OFF TRACK TO 2050?: A STUDY OF PRESENT AND FUTURE INTERURBAN
TRANSPORTATION EMISSIONS IN BRITISH COLUMBIA, CANADA, RELATIVE
TO ITS GREENHOUSE GAS REDUCTION TARGETS ACT OF 2007

by
Moritz Alexander Schare
B.A., University of Northern British Columbia, 2008
M.A., University of Northern British Columbia, 2012

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
IN
NATURAL RESOURCES AND ENVIRONMENTAL STUDIES

UNIVERSITY OF NORTHERN BRITISH COLUMBIA
FEBRUARY 2016
© Moritz Alexander Schare, 2016

originating from sources in a defined category, such as transportation, in a given
geographical area (for example, global or national or local) over a given time span (the
typical time frame is one year). An inventory usually also contains data to calculate the
emissions. In addition, an inventory can include other emissions-related information or
calculations such as emission factors (the amount of the pollutant emitted per unit activity).
The objective of the research in this dissertation was to develop an emissions inventory for
the transportation sector in British Columbia (BC) that could be used to answer various
policy-related questions about BC's present-day and future transportation emissions.
There are two distinct types of transportation emissions inventories: top-down and
bottom-up. Top-down inventories are based on large-scale, overarching (' top level ' ) data,
such as total fuel sales for a given jurisdiction, that are used to calculate emissions which are
then allocated to the lower levels in some manner within the jurisdiction. Bottom-up
inventories work in the opposite direction, so to speak. They start with small-scale, on-theground (' bottom level ' ) data, such as fuel sales at each sales outlet within a jurisdiction, that
are used to calculate emissions which are then summed up in some manner for the
jurisdiction. My original intent for my dissertation research was to develop an entirely
bottom-up emissions inventory for BC; however, because of the lack of fine-scale data for
some of the modes of transportation considered, a top-down approach was also used.
There is value in compiling detailed, bottom-up inventories at local levels, such as the
province of BC. They can, for example, provide policymakers with comprehensive
information for making decisions. Despite their seeming advantages, there seem to be few
detailed, multi-modal, bottom-up GHG emissions inventories of transportation systems at the
local level. This may be because bottom-up inventories are difficult to compile and because

2

there are few direct negative impacts of the major greenhouse gas, CO2 , at the local level as
there are for air pollutants such as nitrogen oxides. However, high resolution GHG
inventories at the local level may be of distinct benefit to policymakers in making local
decisions regarding GHG emission reductions; for example, in channeling money for
infrastructure into light rail instead of road building. For this reason, I decided to construct
(as nearly as possible) a ' pure ' bottom-up transportation emissions inventory for BC that may
prove beneficial to BC policymakers.
Transportation systems, especially for ' large ' local regions such as BC, can be
divided into urban and interurban components. Urban transportation refers to transportation
within cities and communities, while interurban transportation refers to transportation
between cities and communities. For the purposes of inventorying and policymaking, it is
valuable to disaggregate these two components because the nature of transportation can differ
significantly between them. For instance, commuting is often a large part of urban
transportation but less so for interurban transportation. Also, certain modes, such as air travel,
are generally not applicable within an urban area (i.e., one does not generally fly between two
destinations within a city). In addition, some policy approaches that can be used to reduce
urban transportation emissions, such as encouraging the use of public transit or the creation
of bike lanes, are generally not applicable for interurban transportation. Thus, while both
urban and interurban transportation are important components of the transportation system,
they are distinct and often require distinct policy approaches for emission reductions.
My research focuses solely on interurban transportation. Analyzing interurban
transportation GHG emissions by itself can facilitate policymaking that is tailored
specifically to the unique challenges and requirements of this component of the

