowatt‐hour
 life
cycle
assessment
 life
cycle
impact
 low
emission
boiler
system
 Land
and
Resource
Data
Warehouse
 land
use
and
land‐use
change
 moisture
content
 Mackenzie
Green
Energy
Centre
 megajoule
 megawatt
 x
 
  
 MWh
 
 NGCC
 
 Nm3
 
 NP
 
 NREL
 
 NS
 
 NSPS
 
 ODt
 
 SOM
 
 SP
 
 SS
 
 ST
 
 tCO2
 
 tCO2e
 
 TFL
 
 tkm
 
 TSA
 
 VRI
 
 WBCSD
 WRI
 
  
  
  megawatt‐hour
 natural
gas
combined
cycle
 normal
cubic
metre
 northern
pine
site
 National
Renewable
Energy
Laboratory
 northern
spruce
site
 new
source
performance
standard
 oven
dry
tonnes
 soil
organic
matter
 southern
pine
site
 southern
spruce
site
 steam
turbine
 tonnes
carbon
dioxide
 tonnes
carbon
dioxide
equivalent
 tree
farm
license
 tonne‐kilometre
 timber
supply
area
 vegetation
resource
inventory
 World
Business
Council
for
Sustainable
Development
 World
Resources
Institute
  xi
 
  
  
  
  Acknowledgements
 
 First
and
foremost
I
offer
my
enduring
gratitude
to
my
supervisor
Dr.
Paul
McFarlane
for
 teaching
me
to
think
critically
and
for
encouraging
me
to
explore
my
interests.
Without
 his
mentorship
I
would
never
have
been
able
to
stay
on
task
and
complete
this
thesis.

 
 I
 express
 my
 deepest
 appreciation
 to
 Dr.
 Brian
 Titus
 from
 the
 CFS
 for
 the
 countless
 hours
of
time
he
invested
in
me,
providing
both
 guidance
and
perspective
and
to
Jack
 MacDonald
from
FP
Innovations
‐
FERIC
for
his
patience
and
help.

 
 I
 would
 like
 to
 thank
 Dr.
 John
 Grace
 and
 Dr.
 Harry
 Nelson
 for
 helping
 me
 develop
 a
 relevant
 and
 coherent
 topic
 and
 for
 dedicating
 their
 time
 to
 be
 members
 of
 my
 committee.
 
 I
would
also
like
to
thank
Robbie
Sianchuk,
my
friend
and
office‐mate
who
was
always
a
 welcomed
 distraction.
 Thank
 you
 for
 the
 hours
 of
 insightful,
 mind‐expanding
 and
 seemingly
infinite
conversions.
 
 Special
 thanks
 to
 my
 dearest
 friend
 and
 mentor
 Milton
 Wong
 for
 nurturing
 my
 social
 and
environmental
conscience
and
for
always
leading
by
example.
 
 My
 greatest
 appreciation
 is
 owed
 to
 my
 parents,
 as
 they
 have
 been
 the
 impetus
 for
 everything
 difficult
 I
 have
 ever
 attempted.
 Thank
 you
 for
 always
 asking
 for
 more.
 In
 addition,
I
thank
my
brother
for
always
being
there,
no
matter
what.
 
 Finally,
my
most
heartfelt
thanks
are
owed
to
my
beautiful
wife
Jovanna
for
putting
up
 with
my
procrastination
and
always
supporting
me.
 
 
 xii
 
  
  
  
  Dedication
 
 To
my
parents,

 For
their
enduring
support.
 
  xiii
 
  
  
  
  1
Introduction
 1.1
Background
 Biomass
was
the
most
important
contributor
to
global
energy
supply
prior
to
the
period
 of
rapid
industrialization
beginning
around
1850
 (Victor
&
Victor,
2002).
However,
the
 share
of
global
energy
supply
provided
by
biomass
has
declined
significantly
since
this
 period
 as
 coal
 replaced
 biomass
 as
 the
 most
 important
 fuel
 for
 industrial
 production
 (Victor
 &
 Victor,
 2002).
 In
 fact,
 biomass
 has
 decreased
 from
 providing
 80%
 of
 global
 energy
 supply
 in
 1860
 to
 approximately
 12%
 in
 2000
 (Victor
 &
 Victor,
 2002;
 Jaccard,
 2005).
Of
this,
10.4%
was
in
the
form
of
traditional
uses
(e.g.
home
heating
and
cooking)
 and
 only
 1.6%
 was
 from
 more
 advanced
 forms
 (e.g.
 electricity
 and
 biofuels)
 (Jaccard,
 2005).
 Data
 for
 the
 United
 States
 reveals
 an
 even
 more
 profound
 decline,
 with
 the
 contribution
of
biomass
to
primary
energy
decreasing
from
99%
in
1800
to
only
0.2%
by
 2000
 (Victor
 &
 Victor,
 2002).
 The
 decline
 of
 biomass’
 contribution
 to
 primary
 energy
 both
globally
and
 in
the
US
was
characterized
by
a
steep
decrease
between
1860
and
 1910,
 followed
 by
 a
 more
 gradual
 but
 steady
 decline
 through
 the
 remainder
 of
 the
 twentieth
century
(Victor
&
Victor,
2002).

 
 In
 a
 recent
 International
 Energy
 Outlook
 publication,
 the
 U.S.
 Energy
 Information
 Administration
(EIA)
projected
that
global
energy
consumption
would
grow
by
49%
from
 2007
to
2035
(Energy
Information
Agency,
2009).
Fossil
fuels
should
be
able
to
meet
this
 increase
 in
 demand.
 However,
 there
 is
 increasing
 concern
 over
 their
 environmental
 impacts,
including
climate
change
(Intergovernmental
Panel
on
Climate
Change,
2007).
 During
 this
 same
 time
 period,
 energy
 demand
 in
 British
 Columbia
 is
 also
 predicted
 to
 increase.
The
Government
of
British
Columbia
has
mandated
that
in
order
to
meet
this
 demand,
 all
 new
 electricity
 generation
 projects
 must
 have
 zero
 net
 greenhouse
 gas
 (GHG)
 emissions,
 which
 may
 provide
 some
 potential
 for
 new
 bioenergy
 projects
 (Ministry
of
Energy,
Mines
and
Petroleum
Resources,
2009).
 
 1
 
  
  
  
  Several
systems
exist
for
creating
usable
sources
of
energy
from
biomass
(Figure
1).
The
 energy
 production
 systems
 are
 usually
 categorized
 as
 thermochemical,
 biochemical
 or
 mechanical
 and
 the
 forms
 of
 energy
 that
 are
 created
 can
 include
 gaseous,
 liquid
 and
 solid
fuels,
as
well
as
heat
and
electricity.
The
optimal
energy
product
is
dependant
on
 the
 type
 of
 biomass
 used,
 the
 conversion
 process
 employed
 and
 market
 demand.
 Biomass
 is
 found
 in
 many
 forms
 and
 can
 be
 essentially
 described
 as
 either
 a
 forest
 resource
 or
 agricultural
 resource.
 Agricultural
 resources
 include
 purpose‐grown
 starch
 and
 sugar‐containing
 biomass
 (e.g.
 corn,
 sugar
 cane,
 sugar
 beets)
 used
 for
 first
 generation
 ethanol,
 oil‐containing
 biomass
 (e.g.
 soy
 beans,
 palm,
 rape)
 used
 for
 first
 generation
 biodiesel,
 as
 well
 as
 lignocellulosic
 biomass
 (i.e.
woody
 biomass)
 (e.g.
 crop
 residues
and
switchgrass)
that
can
be
used
along
any
product
stream
outlined
in
Figure
 1.
All
forest
resources
can
essentially
be
referred
to
as
woody
biomass
and
can
either
be
 purpose
 grown
 (e.g.
 plantation
 forest),
 naturally
 occurring
 (e.g.
 existing
 forest)
 or
 a
 waste
product.
Sources
of
waste
resources
include
logging
residues,
mill
residues,
waste
 paper
and
construction
and
demolition
waste.
 

  
 Figure
1.
Simple
diagram
of
bioenergy
processes
and
product
pathways
for
woody
 biomass
resources
(adapted
from
Sivasamy
et
al.,
2008)
 
 2
 
  
  
  
  Each
of
type
of
biomass
has
inherent
strengths
and
weaknesses.
For
instance,
some
of
 the
issues
regarding
the
agricultural
production
of
biomass
are
that
it
is
relatively
land
 and
 energy
 intensive
 (Kaltschmitt
 et
 al.,
 1997).
 In
 addition,
 there
 has
 been
 ongoing
 public
 resistance
 to
 the
 use
 of
 first
 generation
 biofuels,
 which
 stems
 from
 the
 ethical
 implications
of
using
a
food
source
for
energy
(i.e.
Food
vs.
Fuel
debate).
On
the
other
 hand,
 forest
 and
 agricultural
 waste
 or
 residues
 have
 been
 identified
 as
 an
 ideal
 feedstock
 for
 bioenergy
 (Searchinger
 et
 al.,
 2008).
 This
 is
 because
 waste
 biomass
 is
 typically
burned
or
landfilled
if
it
is
not
utilized
for
bioenergy.

 
 Recently
 bioenergy
 has
 been
 proposed
 as
 a
 mitigation
 solution
 to
 global
 warming
 because
 it
 was
 assumed
 that
 biofuels
 and
 electricity
 derived
 from
 biomass
 produced
 fewer
 greenhouse
 gases
 than
 fossil
 energy
 sources
 (Righelato
 &
 Spracken,
 2007;
 Scharlemann
&
Laurance,
 2008).
However
 it
was
only
recently,
through
the
 use
of
life
 cycle
 assessment
 (LCA)
 studies,
 that
 the
 complete
 emissions
 from
 bioenergy
 systems
 have
been
realized
(Section
1.3.3).
In
fact,
when
land
use
and
land‐use
change
impacts
 are
 considered,
 some
 bioenergy
 products
 exhibit
 a
 greater
 contribution
 to
 global
 warming
 than
 their
 fossil
 energy
 counterparts
 (Section
 1.3.2)
 (Fargione
 et
 al.,
 2008;
 Scharlemann
 &
 Laurance,
 2008;
 Searchinger
 et
 al.,
 2008).
 In
 fact,
 Searchinger
 et
 al.
 (2008)
 found
 that,
 in
 some
 circumstances,
 replacing
 gasoline
 with
 first
 generation
 ethanol
would
nearly
double
greenhouse
gas
emissions
over
30
years.

 
 A
 life
 cycle
 assessment
 (LCA)
 involves
investigating
 and
 quantifying
 the
 environmental
 impacts
associated
with
a
product
or
service
within
a
given
set
of
boundary
conditions
 (International
Organization
for
Standardization,
2006).
Essentially
it
is
a
tool
to
analyze
 the
impacts
of
a
process,
product
or
system
over
the
entire
life
cycle.
In
this
thesis
these
 same
principles
are
used
to
calculate
carbon
emissions
over
the
life
cycle
of
a
bioenergy
 system
 that
 generates
 electricity
 from
 biomass.
 This
 thesis
 focuses
 on
 electricity
 generation
 from
 biomass
 for
 two
 reasons.
 First,
 British
 Columbia
 is
 currently
 a
 net
 importer
 of
 electricity,
 and
 it
 has
 been
 predicted
 that
 the
 supply
 gap
 will
 widen
 (BC
  3
 
  
  
  
  Hydro,
2008).
This
means
that
there
is
a
demand
for
new
electricity
generation
projects
 in
the
province.
Second,
it
has
been
shown
that
is
more
efficient
to
convert
biomass
into
 electricity
 than
 biofuels
 such
 as
 ethanol
 across
 a
 range
 of
 feedstocks
 and
 conversion
 technologies
(Campbell
et
al.,
2009).
In
fact,
Campbell
et
al.
(2009)
found
that
vehicles
 powered
 by
 bioelectricity
 produced
 roughly
 double
 the
 emission
 offsets
 than
 those
 powered
by
cellulosic
ethanol
per
unit
area
of
cropland.
 
 There
 is
 an
 estimated
 11.9
 million
 green
 tonnes
 of
 biomass
 residues
 generated
 each
 year
 in
 British
 Columbia
 from
 existing
 forestry
 operations,
 which
 represents
 the
 potential
for
approximately
15
million
MWh/year
of
new
electricity
generation
(Nilsson,
 2009).
That
is
enough
electricity
to
power
roughly
1.5
million
homes
per
year
(Ministry
 of
Energy,
Mines
and
Petroleum
Resources,
2009).
One
of
the
primary
objectives
of
this
 thesis
is
to
determine
the
net
life
cycle
carbon
emissions
and
the
carbon
emissions
per
 MWh
from
a
representative
biomass
to
electricity
bioenergy
process
in
the
 interior
of
 British
Columbia
(Section
1.4).
 
 Several
 methods
 exist
 for
 creating
 electricity
 from
 biomass
 (Figure
 1).
 The
 two
 most
 common
methods
are
combustion
and
gasification,
both
of
which
are
thermochemical
 processes,
which
means
they
utilize
heat
to
transform
biomass’
stored
potential
energy
 into
 usable
 electricity.
 As
 a
 result
 these
 two
 methods
 will
 be
 addressed
 in
 this
 thesis.
 Some
of
the
systems
investigated
include
the
combustion
steam
cycle,
gasification
gas
 engine
 system
 and
 gasification
 combined
 cycle
 (Section
 1.3.7).
 The
 energy
 conversion
 efficiency
 can
 vary
 between
 each
 of
 these
 processes,
 which
 means
 two
 technologies
 may
 produce
 the
 same
 quantity
 of
 electricity
 from
 different
 amounts
 of
 biomass,
 resulting
in
 differing
carbon
emissions
per
MWh.
The
impact
conversion
efficiency
has
 on
life
cycle
carbon
emissions,
along
with
many
 other
factors
such
as
soil
carbon
 loss,
 will
also
be
investigated
in
this
thesis
(Section
1.4).
  4
 
  
  
  
  1.2
Project
Description
 This
 thesis
 investigates
 the
 life
 cycle
 carbon
 emissions
 from
 a
 bioenergy
 process
 that
 creates
electricity
from
forest
residues
available
in
the
interior
of
British
Columbia.
This
 section
of
the
thesis
introduces
the
study
location
and
defines
the
boundary
conditions
 of
the
life
cycle
assessment.
 1.2.1
Study
Area

 This
thesis
focuses
on
the
forested
area
surrounding
Mackenzie,
British
Columbia.
This
 area
was
selected
because
Mackenzie
is
the
location
of
the
proposed
Mackenzie
Green
 Energy
 Centre
 (MGEC),
 a
 75MW
 bioenergy
 plant
 (Mackenzie
 Green
 Energy
 Centre,
 2010).
 The
 proposed
 biomass
 feedstock
 for
 the
 MGEC
 is
 primary
 timber
 processing
 residues
 (e.g.
 sawdust,
 chips,
 slabs,
 hogfuel)
 that
 would
 be
 diverted
 from
 beehive
 burners
 (Mackenzie
 Green
 Energy
 Centre,
 2010).
 In
 order
 to
 assess
 other
 options
 for
 electricity
 generation
 in
 BC,
 this
 thesis
 calculated
 life
 cycle
 carbon
 emissions
 for
 electricity
production
from
a
bioenergy
plant
utilizing
logging
slash
(i.e.
forest
residues),
 as
opposed
to
mill
 residues.
The
use
of
forest
residues
was
investigated
for
this
study
 because
 they
 represent
 a
 large
 source
 of
 potential
 energy
 that
 is
 not
 currently
 being
 utilized
in
any
meaningful
way
(Nilsson,
2009).
This
study
assumed
that
all
of
the
forest
 residues
 created
 annually
 in
 the
 area
 surrounding
 Mackenzie
 would
 be
 accessed
 in
 order
 to
 provide
 a
 suitable
 quantity
 of
 feedstock.
 The
 study
 area
 was
 thus
 defined
 as
 being
a
circular
area
with
a
radius
of
100
km,
with
its
centre
located
over
Mackenzie.
 1.2.2
Process
Life
Cycle
 This
thesis
calculated
the
net
carbon
emissions
that
occurred
during
the
entirety
of
the
 process
 life
 cycle.
 This
 was
 accomplished
 using
 a
 modular
 approach,
 where
 emissions
 from
 each
 life
 cycle
 stage
 were
 calculated
 separately.
 The
 carbon
 flows
 were
 then
 combined
 in
 order
 to
 determine
 net
 process
 emissions.
 The
 methods
 used
 for
 quantifying
each
carbon
flow
are
explained
in
Section
2.2.
The
first
step
in
reaching
this
 objective
 is
 the
 definition
 of
 the
 supply
 chain
 and
 the
 determination
 of
 the
 emission
 sources
located
along
it.
 5
 
  
  
  
  The
process
begins
in
the
forest
with
the
generation
of
forest
residues,
which
are
a
by‐ product
of
logging.
Forest
residues
are
known
by
several
names,
including
logging
slash
 or
roadside
residues
when
they
are
processed
at
roadside.
In
British
Columbia,
trees
are
 typically
 cut
 close
 to
 the
 ground
 and
 hauled
 to
 a
 landing
 at
 the
 roadside.
 From
 there,
 dangle‐head
processors
retrieve
whole
trees
from
the
supply
pile,
delimb
and
top
them,
 and
 discard
 the
 tops
 and
 limbs
 into
 residue
 piles
 (Nilsson,
 2009).
 Roughly
 60%
 of
 the
 harvested
 tree
 mass
 from
 the
 study
 area
 is
 removed
 as
 roundwood
 (Figure
 2).
 The
 remaining
 40%
 is
 waste
 (i.e.
 residues),
 of
 which
 approximately
 half
 is
 recoverable
 (Appendix
 E).
This
process
of
cutting,
 hauling
and
delimbing
is
the
first
step
in
the
life
 cycle
and
it
is
referred
to
from
this
point
on
as
harvesting.
 
 
 Stem
wood
=
60%
w/w
 Removed
from
site
as
 roundwood
for
wood
 products
manufacturing
  
 
  Top
and
limbs
=
40%
w/w
 50%
recoverable
for
 bioenergy
uses
  
 
  
 
 
 
 
 Figure
2.
Diagram
outlining
the
average
proportion
of
residues
to
stem
wood
from
trees
 in
the
study
area
(adapted
from
Appendix
E)
 
 Forest
residues
are
not
currently
utilized
in
any
meaningful
way
in
British
Columbia
and
 as
 a
 result
 they
 are
 generally
 piled
 and
 burned
 in
 the
 forest
 (Province
 of
 British
 Columbia,
 2010[a]).
 However,
 in
 other
 parts
 of
 the
 world,
 and
 particularly
 in
 Scandinavia,
 residues
 are
 used
 to
 provide
 heat
 and
 power
 for
 both
 industrial
 and
 residential
customers
(Lang,
2008).
If
residues
are
to
be
utilized
in
this
manner,
the
next
 6
 
  
  
  
  step
in
the
process
is
chipping
(Figure
3).
Chipping
is
done
in
order
to
make
the
residues
 a
 uniform
 size
 and
 to
 increase
 their
 bulk
 density.
 This
 improves
 the
 transportation
 efficiency
as
it
increases
the
quantity
that
can
be
transported
each
trip.
Once
chipped,
 the
biomass
is
transported
via
semi‐trailers
to
the
facility,
which
is
the
third
step
in
the
 supply
 chain.
 Ideally
 residues
 are
 not
 transported
 over
 long
 distances
 because
 it
 can
 greatly
affect
process
economics
(Nilsson,
2009).
 
 
  
 Figure
3.
Chipping
of
logging
residues
(MacDonald,
2009)
 
 The
final
step
in
the
supply
chain
is
energy
production,
which
occurs
once
the
residues
 have
arrived
at
the
bioenergy
plant.
The
base
case
of
this
thesis
assumed
that
electricity
 is
 produced
 via
 a
 biomass
 combustion
 steam
 cycle.
 However,
 other
 technological
 options
 are
 also
 analysed
 in
 the
 sensitivity
 analysis.
 To
 recap,
 the
 four
 steps
 in
 the
 supply
chain
are
harvesting,
chipping,
transportation
and
energy
production
(Figure
4).
 
 Each
link
in
the
supply
chain
has
associated
carbon
emissions
that
must
be
calculated
to
 determine
 the
 net
 lifecycle
 emissions
 of
 the
 process.
 Six
 carbon
 flows
 are
 assessed
 in
 7
 
  
  
  
  this
 thesis
 (Figure
 4).
 The
 first
 carbon
 flow
 is
 from
 soil
 carbon
 due
 to
 harvesting.
 The
 next
 three
 are
 harvesting,
 chipping
 and
 transportation
 emissions.
 These
 activities
 require
the
use
of
carbon
emitting
machinery
that
is
fueled
by
diesel.
The
fifth
emission
 source
 is
 the
 bioenergy
 plant.
 The
 facility
 emissions
 result
 from
 biomass
 combustion.
 Lastly,
 the
 final
 carbon
 flow
 to
 be
 analysed
 is
 the
 avoided
 slash
 pile
 combustion
 emissions.
 The
 process
 life
 cycle
 and
 the
 six
 associated
 carbon
 flows
 are
 presented
 below
in
Figure
4.

 
  
 Figure
4.
Process
life
cycle
and
associated
carbon
flows
  
  8
 
  
  
  
  1.3
Literature
Review
 This
section
introduces
literature
that
was
relevant
to
this
thesis.
The
factors
affecting
 the
 carbon
 neutrality
 of
 biomass
 are
 introduced
 and
 the
 case
 for
 full
 emission
 accounting
 is
 made.
 Then,
 literature
 is
 analysed
 pertaining
 to
 bioenergy
 life
 cycle
 assessment,
 timber
 harvest
 impacts
 on
 soil
 carbon
 and
 carbon
 emissions
 from
 harvesting,
 chipping
 and
 transporting
 biomass.
 Lastly,
 the
 factors
 affecting
 facility
 emissions
and
the
processes
for
converting
biomass
to
electricity
are
assessed.
 1.3.1
Carbon
Neutrality
of
Biomass
 The
majority
of
guidance
on
the
carbon
footprint
of
biomass
assumes
that
it
is
carbon
 neutral
 (Johnson,
 2009;
 Searchinger
 et
 al.,
 2009).
 For
 example,
 many
 studies
 on
 the
 incineration
of
waste
do
not
take
into
account
the
emissions
of
CO2
from
the
biomass
 contained
 in
 the
 waste
 (Rabl
 et
 al.,
 2007).
 This
 assumption
 is
 based
 on
 the
 reasoning
 that
 as
 biomass
 grows
 it
 sequesters
 carbon
 dioxide
 and
 that
 the
 same
 quantity
 of
 carbon
is
then
released
back
to
the
atmosphere
when
the
biomass
is
combusted.
Rabl
et
 al.
 (2007)
 points
 out
 that
 the
 logic
 of
 this
 practice
 implies
 absurd
 conclusions.
 For
 example,
 the
 CO2
 emitted
 by
 burning
 a
 tropical
 forest,
 if
 not
 counted,
 would
 equalize
 the
climate
impact
of
burning
a
forest
and
preserving
it.
Likewise,
the
benefit
of
adding
 a
carbon
capture
and
storage
(CCS)
system
to
a
bioenergy
plant
would
not
be
evaluated
 because
 the
 CO2
 is
 omitted
 from
 the
 analysis
 in
 the
 first
 place.
 The
 World
 Business
 Council
for
Sustainable
Development
(WBCSD)
and
the
World
Resources
Institute
(WRI)
 recognize
that
presuming
biomass
carbon
neutrality
is
problematic.
However,
they
both
 exclude
biomass
combustion
emissions
from
footprint
definitions
(Johnson,
2009).

 
 A
 carbon
 stock
 refers
 to
 the
 quantity
 of
 carbon
 in
 a
 pool,
 which
 can
 change
 due
 to
 a
 difference
between
additions
and
removals
of
carbon
(Johnson,
2009).
Biomass
should
 only
 be
 considered
 carbon
 neutral
 in
 situations
 where
 there
 is
 no
 change
 in
 carbon
 stocks.
For
example,
in
a
scenario
where
abandoned
crops
are
converted
to
plantations
 the
biomass
source
can
be
considered
carbon
neutral
because
the
carbon
stock
change
  9
 
  
  
  
  is
zero
or
positive
(Zanchi
et
al.,
2010).
In
situations
where
there
is
an
initial
carbon
loss
 (e.g.
conversion
from
a
natural
forest),
the
biomass
should
only
be
considered
carbon
 neutral
 after
 the
 carbon
 stock
 change
 is
 fully
 compensated
 for
 by
 avoided
 fossil
 fuel
 emissions
(Zanchi
et
al.,
2010).
In
situations
where
forests
are
intensively
harvested
for
 fuel
 rather
 than
 preserved,
 the
 biomass
 should
 not
 be
 considered
 carbon
 neutral
 as
 carbon
stocks
are
reduced.
These
three
scenarios
are
examples
of
carbon
stock
changes
 due
to
land
use
and
land‐use
change
(Section
1.3.2).

 
 The
carbon
stock
change
from
utilizing
forest
residues
is
dependent
on
their
baseline
or
 business‐as‐usual
 (BAU)
 usage.
 When
 residues
 are
 left
 on
 the
 forest
 floor
 they
 decompose
slowly,
with
the
majority
of
the
carbon
returning
to
the
atmosphere
and
a
 fraction
remaining
in
the
humus
and
soil.
If
the
residues
were
to
be
used
for
electricity
 generation
 instead,
 all
 of
 the
 carbon
 would
 be
 released
 to
 the
 atmosphere
 instantaneously.
 Therefore,
 when
 harvest
 residues
 that
 are
 typically
 left
 on
 the
 forest
 floor
 are
 used
 for
 bioenergy
 there
 is
 a
 resultant
 carbon
 stock
 loss
 in
 the
 dead
 wood,
 litter
and
soil
pools
(Zanchi
et
al.,
2010).

 
 In
order
to
better
understand
these
dynamics,
Schlamadinger
et
al.
(1995)
investigated
 the
 carbon
 neutral
 (CN)
 factor
 of
 logging
 residues
 used
 for
 bioenergy
 from
 a
 typical
 European
 forest.
 In
 this
 scenario
 carbon
 neutrality
 is
 defined
 as
 the
 ratio
 of
 the
 net
 reduction
 of
 carbon
 emissions
 to
 the
 saved
 carbon
 emissions
 from
 the
 substituted
 reference
 energy
 system.
 It
 was
 assumed
 that
 the
 residues
 would
 typically
 be
 left
 to
 decompose
 on
 the
 forest
 floor
 in
 the
 BAU
 scenario
 and
 that
 they
 would
 be
 offsetting
 coal
 usage
 when
 used
 for
 electricity
 production.
 Schlamadinger
 et
 al.
 (1995)
 reported
 that
after
20‐25
years
the
average
CN
factor
for
logging
residues
was
about
0.6,
which
 means
that
the
residue
bioenergy
system
emitted
60%
less
carbon
per
unit
energy
than
 the
 coal
 system.
 In
 other
 words,
 the
 residue
 bioenergy
 system
 emitted
 0.4
 tonnes
 of
 CO2
 for
 every
 tonne
 of
 CO2
 emitted
 from
 the
 coal
 system.
 Therefore,
 in
 the
 case
 of
  10
 
  
  
  
  substituting
for
coal
power,
 it
would
only
be
justified
to
disregard
60%
of
the
biomass
 combustion
emissions
rather
than
assume
total
carbon
neutrality.
 
 Building
on
the
findings
from
Schlamadinger
et
al.
(1995),
a
second
study
by
Palosuo
et
 al.
 (2001)
 investigated
 the
 carbon
 neutral
 factor
 of
 logging
 residues
 from
 a
 typical
 upland
Finnish
spruce
(Picea
spp.)
forest.
Palosuo
et
al.
(2001)
determined
that
90%
of
 the
carbon
in
the
residues
left
on
site
would
be
released
to
the
atmosphere
within
the
 first
20
years
and
that
the
remaining
10%
would
decompose
very
slowly
over
hundreds
 of
years.
When
compared
to
electricity
produced
from
coal
or
peat,
electricity
produced
 from
 residues
produced
approximately
80%‐90%
less
carbon
emissions
and
as
a
 result
 the
residues
exhibited
a
carbon
neutral
factor
between
0.8
–
0.9.
 
 A
determining
factor
in
the
establishment
of
these
values
is
the
rate
at
which
residues
 decompose.
Schlamadinger
et
al.
(1995)
assumed
that
75%
of
the
carbon
contained
in
 the
residues
would
be
released
to
the
atmosphere
within
20
–
25
years
and
that
25%
of
 the
 carbon
 would
 be
 delivered
 to
 the
 humus
 and
 soil
 pool
 where
 it
 would
 remain
 for
 upwards
 of
 several
 hundred
 years.
 While
 the
 average
 CN
 factor
 reported
 by
 Schlamadinger
 et
 al.
 (1995)
 was
 0.6,
 the
 results
 presented
 a
 range
 of
 0.42
 –
 0.82.
 Palosuo
 et
 al.
 (2001)
 assumed
 a
 greater
 fraction
 of
 the
 residues
 were
 released
 to
 the
 atmosphere
 in
 the
 short
 term
 (i.e.
 80%‐90%)
 and
 as
 a
 result
 a
 higher
 CN
 factor
 was
 reported.
 
 When
the
BAU
scenario
for
wood
residues
is
disposal
in
a
landfill
the
conclusions
can
be
 comparable.
 However,
 wood
 in
 landfills
 usually
 decomposes
 more
 slowly
 than
 in
 the
 forest
and
methane,
a
more
potent
GHG,
is
produced
in
addition
to
CO2
(Zanchi
et
al.,
 2010).
 In
 the
 case
 of
 secondary
 processing
 residues
 or
 post‐consumer
 waste
 for
 example,
landfilling
is
often
the
BAU
disposal
method
(Heller
et
al.,
2004).
According
to
 a
 model
 proposed
 by
 Mann
 &
 Spath
 (2001),
 roughly
 35%
 of
 the
 carbon
 in
 landfilled
 biomass
would
decompose
to
a
gas
with
an
approximate
mixture
of
50%
CO2
and
50%
  11
 
  
  
  
  CH4
 (methane),
 and
 that
 65%
 of
 the
 carbon
 would
 be
 stored
 indefinitely
 (Figure
 5).
 Therefore,
 as
 with
 residues
 typically
 left
 on
 the
 forest
 floor,
 diverting
 residues
 from
 landfills
 to
 produce
 bioenergy
 may
 result
 in
 a
 carbon
 stock
 change
 meaning
 that
 emissions
from
residue
combustion
cannot
automatically
be
considered
carbon
neutral.

 
 
 
  20
ODt
Biomass
 (10
tC)
  
 
 
 
  Stored
Carbon
 Lignin
and
50%
of
the
non‐lignin
 resistant
to
degradation
 (6.5
tC)
  Emitted
Carbon
 Degradation
of
50%
of
the
 cellulose
and
hemicellulose
 (3.5
tC)
  
 
 
  Anaerobic
Degradation
 50%
to
CO2
 (6.4
t
CO2)
or
(1.75
tC)
  Anaerobic
Degradation
 50%
to
CH4
 (2.3
t
CH4 )
or
(1.75
tC)
  
 Figure
5.
Decomposition
of
residues
in
a
landfill
(adapted
from
Mann
and
Spath,
2001)
 
 These
studies
allude
to
the
fact
that
the
emissions
associated
with
bioenergy
are
highly
 dependant
 on
 the
 source
 of
 biomass.
 As
 a
 result
 it
 is
 inaccurate
 to
 assume
 emissions
 from
 all
 biomass
 are
 carbon
 neutral.
 In
 order
 to
 calculate
 accurate
 emissions
 from
 bioenergy
 systems
 it
 is
 necessary
 to
 apply
 full
 accounting
 principles.
 This
 involves
 calculating
 emissions
 and
 removals
 of
 CO2
 at
 each
 stage
 in
 the
 process
 life
 cycle,
 including
emissions
from
land
use
and
land‐use
change
(Section1.3.2)
(Rabl
et
al.,
2007).

 
 Biomass
 residues
 that
 would
 either
 rapidly
 decompose
 or
 be
 burned
 in
 a
 business‐as‐ usual
scenario
are
the
most
attractive
for
bioenergy
purposes
as
they
result
in
a
smaller
 carbon
stock
change
than
biomass
that
would
have
a
large
fraction
of
its
carbon
stored
 in
 the
 long
 term.
 As
 mentioned
 in
 Section
 1.2.2,
 forest
 residues
 generated
 in
 British
 12
 
  
  
  
  Columbia
 are
 typically
 burned
 in
 the
 forest
 at
 the
 harvest
site.
 This
 means
 that
 in
 the
 BAU
 scenario,
 the
 residues
 utilized
 in
 this
 study
 are
 rapidly
 converted
 to
 carbon.
 Therefore,
 the
 facility
 combustion
 emissions
 are
 offset,
 as
 the
 same
 quantity
 of
 CO2
 results
 from
 their
 oxidation
 whether
 it
 occurs
 in
 the
 forest
 or
 in
 a
 bioenergy
 plant
 (Section
 1.3.7).
 
 However,
 emissions
 from
 land
 use
 and
 land‐use
 change
 must
 also
 be
 included
 in
 the
 calculation
 before
 accurate
 emissions
 for
 the
 complete
 bioenergy
 process
can
be
determined.
 1.3.2
Land
Use
and
Land‐use
Change
 Emissions
from
 land
use
and
land‐use
change
must
also
be
 included
in
full
accounting
 practices
to
determine
if
there
is
a
resultant
carbon
stock
change
from
utilizing
biomass.
 These
refer
to
emissions
from
the
land
as
a
result
of
human
activities.
For
example,
soil
 carbon
 emissions
 from
 tilling
 or
 nitrogen
 emissions
 from
 fertilizers
 (Brandao,
 2010).
 Both
of
these
examples
fall
under
the
category
of
land
use
emissions.
Land‐use
change
 emissions,
 on
 the
 other
 hand,
 result
 from
 land
 changing
 uses,
 which
 can
 occur
 either
 directly
 or
 indirectly.
 For
 example,
 when
 forests
are
 converted
 to
 agricultural
 lands
 in
 response
 to
 increasing
 demand
 for
 biomass
 to
 generate
 biofuels,
 previously
 stored
 carbon
may
be
released
to
the
atmosphere
(Searchinger
et
al.,
2008).
This
is
an
example
 of
 direct
 land‐use
 change,
 as
 the
 land
 changes
 in
 usage
 from
 forested
 to
 agricultural
 directly
for
producing
biofuels.
Alternatively,
existing
food
crops
can
be
converted
into
 biomass
crops,
triggering
higher
food
prices,
which
results
in
farmers
around
the
world
 establishing
 more
 agricultural
 lands
 in
 response
 to
 higher
 profits
 (Searchinger
 et
 al.,
 2008).
This
is
an
example
of
indirect
land‐use
change,
as
the
change
occurs
indirectly
as
 a
result
of
agricultural
land
scarcity
elsewhere.

 
 There
 is
 ample
 research
 that
 supports
 the
 need
 for
 complete
 carbon
 accounting
 that
 includes
emissions
from
land
use
and
land‐use
change
(Zanchi
et
al.,
2010;
Gnansounou
 et
al.,
2008;
Searchinger
et
al.
2008;
Fargione
et
al.
2008;
Searchinger
et
al.,
2009).
For
 example,
 Searchinger
 et
 al.
 (2008)
 found
 that
 when
 using
 a
 worldwide
 agricultural
 model
to
estimate
emissions
from
land‐use
change,
corn‐based
ethanol
nearly
doubles
 13
 
  
  
  
  greenhouse
 emissions
 over
 30
 years
 instead
 of
 producing
 the
 commonly
 quoted
 20%
 reduction.
This
has
an
obvious
impact
on
policy,
as
the
prime
objective
of
climate
policy
 is
 mitigation
 of
 global
 warming
 (Righelato
 &
 Spracklen,
 2007).
 Yet
 in
 some
 cases
 bioenergy
results
in
higher
emissions
than
its
fossil
energy
counterparts
(Fargione
et
al.,
 2008).
 Zanchi
 et
 al.
 (2010)
 considered
 that
 a
 large
 part
 of
 the
 current
 controversy
 surrounding
the
carbon
neutrality
of
 biomass
is
the
lack
of
a
 full‐accounting
system
in
 the
LULUC
sector
of
current
climate
policy
agreements.
 
 This
 thesis
 uses
 complete
 accounting
 methodology
 to
 determine
 the
 net
 life
 cycle
 emissions
 from
 a
 bioenergy
 system
 in
 northern
 British
 Columbia.
 The
 biomass
 source
 utilized
 for
 this
 thesis
 is
 acquired
 from
 sustainably
 managed
 forests.
 Therefore,
 there
 will
 be
 no
 land‐use
 change
 emissions
 associated
 with
 its
 use.
 In
 addition,
 this
 thesis
 calculated
 emissions
 from
 soil
 carbon
 resulting
 from
 land
 use
 activities
 (i.e.
 forestry
 activities)
 (Section
 1.3.4).
 The
 next
 section
 discusses
 the
 results
 of
 existing
 life
 cycle
 studies
and
the
methodologies
used
to
calculate
emissions.
 1.3.3
Existing
Life
Cycle
Studies
 This
 thesis
 investigated
 both
 the
 methods
 and
 results
 from
 existing
 life
 cycle
 studies
 related
 to
 carbon
 emissions
 from
 electricity
 production
 processes.
 Life
 cycle
 studies
 have
 been
 conducted
 for
 most
 electricity
 producing
 processes,
 both
 renewable
 and
 fossil.
Existing
studies
relate
emissions
to
a
functional
unit,
which
is
typically
a
kilowatt‐ hour
 (kWh)
 or
 megajoule
 (MJ)
 for
 power
 plants.
 This
 means
 that
 carbon
 dioxide
 emissions,
for
example,
are
typically
presented
in
the
form
grams
of
carbon
dioxide
per
 kilowatt‐hour
(gCO2/kWh)
or
per
megajoule
(gCO2/MJ).
Emissions
are
often
presented
in
 terms
 of
 carbon
 dioxide
 equivalent
 (CO2e)
 when
 multiple
 greenhouse
 gases
 are
 produced.
Studies
pertaining
to
both
fossil
(i.e.
non‐renewable)
energy
sources,
such
as
 coal
 and
 natural
 gas,
 and
 renewable
 sources,
 such
 as
 biomass
 are
 included
 in
 this
 analysis.
 
  14
 
  
  
  
  As
 mentioned
 in
 Section
 1.1,
 there
 is
 a
 growing
 electricity
 supply
 gap
 in
 BC.
 The
 BC
 Energy
 Plan
 highlights
 the
 fact
 that
 the
 Province
 of
 BC
 is
 aiming
 to
 be
 energy
 self
 sufficient
by
2016
(Ministry
of
Energy,
Mines
and
Petroleum
Resources,
2009).
This
will
 be
 accomplished
 through
 a
 combination
 of
 energy
 efficiency
 strategies
 and
 new
 electricity
 projects,
 where
 bioenergy
 from
 forest
 residues
 could
 potentially
 play
 a
 role
 (Ministry
of
Energy,
Mines
and
Petroleum
Resources,
2009).
The
BC
Energy
Plan
outlines
 several
 criteria
 that
 new
 electricity
 projects
 must
 meet,
 including
 the
 criterion
 that
 all
 new
electricity
generating
facilities
achieve
zero
net
greenhouse
gas
emissions
(Ministry
 of
 Energy,
 Mines
 and
 Petroleum
 Resources,
 2009).
 Currently,
 the
 method
 or
 standard
 that
is
used
to
calculated
net
greenhouse
gas
emissions
from
new
projects
has
not
been
 included
 in
 the
 plan.
 This
 thesis
 will
 show
 that
 the
 method
 with
 which
 projects
 are
 assessed
can
greatly
affect
the
reported
net
emissions.
 
 The
 results
 from
 life
 cycle
 studies
 are
 often
 regionally
 specific.
 For
 example,
 carbon
 fluxes
and
balances
experienced
in
tropical,
temperate
and
boreal
forests
are
different
 and
 as
 a
 result,
 the
 impacts
 of
 harvesting
 activities
 in
 these
 three
 forest
 types
 would
 also
be
very
different
(Malhi
et
al.,
1999).
To
date,
a
life
cycle
study
calculating
the
net
 carbon
emissions
from
forest
residue
electricity
generation
has
not
been
completed
in
 British
Columbia.
 
