CARBON BUDGET OF FOREST PRODUCTS HARVESTED FROM
MOUNTAIN PINE BEETLE-ATTACKED FORESTS IN THE
PRINCE GEORGE REGION
by
Wyatt Stanley Klopp
B.Sc., University of British Columbia Okanagan, 2011

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
IN
NATURAL RESOURCES AND ENVIRONMENTAL STUDIES
(BIOLOGY)

UNIVERSITY OF NORTHERN BRITISH COLUMBIA
April 2016
© WyattKlopp,2016

TABLE OF CONTENTS
ABSTRACT .........................................................................................................ii
TABLE OF CONTENTS ..................................................................................... iii
LIST OF TABLES ..............................................................................................vii
LIST OF FIGURES ............................................................................................. ix
LIST OF ACRONYMS & ABBREVIATIONS ....................................................... x
GLOSSARY ........................................................................................................xi
ACKNOWLEDGEMENTS .................................................................................xx
CHAPTER ONE

INTRODUCTION & LITERATURE REVIEW

1.0. INTRODUCTION .......................................................................................... 1
1.1. LITERATURE REVIEW ................................................................................2
1.1.1. Contribution of Forests in Climate Change Mitigation Efforts ............... 2
1.1.2. The Mountain Pine Beetle (MPB) Outbreak in British Columbia .......... 3
1.1.3. Estimation of Forest Greenhouse Gas (GHG) Emissions .................... 7
1.1.3.1. GHG Emissions from MPS-Attacked Forests .............................. 8
1.1.3.2. Carbon Neutral Forestry ............................................................. 14
1.1.4. Estimation of Forest Product GHG Emissions .... ........ ........................ 15
1.1.4.1. National GHG Inventory ............................................................. 16
1 .1 .4.2. Carbon (C) Footprints & Life Cycle Assessment (LCA) ............. 16
1 .1 .4.2.1. Consequential LC As .......................................................... 17
1 .1 .4.2.2. Environmental LC As .......................................................... 18
1 .1 .4.3. Forest Product C Models ........................................................... 19
1.1.4.3.1. BC-HWPv.1 ....................................................................... 19
1 .1 .4.3.2. CBM-FHWP ....................................................................... 19
1.1.4.3.3. HWP-CASE ....................................................................... 20
1.1.4.4. Forest-Forest Product C Models ................................................ 20
1.1.4.4.1. GORCAM ........................................................................... 20
1.1.4.4.2. FICAT ................................ ..................... ..... .... .............. .... 21
1.1.4.4.3. Perez-Garcia et al. (2005) ................................................. 21
1.1.4.4.4. Lamers et al. (2014) ........................................................... 22
1.1.5. Conclusion .......................................................................................... 22
1.2. RESEARCH QUESTIONS .......................................................................... 25
CHAPTER TWO

DEVELOPMENT OF AC BUDGET MODEL FOR
FOREST PRODUCTS HARVESTED FROM MPBATTACKED FORESTS IN THE PRINCE GEORGE
REGION

2.0. INTRODUCTION ........................................................................................ 26
2.1. METHODS & METHODOLOGY ................................................................. 26

iii

2.1.1. Carbon Budget Model for Forest Products .......................................... 26
2.1.2. Experimental Design ........................................................................... 28
2.1.2.1. Experimental Unit & Treatment.. ................................................ 28
2.1.2.2. Case Studies .............................................................................. 30
2.1.2.3. Study Boundaries .................................................. ..................... 31
2.1.2.3.1. Physical ............................................................................. 31
2.1.2.3.2. Spatial ................................................................................ 33
2.1.2.3.3. Temporal. ............................................................ ............... 34
2.1.3. Data Collection ................................... ................................................. 35
2.1.3.1. Lumber (Scenario 1) .................................................................. 35
2.1.3.1.1. Material & Product Flow ..................................................... 35
2.1.3.1.1.1. Lumber Recovery ...................................................... 35
2.1.3.1.1.2. Mill Residue Recovery ............................................... 38
2.1.3.1 .2. Biogenic C Storage ........................................................... .41
2.1.3.1.2.1. Lumber ...................................................................... 41
2.1.3.1.2.2. Paper .............. ........................................................... 42
2.1.3.1.3. GHG Emission Profile ....................................................... .43
2.1.3.1.3.1. Harvesting ................................................................. 43
2.1.3.1.3.2. Transportation .......................................................... .45
2.1.3.1.3.3. Primary Processing Industry: Lumber Sawmill ......... .46
2.1.3.1.3.4. Secondary Processing Industries ............................. .48
2.1.3.1.3.4.1. Chemical Pulp Plant. ........................................ .48
2.1.3.1.3.4.1.1. Paper Plant ............................................... 50
2.1.3.1.3.4.2. Pellet Plant.. ...................................................... 51
2.1.3.1.3.4.3. Bioenergy: Chemical Pulp Plant ....................... 52
2.1.3.1.3.5. Distribution ................................................................ 53
2.1.3.1.3.5.1. Lumber .............................................................. 54
2.1.3.1.3.5.2. Pulp & Paper ..................................................... 55
2.1.3.1.3.5.3. Pellets ............................................................... 56
2.1.3.1.3.6. Use & Disposal. ......................................................... 57
2.1.3.1.3.6.1. Lumber & Paper ................................................ 57
2.1.3.1.3.6.2. Pellets ............................................................... 58
2.1.3.2. Pellets (Scenario 2) ............... ..................................................... 58
2.1.3.2.1. Material & Product Flow ..................................................... 58
2.1.3.2.1.1. Pellet Recovery ......................................................... 58
2.1.3.2.2. GHG Emission Profile ........................................................ 59
2.1.3.2.2.1. Harvesting ................................................................. 59
2.1.3.2.2.2. Transportation ........................................................... 60
2.1.3.2.2.3. Primary Processing Industry: Pellet Plant ................. 61
2.1.3.2.2.4. Distribution ................................................................ 62
2.1.3.2.2.5. Use & Disposal. ......................................................... 62
2.1.4. Calculations .............................. ... ........................................................ 62
2.1.4.1. Material & Product Flow ............................................................. 63
2.1.4.2. Biogenic C Storage .................................................................... 63

iv

2.1.4.3. GHG Emission Factors .............................................................. 64
2.1.5. Sensitivity Analysis .............................................................................. 68

CHAPTER THREE

C BUDGETS OF TWO DIFFERENT FOREST
PRODUCTS(LUMBER,PELLETS)HARVESTED
FROM MPS-ATTACKED FORESTS IN THE PRINCE
GEORGE REGION

3.0. RESULTS ...................................................................................................69
3.0.1. C Fluxes Associated with the Industrial Systems of Lumber and
Pellet Production ....................................................................... .... .. ...... 69
3.0.1.1. Lumber (Scenario 1) .................................................................. 69
3.0.1.1.1. Material & Product Flows ................................................... 69
3.0.1.1.2. Biogenic C Storage ............................................................ 70
3.0.1.1.3. GHG Emission Profile ........................................................ 72
3.0.1.1.3.1. Biogenic GHG Emissions .......................................... 73
3.0.1.1.3.2. Non-Biogenic GHG Emissions ..................... .. ... ........ 76
3.0.1.2. Bioenergy: Pellets (Scenario 2) .................................................. 78
3.0.1.2.1. GHG Emission Profile ........................................................ 78
3.0.1.2.1.1. Biogenic GHG Emissions .......................................... 80
3.0.1.2.1.2. Non-Biogenic GHG Emissions .................................. 80
3.0.2. C Budgets of Forest Products Harvested from MPS-Attacked
Forests ..................................................................................................81
3.0.2.1. Lumber ....................................................................................... 81
3.0.2.1.1. Sensitivity Analyses on Lumber's Case Studies ................ 84
3.0.2.2. Bioenergy: Pellets ..................... .... ............................................. 86
3.0.2.2.1. Sensitivity Analyses on Pellets' Case Studies ... ................ 87
3.1 DISCUSSION ...............................................................................................89
3.1.1. C Budget of Forest Products Harvested from MPS-Attacked Forests.89
3.1.1.1. Direct Influences of the Industrial System on Forest Product
C Budgets ........................................................................................ 91
3.1.1.1.1. Material & Product Flow ..................................................... 92
3.1 .1.1.2. Biogenic C Storage .................. .. ......... ............................... 93
3.1.1.1.3. GHG Emission Profile ................................ ........................ 94
3.1.1.1.3.1. Non-Biogenic GHG Emissions .................................. 94
3.1.1.1.3.2. Biogenic GHG Emissions .......................................... 95
3.1.1.2. Indirect Influences of the Biological System on Forest Product
C Budgets ...................... .................................................................. 96
3.1.2. Providing Context for Environmental Science and Policy .................... 99
3.1.2.1. National GHG Inventories .......................................................... 99
3.1.2.2. Carbon Footprint and LCA Studies ................... ....... ................ 100
3.1 .2.3. Forest-Forest Product C Models .............................................. 101
3.1.3. Importance of Forest Products in Forestry's Efforts to Mitigate
Climate Change ....................................... ........................................... 102

V

3.1.3.1. Indirect Influences of the Industrial System on Forest Product C
Budgets .............. .... ..... .. .............. ..... .. .... .... ... .... .. ............. .. ........... 103
3.1 .4. Role of Forests in Mitigating Climate Change .. .. .... ... ................... .... . 105
CHAPTER FOUR

CONCLUSIONS AND RECOMMENDATIONS FOR
FUTURE RESEARCH
4.0. CONCLUSIONS ....................................................................................... 107
4.1. RECOMMENDATIONS FOR FUTURE RESEARCH ............................... 108
4.1.1 . Direct Influences of the Industrial System on Forest Product C
Budgets ....... ........... ................................................... .. ........ ................. 108
4.1 .2. Indirect Influences of the Industrial System on Forest Product C
Budgets ........ ... ........................................ .. ... ... ... .. .................. ... ........... 109
4.1 .3. Indirect Influences of the Biological System on Forest Product C
Budgets ................................................ ............................................. ... 110
4.1 .4. Role of Forest Products in Forestry .............................................. .... ... 111
REFERENCES CITED ..................................................................................... 112
Appendix I. GHG emission factors for non-biogenic materials and products. 130
Appendix II. GHG emission factors for biogenic material and products ......... 130
Appendix Ill. Conversion factors used in this study ........................................ 130

vi

LIST OF TABLES
Table 2.1. Average lumber recovery values in sawmills ................................... 38
Table 2.2. Lumber and mill residue recovery values ......................................... 40
Table 2.3. Parameters governing biogenic C storage of lumber in use ............. 42
Table 2.4. Parameters governing biogenic C storage of lumber in disposal ..... 42
Table 2.5. Parameters governing biogenic C storage of paper in use . ............. 43
Table 2.6. Parameters governing biogenic C storage of lumber in disposal ..... 43
Table 2.7. GHG emission factors in harvesting forests . .................................... 45
Table 2.8. GHG emission factors in transporting roundwood ............................ 46
Table 2.9. GHG emission factors of a large sawmill ......................................... 48
Table 2.10. GHG emission factors of a chemical pulp plant ............................. 50
Table 2.11. GHG emission factors of a paper plant .......................................... 51
Table 2.12. GHG emission factors of a pellet plant ........................................... 52
Table 2.13. GHG emission factors in distributing lumber .................................. 54
Table 2.14. GHG emission factors in distributing pulp and paper ..................... 56
Table 2.15. GHG emission factors in distributing pellets ................................... 56
Table 2.16. Parameters governing GHG emissions of lumber and paper products
while in disposal .................................................................................................58
Table 2.17. GHG emission factors for pellets in use and disposal. ................... 58
Table 2.18. Pellet recovery for pellet plants sourcing roundwood ..................... 59
Table 2.19. GHG emission factors in harvesting forests ................................... 60
Table 2.20. GHG emission factors for transporting roundwood ........................ 61
Table 2.21. GHG emission factor for processing roundwood at a pellet plant .. 62
Table 2.22. List of the equations used in calculating the C budgets of forest
products harvested from MPS-attacked forests ................................................. 65
Table 2.23. Individual components in lumber's C budget model based on a
reference unit of oven-dry tonne (ODt) of wood fibre ......................................... 67

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Table 2.24. Individual components in pellets' C budget model based on a
reference unit of oven-dry tonne (ODt) of wood fibre ......................................... 68
Table 3.1. The net C balance of lumber production a.) assuming biogenic GHG
emissions are C neutral and b.) assuming biogenic GHG emissions are not C
neutral ................................................................................................................83
Table 3.2. The net C balance of pellet production a.) assuming biogenic GHG
emissions are C neutral and b.) assuming biogenic GHG emissions are not C
neutral ................................................................................................................86

viii

LIST OF FIGURES
Figure 2.1. The material and product flows of MPS-attacked lodgepole pine
(Pinus contorta var. latifolia) roundwood harvested for lumber (a; scenario 1) and
for pellet (b; scenario 2) production ................................................................... 33
Figure 3.1. Material and product flow of 1 m3 of dead merchantable lodgepole
pine (Pinus contorta var. latifolia) roundwood in an industrial system defined by
lumber production (scenario 1) ..........................................................................70
Figure 3.2. Change in biogenic C storage while in use (i.e. product C storage)
and in disposal (i.e. landfill C storage) over time ............................................... 71
Figure 3.3. The distribution of biogenic C storage in an industrial system defined
by lumber production (scenario 1) ..................................................................... 72
Figure 3.4. GHG emission profile of lumber production (scenario 1) based on 1
m3 of dead merchantable lodgepole pine ( P. contorta) roundwood ................... 73
Figure 3.5. Alternative ways of interpreting the biogenic GHG emissions in
lumber production (scenario 1) ..........................................................................75
Figure 3.6. Alternative ways of interpreting the non-biogenic GHG emissions in
lumber production (scenario 1) .......................................................................... 77
Figure 3.7. GHG emission profile in pellet production (scenario 2) based on 1 m3
dead merchantable lodgepole pine (P. contorta) roundwood. The biogenic GHG
emission factors for wood waste and pellet combustion varied in the literature 79
Figure 3.8. Breakdown of the biogenic GHG emissions in pellet production
(scenario 2) ........................................................................................................80
Figure 3.9. Breakdown of the non-biogenic GHG emissions in pellet production
(scenario 2) ........................................................................................................81
Figure 3.10. Results from sensitivity analyses run on the parameters of my C
budget model for lumber production. Individual parameters were increased by
10% and the percentage change reflects the percent change in the C budget's
output .................................................................................................................85
Figure 3.11. Results from sensitivity analyses run on the parameters of my C
budget model for pellet production. Individual parameters were increased by 10%
and the percentage change reflects the percent change in the C model's output
...........................................................................................................................88

ix

LIST OF ACRONYMS & ABBREVIATIONS
AAC

Allowable Annual Cut

AD

Air-Dry

AFOLU

Agriculture, Forests and Other Land Uses

BSKP

Bleached Softwood Kraft Pulp

C

Carbon

C02e

Carbon Dioxide Equivalent

CBM-CFS3

Canadian Budget Model of the Canadian Forest Service

GHG

Greenhouse Gas Emission

HWP

Harvested Wood Products

IPCC

Intergovernmental Panel on Climate Change

LCA

Life Cycle Assessment

MBFM

Thousand Board-Feet

MC

Moisture Content

MPB

Mountain Pine Beetle

OD

Oven-Dry

SFM

Sustainable Forest Management

THLB

Timber Harvesting Land Base

TSA

Timber Supply Area

UNFCCC

United Nations Framework on Climate Change

X

GLOSSARY
Attributional approach:
"Attributional studies attempt to calculate the impacts of the system (i.e., its
attributes) looking at the system as it actually exists, normally without regard to
other systems or alternative courses of action" (Miner and Gaudreault 2013).

Attributional carbon footprint:
"(An attributional) carbon footprint study is simply a life cycle assessment
(LCA) study which is limited to carbon and greenhouse gases[ ... ] Attributional
studies are those intended to characterize the system as it actually exists, without
consideration of how it may affect other systems" (Miner and Gaudreault 2013).

Biogenic GHG emissions:
"Biogenic CO2 emissions are defined as CO2 emissions related to the
natural carbon cycle, as well as those resulting from the combustion, harvest,
combustion, digestion, fermentation, decomposition, or processing of biologically
based materials" (US EPA 2014).

Carbon accounting:
a)

"Carbon accounting comprises the recognition, the non-monetary and
monetary evaluation and the monitoring of greenhouse gas emissions on all
levels of the value chain and the recognition, evaluation and monitoring of
the effects of these emissions on the carbon cycle of ecosystems"
(Stechemesser and Guenther 2012).

xi

b)

"The rules for comparing emissions and removals, as reported, with
commitments assumed by Annex I Parties under the Kyoto Protocol. Thus
accounting means calculating 'debits' and 'credits' with reference to the
agreed target" (Cowie et al. 2006).

Carbon accounting approach:
"The conceptual framework for estimating emissions and removals of
greenhouse gases in inventories. Approach refers to the system boundary,
defining which emissions and removals are to be reported or accounted by each
Party" (Cowie et al. 2006).

Carbon accounting method:
"The calculation framework within an approach for estimating emissions and
removals [... ] of greenhouse gases in inventories [ ... ] In practice, (carbon
accounting) method refers to the measurement and estimation of GHG emissions
[and removals]. That is, the approach defines WHAT is being estimated and
reported in an inventory (determined from the system boundary) while the
method describes HOW the reported values are derived, that is, the techniques
used in estimation. An approach can make use of any method, and within each
approach, there may be more than one method" (Cowie et al. 2006).

Carbon neutral (-ity):
a)

Inherent carbon neutrality - "Biomass was only recently removed from
the atmosphere; returning it to the atmosphere merely closes the cycle"
(Malmsheimer et al. 2011 ).

xii

b)

Carbon-cycle neutrality- "If uptake of carbon (in CO2) by plants over a
given area and time is equal to emissions of biogenic carbon attributable to
that area, biomass removed from that area is carbon-cycle neutral"
(Malmsheimer et al. 2011 ).

c)

Accounting neutrality - "If emissions of biogenic CO2 are assigned an
emissions factor of zero because net emissions of biogenic carbon are
determined by calculating changes in stocks of stored carbon, that biogenic
CO2 is accounting neutral" (Malmsheimer et al. 2011 ).

Carbon sink:
"Any process or mechanism which removes a greenhouse gas (GHG), an
aerosol, or a precursor of a GHG from the atmosphere. A given pool (reservoir)
can be a sink for atmospheric carbon if, during a given period, more carbon is
moving into it than is flowing out" (Cowie et al. 2006).

Carbon source:
"Any process, activity or mechanism that releases a greenhouse gas
(GHG), an aerosol or a precursor to a GHG into the atmosphere" (Cowie et al.
2006).

Climate change mitigation:
a)

"The Intergovernmental Panel on Climate Change (IPCC) defines mitigation
as the implementation of policies to reduce GHG emissions and increase
sinks" (Lempriere et al. 2013).

xiii

b)

" ... the amount of reduction in GHG emissions or increase in removals that
can be achieved by a mitigation activity relative to a baseline or reference
case in a given time period at a given cost per tonne" (Lempriere et al.
2013).

Consequential approach:
"Consequential studies attempt to calculate the impacts resulting as a
consequence of using the system (often compared to pursuing a "business as
usual" course of action)" (Miner and Gaudreault 2013).

Displacement factors:
See Substitution Factor.

Greenhouse gas (GHG) emission:
"Greenhouse gases are those gaseous constituents of the atmosphere,
both natural and anthropogenic, which absorb and emit radiation at specific
wavelengths within the spectrum of thermal infrared radiation emitted by the
Earth's surface, by the atmosphere itself, and by clouds. This property causes
the greenhouse effect. Water vapour (H20), carbon dioxide (CO2), nitrous oxide
(N20), methane (CH4), and ozone (03) are the primary greenhouse gases in the
Earth's atmosphere" (Stocker et al. 2013).

Harvested wood products:
"Wood products are defined as all wood-based material transported from
the forest at harvest" (Cowie et al. 2006).

xiv

Life cycle assessment (LCA):
a)

Life cycle assessment is a " ... compilation and evaluation of the inputs,
outputs and the potential environmental impacts of a product system
throughout its life cycle" (ISO 2006).

b)

"Life cycle assessments (LCA) reduce the many LCI measures into risk
indexes affecting human or ecosystem health with the objective of making
comparisons between alternatives that reveal opportunities for
improvement" (Lippke et al. 2011 ).

Life Cycle Inventory (LCI):
"Life cycle inventories (LCI) measure every input (energy, materials etc.)
and every output(emissions, waste, product and co-products) for every stage of
processing from extraction or regeneration through processing, ultimate use,
maintenance and disposal" (Lippke et al. 2011 ).

Lumber Recovery Factor (LRF):
Lumber recovery factor (LRF) is a metric used to indicate a mill's efficiency
at producing lumber. In British Columbia, lumber recovery is defined as the
amount of lumber recovered in thousand board feet from a specific amount of
roundwood in cubic meters (MFLNRO 2015c).

xv

Merchantable timber:
"A tree or stand that has attained sufficient size, quality and (or) volume to
make it suitable for harvesting. Timber that (a) was older than 75 years on
January 1, 1975, and (b) is on an area of Crown land in sufficient quantities (as
determined by the regional manager) to be commercially valuable when the
timber cruise is submitted" (MFR 2008).

Merchantable volume:
"The amount of sound wood in a single tree or stand that is suitable for
marketing under given economic conditions" (MFR 2008).