3

transportation system. While scholarly work has been done on local transportation GHG
emissions, stand-alone treatment of interurban transportation GHG emissions has so far
received little attention.
The jurisdiction chosen for my research on interurban transportation was the
subnational level, specifically, British Columbia, Canada. There were two main reasons for
choosing this location. First, BC is a very large ' local ' jurisdiction. Despite being only a
province, it is several times larger than many countries, such as Great Britain and Japan
(BCRobyn 2013). BC has a comparatively small population of approximately 4.5-million
residents (BC Stats 2013), of which approximately three million live in the Greater
Vancouver and southern Vancouver Island areas in the southwest of the province. The
remaining population is spread over the rest of the province. This means that interurban
transportation of passengers and freight within the province is significant and, based on
distances that have to be covered, generates significant emissions.
A second justification for focusing my research on BC is that the province in 2007
passed the Greenhouse Gas Reduction Targets A ct, legislating highly ambitious GHG
reduction goals-reducing emissions 33% below 2007 levels by 2020, and reducing
emissions 80% below 2007 levels by 2050 (Parliament of British Columbia 2007). To
achieve this goal and to motivate societal change, the province implemented a carbon tax in
2008 (British Columbia Ministry of Finance 2008). Despite the ambitious GHG reduction
targets set by the province, and despite the fact that transportation-related GHG emissions in
BC are substantial, there has been little or no indication of what measures should or could be
taken to allow transportation to significantly reduce its emissions. All components need to be
examined both individually and collectively in order to determine what changes can be made

4

•

to BC' s transportation system to reduce emissions. My research is designed to provide
policymakers with information on current BC interurban transportation emissions and on
changes to interurban transportation that can be made to help the province to achieve its
GHG reduction targets.
In Canada, total GHG emissions have increased from 613 Mt C0 2e 1 in 1990 to 726

Mt C02e in 2013 (Environment Canada 2015a), an increase of 18%. In the same timeframe,
Canadian transportation emissions increased from 130 to 170 Mt C02e (Environment Canada
2015b ), an increase of 31 %, and thus almost twice the rate of overall emissions. In BC, total
GHG emissions have increased from 51.9 Mt C02e in 1990 to 64.0 Mt C02e in 2013 (British
Columbia Ministry of Environment 2012), an increase of 11 %. Emissions from
transportation increased from 18.6 Mt C0 2e in 1990 to 23.3 Mt C02e in 2012 (British
Columbia Ministry of Environment 2012), an increase of 25% and thus more than twice as
fast as overall emissions. Transportation in BC accounts for 38% of all fossil-fuel based
GHG emissions (British Columbia Ministry of Environment 2012). They are 65% higher
than the global average and 27% higher than the developed country average. Looking to the
future, transportation is expected to be one of the fastest-growing sectors in BC (Vancouver
Public Library 2015).
1.2

Research questions
The research contained in this dissertation was guided by the following two

questions:

C0 2e = CO 2 equivalent. The unit C0 2e is used to provide a common or equivalent unit of measure for the
different warming effects of different GHGs. It represents the amount of CO 2 that would have the same relative
warming effect as the GHGs actually emitted (CO2 Australia Limited 2009).
1

5

(I) What are the present-day total CO2 emissions and emission factors of interurban

passenger and freight transportation in BC?
In my research, the CO2 emissions of individual transportation modes comprising the
BC interurban transportation system were calculated, from which were derived the total
emissions of the BC interurban transportation system. Calculations included only those
emissions directly associated with the operation of the vehicle ("tail-pipe emissions"), and
not those associated with the production of fuel, or other environmental impacts such as
radiative forcing or the emission of other pollutants and GHGs. In addition, emission factors
(EFs) were calculated for each mode of interurban transformation considered for the year
2013, the base year for this research. An emission factor expresses the emissions per unit
activity; in this case, the emissions to transport one passenger or one unit of freight over one
unit of distance. For my research, emissions and emission factors were calculated for CO 2 ,
instead of C0 2 e, because existing data for some modes did not allow calculating C02 e
emissions. Thus, this research analyzes not all GHG emissions associated with transportation
but rather just the subcategory of CO2 , which, however, makes up the vast majority (- 99%)
of transportation GHG emissions. The nine transportation modes used in this research are as
follows :
for passenger transportation:
• private vehicles
• ferries
• aviation
• intercity buses
• trains,
and for freight transportation:
• trucking freight
• marine freight
• rail freight
• aviation freight.
6

(2) What changes to BC interurban transportation can help the province to achieve its
legislated 2020 and 2050 emission reduction targets, and how far above target values will
projected values be if reduction rates are insufficient?
BC has set ambitious GHG reduction targets for 2020 and 2050. In order for these
targets to be met, reducing emissions from interurban transportation will be crucial. To assist
policymakers in this task (and to answer this research question) scenarios of possible future
changes to the BC interurban transportation system were created and analyzed. Scenarios
were created that achieved the-emission reduction targets and that did not meet them. For
scenarios that failed to meet the reduction targets, the cost of buying carbon offsets for excess
emissions was used as one way of illustrating the 'price of failure'.
In my research, it was assumed that interurban transportation should, as is mandated
for the BC economy in general, reduce its emissions 33% below 2007 levels by 2020 and
80% below 2007 levels by 2050. There is no such requirement in the legislation, however.
Even if one sector fails to meet its target, another may exceeds its target, and thus the overall
target could still be met.