 Coal
 power
 is
 the
 reference
 case
 most
 often
 cited
 when
 comparing
 emissions
 from
 alternative
sources
of
electricity.
The
main
reason
for
this
is
that
coal
is
the
most
prolific
 global
energy
source,
accounting
for
approximately
40%
of
global
electricity
production
 in
2006
(Figure
6).
In
addition,
biomass
is
compared
to
coal
because
it
can
be
co‐fired
 and
because
coal
plants
can
be
retrofitted
to
run
solely
off
biomass
feedstocks.

 
  15
 
  
  
  
  
 Figure
6.
Global
net
electricity
production
by
energy
source,
2006
(Energy
Information
 Agency,
2009)
 
 1.3.3.1
Fossil
Energy
Systems
 Spath
et
al.
(1999)
completed
a
life
cycle
assessment
that
analysed
emissions
from
three
 coal‐fired
power
generation
systems.
One
plant
represented
the
average
emissions
and
 efficiency
of
 coal‐fired
 power
plants
operating
in
the
U.S.,
another
 plant
met
the
 New
 Source
Performance
Standards
(NSPS),
and
the
third
was
a
highly
advanced
 plant
that
 utilized
 a
 low
 emission
 boiler
 system
 (LEBS)
 (Spath
 et
 al.,
 1999).
 Emissions
 were
 calculated
from
the
entire
process
life
cycle,
which
included
coal
mining,
transportation
 and
energy
production
(Spath
et
al.,
1999).
Life
cycle
carbon
emissions
from
the
three
 systems
 were
 reported
 to
 be
 1,022
 gCO2/kWh
 from
 the
 average
 coal
 system,
 941
 gCO2/kWh
from
the
NSPS
system
and
741
gCO2/kWh
from
the
LEBS
system
(Table
1).

 
  16
 
  
  
  
  Table
1.
Conversion
efficiencies
and
CO2
emissions
from
three
coal‐powered
electricity
 plants
(Spath
et
al.,
1999)
 Conversion
Efficiency
 Surface
Mining
 Transportation
 Power
Generation
 System
 (%)
 (gCO2/kWh)
 (%)
 (gCO2/kWh)
 (%)
 (gCO2/kWh)
 (%)
 Average
 32
 9
 0.9
 17
 1.7
 996
 97.3
 NSPS
 35
 8
 0.9
 16
 1.7
 917
 97.4
 LEBS
 42
 7
 1.0
 13
 1.8
 721
 97.3
 NSPS
=
New
Source
Performance
Standards;
LEBS
=
low
emissions
boiler
system
  Total
Emissions
 (gCO2/kWh)
 1,022
 941
 741
  
  All
 three
 plants
 utilized
 pulverized
 coal
 boiler,
 similar
 in
 design
 to
 the
 power
 plant
 analysed
in
the
base
case
of
this
thesis
(Section
1.3.7).
Integrated
gasification
combined
 cycle
 (IGCC),
 an
 entirely
 different
 method
 of
 generating
 electricity
 from
 coal,
 was
 not
 included
in
the
analysis
by
Spath
et
al.
(1999).
One
of
the
most
significant
factors
that
 contributed
 to
 the
 emission
 differences
 experienced
 at
 the
 three
 plants
 was
 the
 conversion
 efficiency.
 A
 more
 efficient
 plant
 can
 generate
 the
 same
 quantity
 of
 electricity
using
less
material.
As
a
result,
the
most
efficient
plant
(i.e.
LEBS)
consumed
 353
 g
 of
 coal
 per
 kWh
 of
 electricity
 produced
 on
 average,
 while
 the
 average
 system
 consumed
476
g
coal
/
kWh
(Spath
et
al.,
1999).
The
impact
of
conversion
efficiency
on
 overall
process
emissions
is
discussed
in
detail
in
the
sensitivity
analysis
portion
of
this
 thesis
(Section
3.3.9).
Another
significant
finding
from
Spath
et
al.
(1999)
was
that
in
all
 systems,
emissions
from
mining
and
transportation
were
very
small
when
compared
to
 emissions
 from
 power
 generation.
 It
 was
 also
 noted
 that
 mode
 of
 transportation
 (e.g.
 rail
car,
barge
or
truck)
had
virtually
no
effect
on
the
study
results.
 
 Natural
 gas
 accounts
 for
 approximately
 20%
 of
 energy
 production
 globally
 (Energy
 Information
 Agency,
 2009).
 Therefore,
 for
 completeness
 it
 is
 necessary
 to
 include
 figures
for
life
cycle
carbon
emissions
from
natural
gas
power
generation.
Spath
&
Mann
 (2000)
 completed
 a
 life
 cycle
 assessment
 of
 a
 natural
 gas
 combined‐cycle
 power
 generation
system
with
a
power
rating
of
505
MW
and
a
reported
conversion
efficiency
 of
48.8%.
The
carbon
emissions
from
this
study
were
reported
to
be
approximately
500
 gCO2e/kWh
 electricity
 produced
 (Spath
 &
 Mann,
 2000).
 One
 of
 the
 most
 interesting
 findings
 from
 the
 study
 was
 the
 quantity
 of
 the
 upstream
 emissions.
 Emissions
 from
  17
 
  
  
  
  natural
gas
production
and
distribution
were
reported
as
124.5
gCO2e/kWh,
which
are
 approximately
 one‐third
 the
 emissions
 reported
 from
 power
 plant
 operation
 (372.2
 gCO2e/kWh).
 The
 quantity
 of
 upstream
 emissions
 made
 up
 such
 a
 large
 proportion
 of
 overall
 emissions
 because
 it
 was
 assumed
 that
 1.4%
 of
 the
 gross
 natural
 gas
that
 was
 extracted
 was
 lost
 to
 the
 atmosphere
 as
 fugitive
 emissions
 (Spath
 &
 Mann,
 2000).
 Because
natural
gas
is
primarily
methane,
a
potent
greenhouse
gas
that
contributes
21
 times
 more
 to
 global
 warming
 than
 carbon
 dioxide,
 even
 a
 small
 quantity
 of
 fugitive
 emissions
greatly
altered
the
overall
process
emissions
(Spath
&
Mann,
2000).
This
point
 highlights
 the
 importance
 of
 including
 upstream
 emissions
 (i.e.
 full
 accounting)
 in
 emission
calculations
from
energy
generation
processes.

 1.3.3.2
Renewable
Energy
Systems
 Substantial
 differences
 in
 emissions
 are
 observed
 when
 fossil
 energy
 systems
 are
 compared
to
emissions
from
non‐combustion
based
renewable
energy
systems.
Rule
et
 al.
 (2009)
 examined
 the
 comparative
 sustainability
 of
 four
 renewable
 electricity
 technologies
in
terms
of
their
life
cycle
CO2
emissions.
The
study
incorporated
emissions
 from
construction
to
decommissioning,
including
emissions
from
periodic
maintenance
 (Rule
 et
 al.,
 2009).
 Models
 were
 based
 on
 New
 Zealand
 power
 plants,
 comprising
 geothermal,
 large‐scale
 hydroelectric,
 a
 proposed
 tidal
 installation,
 and
 wind‐farm
 electricity
generation
(Rule
et
al.,
2009).
Rule
et
al.
(2009)
reported
life
cycle
emissions
 of
 1.8
 gCO2/kWh
 for
 tidal
 power,
 3.0
 gCO2/kWh
 for
 wind
 power,
 4.6
 gCO2/kWh
 for
 hydroelectric
power,
and
5.6
gCO2/kWh
for
geothermal
power.

 1.3.3.3
Bioenergy
Systems
 There
 has
 been
 a
 surprisingly
 small
 amount
 of
 research
 regarding
 the
 life
 cycle
 emissions
 from
 biomass
 electricity
 generation
 processes.
 However,
 this
 section
 of
 the
 thesis
analyses
three
of
the
most
thorough
life
cycle
studies
that
have
completed
(Heller
 et
al.,
2004;
Carpentieri
et
al.,
2004;
Spath
&
Mann,
2004).
Heller
et
al.
(2004)
compared
 three
electricity
production
cases:
1)
pulverized
coal,
2)
pulverized
coal
(90%,
by
energy)
 co‐fired
with
willow
biomass
(10%,
by
energy),
and
3)
pulverized
coal
(90%,
by
energy)
 18
 
  
  
  
  co‐fired
 with
 biomass
 (10%,
 by
 energy)
 consisting
 of
 willow
 biomass
 (50%,
 by
 weight)
 and
 wood
 residues
 (50%,
 by
 weight).
 In
 these
 cases,
 two
 biomass
 sources
 were
 identified:
purpose
grown
willow
(Salix
spp.)
and
wood
residues.

 
 The
 willow
 biomass
 analysed
 by
 Heller
 et
 al.
 (2004)
 came
 from
 an
 agricultural‐type
 production
system.
Willow
(Salix
spp.)
clones
selected
for
fast
growth
were
planted
in
a
 ‘‘double
 row’’
 system
 of
 15,300
 trees/ha,
 with
 harvesting
 occurring
 every
 3–4
 years.
 Based
 on
 experience
 from
 New
 York
 State,
 an
 average
 annual
 yield
 of
 13.6
 oven
 dry
 tonnes
(ODt)
per
hectare
was
used
(Heller
et
al.,
2004).
The
model
assumed
ammonium
 sulfate
fertilizer
was
added
once
every
three
years
at
a
rate
of
100
kg
of
nitrogen/ha
and
 herbicides
were
used
only
during
field
preparation
and
willow
establishment
(Heller
et
 al.,
2004).
 
 Heller
et
al.
(2004)
reported
that
system
greenhouse
gas
flows,
which
included
carbon
 sequestration
 in
 below
 ground
 biomass,
 were
 3.7
 tCO2e/ha
 over
 23
 years
 of
 willow
 energy
 crops.
 This
 value
 corresponded
 to
 emissions
 of
 0.68
 gCO2e/MJ
 or
 2.45
 gCO2e/kWh
 of
 biomass
 energy
 produced
 (Heller
 et
 al.,
 2004).
 The
 emissions
 from
 the
 portion
 of
 energy
 supplied
 by
 willow
 (Salix
 spp.)
 in
 the
 co‐firing
 process
 is
 therefore
 within
the
range
of
values
presented
by
Rule
et
al.
(2009)
for
tidal,
wind,
hydroelectric
 and
geothermal
power.
However,
Heller
et
al.
(2004)
did
not
include
complete
life
cycle
 emissions.
 Land
 use
 GHG
 flows,
 such
 as
 those
 from
 fertilizers
 and
 soil
 carbon,
 were
 included
but
information
pertaining
to
land‐use
change
was
missing
(i.e.
how
the
land
 was
used
prior
to
the
establishment
of
a
bioenergy
plantation).
 
 Wood
residues
were
the
second
bioenergy
feedstock
considered
by
Heller
et
al.
(2004).
 The
 study
 assessed
 the
 impacts
 of
 utilizing
 forestry
 residues
 from
 logging
 and
 timber
 stand
 improvement
 operations,
 bark
 and
 wood
 residues
 from
 primary
 wood
 product
 mills,
 construction
 and
 demolition
 residues,
 and
 woody
 yard
 trimmings
 (Heller
 et
 al.,
 2004).
 The
 wood
 residues
 had
 a
 significantly
 different
 emission
 profile
 and
 life
 cycle
  19
 
  
  
  
  than
 the
 purpose‐grown
 willow
 (Salix
 spp.)
 crops.
 As
 is
 the
 case
 with
 all
 biomass,
 the
 purpose
grown
willow
(Salix
spp.)
absorbed
carbon
dioxide
from
the
atmosphere
as
 it
 grew.
Because
it
was
grown
specifically
as
an
energy
feedstock,
it
was
given
a
credit
for
 the
 carbon
 absorbed
 during
 growth
 that
 directly
 offset
 the
 emissions
 from
 its
 combustion
 (Heller
 et
 al.,
 2004).
 This
accounting
 method
 is
 only
 justified
 in
 situations
 where
the
impacts
from
land
use
and
land‐use
change
are
also
included.
For
example,
if
 a
natural
forest
was
cleared
to
establish
energy
crops,
the
loss
of
carbon
resulting
from
 converting
a
forest
to
cropland
would
need
to
be
accounted
for
before
the
energy
wood
 could
be
considered
carbon
neutral.
Therefore,
because
land‐use
change
data
was
not
 supplied
in
Heller
et
al.
(2004)
the
credit
given
to
the
purpose‐grown
biomass
is
not
fully
 justified.
 The
 supplemental
 woody
 residues
 did
 not
 receive
 the
 absorption
 credit
 because
 the
 original
 production
 and
 primary
 use
 of
 the
 residues
 were
 poorly
 characterized
 (Heller
 et
 al.,
 2004).
 Instead,
 the
 avoided
 emissions
 that
 would
 have
 occurred
during
the
alternative
disposal
of
the
biomass
were
credited.
 Heller
et
al.
(2004)
assumed
that
there
was
a
saturated
market
for
biomass
residues
and
 that
 if
 the
 residues
 were
 not
 used
 for
 energy
 purposes,
 they
 would
 be
 landfilled.
 The
 model
used
by
Heller
et
al.
(2004)
assumed
that,
if
landfilled,
34.8%
of
the
carbon
in
the
 biomass
would
decompose
to
50%
CO2
and
50%
CH4.
It
was
then
assumed
that
10%
of
 the
 landfill
 methane
 was
 either
 chemically
 oxidized
 or
 converted
 by
 bacteria
 to
 CO2,
 thus
 reducing
 methane
 atmospheric
 emissions
 (Heller
 et
 al.,
 2004).
 As
 mentioned
 above,
these
potential
emissions
were
calculated
and
credited
to
the
system
as
avoided
 emissions.
Unfortunately,
Heller
et
al.
(2004)
did
not
determine
an
associated
figure
for
 gCO2e/kWh
electricity
produced
from
wood
residues.
 
 Heller
 et
 al.
 (2004)
 found
 that
 the
 life
 cycle
 GWP
 emissions
 from
 the
 pulverized
 coal
 base
case
were
978
gCO2e/kWh,
of
which
95%
were
released
in
the
form
of
CO2
at
the
 power
plant.
Emissions
from
the
willow/residue
co‐firing
scenario
were
906
gCO2e/kWh,
 with
 the
 lowest
 emissions
 reported
 from
 the
 all
 willow
 co‐firing
 system,
 at
 883
  20
 
  
  
  
  gCO2e/kWh
(Heller
et
al.,
2004).
The
all
willow
system
had
the
lowest
emissions
because
 the
 willow
 biomass
 combustion
 emissions
 were
 considered
 entirely
 carbon
 neutral,
 while
the
residues
were
not.
The
residues
were
not
considered
carbon
neutral
because
 it
 was
 assumed
 that
 roughly
 65%
 of
 the
 carbon
 contained
 in
 the
 residues
 would
 be
 stored
 indefinitely
 in
 a
 landfill
 had
 they
 not
 been
 used
 for
 bioenergy
 (Section
 1.3.1).
 Also,
 it
 is
 important
 to
 note
 that
 Heller
 et
 al.
 (2004)
 assumed
 there
 was
 zero
 net
 soil
 carbon
sequestration
or
loss
experienced
as
a
result
of
the
willow
(Salix
spp.)
crops.
 
 In
 addition
 to
 the
 three
 primary
 cases
 presented
 by
 Heller
 et
 al.
 (2004),
 hypothetical
 emissions
from
a
 100%
willow
 integrated
biomass
gasification
combined
cycle
(IBGCC)
 system
and
a
direct‐fired
combustion
system
were
calculated
using
a
combination
of
the
 study
 results
 and
 data
 from
 the
 Electric
 Power
 Research
 Institute
 (EPRI).
 It
 was
 concluded
 that
 the
 life
 cycle
 emissions
 from
 the
 IBGCC
 and
 direct‐fired
 combustion
 systems
 would
 be
 substantially
 lower
 than
 the
 coal
 reference
 case,
 at
 40.2
 and
 52.3
 gCO2e/kWh,
 respectively
 (Heller
 et
 al.,
 2004).
 The
 23%
 difference
 between
 the
 IGBCC
 and
 direct‐fired
 systems
 was
 primarily
 due
 to
 the
 conversion
 efficiencies
 of
 the
 processes.
 The
 gasification
 system
 was
 listed
 at
 36%,
 while
 the
 direct‐combustion
 system
exhibited
a
conversion
efficiency
of
27.7%.

 
 Carpentieri
 et
 al.
 (2004)
 examined
 an
 IBGCC
 system
 with
 carbon
 capture
 (80%)
 that
 utilized
 purpose‐grown
poplar
(Populus
spp.)
energy
crops.
The
functional
unit
for
this
 study
was
the
production
of
1
MJ
of
electricity
and
the
boundary
conditions
of
the
study
 included
biomass
production
and
transportation,
plant
construction,
energy
conversion,
 and
plant
maintenance/dismantling.
The
system
was
also
separated
into
four
phases
for
 reporting
purposes:
construction,
biomass
production,
plant
operation,
and
dismantling.
 The
factors
considered
in
the
biomass
production
and
transportation
phase
included
the
 chemical
processes
for
the
production
and
use
of
fertilizers
and
herbicides,
the
biogenic
 emissions
 from
 the
 biomass,
 the
 CO2
 sequestration
 due
 to
 photosynthesis,
 the
 production
 of
 fuel
 and
 its
 utilization
 in
 the
 machines
 for
 biomass
 cultivation
 and
 the
  21
 
  
  
  
  production
of
fuel
and
its
utilization
for
the
biomass
transportation
(Carpentieri,
et
al.,
 2004).
However,
unlike
Heller
et
al.
(2004),
Carpentieri
et
al.
(2004)
did
not
include
any
 information
on
changes
in
below
ground
carbon.
It
was
also
noted
that
the
construction
 of
 machines
 and
 the
 buildings
 necessary
 for
 biomass
 cultivation
 were
 considered
 negligible
and
not
taken
into
account
(Carpentieri
et
al.,
2004)
 
 The
plant
construction
phase
included
the
production
of
the
raw
materials
required
for
 the
 plant
 devices
 and
 the
 emissions
 due
 to
 the
 assembly
 of
 the
 materials,
 while
 the
 operating
phase
comprised
water
consumption
for
the
CO2
removal
section
and
steam
 cycle,
 amines
 production,
 regeneration
 of
 activated
 carbon,
 raw
 materials
 production
 for
maintenance,
stack
emissions
and
process
wastes
(Carpentieri
et
al.,
2004).
Finally,
 the
 dismantling
 phase
 consisted
 of
 the
 energy
 consumption
 for
 dismantling,
 recycling
 and
transporting
of
materials
(Carpentieri
et
al.,
2004).
 
 This
study
determined
that
the
purpose‐grown
poplar
(Populus
spp.)
IBGCC
system
with
 carbon
 capture
 technology
 would
 result
 in
 negative
 carbon
 emissions,
 meaning
 the
 process
would
act
as
a
carbon
sink.
Life
cycle
carbon
emissions
were
reported
as
a
‐165
 gCO2e/MJ
 (i.e.
 ‐593
 gCO2e/kWh)
 (Carpentieri
 et
 al.,
 2004).
 The
 coal
 reference
 case
 presented
 by
 Carpentieri
 et
 al.
 (2004)
 exhibited
 life
 cycle
 carbon
 emissions
 of
 130
 gCO2e/kWh,
which
was
much
lower
than
the
values
presented
by
Spath
et
al.
(1999)
and
 Heller
et
al.
(2004).
The
two
reason
for
this
were
that
the
coal
reference
case
presented
 by
 Carpentieri
 et
 al.
 (2004)
 had
 an
 80%
 efficient
 carbon
 capture
 and
 storage
 (CCS)
 system
 and
 the
 plant
 power
 utilized
 an
 integrated
 coal
 gasification
 combined
 cycle
 (ICGCC)
with
a
high
conversion
efficiency
of
38.8%
(46.6%
without
CCS)
(Table
2).

 
 
  22
 
  
  
  
  Table
2.
Comparison
of
conversion
efficiencies
and
life
cycle
emission
(Spath
et
al.,
 2009;
Carpentieri
et
al.,
2004)
 Study
 Spath
et
al.
(1999)
  System
 Conversion
Efficiency
(%)
 Emissions
(gCO2e/kWh)
 Average
coal
 32
 1,022
 NSPS
 35
 941
 LEBS
 42
 741
 Carpentieri
et
al.
(2004)
 ICGCC
 46.6
 ‐
 IBGCC
+
CCS
 33.9
 178
 ICGCC
+
CCS
 38.8
 130
 *IBGCC
+
CCS
 33.9
 ‐593
 *
Credit
taken
for
sequestration
from
biomass
growth
 NSPS
=
New
Source
Performance
Standards;
LEBS
=
low
emissions
boiler
system;
 ICGCC
=
integrated
coal
gasification
combined
cycle;
 IBGCC
=
integrated
biomass
gasification
combined
cycle;
CCS
=
carbon
capture
and
storage
  
 There
were
two
final
findings
made
by
Carpentieri
et
al.
(2004)
that
were
of
particular
 interest.
 First,
 it
 was
 reported
 that
 the
 biomass
 case
 exhibited
 a
 greater
 life
 cycle
 contribution
 of
 heavy
 metals,
 carcinogenic
 substances,
 and
 ozone
 depletion
 than
 the
 coal
reference
case
(Carpentieri
et
al.,
2004).
While
the
impact
of
these
pollutants
falls
 outside
the
scope
of
this
thesis
they
should
be
included
in
future
research
pertaining
to
 electricity
 generation
 from
 forest
 residues
 (Section
 4.3).
 Second,
 Carpentieri
 et
 al.
 (2004)
 found
 that
 the
 carbon
 emissions
 resulting
 from
 both
 the
 construction
 and
 dismantling
 phases
 of
 the
 process
 life
 cycle
 were
 negligible.
 This
 finding
 was
 relevant
 within
 the
 context
 of
 this
 thesis
 and
 as
 a
 result
 emissions
 from
 these
 phases
 were
 considered
negligible.
 
 The
third
study,
by
Spath
&
Mann
(2004),
compiled
results
from
three
previous
LCA‐type
 studies
 they
 had
 completed
 on
 several
 methods
 of
 thermal‐based
 energy
 generation.
 This
 enabled
 the
 investigation
 of
 10
 different
 electricity
 cases:
 coal‐fired,
 natural
 gas
 combined
cycle
(NGCC),
biomass
/
coal
co‐fired,
direct‐fired
biomass,
and
IBGCC
(Spath
 &
Mann,
2004).
The
impacts
of
each
of
these
systems
were
analysed
with
and
without
 CO2
 removal
 (Spath
 &
 Mann,
 2004).
 The
 process
 life
 cycles
 included
 the
 upstream
 processes
necessary
for
feedstock
procurement
(e.g.
mining
coal,
extracting
natural
gas,
 growing
 dedicated
 biomass,
 diverting
 residue
 biomass
 from
 landfills),
 transportation,
 and
any
construction
of
equipment
and
pipelines
(Spath
&
Mann,
2004).
 23
 
  
  
  
  The
 greenhouse
 gas
 emission
 results
 from
 the
 direct‐fired
 biomass
 and
 IBGCC
 cases
 were
of
particular
interest,
especially
when
compared
to
the
coal
reference
case.
Spath
 &
 Mann
 (2004)
 found
 that
 the
 direct‐fired
 biomass
 case
 without
 carbon
 capture
 technology
 exhibited
 negative
 carbon
 emissions
 emissions
 (‐410
 gCO2e/kWh)
 and
 experienced
a
148%
reduction
 in
emissions
over
the
coal
reference
case.
In
the
IBGCC
 case,
 life
 cycle
 carbon
 emissions
 were
 reduced
 by
 94%
 over
 the
 coal
 reference
 case,
 reported
as
49
gCO2e/kWh
(Spath
&
Mann,
2004).
A
summary
of
the
results
from
Spath
 &
Mann
(2004)
is
presented
in
Table
3.

 
 Table
3.
Net
life
cycle
emissions
for
various
energy
systems
and
their
reduction
against
a
 coal
reference
case
(Spath
&
Mann,
2004)
 Energy
System
 Net
Emissions
(gCO2e/kWh)
 Reduction
from
coal
reference
 Coal‐fired
‐
reference
 847
 N/A
 Coal‐fired
w/
carbon
capture
 247
 ‐71%
 NGCC
 499
 ‐41%
 NGCC
w/
carbon
capture
 245
 ‐71%
 Biomass/coal
co‐fire
 681
 ‐19%
 Biomass/coal
co‐fire
w/
carbon
capture
 43
 ‐95%
 Biomass
direct‐fired
 ‐410
 ‐148%
 Biomass
direct‐fired
w/
carbon
capture
 ‐1368
 ‐262%
 IBGCC
 49
 ‐94%
 
 IBGCC w/
carbon
capture
 ‐667
 ‐179%
 NGCC
=
natural
gas
combined
cycle;
IBGCC
=
integrated
biomass
gasification
combined
cycle
  
 The
more
efficient
IBGCC
case
resulted
in
higher
emissions
than
the
biomass
direct‐fired
 case
 because
 different
 biomass
 sources
 were
 used.
 The
 direct‐fired
 case
 utilized
 biomass
residues,
assumed
to
be
diverted
from
landfills,
while
a
hybrid
poplar
(Populus
 spp.)
energy
crop
was
used
in
the
IBGCC
case
(Spath
&
Mann,
2004).
The
residue
case
 claimed
 avoided
 mulching
 and
 landfill
 emissions
 of
 CO2
 and
 methane
 and
 also
 took
 a
 credit
 for
 the
 CO2
 sequestered
 during
 their
 growth,
 even
 though
 it
 is
 uncertain
 as
 to
 whether
 there
 should
 be
 any
 land‐use
 change
 emissions
 associated
 with
 their
 production
 (i.e.
 whether
 they
 originated
 from
 deforestation
 activities
 or
 sustainable
 forest
harvest).
In
contrast,
the
purpose
grown
case
incurred
emissions
from
agricultural
 practices
and
as
a
result
the
avoided/sequestered
emissions
from
the
direct‐fired
case
 were
 nearly
 double
 those
 from
 the
 IBGCC
 case
 (Table
 4).
 Therefore,
 even
 though
 the
 24
 
  
  
  
  facility
emissions
were
fewer
in
the
IBGCC
case,
the
direct‐fired
case
performed
better
 because
of
the
biomass
source
utilized.
This
analysis
highlights
the
importance
that
the
 biomass
source
has
on
the
overall
emissions
from
bioenergy
processes
(Section
1.3.1).
 Table
 5
 contains
 the
 combined
 results
 from
 the
 three
 bioenergy
 studies
 introduced
 in
 this
section.
 
 Table
4.
Life
cycle
greenhouse
gas
emissions
from
direct‐fired
biomass
combustion
and
 integrated
biomass
gasification
combined
cycle
(Spath
&
Mann,
2004)
 Emissions
(gCO2e/kWh)
 Case
 Avoided/Sequestered
 Transportation
&
Construction
 Direct‐fired
 –
1627
 13
 IBGCC
 –
890
 49
 IBGCC
=
integrated
biomass
gasification
combined
cycle
  
  Facility

 1204
 890
  Net
Atmospheric
 –
410
 49
  
 Table
5.
Collated
net
life
cycle
emission
results
from
Heller
et
al.
(2004),
Carpentieri
et
 al.
(2004)
and
Spath
&
Mann
(2004)
 Energy
System
 Coal‐fired
(Heller
et
al.,
2004)
 Willow/residue/coal
co‐fire
(Heller
et
al.,
2004)
 Willow/coal
co‐fire
(Heller
et
al.,
2004)
 Coal‐fired
(Spath
&
Mann,
2004)
 Biomass
residue/coal
co‐fire
(Spath
&
Mann,
2004)
 Poplar
IBGCC
w/carbon
capture
(Carpentieri
et
al.,
2004)
 Coal‐fired
w/
carbon
capture
(Carpentieri
et
al.,
2004)
 Willow
direct‐fired
(Heller
et
al.,
2004)
 Willow
IBGCC
(Heller
et
al.,
2004)
 Poplar
IBGCC
(Spath
&
Mann,
2004)
 Biomass
residue
direct‐fired
(Spath
&
Mann,
2004)
 Poplar
IBGCC
w/carbon
capture*
(Carpentieri
et
al.,
2004)
 Poplar
IBGCC
w/carbon
capture
(Spath
&
Mann,
2004)
 Biomass
residue
direct‐fired
w/
carbon
capture
(Spath
&
Mann,
2004)
 *
Credit
taken
for
sequestration
from
biomass
growth
 IBGCC
=
integrated
biomass
gasification
combined
cycle
  Net
Emissions
(gCO2e/kWh)
 978
 906
 883
 847
 681
 178
 130
 52
 40
 ‐49
 ‐410
 ‐594
 ‐667
 ‐1368
  
 The
studies
by
Heller
et
al.
(2004),
Carpentieri
et
al.
(2004)
and
Spath
&
Mann
(2004)
are
 relevant
to
this
thesis
for
several
reasons.
Primarily,
they
quantify
comparative
life
cycle
 carbon
 emissions
 for
 various
 bioenergy
 systems
 and
 provide
 some
 benchmarks
 with
 which
to
compare
future
systems.
However,
some
of
the
other
findings
were
of
greater
 relevance
to
this
thesis.
 25
 
  
  
  
  In
addition,
both
Heller
et
al.
(2004)
and
Spath
 &
Mann
(2004)
highlighted
the
impact
 that
the
business‐as‐usual
usage
of
a
bioenergy
feedstock
can
have
on
overall
process
 emissions.
 Specifically,
 diverting
 residues
 from
 a
 landfill,
 where
 a
 large
 portion
 of
 the
 carbon
 in
 the
 biomass
 would
 be
 stored
 indefinitely,
 was
 not
 advantageous
 when
 compared
to
growing
biomass
specifically
for
bioenergy
(assuming
no
land‐use
change
 emissions).
 This
 was
 relevant
 to
 this
 thesis
 because
 the
 biomass
 source
 being
 considered
 is
 forest
 residues,
 which
 would
 be
 burned
 if
 not
 used
 for
 bioenergy.
 The
 importance
 of
 this
 fact
 is
 discussed
 further
 in
 Section
 1.3.1.
 Heller
 et
 al.
 (2004)
 also
 demonstrated
 that
 facility
 conversion
 efficiency
 has
 an
 impact
 on
 overall
 life
 cycle
 carbon
emissions.
Carpentieri
et
al.
(2004)
reported
that
bioenergy
systems
with
carbon
 capture
 systems
 could
 potentially
 act
 as
 a
 carbon
 vacuum,
 highlighting
 the
 important
 role
 bioenergy
 can
 play
 to
 combat
 climate
 change.
 However,
 the
 most
 useful
 finding
 from
 Carpentieri
 et
 al.
 (2004)
 for
 the
 purposes
 of
 this
 thesis
 was
 that
 emissions
 from
 construction,
maintenance
and
demolition
can
be
considered
negligible
with
respect
to
 life
cycle
carbon
emissions
from
a
bioenergy
plant.

 
 However,
 these
 studies
 lacked
 complete
 data
 pertaining
 to
 land
 use
 and
 land‐use
 change
 (LULUC).
 While
 land‐use
 change
 is
 not
 a
 significant
 factor
 for
 the
 purposes
 of
 this
 thesis,
 it
 is
 necessary
 to
 apply
 full‐accounting
 principles
 to
 all
 life
 cycle
 studies
 in
 order
 to
 have
 accurate,
 comparable
 results.
 Searchinger
 et
 al.
 (2009)
 highlighted
 that
 while
the
use
of
biomass
for
energy
is
considered
carbon
neutral
in
current
international
 climate
 change
 policy
 frameworks,
 bioenergy
 sources
 can,
 in
 fact,
 have
 very
 different
 GHG
 emissions
 reduction
 capabilities.
 This
 was
 partly
 evidenced
 by
 the
 results
 from
 Heller
et
al.
(2003)
and
Spath
&
Mann
(2004).
However,
without
complete
LULUC
data
 the
results
are
not
justified.

 
 The
 bioenergy
 life
 cycle
 typically
 consists
 of
 three
 primary
 stages;
 biomass
 growth
 and/or
procurement,
transportation,
and
energy
production.
However,
in
this
thesis
the
 biomass
 growth/procurement
 phase
 is
 separated
 into
 two
 parts;
 harvesting
 and
  26
 
  
  
  
  chipping.
The
reason
for
this
is
that
forest
residues
are
considered
by
most
studies
to
be
 a
 by‐product
 of
 timber
 harvesting
 (Brandao
 et
 al.,
 2010).
 By
 separating
 these
 two
 processes,
 this
 thesis
 will
 be
 able
 to
 determine
 how
 different
 methods
 of
 attributing
 emissions
will
affect
overall
process
emissions
(i.e.
with
or
without
including
upstream
 emissions
 from
 harvesting).
 Currently,
 carbon
 emissions
 from
 the
 transportation
 and
 electricity
production
phases
are
relatively
well
characterized
and
can
be
inferred
from
 existing
 studies.
 However,
 there
 is
 less
 of
 an
 understanding
 around
 the
 impacts
 from
 biomass
procurement.
 
 As
 mentioned
 in
 Sections
 1.3.1
 and
 1.3.2,
 this
 thesis
 uses
 full‐accounting
 principles
 to
 calculate
 emissions
 over
 the
 entire
 bioenergy
 life
 cycle.
 The
 next
 four
 sections
 discuss
 the
 existing
 literature
 pertaining
 to
 the
 six
 carbon
 flows
 outlined
 in
 Figure
 4.
 Section
 1.3.4
discusses
soil
carbon
dynamics,
Section
1.3.5
discusses
emissions
associated
with
 harvesting
 and
 chipping,
 Section
 1.3.6
 discusses
 transportation
 emissions
 and
 Section
 1.3.7
discusses
the
impact
of
facility
type
on
overall
process
emissions.

 1.3.4
Soil
Carbon
 
Soil
 carbon
 in
 active
 exchange
 with
 the
 atmosphere
 constitutes
 approximately
 two‐ thirds
of
all
carbon
 in
the
terrestrial
ecosystems
and
is
estimated
to
be
approximately
 1,395
x
1015
g
(Post
et
al.,
1982).
Approximately
70%
of
soil
carbon
is
contained
in
forest
 ecosystem
soils
 (Jandl
 et
 al.,
 2006).
 It
 is
 therefore
 important
 to
 understand
 forest
 soil
 carbon
 dynamics
 and
 the
 effects
 of
 harvesting
 because
 even
 small
 decreases
 in
 the
 percentage
of
forest
soil
carbon
could
translate
into
large
emissions
due
to
the
large
size
 of
the
soil
carbon
pool.
 
 The
 soil
 carbon
 pool
 is
 a
 representation
 of
 the
 balance
 between
 carbon
 inputs
 from
 litterfall,
 woody
 debris
 and
 root
 turnover
 on
 the
 one
 hand,
 and
 the
 release
 of
 carbon
 from
decomposition
on
the
other.
In
an
unmanaged
forest,
over
time
this
pool
reaches
a
 slightly
 fluctuating
 equilibrium
 level
 (Jandl
 et
 al.,
 2006).
 The
 turnover
 of
 soil
 organic
 matter
(SOM)
depends
on
several
factors,
including
site
conditions,
soil
properties
and
 27
 
  
  
  
  the
 chemical
 quality
 of
 the
 carbon
 (i.e.
 labile
 or
 stable).
 Labile
 carbon
 is
 found
 in
 the
 organic
layers
of
forest
floor,
while
stable
carbon
is
found
below
the
forest
floor
in
the
 mineral
soil
(Jandl
et
al.,
2006).

 
 The
stabilization
of
carbon
in
the
soil
is
much
different
than
the
accumulation
of
labile
 carbon.
Stable
soil
carbon
pools
are
primarily
affected
by
soil
properties
and
structure,
 for
example,
the
quantity
of
reactive
surfaces
of
clay
minerals
and
oxides
(Jandl
et
al.,
 2006).
When
organic
carbon
can
adhere
to
reactive
substances
at
the
mineral
surface
it
 creates
 an
 intimate
 bond,
 which
 leads
 to
 carbon
 stabilization
 (Jandl
 et
 al.,
 2006).
 Soil
 factors
that
decrease
the
accessibility
of
soil
organic
matter
(SOM)
for
microorganisms
 also
improve
carbon
stabilization
(Sollins
et
al.,
1996).
For
example,
small
soil
pores
may
 restrict
entry
by
microorganisms
or
their
extracellular
enzymes,
or
oxygen
levels
may
be
 so
 low
 as
 to
 support
 only
 limited
 microbial
 activity
 (Sollins
 et
 al.,
 1996).
 However,
 carbon
stabilization
can
also
be
affected
by
the
inherent
recalcitrance
of
the
deposited
 organic
 matter
 (Lorenz
 et
 al.,
 2007).
 For
 example,
 biomass
 containing
 alkyl
 carbon
 chains
 (found
 in
 lipids),
 phenolics
 or
 aromatics
 is
 less
 prone
 to
 degradation
 (Lorenz
 et
 al.,
2007).
 
 Conversely,
the
accumulation
or
depletion
of
labile
carbon
in
the
forest
floor
is
mainly
 affected
 by
 site
 conditions.
 Factors
 such
 as
 excess
 soil
 moisture
 or
 low
 temperatures
 inhibit
soil
respiration,
while
soil
disturbances
encourage
respiration
(Jandl
et
al.,
2006).
 Timber
harvesting
reduces
soil
carbon
levels
by
 both
decreasing
the
carbon
input
 rate
 (i.e.
litter
deposition)
and
increasing
decomposition
through
soil
disturbances.
 
 Studies
 have
 shown
 that
 the
 labile
 carbon
 on
 the
 forest
 floor
 is
 the
 fraction
 most
 affected
by
harvesting
practices
(Pennock
&
van
Kessel,
1996;
Olsson
et
al.,
1995;
Nave
 et
al.,
2010).
The
study
by
Pennock
&
van
Kessel
(1996)
was
performed
on
mixedwood
 sites
 in
 central
 Saskatchewan.
 In
 this
 study
 the
 soil
 carbon
 levels
 from
 mature
 stands
 (i.e.
approximately
80
–
100
years
old)
were
compared
to
the
soil
carbon
levels
of
recent
  28
 
  
  
  
  clearcut
sites
to
determine
the
impact
of
harvesting.
Primary
species
on
the
mixedwood
 sites
 included
 trembling
 aspen
 (Populus
 tremuloides)
 and
 white
 spruce
 (Picea
 glauca),
 both
of
which
are
found
within
the
study
area
for
this
thesis
(Section
2.1.4)
(Pennock
&
 van
Kessel,
1996).
Minor
inclusions
of
jack
pine
(Pinus
banksiana)
and
balsam
fir
(Abies
 balsamea)
were
also
noted.
Primary
soil
types
on
the
sites
included
Orthic
Gray
Luvisolic
 and
 Brunisolic
 Gray
 Luvisolic
 with
 significant
 inclusions
 of
 Orthic
 and
 Eluviated
 Eutric
 Brunisolic
 and
 Gleyed
 Gray
 Luvisolic
 (Table
 6).
 Like
 the
 sites
 in
 Pennock
 &
 van
 Kessel
 (1996),
 the
 primary
 soil
 types
 in
 the
 study
 area
 for
 this
 thesis
 were
 Brunisolic
 and
 Luvisolic
 (Ministry
 of
 Environment,
 2010).
 However
 the
 study
 area
 also
 contains
 significant
quantities
of
Humo‐Ferric
Podzolic
soil.
 
 The
study
by
Olsson
et
al.
(1995)
utilized
four
field
experiments
to
assess
the
effects
of
 clearcuts
on
soil
carbon
content
15
–
16
after
harvest.
The
sites
were
initially
harvested
 between
 1974
 and
 1976,
 with
 two
 located
 in
 northern
 Sweden
 and
 two
 in
 southern
 Sweden.
 One
 northern
 site
 and
 one
 southern
 site
 were
 planted
 with
 Norway
 spruce
 (Picea
 abies),
 labeled
 northern
 spruce
 (NS)
 and
 southern
 spruce
 (SS),
 and
 the
 two
 remaining
 sites
 were
 planted
 with
 Scots
 pine
 (Pinus
 sylvestris),
 labeled
 northern
 pine
 (NP)
and
southern
pine
(SP)
(Olsson
et
al.,
1995).
Soil
samples
were
taken
prior
to
the
 initial
harvest
(i.e.
1974
–
1975)
and
again
in
1990
–
1991.
Soil
classifications
were
not
 listed
for
this
study,
however
the
soil
was
described
as
glacial
till
with
moderate
to
high
 stoniness.
 