Moisture content on an oven-dry basis (MCQQ}_
MCoo = 100 x weight of water I oven-dry weight (Briggs 1994)

Moisture content on a wet or original basis (MCwl
MCw = 100 x weight of water I original weight (Briggs 1994)

National greenhouse gas inventory:
" ... accounting conventions for preparing greenhouse gas inventories
submitted by nations under the United Nations Framework Convention on
Climate Change (UNFCCC). The guidelines for developing these greenhouse
gas inventories are issued by the Intergovernmental Panel on Climate Change
(IPCC) and were most recently updated in 2006" (M iner and Gaudreault 2013).

xvi

Non-merchantable forest types:
"Stands that are accessible and otherwise available for harvesting, but are
assumed to be non-merchantable because of stand characteristics (e.g. , small
piece size, incidence of decay, species composition, and low stocking)" (MFR
2008).

Oven-dry weight:
"The only case when wood contains no moisture is when it is kept in an
oven above 100°C. In this environment all water is eliminated and the wood is
referred to as oven dried." (Briggs 1994).

Roundwood:
"Any section of the stem, or of the thicker branches, of a tree of commercial
value that has been felled or cut but has not been processed beyond removing
the limbs or bark, or both, or splitting the section (for fuelwood)" (MFR 2008).

Substitution (emission) factors:
"Substitution refers to replacing product A with product B, such as
substituting wood for cement or biofuel for fossil fuel. Substitution replaces the
LCI footprint of A for Band may cause additional indirect impacts as output
volumes adjust. Each LCI has a system boundary (Lippke et al. 2011 ).

xvii

Sustainable forest management:
"Management that maintains and enhances the long-term health of forest
ecosystems for the benefit of all living things while providing environmental,
economic, social, and cultural opportunities for present and future generations"
(CCFM 2015).

Timber harvested landbase (THLB):
"The Crown forest land base consists of Crown land with forest cover within
the timber supply area (TSA), excluding tree farm licences (TFL), community
forests, woodlots and private lands. The timber harvesting land base (THLB) is
that portion of the Crown forest land base that does not include: protected areas;
areas deemed uneconomic for the protection and conservation of other forest
values , such as wildlife, habitat, biodiversity, recreation , etc.; and areas with
unstable terrain, roads, etc. " (MFR 2008).

Timber supply area (TSA):
"A geographically based administrative area designated under the Forest
Act (Section 7). Timber supply areas have an allowable annual cut as set by the
Chief Forester, and are used to provide a sustainable flow of timber to both
replaceable and non-replaceable forms of volume-based tenures" (MFR 2008) .

Secondary structure:
Secondary structure consists of "tree seedlings, saplings, sub-canopy and
canopy trees that will likely survive a pine beetle attack" (Coates et al. 2006).

xviii

Shelf life:
Shelf life refers to the window of opportunity the wood products industry has
in utilizing a MPS-attacked stand given their "changes in wood properties and
vertical stand structure over time" (Lewis and Hartley 2006). It encompasses "the
rate and process of wood degrade, decay, and fall-down, and (biological)
variables that influence these (vertical stand structure) changes with time" (Lewis
and Hartley 2006). But it also addresses the changes in wood properties as they
relate to the numerous non-biological variables (e.g. technology, market price,
cost) that influence the wood products industry (Lewis and Hartley 2006).

Shrinkage:
"When wood dries below a certain moisture content (MC), referred to as the
fiber saturation point (tsp), it begins to shrink and continues to do so until it is
oven-dry. Conversely, wood that is below tsp will swell as it takes on moisture
and this will continue until tsp is reached. Changes in moisture content above tsp
have no effect on shrinkage and swelling. Fsp varies among species, but a value
of 30% MCoo (23% MCw) is commonly assumed" (Briggs 1994).

xix

ACKNOWLEDGEMENTS
I would like to start off by acknowledging my supervisor for this research project,
Dr. Art Fredeen. Art was invaluable as an even-keeled and insightful mentor
throughout the course of this project. Our weekly meetings helped shape this
manuscript.
I would also like to thank my committee: Dr. Phil Burton for fostering my interest
in natural disturbances and issues of salvage logging after insect outbreaks, Dr.
Ian Hartley for lending his expertise and insight into the wood products industry,
and Dr. Steve Helle for providing guidance on the carbon modeling layout and
sensitivity analysis work for this project.
I would like to thank the Pacific Institute for Climate Solutions (PICS) for providing
financial support this project. And I would also like to acknowledge that additional
funding was also provided by Art's NSERC Discovery Grant. UNBC's Office of
Graduate Programs Travel Bursary provided additional funds that permitted me
to attend a carbon modeling workshop and numerous academic conferences and
conventions.
I would like to say special thanks to University of Northern British Columbia
(UNBC), Natural Resources and Environmental Studies Institute (NRESi), Lheidli
T'enneh Band and the City of Prince George for the opportunity to obtain a
Masters degree at UNBC. The NRESi faculty and weekly colloquium series will
surely be missed!
And lastly, I would like to thank my friends and family for their on-going support
throughout the course of completing my Masters' Degree.

xx

Chapter 1.
INTRODUCTION & LITERATURE REVIEW
1.0. INTRODUCTION
There is widespread agreement on the need to address human-induced
climate change (Steffen et al. 2011, Sarnosky et al. 2012). Anthropogenic
greenhouse gas (GHG) emissions have increased rapidly since the pre-industrial
era at a rate unprecedented in Earth's geological history (Steffen et al. 2011 ).
These GHG emissions are one of the predominant anthropogenic drivers in
human-induced climate change, and left unabated, they risk further warming and
long-lasting changes to the climate system (Stocker et al. 2013). Recent
international negotiations have focused on curtailing these emissions and have
devised a series of reporting schemes and policy mechanisms for countries
willing to commit themselves to these efforts (UNFCCC 2016).
Forests (and forest-related) activities have been proposed as an effective
area within which to enact climate change policy reform (Nabuurs et al. 2007).
While many of these proposals seek to develop policies that prevent
deforestation in tropical regions, there is growing interest globally to decrease
GHG emissions through more intensive forest and forest product management.
This notion has led into an exploration of potential activities that could aid in
these efforts of climate change abatement (Dymond 2012, Chen et al. 2014,
Smyth et al. 2014). In many countries, such as Canada, these discussions have

1

also included additional goals of climate change adaptation and mitigation (Kurz
et al. 2013, Lempriere et al. 2013, Gauthier et al. 2014).

1.1. LITERATURE REVIEW
1.1.1.

Contribution of Forests in Climate Change Mitigation Efforts
Forests play an important role in regulating the global climate system

(Bonan 2008). They influence this system through various processes that affect
the planet's energetics, hydrological cycle, and biogeochemical cycles. These
processes have complex interactions, which ultimately can have either beneficial
or adverse effects on the climate system. This concept is exemplified in Bonan
(2008), whereby global forest reforestation and afforestation efforts benefit
climate change by sequestering carbon dioxide (CO2) and by providing
evaporative cooling in tropical forests, but adversely affect the system by
lowering the albedo of boreal forests. These phenomena represent known
processes affecting the climate system but their net climate forcing has been
difficult to quantify and include in climate change policy (Bonan 2008).
Instead, mitigation efforts have largely focused on the forest carbon (C)
cycle and its contribution to global C cycling (Nabuurs et al. 2007). The forest C
cycle continuously sequesters (via photosynthesis) and respires (via auto- and
heterotrophic respiration) vast amounts of carbon dioxide (CO2), a known
greenhouse gas (GHG) agent. The net result of these CO2 fluctuations has offset
approximately one-third of annual global GHG emissions since 1990 (Pan et al.
2011 ). Yet direct and indirect forestry activities (e.g. agricultural expansion,

2

deforestation) are also responsible for emitting large quantities of GHG emissions
annually (Smith et al. 2014). Therefore, the management of this natural resource
is an opportunity for simultaneously increasing C sequestration while decreasing
C emissions in forests and in forest-related activities.

1.1.2.

The Mountain Pine Beetle (MPB) Outbreak in British Columbia
The mountain pine beetle (MPB; Dendroctonus ponderosae Hopkins)

epidemic marks one of the largest natural disturbances ever recorded in the
province of British Columbia (BC), Canada (Safranyik and Carroll 2006). This
native insect reached its epidemic phase by surpassing a series of ecological
thresholds (Raffa et al. 2008, Bentz et al. 2010). The successful bark beetle kills
mature pine trees directly by overcoming a tree's chemical defenses and
indirectly by inoculating the host with a blue stain fungus (Safranyik and Carroll
2006). The preferred tree host for the beetle is mature lodgepole pine (Pinus
contorta Dougl. ex Loud. var. latifolia Engelm.). In the years following attack, the
infected tree host dies and quickly loses its moisture content, resulting in
checking or cracking of the wood (Lewis and Hartley 2006). This damage to the
timber, in addition to the unaesthetic blue stain in the sapwood, decreases the
product value recovery in BC's forest products industry (Bogdanski et al. 2011 ).
Currently, the MPB outbreak has attacked over 723 million m3 of trees and
18.3 million hectares of BC forests (MFLNRO 2013). Susceptible lodgepole pine
trees are not, however, distributed evenly across the forested landscape. As a
result, British Columbia's timber harvesting land base (THLB) has an uneven

3

distribution in the severity of beetle-attacked trees. The THLB is divided into
individual timber supply areas (TSA) and covers an area of 22 million hectares
(MFML 2010). In response to the MPB epidemic, the TSAs hardest hit by the
MPB have had uplifts in their allowable annual cuts (AAC) (MFLNRO 2012a).
The purpose of these uplifts is meant to harvest wood from these attacked stands
before they become uneconomically feasible to salvage and restock.
In 2001, it was estimated that there was approximately 2.2 billion m3 of
timber on THLB areas, with pine consisting of approximately half of this volume
(FPB 2014). Since then, industry has harvested over 500 million m3 - 60% of
which has being dedicated to the salvaging of pine. Meanwhile, various
researchers have advocated for better retention of well-stocked MPS-attacked
stands during these salvaging efforts - rather than simply harvesting pine and/or
pine-leading stands (Coates et al. 2006, Dhar and Hawkins 2011 ). It is thought
that these well-stocked forests, if protected, would alleviate some of the predicted
slumps in the upcoming midterm timber supply gap (Coates et al. 2006, Burton
2010, MFLNRO 2012b) . Whether or not these recommendations were fully taken
into consideration has yet to be seen. Forestry audits by the Forest Practices
Board (FPB) (2007) found general adherence to provincial recommendations
(Eng 2004, Snetsinger 2005) in earlier years, but has since found some neglect
(MFLNRO 2011 , FPB 2014).
The purpose of previous and current AAC uplifts is meant to encourage
the explicit salvaging and restocking of public forests attacked by the MPB

4

outbreak (MFLNRO 2012a). This has meant consciously overharvesting the longrun sustained yield of timber in heavily impacted TSAs (Burton 2010), with the
intention of mitigating future economic and social losses with current economic
and social prosperity.
In recent discussions focused on addressing the issue of a timber supply
shortage, new areas of previously unmanaged forested areas are being
considered for harvest to mitigate the drop in timber supply (MFLNRO 2012b).
These areas included previously protected areas (e.g. old-growth forest
management areas, forest retained to meet visual quality objectives, ungulate
winter range), which were set in place to ensure a diverse range of values from
forests. Equitable forest governance thus will be a challenge moving forward as
conflicts arise from various forest stakeholders as the timber supply becomes
constrained in the coming years (Burton 2010). Moreover, the concept of
sustainable forest management and its certification faces adversity during these
times, as the sustainability of forestry in the province has not been demonstrated.
With that in mind, a prominent alternative to increasing the THLB is
increasing the utilization of BC's forest resource (Special Committee on Timber
Supply 2012) . Proponents of this concept advocate for more intensive
management of harvest residues (i.e. tree tops and slash, broken and
unmerchantable logs) for bioenergy purposes. It is thought that this could help
salvage additional revenue, which could help leverage the overall feasibility in
salvage logging campaigns. Such notions are often predicated on supplying a

5

low GHG emission fuel (i.e. harvest residue) to emerging bioenergy industries in
domestic and international markets. To date, however, the emergence of these
industries has been supported by an abundance of mill residues - which were
formerly considered wood waste in the province. The extraction of harvest
residues, on the other hand, requires increased expenses in obtaining wood fibre
from the forest, and they are not considered to be economically feasible (Abbas
et al. 2011 ). [With that said, the provincial government has recently created
special forest tenures for parties interested in pursuing such goals (MFLNRO
2012c, 201 Sb).]
A more controversial forestry practice meant to intensify forest
management is the explicit harvesting of forests solely for bioenergy
(Greenpeace 2011, McKechnie et al. 2011, RSPB 2012). In the post-epidemic
phase of the MPB outbreak, it is expected that there will be an abundance of
MPS-attacked forests that are not economically feasible to harvest for traditional
forest product industries, but will slowly release C as the dead trees decompose.
Under this paradigm, the use of bioenergy markets to pay for the salvage and
restocking of these forests is a convincing argument. However, this conviction
largely depends on whether or not there are any perceived climate change
mitigation benefits in such activities (Lamers et al. 2014).
In summary, the MPB outbreak has shone light on a difficult question for
forestry policymakers: whether or not to promote bioenergy (and thereby climate
change mitigation objectives) in their continued salvage logging campaign. Such

6

questions are complex, and difficult to answer given the current disparity between
environmental science research and government policy on this issue. For
example, the provincial government currently reports the GHG emissions from
forests and forest product industries separately in its GHG inventory report. This
decision in turn, makes it difficult for environmental science research to inform
the current situation of the MPB outbreak and to promote the bioenergy industry
in a clear and timely manner.

1.1.3.

Estimation of Forest Greenhouse Gas (GHG) Emissions
Since 1992, multiple nations have joined an international treaty, the United

Nations Framework Convention on Climate Change (UNFCCC), which
recognizes the importance of international collaboration in reducing global
greenhouse gas (GHG) emissions (UNFCCC 2016). This convention specifies a
universal framework for natiohs to collaborate in climate change mitigation and
adaption activities. These international negotiations coincide with another treaty,
the Kyoto Protocol, which is an internationally binding commitment to emission
reduction targets. Canada withdrew from this latter commitment in 2011, but
continues to estimate forest GHG emissions at national and provincial levels in
accordance with the UNFCCC (MOE 2012, EC 2015).
In this context, Canada's managed forest has intermittently acted as both
a C sink (contributing to net C storage) and as a C source (resulting in net C
emissions) over the past few years (EC 2015). Canada does not, however, report
these emissions in its National Inventory Report, which is annually submitted to

7

the UNFCCC. The threat of natural disturbances (e.g. fire, insect outbreaks) puts
the nation's forest C balance at risk and creates uncertainty in their estimation
and reporting (Kurz et al. 2008a, 2008b, Metsaranta et al. 2010, EC 2015).
Additionally, these natural disturbances are also expected to increase in
frequency and severity as global temperatures increase in the northern
hemisphere (Price et al. 2013). As a consequence, there is interest in reducing
this uncertainty by adapting to climate-induced changes in forests and by
mitigating their overall impact (Lempriere et al. 2013, Gauthier et al. 2014).
Current disturbances most affecting Canada's forest C balance are wildfires, the
MPB outbreak in British Columbia, and annual timber harvesting (EC 2015).

1.1.3.1.

GHG Emissions from MPS-Attacked Forests

Historically (1990-2002), BC's managed forest has been estimated as a C
sink (net C storage) in its provincial GHG inventory, using the Carbon Budget
Model of the Canadian Forest Service (CBM-CFS3) (MOE 2012). Since 2002,
however, provincial forests have transitioned into a C source (with net C
emissions). This transition from sink to source has been attributed to the MPB
outbreak, wildfires and increases in annual harvesting. In 2012, forest emissions
were estimated as: -49.2 Mt CO2 being sequestered by net primary productivity
and decay of organic matter, 63.1 Mt CO2 being emitted by harvesting, 17.1 Mt
CO2 being emitted by wildfires, and 8.0 Mt CO2 being emitted through slash
burning (MOE 2012) 1 . This C accounting approach presents a "snapshot" of the

1

Negative values here connote GHG removals, while positive values connote GHG emissions.

8

current C balance of BC forests, but it does not inform policy makers of the total
C emissions attributable to the MPB outbreak, nor any proactive forest
management activities that could help alleviate the damages caused by the
beetle outbreak.
Kurz et al. (2008a) predict that the MPS-attacked region will be a C source
both during, and immediately after, the beetle outbreak. The net outcome of this
outbreak, therefore, is predicted to be a large source of GHG emissions to the
atmosphere (-900 MtC0 2e over 21 years). The paper suggests that disturbances
of this magnitude create a possible feedback loop, whereby this disturbance
accentuates climate change by increasing the likelihood of subsequent natural
disturbances (Kurz et al. 2008a). This perspective of MPS-attacked forests being
a large C source is a predominant notion in the grey and scientific literature. [In
retrospect however, the current provincial projections of 55% pine mortality are
far less than the Province's initial predictions in 2006 of 80% mortality, which was
the provincial MPB projection used by Kurz et al. (2008a) in their C modeling
(MFLNRO 201 Se).]
In Metsaranta et al. (2011 ), BC's forested land base (-67 million ha) was
predicted to act as a C source for most of the 21st century. This C source reflects
a history of natural and anthropogenic disturbances on the land base, as well as
expected future disturbances. The duration of BC's forest C source is expected to
be highly variable and uncertain given Metsaranta et al. 's (2011) most optimistic
and pessimistic scenarios, which tested the effects of future changes in growth

9

rates, decay rates, and area burned by wildfire on the cumulative impact and
recovery of provincial forests.
Coinciding with these modeling predictions by Kurz et al. (2008a) and
Metsaranta et al. (2011) are field measurements using the eddy covariance
technique. Studies using this technique have found sub-boreal spruce stands in
BC to act intermittently as a slight C sink or a slight C source following MPB
attack (Brown et al. 2010, 2011 ). These findings seemingly contradict the
widespread notion that these stands are an immediate and large source of C
emissions in need of immediate attention (Kurz et al. 2008a). Other studies
corroborate Brown et al. 's (2010) findings by demonstrating that stand-level C
fluxes can be minimal post-disturbance and can return to a C sink status shortly
after these types of events - albeit at a lower C sink strength than predisturbance (Edburg et al. 2011, Pfeifer et al. 2011, Goetz et al. 2012). Forests
are thought to follow many different C trajectories depending on the nature of the
natural disturbance regime (e.g. severity, frequency), as well as other variations
in climate and environmental conditions (Goetz et al. 2012) . The presence of
secondary structure and residual vegetation in Brown et al. 's (2010) MPSattacked stands were found to mitigate the loss in net primary productivity (from
dead trees) and increase in heterotrophic respiration (i.e. decomposition,
respiration) (Bowler et al. 2012, Emmel et al. 2014). [Kurz et al. (2008a)
recognized the beetle outbreak as a non-stand replacing disturbance event but it

10

was parameterized based on the percentage of tree crowns killed rather than the
secondary structure and residual vegetation present.]
In unmanaged (or conserved) forests, the long-term recovery back to predisturbance C status is hindered as dead trees begin to fall and decay (Edburg et
al. 2011, Harmon et al. 2011, 2013). While many of these snags are expected to
fall relatively quickly (Angers et al. 2010, Edburg et al. 2011 ), others are expected
to remain standing for some time (Axelson et al. 2010, Amoroso et al. 2013). The
duration of this dead (and potentially decaying) wood is thought to be a complex
and important temporal aspect of forest C dynamics, and yet, it is too often
simplified in C modeling by assigning default decay functions to snags as
downed wood (Boudewyn et al. 2007), and by omitting any secondary structure
and residual vegetation photosynthetic response.
In managed forests, the C budget basis for deciding to harvest MPBattacked forests is not apparent. This is a result of the magnitude of this
disturbance and its unprecedented impact on BC's timber supply. By harvesting a
stand, a large and persistent (8-1 O years) C source is created on-site (Fredeen et
al. 2007). This C source, in turn, has traditionally been offset by forest regrowth
under the presumption of a sustainable rotation at the stand-level and/or
sustainable forest management at the landscape-level. Due to the extensive
damage caused by the MPB, however, these presumptions are now less evident,
and likely debatable. [To date, forest C modeling has not explicitly mentioned nor
adequately addressed the issue of whether or not MPS-attacked stands should

11

be harvested above sustainable harvesting rates (i.e. > long-run sustained yield)
(Kurz et al. 2008a, Metsaranta et al. 2011, Lamers et al. 2014).]
From a pragmatic standpoint, the MPS-attacked stands that can be
harvested profitably are done so regardless of these C considerations. The
decision on whether or not to harvest MPS-attacked stands from a C budget
basis however, has been largely deferred until more is known about the C
budgets of forest products harvested from these forests. One of the general
observations in forestry has been that forest products offer temporary C storage.
Traditionally, forest C budgets were ecological concepts typically discussed
without accounting for the C in forest products, or treated as if the C from these
products were emitted immediately (Kurz et al. 2008a). Therefore, better C
accounting of the C storage held in forest products has resulted in reduced
estimates of forest C emissions by refining their forest C budgets (Metsaranta et
al. 2011, MOE 2012).
Proponents of bioenergy claim overall reductions in GHG emissions by
replacing fossil fuel derived GHG emissions with renewable, biomass-derived
GHG emissions. This claim is typically argued on the basis of contributing to
GHG mitigation. Mitigation is defined here as the amount of GHG emissions
avoided by either reducing emissions or increasing removals (i.e. C storage) that
can be achieved by promoting a mitigation activity relative to a baseline (or
business as usual) condition (Lempriere et al. 2013). The concept of GHG
mitigation is pervasive in environmental policy, and yet, its validity in

12

environmental science remains unclear. While it is generally accepted that
bioenergy's use of wood waste materials reduces GHG emissions, there are also
those who advocate bioenergy generation through the harvesting of healthy (nonmerchantable) and/or damaged forests.
The addition of the mitigation concept to both forestry and bioenergy
industries has led to confusion in the decision-making process of whether or not
to harvest MPS-attacked stands from a C budget standpoint. The versatility of the
mitigation concept in forest and forest product systems has led to a profusion of
perspectives in the literature. Some of these perspectives relevant to MPBattacked forests include: (1) accelerating their recovery by restocking the forest,
(2) storing and using what would otherwise be "dead and decaying" timber in
forest products and (3) substituting more fossil-fuel dependent products and
processes. These are certainly apparent and convincing arguments; however,
there is little support for any of them in environmental science and policy.
Traditionally, studies have been built on an attributional approach that
describes a system as it exists (Miner and Gaudreault 2013), and it relies on
direct measurements built on a strong environment science foundation. The
contemporary concept of mitigation however, uses a consequential approach
(Miner and Gaudreault 2013). This approach describes how the system changes
in response to decisions and is more commonly associated with environmental
policy than environmental science. The advantage of this approach is that it can
be a strong and effective tool in promoting climate change mitigation efforts.