1.3

Methodology
To answer the two research questions above, I developed an Excel-based model

which I termed SMITE (Simulator for Multimodal Interurban Transportation Emissions).
The model contains data for and calculates present-day emissions (which for this research
was the time period between 2007 and 2013) and contains formulas for calculating future
emissions for various scenarios, starting from the calculated present-day emissions.
The first element of SMITE, and the first step of my research, was to quantify the
current CO 2 emissions of interurban passenger and freight transportation in BC. This
7

provided answers to the first research question. As much as possible, a bottom-up approach
to constructing an emissions inventory of interurban transportation emissions was pursued.
The basic formula for calculating CO2 emissions for each interurban transportation mode was
as follows:

Where,
ET M = BC emissions for transportation mode M (tonnes of CO 2)
EFM
= BC-specific emission factor for mode M (tonnes CO 2 emitted per unit distance)
DM
= Distance for activity of mode M (e.g., kilometres driven, flown, sailed)
My methodology consisted of compiling detailed usage data for each mode,
calculating emissions, and computing BC-specific EFs. If BC-specific EFs could not be
computed, they were obtained from alternate sources. All data and calculations were
contained in Excel spreadsheets.
The second element of SMITE, and the second step of the research, was to devise and
analyze scenarios of future emissions. This provided answers to the second question. The
scenarios are characterized by rates of change; in other words, contain parameters
representing annual increases or decreases in emissions between the present and 2050. The
scenarios, with a few exceptions, cannot model specific changes to the transportation system,
such as a modal shift in cars from gasoline to diesel or the effect of public awareness
campaigns on public transit use, unless those changes can be translated into a rate-change
parameter. Calculating future emission scenarios permitted analysis of which change rates
allow BC to meet (or not meet) its legislated 2020 and 2050 emission reduction targets.

In addition to emissions, carbon offset costs were calculated in the SMITE model for
each scenario as a way of illustrating monetary cost associated with the various scenarios.
The carbon offset prices are input into the model and costs to offset the discrepancies
8

between the scenario emissions and the 2020 and 2050 target values were calculated. Thus,
the model determined either (1) the ' income ' derived for those scenarios for which the
legislated target values were exceeded and excess offsets could be sold by the province on
the market or (2) the ' expense ' incurred for those scenarios for which the legislated target
values were not exceeded and thus offsets had to be purchased by the province on the market.
For the purposes of this research, it was assumed a viable market existed for buying and
selling these carbon offsets.
The rates of change incorporated into scenarios chosen for this research generally
ranged from reducing emissions by approximately -5% per year to increasing them by up to
+5% per year. The annual compound reduction rate to achieve an 80% reduction by 2050
over 2007 levels is approximately -4%, which is why modelling higher reduction rates is not
necessary in order to meet the legislated reduction targets. While not meant to be exhaustive,
the modelled scenarios bracket a spectrum of 'plausible' and ' realistic ' scenarios; namely,
with rates of change of generally -5% to +5% . In total, 106 scenarios were modelled.

1.4

Major research findings
In this section, major research findings are outlined. Relative to providing input into

the policy process, the short, simple conclusion from my research is that for BC to be able to
meet its target ofreducing emissions 80% below 2007 levels by 2050-assuming that
interurban transportation emissions must be reduced by this amount-the province will be
required to introduce dramatic changes to interurban transportation sooner rather than later.
1.4.1 Introduction to answers to Research Question 1
Interurban transportation of passengers and freight produced approximately
11 ,194,000 tonnes of CO2 in 2013, the base year for this study. Passenger transportation
9

accounted for 22% of these emissions, while freight transportation accounted for the
remaining 78%. Freight emissions were nearly four-fold those of passenger transportation.
According to provincial data, total BC GHG 2 emissions in 2013 were 64,000,000
tonnes C02 e (British Columbia Ministry of Environment 2012). My calculated interurban
transportation emissions were approximately 17 .8% of this total. Table 1.1 lists individual
interurban transportation modes analyzed in my research along with their contributions to the
interurban total, their respective passenger or freight sector total, and their EFs.
Table 1.1: Summary of BC transportation mode emissions and emission factors
Percentage of
total
interurban
transportation
emissions