 Pennock
 &
 van
 Kessel
 (1996)
 reported
 that
 reductions
 in
 soil
 carbon
 stocks
 are
 significant
compared
to
the
gain
of
carbon
in
biomass
of
the
maturing
forest
for
at
least
 20
years
following
clear
cuts.
Considering
this
study
was
performed
on
similar
soil
types
 to
those
found
 in
the
study
area,
with
a
similar
 species
profile,
these
findings
support
 the
assumption
that
forest
floor
soil
carbon
is
depleted
as
a
result
of
harvesting.
Olsson
 et
al.
(1995)
reported
that
regenerating
forests
remained
net
carbon
sources
for
at
least
 14
 years
 after
 logging
 due
 to
 increased
 rates
 of
 soil
 respiration.
 A
 reduction
 in
 forest
  29
 
  
  
  
  floor
 carbon
 was
 found
 at
 all
 four
 sites,
 with
 the
 greatest
 loss
 experienced
 on
 the
 NS
 and
 SS
 sites.
 However,
 no
 significant
 difference
 was
 reported
 between
 the
 NS
 and
 SS
 sites
 or
 the
 NP
 and
 SP
 sites
 (Olsson
 et
 al.,
 1995).
 These
 findings
 also
 support
 the
 assumption
 that
 harvesting
 reduces
 forest
 floor
 soil
 carbon
 and
 suggest
 that
 species
 type
is
an
important
factor
affecting
soil
carbon
loss.
 
 Nave
 et
 al.
 (2010)
 used
 meta‐analysis
 to
 test
 a
 database
 of
 432
 soil
 carbon
 response
 ratios
drawn
from
temperate
forest
harvest
studies
around
the
world.
The
majority
of
 the
 studies
 were
 performed
 in
 the
 United
 States,
 however
 studies
 from
 Australia,
 Canada,
 Denmark,
 Germany,
 Korea,
 New
 Zealand,
 Spain
 and
 Tazmania
 were
 also
 included
 in
 the
 analysis.
 One
 of
 the
 most
 important
 overall
 findings
 from
 Nave
 et
 al.
 (2010)
was
that,
on
average,
harvesting
induced
a
30%
decrease
in
forest
floor
carbon.
 However
 this
 finding
 was
 highly
 dependant
 on
 forest
 type
 as
 coniferous/mixedwood
 forests
experienced
an
average
loss
of
20%,
while
hardwood
forests
experienced
much
 higher
losses
of
approximately
36%
on
average.
 
 It
 is
 important
 to
 note
 that
 there
 is
 also
 skepticism
 regarding
 the
 loss
 of
 soil
 carbon
 following
 harvest.
 Johnson
 (1992)
 completed
 an
 analysis
 of
 existing
 literature
 on
 soil
 carbon
 change
with
respect
to
forest
harvesting,
cultivation,
site
preparation,
burning,
 fertilization,
nitrogen
 fixation,
and
species
change.
Most
studies
showed
no
significant
 change
 (i.e.
 +/–
 10%)
 in
 soil
 carbon
 content
 unless
 intense
 burning
 or
 cultivation
 followed
harvesting.
Cultivation,
on
the
other
hand,
resulted
in
large
soil
carbon
losses
 of
up
to
50%
in
most
cases
(Section
1.3.4).

 
 Results
regarding
mineral
soil
carbon
loss
due
to
harvesting
seem
to
be
divided
as
well
 with
increased
soil
carbon
values
being
reported
in
some
instances
(Olsson
et
al.,
1995).
 Studies
 reporting
 mineral
 soil
 losses
 have
 returned
 varying
 results
 that
 are
 highly
 dependent
on
soil
type.
For
example,
the
meta‐analysis
completed
by
Nave
et
al.
(2010)
 discovered
 that
 no
 significant
 mineral
 carbon
 changes
 were
 experienced
 following
  30
 
  
  
  
  timber
 harvesting
 on
 Alfisol
 and
 Spodosol
 soil
 types
 (Table
 8),
 and
 that
 overall
 coniferous/mixed
 forests
 showed
 no
 significant
 change
 in
 mineral
 soil
 carbon
 storage
 ability.
 However,
 Nave
 et
 al.
 (2010)
 also
 reported
 statistically
 significant
 mineral
 soil
 carbon
 losses
of
13%
and
7%
from
Inceptisol
and
Ultisol
soils,
respectively.
Pennock
&
 van
 Kessel
 (1996)
 also
 reported
 reductions
 in
 mineral
 soil
 carbon
 due
 to
 harvesting,
 although
admittedly
much
lower
than
in
the
surface
layers
of
soil
(Table
8).
 
 Table
6.
Canadian
and
American
soil
classifications
(Steila,
2008)
 American
Classification
 Inceptisols
 Ultisols
 Alfisols
 Spodosols
  Canadian
Classification
 Brunisolic,
some
Gleysolic
and
Podzolic
 No
Equivalent
 Luvisolic,
some
Gleysolic
 Podzolic
  
 
 Another
 study,
 published
 Fredeen
 et
 al.
 (2005),
 was
 performed
 in
 the
 Aleza
 Lake
 Research
 Forest,
 located
 approximately
 60
 km
 south
 of
 the
 study
 area.
 This
 study
 looked
into
the
soil
carbon
differences
between
first
growth
and
second
growth
stands.
 Upland
 sites
 at
 the
 Aleza
 Lake
 Research
 Forest
 are
 composed
 largely
 of
 fine
 textured
 Luvisolic
 and
 associated
 Luvic
 Gleysolic
 soils
 (Fredeen
 et
 al.,
 2005).
 These
 soils
 are
 characterized
 by
 some
 accumulation
 of
 organic
 matter
 near
 the
 surface,
 with
 distinct
 blocky,
clay‐enriched
deeper
horizons,
resulting
in
shallow
root
systems
(to
25cm
deep).
 Coarser
textured
Orthic
Humo‐Ferric
Podzols
are
found
on
less
than
20%
of
the
upland
 sites,
and
are
characterized
by
much
deeper
root
penetration
(Fredeen
et
al.,
2005).

 
 Slightly
 lower
 levels
 of
 forest
 floor
 carbon
 were
 reported
 for
 second
 growth
 stands,
 however
 the
 findings
 were
 not
 statistically
 significant
 (Table
 7).
 Mineral
 soil
 carbon
 levels
 were
 also
 reported
 as
 approximately
 2‐6%
 lower
 in
 second
 growth
 stands,
 but
 again
the
difference
was
not
significant
(Table
7).
The
findings
of
Fredeen
et
al.
(2005)
 support
those
of
Nave
et
al.
(2010),
as
the
soil
types
found
in
the
Aleza
Lake
Research
 Forest
are
the
Canadian
equivalents
of
Alfisols
and
Spodosols.
  31
 
  
  
  
  
 Table
7.
Mean
(±SD)
mineral
soil
and
forest
floor
carbon
stocks
in
tonnes
carbon
per
 hectare
by
forest
stand
age
and
soil
type
at
the
Aleza
Lake
Research
Forest
(Fredeen
et
 al.,
2005)
 Age‐class,
soil
type
 Old
growth,
coarse
 Second
growth,
coarse
 Old
growth,
fine
 Second
growth,
fine
  0‐6.6
 24.6±9.8
 18.8±9.7
 28.2±7.6
 21.2±5.5
  Sampled
Interval
(cm)
 10‐16.6
 20‐26.6
 11.5±6.0
 8.0±6.1
 13.2±5.2
 10.2±5.9
 14.7±6.4
 6.7±1.5
 12.9±3.5
 9.5±3.0
  
 40‐46.6
 
 5.8±5.1
 
 7.3±3.5
 
 3.1±0.9
 
 4.4±2.5
 
  Pit
Total
(cm)
 0‐46.6
 0‐106.6
 85±20
 115±26
 83±22
 112±24
 83±13
 110±16
 78±12
 106±17
  Forest
Floor
 Carbon
Stock
 78±54
 27±6
 35±6
 29±5
  
 Table
8.
Collated
results
from
soil
carbon
studies
 Study
 Johnson
(1992)
 Pennock
&
van
Kessel
(1996)
  Forest
Type
 Various
 Mixedwood
  Soil
Type
 Various
 Luvisolic
and
 Brunisolic
  Forest
Floor
 +/–

10%
 ‐24%
  Mineral
Soil

 +/–

10%
 Low
  Olsson
et
al.
(1995)
  Coniferous
  Glacial
till
  Yes
  No
  Nave
et
al.
(2010)
  Coniferous
  Alfisol
 Spodosol
 Inceptisol
 Ultisol
  ‐20%
 ‐20%
 ‐20%
 ‐20%
  0
 0
 ‐13%
 ‐7%
  Conclusion
 Not
significant

 Significant
 organic
carbon
 loss
(6‐20
years)
 Significant
 organic
carbon
 loss
(15‐16
years)
 Significant
loss
of
 organic
soil
 Inconclusive
 mineral
soil
loss
  
 1.3.4.1
Upstream
Attribution
 Typically
 upstream
 emissions
 from
 soil
 carbon
 are
 not
 attributed
 forest
 residues
 because
 they
 are
 considered
 a
 by‐product
 of
 timber
 harvest.
 However,
 this
 study
 attributes
some
of
the
soil
carbon
losses
from
harvesting
to
residues.
This
is
because
as
 their
 use
as
a
bioenergy
feedstock
 becomes
more
prevalent
they
may
 be
treated
as
a
 co‐product
 to
 timber,
 which
 could
 require
 the
 attribution
 of
 complete
 upstream
 emissions.
 Therefore,
 it
 is
 important
 to
 accurately
 depict
 soil
 carbon
 flows
 due
 to
 harvesting
 because
 even
 small
 losses
 could
 substantially
 change
 the
 emission
 performance
of
bioenergy
due
to
the
size
of
the
soil
carbon
pool.
Unfortunately
this
is
 very
 difficult
 to
 accomplish
 solely
 through
 literature.
 For
 example,
 it
 was
 reported
 by
 Johnson
 (1992)
 that
 several
 significant
 issues
 were
 encountered
 from
 trying
 to
 32
 
  
  
  
  synthesize
data
on
forest
management
and
soil
carbon
from
literature,
which
stemmed
 from
differences
in
sampling
protocols
(e.g.
space
and
time)
and
intensity.
 
 There
 were
 many
 important
 outcomes
 from
 the
 soil
 carbon
 studies
 introduced
 in
 this
 section.
 Perhaps
 most
 importantly,
 the
 varying
 results
 of
 the
 studies
 highlight
 the
 difficulty
 in
 attaining
 accurate
 soil
 carbon
 data.
 Some
 of
 the
 factors
 that
 influenced
 results
were
soil
type,
forest
species,
sampling
depth
and
time
since
harvest.
Based
on
 the
 reported
 results,
 it
 is
 not
 likely
 that
 mineral
 soil
 carbon
 loss
 will
 occur
 in
 any
 meaningful
quantity
as
a
result
of
harvesting
in
the
study
area
(Table
8).
Other
studies
 suggest
that
organic
carbon
in
the
forest
floor
is
reduced
as
a
direct
result
of
harvesting.
 However,
 the
 exact
 amount
 varies
 from
 site
 to
 site.
 In
 this
 thesis,
 the
 average
 loss
 of
 20%
 reported
 from
 Nave
 et
 al.
 (2010)
 will
 be
 used
 because
 it
 was
 the
 outcome
 of
 a
 meta‐analysis
of
432
different
soil
carbon
studies.
In
addition,
a
range
of
different
values
 will
be
considered
in
a
sensitivity
analysis
to
assess
the
impact
of
soil
carbon
on
overall
 life
cycle
emissions.
Lastly,
the
soil
carbon
densities
reported
in
Fredeen
et
al.
(2005)
will
 be
used
to
approximate
the
initial
soil
carbon
levels
for
the
study
area
(Table
7).
 1.3.5
Harvesting
and
Chipping
Emissions
 Calculating
 emissions
 from
 harvesting
 and
 chipping
 is
 quite
 straightforward.
 Emissions
 from
 these
 stages
 in
 the
 process
 life
 cycle
 are
 a
 result
 of
 fossil
 fuel
 combustion
 (specifically
 diesel)
 by
 machinery
 and
 are
 therefore
 much
 easier
 to
 calculate
 than
 emissions
 from
 soil
 carbon,
 which
 are
 highly
 variable
 and
 dependant
 on
 numerous
 factors.
 
 Whole
tree
harvesting
is
the
preferred
method
of
harvesting
in
the
northern
interior
of
 British
Columbia
(Nilsson,
2009).
In
this
process
a
feller‐buncher
cuts
trees
close
to
the
 ground
and
stacks
them
in
a
pile
suitable
for
access
by
a
skidder,
which
then
transports
 the
cut
trees
to
the
roadside.
Once
at
roadside,
the
trees
are
separated
into
roundwood
 and
residues
(i.e.
tops
and
limbs)
by
a
dangle‐head
processor.
In
the
business‐as‐usual
  33
 
  
  
  
  scenario
roundwood
is
removed
on
trucks
and
the
residues
are
left
at
the
roadside
to
be
 burned.
 
 A
 life
 cycle
 impact
 (LCI)
 study
 by
 Johnson
 et
 al.
 (2006)
 catalogued
 the
 fuel
 use
 from
 logging
 operations
 in
 the
 Pacific
 Northwest
 and
 Southeast
 United
 States
 in
 order
 to
 determine
 differences
 among
 different
 harvesting
 methods.
 The
 system
 of
 harvesting
 analyzed
 from
 the
 Pacific
 Northwest
 was
 hand
 felling,
 while
 in
 the
 Southern
 United
 States
a
similar
harvesting
method
to
the
method
assumed
for
this
thesis
was
assessed.
 The
fuel
use
results
from
Johnson
et
al.
(2006)
are
presented
in
Table
9.
 
 Table
9.
Fuel
use
from
harvesting
operations
(Johnson
et
al.,
2006)
  
 
  
  Pacific
Northwest
 Felling:
 Hand
felling
 Yarding:
 Large
yarder
 Southeast
United
States
 Felling:
 Large
feller
buncher
 Skidding:
 Medium
grapple
skidder
 Processing:
 Stroke
delimber
 CCF
=
100
cubic
feet;
ODt
=
oven
dry
tons
  Diesel
Use
 3 Gal/CCF
 m /ODt
 0.08
 0.00023
 1.80
 0.00507
 
 
 0.27
 0.00076
 1.19
 0.00335
 0.22
 0.00062
  In
 order
 to
 utilize
 residues
 for
 bioenergy
 they
 must
 first
 be
 piled
 and
 then
 chipped.
 Piling
is
primarily
accomplished
with
a
bulldozer
but
there
are
several
machines
capable
 of
 chipping
 residues
 (MacDonald,
 2010[a]).
 The
 BiOS
 model
 developed
 by
 FP
 Innovations
 (Section
 2.1.1)
 lists
 values
 for
 various
 grinding
 machinery
 (Table
 10).
 However
a
horizontal
grinder
is
the
machine
most
commonly
used
for
chipping
residues
 in
northern
BC
(MacDonald,
2010[a]).
 
  34
 
  
  
  
  Table
10.

Fuel
use
of
chipping
equipment
(MacDonald,
2010[a])
 Machine
  
  
  
  Bulldozer
 Terrain
chipper
 Roadside
chipper
(400
kW)
 Roadside
chipper
(600
kW)
 Horizontal
grinder
 Tub
grinder
 ODt
=
oven
dry
tons
  3  Fuel
Use
(m /ODt)
 .0138
 .0284
 .0673
 0.100
 0.104
 0.083
  
 To
estimate
the
emissions
from
harvesting
and
chipping
it
is
important
to
determine
the
 quantity
of
residues
that
will
be
processed
and
have
accurate
fuel
consumption
values.
 In
addition,
the
fuel
consumption
rates
outlined
in
Tables
9
and
10
are
altered
by
±100%
 in
 the
 sensitivity
 analysis
 in
 order
 to
 assess
 the
 impact
 that
 fuel
 consumption
 by
 harvesting
and
chipping
machinery
has
on
overall
process
emissions.
 1.3.6
Transportation
Emissions
 Transportation
 emissions,
 like
 harvesting
 and
 chipping,
 are
 directly
 related
 to
 the
 amount
 of
 material
 being
 processed
 (i.e.
 more
 biomass
 being
 processed
 translates
 to
 more
 emissions)
 and
 are
 a
 result
 of
 fossil
 fuel
 combustion.
 However,
 transportation
 emissions
 are
 additionally
 affected
 by
 biomass
 moisture
 content.
 When
 biomass
 moisture
 content
 increases
 so
 does
 its
 mass
 because
 it
 contains
 more
 water.
 While
 it
 swells
slightly,
the
increase
in
volume
is
not
proportional
to
the
increase
in
mass,
which
 means
 as
 biomass
 moisture
 content
 increases,
 so
 does
 its
 density
 (Glass
 &
 Zelinka,
 2010).
 Moisture
 content
 also
 has
 a
 negative
 effect
 on
 transportation
 payload
 (i.e.
 the
 higher
the
moisture
content
the
less
the
quantity
of
potential
energy
transported
each
 trip
 and
 the
 more
 trips
 that
 are
 necessary)
 (MacDonald,
 2010[b]).
 Therefore,
 if
 transported
over
the
same
distance,
residues
with
a
higher
moisture
content
will
result
 in
 greater
 emissions
 as
 transportation
 fuel
 consumption
 is
 a
 function
 of
 tonne‐ kilometres
traveled
(Table
11).
 
  35
 
  
  
  
  Table
11.
Transportation
fuel
consumption
values
(National
Renewable
Energy
 Laboratory,
2010;
Martensson,
2008)
 Source
 U.S.
LCI
database
 
 Volvo
Trucks
 
 
 
  Value
 Single‐unit
truck
 Combination
truck
 Low‐end
efficiency
–
full
truck
 Low‐end
efficiency
–
empty
truck
 High‐end
efficiency
–
full
truck
 High‐end
efficiency
–
empty
truck
  3  Diesel
Consumption
(m /tkm)
 0.000058
 0.000027
 0.000010
 0.000022
 0.000008
 0.000018
  
 
 In
 Table
 11
 the
 fuel
 consumption
 value
is
 higher
 for
 a
 single‐unit
 truck
 because
 it
 is
 a
 smaller
 truck
 that
 transports
 less
 biomass
 per
 trip
 than
 a
 combination
 truck.
 In
 most
 cases
transporting
a
smaller
load
will
be
less
efficient
per
tonne‐kilometer.
The
reason
 for
this
is
that
that
fuel
consumption
is
not
solely
related
to
the
mass
of
material
being
 transported.
 Trucks
 must
 also
 overcome
 air
 resistance,
 which
 is
 a
 function
 of
 speed
 rather
 than
 mass.
 Therefore,
 assuming
 similar
 frontal
 areas,
 a
 truck
 carrying
 a
 larger
 load
 will
 register
 a
 more
 favourable
 value
 for
 fuel
 consumption
 per
 tonne‐kilometer.

 This
 is
 evidenced
 by
 the
 example
 from
 Martenson
 (2008)
 where
 the
 consumption
 per
 tkm
more
than
doubled
when
the
truck
was
empty
(Table
11).
 
 In
addition
to
determining
the
residue
quantity
and
fuel
 consumption
values,
 it
is
also
 important
 to
 determine
 residue
 moisture
 content
 to
 calculate
 an
 accurate
 value
 for
 transportation
 fuel
 consumption.
 The
 effects
 of
 moisture
 content,
 payload
 and
 fuel
 consumption
data
on
overall
emissions
are
assessed
in
Section
3.3.4
of
this
thesis.

 1.3.7
Facility
Emissions
 The
two
methods
of
producing
electricity
from
biomass
that
are
being
considered
in
this
 thesis
 are
 combustion
 and
 gasification.
 In
 the
 combustion
 process,
 biomass
 ignition
 occurs
 at
 a
 temperature
 of
 approximately
 550oC
 and
 assuming
 a
 sufficient
 air
 supply,
 once
ignition
occurs
it
may
be
difficult
to
stop
the
process
before
complete
combustion
 of
the
material
occurs
(Quaak
et
al.,
1999).

  36
 
  
  
  
  In
 this
 process
the
 goal
 is
to
 achieve
 near‐complete
 combustion,
 where
 virtually
 all
 of
 the
 biomass’
 intrinsic
 energy
 is
 converted
 into
 heat.
 Electricity
 generation
 from
 a
 combustion
 system
 relies
 on
 the
 use
 of
 a
 Rankine
 cycle,
 also
 known
 as
 a
 steam
 cycle
 (DeMeo
 &
 Galdo,
 1997).
 In
 this
 process,
 biomass
 is
 combusted
 with
 excess
 air,
 producing
heat,
which
in
turn
produce
steam
in
the
heat
exchange
portion
of
the
boiler
 (DeMeo
 &
 Galdo,
 1997).
 The
 high‐pressure
 steam
 then
 flows
 to
 a
 turbine/generator
 where
 it
 expands,
 creating
 electricity
 (Wiltsee,
 2000).
 The
 low‐pressure
 steam
 is
 then
 condensed
and
cycled
back
through
the
heat
exchanger
(DeMeo
&
Galdo,
1997).
This
is
 the
most
widely
used
method
of
generating
electricity
from
biomass
(Figure
7).
 
 FLUE
GAS
  HIGH‐PRESSURE

 TURBINE
 STEAM
  
 GENERATOR
  
 ELECTRICITY
  
 
 
  BOILER
 WATER
 LOW‐PRESSURE

 STEAM
 BIOMASS
  
 
 
  COMBUSTION
  CONDENSER
  
 
 
  Figure
7.
Biomass
combustion
process
(adapted
from
DeMeo
&
Galdo,
1997)
 
 With
 gasification,
 temperatures
 range
 from
 750
 oC
 at
 the
 low
 end
 for
 fluidised‐bed
 gasifiers
up
to
1,200
 oC
in
the
hearth
zone
for
older
technology
fixed‐bed
gasifiers,
and
 in
 all
 cases
 results
 in
 complete
 thermal
 decomposition
 of
 the
 biomass
 (Quaak
 et
 al.,
 1999).
 The
 process
 of
 biomass
 gasification
 involves
 exposing
 biomass
 to
 the
 high
 temperatures
emitted
by
its
partial
 combustion,
 with
a
limited
supply
of
a
gasification
 medium,
such
as
air,
oxygen
or
steam
(Sivasamy
et
al.,
2008).
Inside
the
gasifier
several
 processes
take
place
that
lead
to
the
creation
of
a
gaseous
mixture
known
as
product
 37
 
  
  
  
  gas
or
producer
gas
(Figure
8).
First,
as
the
biomass
heats
up
it
goes
through
a
drying
 phase,
 releasing
 steam.
 The
 biomass
 then
 goes
 through
 a
 reaction
 called
 pyrolysis.
 During
 pyrolysis
 the
 biomass
 turns
 to
 char,
 releasing
 tar
 and
 gases
 such
 as
 hydrogen
 (H2),
 carbon
 monoxide
 (CO),
 methane
 (CH4)
 and
 water
 vapour
 (McKendry,
 2002;
 Sivasamy
et
al.,
2008).
A
portion
of
the
produced
gas,
as
well
as
some
tar
and
some
of
 the
solid
biomass,
is
oxidized
releasing
carbon
dioxide,
steam
and
heat
that
continues
to
 fuel
the
process
(Sivasamy
et
al.,
2008).
Lastly,
a
reduction
step
takes
place
where
some
 larger
 molecules,
 such
 as
 CO2
 and
 H2O,
 are
 reduced
 to
 smaller
 molecules,
 such
 as
 CO
 and
H2
(Sivasamy
et
al.,
2008).

 
 The
 product
 gas
 mixture
 changes
 in
 both
 calorific
 value
 (CV)
 and
 composition,
 depending
 on
 the
 fuel
 type
 and
 gasification
 agent
 (McKendry,
 2002).
 Low
 CV
 product
 gas
 (Table
 12)
 is
 created
 by
 using
 an
 air/steam
 mixture
 as
 the
 gasifying
 medium
 and
 typically
 has
 an
 energy
 content
 in
 the
 range
 of
 4
 –
 6
 MJ/Nm3
 (McKendry,
 2002).
 
 In
 comparison,
 natural
 gas
 typically
 has
 a
 calorific
 value
 in
 the
 range
 of
 35
–
 38
 MJ/Nm3
 (National
 Renewable
 Energy
 Laboratory,
 2010).
 Low
 CV
 gas
 can
 be
 combusted
 to
 generate
heat
for
industrial
processes
(e.g.
drying)
and
is
suitable
for
use
in
a
gas
engine
 (McKendry,
2002).
Medium
CV
gas
(12
–
18
MJ/Nm3)
is
created
by
using
oxygen
as
the
 gasification
 medium,
 with
 steam
 injection
 (McKendry,
 2002).
 Using
 oxygen
 is
 more
 expensive
 than
 air.
 However,
 the
 benefit
 is
 that
 the
 product
 gas
 is
 not
 diluted
 by
 the
 presence
of
nitrogen,
which
is
found
in
air
(i.e.
approximately
78%),
resulting
in
a
higher
 CV
gas.
Lastly,
the
use
of
hydrogen
as
the
gasification
medium
results
in
the
formation
 of
 high
 CV
 gas
 (40
 MJ/
 Nm3)
 (McKendry,
 2002).
 This
 process
 is
 the
 most
 costly
 of
 the
 three.
 Both
 medium
 and
 high
 CV
 gases
 are
 suitable
 as
 a
 feedstock
 for
 chemical
 conversion
into
methane,
and
for
use
in
a
gas
turbine
(McKendry,
2002).
 
  38
 
  
  
  
  
 Figure
8.
Flow
diagram
of
gasification
process
(Sivasamy
et
al.,
2008)
 
 Table
12.
Typical
composition
of
low
CV
producer
gas
from
wood
biomass
gasified
in
air
 (Sivasamy
et
al.,
2008)
 Component
 Nitrogen
 Carbon
Monoxide
 Hydrogen
 Carbon
Dioxide
 Methane
  Wood
Gas
(Vol
%)
 50
–
54
 17
–
22
 12
–
20

 9
–
15

 2
–
3
  
 
 Unlike
 combustion,
 which
 relies
 on
 the
 Rankine
 cycle,
 gasification
 can
 be
 used
 to
 generate
 electricity
 using
 a
 variety
 of
 technologies,
 such
 as
 a
 gas
 engine
 system
 or
 combined
cycle
gas
turbine
(Dornbug
&
Faaij,
2001).
A
gas
engine
works
much
like
a
car
 engine.
 Product
 gas
 is
 combusted
 in
 cylinders,
 which
 push
 pistons
 that
 drive
 a
 39
 
  
  
  
  crankshaft.
 The
 engine
 is
 connected
 to
 a
 generator,
 and
 in
 more
 modern
 units,
 a
 turbocharger
as
well
(GE
Energy,
2011).

 
 A
combined
cycle
system,
on
the
other
hand,
works
in
two
steps.
In
the
first
step
clean
 product
gas
is
combusted
in
a
gas
turbine,
which
powers
a
generator
(Carpentieri
et
al.,
 2004).
 In
 this
 type
 of
system
 the
exhaust
 leaving
 the
 gas
 turbine
 is
 still
 extremely
 hot
 and
so
it
can
be
routed
through
a
heat
recovery
steam
generator
(HRSG),
which
creates
 steam
from
the
hot
exhaust
gas
(DeMeo
&
Galdo,
1997).
Hence,
the
second
step
of
this
 cycle
involves
generating
power
using
a
Rankine
cycle
(Carpentieri
et
al.,
2004).

 
 Besides
cost,
a
fundamental
difference
between
each
system
is
their
thermal
efficiency
 (i.e.
 conversion
 efficiency
 or
 process
 efficiency).
 This
 refers
 to
 the
 systems’
 ability
 to
 convert
 the
 stored
 solar
 energy
 in
 the
 biomass
 into
 thermal
 energy
 and
 subsequently
 into
 electricity.
 Thermal
 efficiency
 plays
 an
 important
 role
 in
 determining
 the
 net
 emissions
 from
 a
 bioenergy
 process,
 which
 will
 be
 investigated
 in
 detail
 in
 this
 thesis.
 However,
it
is
important
to
note
that
thermal
efficiency
is
governed
by
the
second
law
 of
thermodynamics
(Brown,
2004).
The
law
states
that
no
process
is
possible
where
the
 sole
outcome
is
the
absorption
of
heat
from
a
reservoir
and
its
complete
conversion
into
 work
(Brown,
2004).
 This
means
that
 it
is
 impossible
convert
heat
into
electricity
with
 100%
efficiency.
 
 Beginning
with
combustion
there
are
a
range
of
conversion
efficiencies
that
have
been
 reported
from
numerous
studies.
For
example,
the
electrical
conversion
efficiency
of
a
 system
 utilizing
 wood‐fired
 stoker
 boiler
 was
 reported
 as
 23%
 (50MW)
 and
 27.7%
 (60MW)
 from
 DeMeo
 &
 Galdo
 (1997)
 or
 24.6%
 (50MW)
 from
 McGowan
 &
 Wiltsee
 (1996).
 A
 report
 published
 by
 Wiltsee
 (2000)
 lists
 the
 conversion
 efficiencies
 of
 numerous
existing
biomass
combustion
facilities
(Table
13).
  40
 
  
  
  
  Table
13.
Names,
types,
power
ratings
and
conversion
efficiencies
of
some
existing
 biomass
combustion
facilities
(Wiltsee,
2000)
 Name
 Facility
Type
 Williams
Lake
Generating
Station
 vibrating
grate
boiler
 Boralex
Stratton
Energy
 stoker
boiler
 Grayling
Generating
Station
 stoker
boiler
 Tracy
Biomass
Plant
 stoker
boiler
 Multitrade
Project
 
stoker
boiler
 McNeil
Generating
Station
 stoker
boiler
 Kettle
Falls
Station
 stoker
boiler
 Shasta
 stoker
boiler
 
 HHV
=
higher
heating
value
  Rating
(MWe)
 60
 45.0
 36
 18.5
 79.5
 50.0
 46.0
 49.9
  
Conversion
Efficiency,
HHV,
%
 29.2
 25.3
 25.1

 24.4
–
25.3
 24.0
–
25.1
 24.2
–
24.9
 24.2
 19.8

  
 The
 range
 of
 values
 experienced
 at
 the
 facility
 stems
 from
 several
 factors
 considering
 the
 conversion
 efficiency
 is
 essentially
 a
 product
 of
 the
 boiler
 efficiency,
 turbine
 efficiency
 and
 generator
 efficiency
 combined.
 Additionally,
 boiler
 efficiency
 is
 affected
 by
fuel
moisture
content.
For
example,
DeMeo
&
Galdo
(1997)
state
that
a
wood‐fired
 stoker
boiler
efficiency
estimated
at
70%
for
a
50%
moisture
content
fuel
 improves
to
 83%
for
a
10%
moisture
content
fuel,
assuming
 30%
excess
air,
19.96
MJ/kg
dry
feed,
 and
a
flue
gas
exit
temperature
of
177
°C.
 
 As
mentioned
earlier,
electricity
can
be
generated
from
gasification
using
a
number
of
 different
 systems,
 such
 as
 gas
 engines
 or
 a
 combined
 cycle.
 Additionally,
 there
 are
 numerous
 types
 of
 gasifiers
 (e.g.
 downdraft,
 updraft,
 fluidised
 bed,
 etc.).
 Conversion
 efficiencies
for
modern
gas
engine
systems
have
been
claimed
to
reach
as
high
as
48.7%
 (9.5MW)
(GE
Energy,
2011),
but
on
average
are
closer
to
33%
(Dornburg
&
Faaij,
2001).
 Combined
cycle
systems
typically
have
the
highest
conversion
efficiencies,
ranging
from
 31%
 ‐
 48%,
 because
 they
 are
 able
 to
 utilize
 waste
 heat
 for
 energy
 (Dornburg
 &
 Faaij,
 2001).
 Of
 course
 this
 is
 dependant
 on
 technology,
 with
 more
 expensive
 oxygen‐based
 gasification
or
pressurized
gasification
systems
achieving
better
results
(Table
14).
  41
 
  
  
  
  Table
14.
Gasification
systems
and
conversion
efficiencies
(Dornburg
&
Faaij,
2001)
  
  Technology
 Power
Cycle
 Downdraft
gasification
(A)
 Gas
engine
 Fluidised
bed
gasification
(A)
 Gas
engine
 Fluidised
bed
gasification
(A)
 Combined
cycle
 Fluidised
bed
gasification
(P)
 Combined
cycle
 (A)
=
atmospheric;
(P)
=
pressurized
  Scale
(MWth)
 0.01‐3
 3‐30
 10‐300
 20‐300
  Efficiency
Range
(%)
 30
 30‐34
 31‐44
 42‐48
  
 As
with
combustion
systems,
gasification
conversion
efficiencies
are
also
dependant
on
 factors
 such
 as
 turbine
 efficiency
 and
 fuel
 moisture
 content.
 However
 one
 factor
 that
 has
not
yet
been
discussed
is
scale.
Dornburg
&
Faaij
(2001)
investigated
the
efficiency
 of
biomass
combustion
and
gasification
technologies
in
relation
to
scale
and
found
that
 conversion
 efficiencies
 generally
 improve
 with
 increasing
 scales.
 For
 example,
 grate‐ fired
 biomass
combustion
systems
improved
from
roughly
23%
for
a
 10MW
system
to
 roughly
33%
for
a
200MW
system
(Dornburg
&
Faaij,
2001).
Along
the
same
scales,
an
 atmospheric
IBGCC
system
improves
from
roughly
35%
to
45%
(Dornburg
&
Faaij,
2001).
 This
 highlights
 the
 fact
 that
 conversion
 efficiency
 is
 highly
 variable
 and
 depends
 on
 many
 factors
 including
 feedstock
 and
 technology
 choice.
 As
 a
 result,
 the
 impact
 of
 conversion
 efficiency
 on
 process
 emissions
 and
 emissions
 per
 unit
 energy
 (i.e.
 kgCO2/MWh)
is
discussed
in
Section
3.3.9.
 
 However,
 these
 issues
 aside,
 the
 overriding
 similarity
 between
 all
 combustion
 and
 gasification
 systems
 is
that
 they
 convert
 biomass
 into
 energy
 and
 release
 nearly
 all
 of
 the
carbon
stored
in
the
biomass
as
CO2.
As
a
result,
in
this
thesis
it
is
assumed
that
the
 quantity
of
carbon
emitted
from
the
facility
is
equal
to
the
quantity
of
carbon
contained
 in
 the
 processed
 biomass.
 As
 mentioned
 in
 Section
 1.3.1,
 the
 carbon
 contained
 in
 the
 residues
would
rapidly
be
released
to
the
atmosphere
in
the
business‐as‐usual
scenario.
 Therefore,
 it
 is
 assumed
 that
 the
 facility
 combustion
 emissions
 are
 entirely
 offset,
 leaving
 emissions
 from
 soil
 carbon,
 harvesting,
 chipping
 and
 transportation
 as
 the
 contributing
factors
to
net
emissions
for
this
thesis.
  42
 
  
  
  
  1.4
Study
Objectives
 The
primary
objectives
of
this
study
are
twofold:
 
 (1)
 Calculate
 the
 lifecycle
 carbon
 emissions
 per
 MWh
 of
 electricity
 generated
 through
 thermal
processing
of
forest
residues
in
Northern
BC.
 
 (2)
Determine
which
factors
have
the
greatest
influence
on
life
cycle
carbon
emissions
 by
performing
a
sensitivity
analysis.
 
  
  43
 
  
  
  
  2
Materials
and
Methods
 This
 section
 of
 the
 thesis
 explains
 the
 methods
 used
 for
 accomplishing
 the
 primary
 objectives
 of
 this
 thesis.
 These
 objectives
 were
 accomplished
 in
 three
 steps.
 The
 first
 step
 in
 this
 process
 was
 to
 approximate
 the
 volume
 of
 biomass
 generated
 annually
 in
 the
 study
 area
 (Section
 2.1).
 The
 second
 step
 involved
 approximating
 emissions
 from
 each
 step
 in
 the
 bioenergy
 supply
 chain
 (Section
 2.2).
 Finally,
 the
 third
 step
 involved
 calculating
 the
 electrical
 output
 based
 on
 plant
 conversion
 efficiency
 and
 biomass
 energy
 content
 in
 order
 to
 determine
 the
 quantity
 of
 emissions
 per
 unit
 of
 energy
 generated
(Section
2.3).
 
 For
this
thesis
it
was
important
to
clearly
identify
a
base
case
or
reference
case
(not
to
 be
 confused
 with
 a
 baseline
 or
 business‐as‐usual
 scenario).
 The
 base
 case
 defines
 the
 reference
 process
 conditions
 and
 assumptions
 used
 by
 this
 thesis.
 It
 is
 the
 base
 case
 against
which
the
results
of
other
studies
and
the
sensitivity
analysis
will
be
compared.

 
 The
 four
 life
 cycle
 stages
 considered
 in
 this
 thesis
 were
 Harvesting,
 Chipping,
 Transportation
and
Energy
Production
(Section
1.2).
The
base
case
made
the
following
 assumptions:
 
 1.
The
harvesting
stage
involved
(Section
2.2.2):
 •  cutting
trees
with
a
feller‐buncher,
  •  skidding
them
to
the
landing
with
a
medium
grapple
skidder,
and
  •  trimming
and
topping
with
a
stroke
delimber
  
 2.
The
chipping
stage
involved
(Section
2.2.3):

 •  piling
residues,
and
  •  chipping
residues
with
a
horizontal
grinder
  
  44
 
  
  
  
  3.
Tranportation
of
residues
to
the
bioenergy
facility
was
accomplished
in
16
ODt
loads
 using
semi‐trailers
(Section
2.2.4)
 
 4.
Residues
were
subsequently
converted
to
electricity
via
a
direct
combustion
process
 (Section
 2.2.5).
 The
 operating
 parameters
 of
 the
 bioenergy
 plant
 found
 in
 Williams
 Lake,
 BC
 was
 assumed
 for
 the
 reference
 case
 of
 this
 thesis
 (i.e.
 60
 MWe
 stand‐alone
 biomass
combustion
with
a
conversion
efficiency
of
29.2%)
(Wiltsee,
2000).

 
 5.
In
addition
to
the
emissions
resulting
from
fossil
fuel
and
biomass
combustion
within
 these
 four
 life
 cycle
 stages,
 carbon
 fluxes
 arising
 from
 soil
 carbon
 were
 quantified
 (Section
 2.2.1).
 In
 the
 reference
 case
 a
 20%
 decrease
 in
 forest
 floor
 soil
 carbon
 was
 assumed
 in
 the
 areas
 that
 are
 disturbed
 due
 to
 harvesting.
 These
 emissions
 were
 allocated
between
timber
and
residues
based
on
mass.

 
 6.
Avoided
emissions
were
calculated
by
assuming
that
residues
not
used
for
bioenergy
 would
be
burned
and
completely
converted
to
carbon
dioxide
(Section
2.2.6)
  2.1
Biomass
Data
and
Calculations
 Keeping
 in
 mind
 the
 primary
 objectives
 introduced
 in
 Section
 1.4,
 the
 secondary
 objectives
 for
 this
 phase
 of
 the
 study
 were
 to
 approximate
 the
 quantity
 of
 forest
 residues
 generated
 each
 year
 within
 the
 study
 area
 and
 to
 determine
 the
 transport
 distances
 from
 each
 operating
 area
 to
 the
 energy
 plant
 in
 Mackenzie.
 This
 was
 accomplished
using
the
BiOS
model
that
was
created
by
FP
Innovations.
This
section
of
 the
report
discusses
how
the
BiOS
model
functions,
as
well
as
the
inputs
it
requires.
This
 section
also
explains
the
source
of
the
data
used
in
the
model
and
how
it
was
adapted
 for
use
in
this
thesis.
 2.1.1
BiOS
Model
 This
 thesis
 used
 the
 BiOS
 model
 developed
 by
 FP
 Innovations
 to
 approximate
 the
 quantity
of
recoverable
biomass
from
each
operating
area
in
the
study
area
(Figure
13).
 45
 
  
  
  
  This
 was
 accomplished
 using
 stand
 data
 (Sections
 2.1.2
 and
 2.1.3),
 harvest
 volume
 (Section
2.1.4)
and
transportation
distances
(Section
2.1.5)
specific
to
the
study
area.
By
 using
these
various
inputs,
the
BiOS
model
calculated
recoverable
biomass
through
the
 use
 of
 allometric
 equations
 and
 relationships.
 Specifically,
 the
 model
 requires
 the
 following
information:

 •  Stand
density
  •  Species
type
  •  DBH
  •  Cut
volume
  •  Tree
height
  •  Transport
distance
  
 2.1.2
Stand
Data
 Stand
 data
 were
 acquired
 from
 the
 Environment
 Canada
 Land
 and
 Resource
 Data
 Warehouse
 (LRDW)
 and
 came
 from
 the
 Vegetation
 Resource
 Inventory
 (VRI)
 ‐
 Forest
 Vegetation
 Composite
 Polygons
 and
 Rank
 1
 dataset.
 The
 VRI
 dataset
 is
 a
 file‐based
 geodatabase
containing
vegetation
cover
from
the
Ministry
of
Forests.
Vegetation
cover
 is
comprised
of
spatial
layers
for
the
collection,
 manipulation
and
production
of
forest
 inventory
data
(Province
of
British
Columbia,
2010[b]).
The
dataset
is
extremely
large
so
 the
 Province
 has
 provided
 it
 as
 a
 configurable
 product.
 This
 means
 that
 an
 area
 of
 interest
(AOI)
must
be
selected
before
the
dataset
can
be
received.