13

However, it requires that the modeling accurately portray business-as-usual and
alternative scenarios. This is difficult to do in BC given the uncertainty in C
cycling of MPS-attacked forests and lack of established (or verified) parameters
for the forest product industry.
In summary, the decision of whether MPS-attacked forests should be
harvested (above sustainable levels) is obscured by the diversity of perspectives
on how the Province can mitigate the C budget impact of the outbreak. This is
problematic given the rudimentary knowledge and history of forest C science and
management in the province. The MPB outbreak and salvage logging have had
an unprecedented impact on the forest C cycle and there remains a great deal of
uncertainty in the long-term modeling of post-disturbance C cycling, making it
difficult to establish accurate and credible baselines for MPS-attacked forests and
their potential forest product streams.

1.1.3.2

Carbon Neutral Forestry

Forestry results in C emissions from the biological (i.e. forest) and industrial
(i.e. forest product) systems (Gower 2003). It is generally assumed that these C
emissions are offset by C sequestration in a sustainably managed forest
landbase. C neutral forestry (or C-cycle neutrality) is when C emissions are
balanced by C sequestration at the landscape-level (Malmsheimer et al. 2011 ).
One of the issues with the C neutral concept being applied in BC is the
ambiguity between environmental science and policy defined forest and forestry
C neutrality (Klopp and Fredeen 2014) . The province's timber supply is unlikely to

14

be sustainable given the unprecedented impact of the MPS outbreak and
excessive salvage logging above the AAC (Burton 2010). Sustainably managed
forests are one of the prerequisites for assuming C neutrality. As a result, it is
possible that not all biogenic GHG emissions can be considered C neutral and
may need to be included in the C accounting of the forest products industry.

1.1.4.

Estimation of Forest Product GHG Emissions
Traditionally, C accounting has assumed forest products' GHG emissions

to follow a default trajectory equivalent to their immediate combustion on site
(UNFCCC 2003, Eggleston et al. 2006). These forest product emissions fell
under the Agriculture, Forestry, and Other Land Use (AFOLU) section according
to the UNFCCC. In light of recent amendments, forest products can now be
estimated and/or reported as part of a nation's submission to the UNFCCC.
Nations are still required to report forest products under the default assumption in
the AFOLU section but they can now offset these emissions by claiming any C
storage held in forest products. Dymond (2012) recently found that BC could
benefit from this new amendment in the UNFCCC
There are numerous C accounting approaches that are used to inform
environmental science and policy on forest products, each having their own
strengths and weaknesses. A C accounting approach is defined here as the
"conceptual framework for estimating emissions and removals of greenhouse
gases in inventories" (Cowie et al. 2006). In other words, the "approach refers to
the system boundary, defining which emissions and removals are to be reported

15

or accounted by each (entity)" (Cowie et al. 2006). The establishment of system
boundaries is important due to the complexity of the forest products industrial
system, and as a result, various entities have created different C accounting
approaches to distinguish the GHG emissions for which they are accountable.

1.1.4.1.

National GHG Inventory

The C accounting approach in National GHG Inventories is set up to
estimate and report a Party's forest products GHG emissions according to the
conventions required in submitting a national inventory to the UNFCCC
(Eggleston et al. 2006). The intent of this inventory is to provide an international
basis for quantifying and comparing a Party's efforts towards climate change
mitigation (UNFCCC 2016). One of its limitations is that it does not quantify all of
the GHG emissions attributable to an activity or product, but instead distributes
them amongst various sectors (e.g. industrial, waste, forestry, etc.) (MOE 2012).
This makes it difficult for a Party to equitably inform decision-making in forestry
activities.

1.1.4.2.

Carbon (C) Footprints and Life Cycle Assessments (LCA)

Carbon footprint studies all share a similar goal in their desire to quantify
GHG emissions attributable to an entity or product (Miner and Gaudreault 2013).
The most common and standardized approach for forest products is the life cycle
assessment (LCA) (ISO 2013). This approach is an analytical tool used to help
industries define the environmental impact of their products (ASMI 2012,
Puettmann et al. 2010). The strength (or weakness) in this approach centers on

16

its definition of scope, which delimits the environmental impacts to those solely
attributable to the reporting entity. These impacts typically include upstream GHG
emissions, but rarely downstream GHG emissions. One of the limitations of this
approach for my purposes is its scope definition. The environmental impact
assessment in LCAs is only relevant to its defined boundary, rather than the
overall environmental impacts these products have on their forest and forest
product systems.

1.1.4.2.1. Consequential LCA
Another strength of the LCA is that it enables comparisons between
alternative and competing products and processes. This type of LCA approach is
known as a consequential LCA (CLCA) . A large component of CLCAs is
substitution, which" ... refers to replacing product A with product B, such as
substituting wood for cement or biofuel for fossil fuel. " In forest product C
accounting, the avoided emissions generated in forming CLCAs (i.e. substitution
emission factors) have been shown to represent significant climate benefits
(Darnen and Faaij 2006, Sathre and O'Connor 2010). Sathre and O'Connor
(2010) performed a meta-analysis on substitution emission factors in the forest
products industry and found an average substitution emission factor of 2.1 tC
removed per tC of wood product. The limitation of using this approach for my
purposes is that CLCAs are built on the concept of functional units. [A functional
unit is the "quantified performance of a product system for use as a reference
unit" (ISO 2006).] That is, by calculating a single substitution factor for a product,

17

only GHG emissions attributable to the product are included, rather than all the
GHG emissions attributable to the material or activity (Sathre and O'Connor
2010). This potentially allows a product with inefficient material use to compete
with a product with a more efficient material use. In this manner, Sathre and
O'Connor (2010) hypothesize that a single substitution factor may not necessarily
inform the most efficient use of the biomass material in addressing climate
change concerns.

1.1.4.2.2. Environmental LCA
Environmental LCAs (or industrial forest C budgets) are differentiated from
traditional LCAs by the fact that they are material-oriented rather than productoriented (like traditional LCAs). While they are far less common and/or
standardized, they are known to offer unique insight into the environmental
impacts of the forest product industry system. White et al. (2005) found that their
industrial forest C budget could help inform management with different levels of
forest governance (e.g. state, national, and private non-industrial). The authors' C
budget for forest products (7 gC m- 2 y( 1) was not seen as making a significant
contribution to the overall forest C budget (-904 to 341 gC m-2 y(1). Similarly,
Ingerson (2011) tracked the material flow of forest products and found that very
little of the original timber remains in use at 100 years (-1%). The limitation of
this approach is that there is no precedent in BC for an environmental LCA and it
would require extensive amounts of time and capacity to begin building this type
of inventory for the forest products industry.

18

1.1.4.3.

Forest Product C Models

1.1.4.3.1. BC-HWPv.1
Dymond (2012) created a spreadsheet model (BC-HWPv.1) for forest
products harvested in British Columbia. The intent of this model was to inform the
provincial government on the GHG mitigation potential of including forest
products and their activities in BC's GHG inventory. Specifically, this model
estimated the C storage in forest products by establishing a series of C stocks
and flows of products throughout its industrial system. The limitation of the BCHWPv.1 model for my purposes is that it only measures the biogenic C stored in
forest products and not the GHG emissions associated with their industrial
systems.

1.1.4.3.2. CBM-FHWP
Smyth et al. (2014) created a forest products model (CBM-FHWP) meant
to inform the government of the greatest mitigation potential of its forest products.
The design of the model is unique in that it applies a "systems perspective" to
Canada's forest sector. This "systems perspective" includes the GHG emissions
avoided by adopting a forest product system over more industrial systems (i.e.
cement, steel or coal systems). The limitation of using the CBM-FHWP model for
my purposes is a lack in transparency in how the authors derived the substitution
emission factors used in their "systems perspective". An understanding of the
derivation of substitution is paramount in discussing the GHG mitigation benefits

19

of forest products, as it is known to significantly alter the outcomes of scientific
research.

1.1.4.3.3. HWP-CASE
Chen et al. (2014) created a model (HWP-CASE) meant to provide a
comprehensive assessment of C stocks and emissions for Canada's forest
sector. This model's intent is very similar to that proposed by Lempriere et al.
(2013) and Smyth et al. (2014) but its C accounting is set up differently. Chen et
al. (2014) measured the C storage, GHG emissions, and avoided GHG
emissions (when substituting forest products for fossil fuel intensive products).
One of the limitations of the HWP-CASE model for my purposes is its scale, as it
is currently configured to inform the country's forestry sector rather than standand/or regional scaled studies.

1.1.4.4.

Forest-Forest Product C Models

1.1.4.4.1. GORCAM
Schlamadinger and Marland (1996) created a forest-forest product C
model (GORCAM) meant to inform policy-makers of the merits of adopting forest
and bioenergy strategies in reducing CO2 emissions into the atmosphere. The
forest product model component of the GORCAM model is confined to four
"displacement factors" (i.e. substitution emission factors) for biofuel (0.60-1 MgC
MgC- 1), long-lived products (0.5-1 MgC MgC- 1), short-lived products (0.25-0.5
MgC MgC-1 ), and very short-lived products (0.25-0.5 MgC MgC- 1 ). The derivation
of these factors is unknown. The limitation of GORCAM model for my purposes is

20

then the applicability of these displacement (i.e. substitution) emission factors in
North America. Their factors are derived from observed trends in Europe and its
applicability remains unclear due to its differences in various industrial sectors
(e.g. forestry, construction , energy) .

1.1.4.4.2. FICAT
The Forest Products Association of Canada (FPAC) used the FICAT
model in its assessment of the environmental performance of Canadian forest
products (FPAC 2011 ). This model creates C footprints of forest-based
manufacturing activities according to UNFCCC (2003) and WRI/WBCSD GHG
Protocol (2011) based methods. FPAC (2011) used this model to outline the C
footprints of a variety of forest product activities, including 12 different forest
products and 16 different pathways. These activities were then compared based
on their C footprints and their socio-economic potential. The strength of this
model is its ability to inform industry of its own operational GHG emissions in
addition to its upstream impacts on the forest system. The limitation of this model
for my purposes is its scope definition as it only accounts for the GHG emissions
for which it is responsible , rather than the total GHG emissions.

1.1.4.4.3. Perez-Garcia et al. (2005)
Perez-Garcia et al. (2005) created a C model with the purpose of informing
others of the ability of forest products to reduce atmospheric GHG emissions
through their displacement of functionally equivalent products in housing
construction. They modeled the forest system, the forest product system, and

21

avoided emissions displaced by forest product system. Their results demonstrate
reduced GHG emissions in constructing houses with forest products and suggest
more intensive forest management. This modeling framework and discussion of
its results has continued in more recent publications (such as Lippke et al. 2011)
and has been expanded to incorporated bioenergy products in their assessments
(Lippke et al. 2012). The limitation of this C model for my purposes is its
applicability to BC forest products. The intent of the model was specific to
engineered forest products (e.g. I-joists) in housing construction and is not
necessarily specific in its handling of traditional forest products (e.g. lumber).

1.1.4.4.4. Lamers et al. (2014)
Lamers et al. (2014) created a C model specific to forestry in MPS-attacked
forests. The authors used the Carbon Budget Model of the Canadian Forest
Sector (CBM-CFS3) for the forest component and their own calculations for the
forest product component of their model. In their model they calculated the forest
and forest products systems using a consequential approach. In doing so, they
found overall reductions in forest C emissions when harvesting MPS-attacked
stands for lumber and for pellets. The limitation of their model for my purposes is
a lack of transparency and validity in their C modeling of the forest industrial
system.

1.1.5.

Conclusion
In summary, my literature review found that there is a precedent for C

accounting and modeling of forest products harvested from MPS-attacked forests

22

in British Columbia. Lamers et al. (2014) demonstrated reductions in overall C
emissions by harvesting MPB forests for lumber and for bioenergy (i.e. pellets).
In their study, they found lumber as the preferable forest product over pellets, but
also found that harvesting heavily damaged stands(> 85% MPB attack) for
pellets was acceptable, assuming lumber's ineligibility. This implies that the
explicit harvesting of MPS-attacked stands for bioenergy would be preferable
over inaction (from a C budget perspective) during BC 's salvage logging
campaign. With that said, there are some concerns I have in their modeling of the
forest product system.
In their assessment, Lamers et al. (2014) chose to include substitution
emission factors for their forest products. The benefits of avoided emissions in
substitution are well known but poorly established. The authors borrowed a
substitution emission factor of 1.7 tC tc-1 for lumber products from Sathre and
O'Connor (2010). Sathre and O'Connor (2010) derived this factor based on a
meta-analysis of reported displacement factors in the literature, which came from
a broad array of C budgets performed in different countries, under different
scopes, and for different wood products.
Generally, a substitution emission factor is the difference between the
fossil fuel emissions (i.e. non-biogenic GHG emissions) attributable to a forest
product with those emissions attributable to a functionally equivalent product
(such as steel or cement). These factors are product specific, meaning they are
distinct from all other products in a forest product system. Lamers et al.'s (2014)

23

forest product modeling component aggregates the substitution emission factors
for lumber and for pellets (derived from sawdust and shavings) in their modeling
of timber harvested for lumber production. They did not include a separate factor
for pulp or paper, but instead assumed that these mill residues are short-lived
and accounted for them as biogenic GHG emissions. This is somewhat
problematic given the C accounting of biogenic GHG emissions in their model.
Lamers et al.'s (2014) C accounting has biogenic GHG emissions
occurring as a default function of forest's GHG emissions. This obscures the
biogenic GHG emissions specific to forest products and clouds them with those
biogenic GHG emissions specific to the forest. While it was the intent of the
authors to determine whether or not MPS-attacked forests should be harvested
for forest products, it remains unclear what exact contribution the forest products
made to the overall C budget of forestry. Moreover, their modeling of forest
products is fairly abstract, rather than established and supported by data.
In this thesis, I create a C budget model for two forest products, lumber
and pellets, harvested from MPS-attacked forests in the Prince George region.
The purpose of my model and the selection of these forest products was to
establish a context for each of these products in BC by tracking their material and
product flows, biogenic C storage and the GHG emissions of their industrial
systems. My primary objective was to estimate and to compare forest products
based on their forest product C budgets. Dymond (2012) set precedent for the
tracking of biogenic C stored in BC forest products, but otherwise there has not

24

been a study detailing the forest products harvested from MPS-attacked forests.
My secondary objective was to explore the influence of biogenic GHG emissions
on forest product C budgets. This was chosen to specifically address the C
accounting issues and concerns in assuming C-neutrality of biogenic GHG
emissions.

1.2. RESEARCH QUESTIONS
1.) How does the choice in forest product (e.g. lumber vs. pellets) affect the C
budget of the forest product industrial system when processing roundwood
harvested from forests attacked by mountain pine beetle?
2.) How does the assumption of C neutrality influence the overall C budget of the
forest product industrial system?

25

Chapter 2.
DEVELOPMENT OF AC BUDGET MODEL FOR FOREST PRODUCTS
HARVESTED FROM MPS-ATTACKED FORESTS IN THE PRINCE GEORGE
REGION
2.0. INTRODUCTION

In the post-epidemic phase of the mountain pine beetle (MPB) outbreak,
there currently lacks an equitable carbon (C) budget model capable of
considering different forest products harvested from provincial forests. The
precedent set by contemporary C modelers has revealed the potential for forest
products to reduce atmospheric GHG emissions and aid in climate change
mitigation. These benefits, however, are highly context specific, and oftentimes
obscure. This chapter outlines the development of a C budget model with the
intent of establishing current knowledge of BC's forest product industrial system.

2.1. METHODS & METHODOLOGY
2.1.1.

Carbon Budget Model for Forest Products
I developed a C budget model for forest products harvested from MPB-

attacked forests. This model estimated: (a) the material and product flows; (b)
biogenic C storage; and (c) greenhouse gas (GHG) emissions of the forest
product industrial system. The material and product flows were based on
prevalent trends in the BC Interior (MFLNRO 201 Sc). Biogenic C storage was
modeled using various flows and stocks of the forest products in use and in
disposal, according to Dymond (2012). This is important because each forest

26

product has a different product recovery, use or uses, half life or half lives, and
disposal methods. Greenhouse gas emissions were separated based on whether
or not they were derived from forest biomass (i.e. biogenic). This is due to the
fact that biogenic GHG emissions are oftentimes thought of as C-neutral
emissions (i.e. zero emissions), and therefore are not always accounted for in C
budgets. However, mounting evidence suggests that forest product C budgets
should, at the very least, be estimated in the absence of forest C budget
accounting (Searchinger et al. 2009, Helin et al. 2013).
My C accounting approach quantified the direct GHG emissions at each of
the life stages of forest products. Each forest product system (or life cycle) was
broken down into its individual life stage components, which were: harvest,
transportation, primary processing, secondary processing, distribution, use and
disposal. My approach characterized the system as it exists, rather than how it
may have affected other industrial systems. This meant that we did not do a
consequential study and compare my forest product industrial systems with other
industrial systems (i.e. considering substitution effects). The purpose of my
approach was to build a strong foundation of environmental science before
addressing the persistent and complex issues of additionality and leakage in
consequential (or political) studies.
The C accounting method in my C model aggregated individual data sets at
each of a forest product's life stages. This was chosen over more extensive
sampling of the entire industrial system due to the limited time and capacity for

27

meaningful and complete stakeholder involvement in the development of a
complete GHG inventory.

2.1.2.

Experimental Design

The intent of my experimental design was to determine the C budgets of
forest products harvested from MPS-attacked forests. While seemingly apparent,
the experimental design required rigorous examination of experimental units,
treatments, and the various contexts of forest product C budgets.

2.1.2.1.

Experimental Unit & Treatment

My experimental unit was based on the oven-dry (OD) mass of 1 m3 of
dead merchantable lodgepole pine (Pinus contorta var. latifolia Engelm.)
roundwood. This material unit (m 3 ) was selected for its ability to equitably inform
environmental science and policy on the most efficient use of this biomass
material in reducing forest product C emissions. This unit did not account for
timber utilization (i.e. harvest residues) or the composition of harvest (i.e. %
green versus dead trees) prior to harvest. These issues are no doubt important
and will need to be addressed in subsequent studies that incorporate a forest C
modeling component.
The selection of pine's OD weight, as opposed to green or air-dry weight,
was chosen to avoid complex wood-water interactions (i.e. shrinkage) and
facilitate the C accounting between the inter-industry trading of mill residues
(Shmulsky and Jones 2011 ). I assumed that there was not a significant change in
OD weight of MPS-attacked roundwood from time since death.

28

My experiment was set up as a comparison between two very different
forest products: lumber and pellets. These products were chosen to act as
current and proposed uses of roundwood during BC's salvage logging campaign.
Both products are thought to be beneficial in the Province's plans for reducing
GHG emissions, but are thought to contribute to this initiative in two very different
ways. Lumber is a long-lived forest product and temporarily stores C held in its
wood fibre, whereas pellets are a short-lived forest product with low GHG
1

emissions (i.e. kg C02e kJ" ) compared to fossil fuels (assuming pellets are C
neutral).
In my first scenario (scenario 1), roundwood was harvested for lumber
production. The function of this scenario was to establish a baseline for the
predominant use of roundwood in the BC Interior. This scenario was selected
due to a general lack of studies on the C balance of lumber in the province, as
well as concerns over the beetle's impact on lumber's recovery and how this
might impact its C budget.
In my second scenario (scenario 2), roundwood was harvested for pellets.
This proposed wood product was chosen as an attractive alternative to lumber
due to its ability to use MPS-attacked timber as its wood quality declines. This
scenario reflects growing scientific curiosity (Lamers et al. 2014, Lloyd et al.
2014), claims by government and industry (EMPR 2007, 2008; BC Hydro 2012),
concerns of non-governmental organizations (Nikiforuk 2011, Greenpeace 2011,
RSPB 2012), and in some cases, actual practice.