 
 The
 acquired
 dataset
 comprised
 65,536
 rows
 of
 data,
 each
 representing
 one
 polygon.
 These
data
represented
the
specific
vegetation
cover
for
the
entire
study
area.
The
key
 information
contained
in
the
dataset
included:
 •  Polygon
area
  •  Basal
area
  •  Live
stems
per
hectare
  •  Percentage
of
each
species
  •  Dead
stems
per
hectare
  •  Average
height
of
each
species

  •  Leading,
 second,
 third
 and
 fourth
species
types
  46
 
  
  
  
  2.1.3
Polygon
Selection
 The
 BiOS
 model
 was
 designed
 for
 use
 with
 an
 existing
 harvesting
 plan.
 One
 of
 the
 program’s
limitations
is
that
it
can
only
handle
a
maximum
of
approximately
100
rows
of
 data,
which
is
the
upper
limit
of
the
number
of
 cut
blocks
that
would
be
included
in
a
 typical
harvesting
plan
(MacDonald,
2010[a]).
In
order
to
enable
the
program
to
be
used
 in
this
study,
it
was
necessary
to
select
100
polygons
from
the
65,536
that
covered
the
 entire
study
area.
 
 The
first
step
in
this
process
was
the
exclusion
of
the
polygons
that
would
be
unrealistic
 to
include
in
a
harvest
plan.
Polygons
with
an
average
DBH
of
less
than
15
cm
or
a
stand
 density
of
less
than
250
stems/ha
were
excluded
because
they
were
either
too
young
or
 sparse
to
be
considered
for
harvesting
(MacDonald,
2010[a]).
Excluding
these
polygons
 reduced
the
dataset
to
28,476
polygons.
Of
the
four
metrics
that
contribute
to
biomass
 volume
 (stand
 density,
 DBH,
 height
 and
 species
 type),
 stand
 density
 and
 DBH
 were
 chosen
as
the
most
significant
factors.
A
frequency
distribution
of
these
two
factors
was
 performed
from
the
remaining
large
dataset
of
28,476
polygons.
This
was
accomplished
 using
the
lower
limit
of
15cm
for
DBH,
increasing
by
2.5cm,
and
the
lower
limit
of
250
 stems/ha
for
stand
density,
increasing
by
250
stems/ha
(Table
15).
These
distributions
 were
also
plotted,
showing
that
the
majority
of
stands
had
an
average
DBH
between
15
 –
30cm
(Figure
9)
and
a
stand
density
between
250
–
1500
stems/ha
(Figure
10).
 
 From
the
harvestable
polygons,
100
were
selected
randomly
for
use
in
the
BiOS
model.
 These
 were
 selected
 to
 match
 the
 same
 distribution
 of
 DBH
 and
 stand
 density
 as
 the
 large
dataset
(Table
15,
Figures
9
and
Figure
10)
to
achieve
a
representation
similar
to
 that
 of
 the
 entire
 study
 area.
 Complete
 stand
 data
 for
 the
 selected
 polygons
 can
 be
 found
in
Appendix
C
and
Appendix
D.
 
  47
 
  
  
  
  Table
 15.
 Breast
 height
 diameter
 and
 stand
 density
 frequency
 distributions
 from
 both
 the
 harvestable
 polygons
 and
 the
 selected
 polygons
 (adapted
 from
 Province
 of
 British
 Columbia,
2010[b])
 Bin
 (cm)
 15.0
 17.5
 20.0
 22.5
 25.0
 27.5
 30.0
 32.5
 35.0
 37.5
 40.0
 42.5
 45.0
 47.5
 50.0
 52.5
 55.0
 57.5
 60.0
 62.5
 65.0
 Over
 Sum
  DBH
 Large
Set
 Frequency
 Percentage
 1674
 6%
 2971
 10%
 4280
 15%
 4443
 16%
 4405
 15%
 3678
 13%
 2318
 8%
 1279
 4%
 1075
 4%
 949
 3%
 644
 2%
 347
 1%
 194
 1%
 99
 0%
 42
 0%
 24
 0%
 23
 0%
 6
 0%
 7
 0%
 7
 0%
 0
 0%
 9
 0%
 28476
 100%
  Selected
 Frequency
 6
 10
 15
 16
 15
 13
 8
 4
 4
 3
 3
 1
 1
 0
 0
 1
 0
 0
 0
 0
 0
 0
 100
  (stems/ha)
 250
 500
 750
 1000
 1250
 1500
 1750
 2000
 2250
 2500
 2750
 3000
 3250
 3500
 3750
 4000
 Over
 
 
 
 
 
 
  Density
 Large
Set
 Frequency
 Percentage
 1851
 7%
 4799
 17%
 6415
 23%
 6645
 23%
 4502
 16%
 2554
 9%
 986
 3%
 475
 2%
 120
 0%
 71
 0%
 37
 0%
 9
 0%
 10
 0%
 1
 0%
 0
 0%
 0
 0%
 1
 0%
 
 
 
 
 
 
 
 
 
 
 28476
 100%
  Selected
 Frequency
 7
 17
 23
 23
 16
 9
 3
 2
 0
 0
 0
 0
 0
 0
 0
 0
 0
 
 
 
 
 
 100
  
 
  48
 
  
  
  
  
  
 Figure
9.
DBH
frequency
distributions
of
harvestable
polygons
(above)
versus
the
 selected
polygons
(below)
 
 
  49
 
  
  
  
  
  
 Figure
10.
Stand
density
frequency
distribution
of
the
harvestable
polygons
(above)
 versus
the
selected
polygons
(below)
 
 2.1.4
Annual
Allowable
Cut
Calculation
 The
harvest
volume
for
the
study
area
was
the
next
input
to
be
calculated
for
the
BiOS
 model
 once
 stand
 data
 were
 acquired.
 In
 British
 Columbia,
 the
 provincial
 harvest
 volume
 is
 known
 as
 the
 annual
 allowable
 cut
 (AAC)
 and
 is
 set
 by
 the
 Chief
 Forester
 through
a
process
known
as
the
Timber
Supply
Review
(Figure
11)
(Pedersen,
2003).
 
 50
 
  
  
  
  
  
 Figure
11.
Timber
Supply
Review
process
(Pedersen,
2003)
 
 The
 Minister
 of
 Forests,
 Lands
 and
 Natural
 Resource
 Operations
 apportions
 the
 AAC
 following
 the
 determination
 by
 the
 Chief
 Forester
 (Bogle,
 2011).
 Currently,
 the
 AAC
 is
 apportioned
 into
four
types
of
forest
management
units.
On
BC
public
 lands
there
are
 37
 Timber
 Supply
 Areas
 (TSAs),
 34
 Tree
 Farm
 Licenses
 (TFLs),
 approximately
 800
 Woodlot
 Licenses
 and
 16
 Community
 Forest
 Agreements
 (Bogle,
 2011;
 Province
 of
 British
Columbia,
2011[a]).
TSAs
are
large
areas
of
Crown
land
where
multiple
volume‐ based
 licenses
 are
 held
 (Bogle,
 2011).
 TFLs
 on
 the
 other
 hand,
 are
 area‐based
 agreements
between
the
province
and
a
single
private
interest
(Bogle,
2011).
Woodlot
 Licenses
area
also
area‐based
and
contain
a
maximum
of
400
hectares
of
Crown
land
on
 the
coast
and
600
hectares
in
the
interior
(Province
of
British
Columbia,
2011[b]).
Lastly,
 Community
Forest
Agreements
are
small
area‐based
licenses
that
are
typically
managed
  51
 
  
  
  
  by
 local
 governments,
 communities
 or
 First
 Nations
 (Province
 of
 British
 Columbia,
 2011[a]).
 
 The
 study
 area
 around
 Mackenzie
 is
 unusual
 as
 it
 contains
 the
 intersection
 point
 between
three
TSAs.
Contained
in
the
study
area
are
portions
of
the
Dawson
Creek
TSA,
 Mackenzie
TSA,
and
Prince
George
TSA
(Figure
12).
 
 
  
 Figure
12.
Project
Area
and
TSA
boundaries
(adapted
from
Ministry
of
Forests,
Lands
 and
Natural
Resource
Operations,
2010)

 
 This
overlap
made
it
difficult
to
estimate
a
suitable
harvest
volume
for
the
study
area
as
 each
TSA
has
a
very
different
harvest
profile.
The
Prince
George
TSA
is
the
largest
of
the
 three
 TSAs
 with
 an
 area
 of
 approximately
 7.5
 million
 hectares
 and
 it
 has
 by
 far
 the
 largest
AAC
at
14.94
million
m3.
It
is
administered
by
the
Prince
George
Forest
District,
 Vanderhoof
 Forest
 District
 and
 Fort
 St.
 James
 Forest
 District.
 The
 reason
 the
 Prince
 George
TSA
has
such
a
high
AAC
is
because
it
was
greatly
affected
by
the
mountain
pine
 beetle
infestation.
In
fact,
it
is
predicted
that
there
will
be
over
200
million
m3
of
beetle‐ killed
 lodgepole
 pine
 (Pinus
 contorta)
 in
 the
 Prince
 George
 TSA
 by
 2024
 (Ministry
 of
 Forests
and
Range,
2008).
Other
dominant
species
in
the
TSA
include
balsam
fir
(Abies
 balsamea)
and
sub‐alpine
fir
(Abies
lasiocarpa).

 
 52
 
  
  
  
  The
 Mackenzie
 TSA
 is
 administered
 by
 the
 Mackenzie
 Forest
 District
 and
 is
 the
 next
 largest
 at
 6.1
 million
 hectares
 but
 with
 a
 much
 smaller
 AAC
 of
 3.05
 million
 m3.
 Lower
 elevations
 are
 characterized
 by
 hybrid
 white
 spruce
 (Picea
 glauca),
 lodgepole
 pine
 (Pinus
 contorta),
 and
 boreal
 black
 spruce
 (Picea
 mariana),
 while
 trees
 are
 typically
 absent
 in
 the
 high
 mountain
 elevations.
 Flat
 terrain
 in
 the
 Mackenzie
 TSA
 typically
 consists
 of
 hybrid
 spruce
 (Picea
 cross),
 lodgepole
 pine
 (Pinus
 contorta)
 and
 trembling
 aspen
(Populus
tremuloides)

(Ministry
of
Forests,
2000[a]).

 
 Lastly,
the
Dawson
Creek
TSA,
administered
by
the
Peace
Forest
District,
is
the
smallest
 of
 the
 three
 with
 an
 area
 of
 750,000
 hectares
 and
 has
 an
 AAC
 of
 1.86
 million
 m3
 (Ministry
of
Forests
and
Range,
2010).
Stands
range
from
low‐elevation
boreal
forests
of
 black
spruce
(Picea
mariana),
white
spruce
(Picea
glauca)
and
trembling
aspen
(Populus
 tremuloides),
 to
 high‐elevation
 forests
 of
 Engelmann
 spruce
 (Picea
 engelmannii)
 
 and
 sub‐alpine
fir
(Abies
lasiocarpa)
(Ministry
of
Forests,
2000[b]).

 
 AAC
determination
is
typically
a
complex
process
that
considers
many
factors
including
 stand
 age,
 species
 types,
 growth
 curves,
 mortality
 rates,
 riparian
 areas
 and
 wildlife
 habitat
 while
 trying
 to
 meet
 both
 the
 social
 and
 economic
 goals
 of
 numerous
 stakeholders
 (Ministry
 of
 Forests
 and
 Range,
 2008;
 Pedersen,
 2003).
 Equation
 1
 was
 used
to
estimate
AAC
for
the
purposes
of
this
thesis,
as
it
was
not
feasible
to
calculate
a
 precise
 AAC
 for
 the
 study
 area.
 This
 involved
 determining
 the
 area
 of
 each
 TSA
 contained
within
the
study
area
(Ac)
(i.e.
coloured
areas
in
Figure
12),
and
a
harvest
rate
 per
hectare
for
each
TSA
by
dividing
the
TSA
AAC
by
the
TSA
area.
The
assumption
made
 with
this
calculation
is
that
the
AAC
is
dispersed
evenly
across
this
study
area.
 
 
 

 
  53
 
  
  
 TSA 3  
  Vh =  " AACtsa % '
 
 Atsa &  ( A $# c  TSA1  
  
  
  
  
  
  [1]
  
 !  where,
Vh
is
the
harvest
volume
(m3),
Ac
is
the
contained
TSA
area
(ha),
AACtsa
is
the
TSA
 AAC

(m3)
and
Atsa
is
the
area
of
the
TSA
(ha).
 
 In
 addition
 to
 being
 used
 in
 the
 BiOS
 model
 to
 calculate
 the
 quantity
 of
 residues
 generated
annually,
the
harvest
volume
(Vh)
is
also
used
to
calculate
the
mass
of
timber
 removed
from
the
study
area
(Section
3.1.1).
Timber
mass
is
used
in
this
thesis
because
 emissions
 are
 attributed
 between
 timber
 and
 residues
 on
 a
 per
 unit
 mass
 basis.
 An
 average
oven
dry
wood
density
of
430
kg/m3
was
assumed
for
this
study,
reported
for
 lodgepole
 pine
 (Pinus
 contorta)
 by
 Glass
 &
 Zelinka
 (2010),
 and
 the
 mass
 of
 timber
 removed
annually
from
the
study
area
is
calculated
using
Equation
2:
 
 
  qt = Vh " # basic 
 
  
  
  
  
  
  
  
  [2]
  
 !  where,
qt
is
the
mass
of
recovered
timber
(kg),
Vh
is
the
harvest
volume
(m3)
and
ρbasic
is
 the
average
oven
dry
timber
density
(kg/
m3).
 
 2.1.5
Operating
Areas
and
Transportation
Distances
 The
 final
 input
 to
 be
 determined
 for
 the
 BiOS
 model
 was
 the
 biomass
 transportation
 distance.
 Rather
 than
 assume
 an
 average
 transport
 distance,
 the
 distance
 from
 each
 harvest
site
to
the
energy
production
site
in
Mackenzie
was
calculated.
The
first
step
in
 this
process
was
the
identification
of
10
operating
areas
(labeled
i
through
x)
within
the
 study
area.
The
operable
area
was
chosen
as
the
area
within
30
km
of
a
major
roadway.
 The
primary
operating
zone
is
the
unshaded
portion
(Figure
13),
located
no
more
than
 15
 km
 from
 a
 major
 roadway,
 while
 the
 secondary
 zone
 (red
 shading)
 is
 located
 between
15
and
30
km
from
a
major
roadway.
It
was
assumed
that
70%
of
the
harvest
 took
 place
 in
 the
 primary
 zone
 and
 30%
 in
 the
 secondary
 zone.
 Following
 these
 54
 
  
  
  
  constraints,
 the
 100
 selected
 polygons
 (i.e.
 harvest
 locations)
 were
 dispersed
 throughout
the
study
area,
with
10
in
each
operating
area.

 
 Once
 the
 harvest
 locations
 were
 identified,
 the
 distance
 from
 each
 polygon
 to
 Mackenzie
 was
 calculated
 using
 a
 program
 called
 On‐Screen
 Takeoff
 created
 by
 On
 Center
 Software.
 On‐Screen
 Takeoff
 is
 construction
 estimation
 software
 that
 was
 designed
 for
 calculating
 lengths,
 areas
 and
 volumes
 by
 tracing
 lines
 on
 construction
 drawings
and
tabulating
the
results
(On
Center
Software,
Inc.,
2011).
It
was
possible
to
 determine
 the
 transport
 distance
 by
 loading
 the
 map
 of
 the
 study
 area
 into
 the
 program,
 calibrating
 the
 drawing
 scale,
 and
 tracing
 the
 route
 to
 Mackenzie
 along
 the
 major
roadways.

 
 To
begin
this
process,
a
collection
point
within
each
operating
area
was
chosen
(labeled
 i
through
x
in
Figure
13),
and
the
distance
from
each
collection
point
to
Mackenzie
was
 calculated.
The
distances
from
collection
point
to
Mackenzie
were
separated
into
main‐ road
and
highway
distances
(Table
16).
Next,
the
distance
from
each
harvest
site
to
its
 associated
 collection
 point
 was
 calculated
 (10
 for
 each
 operating
 region)
 and
 the
 average
 of
 these
 10
 distances
 was
 then
 submitted
 as
 the
 distance
 traveled
 by
branch
 road
for
each
operating
area
in
the
model
(Table
16).
Lastly,
BiOS
includes
an
average
 assumption
of
2
km
of
on‐block
transportation
for
all
biomass.
  55
 
  
  
  
  Legend
 Collection
Point
 Region
Boundary
 
 Roadway
  
  Primary
Zone
  
  Secondary
Zone
  
 
 
  
  
 (Adapted
 from
 Figure
 13.
 Operating
 areas
 and
 major
 roadways
 within
 the
 study
 area
 
  Google
Maps)
  Operating
Area
  
 Table
16.
Average
one‐way
haul
distances
from
operating
areas
to
Mackenzie
 
 
 
 Average
one‐way
haul
distance
(km)
 
 
 On‐block
 Branch
Road
 Main
Road
 Highway

 
  
 
  I
 II
 III
 IV
 V
 VI
 VII
 VIII
 IX
 X
  2
 2
 2
 2
 2
 2
 2
 2
 2
 2
  11
 15
 20
 11
 17
 20
 18
 10
 10
 13
  0
 32
 26
 26
 83
 27
 27
 27
 89
 26
  0
 
 0
 95
 
 0
 
 0
 153
 
 36
 77
 
 0
 9
  
  Total
 13
 49
 143
 39
 102
 202
 82
 116
 101
 145
  
 
 
 
 
  56
 
  
  
  
  2.2
Carbon
Flow
Calculations
 This
 section
 outlines
 the
 formulas
 and
 figures
 used
 to
 calculate
 the
 carbon
 emissions
 from
 the
 various
 processes
 along
 the
 bioenergy
 supply
 chain.
 These
 include
 the
 soil
 carbon
 flux,
 emissions
 from
 harvesting
 and
 chipping,
 transportation
 emissions,
 facility
 emissions
 and
 avoided
 decomposition
 emissions
 (Figure
 14).
 Equation
 3
 was
 used
 in
 order
to
calculate
the
net
lifecycle
emissions
along
the
bioenergy
supply
chain:
 
 
  E net = E s + E h + E c + E t + E f " E a 
 
  
  
  
  
  [3]
  
 !  where,
Enet
are
the
net
 lifecycle
emissions,
 Es
are
the
soil
carbon
emissions,
Eh
are
the
 harvest
emissions,
Ec
are
the
chipping
emissions,
Et
are
the
transportation
emissions,
Ef
 are
 the
 facility
 emissions
 and
 Ea
 are
 the
 avoided
 emissions.
 Carbon
 flows
 were
 measured
in
tonnes
carbon
dioxide
(tCO2).
 
  
 Figure
14.
Life
cycle
carbon
flows
 
 2.2.1
Soil
Carbon
Emissions
 The
 soil
 carbon
 pool
 is
 a
 representation
 of
 the
 balance
 between
 carbon
 inputs
 from
 litterfall,
 woody
 debris
 and
 root
 turnover
 on
 the
 one
 hand,
 and
 the
 release
 of
 carbon
 from
 decomposition
 on
 the
 other
 (Jandl
 et
 al.,
 2006).
 In
 a
 business‐as‐usual
 scenario,
 this
 pool
 would
 reach
 an
 equilibrium
 level
 that
 would
 fluctuate
 slightly
 depending
 on
 several
 factors
 including
 temperature,
 moisture
 and
 stand
 age.
 However,
 harvesting
 reduces
 soil
 carbon
 levels
 by
 decreasing
 the
 carbon
 input
 rate
 and
 increasing
 decomposition
(Jandl
et
al.,
2006).
While
carbon
deposition
is
reduced
substantially
on
 57
 
  
  
  
  the
harvested
area,
it
still
occurs
at
baseline
levels
in
the
rest
of
the
study
area.
In
this
 study
the
carbon
deposited
in
the
unharvested
areas
has
been
regarded
as
part
of
the
 business‐as‐usual
 scenario
 and
 therefore
 was
 not
 included
 in
 the
 soil
 carbon
 calculations.

 
 The
 turnover
 of
 soil
 organic
 matter
 (SOM)
 depends
 on
 several
 factors,
 some
 of
 which
 are
 directly
 or
 indirectly
 influenced
 by
 forest
 management
 practices.
 The
 factors
 affecting
SOM
turnover
include
site
conditions,
soil
properties
and
the
chemical
quality
 of
 the
 carbon
 (i.e.
 labile
 or
 stable)
 (Jandl
 et
 al.,
 2006).
 Labile
 carbon
 is
 found
 in
 the
 organic
layers
of
forest
floor
and
stable
carbon
is
found
below
in
the
forest
floor
in
the
 mineral
soil
(Jandl
et
al.,
2006).
Total
emissions
from
soil
carbon
were
determined
to
be
 a
function
of
losses
from
the
mineral
soil
combined
with
losses
from
the
forest
floor,
as
 presented
in
Equation
4:

 
 
  E s = em + e f 
 
  
  
  
  
  
  
  [4]
  
 !  
Where,
Es
is
net
soil
carbon
flux,
em
is
the
carbon
loss
from
the
mineral
soil
and
ef
is
the
 carbon
loss
from
the
forest
floor,
all
measured
in
tCO2.
 
 As
 presented
 in
 the
 literature
 review
 (Section
 1.3.4),
 studies
 that
 investigated
 mineral
 soil
 carbon
 losses
 following
 harvest
 returned
 varying
 results,
 with
 several
 studies
 reporting
the
losses
to
be
insignificant
(Fredeen
et
al.,
2005;
Jandl
et
al.,
2006;
Johnson
 &
 Curtis,
 2001).
 As
 a
 result,
 mineral
 soil
 losses
 were
 considered
 to
 be
 negligible
 and
 therefore,
em
in
Equation
4
was
assumed
to
be
zero.
In
order
to
assess
the
significance
 of
this
assumption
the
outlying
literature
values
were
included
in
the
sensitivity
analysis
 portion
of
this
thesis
(Section
3.3).
 
 Studies
 have
 shown
 that
 the
 labile
 carbon
 in
 the
 forest
 floor
 is
 most
 affected
 by
 harvesting
practices
(Nave
et
al.,
2010;
Pennock
&
van
Kessel,
1996;
Jandl
et
al.,
2006;
 58
 
  
  
  
  Olsson
et
al.,
1995).
The
accumulation
or
depletion
of
labile
carbon
in
the
forest
floor
is
 affected
 by
 site
 conditions,
 where
 factors
 such
 as
 excess
 soil
 moisture
 or
 low
 temperatures
 inhibit
 soil
 respiration
 (i.e.
 emissions)
 and
 soil
 disturbances
 from
 harvesting
 encourage
 respiration
 (Jandl
 et
 al.,
 2006).
 Figure
 15
 illustrates
 what
 is
 assumed
to
happen
to
soil
 carbon
following
harvesting
for
the
purposes
of
this
thesis.
 The
 labile
 carbon
 found
 in
 the
 forest
 floor
 decreases
 drastically
 following
 harvest
 and
 gradually
returns
to
the
post
harvest
level,
while
the
mineral
carbon
remains
constant
 throughout
the
entire
rotation.
Even
though
the
forest
floor
carbon
level
returns
to
its
 post
harvest
level,
the
average
carbon
content
over
one
rotation
is
clearly
reduced
by
 harvesting.

 
  
 Figure
15.
Simulation
of
soil
carbon
dynamics
following
harvest
of
standing
forest
 biomass
(adapted
from
Jandl
et
al.,
2006)
 
  59
 
  
  
  
  Values
 for
 three
 parameters
 were
 needed
 to
 calculate
 soil
 carbon
 emissions
 following
 harvest:
area
disturbed,
carbon
density
of
the
soil
and
expected
percent
carbon
loss
(k).
 The
area
of
forest
disturbed
was
calculated
from
the
timber
harvest
volume
in
Table
21
 and
the
standing
timber
volume
in
Table
23
(Section
3.1)
using
Equation
5:

 
 
  Acut =  Vh 
 "t  
  
  
  
  
  
  
  
  [5]
  
 !  where,
Acut
is
the
area
disturbed
by
harvest
each
year
(ha),
Vh
is
the
harvest
volume
(m3)
 and
ρt
is
the
average
recoverable
timber
volume
(m3/ha).
 
 The
average
carbon
density
values
reported
by
Fredeen
et
al.
(2005)
were
used
in
this
 thesis
(Table
17).
These
data
were
assumed
to
be
the
most
accurate
attainable
values
 because
 the
 Fredeen
 et
 al.
 (2005)
 study
 was
 performed
 in
 the
 Aleza
 Lake
 Research
 Forest,
 located
 less
 than
 100
 km
 from
 the
 thesis
 study
 area.
 In
 addition,
 an
 average
 forest
floor
carbon
loss
of
20%
was
assumed
for
this
study.
As
presented
Section
1.3.4,
 this
is
the
average
soil
carbon
loss
observed
for
coniferous/mixed
forests
from
a
meta‐ analysis
of
existing
forest
carbon
studies
(Nave
et
al.,
2010).
 
 Table
17.
Soil
carbon
density
per
hectare
and
emission
factors
from
harvesting
(adapted
 from
Fredeen
et
al.,
2005;
Nave
et
al.,
2010)
 
 
 
 Forest
Floor
 Mineral
Soil
  Carbon
Density
(tC/ha)
 Second
Growth
 Old
Growth
 
 Fine
 Coarse
 Fine
 Coarse
 Average
 29(±5)
 27(±6)
 35(±6)
 78(±54)
 42.25
 106(±17)
 112(±24)
 110(±16)
 115(±26)
 110.75
  k
Value
(%
emitted)
 
 Average
 20
 0
  
 
 Equation
6
was
used
was
then
used
to
calculate
carbon
emissions
from
the
forest
floor
 (ef)
 in
 the
 study
 area.
 Total
 emissions
 were
 divided
 by
 total
 quantity
 removed
 and
 multiplied
by
biomass
quantity
to
yield
the
emissions
attributed
to
biomass:
 
 60
 
  
  
  
  # k " & e f = (Acut qb )% f f ( 
 $ qt + qb '  
  
  
  
  
  
  
  [6]
  
 where,
 ef
 is
 the
 carbon
 emitted
 from
 the
 forest
 floor
 (tC),
 Acut
 is
 area
 disturbed
 by
  !  harvest
(ha),
kf
is
the
emission
factor
from
the
forest
floor
(%),
ρf
is
the
carbon
density
of
 the
forest
floor
(tC/ha),
qb
is
the
quantity
of
biomass
removed
each
year
(tonnes)
and
qt
 is
the
quantity
of
timber
removed
each
year
(tonnes).

 
 Equation
7
would
be
used
to
calculate
the
emissions
from
the
mineral
soil
(em)
had
the
 study
 occurred
 in
 an
 area
 where
 significant
 losses
 were
 expected.
 Additionally,
 this
 is
 the
equation
that
will
be
used
in
the
sensitivity
analysis:
 
 
  #k " & em = (Acut qb )% m m ( 
 $ qt + qb '  
  
  
  
  
  
  [7]
  
 where,
em
are
the
carbon
emissions
from
the
forest
floor
(tC),
Acut
is
area
disturbed
by
  ! harvest
(ha),
k 
is
the
emission
factor
from
the
forest
floor
(%),
ρ 
is
the
carbon
density
 m m of
the
forest
floor
(tC/ha),
qb
is
the
quantity
of
biomass
removed
each
year
(tonnes)
and
 qt
is
the
quantity
of
timber
removed
each
year
(tonnes).
 
 Both
ef
and
em
are
calculated
in
term
of
tonnes
carbon
from
these
equations.
The
values
 would
 then
 need
 to
 be
 converted
 to
 tonnes
 CO2
 before
 determining
 Es
 by
 using
 Equation
 5.
This
conversion
 is
accomplished
by
 multiplying
the
ef
and
em
 by
3.67
tCO2
 per
tonne
carbon
(United
States
Environmental
Protection
Agency,
2005).
 2.2.2
Harvesting
Emissions
 Harvesting
 emissions
 result
 from
 the
 combustion
 of
 fossil
 fuels
 used
 to
 power
 the
 harvesting
 machinery.
 The
 processes
 involved
 in
 harvesting
 are
 cutting,
 bunching
 and
 dragging
of
trees
to
the
landing,
followed
by
topping
and
delimbing.
Carbon
emissions
 61
 
  
  
  
  from
 the
 diesel
 used
 during
 these
 processes
 are
 directly
 related
 to
 the
 amount
 of
 biomass
 harvested.
 The
 fuel
 use
 data
 for
 the
 various
 harvesting
 equipment
 has
 been
 adapted
 from
 a
 U.S.
 life
 cycle
 impact
 (LCI)
 study
 (Johnson
 et
 al.,
 2006)
 by
 converting
 diesel
 use
 in
 gal/CCF
 to
 m3/ODt
 assuming
 an
 average
 density
 of
 430
 kg/m3
 for
 the
 biomass.
Johnson
et
al.
(2006)
compared
both
thinning
and
harvesting
operations
in
the
 Southeast
 U.S.
 and
 the
 Pacific
 Northwest.
 In
 addition
 to
 the
 Pacific
 Northwest
 containing
 larger
 trees
 than
 what
 are
 typically
 found
 in
 the
 Northern
 Interior,
 which
 could
 affect
 harvesting
 fuel
 use
 rates,
 the
 values
 for
 the
 Pacific
 Northwest
 were
 not
 considered
 applicable
 for
 use
 in
 this
 thesis
 because
 Johnson
 et
 al.
 (2006)
 assumed
 harvesting
would
occur
via
hand
felling
with
the
help
of
large
yarder
to
transport
logs
to
 the
landing
due
to
steep
terrain.
Harvesting
in
the
Southern
U.S.
however,
is
achieved
 with
 a
 large
 feller
 buncher
 and
 medium
 grapple
 skidder,
 with
 the
 help
 of
 a
 stroke
 delimber
 for
 processing
 (Johnson
 et
 al.,
 2006).
 Therefore,
 the
 fuel
 use
 values
 from
 harvesting
in
the
Southeast
U.S.
were
used
for
this
study
(Table
18),
as
the
harvesting
 process
 is
 comparable
 to
 what
 is
 typically
 experienced
 in
 the
 Northern
 Interior
 (MacDonald,
2010[b]).

 
 Table
18.
Harvesting
fuel
usage
(Johnson
et
al.,
2006)
 Process
 Machine
 Felling
 Large
feller
buncher
 Skidding
 Medium
grapple
skidder
 Delimbing
 Stroke
delimber
 *
CCF
=
100
cubic
feet
of
solid
wood
  Diesel
Use
(Gal/CCF)
 0.27
 1.19
 0.22
  3  Fuel
Consumption
(m /ODt)
 0.00076
 0.00335
 0.00062
  
 Using
 the
 fuel
 consumption
 rate
 for
 each
 machine
 type,
 in
 conjunction
 with
 the
 total
 quantity
 of
 biomass
 processed,
 the
 total
 fuel
 use
 was
 calculated.
 Carbon
 emissions
 in
 tonnes
were
then
calculated
based
on
diesel
fuel’s
average
carbon
density
of
7.31x10‐4 
 tonnes
 carbon
 per
 litre
 or
 2.68
 tCO2/m3
 (Oak
 Ridge
 National
 Laboratory,
 2010)
 using
 Equation
8:
 
 
 62
 
  
 
  
 E h = ( " d # qb )(C f + Cs + Cd ) 
 
  
  
 
  
  
  
  [8]
  
 !  where,
 Eh
 are
 the
 total
 carbon
 emissions
 from
 harvesting
 (tCO2),
 ρd
 is
 the
 average
 carbon
density
of
diesel
(tCO2/m3),
Cf
is
the
fuel
consumption
of
the
feller
(m3/ODt),
Cs
is
 the
fuel
consumption
of
the
skidder
(m3/ODt),
Cd
is
the
fuel
consumption
of
the
dangle‐ head
processor
(m3/ODt)
and
qb
is
the
quantity
of
biomass
processed
(ODt).
 2.2.3
Piling
and
Chipping
Emissions
 Piling
and
chipping
represent
the
first
step
in
the
production
of
bioenergy
from
forest
 residues
 for
 most
 carbon
 accounting
 schemes,
 as
 they
 are
 created
 as
 a
 by‐product
 of
 timber
harvest
operations.
If
not
utilized
for
bioenergy
production,
residues
are
typically
 burned
in
the
forest
(Province
of
British
Columbia,
2010[a]).

 
 Piling
and
chipping
is
an
important
step
in
the
supply
chain,
as
it
must
take
place
to
get
 residues
 into
 a
 uniform
 size
 and
 to
 increase
 their
 density
 so
 they
 are
 more
 easily
 transported.
While
chipping
is
the
crucial
process
in
this
step,
proper
piling
is
important
 as
it
promotes
drying
and
improves
productivity
of
subsequent
processes
(MacDonald,
 2010[b]).
 
 Compared
to
piling,
the
chipping
process
is
quite
energy
intensive;
it
uses
approximately
 ten
times
more
fuel
to
chip
a
tonne
of
 biomass
than
to
pile
 it.
The
two
main
machine
 options
 for
 chipping
 are
 the
 grinder
 or
 chipper.
 Due
 to
 the
 rough
 terrain
 in
 British
 Columbia,
the
most
commonly
used
option
is
a
horizontal
grinder
(MacDonald,
2010[a]).
 The
 productivity
 and
 fuel
 usage
 of
 a
 horizontal
 grinder
 is
 approximately
 25
 ODt/hour
 and
 0.120
 m3/hour,
 respectively
 (MacDonald,
 2010[a]).
 The
 fuel
 usage
 rates
 for
 the
 harvesting
and
chipping
stages
are
summarized
in
Table
19.
 
 
  63
 
  
  
  
  Table
19.
Harvesting
and
chipping
fuel
usage
(MacDonald,
2010[a])
 Process
 Piling
 Chipping
  3  Productivity
(ODt/hour)
 32
 25
  Fuel
Use
(m /hour)
 0.016
 0.120
  3  Fuel
Consumption
(m /ODt)
 0.00050
 0.00480
  
 
 Using
 the
 fuel
 consumption
 rate
 for
 each
 machine
 type,
 in
 conjunction
 with
 the
 total
 quantity
 of
 biomass
 processed
 and
 the
 average
 carbon
 density
 of
 diesel
 fuel
 (2.68
 tCO2/m3),
the
total
fuel
use
was
calculated
using
Equation
9:
 
 
  E c = ( " d # qb )(C p + Cc ) 
  
  
  
  
  
  
  [9]
  
 !  where,
Ec
are
the
total
emissions
from
chipping
(tCO2),
 ρd
is
the
average
carbon
content
 per
cubic
metre
of
diesel
(tCO2/m3),
qb
is
the
quantity
of
biomass
processed
(ODt),
Cp
is
 the
fuel
consumption
from
piling
(m3/ODt)
and
Cs
is
the
fuel
consumption
of
the
grinder
 (m3/ODt).
 2.2.4
Transportation
Emissions
 Transportation
 emissions
 were
 calculated
 by
 using
 the
 results
 of
 the
 BiOS
 model
 in
 conjunction
with
data
from
the
U.S.
Department
of
Energy
National
Renewable
Energy
 Laboratory
 (NREL).
 NREL
 maintains
 the
 U.S.
 Life
 Cycle
 Impact
 (LCI)
 Database,
 which
 contains
 quantified
 material
 and
 energy
 flows
 for
 common
 unit
 processes
 (National
 Renewable
Energy
Laboratory,
2010).

 
 The
 transportation
 of
 chipped
 harvest
 residues
 in
 British
 Columbia
 would
 most
 likely
 occur
 by
 semi‐trailer
 (MacDonald,
 2010[a]).
 The
 fuel
 consumption
 data
 for
 a
 semi‐ trailer,
also
known
as
a
combination
truck,
was
acquired
from
the
U.S.
LCI
database.
The
 fuel
 consumption
 value
 used
 was
 2.72x10‐5
 m3
 of
 diesel
 per
 tonne‐kilometre
 traveled
 (tkm),
 corresponding
 to
 8.0x10‐5
 tCO2/tkm
 (National
 Renewable
 Energy
 Laboratory,
 2010).

 
 64
 
  
  
  
  Truck
weight,
payload
weight
and
distance
traveled
are
the
three
factors
necessary
for
 calculating
 tonne‐kilometres
 traveled.
 First,
 an
 empty
 semi‐trailer
 (me)
 (i.e.
 truck
 and
 trailer)
 weights
 approximately
 26,000
 pounds
 or
 12
 tonnes
 (Schroeder
 et
 al.,
 2007).
 Payload
was
determined
to
be
approximately
24
tonnes
(green
chips)
by
subtracting
the
 truck‐trailer
 weight
 of
 12
 tonnes
 from
 the
 mass
of
 a
 fully
 loaded
 semi‐trailer
 (mf)
 (i.e.
 80,000
lbs
or
36
tonnes).
However,
the
quantity
of
potential
energy
transported
(i.e.
the
 oven
 dry
 mass
 of
 biomass
 transported
 each
 trip)
 is
 highly
 dependant
 on
 moisture
 content
(MC).
The
reason
for
this
is
that
mass
increases
occur
more
rapidly
than
volume
 with
 increasing
 moisture
 content.
 It
 was
 assumed
 for
 this
 study
 that
 the
 moisture
 content
of
the
chipped
residues
being
transported
would
be
approximately
50%,
as
the
 average
 moisture
 content
 of
 green
 trees
 in
 British
 Columbia
 is
 between
 45%‐55%
 (ENVINT
 Consulting,
 2008).
 Considering
 this,
 24
 tonnes
 of
 50%
 MC
 chips
 would
 weigh
 approximately
 16
 tonnes
 when
 oven
 dry
 (Glass
 &
 Zelinka,
 2010).
 Therefore,
 it
 was
 assumed
 in
 this
 study
 that
 16
 tonnes
 of
 oven
 dry
 residues
 weighing
 24
 tonnes
 were
 transported
by
each
truckload.
The
BiOS
model
required
inputs
to
be
in
ODt.
Lastly,
the
 distance
traveled
is
a
function
of
the
number
of
trips
made.
The
number
of
trips
(T#)
was
 calculated
by
dividing
the
quantity
of
residues
produced
in
each
operating
area
anually
 by
 the
 payload
 of
 16
 ODt
 (Table
 16
 in
 Section
 2.1.5).
 Finally,
 emissions
 from
 transportation
were
calculated
using
Equation
10:
 
 X  
  E t = etkm (me + m f ) • # (T# " dow ) 
  
  
  
  

  
  [10]
  I  
  !  where
Et
is
the
total
transportation
emissions
(tCO2),
etkm
is
the
carbon
emissions
per
 tonne‐kilometer
traveled
by
combination
truck
(tCO2/tkm),
me
is
the
truck
weight
when
 empty
(t),
mf
is
the
truck
weight
when
full
(t),
T#
is
the
number
of
trips
per
operating
 area
and
dow
is
the
average
one‐way
transportation
distance
from
each
operating
area
 (km)
(Table
16).
  65
 
  
  
  
  2.2.5
Facility
Emissions
 Carbon
 emissions
 from
 the
 bioenergy
 plant
 result
 from
 the
 combustion
 of
 residues
 to
 generate
 electricity.
 There
 are
 also
 emissions
 associated
 with
 the
 plant
 construction,
 maintenance,
 and
 disassembly.
 However,
 an
 LCA
 study
 of
 an
 integrated
 biomass
 gasification
 combined‐cycle
 (IBGCC)
 plant
 by
 Carpentieri
 et
 al.
 (2004)
 found
 that
 the
 emissions
 from
 these
 phases
 were
 negligible
 compared
 to
 the
 emissions
 from
 energy
 conversion.
Therefore,
annual
carbon
emissions
from
the
facility
were
determined
to
be
 equal
 to
 the
 amount
 of
 carbon
 contained
 in
 the
 processed
 biomass
 (qb)
 (Table
 23)
 assuming
complete
combustion
or
gasification.
Also
for
the
purposes
of
this
thesis
it
was
 assumed
 that
 the
 oven
 dry
 biomass
 consisted
 of
 50%
 carbon
 (Skog,
 2008).
 Therefore,
 emissions
were
calculated
using
Equation
11
with
ρb
assumed
to
be
500
kgC/ODt
or
1.84
 tCO2/ODt
biomass:

 

 
  E f = qb " # b 
 
  
  
  
  
  
  
  
  [11]
  
  !  where,
Ef
are
the
carbon
emissions
from
the
bioenergy
facility
(tCO2),
qb
is
the
quantity
 of
biomass
processed
(ODt)
and
ρb
is
the
average
carbon
content
of
biomass
(tCO2/ODt).