29

2.1.2.2.

Case Studies

Case studies were used in my project to describe the C budget in two
different contexts. These contexts represent two extremes in the treatment of
biogenic GHG emissions. The intent of these case studies was to explore and
demonstrate the sensitivity of forest product C budgets in the absence of the
forest C modeling. This evaluation was prompted by the extensive damage to BC
forests in the wake of the MPB outbreak and subsequent salvaging efforts.
My first case study (case 1) accepted the C-neutral forestry assumption,
which meant excluding all biogenic GHG emissions from the C budget. This is a
prominent assumption in forest product C accounting and modeling. It is
predicated on the assumption that "(the) uptake of C (in CO2) by plants over a
given area and time is equal to emissions of biogenic C attributable to that area"
(Malmsheimer et al. 2011 ).
My second case study (case 2) rejected the C-neutral forestry assumption,
which meant including all biogenic GHG emissions in the C budget. I rejected this
assumption based on the unlikely event that forest management is a completely
C-neutral activity and that a failure to acknowledge these biogenic GHG
emissions may lead to erroneous conclusions on the most appropriate use of this
wood material. Lamers et al. (2014) found that harvesting stands exclusively for
bioenergy (i.e. pellets) did not meet a C-neutral definition unless the harvested
stands contained a high proportion of dead pine (>85%). Other researchers
testing the validity of C-neutral claims in healthy forests have found mixed results

30

(McKechnie et al. 2011, Holtsmark 2012, Mitchell et al. 2012, Schulze et al.
2012).
Cases 1 and 2 in my study, therefore, represent the extremes of 100%
and 0% C neutrality, respectively. However, I acknowledge that the true effect of
forestry on the C balance of the MPS-attacked forests likely lies somewhere in
between these two extremes. It is difficult to know exactly where BC forests are
on this spectrum of C neutrality (0-100%) due to a lack of studies examining the
landscape-level impacts on the forest C balance.

2.1.2.3.

Study Boundaries

The establishment of study boundaries is necessary in the construction of
a C budget model for forest products. The forest product system can be a fairly
complex and extensive system, so the establishment of boundaries can be a
fairly subjective procedure. To be as transparent and honest about my study
boundary as possible, I have defined the physical, spatial, and temporal aspects
of my C budget model. [Once this boundary is set, the GHG emissions reported
in the C budget then reflect the boundaries imposed on the industrial system.]

2.1.2.3.1. Physical
The physical (or procedural) boundary determines which activities and
processes of the forest product's industrial system are included in its C budget
(Sathre and O'Connor 2010, Miner and Gaudreault 2013).
My forest product C budget includes the material and product flows of
forest products (Figure 2.1.). This unusual in that primary processing residues

31

(i.e. by-products) and their GHG emissions are accounted for in my model.
Typically, modeling efforts have either focused entirely on the main product or
assumed that the residues are immediately combusted. My model does not,
however, include all of the upstream and downstream uses of the various nonforest product materials and products used in this system (e.g. bleach from
chemical pulp plants).
My forest product C model includes the biogenic C storage of forest
products in use and in disposal. It is well known that long-lived forest products
can temporarily store C while in use, but they can also store C while in disposal.
This storage changes over time - as these products are eventually disposed of in
landfills. As a result, my model was designed to look at biogenic C storage over
time.
My forest C budget accounts for all of the direct GHG emissions
attributable to a forest product's industrial system. These emissions are
categorized here as being either biogenic or non-biogenic GHG emissions.
Biogenic GHG emissions are biomass-derived GHG emissions that are not
typically included in forest product C budgets under a C-neutral assumption .
Cases have been made, however, that advocate for including biogenic GHG
emissions in forest product C budgets when the forest system is not considered
(Searchinger et al. 2009, Helin et al. 2013). Non-biogenic GHG emissions are the
remaining GHG emissions that are not biomass derived and/or eligible for C-

32

neutral status. [Note: Methane (CH4) and nitrous oxide (N20) derived from
biomass are not considered C neutral.]
MATERIAL & PRODUCT FLOW

a.) Scenario 1 - Lumber Harvest
Timber

Harvest

Hogfuel
Pulp Chips

.-------i
·--- ---J
Ex. Hog. t

Transport
Sawdust &
Shavings

b.) sc-rlo 2 - Pellet Harvest
Timber

Harvest

Pulp
Plant

Transport

Pellet
Plant

r"-.__ _ _ _ __,

Pellets

Distribution

Figure 2.1. The material and product flow of MPS-attacked lodgepole pine (Pinus
contorta var. latifolia) roundwood harvested for lumber (a; scenario 1)
or for pellet (b; scenario 2) production (adapted from Klopp and
Fredeen 2014).
2.1.2.3.2. Spatial
The spatial boundary defines the physical (or geographical) area of a
forest product C budget (Sathre and O'Connor 2010, Miner and Gaudreault
2013). The forest product industrial system includes regional and industrial
processes (such as harvesting, transportation, primary and secondary
processing) , but it can also include international distribution , secondary
processing, use, and disposal. In my study, I chose to include both regional and
international aspects of the forest product industrial system. It was assumed that
some of the largest GHG emissions occur abroad. Moreover, the establishment
of spatial (or political) boundaries has been known to skew the results of forest
product C budgets (Nabuurs and Sikkema 2001 ).

33

Another aspect of establishing a spatial boundary is that it determines how
biological processes are perceived and/or treated (Miner and Gaudreault 2013).
In particular, the spatial boundary representing forestry and photosynthesis (as
biological processes) at the stand- and landscape-level are important in
discussing biogenic GHG emissions attributable to the forest product industrial
system. In my study, I chose to discuss the spatial boundary of my C budget in a
landscape-level setting. This was chosen based on landscape-level concerns,
(such as overharvesting during the MPS outbreak) and the potential
shortsightedness of a stand-level spatial boundary.

2.1.2.3.3. Temporal
The establishment of a temporal boundary is problematic in forest product
C budgets, as it is known to significantly affect the outcomes of their budgets
(Miner and Perez-Garcia 2007). In short, various temporal boundaries exist and
have been used in a variety of applications. Short-term studies are promoted on
the importance of short-term mitigation efforts and their ability to limit the
uncertainty of C modeling. These studies include a focus on quick and tangible
improvements during the rapid transitions of a forest product's early life stages. In
contrast, long-term studies demonstrate the actual net impacts of forest products
over the course of their lifetime. The accuracy of their portrayal is reliant on:
current patterns of wood use and methods of disposal (Smith et al. 2006); the
mathematical formulation of use and disposal functions (Marland et al. 2011 );
and the disposal technology available.

34

In my study, the temporal boundary was set at 100 years. With that said,
the establishment of a timeline is fairly arbitrary. Generally, timelines are
synchronized with the forest management rotation or set to an arbitrary, roundnumbered standard (for example, 100 years). In my case, a 100-year timeframe
is the most pragmatic as it aligns with current conventions in the Province's GHG
inventory.

2.1.3.

Data Collection
My forest product C budget model relied on a variety of resources, which

help construct the material and product flow, biogenic C storage, and the GHG
emission profile of my forest product industrial systems. A detailed overview of
the current and most representative data available in BC is presented in the
following sections.

2.1.3.1.

Lumber (Scenario 1)

2.1.3.1.1. Material & Product Flow
2.1.3.1.1.1.

Lumber Recovery

An exact estimate of the lumber recovery of MPS-attacked roundwood is
uncertain for a variety of reasons. Lumber recovery is naturally variable between
sawmills due to differences in the log size and quality (e.g. defects, grade) and a
sawmill's machinery (Shmulsky and Jones 2011 ); but there is also a general lack
of information on the lumber recovery of MPS-attacked roundwood.
At the peak of the MPB outbreak, the shelf life and lumber recovery of MPBattacked timber was a large concern held by many researchers (Lewis and

35

Hartley 2006, Orbay and Goudie 2006, Byrne et al. 2006). At this time it was
found that grey-stage (>five years since attack) roundwood had a lumber
recovery factor of 0.241 mbfm m-3 (mbfm: thousand board feet) with current
technology (Orbay and Goudie 2006) (Table 2.1.). Forestry Innovation
Investment Ltd. (2016) has since continued the examination of lumber recovery
of MPS-attacked roundwood but it is now considered market research and is not
publically available.
British Columbia's Ministry of Forests, Lands and Natural Resource
Operations (MFLNRO) Competitiveness and Innovation Branch reported an
average lumber yield of 44.5% (on a volume basis) for Interior sawmills in 2012
(MFLNRO 2015c). This yield is calculated by dividing total lumber output by total
roundwood input in the Interior. This value represents a fair estimate of the
average lumber recovery in the Interior. One of its limitations is that it averages
multiple tree species and sizes, and thus limits its usefulness in determining MPS
lumber recovery. For example, the allowable annual cut (AAC) uplifts increased
pine utilization in heavily impacted TSAs, and yet, the average lumber recovery in
the BC Interior remains unchanged over this period of time (MFLNRO 2015c)
British Columbia's Ministry of Forests, Lands and Natural Resource
Operations Timber Pricing Branch estimates specific lumber recovery yields by
species, condition and size (MFLNRO 2015a). Their recoveries are calculated
using a set of "log rules," which the Branch uses to estimate the expected
recovery of lumber (Shmulsky and Jones 2011 ). Lumber recovery for healthy

36

lodgepole pine ranges from 0.091 to 0.254 mbfm m·3 based on their log top
diameter class (4.5-5.49 cm and +100 cm) (MFLNRO 2015a). [Due to the
bucking of timber at the landing , some logs will have a large top diameter.]
Lumber recovery from dead lodgepole pine can also be estimated with net
adjustments (MFLNRO 2015a). However, these yields are suspect in being more
conservative (i.e. lower) than their actual recoveries when compared to those
described by the Competitiveness and Innovation Branch.
Life cycle assessments (LCA) performed in Canada and in the US Inland
Northwest had lumber yields of 43 and 56% (on an oven-dry weight basis) ,
respectively (Puettmann et al. 2010, ASMI 2012) . This recovery value applies
conversion factors (ODt m·3 ) to relate the material and products into common
units (i.e. ODt). The limitation of their lumber yields is a lack of geographical and
temporal representativeness of the BC forest products industry, let alone any
technological refinements during the MPB outbreak.
Carbon models typically assume lumber yields between 45-50% (on an
oven-dry weight basis) (Schlamadinger et al. 1996, Lippke et al. 2012, Chen et
al. 2014, Smyth et al. 2014) . The origins of these estimates are not always
apparent and many simply rely on that suggested by the IPCC and UNFCCC
(UNFCCC 2003, Eggleston et al. 2006). Lamers et al. 's (2014) model of forest
products used a lumber yield of 50% for MPS-attacked roundwood.
In my study, the lumber recovery value was taken from Orbay and Goudie
(2006) (Table 2.1.). This was taken to be a fair estimate given the low estimates

37

by MFLNRO's Timber Pricing Branch and the higher recoveries reported in the
MFLNRO's Competitiveness and Innovation Branch, LCAs, and C modeling.

•

T bl 21 A

b
Lumber Recovery
mbfm m-3
% m3

NA
NA
0.24*
NA
NA
NA
NA
0.09-0.25
0.28

·11
% ODt

Reference

50
42
NA
50
52
43
50*
NA
NA

Milota et al. 2005
Milota et al. 2005
Orbay and Goudie 2006
Chen et al. 2008
Puettmann et al. 2010
ASMI 2012
Lamers et al. 2014
MFLNRO 2015a
MFLNRO 2015c

NA
NA
(38)*
NA
NA
NA
NA
(14- 40)
45

mbfm m- : Thousand board feet (mbfm) per cubic meter (m ) of roundwood .
3

3

3

% m : Percent of m lumber recovered from m roundwood .
% ODt: Percent of oven-dry tonne (ODt) lumber recovered from ODt roundwood .
3

1

():Applied Nielson et al. 's (1985) conversion factor of 1.594 m mbfm- .
• : Specific lumber recovery yields for MPS-attacked roundwood .
NA: Not available

2.1.3.1.1.2.

Mill Residue Recovery

Mill residues originate from the various processing phases of lumber
sawmills (i.e. wood handling, sawing , planing) (Shmulsky and Jones 2011 ). They
typically include : pulp chips , sawdust, shavings, and hogfuel (i.e. bark and other
mixed wood waste) (MFLNRO 2015c). While mill residues often form the majority
of the original roundwood harvested, there is little governance in their use. Their
volume , composition, and use depend on the sawmill and its prerogatives, as
well as the surrounding forest product industries (e.g. pulp and paper plants,
pellet plants) (Nielson et al. 1985). The wood fibre arrangements in this
manuscript follow those described in MLFNRO (2015) (Figure 2.1.).

38

Pulp chips are created using a chipping headrig in lumber sawmills
(Shmulsky and Jones 2011 ). Pulp chips produced in sawmills are valued in the
BC Interior as an important feedstock for the pulp and paper industry (Cantor
Pulp Inc. 2012ab, MLFNRO 2015). In the Prince George region, there exist two
chemical pulp plants (Intercontinental Pulp Mill, Northwood Pulp Mill) and one
chemical pulp and paper mill (Prince George Pulp and Paper Mill) (MFLNRO
201 Sc). It was assumed that the chemical pulp produced from the region was
exported (based on pulp production capacities) to a paper mill outside of British
Columbia (MFLNRO 201 Sc).
Sawdust and shavings are created as a result of the sawing and planing
processes in lumber sawmills (Shmulsky and Jones 2011 ). Both mill residues are
valued in the province as important feedstocks for the pellet industry (MLFNRO
201 Sc). In the Prince George area, there are two pellet plants: Pacific Bioenergy
and Pinnacle Pellet. [Alternative markets for these residues in the province
include: medium-density fibreboard and particleboard. These alternative wood
product industries are not, however, currently present in the Northern Interior
(MFLNRO 201 Sc).]
Hogfuel is created as a mixture of mill residues collected throughout all the
processes in lumber sawmills (Shmulsky and Jones 2011 ). This mill residue can
include a mix of sawdust (green and/or dry), shavings (green and/or dry), bark,
and trim ends. The end result creates a residue with variable moisture content.
As such, it is valued as a source of cheap and C-neutral fuel for bioenergy

39

(Nyboer 2015ab). The largest quantities of hogfuel are typically consumed in
SC's pulp and paper industry (MFLNRO 2015c).
In my study, I used the mill residue composition and recoveries cited in
ASMI (2012) and the wood fibre arrangements in MFLNRO (2015c) to delineate
the material flow of mill residues from the lumber sawmill to their respective
processing industries (Table 2.2.). To date, there is little information specifically
on mill residue composition and recoveries while processing MPS-attacked
roundwood. My chosen lumber recovery factor (from Orbay and Goudie 2006)
did not give a breakdown of the mill residues for MPS-attacked roundwood.
Therefore, I adapted ASMl's (2012) residue breakdown by distributing total
residue volume evenly across the mill residues.

T bl 2 2 L
Lumber
50
42
51
52
43
NA
50
45

b
Pulp
ch·1ps

d ·11
"d
Sawdust &
Sh avmgs
.
Hogfuel

% of roundwood

29
32
25
26
35
58
NA
41111111

"Processing residues"

12
9
NA
11
12
25
15
12

10
17
2511
11
11
17
351111
NA

.
Shrinkage
NA
NA
NA
NA
NA
NA
NA
2

Ref erence
Milota et al. 2005
Milota et al. 2005
Chen et al. 2008
Puettmann et al. 201 O
ASMI 2012
Canfor Corp. 2013
Lamers et al. 2014
MFLNRO 2015c

1111 "Short-lived wood products"
111111 "By-product chips"
NA: not available

40

2.1.3.1.2. Biogenic C Storage
2.1.3.1.2.1.

Lumber

Lumber has biogenic C storage in its use and disposal life stages. Lumber
has many different uses in North America (Table 2.3.). These uses of lumber in
turn determine the half-life (t 112 ) of the product, which dictates how much lumber
(i.e. biogenic C) remains in use (Smith et al. 2006). Conversely, half-life also
dictates how much lumber is sent to disposal. Lumber stores biogenic C in
landfills via its degradable and non-degradable components (Table 2.4.). The
degradable component succumbs to decay over time, whereas the nondegradable is thought of as permanent.
In my study, lumber's use and disposal was modeled after Dymond 's (2012)
BC-HWPv.1 model (Table 2.3., 2.4.). This model was created to monitor the
biogenic C storage of BC forest products in North America. Alternatives to this C
model include Chen et al. (2014), which examined the biogenic C storage of
Canadian forest products (in general) in North America.

41

Residential upkeep and
moveable homes
Furniture & other
roducts

10 (38)

NA

46 20

Other
Landfill
Rec cle
t

112

NA

NA
NA

: half-life

14% burned,
9% recycled ,
8% composted,
67% landfilled,
and 2% dum ed 11
23%
29 ears

Disposal site

Solid-waste disposal sites, landfill and open dumps, are differentiated from one another based
on the presence of oxygen. Open dumps refers to sites "where oxygen is available to decompose
all wood and paper over time, and landfills, where a covering is placed over waste periodically
and oxygen is sealed out" (Skog 2008).

2.1.3.1.2.2.

Paper

Paper has biogenic C storage in its use and disposal life stages. In contrast
to lumber, paper is not generally given a specific use, but is instead given a
generic half-life of 2.5 years (Table 2.5.). The brevity of paper's half-life results in
very little biogenic C storage while in use. The bulk of paper's biogenic C storage
is found as non-degradable portions in disposal (Table 2.6.).

42

In my study, paper's use and disposal was modeled after Dymond 's (2012)
BC-HWPv.1 model (Table 2.5., 2.6.).

Disposal Site

14% burned,
50% recycled,
5% composted,
31% landfilled,
and 1% dum ed 11
56%
14.5 ears

16% burned,
66% recycled,
5% composted,
12% landfilled ,
and 1% dum ed 11
56%
14.5 ears

Solid-waste disposal sites, landfill and open dumps, are differentiated from one another based
on the presence of oxygen . Open dumps refers to sites "where oxygen is available to decompose
all wood and paper over time", and landfills, where a covering is placed over waste periodically
and oxygen is sealed out" (Skog 2008).

2.1.3.1.3. GHG Profile of Lumber (Scenario 1)
2.1.3.1.3.1.

Harvesting

British Columbia's Interior forests are predominantly managed under a
clearcut-with-reserves silvicultural system (MFLNRO 2015d). The harvesting
event creates a large and persistent C source on site (Fredeen et al. 2007).
These emissions are not, however, attributed to the forest products industrial
system , but rather, to the forest system. Instead, the harvesting emissions
discussed here refer to the emissions surrounding the industrial harvesting
system (e.g. harvesting , skidding, delimbing) - the most common system in the
Interior being ground-based (MFLNRO 2015d).

43

Data collection on the harvesting system is primarily derived from energy
consumption surveys by industry and academia (Karjailainen et al. 1996, Samba
2002, Oneil et al. 2010). These surveys often collect energy consumption for:
preharvest activities (e.g. right-of-way logging, road construction), camp, harvest,
and silviculture (e.g. site preparation, replanting) unit processes. The primary
method of calculating their harvesting efficiency is by dividing total energy
consumption of the entire forest operation by the total merchantable volume
harvested (Oneil et al. 2010). As a result, much of the information regarding
harvesting-specific parameters is lost.
For my study, I used the energy consumption values reported in Samba
(2002). This study examined forest operations (i.e. harvesting, hauling) of
roundwood in Western Canada, including a few sites in the Prince George
region. The study's forest conditions had an average cutblock area of 24 ha,
merchantable timber volume of 244 m3 ha- 1, and a volume of 5,856 m3 (Samba
2002). This study was found to be the most comprehensive and representative
set of data of current forest operations in the region. Other sources of data
reviewed include: harvest data from industry (NRCan 2010, ASMI 2012) ,
research (MacDonald 2006), and academia (Berg and Karkalainen 2003) (Table
2.7.). But overall, there has been very little (public) research conducted on the
current GHG emission efficiencies of salvage logging operations in the BC
Interior. Instead, much of the research during these campaigns has focused on
the economic feasibility of such operations (MacDonald 2006).

44

10
5

6

9
NA

NA: not available

2.1.3.1.3.2.