 2.2.6
Avoided
Emissions
 Avoided
 emissions
 are
 the
 final
 carbon
 flows
 to
 be
 calculated.
 Carbon
 flows
 from
 the
 business‐as‐usual
 scenario
 must
 be
 calculated
 and
 subtracted
 from
 the
 study
 scenario
 to
 determine
 the
 net
 effect
 of
 utilizing
 forest
 residues
 for
 energy.
 This
 study
 assumes
 the
 business
 as
 usual
 scenario
 for
 the
 British
 Columbia
 central
 interior
 is
 that
 forest
 residues
are
made
into
slash
piles
and,
after
some
time
to
season,
burned
at
a
safe
time
 of
 year
 (Province
 of
 British
 Columbia,
 2010[a]).
 However,
 if
 slash
 burning
 was
 not
 the
 business‐as‐usual
 scenario
 it
 would
 greatly
 affect
 the
 outcome
 of
 the
 study.
 This
 is
 discussed
in
the
sensitivity
analysis
portion
of
the
thesis
(Section
3.3).

 
  66
 
  
  
  
  The
primary
purpose
of
burning
slash
piles
is
to
reduce
the
fire
hazard
posed
by
logging
 residues
in
a
safe
and
efficient
manner
(Vogl
&
 Ryder,
 1969).
For
the
purposes
of
this
 thesis,
complete
combustion
 of
the
biomass
is
assumed.
Therefore,
avoided
emissions
 are
 equal
 to
 the
 carbon
 contained
 in
 the
 biomass,
 which
 entirely
 offset
 the
 emissions
 incurred
at
the
facility:
 
 
  E a = qb " # b 
 
  
  
  
  
  
  [12]
  
  !  where,
Ea
are
the
avoided
emissions
from
utilizing
waste
biomass,
qb
is
the
quantity
of
 biomass
burned
and
ρb
is
the
average
carbon
content
of
biomass.
 
  2.3
Electrical
Output
 Electrical
output
of
the
bioenergy
plant
is
calculated
using
three
variables:
the
quantity
 of
biomass,
the
energy
contained
in
the
biomass
(i.e.
energy
density)
and
the
conversion
 efficiency
of
the
facility.
The
base‐case
of
this
thesis
assumed
that
all
of
the
recoverable
 biomass
 from
 the
 study
 area
 was
 used
 for
 bioenergy
 production.
 This
 value
 (qb)
 is
 calculated
 in
 Section
 3.1.
 The
 typical
 energy
 density
 (ρE)
 for
 wood
 fuels
 (HHV)
 is
 approximately
20‐22
GJ/t
(Oak
Ridge
National
Laboratory,
2010).
Mani
(2007)
cites
the
 energy
 density
 of
 logging
 slash
 as
 being
 slightly
 lower
 at
 18.9
 GJ/t,
 compared
to
 20.4
 GJ/t
for
wood
chips,
while
the
United
States
Department
of
Energy
(2010)
lists
the
oven
 dry
 energy
 content
 of
 wood
 fuels
 as
 19.8
 GJ/t.
 These
 differences
 are
 mainly
 due
 to
 species
 variation.
 Because
 there
 is
 so
 much
 variability
 between
 different
 wood
 fuel
 samples,
this
thesis
assumed
an
average
energy
content
for
logging
residues
of
20
GJ/t.

 
 While
the
range
of
values
for
energy
density
is
quite
narrow,
the
thermal
efficiency
of
 stand‐alone
 wood‐energy
 plants
 is
 affected
 greatly
 by
 the
 conversion
 technology
 chosen,
and
for
a
60
MW
plant,
the
efficiency
can
range
from
approximately
25%‐45%
 (Dornburg
&
Faaij,
2001).
The
base‐case
thermal
efficiency
(ηth)
assumed
for
this
study
 was
29.2%,
which
has
been
reported
for
the
Williams
Lake
Power
Plant
(Wiltsee,
2000).
 67
 
  
  
  
  Using
 these
 three
 values,
 the
 electrical
 output
 of
 the
 plant
 was
 determined
 using
 Equation
13:

 
 
  Oe = qb " # E " $th 
  
  
  
  
  
  
  
  [13]
  
  !  where,
 Oe
 is
 the
 electrical
 output
 (GJ),
 qb
 is
 the
 quantity
 of
 biomass
 (ODt), ρE
 is
 the
 energy
 density
 or
 wood
 heat
 content
 of
 biomass
 (GJ/ODt),
 and
 ηth
 is
 the
 electrical
 conversion
efficiency
of
the
facility
(%).
 
 As
mentioned
at
the
beginning
of
Section
2,
the
power
plant
assumed
for
the
base
case
 is
 a
 stand‐alone
 biomass
 combustion
 facility
 with
 a
 two‐drum,
 top‐hung
 watertube
 design
 boiler
 that
 can
 deliver
 71
 kg/s
 of
 110
 bar, 510
 °C
 steam,
 and
 the
 turbine/generator
 provides
 a
 guaranteed
 net
 electric
 output
 of
 60
 MW,
 with
 the
 capability
to
achieve
67‐68
MW
(Wiltsee,
2000).
The
plant
consumes
more
than
550,000
 t/yr
 of
 green
 wood
 waste,
 from
 which
 it
 produces
 roughly
 500,000
 MWh
 (Wiltsee,
 2000).
 The
 fuel
 mix
 is
 approximately
 40%‐50%
 bark,
 and
 the
 rest
 is
 an
 assortment
 of
 sawdust,
chips,
and
slabs,
with
typical
moisture
 content
ranging
from
37%‐38%
during
 the
summer
to
50%
during
the
winter
(Wiltsee,
2000).
In
order
to
check
the
suitability
of
 these
values
and
the
study
methodology,
the
wood
heat
content
(i.e.
energy
density)
of
 the
green
wood
fuel
used
in
Williams
Lake
was
calculated
to
be
approximately
11.2
GJ/t
 by
solving
for
 ρE
in
Equation
13
using
the
values
for
electrical
output,
biomass
quantity
 and
thermal
efficiency
presented
by
Wiltsee
(2000).
A
wood
heat
content
of
 11.2
GJ/t
 falls
 in
 the
 middle
 of
 the
 expected
 wood
 heat
 content
 range
 for
 wood
 fuels
 with
 a
 moisture
 content
 of
 40%‐45%,
 which
 confirms
 the
 suitability
 of
 Equation
 13
 for
 this
 study
(Table
20).
 Table
20.
The
effect
of
moisture
content
on
wood
heat
content
(U.S.
Department
of
 Energy,
2010)
 Moisture
Content
(%)
 0
 15
 20
 HHV
(Btus/lb)
 8500
 7275
 6800
 HHV
(GJ/t)
 19.8
 16.9
 15.8
 
 Btus
=
British
thermal
units;
GJ
=
gigajoule
  25
 6375
 14.8
  30
 5950
 13.8
  35
 5535
 12.9
  40
 5100
 11.9
  45
 4575
 10.6
  50
 4250
 9.9
  68
 
  
  
  
  3
Results
and
Discussion
 The
 results
 and
 discussion
 are
 presented
 in
 three
 sections
 of
 this
 chapter.
 The
 first
 section
outlines
the
results
of
the
BiOS
model
and
the
base
case
electrical
output
of
the
 plant.
 In
 the
 second
 section,
 the
 carbon
 flow
 calculations
 and
 net
 annual
 carbon
 emissions
from
each
life
cycle
stage
are
compared
on
an
absolute
basis,
as
well
as
per
 ODt
and
per
kWh.
Finally,
the
third
section
contains
a
series
of
sensitivity
analyses
that
 look
 into
 how
 the
 consideration
 of
 different
 scenarios,
 literature
 values
 and
 assumptions
would
affect
the
study
results.
  3.1
BiOS
Results
and
Electrical
Output
 3.1.1
BiOS
Values
and
Results
 The
estimated
study
area
harvest
volume
(Vh)
was
the
first
value
to
be
calculated
for
the
 BiOS
model.
This
was
done
using
Equation
1
from
Section
2.1.4.
This
calculation
yielded
 a
harvest
volume
of
3.42
million
m3
for
the
study
area
(Table
21).
 
 Table
21.
Timber
harvest
estimation
 Timber
Supply
Area
 Prince
George
TSA
(1)
 Mackenzie
TSA
(2)
 Dawson
Creek
TSA
(3)
 Total
  AAC

 3 (M
m )
 14.94
 3.05
 1.86
 19.85
  TSA
Area
 (M
ha)
 7.5
 6.1
 2.3
 15.9
  Area
Contained

 (M
ha)
 1.08
 1.31
 0.75
 3.14
  Prorated
AAC
 3 (M
m )
 2.15
 0.66
 0.61
 3.42
  
 
 When
a
harvest
volume
of
3.42
million
m3
was
used,
the
BiOS
model
calculated
that
the
 annual
amount
of
recoverable
biomass
from
the
10
operating
areas
was
approximately
 258,200
 ODt
 (Table
 22).
 The
 number
 of
 trips
 (T#)
 made
 from
 each
 operating
 area
 to
 Mackenzie
 was
 then
 calculated
 by
 dividing
 the
 residue
 quantity
 generated
 in
 each
 operating
area
by
the
assumed
truck
payload
of
16
tonnes
(Table
22).

 
  69
 
  
  
  
  Table
22.
Biomass
quantity,
number
of
trips
and
average
one‐way
transportation
 distances
from
operating
areas
to
Mackenzie
(results
from
BiOS
Model)
  Operating
Area
  
  
  
 I
 II
 III
 IV
 V
 VI
 VII
 VIII
 IX
 X
 Sum
  Biomass
(ODt)
 Number
of
Trips
(T#)
 14,800
 924
 17,400
 1,084
 43,400
 2,710
 51,800
 3,238
 27,900
 1,743
 7,500
 469
 36,500
 2,283
 25,200
 1,575
 15,900
 992
 17,900
 1,119
 258,200
 
  Average
One‐way
Transport
Distance
(km)
 13
 50
 143
 39
 102
 202
 82
 116
 101
 145
 
  
 
 In
 addition
 to
 being
 used
 in
 the
 BiOS
 residue
calculation,
 harvest
 volume
 was
 used
to
 estimate
the
quantity
of
timber
removed
from
the
study
area
annually.
Using
Equation
2
 from
Section
2.1.4,
the
quantity
of
timber
removed
from
the
study
area
was
estimated
 to
be
1,471,000
ODt
(Table
23).
The
BiOS
model
 also
returned
an
average
recoverable
 timber
volume
(ρt)
of
298.25
m3/ha
(Table
23).
 
 Table
23.
Recovered
biomass
and
timber
recovery
rate
(results
from
BiOS
model)
 Recovered
Biomass
 (ODt)
 258,200
  Recovered
Timber
 (ODt)
 1,471,000
  Total
mass
removed
 (ODt)
 1,729,200
  Timber
Recovery
Rate
 3 (m /ha)
 298.25
  
 3.1.2
Base
Case
Electrical
Output

 As
mentioned
in
Section
2.3,
the
wood
heat
content
(ρE)
assumed
for
this
study
was
20
 GJ/t.
 This
 was
 because
 the
 units
 returned
 from
 the
 BiOS
 model
 for
 residue
 quantity
 were
 oven
 dry
 tonnes
 (ODt)
 and
 Equation
 13
 (Section
 2.3)
 used
 to
 calculate
 the
 electrical
 output
 of
 the
 facility
 was
 also
 based
 on
 residue
 inputs
 in
 over
 dry
 tonnes.
 Using
 equation
 13,
 the
 quantity
 of
 electricity
 generated
 was
 calculated
to
 be
 418,900
 MWh
 in
 the
 base‐case.
 However,
 this
 value
 could
 change
 substantially
 based
 on
 the
 technology
employed
(Section
3.3.5).
  70
 
  
  
  
  3.2
Carbon
Flows
and
Emissions
 This
 section
 presents
 the
 results
 of
 the
 carbon
 flow
 calculations.
 A
 summary
 of
 the
 results
is
outlined
initially
and
the
results
for
each
life
cycle
stage
are
then
presented
in
 Sections
3.2.1
to
3.2.5.
These
flows
are
discussed
on
an
absolute
basis
for
each
life
cycle
 stage,
as
well
as
per
ODt
biomass
processed
and
per
MWh
electricity
generated.
The
net
 annual
emissions
from
the
bioenergy
life
cycle
were
calculated
to
be
68,500
tonnes
CO2.
 This
 value
 corresponds
 to
 a
 value
 of
 265
 kgCO2/ODt
 biomass
 processed
 or
 164
 kgCO2/MWh
 electricity
 generated.
 An
 overview
 of
 the
 results
 can
 be
 found
 below
 in
 Table
24
and
Figure
16,
with
detailed
calculations
presented
in
Appendix
A.
 
 Table
24.
Life
cycle
emission
results
 Life
Cycle
Stage
 Soil
Carbon
 Harvesting
 Chipping
 Transportation
 Facility
 Avoided
 Net
  
Absolute
(Tonnes
CO2)
 56,000
 3,300
 3,700
 5,600
 472,500
 ‐472,500
 68,500
  Life
Cycle
Emissions
 Per
ODt
(kgCO2/ODt
biomass)
 217
 13
 14
 22
 1830
 ‐1830
 265
  Per
MWh
(kgCO2/MWh)
 134
 8
 9
 13
 1128
 ‐1128
 164
  
 
  Carbon
Emissions
(kgCO2/MWh)
  
  Figure
16.
Carbon
emissions
per
megawatt‐hour
electricity
generated
  71
 
  
  
  
  3.2.1
Soil
Carbon
Emissions
 Emissions
from
soil
 carbon
were
determined
using
Equations
4
–
7
(Section
2.2.1)
and
 they
were
the
second
highest
of
all
lifecycle
stages.
Under
the
given
study
parameters,
 approximately
 360,000
 tCO2
 would
 be
 emitted
 annually
 across
 the
 study
 area
 due
 to
 harvesting
 practices.
 Emissions
 of
 217
 kg
 kgCO2/ODt
 were
 calculated
 by
 dividing
 the
 total
 soil
 carbon
 emissions
 by
 the
 total
 quantity
 of
 biomass
 removed
 annually
 (i.e.
 roundwood
and
residues).
It
was
then
determined
that
56,000
tCO2
was
attributable
to
 forest
 residues
 by
 multiplying
 the
 emissions
 rate
 by
 the
 quantity
 of
 resides
 removed.
 This
value
corresponded
to
a
value
of
134
kgCO2/MWh
(Table
25).
Considering
that
the
 biomass
 combustion
 emissions
 were
 entirely
 offset
 by
 the
 avoided
 emissions,
 soil
 carbon
 provided
the
largest
contribution
to
 overall
emissions
at
82%
of
net
emissions.
 Additionally,
 the
 CO2
 emitted
 from
 soil
 carbon
 is
 equal
 to
 approximately
 12%
 of
 total
 carbon
 contained
 in
 the
 biomass
 removed.
 Reducing
 emissions
 from
 soil
 carbon
 is
 therefore
an
important
topic
to
study
in
order
to
reduce
the
net
emissions.
A
sensitivity
 analysis
including
the
range
of
soil
carbon
literature
values
can
be
found
in
Section
3.3.
 
 Table
25.
Soil
carbon
emission
results
 Total
Soil
C
Loss
 (tCO2 )
 360,000
  Total
Biomass
Removed
 per
Tonne
 (ODt)
 (kgCO2/ODt)
 1,700,000
 217
  Attributed
Loss

 per
MWh
 (tCO2 )
 (kgCO2/MWh)
 56,000
 134
  
 3.2.2
Harvesting
and
Chipping
Results
 Emissions
from
harvesting
and
chipping
are
the
result
of
fuel
combustion
in
machinery
 and
 they
 were
 calculated
 using
 Equations
 8
 and
 9
 (Sections
 2.2.2
 and
 2.2.3).
 While
 absolute
emissions
change
as
a
function
of
the
quantity
of
timber
harvested
or
residues
 chipped,
 the
 emissions
 per
 unit
 of
 biomass
 processed
 stay
 the
 same.
 Total
 annual
 emissions
from
the
harvesting
and
chipping
phases
of
the
study
amounted
to
6,900
tC
 or
17
kgCO2/MWh.
The
greatest
fuel
consumption
was
observed
for
chipping,
emitting
 approximately
13
kgCO2/ODt
processed
or
3,300
tonnes
CO2
annually.
This
translated
to
  72
 
  
  
  
  approximately
 8
 kgCO2/MWh
 in
 the
 base
 case.
 Skidding
 was
 the
 other
 significant
 emissions
 source
 at
 9
 kgCO2/ODt
 or
 5.5
 kgCO2/MWh.
 Annual
 emissions
 from
 felling,
 delimbing
 and
 piling
 were
 relatively
 small
 at
 approximately
 500,
 400,
 and
 350
 tonnes
 CO2,
respectively.
Figure
17
illustrates
the
emissions
from
each
process
in
kgCO2/MWh.
  Emissions
(kgCO2/MWh)
  
  
 Figure
17.
Emissions
from
harvesting
and
chipping
activities
 
 3.2.3
Transportation
Results
 Equation
10
(Section
2.2.4)
was
used
to
calculate
transportation
emissions,
with
tonne
 kilometres
 traveled
 being
 calculated
 by
 determining
 the
 total
 one‐way
 travel
 distance
 and
the
mass
transported
(Table
26).
Annual
transportation
emissions
were
calculated
 to
 be
 5,600
 tCO2
 using
 the
 value
 for
 emissions
 per
 tonne
 kilometer
 traveled,
 found
 in
 the
 U.S.
 LCI
 database
 (National
 Renewable
 Energy
 Laboratory,
 2010).
 This
 value
 corresponded
to
emissions
 of
 21.8
kgCO2/ODt
or
13
kgCO2/MWh,
which
was
19%
less
 than
the
combined
emissions
from
harvesting
and
chipping.
A
sensitivity
analysis
using
 various
literature
values
for
fuel
economy
can
be
found
in
Section
3.3.
 
 73
 
  
  
  
  Table
26.
Tonne‐kilometres
traveled
and
transportation
emissions
 One‐way
Distance

 (km)
 1,485,700
  Empty
Weight

 (t)
 12
  Full
Weight

 (t)
 36
  Tonne‐Kilometres

 Emissions

 (tkm)
 (tCO2 )
 71,313,400
 5,600
  Emissions

 (kgCO2/ODt)
 22
  
 3.2.4
Facility
and
Avoided
Emission
Results
 As
 mentioned
 in
 Section
 2.2.5,
 facility
 emissions
 were
 calculated
 by
 assuming
 the
 entirety
 of
 the
 carbon
 contained
 in
 the
 processed
 biomass
 was
 emitted
 through
 biomass
 combustion.
 Assuming
 the
 oven
 dry
 biomass
 was
 50%
 carbon,
 and
 using
 Equation
11
from
Section
2.2.5,
annual
facility
emissions
were
calculated
to
be
472,500
 tCO2,
 corresponding
 to
 a
 value
 of
 1,800
 kgCO2/ODt.
 Then,
 using
 the
 quantity
 of
 electricity
 generated
 in
 the
 base
 case,
 emissions
 were
 calculated
 to
 be
 1,138
 kgCO2/MWh.
Because
it
was
assumed
that
logging
slash
was
burned
in
the
business‐as‐ usual
 scenario,
 avoided
 emissions
 were
 calculated
 in
 the
 same
 manner.
 Therefore
 the
 annual
 avoided
 emissions
 completely
 offset
 the
 facility
 emissions
 at
 ‐472,500
 tCO2
 (Table
24).
 
 It
is
important
to
note
that
the
facility
emissions
were
nearly
seven
times
greater
than
 the
 net
 annual
 lifecycle
 emissions,
 a
 fact
 that
 highlights
 the
 importance
 of
 accurately
 calculating
 the
 avoided
 emissions.
 Forest
 residues
 generated
 in
 Northern
 BC
 are
 typically
burned
in
slash
piles
in
the
forest
(Province
of
BC,
2010[b]).
However,
if
there
 was
another
use
for
them,
or
if
they
were
piled
and
left
to
decompose,
facility
emissions
 would
not
be
completely
offset
which
could
greatly
affect
the
net
emissions
profile.
This
 is
discussed
in
a
sensitivity
analysis
performed
in
Section
3.3.
 3.2.5
Net
Emissions
 Net
annual
life
cycle
emissions
were
calculated
to
be
68,500
tonnes
CO2
using
Equation
 3
 from
 the
 beginning
 of
 Section
 2.2.
 Next
 emissions
 were
 calculated
 to
 be
 265
 kgCO2/ODt
 by
 dividing
 the
 net
 emissions
 by
 the
 quantity
 of
 residues
 processed
(Table
  74
 
  
  
  
  24).
Finally,
by
dividing
the
net
annual
emissions
by
the
quantity
of
electricity
generated
 in
the
base
case,
emissions
were
calculated
to
be
164
kgCO2/MWh
(Figure
16).

 
 When
compared
to
other
literature
values
for
biomass‐derived
electricity,
the
emissions
 per
 MWh
 do
 not
 appear
 favourable,
 with
 the
 reason
 being
 that
 upstream
 emissions
 from
 harvesting
 practices
 and
 potential
 losses
 from
 soil
 carbon
 are
 not
 typically
 attributed
 to
 residues.
 In
 addition,
 previous
 studies
 have
 included
 or
 excluded
 carbon
 capture
 (Table
 27).
 The
 biomass‐based
 investigations
 that
 did
 not
 capture
 the
 CO2
 reported
process
emissions
ranging
from
‐410
to
52
kgCO2/MWh.
In
order
to
compare
 the
 results
 of
 this
 study
 with
 previous
 investigations
 the
 soil
 carbon
 emissions
 of
 134
 kgCO2/MWh
 should
 be
 removed
 from
 the
 calculation
 and
 the
 resultant
 net
 emissions
 would
 be
significantly
more
 favourable
at
30
kgCO2/MWh
(this
 is
addressed
further
in
 the
sensitivity
analysis
in
Section
3.3).
 This
value
is
comparable
to
previously
reported
 net
 emissions
 per
 MWh.
 Further,
 if
 soil
 carbon
 emissions
 were
 removed
 and
 the
 bioenergy
plant
used
in
this
study
was
fitted
with
carbon
capture
technology
that
was
 80%
 efficient
 like
 in
 Carpentieri
 et
 al.
 (2004),
 the
 net
 emissions
 would
 be
 ‐873
 kgCO2/MWh
which
is
at
the
lower
end
of
the
values
reported
by
previous
studies
(Table
 27).
 In
 addition,
 the
 results
 of
 this
 study
 show
 that
 even
 with
 upstream
 emissions
 included,
electricity
from
the
direct‐fired
combustion
of
forest
residues
performs
much
 better
 than
 coal‐fired
 and
 biomass/coal
 co‐fired
 systems
 (without
 carbon
 capture)
 as
 the
improvement
over
similar
fossil
energy
systems
ranges
from
317%‐498%
(Table
27).
 
 These
 results
 highlight
 the
 importance
 of
 using
 full
 accounting
 methodology
 and
 the
 impact
 that
 emission
 attribution
 has
 on
 overall
 emissions.
 However,
 it
 is
 necessary
 to
 perform
a
sensitivity
analysis
in
order
to
determine
the
impact
that
each
value
has
on
 emissions,
 such
 as
 machinery
 fuel
 consumption
 or
 facility
 conversion
 efficiency.
 Therefore,
 in
 Section
 3.3
 a
 range
 of
 different
 values
 from
 literature
 are
 assessed
 to
 determine
the
ability
of
each
step
in
the
process
life
cycle
to
impact
overall
emissions.

 
  75
 
  
  
  
  Table
27.
Net
Global
Warming
Potential
(kgCO2e/MWh)
from
various
energy
systems
 and
their
difference
from
this
study
 Energy
System
&
Study
 Coal‐fired
(Heller
et
al.,
2004)
 Willow/residues/coal
co‐fire
(Heller
et
al.,
2004)
 Willow/coal
co‐fire
(Heller
et
al.,
2004)
 Coal‐fired
(Spath
&
Mann,
2004)
 Biomass
residues/coal
co‐fire
(Spath
&
Mann,
2004)
 Poplar
IBGCC
w/
carbon
capture
(Carpentieri
et
al.,
2004)
 Base
Case
 Coal‐fired
w/
carbon
capture
(Carpentieri
et
al.,
2004)
 Willow
direct‐fired
(Heller
et
al.,
2004)
 Willow
IBGCC
(Heller
et
al.,
2004)
 Base
Case
minus
soil
carbon
 Poplar
IBGCC
(Spath
&
Mann,
2004)
 Biomass
residue
direct‐fired
(Spath
&
Mann,
2004)
 Poplar
IBGCC
w/
carbon
capture*
(Carpentieri
et
al.,
2004)
 Poplar
IBGCC
w/
carbon
capture
(Spath
&
Mann,
2004)
 Base
Case
minus
soil
carbon
w/
carbon
capture
 Biomass
residue
direct‐fired
w/
carbon
capture
(Spath
&
Mann,
2004)
 *
Credit
taken
for
sequestration
from
biomass
growth
  Net
GWP
(kgCO2 e/MWh)
 978
 906
 883
 847
 681
 178
 164
 130
 52
 40
 30
 ‐49
 ‐410
 ‐594
 ‐667
 ‐873
 ‐1368
  Difference
(%)
 498
 454
 440
 418
 317
 9
 0
 ‐21
 ‐68
 ‐76
 ‐82
 ‐130
 ‐351
 ‐463
 ‐508
 ‐632
 ‐937
  
  3.3
Sensitivity
Analysis
&
Discussion
 This
 section
 of
 the
 thesis
 looks
 into
 the
 importance
 each
 lifecycle
 stage
 (Figure
 14)
 in
 determining
 the
 overall
 process
 emissions
 and
 performance
 by
 considering
 a
 range
 of
 reasonable
 values
 and
 conditions.
 It
 also
 assesses
 the
validity
 of
 the
 assumptions
 that
 have
 been
 made
 and
 identifies
 the
 most
 important
 intervention
 points
 to
 minimize
 emissions.
 Various
 values
 are
 presented
 for
 soil
 carbon
 emissions,
 fuel
 economy
 and
 payload
 size,
 and
 facility
 conversion
 efficiency.
 This
 section
 also
 discusses
 how
 an
 alternate
business‐as‐usual
residue
disposal
scenario
or
accounting
methodology
would
 affect
 emissions,
 and
 the
 best‐case
 emission
 scenario.
 The
 results
 of
 the
 sensitivity
 analysis
are
presented
in
Section
3.3.9.

 3.3.1
Forest
Floor
and
Mineral
Soil
Carbon
Emissions
 Soil
 carbon
 losses
 were
 calculated
 using
 Equation
 4
 (Section
 2.2.1)
 with
 km
 =
 0
 (i.e.
 negligible
soil
carbon
losses)
and
kf
=
0.2
(i.e.
20%
loss
of
forest
floor
soil
carbon).
Five
 additional
soil
carbon
scenarios
were
considered
to
assess
the
importance
of
soil
carbon
  76
 
  
  
  
  in
 determining
 the
 net
 emissions
 and
 emissions
 per
 MWh.
 In
 the
 first
 scenario
 it
 was
 assumed
 that
 there
 were
 no
 losses
 from
 soil
 carbon.
 This
 was
 assumed
 in
 order
 to
 determine
the
effect
of
including
soil
carbon
losses
in
the
emission
calculation,
as
many
 studies
 ignore
 emissions
 from
 soil
 carbon.
 The
 second
 scenario,
 on
 the
 other
 hand,
 assumed
 a
 forest
 floor
 carbon
 loss
 of
 10%
 due
 to
 harvesting.
 In
 this
 scenario
 a
 post‐ harvest
soil
carbon
loss
of
20%
was
assumed,
followed
by
a
gradual
return
to
the
pre‐ harvest
 level.
 As
 a
 result
 the
 average
 soil
 carbon
 level
 was
 approximately
 10%
 lower
 than
the
initial
level
over
one
harvest
rotation
(Figure
18).
This
puts
the
average
forest
 floor
 soil
 carbon
 content
 as
 10%
 lower
 than
 in
 a
 no‐harvest
 scenario.
 This
 scenario
 assumed
that
there
were
no
changes
to
the
mineral
soil
carbon.
 
 
  
 Figure
18.
Graphic
depiction
of
Soil
Carbon
Scenario
2
(adapted
from
Jandl
et
al.,
2006)
 
 Scenarios
 three
 and
 four
 were
 chosen
 from
 the
 Aleza
 Lake
 Research
 Forest
 study
 by
 Fredeen
et
al.
(2005).
In
this
study,
mineral
soil
carbon
was
reduced
by
2‐6%
in
second
 growth
 stands
 compared
 to
 old‐growth
 stands
 (Fredeen
 et
 al.,
 2005).
 This
 loss
 wasn’t
 found
to
be
statistically
significant
but
the
range
of
values
is
assessed
nonetheless.
The
 fifth
and
final
scenario
was
based
on
findings
from
Nave
et
al.
(2010),
which
found
that
 77
 
  
  
  
  Inceptisol
 soil
 carbon
 storage
 was
 reduced
 by
 13%
 following
 harvest.
 However,
 it
 was
 also
reported
that
this
reduction
was
neither
permanent
nor
unavoidable.
The
results
of
 these
scenarios
can
be
found
in
Section
3.3.9.
To
recap,
the
five
scenarios
are
presented
 below,
with
coefficients
for
Equation
4
specified
in
brackets:
 
 Soil
Carbon
Base
Case.
No
mineral
soil
loss,
20%
from
forest
floor
(km
=
0;
kf
=
0.2)
 Soil
Carbon
Scenario
1.
No
mineral
soil
loss,
No
loss
from
forest
floor
(km
=
0;
kf
=
0)
 Soil
Carbon
Scenario
2.
No
mineral
soil
loss,
10%
from
forest
floor
(km
=
0;
kf
=
0.1)
 Soil
Carbon
Scenario
3.
2%
mineral
soil
loss,
20%
from
forest
floor
(km
=
0.02;
kf
=
0.2)
 Soil
Carbon
Scenario
4.
6%
mineral
soil
loss,
20%
from
forest
floor
(km
=
0.06;
kf
=
0.2)
 Soil
Carbon
Scenario
5.
13%
mineral
soil
loss,
20%
from
forest
floor
(km
=
0.13;
kf
=
0.2)
 3.3.2
Harvesting
Emissions
 Two
 scenarios
 were
 evaluated
 to
 assess
 the
 impact
 that
 harvesting
 equipment
 fuel
 usage
 had
 in
 affecting
 overall
 emissions.
 This
 was
 achieved
 by
 assessing
 both
 a
 100%
 increase
and
100%
decrease
in
diesel
fuel
use.
The
results
of
this
analysis
can
be
found
 in
Section
3.3.9.
 
 Harvesting
Base
Case.
0.0047
m3/ODt
(8
kgCO2/MWh)
 Harvesting
Scenario
1.
100%
increase
in
harvesting
equipment
fuel
usage

 Harvesting
Scenario
2.
100%
decrease
in
harvesting
equipment
fuel
usage
 3.3.3
Chipping
Emissions
 Two
 scenarios
 were
 also
 considered
 to
 assess
 the
 impact
 of
 fuel
 usage
 by
 chipping
 equipment
on
life
cycle
emissions.
As
with
harvesting
equipment,
this
was
accomplished
 by
analysing
the
effects
a
100%
increase
and
100%
decrease
in
fuel
use.
 
 Chipping
Base
Case.
.0053
m3/ODt
(9
kgCO2/MWh)
 Chipping
Scenario
1.
100%
increase
in
chipping
equipment
fuel
usage

 Chipping
Scenario
2.
100%
decrease
in
chipping
equipment
fuel
usage
 78
 
  
  
  
  3.3.4
Transportation
Emissions
 Four
different
scenarios
were
assessed
to
determine
the
importance
 of
transportation
 emissions.
The
principal
driver
of
transportation
emissions
is
the
combustion
of
fuel
and
 there
 are
 several
 factors
 that
 affect
 this
 value,
 including
 vehicle
 fuel
 efficiency
 and
 payload
size
(Equation
10).
The
first
transport
scenario
used
an
alternate
value
for
fuel
 use
per
tonne
kilometer.
The
 U.S.
LCI
database
contains
two
values
for
transportation
 fuel
use,
one
for
single‐unit
trucks
and
one
for
combination
trucks
(National
Renewable
 Energy
Laboratoty,
 2010).
The
value
for
combination
truck
transportation
was
used
as
 the
 base‐case
 in
 this
 study.
 The
 first
 transport
 scenario
 evaluated
 in
 the
 sensitivity
 analysis
 used
 the
 value
 for
 single‐unit
 trucks.
 In
 this
 scenario,
 0.058
 L
 of
 diesel
 is
 consumed
per
tonne
kilometer,
compared
to
0.027
L
in
the
base‐case.

 
 The
next
two
scenarios
considered
alternate
values
for
fuel
efficiency.
Over
time,
trucks
 have
 become
 more
 fuel‐efficient
 and
 modern
 trucks
 are
 able
 to
 transport
 the
 same
 quantity
of
goods
using
significantly
less
fuel.
For
example,
the
average
fuel
economy
of
 European
 tractor‐trailer
 combinations
 has
 been
 improved
 by
 approximately
 27%
 over
 the
past
25
years
(Table
28).

 
 Table
 28.
 Average
 fuel
 economy
 over
 time
 for
 a
 European
 tractor
 and
 semi‐trailer
 (Martensson,
2008)
 Average
Fuel
Economy
for
a
European
Tractor
and
Semi‐trailer
 3 Model
Year
 m /100
km
 1980
 0.044
 1990
 0.035
 1993
 0.033
 1998
 0.031
 2001
 0.030
 2006
 0.029
  Percent
 100
 81
 75
 70
 68
 63
  
 
 Current
 fuel
 economy
 values
 for
 Volvo
 trucks
 were
 used
 in
 the
 second
 and
 third
 scenarios
and
ranged
from
0.021–0.026
m3/100
km
when
empty
(i.e.
1.8x10‐5–2.2x10‐5
  79
 
  
  
  
  m3/tkm)
 to
 .029
 –.035
 m3/100
 km
 when
 full
 (i.e.
 0.8x10‐5–1.0x10‐5
 m3/tkm)
 (Martensson,
2008).
Both
the
low‐end
and
high‐end
values
were
considered.
 
 In
 the
 fourth
 scenario,
 the
 effect
 that
 payload
 has
 on
 emissions
 was
 evaluated.
 By
 increasing
 payload
 (i.e.
 amount
 transported
 each
 trip)
 the
 total
 number
 of
 tonne‐ kilometres
is
reduced.
This
is
because
the
weight
of
the
truck
is
considered
in
the
tonne‐ kilometre
calculation,
and
with
a
larger
payload,
fewer
empty
trips
are
made.

 
 There
are
three
options
for
the
payload
input
in
 the
BiOS
model.
They
include
a
semi‐ trailer
with
a
16
ODt
payload,
a
B‐train
with
a
25
ODt
payload,
or
a
combination
of
the
 two.
 A
 B‐train
 is
 larger
 than
 a
 semi‐trailer
 (Figure
 19)
 and
 a
 more
 efficient
 means
 of
 transport.
 However,
 its
 operation
 is
 limited
 to
 only
 the
 highest
 standard
 roads
 (MacDonald,
2010[a]).


 
  
 Figure
19.
B‐train
chip
hauler
(Hank's
Truck
Pictures,
2010)
 
 The
 fourth
 scenario
 considered
 how
 the
 use
 of
 a
 B‐train
 would
 affect
 the
 overall
 emissions
 of
 the
 process
 compared
 to
 a
 semi‐trailer
 (i.e.
 base‐case).
 Specifically,
 a
 25
 ODt
load
requires
58.5
million
tkm,
rather
than
71.3
million
tkm
with
a
16
ODt
payload.
 80
 
  
  
  
  It
should
be
noted
that
a
residues
moisture
content
of
50%
was
assumed,
meaning
the
 actual
 payloads
 of
 the
 B‐train
 and
 semi‐trailer
 were
 37.5
 tonnes
 and
 24
 tonnes,
 respectively.
To
recap,
the
four
transport
scenarios
were:
 
 Transport
Base
Case.
U.S.
LCI
data
for
combination
truck
–
2.7x10‐5
m3/tkm
 Transport
Scenario
1.
U.S.
LCI
data
for
single‐unit
truck
–
5.8x10‐5
m3/tkm
 Transport
Scenario
2.
Low
Volvo
–
1.8x10‐5
m3/tkm
empty,
0.8x10‐5
m3/tkm
full
 Transport
Scenario
3.
High
Volvo
–
2.2x10‐5
m3/tkm
empty,
1.0x10‐5
m3/tkm
full
 Transport
Scenario
4.
25
ODt
load
–
58.5
million
tkm
 3.3.5
Facility
Emissions
 Facility
 conversion
 efficiency
 is
 greatly
 affected
 by
 the
 technology
 employed.
 As
 mentioned
 in
 Section
 3.2,
 conversion
 efficiencies
 can
 vary
 by
 more
 than
 20%,
 even
 when
using
the
same
feedstock.
The
facility
type
considered
in
the
base
case
is
a
grate‐ fired
 biomass
 combustion
 system
 that
 creates
 power
 using
 a
 steam
 cycle,
 and
 has
 a
 conversion
 efficiency
 of
 29.2%
 (Wiltsee,
 2000).
 Five
 bioenergy
 systems
 with
 different
 conversion
efficiencies
were
considered
in
the
facility
sensitivity
analysis.
This
was
done
 to
 assess
 the
 impact
 that
 technology
 has
 on
 bioenergy
 emissions.
 It
 should
 be
 noted
 that
changing
technology
changes
the
quantity
of
electricity
produced,
not
the
absolute
 quantity
of
CO2
emissions.
However,
improving
conversion
efficiency
effectively
reduces
 the
quantity
of
CO2
released
per
MWh
generated.

 
 Included
in
the
five
scenarios
are
both
combustion
and
gasification
systems,
and
steam
 cycle,
 gas
 engine
 and
 combined
 cycle
 power
 cycles.
 The
 five
scenarios
 included
 in
 the
 facility
sensitivity
analysis,
with
conversion
efficiencies
included
in
brackets,
were:
 
 Facility
Base
Case.
Grate
firing,
steam
turbine
(29.2%)
 Facility
Scenario
1.
Grate
firing,
steam
turbine
(27%)
 Facility
Scenario
2.
Fluidised
bed
combustion,
steam
turbine
(30%)
 Facility
Scenario
3.
Fluidised
bed
gasification,
gas
engine
(atmospheric)
(33%)
 81
 
  
  
  
  Facility
Scenario
4.
Fluidised
bed
gasification,
combined
cycle
(atmospheric)
(38%)
 Facility
Scenario
5.
Fluidised
bed
gasification,
gas
engine
(pressurized)
(44%)
 
 The
conversion
efficiencies
for
the
five
scenarios
were
acquired
from
Dornburg
&
Faaij
 (2001),
and
can
be
found
in
Table
29
along
with
other
pertinent
information,
including
 the
quantity
of
electricity
that
could
be
generated.
With
the
exception
of
the
gas
engine
 system,
the
conversion
efficiencies
correspond
with
efficiency
values
of
a
50
MW
power
 plant.
 Typically
 the
 maximum
 power
 rating
 for
 a
 fluidised
 bed
 gasification
 gas
 engine
 system
is
30
MW
(Dornburg
&
Faaij,
2001).
However,
for
completeness
this
technology
 has
also
been
included
in
this
thesis.
 