NA
NA
NA
22
10

Sambo 2002
Berg and Karkalainen 2003
Berg and Karkalainen 2003
NRCan 2010
ASMI 2012

Transportation

The transportation system tracks roundwood loaded at a harvest landing
site and hauled to its designated mill (BCIT 1996, MacDonald 1999). It is often
suggested that transportation is a limiting factor in MPB salvage logging
operations due to its high cost. The damage caused by the beetle has decreased
lumber value recovery ($ m-3 ) of roundwood , making the supply chain less
efficient. Known parameters impacting the transportation system 's cost efficiency
include: the diversity in roadways, speed, weight, distance, cycle-time, and fuel
consumption (MacDonald 2006, MFLRNO 2008). These are discussed in detail in
MacDonald (2006) and are the focus of ongoing research by the Forest
Engineering Research Institute of Canada (FERIC) (Jokai 2006ab, MacDonald
2006). In contrast, little research has been conducted explicitly on the energy
consumption and/or efficiency of salvage logging operations in the BC Interior
(Sambo 2002, Lindroos et al. 2011 ).
Data collection on the transportation system's energy consumption is
typically collected via surveys (Sambo 2002, Oneil et al. 2010 , ASMI 2012).
Hauling distances are cited on a one-way basis, although most surveys include

45

backhaul in their overall energy consumption reporting (Sambo 2002, Puettmann
et al. 2010, ASMI 2012). Similar to harvesting, the transportation system's energy
efficiency is calculated by dividing the total energy consumption by the total
merchantable timber volume (Oneil et al. 2010). As a result, much of the
information regarding transportation-specific parameters is lost.
In my study, the transportation of roundwood is derived from Sambo (2002)
(Table 2.8.). The energy consumption value here reflects an average value for
the entire transportation system, meaning it covers a round trip and its various
road conditions (e.g. highway, off-road). The average one-way haul distance for
my study was 106 km (Sambo 2002). This distance is thought to be fairly
representative of the region and falls within the distances discussed elsewhere in
the literature, such as: 50-200 km (MacDonald 2006), 102 km (ASMI 2012) and
<300 km (Lamers et al. 2014).

f

T bl 2 8 GHG
.3

Non-Biogenic

kg C02e m roundwood
10 (106 km)
6 (NA)
3 (NA)
10 (88 km)
NA

NA: not available

2.1.3.1.3.3.

t

•

. t

kg C02e m
NA
NA
NA
NA
7 NA

.3

1u mber

•

d

d
R f
e erence

Sambo 2002
Berg and Karkalainen 2003
Berg and Karkalainen 2003
NRCan 2010
ASMI 2012

Primary Processing Industry: Lumber Sawmill

The primary roundwood processing industry operating in the Interior is the
large dimensional lumber sawmill (MFLNRO 2015c). Each sawmill is specialized
with specific headrigs (i.e. saws, chippers) and kilns to match selected tree

46

species, lengths, and moisture contents of incoming roundwood from the
surrounding timber supply, while at the same time, meeting international market
demands (Bogdanski et al. 2011 ). Dimensional lumber is valued as a low input,
low cost structural product used primarily as timber framing in new residential
housing (Shmulsky and Jones 2011 ). The MP B's impact on lumber quality
threatens the industry's share in these markets because of its decreased product
value recovery($ m-3 ; Bogdanski et al. 2011 ).
Data collection on sawmill energy consumption is collected via survey.
These surveys can include intensive life cycle assessments (LCA) of forest
products (ASMI 2012, Puettmann et al. 2013ab) or extensive industrial surveys
(Nyboer 201 Sb). The trade-off between these surveys is a more detailed material
flow and energy consumption in LCA, over better representation (e.g.
geographical, temporal) of the industry in broader industrial surveys. In both
cases, energy consumption is calculated by dividing annual energy use over
annual product output, on a product volume basis (Nyboer 201 Sb).
In my study, I used the primary processing data from an LCA survey of
Canadian softwood lumber (ASMI 2012) (Table 2.9.). This inventory presented
the most comprehensive view of the typical Canadian sawmill. Granted, further
refinement is still needed for processing MPS-attacked roundwood explicitly.
[However, the feasibility of doing so remains unlikely.] Anecdotal evidence of the
beetle 's impact on the energy efficiency of sawmills has suggested that wood
handling and drying would increase the overall energy consumption (Lewis and

47

Hartley 2006). However, this is difficult to quantify given the mix of tree species
and overall improvements in energy efficiency experienced by most sawmills in
recent years (Nyboer 2015b).

T bl 2 9 GHG
Non-Biogenic

f

t

kg C02e m-3

kg C02e tc-1

44
32
113
103
49
NA
170

NA
NA
NA
NA
NA
297
NA

m- : cubic meter of lumber

f

I •
Biogenic

kg C0 2e m-3
97
O*
102
135
198
O*
O*

Reference
NRCan 2010
ASMI 2012
Puettmann and Oneil 2013
Puettmann et al. 2013a
Puettmann et al. 2013b
Chen et al. 2014
N boer2015b

* biogenic CO 2 emissions are confidential and/or C neutral (i.e. zero GHG emissions) .

NA: not available.

2.1.3.1.3.4.

Secondary Processing Industries

2.1.3.1.3.4.1.

Chemical Pulp Plant

Chemical pulp plants are the predominant recipients of pulp chips in the
British Columbia (Nyboer 2015a). This industry is characterized by its distinctive
use of chemicals in the pulping process of wood fibre (i.e. pulp chips) (Shmulsky
and Jones 2011 ). The chemical pulping process removes lignin from the wood
fibre , decreasing overall pulp yield to approximately 45-4 7% (Shmulsky and
Jones 2011 , Cantor Pulp 2012ab). The main pulp product from this process is
bleached Kraft market pulp (BKMP). This pulp is typically exported and
processed into paper and paper products via paper mills in other countries (IC
2015).

48

Chemical pulp plants are very energy intensive (Browne and Williamson
1999, Francis et al. 2004, NRCan 2008, US DOE 2005). In British Columbia,
much of the energy consumed is generated in-house using spent cooking
chemicals (i.e. black liquor) and hogfuel sourced from neighboring sawmills
(Cantor Corp. 2013, Nyboer 2015a). A pulp plant's energy mix (i.e. % coal,
natural gas, biomass) is plant-specific, and thus, the GHG emissions from this
industry can vary quite substantially among plants. Energy audits typically focus
on energy consumption specific to a pulp plant's unit processes (e.g. chip
conveying, digester, washing and screening, etc.) rather than its energy mix
(NRCan 2008). As a result, the GHG emissions from this industry are often
obscure.
In my study, energy consumption data of a chemical pulp plant were taken
from an environmental product declaration of a chemical pulp plant located in
Prince George (Cantor Pulp Inc. 2012a) {Table 2.10.). This was found to be the
most geographically representative documentation of the pulp industry in the
region. The plant's energy efficiency in 2012 was 38.3 GJ ADr 1 , which was
composed of: 1.3% hydroelectric, 90.2% biomass, and 8.5% fossil fuels. This
agrees well with similar energy consumption values of 36-37 GJ ADr 1 reported
elsewhere in the literature (NRCan 2008, Nyboer 2015a).

49

T bl 210 GHG
f t
Non-Biogenic
kg C02e ADr 1 kg C02e tc-1
171
NA
240
NA
NA
578 11
NA
109811
325
NA

f
h . II
Biogenic
kg C0 2e ADr 1
2951
3110
O*
O*
O*

I

I

I

t

Reference
Cantor Pulp Inc. 2012a
Cantor Pulp Inc. 2012b
Chen et al. 2014
Chen et al. 2014
N boer2015a

ADt: air-dry tonne (ADt) of pulp.
O* biogenic CO2 emissions are confidential and/or C neutral (i. e. zero GHG emissions).
,i "cradle to gate" refers to allocating upstream GHG emissions into an industry's GHG total.
NA: not available

2.1.3.1.3.4.1 .1. Paper Plant
Paper plants receive market pulp from chemical pulp plants and process it
into paper. They do this by preparing, blending , pressing, and drying pulp into a
desired paper product (US DOE 2005). The two largest paper and writing
products from market pulp are : coated and uncoated freesheet paper (AFPA
2012). Coated paper can be used in glossy magazines and catalogs, while
uncoated paper is commonly used as freesheet paper. The two largest importers
and producers of these paper products are United States and China (IC 2015).
Paper plants are energy intensive (US DOE 2005, NRCan 2008) . Their
energy consumption can range anywhere between 11.1 to16.1 GJ ADr 1 (US
DOE 2005, Nyboer 2015a). Similar to chemical pulp plants, the energy mix of
paper plants is known to vary among individual plants. For example, paper plants
in China are mainly based on coal (Newell and Vos 2012) , whereas plants in the
US and Canada have a more diverse mix of fossil and renewable fuels (US DOE
2005, Newell and Vos 2012, Nyboer 2015a).

50

In my study, I used the energy consumption values of an average Canadian
paper plant (Nyboer 2015a) (Table 2.11.). The rationale behind this decision was
based on insufficient technical coverage of paper plants in the US and China.
There are a few studies examining GHG emissions of paper plants and those
that do are obscured in scoping issues (i.e. cradle to gate) and/or C-neutral
reporting of biogenic GHG emissions.

T bl 211 GHG
Non-Biogenic
kg C02e ADr 1
139711
217511
97511
119911
NA
175

f

kg C02e tc-1
NA
NA
NA
NA
16281
NA

t

f

• • •I
Biogenic

kg C0 2e ADr 1

O*
O*
O*
O*
O*
O*

t

Reference
Newell and Vos 2012
Newell and Vos 2012
AFPA 2012
AFPA 2012
Chen et al. 2014
N boer2015a

ADt: air-dry tonne of paper.
O* biogenic CO 2 emissions are confidential and/or C neutral (i.e. zero GHG emissions).
11 "cradle to gate" refers to allocating upstream GHG emissions into an industry's GHG totals.
NA: not available

2.1.3.1.3.4.2.

Pellet Plant

Pellet plants are the primary beneficiaries of the sawdust and shavings from
lumber sawmills in the BC Interior (MFLNRO 2015c). These plants grind and
pelletize these mill residues into pellets, which can then be exported to their
primary markets in the European Union.
The energy consumption in pellet plants is relatively minor 1.6 to 3.8 GJ r 1
(Magelli et al. 2009, Pa et al. 2012). The dryers are the largest consumer of
energy in the pellet plant. Consequently, sawdust and shavings are typically
separated into two distinct lines in BC pellet mills, each with their own dryer to
increase efficiency (Envirochem Inc. 2008). Moreover, the energy demands of

51

these dryers can then be met by burning oversized wood particles in a boiler or
by burning natural gas in their boilers (Magelli et al. 2009).
In my study, the pellet plant energy consumption data were taken from Pa
et al. (2012) (Table 2.12.). This study surveyed various pellet plants in the BC
Interior. Due to the extensive use of electricity and biomass in their surveyed
pellet plants, their energy consumption and GHG emissions are quite low in
comparison to other pellet plant studies cited in the literature.

28
19311
15
16711
8
29

O*
0
O*
0
105
O*

Magelli et al. 2009
Magelli et al. 2009
Sikkema et al. 2010
Sikkema et al. 201 O
Pa et al. 2012
Lamers et al. 2014

ODt: oven-dry tonne pellet
11 natural gas-fired boiler
O*: biogenic CO 2 emissions are confidential and/or C neutral (i.e . zero GHG emissions).

2.1.3.1.3.4.3.

Bioenergy: Chemical Pulp Plant

Chemical pulp plants are the largest consumers of hogfuel in the Interior
(Dymond and Kamp 2014, MFLNRO 2015c, Nyboer 2015a). A recent survey of
the bioenergy industry in BC found a wide range in net calorific values for wood
fibre in different energy applications (3-16.8 GJ r 1) (Dymond and Kamp 2014).
The biogenic GHG emission factor of 1.622 kg C02e kg·1 for hogfuel (i.e. wood
waste) combustion (MOE 2012).

52

In my study, I chose not to report hogfuel combustion explicitly, but instead,
assumed that it was consumed as part of my chemical pulp plants' requirements
for purchased hogfuel.

2.1.3.1.3.5.

Distribution

The distribution of wood products encompasses the transportation of forest
products from their processing facility to supplier distribution centers; distribution
centers to retailer distribution centers; and retailer distribution centers to their
final use (Gower et al. 2006). The distribution model from processing facilities to
supplier distribution centers was found to differ among BC forest companies (IBI
2006). The first model is the "produce to order" distribution system whereby
production is matched with specific orders and vessel deliveries. The second
model is the "shipped to order" systems whereby forest product industries have a
set production and ship from their distribution center. With that said, there is a
general lack of data collected on the distribution of forest products (i.e. supply
chains). The few studies that have examined distribution have shown that it is
relatively small component of a product's total GHG emissions in comparison to
manufacturing and disposal GHG emissions (Winistofer 2005, Gower et al. 2006,
AFPA 2012, Newell and Vos 2012).
In my study, the general GHG emission factors I used for product
distribution were taken from CN (2015), as seen in Sikkema et al. (2013). These
factors were: 0.114 kg C02e r 1 km- 1 by truck, 0.01785 kg C0 2e r 1 km- 1 by train,
and 0.010732 kg C02e r 1 km-1 by boat.

53

2.1.3.1.3.5.1.

Lumber

In 2014, British Columbia exported 5,755 million dollars' worth of softwood
lumber out of the province (IC 2015). The destinations of these exports were:
United States (53%), China (25%), Japan (13%), Taiwan (2%), Philippines (2%),
and others (5%) (IC 2015). The United States has traditionally been the leading
importer of BC lumber, however exports to Asia are on the rise. The distribution
systems can be very different between markets, which affects their overall GHG
emissions. Meil et al. (2004) performed a study of the distance travelled by their
building materials. In one of their case studies, wood was transported a very
short distance (120 km) by truck to the construction site. In their other case study,
wood was shipped very long distances by train (2,538 km) and truck (60 km).
In my study, I assumed that lumber was distributed to the US Midwest (Meil
et al. 2004) (Table 2.13.). That study estimated the distribution emissions of
lumber from British Columbia to Minnesota, and it is thought that this study is
fairly representative of the distribution network established for lumber in North
America (Upton et al. 2008).

f t
· d. t ·b r
Tabl 213 GHG
Non-Biogenic
kg C02e m-3
kg C02e r1
18 (120 km)
32 (2,598 km)
NA
NA
NA

NA
NA
NA
22 (NA)
109 8,805 km

I

b

Reference
Meil et al. 2004
Meil et al. 2004
Gower et al. 2006
US EPA 2015
Sikkema et al. 2013

Meil et al. (2004): 120 km by truck
Meil et al. (2004): 2538 km by train and 60 km by truck.
Sikkema et al. (2013): 780 km by train , 7295 km by boat, and 100 km by truck.
NA: not available

54

2.1.3.1.3.5.2.

Pulp & Paper

In 2014, British Columbia exported 3 billion dollars worth of pulp out of the
province (IC 2015). The destinations of these exports were: China (56%), United
States (18%) , Japan (7%), Italy (5%) , South Korea (3%), and others (11 %).
Studies examining pulp's distribution have assumed long transportation distances
to China (e.g. 8,050 km) or to the US Midwest (e.g. 2,414) (Gower 2006, Newell
and Vos 2012) . Studies examining paper's distribution , on the other hand, have
assumed very short transportation distances (AFPA 2012). But overall, the GHG
emission factor for pulp and paper distribution is thought to be relatively low in
comparison to their other life stages (e.g. chemical pulp plant) (Gower 2006,
Newell and Vos 2012).
In my study, I assumed that pulp was distributed to the US Midwest (Newell
and Vos 2012) and I assumed the average GHG emission factor for paper
distribution in the United States (AFPA 2012) (Table 2.14.). Pulp was distributed
to the United States as opposed to China (i.e. the leading importer of Canadian
chemical pulp) in my C model because of limited information on Chinese paper
plants.

55

T bl 214 GHG
Non-Biogenic
kg C0 e r 1

f

t

· d. t ·b

r

I

I

I

d I

I

Reference

2

Newell and Vos 2012
Newell and Vos 2012
AFPA 2012
Sikkema et al. 2013

187 (8,050 km)
23 (2,414 km)
28* (NA)
29 1,100 km

*4.25 kg C02e ream· 7 0.00215 ADt ream· x 1.4% = 28 kg C02e r
Newell and Vos (2012): 8050 km by boat.
Newell and Vos (2012): 2414 km by train.
Sikkema et al. (2013): 50 km by truck, 1000 km by train, and 50 km by truck .
NA: not available

2.1.3.1.3.5.3.

Pellets

In 2014, BC exported 200 million dollars' worth of pellets (IC 2015). The
destinations of these pellets were: United Kingdom (67%), Italy (14%), South
Korea (11 %), Japan (6%), and United States (2%). In contrast, much of the
literature on BC pellets has focused on the export of pellets to Sweden (Magelli et
al. 2009), Netherlands (Pa et al. 2012) or EU-27 (Sikkema et al. 2013).
In my study, I assumed the distribution of pellets described by Pa et al.
(2012) (Table 2.15.). This study includes the pellets' travel by train from Prince
George to the port of Vancouver and by ship to the Port of Rotterdam.

f
T bl 215 GHG
Non-Biogenic
kg C0 e r 1
2

221 (16,300 km)
96 (17,400 km)
206 (17,500 km)
135 NA

t

· d. t ·b r

I

I

II t

Reference
Magelli et al. 2009
Sikkema et al. 201 O
Pa et al. 2012
Sikkema et al. 2013

Magelli et al. (2009) : 137 km by truck, 750 km by train , and 15500 km by boat.
Sikkema et et al. (2010) : 207 km by truck, 781 km by train , and 16500 km by boat.
Pa et al. (2012) : 125 km by truck, 840 km by train, and 16668 km by boat.
NA: not available

56

2.1.3.1.3.6.

Use & Disposal

The use and disposal of forest products is often thought to have the
greatest contribution to forest product C budgets (Miner and Perez-Garcia 2007,
Heath et al. 2010). This contribution, however, is also met with high degree of
uncertainty. Due to the longevity of some forest products, modeling is required to
estimate their impact over time. This requires intensive use of assumptions with
regards to forest product use (e.g. single-family home, multi-family home,
commercial) and its longevity (i.e. half-life); methods of forest product disposal
(e.g. recycling, landfilling, combustion); and disposal sites and their
decomposition rates, conditions and technologies (i.e. methane capture).

2.1.3.1.3.6.1.

Lumber & Paper

Lumber and paper's disposal GHG emissions are comprised of biogenic
(i.e. CO2) and non-biogenic GHG emissions (i.e. CH4) arising from their
decomposition in landfills. When these products decompose, they generate CO2
and CH4 emissions. The CH4 generated emissions can be converted into CO2
using CH4 capture technology, or it can be oxidized by soils in the landfill. The
remaining CH4 emitted is a potent GHG emission not eligible for a C-neutral
status even though it is derived from biomass (MOE 2012).
In my study, I used the disposal parameters provided in Dymond (2012)
(Table 2.16.). These parameters had a higher percentage of landfills with CH 4
capture technology and efficiency and a lower CH4 oxidation rate than similar
studies performed elsewhere in Canada (Chen et al. 2014).

57

Table 2.16.

Parameters governing GHG emissions of lumber and paper
roducts while in dis osal.

.....

~

~~

82%

Percentage of landfills with
CH4 ca ture technolo
CH4 capture efficiency

78%

51%

CH4 oxidation in soils

22%

36%

2.1.3.1.3.6.2.

Pellets

In my study, pellets' use and disposal was modeled after pellet stove in Pa
et al. (2013) (Table 2.17.). This study estimated the GHG emission factor for
burning pellets in a pellet stove. Biogenic GHG emission factors for pellet
combustion were sparse in the scientific literature due to the assumption that
these emissions are C neutral.

T bl 217 GHG
Non-Biogenic

f

t

kg C02e r1

NA
NA

4511
19

NA

f

• II t .

d d" •

kg C02e r1

R f
e erence

O*
O*
1731
1731
O*

Magelli et al. 2009
Sikkema et al. 2010
Pa et al. 2013
Pa et al. 2013
Sikkema et al. 2013

Biogenic

* biogenic CO 2 emissions are confidential and/or C neutral (i.e . zero GHG emissions).
t : tonne pellet

11 pellet stove

NA: not available

2.1.3.2.

Pellets (Scenario 2)

2.1.3.3.

Material and Product Flow

2.1.3.3.1. Pellet Recovery
There is not a whole lot of information on pellet recovery specific to pellet
plants sourcing roundwood. Most of the work on pellet recovery has come from

58

pellet plants sourcing sawmill residues. Zhang et al. (2010) assumed a recovery
of 85% (Zhang et al. 2010) for roundwood processing at a pellet plant. The
remaining 15% of the harvested roundwood is burned in the pellet plant's dryers.
In my study, I used Zhang et al.'s (2010) pellet recovery of 85% (on an
oven-dry weight basis). This recovery was consistent with that reported by Katers
et al. (2012) for a pellet plant in the US Inland Northwest sourcing roundwood
(Table 2.18.).

Zhang et al. 201 O
Katers et al. 2012

2.1.3.2.2. GHG Emission Profile
2.1.3.2.2.1.

Harvesting

The predominant silvicultural system in the BC Interior is the clearcut-with
reserves-system (MFLNRO 201 Sd). The transition from traditional forest
operations (for lumber, plywood, etc.) to proposed operations for pellets depends
largely on the material input demands (e.g. whole logs, chips) of the receiving,
primary processing facility (i.e. pellet plant) (Lindroos et al. 2011 ). As a result, the
silvicultural and harvesting systems vary according to these mill demands. The
notion of harvesting MPS-attacked forests for bioenergy also coincides with the
notion of a more intensive harvest. For example, studies examining salvage
operations have compared chipping whole trees and solely using harvest
residues. However, the consensus on this matter has been that it is not

59

economically feasible to chip these materials on site (MacDonald 2006, Abbas et
al. 2011 ). The alternative then has had the pellet industry adopting more
traditional harvesting systems in collecting roundwood as whole logs. Zhang et
al. (2010) assumed traditional harvesting systems in their modeling of a pellet
plant sourcing roundwood.
In my study, harvesting GHG emissions were taken from an energy
consumption survey reported in Samba (2002) (Table 2.19.). This was the same
survey that was used to construct an emission factor for harvesting in scenario 1.
One of the advantages of this overlap is that the assumptions regarding the
forest conditions are the same. Samba's (2002) forest conditions had an average
1

cutblock area of 24 ha, merchantable timber volume of 244 m3 ha- , and a
volume of 5,856 m 3 . A contemporary study by Zhang et al. (2010) did an LCI on
whole-tree harvesting for pellets in northern Ontario that had a stocking of 150 m

T bl 219 GHG
-3

kg C02e m rw

10

NA
NA

f t
. h
r •f t
Non-Biogenic
-1
-1
kg C02e ODt rw
kg C02e ODt pellet

NA
36

NA

m rw: cubic meter (m ) of roundwood .
ODtrw: oven-dry tonne (ODt) of roundwood.
0Dtpeue1: ODT of pellet.
NA: not available

2.1.3.2.2.2.

NA
NA

60.6

3

Reference
Samba 2002
Zhang et al. 2010
Lamers et al. 2014

Transportation

The transportation system and equipment used to support the pellet
industry can accommodate a wide range of material inputs (MacDonald 1999,

60

2006). These inputs include whole logs, short logs, residues, and wood chips
(Lindroos et al. 2011 ). Due to economic feasibility, some studies have opted to
transport whole logs directly to the pellet plants (Zhang et al. 2010, Lamers et al.
2014).
In my study, the emission factor for transportation is taken from Sambo
(2002) (Table 2.20.). This inventory models the transport of logs from the harvest
area to the primary processing facility (i.e. pellet plant). The GHG emission factor
reported by Sambo (2002) is similar to those reported elsewhere in the literature.

T bl 2 20 GHG
-3

kg Co 29 m rw
10

f t
f t
• • d
Non-Biogenic
CO
-1
-1
kg
29 O Dt rw
kg C 0 29 ODt pellet

NA

NA
NA

27

NA

m rw: cubic meter (m ) of roundwood .
ODtrw: oven-dry tonne (ODt) of roundwood .
0Dtpeue1: ODt of pellet.
NA: not available

2.1.3.2.2.3.

NA
NA
16

d
Reference
Sambo 2002
Zhang et al. 2010
Lamers et al. 2014

Primary Processing Industry: Pellet Plant

Pellet plants sourcing whole logs would require an additional processing
line to that commonplace in the BC Interior. Pellet plants sort wood fibre based
on the wood particle size and moisture content of their input materials. The
process line for roundwood would require it to be debarked, chipped, ground, and
dried before becoming integrated into the main processing line (Zhang et al.
2010). Roundwood's drying requirements result in greater energy consumption
and GHG emissions compared to pellet plants sourcing sawdust and shavings
(Katers et al. 2012).

61

In my study, my pellet plant GHG emission data were based on Zhang et al.
(2010) (Table 2.1.). A detailed GHG inventory of BC pellet plants processing
whole logs was not available, so Zhang et al. (2010) was used as it modeled a
whole-tree pellet processing facility in Ontario.
Table 2 21 GHG
·s .
Non-Biogenic
kg C02e oor1

f

• oc s· • o
Biogenic
kg C02e oor1
t

34
59

8

19011

f

O*
O*
O*

d ood t

• II t •I t

Reference
Zhang et al. 201 O
Sikkema et al. 2010
Pa et al. 2012
Lamers et al. 2014

ODt: oven-dry tonne (ODt) of pellet.
O* biogenic CO 2 emissions are confidential and/or C neutral (i.e. zero GHG emissions).
~Assumed Lamers et al. (2014) combined biogenic and non-biogenic GHG emission factors .

2.1.3.2.2.3.

Distribution

Refer back to the section on pellet distribution described in lumber's
industrial system (scenario 1) (§ 2.1 .3.1.3.5.3.) .

2.1.3.2.2.4.

Use & Disposal

Refer back to the section on pellet use and disposal described in lumber's
industrial system (scenario 1) (§ 2.3.1.3.6.2.).

2.1.4.

Calculations
Various calculations and conversions were required in compiling all of the

data collected for each of my industrial forest product systems (Table 2.22.,
Appendix 1-111). They were compiled according to a reference unit. This reference
unit was an oven-dry tonne (ODt) of wood fibre (Table 2.23., 2.24.).
I calculated my C budgets based off of this reference unit framework using
my experimental unit of 1 m3 roundwood (or 0.409 ODt m-3 ).

62

2.1.4.1.

Material & Product Flow

My lumber recovery yield was measured on a thousand-board-foot basis
(mbfm) at -15% MC00 (Orbay and Goudie 2006). This yield assumes that the
moisture content (MC) is above the fibre saturation point (FSP) of wood (-30%
MC 00 ). However, current findings by FIi (2009) suggest that roundwood is below
the FSP point (at 20.4% MCoo) at which point MC adversely affects volume. My
study converted "green" volume of roundwood (i.e. MC present in the field) to an
oven-dry (OD) volume to facilitate C accounting amongst material and product
flows (Table 2.22a.) (Briggs 1994 p.5).

2.1.4.2.

Biogenic C Storage

The amount of forest products in use was calculated using a first order
decay function {Table 2.22b.) (Eggleston et al. 2006 p.12.11 ). This function was
prescribed to each of a forest product's uses and half-lives (Tables 2.3. to 2.5.).
The amount of forest product still in use at year 100 was then converted into
biogenic C storage {Table 2.22c.) (MOE 2011 p.87).
The amount of forest products in landfill disposal was calculated by
summing up the amount of products left in its degradable and non-degradable
pools. The non-degradable portion of forest products goes unchanged, whereas
the degradable portion of forest products decomposes. Its decomposition over
time is calculated using a first order decay function (Table 2.22b.) (Eggleston et
al. 2006 p.12.11 ). Biogenic C storage is then calculated by converting the total
amount of forest products remaining in disposal at Year 100 into units of carbon

63

(Table 2.22e) (MOE 2011 p.87).

2.1.4.3.

GHG Emissions Factors

The non-biogenic GHG emission factors for most of forest product's life
stages were calculated by multiplying energy consumed per unit product by
energy-specific GHG emission factors (Table 2.22f., Appendix I) (MOE 2011
p.89). The biogenic GHG emission factors for each of these life stages were
calculated in a similar fashion (Table 2.22f., Appendix II) (MOE 2011 p.98).
These product-based GHG emission factors are then converted to materialbased factors using various product-material conversion factors (Appendix Ill).
The non-biogenic GHG emission factors for distribution were based on
CAS's (2011) amount and distribution approach for product distribution (Table
2.22h.) (MOE 2011 p.95).
The non-biogenic GHG emission factors for landfill disposal were calculated
by first deriving the methane (CH4) generated from the forest product under
conditions of landfill disposal. The amount of CH4 generated is calculated by
multiplying the amount of material decomposed with the fraction that emits as
CH4 (Table 2.22i.). The actual CH4 emitted is then calculated by multiplying the
amount of CH4 generated by the percentage of CH4 recovery (and its efficiency),
as well as the CH4 that is oxidized by soil (Table 2.22j.).
The biogenic GHG emission factors for landfill disposal were calculated by
deriving the amount of CO2 generated and emitted in the landfill (Table 2.22k).

64

Table 2.22.

c)

List of equations used in calculating the C budgets of forest
roducts harvested from MPS-attacked forests.

GHGco2,HWPin-use,t

=

Lk[L~=o(mk,t,x X fc,in-use ,k,(100-x)) X (1 - fProduction loss,k)] X fc,wood X

d

C = Chw x 1-Df

e)

GHGco2 ,HWP inlandfill,t

+ Chw x Df x e-k t

M':;

=

Lk[L~=o(mk,t ,x X fc ,inlandfill,k,(100-x) X (1- fProductionloss,k)] X fc ,wood X

GHG Emission Factors
f) GHG·,EmissionSource·,t = EFi, · X Ali X CF
g) GHG·,PEB BEB,t = Lb EFb, . X ALb,t X CFb

h) GHG·,PE6 BE6,t = Lm[EFm, · X L (Dm,

02

M':;

02

X Cm, ,t) X CFm]

= 1-Rcol X M en X 1-Roxi
k) GHGco2,HWPinlandfill,t =
Memit

Lk { L~=O [ ( mk,t,x X (1 - fc.in-use ,k,(100-x) -

fc ,non landfill,k ,(100-x) -

fc,in landfill,k,(100-x) ) X (1 - fProduction loss,k) ] X fc,wood X CH4,LFG X

Glossary:
AL;or ALb,t

CF or CFb or CFm

CH4,LFG

Chwp
Cm,9,t

EF;,i or EFb,i or EFm,i
fc, wood
fc,in-use,k, 100-x
f C,in-landfi/1,k, (100-x)
fc,non tandfill,k,(100-x)

MWcH4}
MW

The quantity of input/output or "activity level" for emission source I
(e.g . volume of fuel combusted, amount of fertilizer applied, etc.).
The conversion factor to be used when the units of the activity level do
not match those of the emission factor. Where both the activity level and
emission factor are expressed in the same units, CF would be set to 1.
Molar% CH 4 in landfill gas .
C in harvested wood products
Total quantity of material, equipment, input, or personnel g transported
the same distance using transport mode m during reporting period t.
Where the same type of good is transported different distances to arrive
at the project or baseline site, they should be treated as separate goods
for the purposes of this calculation.
fraction of Chwp that decomposes
transport distance for material , equipment, input, or personnel g using
transport mode m.
the emission factor for GHG j,b,m and emission source I
[e.g. tonne C0 2/(activity or input/output)]
the fraction of the dry mass of wood , excluding bark, that is carbon.
the fraction of C in HWPs of type k that remain in-use after (100-x)
years.
The fraction of C in HWPs of type k that remains in landfill after (100-x}
years.
The fraction of carbon in HWPs of type k that have been discarded but
not sent to landfill after 100 - x years.

65

fproduction loss, k

fsp

GHGco2, HWP,in landfill, t

GHGco2, HWP in-use, t

GHGj,Emission Source,l,t
GHGj,PEBIBEB,I

GHGj,PE6/BE6,t

GHG 1

HF

l(t)
k
k
M1
Mgen
m k, t, x

MCoo,x
MWc
MWco2
MWcH4
p

SHvx
Shv1
R col
Roxi

t

t,12
X

The fraction of wood mass lost as residuals during production of HWP k
fibre saturation point (-30% MCoo)
Mass of carbon dioxide, in tonnes, that remains stored in in landfill
project or baseline HWPs harvested in reporting period t, 100 years
after initial sequestration in the tree from which it is derived or after the
start of the project, whichever is later.
Mass of carbon dioxide, in tonnes , that remains stored in in-use project
or baseline HWPs harvested in reporting period t, 100 years after initial
sequestration in the tree from which it is derived or after the start of the
project, whichever is later.
emissions of GHG j from emission source I during reporting period t.
Emissions of GHGi , in tonnes, from on-site vehicle and equipment fuel
combustion during reporting period t. Note that for this SSP, only CH4
and N20 are to be reported , as CO 2 is tracked as part of forest C
pools.
emissions of GHGi , in tonnes , from transportation of materials,
equipment, inputs, and personnel to the project I baseline site during
reporting period t.
GHG em ission factor per unit energy (or fuel) (see Appendix I)
half life
inflow of HWP in a particular year
constant annual decomposition rate (k = ln(2) + t,12)
relevant HWP types .
methane fraction in landfill emissions
methane generated by decomposition
dry mass, in tonnes , of harvested wood , minus bark, harvested in
reporting period t, that grew x years prior to harvest, and that will be
processed into HWP k.
intermediate moisture content
molecular weight of carbon
molecular weight of CO2
molecular weight of CH4
A number of years prior to the harvest. x ranges from O (i.e. the year of
harvest) to p , where p represents the lesser of the age in years of the
oldest tree that is harvested in a given reporting period ; and the
number of years from project start to the end of reporting period.
volumetric shrinkage
volumetric shrinkage constant from 0-30% MC 00
landfill CH4 collection rate
the oxidation rate for CH 4 reaching the top layer of waste covering soil.
year
half life
A number of years prior to the harvest. x ranges from O (i.e. the year of
harvest) to p , where p represents the lesser of the age in years of the
oldest tree that is harvested in a given reporting period ; and the
number of years from project start to the end of reporting period.

66

Table 2.23. Individual components in lumber's C budget model based on a
reference unit of oven-d tonne ODt of wood fibre.

Reference
Orbay and Goudie
2006
ASMI 2012
ASMI 2012

%00

Lumber Recovery
Sawdust & Shavings
Recove
Chemical Pulp Recovery

Harvest
Trans ortation
Primar Processin
Secondary Processing:
Chemical Pul Plant
Secondary Processing:
Pa er Plant
Secondary Processing:
Pellet Plant
Distribution: Lumber
Distribution: Pulp &
Pa er
Distribution: Pellets

40

35
9

ASMI 2012
Cantor Pulp Inc.
2012a

16
46

Biogenic
k C02e 0Df1
0
0
67
1532

Non-Biogenic
k C02e 0Df1
24
27
21
89

615

199

Sambo 2002
Sambo 2002
ASMI 2012
Cantor Pulp Inc.
2012a
Nyboer et al. 2015

94

7.4

Pa et al. 2012

64
51

64
51

183
262
892
1541

28
377
1286
40

Meil et al. 2004
Newell and Vos
2012, AFPA 2012
Pa et al. 2012
D mond 2012
D mond 2012
Pa et al. 2013

Reference

67

Table 2.24. Individual components in pellets' C budget model based on a
reference unit of oven-d tonne ODt of wood fibre.

Harvest
Trans ortation
Primary Processing:
Pellet Plant
Distribution
Use & Dis osal: Pellets

Biogenic
k C02e 0Df 1
0
0
235

Non-Biogenic
k C02e oor1
24
26
2

Samba 2002
Samba 2002
Zhang et al. 2010

0
1419

183
37

Pa et al. 2012
Pa et al. 2013

Reference

Sensitivity Analysis

A sensitivity analysis was performed on the C budgets of my two
scenarios and their respective case studies. This analysis was performed by
increasing individual parameter values by 10% and by observing their overall
impact on the net C balance. The purpose of running a sensitivity analysis is to
determine the most sensitive parameters in a model. My intent was to know
where the greatest point of emphasis should be placed in refining parameters in
the forest product industrial system.

68

Chapter 3.
C BUDGETS OF TWO DIFFERENT FOREST PRODUCTS (LUMBER,
PELLETS) HARVESTED FROM BEETLE-ATTACKED FORESTS IN THE
PRINCE GEORGE REGION.
3.0. RESULTS
3.0.1.

C Fluxes Associated with the Industrial Systems of Lumber and
Pellet Production

3.0.1.1.

Lumber (Scenario 1)

3.0.1.1.1. Material & Product Flows

In an industrial system defined by lumber production (scenario 1),
roundwood harvested from MPS-attacked forests is first processed at a lumber
sawmill (Figure 3.1.). The lumber recovery for MPS-attacked roundwood at this
sawmill was modeled with a 40% recovery yield of the original roundwood ovendry (OD) weight. Meanwhile, the mill residue recovery at the sawmill was 60%
and it was composed of 35% pulp chips, 16% hogfuel, and 9% sawdust and
shavings.
Sawmill residues were processed in secondary processing industries
(Figure 3.1.). These industries included chemical pulp, paper, and pellet plants.
The chemical pulp plant recovered 16% of original roundwood volume as a
chemical pulp product. It was assumed that the paper plant maintained this
recovery during its processing of chemical pulp into paper (i.e. -100% recovery).
The pellet plant recovered 8% of the original roundwood volume as pellets.

Collectively, the secondary processing industries equated to a 24%
recovery of the original roundwood volume as secondary wood products (i.e.
paper and pellets). The remaining primary and secondary mill residues (i.e. black
liquor and hogfuels) amounted to 36% of original roundwood volume and were
used as a source of biomass fuel in bioenergy processes.
40% lumber

100%
round wood

Figure 3.1.

Material and product flow of 1 m3 of dead merchantable
lodgepole pine (Pinus contorta var. latifolia) roundwood in an
industrial system defined by lumber production (scenario 1). For
details, see Table 2.23.

3.0.1.1.2. Biogenic C Storage

Lumber and paper were the only forest products capable of physically
storing biogenic carbon (C) in lumber production (Figure 3.2. , 3.3.). Their storage
was confined to the use and disposal stages of this industrial system.
The biogenic C stored in forest products while in use decreased over time
(Figure 3.2.). In the beginning (Year 0), forest products are in use and amount to
56% of the original roundwood volume being stored (Table 3.3). At Year 100,
70

forest product remaining in use amounted to 9% of the original roundwood
volume and its composition at this time was predominantly lumber (Figure 3.3.).
The biogenic C stored in forest products while in disposal increased over
time (Figure 3.2.). In the beginning (Year 0), there were no forest products in
disposal, and so, there was no biogenic C storage. At Year 100, products in
disposal amounted to 26% of the original roundwood volume being stored (Figure
3.3.). The composition of this storage at Year 100 was 19% lumber and 7%
paper.
..........

C")
I

E

0

C\I

-50

Q)

0

()
0)

-100

en

-150

~
..__.

C

0

YearO

Year100

·u5 -200
en

E -250

-

-300

:::J

>

-350

~ Landfill C Storage

w

-400

•

w
C

Q)

co

O"

0

C\I

()

Product C Storage

-450

Figure 3.2.

Change in biogenic C storage while in use (i.e. product C
storage) and in disposal (i.e. landfill C storage) over time.

71

100% roundwood

Figure 3.3.

The distribution of biogenic C storage amongst forest products in
lumber's industrial forest product system (scenario 1).

3.0.1.1.3. GHG Emission Profile
In my first scenario, the GHG emissions attributable to lumber production
were complex and extensive (Figure 3.4.). Its GHG emission profile is described
here briefly based on its (1) geographical, (2) temporal, and (3) physical aspects.
(1) The geographical aspect of lumber production spanned many different
geographical areas. The GHG emissions that occurred inside the province were
harvest, transportation, primary processing and most secondary processing. The
GHG emissions that occurred outside of the province were secondary processing
(i.e. paper plant), distribution, and the use and disposal of wood products. (2) The
temporal aspect of GHG emissions in lumber production is long-lasting due to the
longevity of some forest products in use and in disposal. (3) The physical (or
procedural) aspect of GHG emissions in lumber production encompasses
multiple forest products and their life cycles.

72

-

M

'E
Q)

C\I

0

0

~

250

Non-Biogenic GHG

I

200
100

Figure 3.4.

3.0.1.1.3.1.

~ Emissions

II

Biogenic GHG
Emissions

GHG emission profile in lumber production (scenario 1) based on 1
3
m of dead merchantable lodgepole pine (P. contorta) roundwood.
The GHG emissions from Use & Disposal are cumulative over time.
Reference: m3 = 0.409 ODt = 0.2045 tC = 750 kg C02e

Biogenic GHG Emissions

The greatest amount of biogenic GHG emissions in my C budget was
released during the secondary processing of pulp chips at chemical pulp plants
(Figure 3.5.). These emissions were derived from the use of black liquor and
purchased hogfuel. From an accounting perspective, these emissions occurred in
BC; they were emitted immediately, and they pertain to the mill residue stream of
lumber production.
73

My forest C budget calculated biogenic GHG emissions (a) by life stage and
forest product (Figure 3.5a.). Alternatively, the biogenic GHG emissions in my C
budget could be broken down (b) by life stage, (c) by life stage and time, and (3)
by parent wood product. (b) By breaking down biogenic GHG emissions by life
stage, I found that the secondary processing of mill residues was the largest
source of its emissions (Figure 3.5b.). (c) By breaking down these emissions by
life stage and time, I found that secondary processing's emissions are
immediate, in contrast to the prolonged decomposition emissions via forest
product use and disposal (Figure 3.5b,c.). (d) By breaking down biogenic GHG
emissions by their parent wood product I found that paper was the largest emitter
in this system (Figure 3.5d.). Moreover, sawmill residues (i.e. pulp chips, sawdust
and shavings, hogfuel), collectively, were responsible for 60% of the total
biogenic GHG emissions in lumber's industrial system (i.e. 433 kg C0 2e m-3 ).

74

Total Biogenic GHG Emissions:
a.)...QY- life stage and wood P-roduct
Use & Disposal: Pellet (Year 0-100) (12%)

Primary Processing : Sawmill (6%)

Use & Disposal: Paper (Year 0-100) (13%)

Secondary Processing : Pulp Plant (48%)

Use & Disposal: Lumber (Year 0-100) (11%
Secondary Processing : Pellet Plant (1%)
Secondary Processing : Paper Plant (9%)

.bJ....bY. life stag.e

Primary Processing (6%)

Use & Disposal (36%)

Secondary Processing (58%)
Primary Processing (8%)

Secondary Processing (76%)

d..)....Q.Y_P-arent wood P-roduct

Paper (73%)

Figure 3.5. Alternative ways of interpreting the biogenic GHG emissions in
lumber production (scenario 1).

75

3.0.1.1.3.2.

Non-Biogenic GHG Emissions

The greatest amount of non-biogenic GHG emissions was released in the
use and disposal of paper (Figure 3.6.). These emissions are released during the
decomposition of paper in landfills. From an accounting perspective, their
emissions occur outside of BC; they are emitted over time; and they pertain to
the mill residue (or by-product) stream of lumber's industrial system.
My forest C budget calculated non-biogenic GHG emissions (a) by life stage
and by wood product (Figure 3.6a.). Alternatively, the non-biogenic GHG
emissions can be broken down (b) by life stage, (c) by life stage and time, and
(d) by parent wood product. (b) By breaking down the non-biogenic GHG
emissions by life stage, I found that the use and disposal of forest products was
the greatest GHG emitter (Figure 3.6b.). (c) By breaking down these same
emissions by life stage and time, I found that forest product's distribution and
secondary processing GHG emissions are immediate, whereas their use and
disposal GHG emissions occur over the long-term (Figure 3.6b, c). (d) By
breaking down the non-biogenic GHG emissions by its parent wood product I
found that paper was the largest source of non-biogenic GHG emissions (Figure
3.6d.).