 Table
29.
Biomass
power
plant
information
and
potential
electricity
production
using
all
 recoverable
biomass
(Dornburg
&
Faaij,
2001)
 Abbreviation
 GF/ST
 FBC/ST
 FBG/GE
 BIG/CCA
 BIG/CCP
 
  Technology
 Power
Cycle
 Grate
firing
 Steam
cycle
 Fluidised
bed
combustion
 Steam
cycle
 Fluidised
bed
gasification
(A)
 Gas
engine
 Fluidised
bed
gasification
(A)
 Combined
cycle
 Fluidised
bed
gasification
(P)
 Combined
cycle
 (A)
=
atmospheric,
(P)
=
pressurized
  Scale
(MWth)
 10‐200
 10‐200
 3‐30
 10‐300
 20‐300
  Efficiency
(%)
 27
 30
 33
 38
 44
  Electricity
(MWh)
 387,279
 430,310
 473,341
 545,059
 631,121
  3.3.6
Residue
Decomposition
 Logging
slash
created
in
BC
is
typically
piled
and
burned,
releasing
all
of
the
stored
CO2
 back
to
the
atmosphere.
In
this
thesis,
the
carbon
neutral
aspect
of
biomass
combustion
 hinges
on
the
fact
that
it
is
combusted
in
the
business‐as‐usual
scenario.
For
example,
 the
 combustion
 of
 wood
 waste
 that
 was
 going
 to
 be
 converted
 to
 a
 long‐lived
 wood
 composite
 material
 should
 not
 be
 considered
 carbon
 neutral,
 as
 the
 alternative
 use
 would
not
result
in
CO2
emissions
in
the
short
term.

 
 Three
 alternative
 scenarios
 were
 assessed
 for
 forest
 residues.
 Landfilling
 may
 be
 an
 option
 for
 mill
 residues
 or
 post‐consumer
 wastes
 if
 they
 are
generated
 near
 an
 urban
 centre.
 However,
 this
 would
 be
 an
 unlikely
 scenario
 for
 forest
 residues.
 Therefore,
 it
 was
assumed
for
the
three
alternative
scenarios
that
residues
were
left
on
the
cutblock.

 82
 
  
  
  
  In
these
scenarios
the
residues
would
decompose
over
time.
However
the
rate
at
which
 this
occurs
is
not
completely
understood
and
it
is
highly
dependant
on
site
conditions,
 biomass
 size
 and
 dispersion
 (Stone
 et
 al.,
 1998;
 Zanchi
 et
 al.,
 2010).
 In
 a
 study
 that
 monitored
the
decay
 of
coarse
woody
debris
(CWD),
Stone
et
al.
(1998)
reported
that
 after
 65
 years
 anywhere
 from
 4%‐37%
 of
 the
 initial
 quantity
 of
 CWD
 still
 remained.
 However
in
this
study
the
mean
biomass
diameter
was
quite
large,
ranging
from
27cm‐ 46cm
 (Stone
 et
 al.,
 1998).
 Schlamadinger
 et
 al.
 (1995)
 investigated
 the
 carbon
 neutral
 factor
of
logging
residues
from
a
typical
European
forest
and
found
that
if
they
were
left
 on
site
to
decompose
in
the
business‐as‐usual
scenario
they
would
exhibit
an
average
 carbon
neutral
factor
of
0.6.
This
means
it
would
only
be
justifiable
to
disregard
60%
of
 the
 carbon
 emissions
 from
 the
 facility,
 as
 40%
 of
 the
 carbon
 would
 remain
 the
 forest
 ecosystem
in
the
BAU
situation
(Section
1.3.1).
In
a
similar
study,
Palosuo
et
al.
(2001)
 determined
the
CN
factor
of
logging
residues
from
a
typical
upland
Finnish
spruce
(Picea
 spp.)
 forest
 ranged
 from
 0.8
 –
 0.9.
 The
 higher
 CN
 factor
 reported
 by
 Palosuo
 et
 al.
 (2001)
was
due
to
a
higher
decomposition
rate
than
in
Schlamadinger
et
al.
(1995).
 
 Unfortunately
 it
 was
 difficult
 to
 discern
 from
 these
 studies
 what
 would
 occur
 in
 the
 interior
of
northern
BC
if
residues
were
left
to
decompose
rather
than
be
burned.

Most
 likely
residues
would
decompose
more
slowly
than
the
CWD
from
Stone
et
al.
(1998)
as
 the
study
was
performed
on
Vancouver
Island
where
it
is
warmer
and
wetter
than
the
 study
area.
However,
the
diameter
of
the
CWD
from
Stone
et
al.
(1998)
was
quite
large
 which
 would
 slow
 the
 decay
 rate
 when
 compared
 to
 smaller
 logging
 residues.
 As
 a
 result,
the
CN
factors
for
logging
residues
 reported
in
Schlamadinger
et
al.
(1995)
and
 Palosuo
 et
 al.
 (2001)
 were
 equated
 to
 amount
 of
 carbon
 remaining
 in
 the
 forest
 ecosystem
and
used
for
the
sensitivity
analysis.
Palosuo
et
al.
(2001)
reported
a
carbon
 neutral
factor
ranging
from
0.8
–
0.9
(i.e.
10%‐20%
of
carbon
remaining
in
ecosystem),
 while
Schlamadinger
et
al.
(1995)
reported
an
average
carbon
neutral
factor
of
0.6
(i.e.
 40%
of
carbon
remaining
in
ecosystem):
 
  83
 
  
  
  
  Decomposition
Base
Case.
Residues
burned
–
0%
of
C
remaining
in
forest
ecosystem
 Decomposition
Scenario
1.
Fast
decomposition
–
10%
of
C
remaining
in
forest
ecosystem
 Decomposition
Scenario
2.
Medium
rate

–
20%
of
C
remaining
in
forest
ecosystem
 Decomposition
Scenario
3.
Slow
decomposition
–
40%
of
C
remaining
in
forest
ecosystem

 3.3.7
Emission
Attribution

 Under
 typical
 carbon
 accounting
 frameworks,
 such
 as
 those
 for
 the
 European
 Union’s
 cap‐and‐trade
system
and
the
climate
bill
passed
by
the
U.S.
House
of
Representatives,
 CO2
emissions
from
bioenergy
combustion
are
exempt
(Searchinger
et
al.,
2009).
This
is
 because
all
potential
emissions
from
bioenergy
are
supposed
to
be
captured
under
the
 land‐use
 phase
 of
 carbon
 accounting
 according
 to
 IPCC
 guidelines
 (Searchinger
 et
 al.,
 2009).
 However,
 a
 problem
 occurs
 when,
 under
 a
 cap‐and‐trade
 system
 for
 instance,
 bioenergy
emissions
are
considered
to
be
carbon
neutral
but
land‐use
emissions
aren’t
 capped.
 To
 avoid
 this
 problem,
 the
 actual
 carbon
 flows
 and
 avoided
 emissions
 were
 tracked
for
this
study,
rather
than
just
consider
biomass
combustion
as
carbon
neutral
 (i.e.
full
accounting).
This
process
of
tracking
carbon
flows
involved
attributing
a
portion
 of
 the
 upstream
 emissions
 (i.e.
 soil
 carbon
 loss
 and
 harvesting
 emissions)
 to
 the
 residues,
where
typically
these
emissions
are
not
attributed
in
this
way
(Brandao
et
al.,
 2010).
Therefore,
a
scenario
was
included
in
the
sensitivity
analysis
where
no
upstream
 emissions
are
attributed
to
residues
in
order
to
determine
how
much
of
an
impact
this
 had
on
the
outcome
of
the
study:
 
 Attribution
Base
Case.
Upstream
emissions
mass‐allocated
to
biomass
residues
 Attribution
Scenario
1.
No
upstream
emissions
allocation
to
biomass
residues
 3.3.8
Best
Case
Scenario
 The
final
scenario
included
in
the
sensitivity
analysis
was
a
best‐case
scenario.
This
was
 included
 to
 highlight
 how
 making
 different
 assumptions
 could
 impact
 results.
 In
 the
 best‐case
scenario,
forest
residues
were
treated
 as
a
waste
product
with
 no
upstream
 emissions,
the
way
they
are
typically
conceived
in
carbon
accounting
methodologies.
In
 84
 
  
  
  
  addition,
 their
 combustion
 was
 considered
 to
 be
 carbon
 neutral.
 Therefore,
 the
 only
 values
 to
 be
 considered
 in
 the
 emission
 calculation
 were
 piling,
 chipping,
 and
 transportation
 emissions.
 Piling
 and
 chipping
 emissions
 were
 constant
 throughout
 the
 study
 and
 the
 low
 fuel
 consumption
 values
 from
 Martenson
 (2009)
 (i.e.
 Scenario
 2
 –
 Section
3.3.4)
were
used
in
this
scenario.
Finally,
it
was
assumed
that
the
biomass
was
 converted
to
electricity
using
an
IBGCC
process
with
a
conversion
efficiency
of
44%.

 3.3.9
Sensitivity
Analysis
Results
 The
results
from
the
sensitivity
analysis
are
presented
in
Table
30.
This
table
shows
the
 base
process
emissions
and
net
emissions
in
tonnes
CO2,
emissions
in
kgCO2/MWh,
and
 the
percent
the
rate
was
changed
as
a
result
of
each
scenario.
Table
30
shows
that
the
 following
factors
had
the
greatest
impact
on
emissions:
 
 •  Percentage
of
soil
carbon
lost
from
harvesting
  •  Business‐as‐usual
disposal
method
  •  Emission
attribution
method
  •  Facility
conversion
technology
  
 Interestingly,
the
sensitivity
analysis
showed
that
soil
carbon
potentially
has
one
of
the
 greatest
 impacts
 on
 emissions
 because
 of
 the
 magnitude
 of
 the
 carbon
 pool.
 For
 example,
net
emissions
range
from
to
12,600
tonnes
CO2
annually
in
the
scenario
with
 no
soil
carbon
loss
(i.e.
IPCC
good
practices
guide
default
scenario),
to
164,000
tonnes
 CO2
 annually
 in
 the
 scenario
 with
 a
 13%
 reduction
 in
 mineral
 soil
 (Figure
 20),
 which
 corresponded
with
a
rate
change
of
‐82%
and
139%
respectively
(Table
30).
Even
in
the
 scenario
with
a
10%
loss
in
organic
carbon
(versus
20%
in
the
base
case),
emissions
were
 decreased
 by
 approximately
 41%.
 This
 highlights
 the
 need
 for
 applying
 full
 accounting
 and
one
of
the
biggest
problems
in
calculating
accurate
emissions
from
bioenergy,
as
it
 is
difficult
to
quantify
soil
carbon
values
on
a
general
basis.
 
 
 85
 
  
  
  
  Table
30.
Sensitivity
Analysis
results
 Variable
 Scenario
 Emissions
(tCO2)
 Net
Emissions
(tCO2 )
 Rate
(kgCO2/MWh)
 Rate
Change
(%)
 Soil
Carbon
 Base
 56,000
 68,500
 164
 0
 Soil
Carbon
 km
(0.0);
k f
(0.0)
 0
 12,600
 30
 ‐82
 Soil
Carbon
 km
(0.0);
k f
(0.1)
 28,000
 40,500
 97
 ‐41
 Soil
Carbon
 km
(0.02);
k f
(0.2)
 70,600
 83,200
 199
 21
 Soil
Carbon
 km
(0.06);
k f
(0.2)
 100,000
 112,500
 269
 64
 Soil
Carbon
 km
(0.13);
k f
(0.2)
 151,300
 163,800
 391
 139
 Harvesting
 Base
 3,300
 68,500
 164
 0
 Harvesting
 +100%
 6,500
 71,800
 171
 5
 Harvesting
 ‐100%
 0
 65,200
 156
 ‐5
 Chipping
 Base
 3,700
 68,500
 163
 0
 Chipping
 +100%
 7,300
 72,200
 172
 5
 Chipping
 ‐100%
 0
 64,800
 155
 ‐5
 Transport
 Base
 5,600
 68,500
 164
 0
 Transport
 Single‐Unit
 12,200
 75,100
 179
 10
 Transport
 Low
Volvo
 1,900
 64,800
 155
 ‐5
 Transport
 High
Volvo
 2,400
 65,200
 156
 ‐5
 Transport
 25t
load
 4,600
 67,500
 161
 ‐1
 Facility
 Base
 472,500
 68,500
 164
 0
 Facility
 GFC/ST
 472,500
 68,500
 177
 8
 Facility
 FBC/ST
 472,500
 68,500
 159
 ‐3
 Facility
 FBG/GE
 472,500
 68,500
 145
 ‐12
 Facility
 BIG/CC(A)
 472,500
 68,500
 126
 ‐23
 Facility
 BIG/CC(P)
 472,500
 68,500
 109
 ‐34
 Disposal
 Base
 ‐472,500
 68,500
 164
 0
 Disposal
 10%
C
Storage
 ‐425,200
 115,800
 276
 69
 Disposal
 20%
C
Storage
 ‐378,000
 163,000
 389
 138
 Disposal
 40%
C
Storage
 ‐283,500
 257,500
 614
 276
 Attribution
 Base
(Upstream)
 59,200
 68,500
 164
 0
 Attribution
 No
Upstream
 0
 9,300
 22
 ‐86
 All
 Best
Case
 ‐
 5,600
 9
 ‐95
 km
=
mineral
soil
factor;
kf
=
forest
floor
factor;
GFC
=
grate‐fired
combustion;
FBC
=
fluidized
bed
 combustion;
FBG
=
fluidized
bed
gasification;
BIG
=
biomass
integrated
gasification;
ST
=
steam
turbine;
 GE
=
gas
engine;
CC
=
combined
cycle;
(A)
=
atmospheric;
(P)
=
pressurized
  
 
  86
 
  
  
  Emissions
(kgCO2/MWh)
  
  
 Figure
20.
Sensitivity
analysis
results
for
soil
carbon
emissions
 
 While
soil
carbon
emissions
had
the
potential
to
greatly
increase
overall
emissions,
an
 alternate
 business‐as‐usual
 disposal
 method
 for
 harvesting
 residues
 represented
 the
 largest
 potential
 increase
 of
 both
 net
 emissions
 and
 the
 emissions
 per
 MWh.
 
 This
 is
 because
 the
 combustion
 of
 biomass
 at
 the
 facility
 cannot
 be
 considered
to
 be
 carbon
 neutral
 if
 the
 residues
 are
 not
 completely
 converted
 to
 CO2
 in
 the
 business‐as‐usual
 scenario.
Even
if
10%
of
the
carbon
in
the
biomass
was
stored
in
the
BAU
scenario,
the
 emissions
from
the
process
would
increase
by
nearly
70%
(Table
30).
This
was
result
was
 amplified
in
the
scenario
with
20%
carbon
storage,
as
the
emissions
per
MWh
increased
 by
138%
over
the
base
case.
Lastly,
if
40%
of
the
carbon
contained
in
the
biomass
were
 to
 be
 stored
 in
 the
 business‐as‐usual
 scenario,
 the
 net
 process
 emissions
 would
 be
 257,500
tCO2,
corresponding
to
emissions
of
614
kgCO2/MWh.
This
value
was
nearly
as
 high
as
some
of
the
values
for
coal‐based
electricity
(Tables
1
and
27).

 
 These
 results
 highlight
 the
 need
 for
 accurate
 full
 accounting
 of
 emissions.
 In
 some
 situations
 the
 biomass
 combustion
 is
 not
 carbon
 neutral,
 which
 results
 in
 emissionss
 from
 bioenergy
 that
 approach
 those
 from
 coal‐power.
 In
 addition,
 region‐specific
 research
is
needed
that
investigates
what
happens
to
residues
when
they
are
left
on
a
  87
 
  
  
  
  harvest
site,
as
there
is
a
scarcity
of
good
 data.
Decay
rates
are
dependant
on
several
 factors,
 such
 as
 temperature
 and
 precipitation
 and
 as
 a
 result
 they
 seem
 to
 be
 highly
 variable.
 Also,
 the
 relationship
 between
 residue
 decay
 and
 the
 buildup
 of
 humus
 and
 soil
 carbon
 is
 not
 fully
 characterized.
 However,
 all
 of
 these
 results
 support
 the
 use
 of
 biomass
sources
that
are
rapidly
converted
to
carbon
in
the
short
term
and
suggest
that
 diverting
biomass
from
long‐term
storage,
such
as
in
landfills,
is
not
currently
the
best
 strategy
for
bioenergy
expansion.

 
 With
 the
 exception
 of
 the
 best‐case
 scenario,
 the
 greatest
 emission
 reduction
 was
 observed
from
the
attribution
scenario
where
upstream
emissions
from
soil
carbon
and
 harvesting
equipment
were
ignored.
This
is
an
interesting
finding
as
upstream
emissions
 are
typical
ignored
when
calculating
emissions
from
residues.
The
results
of
the
scenario
 were
emissions
of
22
kgCO2/MWh,
which
is
a
reduction
of
86%
when
compared
to
the
 base
case
value
of
164
kgCO2/MWh.
This
was
improved
upon
even
further
in
the
best‐ case
 scenario,
 where
 the
 emissions
 were
 calculated
 to
 be
 only
 9
 kgCO2/MWh
 (Figure
 21).
 Lastly,
 the
 best‐case
 scenario
 represents
 a
 reduction
 of
 95%
 over
 the
 base
 case
 from
 this
 study
 and
 a
 99%
 reduction
 over
 the
 emissions
 from
 coal‐fired
 power
 from
 Heller
et
al.
(2004).

 
  88
 
  
  
  Emissions
(kgCO2/MWh)
  
  
 Figure
21.
Base
case,
attribution
scenario
and
best‐case
life
cycle
emissions
compared
to
 coal
energy
values
(Heller
et
al.,
2004;
Spath
&
Mann,
2004;
Carpentieri
et
al.,
2004)
 
 Again,
 these
 results
 highlight
 the
 necessity
 for
 full
 accounting.
 In
 these
 cases,
 by
 not
 accurately
 attributing
 emissions
 the
 results
 are
 skewed
 in
 favour
 of
 bioenergy.
 These
 results
 have
 major
 implications
 when
 it
 comes
 to
 climate
 policy.
 Considering
 that
 upstream
 emissions
 from
 soil
 carbon
 and
 harvesting
 are
 not
 typically
 attributed
 to
 residues
 under
 most
 cap‐and‐trade
 systems,
 process
 emissions
 would
 most
 likely
 be
 reported
 as
 similar
 to
 the
 no
 upstream
 emission
 scenario
 (i.e.
 22
 kgCO2/MWh).
 Considering
 this
 thesis
 reported
 base
 case
 emissions
 of
 164
 kgCO2/MWh,
 there
 would
 be
 approximately
 142
 kg
 of
 CO2
 unaccounted
 for
 per
 MWh
 of
 electricity
 generated
 in
 this
case.
The
implications
of
this
are
discussed
further
in
Section
4.2.
 
 The
facility
type
does
not
impact
the
total
quantity
of
carbon
emissions
if
it
is
assumed
 that
all
of
the
residues
are
utilized
for
bioenergy
but
it
does
play
an
important
role
when
 looking
at
CO2
emissions
per
MWh.
For
example,
when
comparing
a
pressurized
IBGCC
 system
with
a
conversion
efficiency
of
44%
to
the
base
case
combustion
system
(29.2%),
 CO2
emissions
per
MWh
were
reduced
by
nearly
35%.
This
corresponded
to
a
decrease
 89
 
  
  
  
  of
55.1
kgCO2/MWh
(Figure
22).
However,
these
changes
were
relatively
modest
when
 compared
to
the
changes
experienced
from
the
soil
carbon,
alternate
business‐as‐usual
 scenario
and
attribution
method
scenarios
(Table
30).
  Emissions
(kgCO2/MWh)
  
  
 Figure
22.
Impact
on
net
life
cycle
emissions
comparing
the
base
case
with
conversion
 efficiencies
for
grate‐fired
combustion
steam
turbine
(GFC/ST),
fluidized
bed
 combustion
steam
turbine
(FBC/ST),
fluidized
bed
gasification
gas
engine
(FBG/GE),
 biomass
integrated
gasification
combined
cycle
atmospheric
and
pressurized
(BIG/CCA
 &
BIG/CCP)

 
 Another
way
to
consider
the
effect
of
conversion
efficiency
is
to
calculate
the
biomass
 needed
 to
 generate
 the
 same
 quantity
 of
 electricity
 (Table
 31).
 In
 this
 scenario
 the
 emission
rates
and
rate
changes
are
identical
to
the
results
from
Table
30,
while
the
net
 emissions
experienced
at
the
facility
are
much
lower
in
some
instances.
This
is
because
 the
amount
of
biomass
required
to
generate
a
constant
amount
of
electricity
decreases
 as
 the
 conversion
 efficiency
 increases.
 Increasing
 the
 conversion
 efficiency
 will
 reduce
 the
 quantity
 of
 soil
 carbon
 emissions
 and
 harvesting
 emissions
 attributable
 to
 the
 removed
 residues
 (i.e.
 because
 less
 is
 removed)
 but
 it
 will
 not
 affect
 the
 absolute
 emissions
from
these
sources
(Table
32).
However,
conversion
efficiency
will
affect
the
  90
 
  
  
  
  absolute
 emissions
 from
 chipping
 and
 transportation
 as
 less
 biomass
 is
 processed.
 In
 addition,
both
facility
and
avoided
emissions
are
reduced.
 
 Table
31.
The
effect
of
technology
choice
on
biomass
requirements,
facility
emissions
 and
net
emissions
 Conversion
Efficiency
 Electricity
 Biomass
Required
 Facility
Emissions
 Emissions
 Rate
Change
 Technology
 (%)
 (MWh)
 (ODt)
 (tCO2 )
 (kgCO2/MWh)
 (%)
 Base
 29.2
 418,800
 258,200
 472,500
 164
 0%
 GFC/ST
 27
 418,800
 279,200
 510,900
 177
 8%
 FBC/ST
 30
 418,800
 251,300
 459,900
 159
 ‐3%
 FBG/GE
 33
 418,800
 228,400
 418,000
 145
 ‐12%
 BIG/CCA
 38
 418,800
 198,400
 363,100
 126
 ‐23%
 BIG/CCP
 44
 418,800
 171,300
 313,500
 108
 ‐34%
 GFC
=
grate‐fired
combustion;
FBC
=
fluidized
bed
combustion;
FBG
=
fluidized
bed
gasification;

 BIG
=
biomass
integrated
gasification;
ST
=
steam
turbine;
GE
=
gas
engine;
CC
=
combined
cycle;

 (A)
=
atmospheric;
(P)
=
pressurized
  
  The
effect
of
this
is
that
the
residues
that
are
not
utilized
for
bioenergy
will
be
burned
in
 the
 forest
 as
 the
 business‐as‐usual
 scenario
 outlines.
 The
 emissions
 from
 the
 residues
 that
are
not
utilized
could
be
treated
in
two
manners.
If
they
are
considered
outside
the
 project
boundary,
the
emissions
would
appear
as
they
do
in
Table
32.
However,
if
they
 were
to
be
treated
as
lost
potential,
and
their
emissions
considered
inside
the
project
 boundary,
the
emissions
would
appear
as
they
do
in
Table
33.
This
does
not
affect
the
 total
 quantity
 of
 biomass
 combustion
 emissions
 because
 all
 of
 the
 material
 is
 combusted,
whether
in
the
facility
or
in
the
forest.

 
 Table
32.
The
effect
of
technology
choice
on
emissions
from
each
life
cycle
stage
 Emissions
(tCO2)
 Technology
 Soil
Carbon
 Harvesting
 Chipping
 Transportation
 Facility
 Avoided
 Base
 56,000
 3,300
 3,700
 5,600
 472,500
 ‐472,500
 GFC/ST
 60,500
 3,500
 4,000
 6,100
 510,900
 ‐510,900
 FBC/ST
 54,500
 3,200
 3,600
 5,500
 459,900
 ‐459,900
 FBG/GE
 49,500
 2,900
 3,200
 5,000
 418,000
 ‐418,000
 BIG/CCA
 43,000
 2,500
 2,800
 4,300
 363,100
 ‐363,100
 BIG/CCP
 37,100
 2,200
 2,400
 3,700
 313,500
 ‐313,500
 GFC
=
grate‐fired
combustion;
FBC
=
fluidized
bed
combustion;
FBG
=
fluidized
bed
gasification;

 BIG
=
biomass
integrated
gasification;
ST
=
steam
turbine;
GE
=
gas
engine;
CC
=
combined
cycle;

 (A)
=
atmospheric;
(P)
=
pressurized
  
  91
 
  Net
 68,500
 74,100
 66,700
 60,600
 52,600
 45,500
  
  
  
  Table
33.
The
effect
of
technology
choice
on
emissions
considering
the
combustion
of
 residues
not
utilized
for
bioenergy
 
 Emissions
(tCO2)
 Technology
 Soil
Carbon
 Harvesting
 Chipping
 Transportation
 Facility
 Avoided
 Forest
Burn
 Base
 56,000
 3,300
 3,700
 5,600
 472,500
 ‐472,500
 0
 GFC/ST
 60,500
 3,500
 4,000
 6,100
 510,900
 ‐510,900
 0
 FBC/ST
 54,500
 3,200
 3,600
 5,500
 459,900
 ‐459,900
 12,600
 FBG/GE
 49,500
 2,900
 3,200
 5,000
 418,000
 ‐418,000
 54,500
 BIG/CCA
 43,000
 2,500
 2,800
 4,300
 363,100
 ‐363,100
 109,400
 BIG/CCP
 37,100
 2,200
 2,400
 3,700
 313,500
 ‐313,500
 159,000
 GFC
=
grate‐fired
combustion;
FBC
=
fluidized
bed
combustion;
FBG
=
fluidized
bed
gasification;

 BIG
=
biomass
integrated
gasification;
ST
=
steam
turbine;
GE
=
gas
engine;
CC
=
combined
cycle;

 (A)
=
atmospheric;
(P)
=
pressurized
  
  Tables
32
and
33
highlight
the
compounding
effects
of
technology
choice.
When
a
more
 efficient
means
of
converting
biomass
into
electricity
is
utilized
the
emissions
from
each
 stage
in
the
process
life
cycle
are
reduced,
as
less
biomass
is
required.
However,
the
net
 results
 reported
 in
 the
 two
 tables
 are
 very
 different
 as
 Table
 32
 takes
 an
 electricity
 supply
viewpoint,
while
Table
33
takes
a
biomass
utilization
one.
For
Table
32,
emissions
 are
calculated
solely
based
on
the
quantity
of
biomass
needed
produce
418,000
MWh
of
 electricity,
 while
 Table
 33
 assumes
 that
 any
 residues
 not
 utilized
 for
 electricity
 production
 represent
 lost
 potential.
 As
 a
 result
 these
 emissions
 are
 included
 in
 the
 emission
 calculation,
 which
 yet
 again
 highlights
 the
 impact
 that
 accounting
 methodology
has
on
bioenergy
emissions.
 
 Transportation
 emission
 values
 were
 subject
 to
 large
 changes
 depending
 on
 which
 literature
values
were
used.
For
example,
transportation
emissions
 increased
 by
117%
 when
 the
 data
 for
 single‐unit
 trucks
 was
 used
 and
 decreased
 by
 66%
 when
 the
 most
 fuel‐efficient
 Volvo
 data
 was
 used.
 However,
 since
 transportation
 emissions
 are
 quite
 small
 in
 an
 absolute
 sense,
 these
 changes
 only
 amounted
 to
 a
 10%
 increase
 and
 5%
 decrease
in
emissions
per
MWh,
respectively
(Figure
23).

 
  92
 
  Net
 68,500
 74,100
 79,300
 115,100
 162,100
 204,500
  
  
  Emissions
(kgCO2/MWh)
  
  
 Figure
23.
Sensitivity
analysis
results
for
transportation
emissions
 
 Finally,
 the
 base
 case
 carbon
 emissions
 from
 both
 the
 harvesting
 and
 chipping
 phases
 were
modest.
The
analysis
showed
that
even
when
emissions
from
either
process
were
 doubled,
it
only
increased
net
emissions
per
MWh
by
5%
(Table
30).
These
results
show
 that,
like
transportation,
the
fuel
efficiency
of
harvesting
and
chipping
equipment
does
 not
 have
 a
 significant
 impact
 on
 the
 overall
 study
 outcome.
 As
 a
 result,
 the
 most
 important
 values
 affecting
 the
 emission
 performance
 of
 bioenergy
 processes
 are
 the
 land
impacts
or
carbon
stock
changes
associated
with
the
biomass
fuel
and
the
method
 with
which
it
is
converted
to
energy.
 
  93
 
  
  
  
  4
Conclusion
 4.1
Summary
 The
 first
 portion
 of
 this
 thesis
 estimated
 the
 annual
 life
 cycle
 carbon
 emissions
 for
 a
 hypothetical
 bioenergy
 combustion
 process
 that
 generates
 electricity
 from
 northern
 British
Columbian
forest
residues.
This
was
accomplished
in
two
steps.
First,
using
stand
 data
 acquired
 from
 the
 B.C.
 Land
 and
 Resource
 Data
 Warehouse
 and
 an
 extrapolated
 annual
harvest
volume
of
3.42
million
m3,
the
quantity
of
residues
generated
annually
in
 the
study
area
was
calculated
to
be
258,200
ODt
using
FP
Innovation’s
BiOS
model.
In
 addition
the
annual
timber
harvest
volume
was
calculated
to
be
1,470,800
ODt
and
the
 total
 volume
 removed
 was
 determined
 to
 be
 1,729,000
 ODt.
 The
 distances
 from
 each
 operating
 area
 to
 Mackenzie
 were
 also
 calculated,
 using
 the
 program
 On‐Screen
 Takeoff.
 
 Secondly,
net
annual
carbon
dioxide
emissions
were
calculated
for
the
process
life
cycle,
 which
 involved
 identifying
each
emission
source
 along
the
bioenergy
supply
chain
and
 determining
 how
 it
 would
 be
 affected
 by
 this
 process.
 The
 CO2
 emission
 sources
 included
 soil
 carbon,
 harvesting
 machinery,
 chipping,
 transportation,
 and
 biomass
 combustion.
 Emissions
 that
 were
 avoided
 as
 a
 result
 of
 this
 process
 were
 also
 calculated.

 
 Soil
 carbon
 emissions
 were
 calculated
 by
 determining
 the
 area
 of
 forest
 disturbed
 by
 harvesting
and
allocated
based
on
mass.
For
example,
total
emissions
from
soil
carbon
 were
 calculated
 to
 be
 approximately
 360,000
 tCO2,
 of
 which
 56,000
 tCO2
 were
 attributed
 to
 the
 logging
 residues.
 Emissions
 from
 harvesting,
 chipping,
 and
 transportation
were
calculated
 based
on
fuel
usage
and
determined
to
be
 3,300
tCO2,
 3,700
tCO2
and
 5,600
tCO2
 respectively.
The
facility
combustion
emissions
were
by
far
 the
largest
emission
source,
calculated
to
be
472,500
tCO2
by
determining
the
quantity
 of
 carbon
 contained
 in
 the
 biomass.
 Avoided
 emissions
 from
 slash
 pile
 burning
 were
 94
 
  
  
  
  determined
using
the
same
method
(‐472,500
tCO2)
and
therefore
completely
offset
the
 facility
emissions.
By
adding
the
emissions
from
each
process,
the
net
annual
life
cycle
 emissions
were
calculated
to
be
68,500
tCO2.
 
 The
 second
 portion
 of
 this
 thesis
 involved
 comparing
 the
 emission
 values
 with
 the
 quantity
of
biomass
harvested
and
the
electricity
that
could
be
generated
from
it.
This
 was
done
in
order
to
assess
how
electricity
derived
from
performs
from
a
CO2
emission
 standpoint.
This
was
accomplished
in
three
steps.
First,
the
emissions
were
compared
to
 the
quantity
of
recoverable
forest
residues,
and
CO2
emissions
per
ODt
recovered
were
 calculated.
Second,
a
base
case
was
identified
and
the
quantity
of
electricity
that
would
 be
generated
was
calculated
to
be
418,800
MWh.
The
emissions
were
then
compared
to
 the
quantity
of
generated
electricity,
yielding
a
net
emissions
value
of
164
kgCO2/MWh.
 Lastly,
a
sensitivity
analysis
was
performed
on
several
values
used
in
the
study,
including
 soil
 carbon
 loss,
 harvesting,
 chipping
 and
 transportation
 fuel
 use,
 facility
 conversion
 efficiency,
residue
disposal
method,
and
emission
attribution
method.
  4.2
Conclusions
 There
 were
 many
 important
 outcomes
 from
 this
 thesis.
 First,
 the
 results
 highlight
 the
 magnitude
 of
 biomass
 combustion
 emissions
 and
 the
 role
 that
 the
 business‐as‐usual
 scenario
 plays
 in
 the
 determination
 of
 net
 emissions.
 For
 example,
 if
 the
 business‐as‐ usual
 scenario
 involved
 an
 alternative
 residue
 disposal
 method
 or
 use,
 the
 lifecycle
 carbon
emissions
would
be
greatly
affected.
The
sensitivity
analysis
showed
that
if
40%
 of
the
carbon
contained
in
the
residue
was
fixed
in
long
term
storage
(e.g.
in
products
or
 fixed
in
soil),
annual
emissions
would
increase
by
roughly
275%.
While
this
is
an
unlikely
 scenario
for
forest
residues
in
British
Columbia,
as
research
has
shown
that
most
forest
 residues
 are
 destined
 for
 combustion
 or
 rapid
 decomposition,
 these
 findings
 highlight
 the
 fact
 that
 biomass
 sources
 that
 are
 not
 completely
 converted
 to
 CO2
 in
 the
 short
 term
should
not
be
considered
carbon
neutral.
 
  95
 
  
  
  
  The
findings
on
soil
carbon
emissions
were
also
an
important
outcome
of
this
research.
 Annual
soil
carbon
emissions
were
calculated
to
be
approximately
360,000
tCO2
when
a
 20%
 decrease
 in
 forest
 floor
 (i.e.
 organic)
 soil
 carbon
 due
 to
 harvesting
 was
 assumed.
 Soil
 carbon
 emissions
 attributable
 to
 residues
 contributed
 to
 82%
 of
 the
 net
 annual
 emission
when
emissions
were
allocated
based
on
mass
between
timber
and
residues.
 Typically
 upstream
 emissions
 are
 not
 attributed
 to
 waste
 products
 such
 as
 residues.
 However,
 this
 thesis
 treated
 them
 as
 a
 co‐product
 to
 roundwood
 and,
 as
 a
 result,
 allocated
upstream
emissions
between
roundwood
and
residues
based
on
mass.
 
 The
 soil
 carbon
 sensitivity
 analysis
 emphasized
 the
 importance
 of
 acquiring
 accurate,
 regionally
 specific
 soil
 carbon
 data.
 Results
 from
 various
 soil
 carbon
 studies
 were
 included
 in
 the
 sensitivity
 analysis,
 ranging
 from
 findings
 of
 zero
 net
 loss
 due
 to
 harvesting
to
findings
of
a
20%
reduction
in
organic
carbon
with
a
13%
loss
of
mineral
 carbon.
 The
 emissions
 ranged
 from
 30
 kgCO2/MWh
 to
 391
 kgCO2/MWh
 when
 these
 conditions
 were
 assumed,
 a
 departure
 of
 ‐82%
 and
 139%
 from
 the
 base
 case,
 respectively.
This
upper
limit
highlights
the
magnitude
of
potential
emissions
from
soil
 when
 mineral
 carbon
 is
 depleted.
 However,
 this
 scenario
 is
 not
 likely
 to
 occur
 in
 the
 study
area
based
on
the
majority
of
findings
from
soil
carbon
literature.
The
important
 scenarios
 to
 consider
 are
 the
 more
 realistic,
 small
 reductions
 in
 soil
 carbon
 and
 their
 effect
 on
 net
 emissions.
 Under
 IPCC
 carbon
 accounting
 guidleines
 with
 respect
 to
 forestry,
 the
 default
 scenario
 assumes
 there
 is
 zero
 net
 soil
 carbon
 loss
 due
 to
 harvesting
and
this
assumption

is
supported
by
several
studies.
However,
even
a
10%
 decrease
 in
 soil
 organic
 carbon
 resulted
 in
 net
 soil
 carbon
 emissions
 of
 nearly
 28,000
 tCO2,
 which
 is
 more
 than
 double
 the
 emissions
 from
 harvesting,
 chipping
 and
 transportation
combined.

 
 The
 impacts
 of
 harvesting
 and
 chipping
 were
 relatively
 minor
 when
 compared
 to
 emissions
 from
 both
 soil
 carbon
 and
 biomass
 combustion.
 Interestingly
 however,
 the
 combined
 emissions
 of
 these
 two
 stages
 were
 greater
 than
 those
 from
 biomass
  96
 
  
  
  
  transportation.
 The
 sensitivity
 analysis
 showed
 that
 even
 when
 large
 changes
 in
 fuel
 efficiency
 were
 considered
 (i.e.
 +/‐100%),
 net
 emissions
 were
 only
 slightly
 impacted.
 The
 transportation
 of
 biomass
 was
 also
 a
 relatively
 minor
 source
 of
 emissions,
 contributing
 5,600
 tCO2
 to
 the
 net
 emissions
 of
 68,500
 tCO2.
 As
 with
 harvesting
 and
 chipping
 equipment,
 the
 sensitivity
 analysis
 showed
 that
 fuel
 efficiency
 was
 not
 a
 significant
 factor
 in
 the
 overall
 emission
 profile
 of
 the
 process.
 Improving
 the
 fuel
 efficiency
 of
 trucks
 or
 of
 harvesting
 and
 chipping
 equipment
 such
 as
 feller‐bunchers,
 skidders,
dangle‐head
processors,
and
grinders
may
have
a
significant
impact
in
terms
of
 reduced
fuel
cost,
but
the
contribution
to
net
process
emissions
is
minimal.
Therefore,
 improving
 equipment
 fuel
 efficiency
 should
 not
 constitute
 the
 focus
 of
 bioenergy
 emission
reduction
strategies.
 
 A
variety
of
thermal
conversion
processes
were
considered
to
 determine
the
effect
of
 the
 efficiency
 with
 which
 electricity
 was
 produced
 in
 the
 power
 plant.
 The
 sensitivity
 analysis
 showed
 that
 even
 small
 changes
 in
 thermal
 efficiency
 greatly
 improved
 the
 overall
emissions
(i.e.
per
MWh)
of
the
bioenergy
processes.
For
example,
the
emissions
 improved
 from
 177
 kgCO2/MWh
 to
 145
 kgCO2/MWh
 by
 increasing
 the
 conversion
 efficiency
 from
 27%
 (grate‐fired
 combustion
 scenario)
 to
 33%
 (gas‐engine
 gasification
 scenario).
When
the
two
highest
conversion
efficiencies
were
considered
(i.e.
38%
and
 44%),
the
CO2
emissions
per
MWh
were
improved
by
23%
and
34%
respectively.
These
 findings
suggest
that
updating
existing
combustion
facilities
to
more
modern
gasification
 type
facilities
may
be
an
appropriate
method
of
reducing
the
CO2
emissions
incurred
per
 MWh
generated
or
to
increase
output
without
incurring
significant
new
emissions.
 
 This
 study
 showed
 that
 there
 was
 no
 scenario
 in
 which
 this
 bioenergy
 process
 was
 carbon
 neutral.
 However,
 the
 net
 emissions
 were
 extremely
 low
 when
 residues
 were
 treated
as
a
waste
product
with
no
attribution
of
upstream
emissions
from
harvesting
 and
 soil
 carbon.
 In
 this
 scenario
 the
 net
 emissions
 were
 calculated
 to
 be
 22.2
 kgCO2/MWh.
Lastly,
in
the
best‐case
scenario
emissions
were
determined
to
be
only
8.9
  97
 
  
  
  
  kgCO2/MWh,
which
is
a
99%
reduction
in
CO2
when
compared
to
typical
values
for
coal
 power
generation.