76

Non-Blogenic GHG Emissions
a..)Jzy life stage and wood product
Use & Disposal : Paper (Year 0-100) (36%)

Harves.t 14%)
Transportation (5%)
Primary Processing : Sawmill (4%)
Secondary Processing : Pulp Plant (5%)
Secondary Processing : Paper Plant (6%)
Secondary Processing : Pellet Plant (0%)
Distribution : Lumber (4%)
Distribution : Pulp (2%)
Distribution : Pellets (3%)
Use & Disposal : Lumber (Year 0-100) (30%)

b.Jjzy Iife stag.e

Harvest (4%)
Transportation (5%)
Primary Processing (4%)
Secondary Processing (11 %)

Distribution (10%)
Use & Disposal (66%)

~)Jzy life stage and time
Use & Disposal (1%)
Distribution (29%)
Transportation (14%)

Primary Processing (10%)

g_,_)_by_parent wood product

Pellets (4%) Hogfuel (2%)

Lumber (40%)

Paper (54%)

Figure 3.6.

Alternative ways of interpreting the non-biogenic GHG emissions in
lumber production (scenario 1).

77

3.0.1.2.

Bioenergy: Pellets (Scenario 2)

3.0.1.2.1. GHG Emission Profile
In my second scenario, the GHG emissions attributable to the pellets were
straightforward and simplistic (Figure 3.7.). Its GHG emission profile is discussed
here briefly based on its (1) geographical, (2) temporal, and (3) physical aspects.
(1) The geographical aspect of GHG emissions in pellet production spanned
different countries. The emissions that occurred in BC were: harvest,
transportation, and primary processing. The emissions that occurred outside of
BC were: distribution, use and disposal. (2) The temporal aspect of GHG
emissions in pellet production was short-lived. (3) The physical (or procedural)
aspect was well defined.

78

~ Non-Biogenic GHG Emissions

II Biogenic GHG Emissions

Figure 3.7.

GHG profile of pellet production based on 1 m3 of dead
merchantable lodgepole pine (P. contorta) roundwood . The
biogenic GHG emission factors for wood waste and pellet
combustion varied in the literature (Appendix II).
Reference: 1 m3 =0.409 ODt =0.2045 tC =750 kg C0 2e.

79

3.0.1.2.1.1.

Biogenic GHG Emissions

The greatest amount of biogenic GHG emissions were emitted during the
use and disposal of pellets (Figure 3.8.). Pellets are burned immediately in pellet
stoves. These emissions occur in Europe, in the short term, and are typically not
reported in GHG inventories because they are assumed to be C neutral and are
accounted for as zero GHG emissions.

Biogenic GHG Emissions:
a.)_by life stage and wood product

589 kg CO m-3
2
Primary Processing: Pellet Plant (16%)

Figure 3.8.

Breakdown of the biogenic GHG emissions in the pellet
production (scenario 2).

3.0.1.2.1.2.

Non-Biogenic GHG Emissions

The greatest amount of non-biogenic GHG emissions was emitted during
the distribution of pellets (Figure 3.9.). Pellets are shipped overseas by boat to
international markets. These emissions occur between BC and Europe, in the
short term, and are typically included in pellet C budgets.

80

Non-Biogenic GHG Emissions:
a.)__by life stage and wood product

Transportation ( 11 % )
Primary Processing : Pellet Plant (2%)

Figure 3.9.
3.0.2.

Breakdown of the non-biogenic GHG emissions in pellet
production (scenario 2).

C Budget of Forest Products Harvested from MPS-Attacked
Forests

3.0.2.1.

Lumber

If I accepted the assumption that biogenic GHG emissions are C neutral
(Case 1), roundwood harvested for lumber had net C storage (-82 kg C02e m·3 ;
Table 3.1 a.). In this context, biogenic C storage outweighed the non-biogenic
GHG emissions in my C budget. The largest source of biogenic C storage and
non-biogenic GHG emissions occur at the same life stage. This life stage was the
use and disposal phase of forest products. It was a dynamic life stage that had
both biogenic C storage and non-biogenic GHG emissions change over time. In
the end, the use and disposal phase accounted for 86% of the total C fluxes (i.e.
C storage and GHG emissions) in my C budget. Alternatively, the breakdown of

81

the total C fluxes by parent wood product was 54% lumber and 36% mill
residues.
If I rejected the assumption that biogenic GHG emissions are C neutral
(Case 2), roundwood harvested for lumber had net C emissions (350 kg C02e m-

3; Table 3.1 b.). In this context, the amount of biogenic and non-biogenic GHG
emissions was greater than the biogenic C storage in my C budget. The largest
source of these GHG emissions was in the secondary processing and the use
and disposal of forest products. These emissions differed in that the secondary
processing's emissions occur in the near term, whereas use and disposal's
emissions occur over the long term. Alternatively, it can be said that the use and
disposal phase accounted for 61 % of the total C fluxes in my C budget.

82

Table 3.1. The net C balance of lumber production a.) assuming biogenic GHG
emissions are C neutral and b.) assuming biogenic GHG emissions are not C
neutral.

C

Non-Biogenic
GHG Emissions
kg C02e m-3 (% )

Harvest
Transportation
Primary Processing : Sawmill
Secondary Processing : Pulp Plant
Secondary Processing : Paper Plant
Secondary Processing : Pellet Plant
Distribution : Lumber
Distribution : Pulp & Paper
Distribution : Pellets
Use & Disposal : Lumber (Year 0-100)
Use & Disposal : Paper (Year 0-100)
Use & Dis osal : Pellet Year 0-100

Subtotal

b.)

10 (4)
11 (5)
8 (4)
12 (6)
13 (6)
0 (0)
10 (5)
5 (2)
6 (3)
62 (28)
82 (37)
1 1
221(100)

Biogenic GHG
Emissions
3

kg C02e m- (% )

Biogenic C
Storage
3

kg C02e m- (% )

0 (0)
0 (0)
0 (0)
0 (0)

0 (O)

-251 (83)
-52 (17)

0 (0
0 (0)

-303 (100)

NET C BALANCE

-82

Biogenic GHG
Emissions

Biogenic C
Storage

0 (0)

Not C Neutral
Non-Biogenic
GHG Emissions
kg C02e m-3 (% )

Harvest
Transportation
Primary Processing: Sawmill
Secondary Processing: Pulp Plant
Secondary Processing : Paper Plant
Secondary Processing : Pellet Plant
Distribution : Lumber
Distribution : Pulp & Paper
Distribution : Pellets
Use & Disposal : Lumber (Year 0-100)
Use & Disposal : Paper (Year 0-100)
Use & Disposal: Pellet (Year 0-100

Subtotal

10(4)
11 (5)
8(4)
12(6)
13(6)
0(0)
10(5)
5(2)
6(3)
62(28)
82(37)
1 1)
221 (100)

kg C0 2e m-3 (% )

kg C0 2e m-3 (%)

27(6)
210(49)
39(9)
4(1)

43(10)
57(13)
52(12

-251 (83)
-52(17)

433 (100)

-303 (100)

NET C BALANCE

350

83

3.0.2.1.1. Sensitivity Analyses on Lumber's Case Studies
If I accepted the assumption that biogenic GHG emissions are C neutral
(case 1), the most sensitive parameter in my C budget model was the biogenic C
storage factor applied to lumber in a landfill (Figure 3.10.). This was closely
followed by the lumber recovery factor. Other sensitive parameters in my model
were: a.) material flaw's pulp chip recovery and chemical pulp recovery; b.)
biogenic C storage's lumber use, lumber disposal and paper disposal; and d.)
non-biogenic GHG factors paper disposal and lumber disposal (Figure 3.10.).
The C budget was not, however, sensitive to changes in my biogenic GHG
emission factors nor was it sensitive to small changes in the non-biogenic GHG
emissions factors of life stages (excluding use and disposal life stages).
If I rejected the assumption that biogenic GHG emissions are C neutral
(case 2), the most sensitive parameter was pulp chip recovery (Figure 3.10.).
Other sensitive parameters included: a.) material flaw's lumber recovery and
chemical pulp recovery; b.) biogenic C storage's lumber use, lumber disposal,
and paper use; c.) biogenic GHG emission factor's pulp plant; and d.) nonbiogenic GHG emission factor's lumber disposal and paper disposal (Figure
3.10.). My C budget was sensitive to changes in the biogenic GHG emission
factors but it was not sensitive to small changes (i.e. 10%) in the non-biogenic
GHG emission factors (excluding use and disposal life stages).

84

a.) Material Flow
LumberRecove~ .............. . - - - - - - - - - ~ D
Pulp Chip Recove~ ................................._
Chemical Pulp Recove~ ...............-__,
Sawdust & Shavings Recovery - ,--,
Pellet Recove~ --!~~!......-.-,..........-.-,..........-,-,..........-,-,..........~,...........,....,( ,,_,rT""1rT""1~

0%

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Paper Use

-

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Pulp Plant • - - - - - - Lumber Disposal • - Paper Disposal • - Pellet Disposal
Paper Plant
Sawmill
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d.) Non-Biogenic GHG Emission Factors
Paper Disposal
Lumber Disposal
Paper Plant
Transportation
Harvest
Pulp Plant
Lumber Distribution
Sawmill
Pellet Distribution
Pellet Disposal
Pulp Distribution

liiii-==============:::::i
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Pellet Plant
Not Carbon Neutral
Paper Distribution+,..........-,-,..........~,..........~,.............,..=;:::;::::::;:::;:::;:::::;::::;;:::;:::::;:::;,;.:::;::;::::;::;::::;~

0%

2%

4%

6%

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10%

22% 24%

Percent Change in Output (0/o)
Figure 3.10. Results from the sensitivity analyses run on the parameters of my C
budget model for lumber production. Individual parameters were
increased by 10% and the percentage change reflects the percent
change in the C budget's output.
85

3.0.2.2.

Bioenergy: Pellets

If I accepted the assumption that biogenic GHG emissions are C neutral
(Case 1), roundwood harvested for pellets emitted very little GHG emissions (99
kg C02 e m- 3 ; Table 3.2a.). In this context, the largest amount of GHG emissions
are emitted during the distribution of pellets to European markets (64% of total).

If I rejected the assumption that biogenic GHG emissions are C neutral
(Case 2), roundwood harvested for pellets emitted large amounts of GHG
emissions (688 kg C0 2 e m-3 ; Table 3.2b.). In this context, the largest GHG
emissions are the biogenic GHG emissions emitted during the use and disposal
of pellets (72% of total).
Table 3.2. The net C balance of pellet production a.) assuming biogenic GHG
emissions are C neutral and b.) assuming biogenic GHG emissions are not C
neutral.

Harvest
Transportation
Pellet Plant
Pellet Distribution
Pellet Use & Dis osal
Subtotal

Non-Biogenic GHG Emissions
kg C0 2e m 3 (%)
10 (10)
11 (11)
2 (2)
64 (64)
13 (13
99 (100)
NET C BALANCE

Harvest
Transportation
Pellet Plant
Pellet Distribution
Pellet Use & Dis osal
Subtotal

Non-Biogenic GHG Emissions
kg C02e m-3 (%)
10 (10)
11 (11)
2 (2)
64 (64)
13 13
99 (100)
NET C BALANCE

Biogenic GHG Emissions
kg C0 2e m-3 (%)

O*
O*
0

99
Biogenic GHG Emissions
kg C0 2e m-3 (% )

96 {16)
493 84
589 {100)

688

86

3.0.2.2.1. Sensitivity Analyses on Pellets' Case Studies
In the absence of biogenic GHG emissions (case 1), the most sensitive
parameter in my C budget model is pellet recovery (Figure 3.11.). This was
followed closely by the non-biogenic GHG emission factor for the distribution of
pellets. My C budget model was not sensitive to any changes in the biogenic
GHG emission factors (Figure 3.11 b.) nor was it really affected by small changes
to the non-biogenic GHG emission factors (excluding distribution) (Figure 3.11 c.).
In the presence of biogenic GHG emissions (case 2), the most sensitive
parameter in my forest product model is also pellet recovery (Figure 3.11 a). The
runner-up was the biogenic GHG emission factor applied to use and disposal of
pellets (Figure 3.11 b.). Consequently, my C budget model was sensitive to
changes in the biogenic GHG emissions factor but it was not sensitive to any
small changes in the non-biogenic GHG emission factors (Figure 3.11 c.).

87

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88

3.1. DISCUSSION
3.1.1. C Budget of Forest Products Harvested from MPS-Attacked Forests
The C budgets of my chosen forest products (lumber, pellets) had very
different C balances. Their budgets differed with respect to their industrial (i.e.
forest product) system, as well as their sensitivity to the state of the biological
(i.e. forest) system. Roundwood harvested for lumber (scenario 1) had net C
storage (-82 kg C0 2e m-3 ) and net C emissions (350 kg C02e m-3 ) with and
without the C-neutral assumption, respectively. Meanwhile, roundwood harvested
for pellets (scenario 2) had net C emissions (99 kg C02e m- 3 , 688 kg C02e m-3 )
regardless of the C-neutral assumption. These different C balances demonstrate
the relative variability between two forest products in an industrial forest product
C budget. Taken in this context, lumber was seen as contributing less GHG
emissions to the atmosphere than pellets.
My findings on lumber and pellet C budgets support previous studies
examining these forest products in British Columbia (ASMI 2012, Dymond 2012,
Pa et al. 2012, Lamers et al. 2014). However, in our studies, lumber production
was found to have a lower lumber recovery (due to the MPB attack) and higher
GHG emissions attributed to its mill residues than previous studies (ASMI 2012,
Dymond 2012, Lamers et al. 2014). Pellet production on the other hand, differed
with respect to previous studies by including a biogenic GHG emission factor for
pellet combustion (Pa et al. 2012). Finally, we excluded substitution related
emissions reductions for both lumber and pellets.

89

The C budget of lumber production had large and opposing C fluxes of
biological C storage and GHG emissions. Its biogenic C storage helped offset
GHG emissions regardless of the C neutral assumption. In contrast, the C budget
of pellet production did not have any biogenic C storage to counteract its GHG
emissions. Moreover, its non-biogenic GHG emissions were relatively low in
comparison to its biogenic GHG emissions. Its C budget was thus susceptible to
assumptions regarding the C neutrality. Pellet's C budgets could emit very little or
very large amounts of biogenic GHG emissions based simply on the C-neutrality
of its biogenic GHG emissions.
That said, total GHG emissions (i.e. biogenic and non-biogenic) were
comparable between lumber and pellet production. The mill residues in lumber
production formed the majority of the roundwood harvested from forests and they
were large biogenic GHG emitters. As a result, lumber production was not
impervious to assumptions regarding C neutrality altogether.
Lumber and pellet production differed with respect to the timing of their C
fluxes. Lumber's C storage and GHG emissions (from disposal) are protracted.
Its budget begins as a strong C sink (i.e. net C storage), but it ends as a slight C
source (i.e. net C emissions). Pellets' GHG emissions on the other hand , are
immediate. Its C budget begins as a strong C source from the start. This
observation echoes a concern held by many stakeholders in allowing whole-tree
pellet harvesting (i.e. forest harvesting for bioenergy). The concern is that forest
bioenergy releases more biogenic GHG emissions than forests are capable of

90

sequestering via photosynthesis. In essence, these concerns of "slow in fast out"
ultimately question the validity of any C-neutrality assumptions.
In closing, my forest C budgets were compared based on an experimental
unit of 1 m3 merchantable roundwood. This was chosen based on my desire to
demonstrate the differences between lumber and pellet industrial systems. One
logical alternative to my experimental unit is 1 m3 timber. This could have
examined the differing capabilities of lumber and pellet products in utilizing MPBattacked stands and timber that were deemed "non-merchantable". Lumber is
unable to use non-merchantable timber and it would otherwise decay in the
forest, whereas pellets could utilize this timber for bioenergy. The problem with
this experimental unit is that it requires a forest C modeling component and there
is some uncertainty in the snagfall and decay rates of MPS-attacked forests.

3.1.1.1.

Direct Influences of the Industrial System on Forest Product C
Budgets

The industrial system for lumber production was complex and extensive. It
had difficult geographical, temporal, and physical aspects to account for fully in a
C budget. Consequently, my study focused on establishing actual material and
product flows, biogenic C storage and GHG emission factors for each of the life
stages of lumber and its by-products. The precedent for forest product C
accounting in BC was set by two contemporary studies (Dymond 2012, Lamers
et al. 2014). Dymond (2012) focused the biogenic C storage of BC forest

91

products, while Lamers et al. (2014) focused on the possible avoided emissions
in harvesting MPS-attacked forests and in substituting forest products.
In contrast, the industrial system for pellet production was simplistic and
well documented. It had fairly straightforward geographical, temporal and
physical aspects that could easily be accounted for in a C budget. My study
focused on establishing an emission factor for whole-tree pellet harvesting of
MPS-attacked forests. To date, much of the knowledge on pellets is based on
those pellet industries sourcing sawdust and shavings. Lamers et al. (2014) used
an emission factor for whole-tree pellet processing in BC, but this was taken from
a confidential source and some of their parameters are unclear.

3.1.1.1.1. Material & Product Flow
In lumber production, the material and product flows of its forest products
were important parameters governing lumber's overall C balance. The allocation
of lumber and its mill residues is fairly well represented in the literature (MFLNRO
201 Sc), whereas their composition and/or recovery are not. This is somewhat
problematic given the sensitivity of my C model to lumber and pulp chip recovery.
The chosen lumber recovery factor for MPS-attacked roundwood in my study
was much lower than the recovery used in Lamers et al. (2014), and slightly
lower than the average recovery in the BC Interior (MFLNRO 201 Sc).
Interestingly, lumber recovery became less important when C neutrality was not
assumed. Up until this point, much of the narrative in MPB's salvage logging
campaign has been focused on the lumber recovery factor and its ability to store

92

biogenic C. My study expands this narrative to begin discussions surrounding the
potential ramifications of higher mill residue recoveries and variation in mill
residue composition, without the assurance that C neutrality is being met.
In pellet production, the material and product flow was simple but pivotal in
determining its C balance. The pellet recovery was the most sensitive parameter
in my C budget regardless of the biological context (i.e. C neutral vs. not C
neutral). The pellet recovery factor used in my study was consistent with the
recovery cited in whole-tree pellet plants in the US Midwest (Katers et al. 2012)
and lower than the pellet recoveries cited in pellet plants sourcing sawdust and
shavings in the BC Interior (Pa et al. 2012, Sikkema et al. 2013).

3.1.1.1.2. Biogenic C Storage
Biogenic C storage was a large C flux unique to lumber production. Both
lumber and paper products in this industrial system contributed to its storage.
These products stored C while they were in use and while they were in disposal.
In the end, it was found that only 9% of the original roundwood harvested
remains in use. The bulk of biogenic C storage lay in disposal C pools.
Biogenic C storage was found to play an important role in offsetting total
GHG emissions. My C budget was sensitive to the biogenic C storage factors for
lumber and paper, in use and in disposal. This finding is consistent with that
reported in the literature -- that a forest product's use and disposal is the most
important and uncertain life stage in an industrial forest product system (Miner
and Perez-Garcia 2007, Heath et al. 2010). My study borrowed Dymond 's (2012)

93

parameters for biogenic C storage of BC forest products in North America. This
study adapted the standard use and disposal parameters of North American
forest products to those products harvested from BC forests (Smith et al. 2006,
MOE 2011, Dymond 2012). While many of these parameters are the standard,
they are by no means faultless or certain. Many of the parameters in use and
disposal are the subject ongoing areas of research (Marland et al. 2010).

3.1.1.1.3. GHG Emission Profile
3.1.1.1.3.1.

Non-Biogenic GHG Emissions

The non-biogenic GHG emissions in lumber production were relatively small
and uncertain in comparison with its other C fluxes (i.e. biogenic C storage and
GHG emissions). Yet these non-biogenic GHG emissions are juxtaposed here
with the large GHG savings and simplifying assumptions that occur when
substitution emission factors are used in other studies in place of direct, nonbiogenic GHG emissions (e.g. Lamers et al. 2014). My study found the largest
amount of non-biogenic GHG emissions occurred in the disposal of forest
products. These emissions are included in Sathre and O'Connor's (2010)
substitution emission factor, but there exists considerable variation in the
disposal parameters among the studies used in their meta-analysis.
Meanwhile, the non-biogenic GHG emissions in pellet production were
relatively small in comparison with its biogenic GHG emissions. Contrary to
lumber production, the non-biogenic emissions in pellet production are fairly well
established in the literature. My study found the distribution of pellets to be the

94

largest source of non-biogenic GHG emissions and this was found to be a
consistent finding among similar studies (Magelli et al. 2009, Sikkema et al. 2010,
Pa et al. 2012).

3.1.1.1.3.2.

Biogenic GHG Emissions

The biogenic GHG emissions in lumber and paper C budgets are large in
comparison to its other C fluxes (i.e. biogenic C storage and non-biogenic GHG
emissions). Yet little attention has been brought to the biogenic GHG emissions
that occur at each of the life stages of a forest product's industrial system. They
are typically excluded from this system based on the presumption that it is the
responsibility of the biological (i.e. forest) system to account for forest product
biogenic GHG emissions.
In lumber's C budget, it was found that the biogenic GHG emissions
occurred at various life stages in the short term and in the long term. In the short
term , the largest source of biogenic GHG emissions was in the secondary pulp
processing of pulp chips at chemical pulp plants. In the long term , the largest
biogenic GHG emission was in the use and disposal of forest products. In
contrast, Lamers et al. (2014) did not examine biogenic GHG emissions directly.
Instead, the authors accounted for short-lived forest products (i.e. pulp chips,
hogfuel, sawdust and shavings) as immediate biogenic GHG emissions. Longlived forest products accrue biogenic GHG emissions over time, as products are
no longer being stored in use.

95

In the pellets C budget, biogenic GHG emissions were considerably larger
than its non-biogenic GHG emissions. The largest source of biogenic GHG
emissions was in its use and disposal. While these findings may seem apparent,
most studies treat pellet emissions as C neutral or as if the C is immediately
emitted upon harvest. Lamers et al. (2014) did the latter and treated roundwood
harvested from MPS-attacked forests for pellets as an instantaneous loss to the
system.

3.1.1.2.

Indirect Influences of the Biological System on Forest Product C
Budgets

The treatment of the biological system in forest product C accounting is
problematic given its role in determining whether or not biogenic GHG emissions
are included in the industrial system (Seachinger et al. 2009, Helin et al. 2013).
This C accounting dilemma is best exemplified by the concept of C neutrality.
The term, C neutrality, covers a suite of differing assumptions with regards to the
accounting of biogenic GHG emissions in industrial forest product systems.
These C-neutral definitions are discussed here as: C-cycle neutrality, Caccounting neutrality, and inherent C neutrality (Malmsheimer et al. 2011 ).
Carbon-cycle neutrality is "if (the) uptake of C (in CO2) by plants over a
given area and time is equal to emissions of biogenic C attributable to that area,
(the) biomass removed from that area is (considered) C-cycle neutral"
(Malmsheimer et al. 2011 ). It is typically predicated on the concept of sustainable
forest management (SFM) as a means of bypassing any forest C modeling. The

96

designation of SFM is thought to be legitimatized through its SFM certification
process. This negates the fact, however, that SFM certification programs have
failed to incorporate global C cycles and climate change as part of their criteria
and indicators for SFM designation (Tittler et al. 2001, Lloyd et al. 2014).
Moreover, few SFM certification processes can convincingly ensure SFM in BC
given the recent MPB outbreak and upcoming mid-term timber supply gap. In
fact, many of the SFM certification companies in MPS-attacked forests defer their
definition of "sustainability" to that defined by BC's Chief Forester (Lloyd et al.

2014).
Carbon accounting neutrality, at least from a biogenic perspective, is met "if
(the) emissions of biogenic CO2 are assigned an emissions factor of zero
because net emissions of biogenic C are determined by calculating changes in
stocks of stored C" (Malmsheimer et al. 2011 ). Conventionally, nations did not
have to report biogenic GHG emissions in their industrial sectors, provided that
they reported these emissions as part of the forest's GHG emissions. This is
problematic for the provincial government, as it does not currently report forest
GHG emissions in its GHG inventory. This lack of reporting forfeits its rights to
claim biogenic GHG emissions as C neutral, under a C accounting neutrality
definition (Malmsheimer et al. 2011 ). By not reporting forest GHG emissions,
these biogenic GHG emissions would then need to be included in their respective
industrial sectors. [Instead, the Province considers biogenic GHG emissions as C

97

neutral under inherent C neutral and/or C-cycle neutral definitions, and excludes
them from being reported in its GHG inventory.]
Inherent C neutrality assumes that "biomass was only recently removed
from the atmosphere; (and that by) returning it to the atmosphere (it) merely
closes the cycle" (Malmsheimer et al. 2011 ). It is a simple and obvious definition
that blindly promotes the renewable nature of biological C (i.e. bioenergy) over
the non-renewable nature of fossil fuel C (i.e. natural gas, coal). Its limitation is
that it disregards any incremental steps in forestry's climate change mitigation
efforts, and instead promotes broad and abstract goals of reducing global fossil
fuel consumption. While the latter is the ultimate goal of climate change
mitigation, it may in fact be misguided given the lack of coverage of the biological
system. Biomass is a low-energy fuel and releases more GHG emissions per unit
of energy harnessed than most fossil fuels in the short-term. It remains unclear
what the immediate ramifications will be of increasing the atmospheric CO2
concentration by promoting large quantities of low-energy biomass in exchange
for fossil fuels.
In summary, my study found a lack of convincing evidence to suggest that
forest product biogenic GHG emissions are being managed appropriately in
British Columbia. This is troublesome given the importance of biogenic GHG
emissions on the outcome of forest product C budgets, and the broader
implications of promoting the role of forests, forestry and forest products in
climate change mitigation efforts.

98

3.1.2.

Providing Context for Environmental Science and Policy

The intent of this manuscript was meant to equitably inform environmental
science and policy on the decision of whether or not to harvest MPS-attacked
stands for forest products, and particularly, bioenergy products. As previously
mentioned, my chosen forest products (lumber, pellets) involved very different
industrial processes and systems. Depending on the C accounting approach and
its conventions, the C budget for these forest products was found to vary widely
in the literature. Moreover, additional discrepancies were found in their definitions
(and/or treatment) of the biological system. My study deconstructed many of the
established conventions for this reason, which meant reexamining their scope
and their selection of cut-off criteria and assumptions. My efforts here strive to
provide clarity for how different C accounting contexts and conventions differ
from one another with respect to their perspectives in harvesting MPS-attacked
forests for forest products.

3.1.2.1.

National GHG Inventories

British Columbia currently reports its annual GHG emissions in a provincial
GHG inventory report (MOE 2012). The intent of this inventory is to quantify,
monitor, and inform others of the Province's commitments to addressing climate
change. While this inventory allows BC to clearly communicate its GHG
emissions to a national and international audience, the Province's current C
accounting framework and reporting on forest (and forest product) GHG
emissions is not conducive to activity-related climate change mitigation efforts.

99

The Province does not currently account for forest GHG emissions in its
GHG inventory report, so any forest- (or forest product)- related mitigation activity
results in no overall reductions in provincial GHG emissions. Dymond (2012)
found that the Province could reduce forest GHG emissions by claiming the
biogenic C storage of its forest products, but this would require the Province to
begin accounting forest GHG emissions in its GHG totals as well. British
Columbia's forests are currently estimated as a large C source and outweigh
Dymond's (2012) expected benefits, so it is unlikely these will be pursued
anytime in the near future.
That said, recent studies by Lempriere et al. (2013) and Smyth et al. (2014)
discuss the possibility of including a "systems perspective" to bolster Canada's
forest products industry and its efforts in climate change mitigation. This
"systems perspective" is reminiscent of substitution emission factors and the
faults therein. It remains unclear how these researchers plan on incorporating
these derived benefits into current national and provincial GHG inventories.

3.1.2.2.

Carbon Footprint and LCA Studies

Carbon footprint and LCA studies provide the most standardized and
detailed account of the environmental impacts specific to a forest product
industry and its products. The advantage of this framework is that it provides
tangible environmental impact assessments for the industry and for forest
product consumers. Moreover, they lend themselves to consequential LCA
studies (i.e. substitution emissions factors), which are very popular with tall

100

wooden building and bioenergy advocates because they have shown significant
reductions in global GHG emissions (Sathre and O'Connor 2010).
The limitation of C footprint and LCA studies is that they rely on simplifying
assumptions with regards to their C accounting of industrial and biological
systems. Historically, these studies have been able to exclude the life cycle of
any by-products, under the assumption that they are included in separate LCAs
(where they form the material input for another industrial system). This is
currently not the case. In my study, for example, I found a lack of LCAs specific
to the pulp and paper industry. This is concerning given the fact that secondary
processing of residues is among the largest sources of biogenic and nonbiogenic GHG emissions. Secondly, C footprint and LCA studies rely on the
assumption that the biological system (i.e. biogenic GHG emissions) is C-cycle
neutral. This is an assumption that lacks support and is likely disputable
considering the unprecedented impact of the MPB outbreak on the sustainability
of forestry in the province.

3.1.2.3

Forest-Forest Product C Models

Forest-forest product C models are great analytical tools used to explore the
various aspects of forest and forest product systems. They provide insight into
current and alternative ways of managing and manipulating these systems over
space and time, without always performing physical measurements and/or
treatments. Traditionally, these top-down modeling efforts reflected physical C
fluxes that could then be followed up through validation with actual

101

measurements. In recent years however, these modeling efforts have begun
incorporating indirect (or hypothetical) C fluxes that cannot be validated directly.
Some of the concerns and limitations in modeling forest and forest product
systems are its lack of standardization, transparency, validity and accountability.
Without standardization, the C accounting can be a fairly subjective procedure.
This is problematic considering the different C accounting contexts that are
possible and the different narratives that can be communicated. Without
transparency, the goals and intent of these models are obscured and their
communication is likely to transcend their own context. Lastly, without validity and
accountability, the models are capable of making assumptions that may not in
fact be representative of the actual forest and forest product systems. Instead,
many of these C models keep expanding their forest-forest product systems to
include more consequential impacts associated with the forest product industry
(e.g. Ter-M ikaelian et al. 2015).
3.1.3.

Importance of Forest Products in Forestry's Efforts to Mitigate
Climate Change

The contribution of forest products to the broader impacts of forestry
remains ambiguous in British Columbia. As previously discussed, there are many
different industrial and biological contexts for forest product C accounting. These
different contexts add to the illusion of importance when it comes to forestry's
efforts in mitigating climate change. My discussions of forest products so far have
been largely confined to the direct impacts influencing the forest products

102

industrial system. The focus of the discussion of these forest products will now
shift and include the possible indirect impacts that influence the industrial system.

3.1.3.1.

Indirect Influences of the Industrial System on Forest Product C
Budgets

One of the largest indirect influences on the industrial forest system is the
avoided emissions in substituting (or displacing) other industrial systems. Taken
at face value, the substitution emission factors are capable of making substantial
reductions in global GHG emissions (Sathre and O'Connor 2010). As a result,
they have been heralded as the frontrunner in bolstering forestry's efforts and
ambitions in mitigating climate change (Schlamadinger and Marland 1996, Lippke
et al. 2011, Lamers et al. 2014, Smyth et al. 2014) .
However, Sathre and O'Connor (2010) discuss the possibility that productbased substitution emission factors may not necessarily inform the most efficient
use of biomass from a climate change mitigation perspective. They offer a
singular perspective of a product's ability to reduce global GHG emissions. The
most commonly used substitution emission factor for long-lived wood products
(i.e. lumber) is currently borrowed from a meta-analysis performed by Sathre and
O'Connor (2010). My problems with their meta-analysis are its selection bias,
and the wide range of forest products and uses, country-specific data, and C
accounting methodology and assumptions. For these reasons, my study chose
not to blindly use substitution emission factors and sought to first establish a
baseline for the BC forest product industry. Moreover, Lamers et al. (2014)

103

already used Sathre and O'Connor's (2010) substitution emission factor in their
analysis and established a range of possible scenarios in harvesting MPBattacked forests for forest products.
The biological system (and its biogenic GHG emissions) is not accounted
for in substitution emission factors (Sathre and O'Connor 2010). The concern has
been that by segregating the industrial systems from the biological system,
forestry runs the potential risk of inadvertently deteriorating the biological system
to a point at which forestry is no longer sustainable and/or C neutral. This
concern is most prevalent and contentious in discussions surrounding the Cneutrality of forest bioenergy (McKechnie et al. 2011, Holtsmark 2012, Mitchell et
al. 2012, Schulze et al. 2012).
Recent efforts to validate the C-neutral assumption in forest bioenergy have
consequently included industrial systems' avoided emissions in the modeling of
the biological system. This marks a radical shift in the way C neutrality is
discussed and defined in the literature. These new and emerging C-neutral
concepts include: "C offset point", "break-even period", "C parity period", "time to
C neutrality", and "C sequestration parity" (Ter-Mikaelian et al. 2015). These
concepts differ from traditional definitions of C neutrality in that they incorporate
various avoided emissions in their C-neutrality validation process. They differ
from one another in that they have different break-even points and different
degrees of avoided emissions. The result of these differences has broadened our

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understanding of possible outcomes of harvesting forests for bioenergy, but it has
also created confusion in the word "carbon neutral".
In the midst of these shifting perspectives on C neutrality and forest
bioenergy, my thesis asks the general question of whether or not harvesting
MPS-attacked forests for different forest products is beneficial or detrimental to
forest GHG emissions. This remains a difficult question to answer conclusively
due to the lack of environmental science research on the direct and indirect
impacts of industrial and biological systems. Lamers et al. (2014) did examine
the direct and indirect influences of industrial and biological systems in MPSattacked forests and they found that lumber and pellet products could reach C
sequestration parity in heavily damaged forests when including the avoided
emissions of forests and forest products. However, the validity of the avoided
emissions used in this study remains unclear.

3.1.4.

Role of Forests in Climate Change Mitigation Efforts

It is widely recognized that forests can help mitigate the effects of climate
change. As stated in early versions of the IPCC: "in the long term, a sustainable
forest management strategy aimed at maintaining or increasing forest C stocks,
while producing an annual sustained yield of timber, fibre, or energy from the
forest, will generate the largest sustained mitigation benefit" (Nabuurs et al.
2007). Yet for a province currently in the midst of dealing with the unprecedented
impacts of an MPS outbreak, it has been difficult to establish a credible

105

sustainable forest management strategy incorporating global C cycles and
climate change mitigation aspects in a timely manner.
The environmental science research supporting the role of BC forests in
mitigating climate change is unclear. Kurz et al. (2008) and Metsaranta et al.
(2011) provide the cornerstones of BC's understanding of the regional- and
landscape-level C impacts of the MPB outbreak. Yet these studies are currently
at odds with stand-level studies by Brown et al. (2010) and do not address the
appropriateness of timber harvesting during BC's salvaging logging efforts,
directly. Instead, they demonstrate the impacts of additional harvests indirectly,
as increased C emissions following increasing levels of harvesting in the
allowable annual cut (AAC).
An environmental policy supporting the role of BC forests in mitigating
climate change is not present. The Province has yet to establish any
environmental policies focused directly at forestry's role in mitigating climate
change. This is particularly glaring given the fact that the Province does not
currently include BC's managed forests in its provincial GHG inventory report.
In closing, it is my belief that if the provincial government is truly serious
about curtailing global GHG emissions and mitigating climate change, it will need
to begin reporting forest GHG emissions, in addition to many other industrial
sectors currently not included in the Province's GHG inventory report.

106

Chapter 4.
CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH
4.0. CONCLUSIONS

In the post-epidemic phase of the mountain pine beetle (MPB) outbreak,
there still remains a degree of uncertainty in quantifying carbon (C) budgets for
industrial forest product systems. Consequently, there is also uncertainty in
promoting the broader notion of any mitigation benefits by including global carbon
(C) cycles and climate change objectives into forest management planning and
policy in British Columbia (BC), Canada.
In my study, I looked at the C budgets of forest products harvested from
MPS-attacked forests and found lumber contributed lower GHG emissions than
pellets (in an industrial system). While seemingly modest, my findings contribute
unique insights into the role forest products may play in forestry's climate change
mitigation efforts. For one, the Province does not account for forest nor forest
product GHG emissions in its provincial GHG inventory. Secondly, it lacks
support in its claims that biogenic GHG emissions in the forest products industry
are C neutral. Lastly and most importantly, any discussion surrounding avoided
emissions (or substitution emission factors) of MPS-attacked forests and forest
products is conjecture. Environmental science research is ongoing in its
quantification of the MPB's impact on forests and the forest products industry.

107

4.1. RECOMMENDATIONS FOR FUTURE RESEARCH
My recommendations for future research begin with the targeting of specific
parameters that were found to be problematic in synthesizing a forest product C
budget for MPS-attacked forests, and it ends with broader and more ambitious
research that needs to be performed before these forests are managed for global
C and climate change mitigation efforts.

4.1.1.

Direct Influences of the Industrial System on Forest Product C
Budgets

One of the major limitations in my approach was the limited data available
in constructing forest industry C budgets in BC. In particular, C budgets for
dimensional lumber had many unknowns and uncertainties at each of its life
stages. The C budget for pellets (bioenergy), on the other hand, had fewer
unknowns and uncertainties, with the exception being the lack of data on wholetree pellet processing. Future research needs to perform an environmental LCA
(i.e. similar to White et al. 2005) on roundwood utilized for lumber versus pellets.
To improve these LCAs, greater access to forest industry parameters is needed.
In dimensional lumber C budgets there remain some uncertainties in the product
recoveries at sawmills processing MPS-affected roundwood. While the attention
has been on lumber recovery, mill residue recovery and composition were found
to be of some significance. Future research needs to establish the current lumber
recovery level of MPS-affected roundwood, and to more fully document the
subsequent composition and use of mill residues.

108

Modeling parameters governing forest product use and disposal were
important in deciding the amount of biogenic C stored. My C budget borrowed
use and disposal parameters from Dymond (2012). Future research could
examine specific aspects of these parameters, such as: the allocation of lumber
use and its half-lives, mathematical functions representing the disposal of lumber
by type and use (e.g. first-order decay versus distributed approach) (Marland et
al. 2010), and method of disposal (e.g. open dumps, landfills, combustion).
I found that the greatest uncertainties in GHG emissions from lumber lies in
the secondary processing of its chip byproducts (e.g. chemical pulp plant, paper
plant) and in its disposal stages (e.g. lumber and paper decay). Future research
should examine emissions data for pulp and paper manufacturing and the rates
and efficiency of methane capture and efficiency in landfills.
The timing of GHG emissions between forest products differed. Lumber
emitted emissions over time, whereas pellet emissions were generated
immediately. Levasseur et al. (2010) discuss the possibility of current and future
atmospheric CO2 emissions (i.e. cumulative radiative forcings) changing how we
view the impacts of the timing of GHG emissions on the atmosphere.

4.1.2.

Indirect Influences of the Industrial System on Forest Product C
Budgets

Substitution emission factors allegedly offset large amounts of GHG
emissions from other industrial sectors. These emissions factors are not typically
screened using C offsetting criteria (MOE 2011 ). Future research could examine

109

the actual GHG emission offset by the construction sector using C offsetting
criteria, such as additionality (e.g. ability to prove an action is more than business
as usual) and leakage (e.g. inability to prove an action actually curtailed fossil
fuel dependency).
Similarly, more precise end-use modeling of forest products could yield
substantial GHG emission savings. This could include options for their re-use or
emissions-avoiding options (e.g. bioenergy) (Sikkema et al. 2013).

4.1.3.

Indirect Influences of the Biological System on Forest Product C
Budgets

Many forest C budgets from the scientific literature were found to use Cneutral assumptions to exclude the biogenic GHG emissions from their reporting .
My study found that there is a lack of support for many of the definitions currently
in use. Specifically, C-cycle neutrality was found to be questionable given the
uncertainties of sustainable forest management (e.g. growing inventory of not
satisfactory restocked (NSR) forest lands, uncertainty about future growth and
yield of plantations in the face of climate change, midterm timber supply gaps).
Meanwhile, inherent C neutrality lacked any scientific basis and C accounting
neutrality is not possible given the Province 's policy on forest GHG emissions.
Future research could look into stakeholders' perceptions of C neutrality and
gauge their support for, or lack thereof, in the Province adopting C-accounting
neutrality as its default assumption.

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4.1.4.

Role of Forest Products in Mitigating GHG Emissions

The logical next step in broadening my research would be to validate the Ccycle neutral assumption. Validation of this assumption would require the
integration of the direct C fluxes associated with forest and forest product
systems when harvesting MPS-attacked forests (e.g. actual C balances of
harvested stands using eddy covariance and process-based ecosystem models
such as 3PG) (Landsberg and Waring 1997). The outcome of this research would
shed light on whether or not biogenic GHG emissions are fully accounted for by
the forest. Moreover, the findings from this research could be used to justify
whether or not MPS-attacked forests should be harvested for forest products,
and in particular, whether forest bioenergy should be promoted in SC's salvage
logging campaign and climate change mitigation efforts.

111

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endix I. GHG emission factors for non-bio
BC electricity (kg C02e kWh.1)
heavy fuel oil (kg C02e L·1 )
diesel (kg C0 2e L. 1 )
gasoline (kg C02e L·1 )
propane (kg C0 2e L. 1)
natural as k C0 2e m·3

0.0091
3.146
3.007
2.299
1.529
1.927

Appendix II. C storage and GHG emission factors for biogenic material and
roducts used in this stud
product storage
wood waste (15% MC)
ellets

* C content: 0.5 tC r wood.
1

n/a
n/a

n/a

n/a
1.731'

' C content: 0.434 tC r 1 wood.
' density: 840 kg m·3 @ 50%MCoo

1.594 mbfm m·3
11.4%

basic density of lodgepole pine (Pinus contorta var.
latifolia) wood (Nielson et al. 1985)
thousand board feet per merchantable cubic meter of
roundwood (Nielson et al. 1985, ASMI 2012)
volumetric shrinkage of lodgepole pine wood from green
to dr 30-0% MCoo Nielson et al. 1985 .

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