 
 Perhaps
 the
 most
 important
 outcome
 of
 this
 thesis
 is
 the
 importance
 of
 utilizing
 complete
 accounting
 methodology
 when
 calculating
 emissions
 from
 energy
 processes
 and
 not
 making
 the
 blanket
 assumption
 that
 all
 biomass
 is
 carbon
 neutral.
 While
 the
 biomass
 source
 utilized
 in
 this
 thesis
 can
 be
 considered
 to
 be
 carbon
 neutral,
 the
 sensitivity
 analysis
 showed
 that
 emissions
 could
 increase
 by
 upwards
 of
 275%
 if
 the
 residues
 were
 disposed
 of
 differently
 (or
 a
 different
 biomass
 source
 was
 used).
 In
 addition,
 by
 not
 including
 soil
 carbon
 emissions
 or
 altering
 the
 percent
 of
 soil
 carbon
 lost
 the
 results
 were
 highly
 variable.
 As
 discussed
 in
 Section
 3.3.9,
 these
 results
 have
 serious
 implications
 for
 countries
 operating
 within
 a
 cap‐and‐trade
 system
 or
 in
 a
 carbon
 constrained
 economy.
 As
 a
 result,
 transitioning
 from
 coal
 power
 to
 bioenergy
 appears
 even
 more
 environmentally
 attractive
 than
 it
 actually
 is
 because
 combustion
 emissions
 from
 biomass
 energy
 are
 typically
 treated,
 as
 carbon
 neutral
 and
 upstream
 emissions
are
not
typically
attributed
to
residues.
In
addition,
bioenergy
expansion
is
an
 effective
 method
 for
 reducing
 greenhouse
 gas
 emissions
 that
 will
 only
 become
 more
 enticing
as
caps
shrink
or
carbon
taxes
increase,
two
processes
that
essentially
drive
up
 the
cost
the
of
emitting
carbon.

 
 In
a
British
Columbia
context,
utilizing
forest
residues
for
bioenergy
could
be
a
method
 of
increasing
provincial
energy
supply
without
incurring
significant
new
emissions.
This
 thesis
 showed
 that
 the
 emissions
 from
 harvesting,
 chipping
 and
 transporting
 residues
 would
only
be
a
fraction
of
the
emissions
already
experienced
from
burning
residues
in
 the
 forest.
 In
 addition,
 this
 thesis
 proposes
 that
 although
 utilizing
 forest
 residues
 for
 bioenergy
 will
 not
 result
 in
 any
 new
 emissions
 from
 soil
 carbon,
 a
 portion
 of
 the
 soil
 emissions
 already
 incurred
 should
 be
 allocated
 to
 forest
 residues
 if
 they
 are
 used
 for
 bioenergy,
as
they
are
essentially
a
co‐product
to
roundwood
in
this
case.
Using
this
full
 emission
 accounting
 methodology,
 a
 bioenergy
 combustion
 process
 utilizing
 forest
  98
 
  
  
  
  residues
 from
 the
 Mackenzie
 area
 of
 northern
 BC
 would
 produce
 approximately
 164
 kilograms
of
carbon
dioxide
per
megawatt‐hour
of
electricity.
  
4.3
Potential
Applications
and
Future
Research
 The
conditions
outlined
in
this
study
were
hypothetical
as
there
is
currently
no
facility
in
 Mackenzie
that
generates
electricity
from
forest
residues.
Therefore,
the
values
used
in
 this
 study
 were
 acquired
 from
 literature
 and
 were
 not
 based
 on
 measurements
 performed
in
the
study
area.
While
the
study
is
a
likely
depiction
of
the
life
cycle
carbon
 flows
that
would
be
experienced
if
this
process
was
to
be
initiated
in
Mackenzie
or
an
 area
 with
 similar
 conditions,
 it
 was
 also
 intended
 to
 highlight
 the
 methods
 used
 to
 calculate
life
cycle
emissions.

 
 Nonetheless,
 this
 study’s
 findings
 have
 several
 potential
 applications.
 This
 research
 primarily
outlined
a
method
for
estimating
carbon
emissions
from
bioenergy
processes,
 and
 could
 serve
 as
 a
 reference
 for
 potential
 emissions.
 The
 results
 of
 the
 study
 also
 serve
 to
 highlight
 the
 potential
 quantity
 of
 energy
 wasted
 and
 the
 greenhouse
 gases
 emitted
through
the
combustion
of
logging
slash
piles.

 
 Numerous
areas
for
future
research
stand
out
as
a
result
of
this
study.
First,
improved
 information
on
the
soil
carbon
losses
resulting
from
harvesting
and
 on
the
extent
and
 rates
 of
 decay
 of
 forest
 biomass
 is
 needed.
 Particular
 emphasis
 could
 be
 placed
 on
 looking
at
the
changes
between
different
soil
classifications.
This
research
is
necessary
 because
 soil
 carbon
 emissions
 were
 one
 of
 the
 most
 significant
 contributors
 to
 net
 process
 emissions,
 as
 this
 study
 found
 that
 net
 emissions
 were
 reduced
 by
 82%
 and
 increased
by
139%
when
the
range
of
 literature
 values
for
soil
carbon
emissions
were
 considered,
 Second,
 the
 results
 of
 this
 study
 should
 be
 expanded
 to
 include
 cost
 information.
 A
 cost‐benefit
 analysis
 comparing
 the
 effects
 of
 conversion
 efficiency,
 various
 biomass
 feedstocks
 (e.g.
 mill
 residues
 and
 post‐consumer
 waste)
 and
 net
 process
emissions
is
an
appealing
area
for
future
research.
This
type
of
analysis
would
  99
 
  
  
  
  be
helpful
in
determining
which
areas
could
be
focused
on
to
minimize
emissions
while
 maximizing
 profitability.
 There
 is
 also
 the
 potential
 to
 expand
 this
 study
 to
 include
 combined
 heat
 and
 power
 facilities
 that
 supply
 waste
 heat
 for
industrial
 processes
 or
 district
heating.
Lastly,
this
research
could
be
expanded
to
include
other
environmental
 impacts,
such
as
the
depletion
of
soil
nitrogen.
 
  100
 
  
  
  
  References
 
 BC
Hydro.
2008.
2008
Long‐term
Acquisition
Plan
Application.
Evidentiary
Update.
22
 December
2008.
(Accessed
12
February
2010)
 <http://www.bchydro.com/etc/medialib/internet/documents/info/pdf/ltap_200 8_‐_evidentiary.Par.0001.File.evidentiary_update_20081222.pdf>
 
 Bogle,
T.
2011.
Adopting
new
knowledge
and
models:
The
BC
Perspective.
Forest
 Analysis
Branch,
Province
of
British
Columbia.
Powerpoint
Presentation.
 (Accessed
7
March
2011)
<www.legacyforest.ca/pdfs/powerpoints/Bogle.pdf>
 
 Brandao,
M.,
i
Canals,
L.M.
and
R.
Clift.
2010.
Soil
organic
carbon
changes
in
the
 cultivation
of
energy
crops:
Implications
for
GHG
balances
and
soil
quality
for
use
 in
LCA.
Biomass
and
Bioenergy,
article
in
press,
pp.
1
–
14.
 
 Brown,
R.G.
2004.
Second
Law
of
Thermodynamics
Summary.
Physics
51
Lecture
Notes,
 Department
of
Physics,
Duke
University,
Durham,
North
Carolina.
(Accessed
23
 March
2011)
 <http://www.phy.duke.edu/~rgb/Class/phy51/phy51/node63.html>
 
 Campbell,
J.E.,
Lobell,
D.B.
and
C.B.
Field.
2009.
Greater
Transportation
Energy
and
GHG
 Offsets
from
Bioelectricity
than
Ethanol.
Science,
Vol.
324,
pp.
1055
–
1057.
 
 Carpentieri,
M.,
Corti,
A.
and
L.
Lombardi.
2004.
Life
cycle
assessment
of
an
integrated
 biomass
gasification
combined
cycle
with
CO2
removal.
Energy
Conversion
and
 Management,
Vol.
46,
pp.
1790
–
1808.
 
 DeMeo,
E.A.
and
J.F.
Galdo.
1997.
Renewable
Energy
Technology
Characterizations.
 Electric
Power
Research
Institute
and
U.S.
Department
of
Energy.
Topical
Report,
 TR‐109496,
December
1997.
 
 Dornburg,
V.
and
A.P.C.
Faaij.
2001.
Efficiency
and
economy
of
wood‐fired
biomass
 energy
systems
in
relation
to
scale
regarding
heat
and
power
generation
using
 combustion
and
gasification
technologies.
Biomass
and
Bioenergy,
Vol.
21,
pp.
 91
–
108.

 
 Energy
Information
Agency.
2009.
International
Energy
Outlook
2009.
Energy
 Information
Agency,
Office
of
Integrated
Analysis
and
Forecasting,
US
 Department
of
Energy,
Washington,
DC.
May
2009.
 
 
 
 101
 
  
  
  
  ENVINT
Consulting.
2008.
An
Information
Guide
on
Pursuing
Biomass
Energy
 Opportunities
and
Technologies
in
British
Columbia
for
First
Nations,
Small
 Communities,
Municipalities
and
Industry.
BC
Biomass
Energy
Primer,
ENVINT
 Consulting
for
BC
Ministry
of
Energy,
Mines
and
Petroleum
Resources
&
BC
 Ministry
of
Forests
and
Range.
7
February
2008.
 
 Fargione,
J.,
Hill,
J.,
Tilman,
D.,
Polasky,
S.
and
P.
Hawthorne.
2008.
Land
Clearing
and
 the
Biofuel
Carbon
Debt.
Science,
Vol.
319,
No.
5867,
pp.
1235
–
1238.
 
 Fredeen,
A.L.,
Bois,
C.H.,
Janzen,
D.T.
and
P.T.
Sanborn.
2005.
Comparison
of
coniferous
 forest
carbon
stocks
between
old‐growth
and
young
second‐growth
forests
on
 two
soil
types
in
central
British
Columbia,
Canada.
Ecosystem
Science
and
 Management
Program
and
Faculty
of
Natural
Resources
and
Environmental
 Studies,
University
of
Northern
British
Columbia
(UNBC),
Prince
George,
BC.
 
 GE
Energy.
2011.
J920
Factsheet.
J920,
Gas
Engines
–
Power
Generation,
GE
Energy.
 (Accessed
26
February
2011)
 <http://www.gepower.com/prod_serv/products/recip_engines/en/j920.htm>
 
 Glass,
S.V.
and
S.L.
Zelinka.
2010.
Moisture
Relations
and
Physical
Properties
of
Wood.
 Chapter
4.
General
Technical
Report
FPL‐GTR‐190.
U.S.
Department
of
 Agriculture,
Forest
Service,
Forest
Products
Laboratory.
Madison,
WI.
 
 Gnansounou,
E.,
Panichelli,
L.,
Dauriat,
A.
and
J.D.
Villegas.
2008.
Accounting
for
Indirect
 Land‐Use
Changes
in
GHG
Balances
of
Biofuels.
Ecole
Polytechnique
Federale
de
 Lausanne,
Lausanne,
Switzerland.
March
2008.
 
 Hank’s
Truck
Pictures.
2010.
Arrow
Transportation
Peterbilt
379
B‐Train
chip
hauler
 taken
east
of
Chilliwack.
Alberta
Road
Trip
–
2005,
Day
1.
Hank’s
Truck
Pictures.
 (Accessed
8
February
2011)
 <http://www.hankstruckpictures.com/road_trip_june2005a1.htm>
 
 Heller,
M.,
Keoleian,
G.,
Mann,
M.
and
T.
Volk.
2004.
Life
cycle
energy
and
 environmental
benefits
of
generating
electricity
from
willow
biomass.
Renewable
 Energy,
Vol.
29,
Issue
7,
pp.
1023
–
1042.
 
 Intergovernmental
Panel
on
Climate
Change.
2007.
Climate
Change
2007:
The
Physical
 Science
Basis.
IPCC
Fourth
Assessment
Report,
Cambridge
University
Press,
NY.
 <http://www.ipcc.ch/ipccreports/ar4‐wg1.htm>
 
 International
Organization
for
Standardization.
2006.
ISO
14040:
Environmental
 management
–
Life
cycle
assessment
–
Principles
and
framework.
International
 Organization
for
Standardization.
Geneva,
Switzerland.
 
 102
 
  
  
  
  Jaccard,
M.
2005.

Sustainable
Fossil
Fuels:
The
Unusual
Suspect
in
the
Quest
for
Clean
 and
Enduring
Energy.
Cambridge
University
Press,
New
York,
NY.
 
 Jandl,
R.,
Lindner
M.,
Vesterdal
L.,
Bauwens
B.,
Baritz
R.,
Hagedorn
F.,
Johnson
D.W.,
 Minkkinen
K.
and
K.A.
Byrne.

2006.
How
strongly
can
forest
management
 influence
soil
carbon
sequestration?
Geoderma,
Vol.
137,
pp.
253
–
268.
 
 Johnson,
D.W.
1992.
Effects
of
forest
management
on
soil
carbon
storage.
Water,
Air,
 and
Soil
Pollution.
Vol.
62,
Nos.
1
–
2,
pp.
83
–
121.
 

 Johnson,
D.W.
and
P.S.
Curtis.
2001.
Effects
of
forest
management
on
soil
C
and
N
 storage:
meta
analysis.
Forest
Ecology
and
Management
140.
pp.
227
–
238.

 
 Johnson,
E.
2009.
Goodbye
to
carbon
neutral:
Getting
biomass
footprints
right.
 Environmental
Impact
Assessment
Review,
Vol.
29,
No.
3,
pp.
165
–
168.
 
 Johnson,
L.R.,
Lippke,
B.,
Marshall,
J.D.
and
J.
Comnick.
2006.
Life‐cycle
impacts
of
forest
 resource
activities
in
the
Pacific
Northwest
and
Southeast
United
States.
Wood
 and
Fiber
Science,
Vol.
37,
Corrim
Special
Issue,
pp.
30
–
46.
 
 Kaltschmitt,
M.,
Reinhardt,
G.A.
and
T.
Stelzer.
1997.
Life
Cycle
Analysis
of
Biofuels
 Under
Different
Environmental
Aspects.
Biomass
and
Bioenergy,
Vol.
12,
No.
2,
 pp.
121
–
134.

 
 Lang,
A.
2008.
Energy
From
Wood
–
Policies,
Logistics,
and
Economics
of
Bioenergy
in
 Nordic
Countries.
2008
Gottstein
Fellowship
Report,
J.W.
Gottstein
Memorial
 Trust
Fund,
Clayton
South,
Australia.
 
 Lorenz,
K.,
Lal,
R.,
Preston,
C.M.
and
K.G.J.
Nierop.
2007.
Strengthening
the
soil
organic
 carbon
pool
by
increasing
contributions
from
recalcitrant
aliphatic
 bio(macro)molecules.
Geoderma,
Vol.
142,
pp.
1
–
10.
 
 MacDonald,
J.
2010[a].
Personal
Communication,
E‐mail
Correspondence.
8
September
 2010.

 
 MacDonald,
J.
2010[b].
Forest
feedstock
transportation
logistics
for
small‐to‐medium
 size
biomass
plants.
FP
Innovations.
Powerpoint
Presentation.
26
May
2010.
 
 MacDonald,
P.
2009.
Turning
bug
wood
to
bioenergy.
Logging
&
Sawmilling
Journal.
 February
2010.
(Accessed
12
January
2011)
 <http://www.forestnet.com/LSJissues/Feb_10/bug_wood.php>
 
 Mackenzie
Green
Energy
Centre.
2010.
Mackenzie
Green
Energy
Centre.
(Accessed
21
 February
2010)
<http://www.mackenziegreenenergy.ca/>
 103
 
  
  
  
  Malhi,
Y.,
Baldocchi,
D.D.
and
P.G.
Jarvis.
1999.
The
carbon
balance
of
tropical,
 temperate
and
boreal
forests.
Plant,
Cell
and
Environment,
Vol.
22,
pp.
715
–
 740.
 
 Mani,
S.
2007.
Bioenergy
Research
Initiatives
at
UGA.
Faculty
of
Engineering,
University
 of
Georgia.
Powerpoint
Presentation.
5
July
2007.
 
 Mann,
M.K.
and
P.L.
Spath.
2001.
A
life
cycle
assessment
of
biomass
cofiring
in
a
coal‐ fired
power
plant.
Clean
Production
Processes,
Vol.
3,
pp.
81
–
91.
 
 Martensson,
L.
2008.
Emissions
from
Volvo’s
trucks.
Volvo
Truck
Corporation.
21
May
 2008.
<www.volvo.com/NR/rdonlyres/8F7802B0‐1F27‐49AD‐9864‐ C84BCFFA5CCC/0/Emis_eng_20640_05008.pdf>
 
 McKendry,
P.
2002.
Energy
production
from
biomass
(part
3):
gasification
technologies.
 Bioresource
Technology,
Vol.
83,
pp.
55
–
63.
 
 Ministry
of
Energy,
Mines
and
Petroleum
Resources.
2009.
The
BC
Energy
Plan
–
A
 Vision
For
Clean
Energy
Leadership.
Ministry
of
Energy,
Mines
and
Petroleum
 Resources,
The
Province
of
British
Columbia.
9
April
2009.
(Accessed
23
March
 2010)
<http://www.energyplan.gov.bc.ca/default.htm>
 
 Ministry
of
Environment.
2010.
The
Soil
Map
of
British
Columbia.
Ministry
of
 Environment,
The
Province
of
British
Columbia.
 <http://www.env.gov.bc.ca/soils/landscape/figures/fig321.html>
 
 Ministry
of
Forests.
2000[a].
Information
Report
–
Mackenzie
Timber
Supply
Area.
 Timber
Supply
Review,
Ministry
of
Forests,
Province
of
British
Columbia.
April
 2000.
<http://www.for.gov.bc.ca/hts/tsa/tsa16/docs.htm>
 
 Ministry
of
Forests.
2000[b].
Information
Report
–
Dawson
Creek
Timber
Supply
Area.
 Timber
Supply
Review,
Ministry
of
Forests,
Province
of
British
Columbia.
 September
2000.
<http://www.for.gov.bc.ca/hts/tsa/tsa41/docs.htm>
 
 Ministry
of
Forests
and
Range.
2008.
Information
Report
–
Prince
George
Timber

Supply
 Area.
Timber
Supply
Review,
Ministry
of
Forests
and
Range,
Province
of
British
 Columbia.
November
2008.
<http://www.for.gov.bc.ca/hts/tsa/tsa24/docs.htm>
 
 Ministry
of
Forests,
Lands
and
Natural
Resource
Operations.
2010.
Tree
Farm
License
 (TFL),
Timber
Supply
Area
(TSA),
Forest
Region,
&
Forest
Districts
Location
Map.
 Province
of
British
Columbia.
 <http://www.for.gov.bc.ca/hth/timbertenures/provincial‐map.htm>
 
  104
 
  
  
  
  National
Renewable
Energy
Laboratory.
2010.
Life‐Cycle
Inventory
Database.

National
 Renewable
Energy
Laboratory.
(Accessed
12
April
2010).
 <http://www.nrel.gov/lci/database/default.asp>
 
 Nave,
L.E.,
Vance,
E.D.,
Swantson
C.W.
and
P.S.
Curtis.
2010.
Harvest
impacts
on
soil
 carbon
storage
in
temperate
forests.
Forest
Ecology
and
Management,
Vol.
25,
 pp.
857
–
866.
 
 Nilsson,
B.
2009.
Costs,
CO2‐emissions
and
energy
balance
for
applying
Nordic
methods
 of
forest
biomass
utilization
in
British
Columbia.
Department
of
Forest
Resource
 Management,
Swedish
University
of
Agricultural
Sciences,
Umea,
Sweden.
 
 Oak
Ridge
National
Laboratory.
2010.
Bioenergy
Conversion
Factors.
(Accessed
17
March
 2010).
<http://bioenergy.ornl.gov/papers/misc/energy_conv.html>
 
 Olsson,
B.A.,
Staaf,
H.,
Lundkvist,
H.,
Bengtsson,
J.
and
K.
Rosen.
1995.
Carbon
and
 nitrogen
in
coniferous
forest
soils
after
clear‐felling
and
harvests
of
different
 intensity.
Forest
Ecology
and
Management,
Vol.
82,
pp.
19
–
32.
 
 On
Center
Software,
Inc.
2011.
On
Screen
Takeoff.
On
Center
Software.
(Accessed
25
 January
2011)
<http://www.oncenter.com/products/ost/>
 
 Palosu,
T.,
Wihersaari,
M.
and
J.
Likski.
2001.
Net
Greenhouse
Gas
Emissions
Due
to
 Energy
Use
of
Forest
Residues
–
Impact
of
Soil
Carbon
Balance.
Woody
Biomass
 as
an
Energy
Source
–
Challenges
in
Europe.
EFI
Proceedings
No.
39.
European
 Forest
Institute,
Joensuu,
Finland.
 
 Pedersen,
L.
2003.
Allowable
Annual
Cuts
in
British
Columbia:
The
Agony
and
the
 Ecstasy.
Jubilee
Lecture,
Faculty
of
Forestry,
UBC,
Vancouver.
Powerpoint
 Presentation.
20
March
2003.
 
 Pennock,
D.J.
and
C.
van
Kessel.
1996.
Clear‐cut
forest
harvest
impacts
on
soil
quality
 indicators
in
the
mixedwood
forest
of
Saskatchewan,
Canada.
Department
of
Soil
 Science,
University
of
Saskatchewan,
Saskatoon,
Saskatchewan,
Canada.
 Geoderma,
Vol.
75,
pp.
13
–
32.
 
 Post,
W.F.,
Emanuel,
W.R.,
Zinke,
P.J.
and
A.G.
Stangenberger.
1982.
Soil
Carbon
Pools
 and
World
Life
Zones.
Nature,
Vol.
298,
pp.
156
–
169.
 
 Province
of
British
Columbia.
2010[a].
Slash
and
Wood‐Residue
Burning.
BC
Air
Quality.
 The
Province
of
British
Columbia.
(Accessed
7
September
2010).
 <http://www.env.gov.bc.ca/epd/bcairquality/topics/slash‐burning.html>
 
  105
 
  
  
  
  Province
of
British
Columbia.
2010[b].
VRI
‐
Forest
Vegetation
Composite
Polygons
and
 Rank
1.
Data
Distribution
System.
GeoBC.
Province
of
British
Columbia.
 (Accessed
18
March
2010)
 <https://apps.gov.bc.ca/pub/dwds/addProducts.do?orderId=566927&packaged ProductId=‐2#null>
 
 Province
of
British
Columbia.
2011[a].
History
of
Community
Forests.
Ministry
of
Forests,
 Lands
and
Natural
Resource
Operations.
The
Province
of
British
Columbia.
 (Accessed
24
March
2011)
<
http://www.for.gov.bc.ca/hth/timber‐ tenures/community/history.htm>
 
 Province
of
British
Columbia.
2011[b].
Woodlot
Licence.
Ministry
of
Forests,
Lands
and
 Natural
Resource
Operations.
The
Province
of
British
Columbia.
(Accessed
24
 March
2011)
<
http://www.for.gov.bc.ca/hth/timber‐ tenures/woodlots/index.htm
>
 
 Quaak,
P.,
Knoef
H.
and
H.
Stassen.
1999.
Energy
From
Biomass:
A
Review
of
Combustion
 and
Gasification
Technologies.
World
Bank
Technical
Paper
No.422.
The
World
 Bank,
Washington,
D.C.
March
1999.
 
 Rabl,
A.,
Benoist,
A.,
Dron,
D.,
Peuporiter,
B.,
Spadaro,
J.V.
and
A.
Zoughaib.
2007.
How
 to
Account
for
CO2
Emissions
from
Biomass
in
an
LCA.
International
Journal
of
 Life
Cycle
Assessment,
Vol.
12,
No.
5,
p.
281.
 
 Righelato
R.
and
D.V.
Spracklen.
2007.
Carbon
Mitigation
by
Biofuels
or
by
Saving
and
 Restoring
Forests?
Science,
Vol.
317,
No.
5840,
p.
902.
 
 Rule,
B.M.,
Worth,
Z.J
and
C.A.
Boyle.
2009.
Comparison
of
Life
Cycle
Carbon
Dioxide
 Emissions
and
Embodied
Energy
in
Four
Renewable
Electricity
Generation
 Technologies
in
New
Zealand.
Environmental
Science
&
Technology,
Vol.
43,
 Issue
16,
pp.
6406
–
6413.
 
 Scharlemann,
J.P.W.
and
W.F.
Laurance.
2008.
How
Green
Are
Biofuels?
Science,
Vol.
 319,
No.
5859,
pp.
43
–
44.
 
 Schlamadinger,
B.,
Spitzer,
J.,
Kohlmaier,
G.H.
and
M.
Ludeke.
1995.
Carbon
balance
of
 bioenergy
from
logging
residues.
Biomass
and
Bioenergy,
Vol.
8,
Issue
4,
pp.
221
 –
234.
 
 Schroeder,
R.,
Jackson,
B.
and
S.
Ashton.
2007.
Biomass
Transportation
and
Delivery.
 Sustainable
Forestry
for
Bioenergy
and
Bio‐based
Products:
Trainers
Curriculum
 Notebook.
Forest
Research
Partnership,
Inc.,
Athens,
GA.
pp.
145
–
148.
 
  106
 
  
  
  
  Searchinger,
T.,
Heimlich,
R.,
Houghton,
R.A.,
Dong,
F.,
Elobeid,
A.,
Fabiosa,
J.,
Tokgoz,
S.,
 Hayes,
D.
and
T.
Yu.
2008.
Use
of
US
Croplands
for
Biofuels
Increases
Greenhouse
 Gases
Through
Emissions
from
Land‐Use
Change.
Science,
Vol.
319,
No.
5867,
pp.
 1238
–
1240.
 
 Searchinger,
T.D.,
Hamburg,
S.P.,
Melillo,
J.,
Chameides,
W.,
Havlik,
P.,
Kammen,
D.M.,
 Likens,
G.E.,
Lubowski,
R.N.,
Obersteiner
M.,
Oppenheimer,
M.,
Robertson,
G.P.,
 Schlesinger
W.H.
and
G.D.
Tilman.
2009.
Fixing
a
Critical
Climate
Accounting
 Error.
Science,
Vol.
326,
No.
5952.
pp.
527
–
528.
 
 Sivasamy,
A.,
Zinoviev,
S.,
Foransiero,
P.,
Miertus,
S.,
Müller‐Langer,
F.,
Kaltschmitt,
M.,
 Vogel,
A.,
Thraen,
D.
and
F.
Kemausuor.
2008.
Bio‐Fuels:
Technology
Status
and
 Future
Trends,
Technology
Assessment
and
Decision
Support
Tools.
International
 Centre
for
Science
and
High
Technology,
United
Nations
Industrial
Development
 Organization
(ICS‐UNIDO),
Trieste,
Italy.
 
 Skog,
 K.E.
 2008.
 Sequestration
 of
 carbon
 in
 harvested
 wood
 products
 for
 the
 United
 States.
Forest
Products
Journal,
Vol.
58,
No.
6,
pp.
56
–
72.
 
 Sollins,
P.,
Homann,
P.
and
B.A.
Caldwell.
1996.
 Stabilization
and
destabilization
of
soil
 organic
matter:
mechanisms
and
controls.
Geoderma,
Vol.
74,
pp.
65
–
105.
 
 Spath,
P.L.,
Mann,
M.K.
and
D.R.
Kerr.
1999.
Life
Cycle
Assessment
of
Coal‐fired
Power
 Production.
National
Renewable
Energy
Laboratory,
Golden,
Colorado.
NREL/TP‐ 570‐25119.
June
1999.
 
 Spath,
P.
and
M.
Mann.
2000.
Life
Cycle
Assessment
of
a
Natural
Gas
Combined‐Cycle
 Power
Generation
System.
National
Renewable
Energy
Laboratory,
Golden,
 Colorado.
NREL/TP‐570‐27715.
September
2000.
 

 Spath,
P.
and
M.
Mann.
2004.
Biomass
Power
and
Conventional
Fossil
Systems
with
and
 without
CO2
sequestration
–
Comparing
Energy
Balance,
Greenhouse
Gas
 Emissions
and
Economics.
National
Renewable
Energy
Laboratory,
Golden,
 Colorado.
NREL/TP‐510‐52575.
January
2004.
 
 Steila,
D.
2008.
The
Soils
of
North
America.
Flora
of
North
America.
Volume
1,
Chapter
2.
 Flora
of
North
America
Association.
(Accessed
February
24
2011)
 <http://www.fna.org/Volume/V01/Chapter02>
 
 Stone,
J.N.,
MacKinnon,
A.,
Parminter,
J.
and
K.P.
Lertzman.
1998.
Coarse
woody
debris
 decomposition
documented
over
65
years
on
southern
Vancouver
Island.
 Canadian
Journal
of
Forest
Research,
Vol.
28,
No.
5,
pp.
788
–
793.
 
 
 107
 
  
  
  
  United
States
Environmental
Protection
Agency.
2005.
Metrics
for
Expressing
 Greenhouse
Gas
Emissions:
Carbon
Equivalents
and
Carbon
Dioxide
Equivalents.
 Emissions
Facts,
Office
of
Transportation
and
Air
Quality.
EPA420‐F‐05‐002,
 February
2005.
 
 U.S.
Department
of
Energy.
2010.The
Effect
of
Fuel
Moisture
Content
on
Wood
Heat
 Content.
Appendix
A
–
Converions.
Biomass
Energy
Data
Book.
U.S.
Department
 of
Energy.
(Accessed
28
January
2011)
 <http://cta.ornl.gov/bedb/appendix_a.shtml>
 
 Victor,
N.M.
and
D.G.
Victor.
2002.
Macro
Patterns
in
the
Use
of
Traditional
Biomass
 Fuels.
Working
Paper
#10,
Program
on
Energy
and
Sustainable
Development,
 Stanford
University,
Stanford,
CA.
 
 Vogl,
R.
and
C.
Ryder.
1969.
Effects
of
Slash
Burning
on
Conifer
Reproduction
in
 Montana’s
Missions
Range.
Northwest
Science,
Vol.
43,
No.
3,
pp.
137
–
145.
 
 Wiltsee,
G.
2000.
Lessons
learned
from
existing
biomass
power
plants.
National
 Renewable
Energy
Laboratory.
Golden,
Colorado.
NREL/SR‐570‐26946.
February
 2000.
 
 Zanchi,
G.,
Pena,
N.,
and
N.
Bird.
2010.
The
upfront
carbon
debt
of
bioenergy.
Joanneum
 Research.
Elisabethstrasse
5,
A‐8010,
Graz,
Austria.
May
2010.
 
  108
 
  
  
  
  Appendices
 Appendix
A:
Sample
Calculations
 Equation
1.
Harvest
Volume
 
 " AACtsa % A ( c $# A '& 
 
 tsa TSA1  TSA 3  Vh =  
  
  
  
  
  
  
  
 !  where,
Vh
is
the
harvest
volume
(m3),
Ac
is
the
contained
TSA
area
(ha),
AACtsa
is
the
TSA
 AAC

(m3)
and
Atsa
is
the
area
of
the
TSA
(ha)
 
 " 14.94 Mm 3 % " 3.05Mm 3 % " 1.86Mm 3 % Vh = 1.08Mha$ ' + 1.31Mha$ ' + 0.75Mha$ '
 # 7.5Mha & # 6.1Mha & # 2.3Mha &  Vh = 2.15Mm 3 + 0.66Mm 3 + 0.61Mm 3 
 !  Vh = 3.42Mm 3 
  !  Equation
2.
Mass
of
Recovered
Timber
  !  
 qt = Vh " # basic 
 
  
  
  
  
  
  
  
  
  
 !  where,
qt
is
the
mass
of
recovered
timber
(kg),
Vh
is
the
harvest
volume
(m3)
and
ρbasic
is
 the
average
oven
dry
timber
density
(kg/
m3)
 
 qt = 3.42Mm 3 " 430kg /m 3 
 
  
  
  
  qt = 1,471,000,000 kg 
 !  
  !  109
 
  
  
  
  Equation
3.
Net
Life
Cycle
Emissions
 
 E net = E s + E h + E c + E t + E f " E a 
 
  
  
  
  
  
  
 !  where,
Enet
are
the
net
 lifecycle
emissions,
 Es
are
the
soil
carbon
emissions,
Eh
are
the
 harvest
emissions,
Ec
are
the
chipping
emissions,
Et
are
the
transportation
emissions,
Ef
 are
the
facility
emissions
and
Ea
are
the
avoided
emissions,
all
measured
in
tCO2.
 
 E net = 56,000tCO2 + 3,300tCO2 + 3,700tCO2 + 5,600tCO2 + 472,000tCO2 " 472,000tCO2 
 E net = 68,500tCO2 
  !  Equation
4.
Soil
Carbon
Emissions
  !  
 E s = em + e f 
 
  
  
  
  
  
  
  
  
 !  
Where,
Es
is
net
soil
carbon
flux,
em
is
the
carbon
loss
from
the
mineral
soil
and
ef
is
the
 carbon
loss
from
the
forest
floor,
all
measured
in
tCO2
 
 E s = 0 + 56,000tCO2 
 
  
  
  
  
  
  
  E s = 56,000tCO2 
 !  Equation
5.
Area
Disturbed
  !  
 Acut =  Vh 
 "t  
  
  
  
  
  
  
 !  where,
Acut
is
the
area
disturbed
by
harvest
each
year
(ha),
Vh
is
the
harvest
volume
(m3)
 and
ρt
is
the
average
recoverable
timber
volume
(m3/ha)
 
  110
 
  
  Acut =  
  
  3.42Mm 3 
 298.25m 3 /ha  Acut = 11,500ha 
  !  Equation
6.
Forest
Floor
Emissions
  !  
  # k " & e f = (Acut qb )% f f ( 
 $ qt + qb '  
  
  
  
  
  
  
  
 where,
 ef
 is
 the
 carbon
 emitted
 from
 the
 forest
 floor
 (tC),
 Acut
 is
 area
 disturbed
 by
  !  harvest
(ha),
kf
is
the
emission
factor
from
the
forest
floor
(%),
ρf
is
the
carbon
density
of
 the
forest
floor
(tC/ha),
qb
is
the
quantity
of
biomass
removed
each
year
(tonnes)
and
qt
 is
the
quantity
of
timber
removed
each
year
(tonnes)
 
  # & 20% " 42.25tC /ha e f = (11,500ha " 258,200ODt)% (
 $1,471,000ODt + 258,000ODt ' " 8.45tC /ha % e f = (2,970,000,000ha • ODt)$ '
 #1,729,000ODt &  ! ! !  e f = 15,000tC 
or
 e f = 56,000tCO2 
 Equation
7.
Mineral
Soil
Emissions
 ! 
  #k " & em = (Acut qb )% m m ( 
 $ qt + qb '  
  
  
  
  
  
  
  
 where,
em
are
the
carbon
emissions
from
the
forest
floor
(tC),
Acut
is
area
disturbed
by
  !  harvest
(ha),
km
is
the
emission
factor
from
the
forest
floor
(%),
ρm
is
the
carbon
density
 of
the
forest
floor
(tC/ha),
qb
is
the
quantity
of
biomass
removed
each
year
(tonnes)
and
 qt
is
the
quantity
of
timber
removed
each
year
(tonnes)
 
 111
 
  
  
  
  # & 0% "110.75tC /ha em = (11,500ha " 258,200ODt)% (
 $1,471,000ODt + 258,000ODt ' em = O tCO2 

 !  Equation
8.
Harvesting
Emissions
  !  
 E h = ( " d # qb )(C f + Cs + Cd ) 
 
  
  
  
  
  
  
  
 !  where,
 Eh
 are
 the
 total
 carbon
 emissions
 from
 harvesting
 (tCO2),
 ρd
 is
 the
 average
 carbon
density
of
diesel
(tCO2/m3),
Cf
is
the
fuel
consumption
of
the
feller
(m3/ODt),
Cs
is
 the
fuel
consumption
of
the
skidder
(m3/ODt),
Cd
is
the
fuel
consumption
of
the
dangle‐ head
processor
(m3/ODt)
and
qb
is
the
quantity
of
biomass
processed
(ODt)
  !  
 E h = (2.7tCO2 /m 3 " 258,200ODt)(0.00076m 3 /ODt + 0.00335m 3 /ODt + 0.00062m 3 /ODt) 
 E h = (697,000tCO2 • ODt /m 3 )(0.0047m 3 /ODt) 
 
 E h = 3,300tCO2 
  !  Equation
9.
Chipping
Emissions
  !  
 E c = ( " d # qb )(C p + Cc ) 
  
 !  
  
  
  
  
  
  
  where,
Ec
are
the
total
emissions
from
chipping
(tCO2),
 ρd
is
the
average
carbon
content
 per
cubic
metre
of
diesel
(tCO2/
m3),
qb
is
the
quantity
of
biomass
processed
(ODt),
Cp
is
 the
fuel
consumption
from
piling
(m3/ODt)
and
Cs
is
the
fuel
consumption
of
the
grinder
 (m3/ODt)
 
 E c = (2.7tCO2 /m 3 " 258,200ODt)(0.0005m 3 /ODt + 0.0048m 3 /ODt) 
  E c = (697tCO2 • ODt /L)(5.3L /ODt) 
 !  ! !  E c = 3,700tCO2 
  
 112
 
  
  
  
  Equation
10.
Transportation
Emissions
 
 X  E t = etkm (me + m f ) • # (T# " dow ) 
 
  
  
  

  
  
  I  
  !  where
Et
is
the
total
transportation
emissions
(tCO2),
etkm
is
the
carbon
emissions
per
 tonne‐kilometer
traveled
by
combination
truck
(tCO2/tkm),
me
is
the
truck
weight
when
 empty
(t),
mf
is
the
truck
weight
when
full
(t),
T#
is
the
number
of
trips
per
operating
 area
and
dow
is
the
one‐way
transportation
distance
from
each
operating
area
(km).
 
 E t = (8 "10#5 tCO2 /tkm) " (12t + 36t) "  !  [(924 "13km) + (1,084 " 50km) + (2,710 "143km) + (3,238 " 39km) + (1,743 "102km) 
 (469 " 202km) + (2,283 " 82km) + (1,575 "116km) + (992 "101km) + (1,119 "145km)] 
  !  E t = (8 "10#5 tCO2 /tkm) " (48t) " (1,486,000km) 
  !  E t = (8 "10#5 tCO2 /tkm) " (70,000,000tkm) 
  !  E t = 5,600tCO2 
  !  Equation
11.
Facility
Emissions
  !  
  E f = qb " # b 
 
  
  
  
  
  
  
  
  
  
  !  where,
Ef
are
the
carbon
emissions
from
the
bioenergy
facility
(tCO2),
qb
is
the
quantity
 of
biomass
processed
(ODt)
and
ρb
is
the
average
carbon
content
of
biomass
(tCO2/ODt)
 
  E f = 258,200ODt " 0.5tC /ODt 
 E f = 129,100tC 
or
 472,500tCO2 
 ! !  
 
  ! 113
 
  
  
  
  Equation
12.
Avoided
Emissions
 
  E a = qb " # b 
 
  
  
  
  
  
  
  
  !  where,
Ea
are
the
avoided
emissions
from
utilizing
waste
biomass,
qb
is
the
quantity
of
 biomass
burned
and
ρb
is
the
average
carbon
content
of
biomass
 
  E a = "258,200ODt # 0.5tC /ODt 
 E a = "129,100tC 
or
 472,500tCO2 
 ! !  Equation
13.
Electrical
Output
 ! 
  Oe = qb " # E " $th 
  
  
  
  
  
  
  
  
  
  !  where,
 Oe
 is
 the
 electrical
 output
 (GJ),
 qb
 is
 the
 quantity
 of
 biomass
 (ODt), ρE
 is
 the
 energy
 density
 or
 wood
 heat
 content
 of
 biomass
 (GJ/ODt),
 and
 ηth
 is
 the
 electrical
 conversion
efficiency
of
the
facility
(%)
 
  Oe = 258,200ODt " 20GJ /ODt " 29.2% 
 Oe = 1,508,000GJ 
or
 418,900MWh 
 ! !  !  114
 
  
  
  
  Appendix
B:
BiOS
Species
Abbreviations
 Abbreviation
 FI
 Py
 Hw
 Sw
 Se
 Lw
 Pl
 Sb
 Bl
 Ac
 At
 Bw
 Pw
 Fd
 Ss
 Cw
 Bxx
 Bxx
 Hm
 Cy
 Ra
 Sp
  Name
 Interior
Douglas‐fir
 Ponderosa
pine
 Western
hemlock
 White
spruce
 Englemann
spruce
 Western
larch
 Lodgepole
pine
 Black
spruce
 Subalpine
fir
 Black
cottonwood
 Trembling
aspen
 White
birch
 Western
white
pine
 Coastal
Douglas‐fir
 Sitka
spruce
 Western
red
cedar
 Pacific
silver
fir
 Grand
fir
 Mountain
hemlock
 Yellow
cedar
 Red
alder
 Shore
pine
  Type
 Softwood
 Softwood
 Softwood
 Softwood
 Softwood
 Softwood
 Softwood
 Softwood
 Softwood
 Hardwood
 Hardwood
 Hardwood
 Softwood
 Softwood
 Softwood
 Softwood
 Softwood
 Softwood
 Softwood
 Softwood
 Hardwood
 Softwood
  
  115
 
  
  
  
  Appendix
C:
Polygon
Area,
Diameter
 At
 Breast
Height
(DBH),
Basal
Area,
 Live
 Stems
 Per
 Hectare
 And
 Dead
 Stems
 Per
 Hectare
 Of
 The
 100
 Representative
Polygons
 Polygon
Name
 CUT_V_06
 CUT_II_06
 CUT_III_17
 CUT_VII_02
 CUT_V_01
 CUT_X_01
 CUT_X_02
 CUT_VII_06
 CUT_II_07
 CUT_III_13
 CUT_VI_02
 CUT_V_08
 CUT_VIII_05
 CUT_I_03
 CUT_II_03
 CUT_IV_10
 CUT_IV_18
 CUT_VIII_07
 CUT_IV_02
 CUT_II_08
 CUT_VII_03
 CUT_IV_01
 CUT_III_09
 CUT_IV_04
 CUT_VII_10
 CUT_IV_13
 CUT_II_01
 CUT_VII_08
 CUT_V_07
 CUT_IX_05
 CUT_VIII_03
 CUT_X_08
 CUT_V_12
 CUT_VII_04
 CUT_IX_06
 CUT_V_02
 CUT_VIII_04
 CUT_III_15
 CUT_IV_14
 CUT_III_02
 CUT_VI_03
 CUT_VII_09
 CUT_X_05
 CUT_IV_05
 CUT_III_10
 CUT_VII_01
  Polygon
Area
 (ha)
 24.8
 22.2
 21.3
 20.8
 20.7
 21.3
 21.1
 21.1
 22.0
 21.5
 24.7
 23.8
 24.5
 22.5
 23.8
 20.3
 20.0
 20.0
 21.1
 21.1
 21.1
 21.4
 21.5
 23.2
 23.2
 24.1
 24.7
 24.8
 24.8
 24.9
 25.0
 24.6
 20.8
 20.8
 20.9
 20.9
 22.1
 21.0
 21.0
 21.0
 21.0
 21.0
 22.9
 22.9
 22.9
 23.2
  Average
DBH
 (cm)
 14.0
 14.3
 13.6
 14.4
 14.4
 13.9
 15.1
 16.8
 16.1
 17.2
 16.0
 17.1
 17.4
 17.3
 16.2
 16.2
 18.6
 18.5
 19.4
 17.9
 18.6
 17.8
 19.1
 19.2
 18.6
 19.0
 18.8
 20.0
 17.7
 19.0
 19.9
 22.1
 21.3
 21.0
 21.9
 20.4
 21.4
 21.7
 21.2
 22.4
 21.6
 21.1
 21.4
 21.7
 20.8
 22.3
  Basal
Area

 2 (m 
/
hectare)
 31.0
 26.8
 18.6
 18.0
 32.5
 27.3
 15.7
 33.0
 24.5
 26.1
 28.4
 34.1
 20.6
 35.3
 18.5
 30.3
 30.6
 36.1
 35.2
 20.1
 27.5
 15.1
 19.0
 25.8
 51.1
 38.6
 30.5
 25.0
 37.0
 38.6
 46.7
 45.2
 18.3
 29.9
 44.9
 44.5
 35.4
 18.8
 26.0
 29.7
 44.6
 30.8
 16.2
 26.7
 30.2
 21.3
  Live
Stems

 (stems
/
ha)
 1543
 1616
 1230
 1098
 1642
 1811
 851
 1491
 1195
 1095
 1413
 1294
 864
 1485
 896
 1432
 1122
 1163
 1193
 796
 931
 599
 635
 892
 1850
 1093
 1095
 774
 1485
 1068
 1485
 1181
 515
 850
 1197
 1319
 973
 501
 705
 741
 852
 881
 448
 670
 871
 535
  Dead
Stems

 (stems
/
ha)
 470
 50
 45
 5
 364
 0
 25
 3
 0
 25
 10
 194
 0
 15
 0
 30
 0
 173
 0
 5
 79
 8
 25
 2
 28
 271
 5
 25
 20
 299
 10
 0
 0
 10
 0
 40
 15
 10
 30
 15
 359
 0
 0
 50
 15
 10
  116
 
  
  
  Polygon
Name
 CUT_VIII_06
 CUT_I_02
 CUT_X_04
 CUT_III_08
 CUT_III_14
 CUT_V_05
 CUT_IX_03
 CUT_III_01
 CUT_IV_11
 CUT_VIII_01
 CUT_III_16
 CUT_IV_17
 CUT_IV_12
 CUT_VII_07
 CUT_I_04
 CUT_IV_19
 CUT_IX_01
 CUT_III_18
 CUT_IV_08
 CUT_IV_16
 CUT_V_09
 CUT_II_04
 CUT_II_05
 CUT_V_10
 CUT_V_04
 CUT_IV_15
 CUT_III_06
 CUT_III_19
 CUT_X_03
 CUT_X_06
 CUT_IV_07
 CUT_IV_09
 CUT_III_11
 CUT_VIII_08
 CUT_VII_05
 CUT_VI_04
 CUT_I_01
 CUT_III_07
 CUT_IX_02
 CUT_IV_03
 CUT_IX_04
 CUT_III_05
 CUT_III_12
 CUT_II_02
 CUT_X_07
 CUT_V_11
 CUT_IV_06
 CUT_VI_01
 CUT_III_04
 CUT_IV_20
  Polygon
Area
 (ha)
 23.2
 24.7
 24.7
 24.6
 24.4
 23.9
 23.8
 22.4
 22.4
 21.9
 21.9
 21.8
 21.3
 20.8
 20.6
 20.3
 20.6
 21.0
 21.0
 21.8
 21.8
 22.1
 22.3
 23.4
 23.4
 23.5
 24.8
 24.5
 24.4
 21.5
 24.6
 21.9
 21.6
 21.6
 21.5
 21.2
 20.8
 21.6
 21.6
 22.8
 23.5
 24.9
 24.9
 22.2
 21.4
 20.6
 21.7
 20.7
 20.8
 23.1
  Average
DBH
 (cm)
 21.6
 23.7
 24.5
 23.8
 24.1
 24.2
 24.5
 23.5
 23.5
 23.6
 24.6
 24.4
 23.5
 22.6
 24.3
 24.0
 25.5
 27.4
 26.4
 25.5
 27.0
 27.0
 26.1
 27.4
 27.0
 27.2
 25.4
 26.9
 26.0
 29.3
 29.2
 27.9
 29.6
 27.9
 28.6
 28.2
 28.6
 32.0
 31.6
 31.4
 32.4
 32.6
 33.1
 34.7
 33.2
 37.4
 37.2
 37.3
 39.3
 38.5
  
 Basal
Area

 2 (m 
/
hectare)
 36.3
 45.3
 39.8
 34.4
 54.8
 36.7
 41.2
 59.8
 45.8
 44.9
 44.9
 55.1
 28.9
 40.0
 45.6
 29.6
 8.7
 35.9
 34.1
 18.5
 30.0
 35.7
 40.0
 35.5
 35.3
 40.0
 50.4
 45.2
 27.5
 17.4
 16.7
 38.0
 54.9
 45.1
 44.8
 40.3
 53.2
 41.2
 54.7
 35.1
 34.9
 43.2
 44.0
 39.8
 45.0
 40.9
 40.0
 41.0
 45.8
 49.7
  Live
Stems

 (stems
/
ha)
 963
 1027
 847
 651
 1166
 715
 855
 1261
 1055
 985
 949
 1070
 629
 999
 973
 616
 132
 564
 604
 346
 451
 575
 697
 573
 559
 608
 948
 780
 478
 247
 247
 595
 797
 736
 697
 643
 825
 484
 424
 453
 397
 498
 493
 397
 410
 346
 194
 349
 348
 199
  Dead
Stems

 (stems
/
ha)
 25
 0
 0
 122
 36
 80
 20
 119
 5
 40
 0
 108
 40
 0
 9
 39
 38
 44
 20
 15
 75
 50
 50
 30
 57
 82
 44
 15
 40
 10
 2
 25
 0
 0
 0
 0
 5
 30
 274
 0
 25
 20
 20
 25
 110
 25
 174
 25
 30
 227
  117
 
  
  
  Polygon
Name
 CUT_III_03
 CUT_VIII_02
 CUT_V_03
 CUT_I_05
  
  Polygon
Area
 (ha)
 21.1
 24.8
 20.5
 23.7
  Average
DBH
 (cm)
 39.1
 40.1
 50.1
 44.2
  
 Basal
Area

 2 (m 
/
hectare)
 44.8
 53.7
 67.9
 30.0
  Live
Stems

 (stems
/
ha)
 373
 169
 295
 190
  Dead
Stems

 (stems
/
ha)
 0
 257
 50
 5
  
  118
 
  
  
  
  Polygon
Name
  Leading
Species

  Leading
Percent
  Leading
Density
  Leading
Height
  Second
Species

  Second
Percent
  Second
Density
  Second
Height
  Third
Species
  Third
Percent
  Third
Density
  Average
Height
  Fourth
Species
  Fourth
Percent
  Fourth
Density
  Average
Height
  Appendix
 D:
 Leading,
 Second,
 Third
 And
 Fourth
 Species
 Types,
 Species
 Percent
Of
Stand,
Stand
Densities
And
Heights
Of
The
100
Representative
 Polygons
  CUT_V_06
 CUT_II_06
 CUT_III_17
 CUT_VII_02
 CUT_V_01
 CUT_X_01
 CUT_X_02
 CUT_VII_06
 CUT_II_07
 CUT_III_13
 CUT_VI_02
 CUT_V_08
 CUT_VIII_05
 CUT_I_03
 CUT_II_03
 CUT_IV_10
 CUT_IV_18
 CUT_VIII_07
 CUT_IV_02
 CUT_II_08
 CUT_VII_03
 CUT_IV_01
 CUT_III_09
 CUT_IV_04
 CUT_VII_10
 CUT_IV_13
 CUT_II_01
 CUT_VII_08
 CUT_V_07
 CUT_IX_05
 CUT_VIII_03
 CUT_X_08
 CUT_V_12
 CUT_VII_04
 CUT_IX_06
 CUT_V_02
 CUT_VIII_04
 CUT_III_15
 CUT_IV_14
 CUT_III_02
  PL
 AT
 PL
 BL
 PL
 AT
 BL
 BL
 BL
 BL
 PL
 PLI
 SE
 SW
 BL
 BL
 SE
 PL
 BL
 BL
 PL
 BL
 BL
 BL
 PL
 PL
 BL
 BL
 PL
 PL
 BL
 PL
 BL
 BL
 BL
 PL
 PL
 SW
 BL
 SW
  98
 50
 100
 100
 40
 80
 100
 100
 90
 80
 100
 95
 60
 60
 90
 80
 70
 80
 100
 100
 100
 100
 100
 89
 98
 100
 50
 100
 90
 100
 65
 80
 90
 60
 80
 90
 80
 55
 70
 55
  1512
 808
 1230
 1098
 657
 1449
 851
 1491
 1076
 876
 1413
 1229
 518
 891
 806
 1146
 785
 930
 1193
 796
 931
 599
 635
 794
 1813
 1093
 548
 774
 1337
 1068
 965
 945
 464
 510
 958
 1187
 778
 276
 494
 408
  18.6
 16.6
 13.7
 12.3
 14.7
 15.6
 15.4
 17.3
 16.3
 12.3
 15.9
 18.7
 16.7
 19.3
 16.3
 17.8
 17.7
 20.4
 16.3
 15.8
 18.3
 16.8
 16.6
 14.3
 24.9
 22.4
 16.6
 18.2
 17.4
 21.4
 18.6
 24.2
 18.0
 17.2
 21.3
 20.3
 20.3
 23.5
 17.3
 24.1
  SB
 PL
 
 
 FI
 PL
 
 
 SW
 SW
 
 SW
 BL
 BL
 SE
 SW
 PL
 SW
 
 
 
 
 
 SE
 SW
 
 SW
 
 AT
 
 SW
 BL
 SW
 SE
 SE
 SW
 SW
 BL
 SW
 BL
  2
 40
 
 
 30
 10
 
 
 10
 20
 
 5
 30
 40
 10
 20
 30
 20
 
 
 
 
 
 11
 2
 
 30
 
 5
 
 25
 10
 10
 40
 20
 5
 15
 30
 30
 45
  31
 646
 
 
 493
 181
 
 
 120
 219
 
 65
 259
 594
 90
 286
 337
 233
 
 
 
 
 
 98
 37
 
 329
 
 74
 
 371
 118
 52
 340
 239
 66
 146
 150
 212
 333
  16.0
 18.5
 
 
 15.0
 14.6
 
 
 18.4
 17.3
 
 20.6
 16.8
 16.3
 21.3
 21.8
 16.3
 20.8
 
 
 
 
 
 13.4
 26.6
 
 21.0
 
 18.4
 
 20.7
 19.3
 21.1
 27.1
 24.2
 21.6
 23.5
 20.0
 21.3
 17.9
  
 SW
 
 
 BW
 SW
 
 
 
 
 
 
 PL
 
 
 
 
 
 
 
 
 
 
 
 
 
 PL
 
 SW
 
 PL
 AT
 
 
 
 AT
 BL
 PL
 
 
  
 10
 
 
 20
 10
 
 
 
 
 
 
 10
 
 
 
 
 
 
 
 
 
 
 
 
 
 20
 
 5
 
 10
 5
 
 
 
 5
 5
 10
 
 
  
 162
 
 
 328
 181
 
 
 
 
 
 
 86
 
 
 
 
 
 
 
 
 
 
 
 
 
 219
 
 74
 
 149
 59
 
 
 
 66
 49
 50
 
 
  
 17.6
 
 
 14.8
 15.1
 
 
 
 
 
 
 16.8
 
 
 
 
 
 
 
 
 
 
 
 
 
 18.8
 
 17.9
 
 19.7
 21.8
 
 
 
 20.9
 21.9
 21.8
 
 
  
 
 
 
 SE
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 EP
 
 
 
 
 
 AT
 
 
  
 
 
 
 10
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 5
 
 
 
 
 
 5
 
 
  
 
 
 
 164
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 59
 
 
 
 
 
 25
 
 
  
 
 
 
 14.8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 21.8
 
 
 
 
 
 21.8
 
 
  119
 
  Leading
Percent
  Leading
Density
  Leading
Height
  Second
Species

  Second
Percent
  Second
Density
  Second
Height
  Third
Species
  Third
Percent
  Third
Density
  Average
Height
  Fourth
Species
  Fourth
Percent
  Fourth
Density
  Average
Height
  
  Leading
Species

  
  Polygon
Name
  
  CUT_VI_03
 CUT_VII_09
 CUT_X_05
 CUT_IV_05
 CUT_III_10
 CUT_VII_01
 CUT_VIII_06
 CUT_I_02
 CUT_X_04
 CUT_III_08
 CUT_III_14
 CUT_V_05
 CUT_IX_03
 CUT_III_01
 CUT_IV_11
 CUT_VIII_01
 CUT_III_16
 CUT_IV_17
 CUT_IV_12
 CUT_VII_07
 CUT_I_04
 CUT_IV_19
 CUT_IX_01
 CUT_III_18
 CUT_IV_08
 CUT_IV_16
 CUT_V_09
 CUT_II_04
 CUT_II_05
 CUT_V_10
 CUT_V_04
 CUT_IV_15
 CUT_III_06
 CUT_III_19
 CUT_X_03
 CUT_X_06
 CUT_IV_07
 CUT_IV_09
 CUT_III_11
 CUT_VIII_08
 CUT_VII_05
 CUT_VI_04
 CUT_I_01
 CUT_III_07
 CUT_IX_02
  PL
 BL
 BL
 SE
 BL
 BL
 SW
 SW
 SW
 PL
 PL
 PL
 SW
 PL
 PL
 PL
 BL
 PL
 BL
 BL
 SW
 PL
 PL
 SW
 BL
 SW
 BL
 BL
 BL
 SW
 PL
 PL
 PL
 PL
 SW
 BL
 BL
 PL
 SE
 SW
 SE
 PL
 SW
 SW
 PL
  50
 80
 100
 90
 80
 95
 70
 85
 60
 75
 75
 90
 65
 70
 40
 85
 90
 95
 55
 90
 60
 90
 50
 60
 90
 80
 80
 80
 70
 60
 80
 90
 40
 60
 60
 80
 40
 90
 80
 90
 90
 80
 60
 60
 90
  426
 705
 448
 603
 697
 508
 674
 873
 508
 488
 875
 644
 556
 883
 422
 837
 854
 1017
 346
 899
 584
 554
 66
 338
 544
 277
 361
 460
 488
 344
 447
 547
 379
 468
 287
 198
 99
 536
 638
 662
 627
 514
 495
 290
 382
  23.9
 15.6
 14.6
 20.8
 19.3
 13.4
 23.3
 24.5
 24.3
 23.4
 24.1
 26.3
 28.3
 25.9
 23.4
 23.2
 21.3
 24.4
 19.7
 20.3
 26.5
 26.3
 26.4
 26.8
 22.3
 25.9
 21.4
 25.5
 27.5
 27.0
 26.3
 25.3
 26.2
 27.1
 26.7
 25.4
 26.6
 28.1
 31.2
 28.3
 30.2
 24.2
 28.7
 30.7
 29.2
  AT
 SE
 
 BL
 SW
 SW
 PL
 EP
 BL
 AT
 SW
 SW
 PL
 SW
 AT
 SW
 SE
 SW
 SW
 SE
 PL
 SW
 SW
 PL
 SW
 SB
 SE
 SE
 SW
 BL
 SW
 SE
 SW
 AT
 PL
 SW
 AT
 SW
 BL
 PL
 BL
 SB
 AT
 BL
 SW
  30
 20
 
 10
 20
 5
 15
 15
 40
 15
 25
 10
 25
 15
 30
 10
 10
 3
 35
 10
 40
 10
 30
 30
 10
 20
 20
 20
 30
 30
 20
 10
 30
 20
 30
 20
 30
 10
 20
 10
 10
 15
 20
 40
 10
  256
 176
 
 67
 174
 27
 144
 154
 339
 98
 292
 72
 214
 189
 317
 99
 95
 32
 220
 100
 389
 62
 40
 169
 60
 69
 90
 115
 209
 172
 112
 61
 284
 156
 143
 49
 74
 60
 159
 74
 70
 96
 165
 194
 42
  25.0
 17.4
 
 18.6
 22.4
 16.5
 21.6
 20.4
 21.3
 23.5
 25.3
 27.8
 23.6
 29.4
 24.5
 24.3
 25.2
 26.9
 25.8
 24.2
 22.3
 28.7
 28.8
 24.4
 24.3
 19.8
 23.2
 27.5
 28.6
 24.9
 28.6
 27.8
 25.6
 29.2
 24.3
 28.4
 23.5
 30.4
 27.2
 26.2
 28.2
 25.3
 26.0
 26.7
 29.7
  BW
 
 
 
 
 
 BL
 
 
 SE
 
 
 BL
 FD
 SW
 BL
 
 AT
 PL
 
 
 
 SB
 BL
 
 
 
 
 
 PL
 
 
 AT
 SW
 BL
 
 SB
 
 
 
 
 SE
 PL
 
 
  10
 
 
 
 
 
 10
 
 
 10
 
 
 10
 10
 20
 5
 
 1
 10
 
 
 
 20
 10
 
 
 
 
 
 10
 
 
 20
 15
 10
 
 20
 
 
 
 
 3
 20
 
 
  85
 
 
 
 
 
 96
 
 
 65
 
 
 86
 126
 211
 49
 
 11
 63
 
 
 
 26
 56
 
 
 
 
 
 57
 
 
 190
 117
 48
 
 49
 
 
 
 
 19
 165
 
 
  24.4
 
 
 
 
 
 22.4
 
 
 23.4
 
 
 25.9
 27.6
 23.9
 23.8
 
 25.6
 22.8
 
 
 
 27.6
 25.6
 
 
 
 
 
 25.9
 
 
 25.9
 28.2
 25.5
 
 25.1
 
 
 
 
 24.8
 27.4
 
 
  SE
 
 
 
 
 
 SB
 
 
 
 
 
 
 BL
 BL
 
 
 SB
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 BL
 BL
 
 
 BW
 
 
 
 
 BW
 
 
 
  10
 
 
 
 
 
 5
 
 
 
 
 
 
 5
 10
 
 
 1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 10
 5
 
 
 10
 
 
 
 
 2
 
 
 
  85
 
 
 
 
 
 48
 
 
 
 
 
 
 63
 106
 
 
 11
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 95
 39
 
 
 25
 
 
 
 
 13
 
 
 
  24.4
 
 
 
 
 
 22.4
 
 
 
 
 
 
 27.6
 23.9
 
 
 25.6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 25.9
 28.2
 
 
 25.1
 
 
 
 
 24.8
 
 
 
  120
 
  Leading
Percent
  Leading
Density
  Leading
Height
  Second
Species

  Second
Percent
  Second
Density
  Second
Height
  Third
Species
  Third
Percent
  Third
Density
  Average
Height
  Fourth
Species
  Fourth
Percent
  Fourth
Density
  Average
Height
  
  Leading
Species

  
  Polygon
Name
  
  CUT_IV_03
 CUT_IX_04
 CUT_III_05
 CUT_III_12
 CUT_II_02
 CUT_X_07
 CUT_V_11
 CUT_IV_06
 CUT_VI_01
 CUT_III_04
 CUT_IV_20
 CUT_III_03
 CUT_VIII_02
 CUT_V_03
 CUT_I_05
  BL
 SW
 SW
 SW
 SW
 PL
 SW
 PL
 SW
 SW
 PL
 SW
 PL
 FI
 SE
  80
 60
 45
 50
 70
 80
 60
 64
 65
 85
 80
 90
 80
 40
 90
  362
 238
 224
 247
 278
 328
 208
 124
 227
 296
 159
 336
 135
 118
 171
  20.3
 32.3
 33.5
 35.5
 30.4
 34.2
 36.3
 28.3
 31.3
 32.3
 30.2
 36.1
 33.3
 38.4
 32.1
  SW
 PL
 PL
 BL
 BL
 SW
 FI
 SW
 BL
 BL
 SW
 AC
 AT
 SW
 BL
  20
 20
 40
 30
 30
 20
 30
 22
 20
 10
 20
 10
 10
 40
 10
  91
 79
 199
 148
 119
 82
 104
 43
 70
 35
 40
 37
 17
 118
 19
  23.3
 30.1
 31.3
 30.4
 26.3
 36.5
 38.3
 31.8
 27.3
 28.3
 34.5
 37.1
 34.4
 37.3
 22.2
  
 BL
 BL
 PL
 
 
 PL
 BL
 PL
 PL
 
 
 SW
 PL
 
  
 20
 10
 20
 
 
 10
 14
 15
 5
 
 
 10
 10
 
  
 79
 50
 99
 
 
 35
 27
 52
 17
 
 
 17
 30
 
  
 31.2
 32.4
 32.9
 
 
 37.3
 30.0
 29.3
 30.3
 
 
 33.9
 37.9
 
  
 
 AT
 
 
 
 
 
 
 
 
 
 
 BL
 
  
 
 5
 
 
 
 
 
 
 
 
 
 
 10
 
  
 
 25
 
 
 
 
 
 
 
 
 
 
 30
 
  
 
 32.4
 
 
 
 
 
 
 
 
 
 
 37.9
 
  
  121
 
  
  
  
  Appendix
 E:
 BiOS
 Results
 Showing
 Harvest
 Volume
 And
 Volume
 Of
 Roundwood
Removed
Per
Hectare
 Polygon
 Name
 CUT_V_06
 CUT_II_06
 CUT_III_17
 CUT_VII_02
 CUT_V_01
 CUT_X_01
 CUT_X_02
 CUT_VII_06
 CUT_II_07
 CUT_III_13
 CUT_VI_02
 CUT_V_08
 CUT_VIII_05
 CUT_I_03
 CUT_II_03
 CUT_IV_10
 CUT_IV_18
 CUT_VIII_07
 CUT_IV_02
 CUT_II_08
 CUT_VII_03
 CUT_IV_01
 CUT_III_09
 CUT_IV_04
 CUT_VII_10
 CUT_IV_13
 CUT_II_01
 CUT_VII_08
 CUT_V_07
 CUT_IX_05
 CUT_VIII_03
 CUT_X_08
 CUT_V_12
 CUT_VII_04
 CUT_IX_06
 CUT_V_02
 CUT_VIII_04
 CUT_III_15
 CUT_IV_14
 CUT_III_02
 CUT_VI_03
 CUT_VII_09
 CUT_X_05
 CUT_IV_05
 CUT_III_10
 CUT_VII_01
  Harvest
Volume
 3 (m )
 19612.01
 19104.72
 11587.14
 7440.14
 18066.83
 16489.35
 8068.84
 20638.54
 14587.80
 13102.96
 22437.39
 27251.09
 13931.86
 24711.15
 11220.61
 20064.54
 22910.92
 30443.73
 22259.18
 11807.66
 23191.17
 9327.38
 11629.49
 14300.65
 59797.10
 33878.38
 23015.67
 17006.64
 31572.18
 31738.13
 36002.08
 51575.98
 13253.09
 24749.43
 38605.57
 43735.07
 35003.71
 17149.52
 18530.64
 25341.24
 37321.38
 19572.86
 9375.28
 20739.79
 23228.56
 11426.95
  Roundwood
 3 (m /ha)
 171.03
 166.61
 101.05
 64.88
 157.56
 143.80
 70.37
 179.99
 127.22
 114.27
 195.67
 237.65
 121.50
 215.50
 97.85
 174.98
 199.80
 265.49
 194.12
 102.97
 202.25
 81.34
 101.42
 124.71
 521.48
 295.45
 200.72
 148.31
 275.34
 276.78
 313.97
 449.79
 115.58
 215.84
 336.67
 381.41
 305.26
 149.56
 161.60
 221.00
 325.47
 170.69
 81.76
 180.87
 202.57
 99.65
  122
 
  
  
 Polygon
 Name
 CUT_VIII_06
 CUT_I_02
 CUT_X_04
 CUT_III_08
 CUT_III_14
 CUT_V_05
 CUT_IX_03
 CUT_III_01
 CUT_IV_11
 CUT_VIII_01
 CUT_III_16
 CUT_IV_17
 CUT_IV_12
 CUT_VII_07
 CUT_I_04
 CUT_IV_19
 CUT_IX_01
 CUT_III_18
 CUT_IV_08
 CUT_IV_16
 CUT_V_09
 CUT_II_04
 CUT_II_05
 CUT_V_10
 CUT_V_04
 CUT_IV_15
 CUT_III_06
 CUT_III_19
 CUT_X_03
 CUT_X_06
 CUT_IV_07
 CUT_IV_09
 CUT_III_11
 CUT_VIII_08
 CUT_VII_05
 CUT_VI_04
 CUT_I_01
 CUT_III_07
 CUT_IX_02
 CUT_IV_03
 CUT_IX_04
 CUT_III_05
 CUT_III_12
 CUT_II_02
 CUT_X_07
 CUT_V_11
 CUT_IV_06
 CUT_VI_01
 CUT_III_04
 CUT_IV_20
  Harvest
Volume
 3 (m )
 34140.02
 46787.84
 37870.19
 32903.89
 61187.60
 41929.62
 46847.30
 67827.09
 51878.39
 48085.37
 38503.49
 59968.69
 25033.19
 32477.52
 50007.77
 35364.59
 8293.55
 37818.96
 29816.87
 18182.27
 22483.95
 34001.96
 41839.51
 37298.05
 40439.70
 43203.98
 58086.57
 58584.56
 28551.29
 17562.58
 18916.77
 49154.52
 67794.51
 53685.08
 54659.99
 46814.91
 66099.30
 46556.91
 46024.47
 29993.54
 44496.74
 59762.99
 60204.90
 45134.23
 57344.59
 57200.15
 28137.71
 49200.35
 56521.02
 33396.71
  
 Roundwood
 3 (m /ha)
 297.73
 408.03
 330.26
 286.95
 533.61
 365.66
 408.55
 591.51
 452.42
 419.34
 335.78
 522.98
 218.31
 283.23
 436.11
 308.41
 72.33
 329.81
 260.03
 158.56
 196.08
 296.53
 364.88
 325.27
 352.67
 376.77
 506.56
 510.91
 248.99
 153.16
 164.97
 428.67
 591.22
 468.18
 476.68
 408.27
 576.44
 406.01
 401.37
 261.57
 388.05
 521.18
 525.04
 393.61
 500.09
 498.83
 245.38
 429.07
 492.91
 291.25
  123
 
  
  
 Polygon
 Name
 CUT_III_03
 CUT_VIII_02
 CUT_V_03
 CUT_I_05
  Harvest
Volume
 3 (m )
 67152.01
 33974.74
 86619.28
 37373.81
  
 Roundwood
 3 (m /ha)
 585.62
 296.29
 755.39
 325.93
  
 
  124
 
  
  
  
  Appendix
 F:
 Roundwood,
 Total
 Residues
 And
 Recovered
 Residues
 As
 A
 Percent
Of
Total
Polygon
Biomass
 Polygon
 Name
 CUT_V_06
 CUT_II_06
 CUT_III_17
 CUT_VII_02
 CUT_V_01
 CUT_X_01
 CUT_X_02
 CUT_VII_06
 CUT_II_07
 CUT_III_13
 CUT_VI_02
 CUT_V_08
 CUT_VIII_05
 CUT_I_03
 CUT_II_03
 CUT_IV_10
 CUT_IV_18
 CUT_VIII_07
 CUT_IV_02
 CUT_II_08
 CUT_VII_03
 CUT_IV_01
 CUT_III_09
 CUT_IV_04
 CUT_VII_10
 CUT_IV_13
 CUT_II_01
 CUT_VII_08
 CUT_V_07
 CUT_IX_05
 CUT_VIII_03
 CUT_X_08
 CUT_V_12
 CUT_VII_04
 CUT_IX_06
 CUT_V_02
 CUT_VIII_04
 CUT_III_15
 CUT_IV_14
 CUT_III_02
  Total
 
Recovered
 Biomass

 Logging

 Residue
 (ODt/ha)
 (ODt/ha)
 (ODt/ha)
 146.13
 68.41
 39.27
 134.61
 66.64
 34.23
 93.74
 40.42
 27.05
 70.06
 25.95
 22.49
 145.08
 63.02
 41.61
 118.93
 57.52
 30.97
 69.94
 28.15
 21.25
 161.03
 71.99
 45.09
 116.38
 50.89
 33.20
 98.65
 45.71
 26.76
 144.02
 78.27
 32.90
 162.61
 95.06
 33.56
 99.42
 48.60
 25.62
 171.76
 86.20
 43.06
 89.47
 39.14
 25.51
 154.98
 69.99
 43.02
 145.16
 79.92
 32.61
 173.11
 106.20
 33.05
 159.73
 77.65
 41.39
 88.80
 41.19
 24.07
 130.22
 80.90
 24.32
 70.05
 32.54
 18.96
 83.94
 40.57
 21.88
 103.37
 49.89
 26.98
 329.57
 208.59
 59.49
 185.60
 118.18
 33.12
 148.80
 80.29
 34.30
 119.94
 59.32
 30.53
 184.44
 110.13
 36.83
 174.48
 110.71
 31.34
 234.88
 125.59
 54.75
 271.99
 179.91
 44.94
 89.34
 46.23
 21.65
 156.03
 86.33
 34.82
 252.48
 134.67
 59.03
 233.45
 152.56
 39.56
 187.30
 122.10
 31.90
 99.70
 59.82
 19.75
 119.78
 64.64
 27.60
 151.05
 88.40
 31.12
  Unrecovered Residue
 (ODt/ha)
 38.45
 33.73
 26.27
 21.61
 40.45
 30.43
 20.53
 43.95
 32.29
 26.18
 32.86
 33.99
 25.20
 42.50
 24.82
 41.97
 32.64
 33.86
 40.69
 23.54
 25.00
 18.55
 21.49
 26.51
 61.49
 34.30
 34.21
 30.08
 37.48
 32.42
 54.54
 47.14
 21.46
 34.88
 58.78
 41.32
 33.29
 20.13
 27.54
 31.53
  Roundwood
 (%)
 47
 50
 43
 37
 43
 48
 40
 45
 44
 46
 54
 58
 49
 50
 44
 45
 55
 61
 49
 46
 62
 46
 48
 48
 63
 64
 54
 49
 60
 63
 53
 66
 52
 55
 53
 65
 65
 60
 54
 59
  Total
 Residues
 (%)
 53
 50
 57
 63
 57
 52
 60
 55
 56
 54
 46
 42
 51
 50
 56
 55
 45
 39
 51
 54
 38
 54
 52
 52
 37
 36
 46
 51
 40
 37
 47
 34
 48
 45
 47
 35
 35
 40
 46
 41
  
Recovered
 Residues
 (%)
 27
 25
 29
 32
 29
 26
 30
 28
 29
 27
 23
 21
 26
 25
 29
 28
 22
 19
 26
 27
 19
 27
 26
 26
 18
 18
 23
 25
 20
 18
 23
 17
 24
 22
 23
 17
 17
 20
 23
 21
  125
 
  
  Polygon
 Name
 CUT_VI_03
 CUT_VII_09
 CUT_X_05
 CUT_IV_05
 CUT_III_10
 CUT_VII_01
 CUT_VIII_06
 CUT_I_02
 CUT_X_04
 CUT_III_08
 CUT_III_14
 CUT_V_05
 CUT_IX_03
 CUT_III_01
 CUT_IV_11
 CUT_VIII_01
 CUT_III_16
 CUT_IV_17
 CUT_IV_12
 CUT_VII_07
 CUT_I_04
 CUT_IV_19
 CUT_IX_01
 CUT_III_18
 CUT_IV_08
 CUT_IV_16
 CUT_V_09
 CUT_II_04
 CUT_II_05
 CUT_V_10
 CUT_V_04
 CUT_IV_15
 CUT_III_06
 CUT_III_19
 CUT_X_03
 CUT_X_06
 CUT_IV_07
 CUT_IV_09
 CUT_III_11
 CUT_VIII_08
 CUT_VII_05
 CUT_VI_04
 CUT_I_01
  
 Total
 
Recovered
 Biomass

 Logging

 Residue
 (ODt/ha)
 (ODt/ha)
 (ODt/ha)
 196.80
 130.19
 32.51
 132.28
 68.28
 32.15
 64.99
 32.70
 16.24
 122.93
 72.35
 25.11
 154.10
 81.03
 36.65
 77.60
 39.86
 18.96
 192.36
 119.09
 36.15
 254.63
 163.21
 44.86
 218.55
 132.10
 42.78
 166.55
 114.78
 25.05
 310.52
 213.44
 47.00
 209.90
 146.26
 30.71
 248.33
 163.42
 41.48
 349.53
 236.60
 54.86
 271.33
 180.97
 44.03
 246.73
 167.74
 38.34
 248.43
 134.31
 57.12
 299.72
 209.19
 43.66
 148.62
 87.32
 30.44
 214.35
 113.29
 50.67
 258.73
 174.44
 40.98
 177.47
 123.36
 26.12
 42.46
 28.93
 6.56
 195.35
 131.93
 30.82
 189.69
 104.01
 42.83
 96.01
 63.43
 15.91
 139.97
 78.43
 30.70
 210.53
 118.61
 45.84
 253.91
 145.95
 53.73
 203.43
 130.11
 35.99
 199.11
 141.07
 27.88
 211.93
 150.71
 29.38
 299.71
 202.63
 47.17
 292.55
 204.36
 42.53
 149.19
 99.60
 24.16
 106.98
 61.26
 22.76
 107.22
 65.99
 20.36
 239.09
 171.47
 32.37
 356.84
 236.49
 58.71
 271.83
 187.27
 40.91
 284.11
 190.67
 45.47
 232.47
 163.31
 33.30
 329.43
 230.58
 47.65
  Unrecovered Residue
 (ODt/ha)
 34.10
 31.86
 16.04
 25.47
 36.42
 18.77
 37.12
 46.55
 43.66
 26.72
 50.08
 32.93
 43.43
 58.07
 46.33
 40.65
 57.00
 46.86
 30.86
 50.38
 43.31
 27.98
 6.96
 32.60
 42.84
 16.68
 30.83
 46.08
 54.23
 37.32
 30.16
 31.84
 49.91
 45.66
 25.43
 22.95
 20.88
 35.25
 61.63
 43.64
 47.97
 35.86
 51.21
  
  Roundwood
 (%)
 66
 52
 50
 59
 53
 51
 62
 64
 60
 69
 69
 70
 66
 68
 67
 68
 54
 70
 59
 53
 67
 70
 68
 68
 55
 66
 56
 56
 57
 64
 71
 71
 68
 70
 67
 57
 62
 72
 66
 69
 67
 70
 70
  Total
 Residues
 (%)
 34
 48
 50
 41
 47
 49
 38
 36
 40
 31
 31
 30
 34
 32
 33
 32
 46
 30
 41
 47
 33
 30
 32
 32
 45
 34
 44
 44
 43
 36
 29
 29
 32
 30
 33
 43
 38
 28
 34
 31
 33
 30
 30
  
Recovered
 Residue
 (%)
 17
 24
 25
 20
 24
 24
 19
 18
 20
 15
 15
 15
 17
 16
 16
 16
 23
 15
 20
 24
 16
 15
 15
 16
 23
 17
 22
 22
 21
 18
 14
 14
 16
 15
 16
 21
 19
 14
 16
 15
 16
 14
 14
  126
 
  
  
  Polygon
 Name
 CUT_III_07
 CUT_IX_02
 CUT_IV_03
 CUT_IX_04
 CUT_III_05
 CUT_III_12
 CUT_II_02
 CUT_X_07
 CUT_V_11
 CUT_IV_06
 CUT_VI_01
 CUT_III_04
 CUT_IV_20
 CUT_III_03
 CUT_VIII_02
 CUT_V_03
 CUT_I_05
 
  Total
 
Recovered
 Unrecovered Biomass

 Logging

 Residue
 Residue
 (ODt/ha)
 (ODt/ha)
 (ODt/ha)
 (ODt/ha)
 253.60
 162.41
 44.76
 46.43
 220.05
 160.55
 28.33
 31.17
 181.83
 104.63
 38.42
 38.78
 229.12
 155.22
 35.89
 38.01
 297.10
 208.47
 42.68
 45.94
 314.74
 210.01
 51.03
 53.70
 237.81
 157.44
 39.21
 41.15
 272.91
 200.04
 34.64
 38.23
 284.76
 199.53
 41.07
 44.16
 138.80
 98.15
 19.53
 21.11
 249.61
 171.63
 37.75
 40.23
 279.50
 197.16
 39.60
 42.74
 157.43
 116.50
 19.39
 21.54
 327.94
 234.25
 44.91
 48.79
 159.82
 118.52
 19.55
 21.75
 441.09
 302.16
 67.31
 71.62
 181.35
 130.37
 24.38
 26.59
 Averages
 190.74
 119.30
 35.19
 36.25
  
  Roundwood
 (%)
 64
 73
 58
 68
 70
 67
 66
 73
 70
 71
 69
 71
 74
 71
 74
 69
 72
  Total
 Residues
 (%)
 36
 27
 42
 32
 30
 33
 34
 27
 30
 29
 31
 29
 26
 29
 26
 31
 28
  
Recovered
 Residue
 (%)
 18
 13
 21
 16
 14
 16
 16
 13
 14
 14
 15
 14
 12
 14
 12
 15
 13
  60
  40
  20
  
 
  127
 
  

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