Percentage of
passenger
interurban
transportation
emissions

1,916,000

17.1

78.4

202

Ferry

342,000

3.1

14.0

260- 1,781

Passenger aviation

167,000

1.5

6.8

75 - 386

Intercity buses

13,000

0.1

0.5

57* - 137*

Passenger trains

5,000

<0.1

0.2

117*

Trucking freight

5,431,000

48.5

62.1

196

Marine freight

1,883,000

16.8

21.5

n/a

Rail freight

1,428,000

12.8

16.3

15

9,000

0.1

0.1

940-6,810

11 ,194,000

100%

Transportation
mode

Emissions
by mode
(tonnes
CO2)

Percentage of
freight
interurban
transportation
emissions

EF
(g C02/pkm
for
passengers; g
C02/tkm for
freight)
(range where
available or
average)

Passenger transportation
Private vehicles

Freight transportation

A via ti on freight
Total

2

100%

100%

While the province accounts for all GHGs (C0 2e), rather than just the CO 2 considered in this research, the
C0 2e value for transportation is generally only approximately 1% larger than the CO 2 value.

10

Table 1.1 Legend:
pkm = Passenger-kilometre
tkm
= Tonne-kilometre
*
= Value could not be calculated independently and was taken from the literature.
1.4.2

Introduction to answers to Research Question 2
Modelled scenarios fall into two categories, those that meet the GHG reduction

targets, and those that do not. To meet the reduction targets, it was assumed that interurban
transportation should have to reduce its emissions by the same percentages as the economy in
general, namely 33% below 2007 levels by 2020 and 80% below 2007 levels by 2050,
although the BC legislation does not mandate that specific sectors reduce their emissions by
specific percentages.
Four types of scenarios were generally unable to meet either the 2020 or 2050
emission reduction targets. These modelled situations in which (1) emissions increase at any
point between 2007 and 2050, (2) a business-as-usual approach is followed for a given period
of time before applying sustained emission reductions (in other words, emission increases or
decreases continue the trajectory set by the 2007-2013 rate of change), (3) emissions remain
unchanged for a given period of time before applying sustained emissions reductions, or (4)
reduction rates are too small (less than 3% per annum). The scenario with the least
favourable conditions modelled-an increase of 5% per year for all modes- resulted in
projected 2020 emissions of 15.66 million tonnes CO 2 (over 100% above the target value of
7.6 million tonnes CO2 ), and projected 2050 emissions of 67.4 million tonnes CO2 (almost
3,000% above the target value of 2.3 million tonnes CO2 ). This scenario would result in total
offset costs to the province of nearly $98 billion by 2050.
Only two scenarios, of the 106 used in my research, were able to meet both the 2020
and 2050 emission reduction targets. One, Scenario 6, which is described in detail in Chapter
11

5, used backcasting, which involved every mode changing its emissions by the exact
percentage rates to meet a 33% reduction by 2020, and then using a reduction rate of -3.83%
to meet the 80% reduction target for 2050), while the other, Scenario 96, which is also
described in detail in Chapter 5, used each modes ' rate of change from 2007 to 2013, minus
5%. (Again, these scenarios use only percentage change of emissions and do not incorporate
specific changes to BC's interurban transportation system.) However, such scenarios could
represent significant technological advances or infrastructure investments such as upgrading
to an extensive, electrified, and hence zero emission, railway system. Meeting only the 2050
target requires somewhat less stringent changes because there is more time to accomplish
technological and societal changes.
No scenario that achieves either the 2020 or 2050 targets, or both, would likely be
easy to implement. My scenarios only model rates of change, not actual, concrete changes;
however, the high rates of change needed to hit the targets implies that major transformations
in technology, public policy, demographics, and/or social behaviour will have to take place to
have a chance of meeting either target. However, on the positive side, if one assumes a viable
offset market in which the province could sell its excess carbon credits, several of the
scenarios modelled that encompassed reduction rates of up to 5% per year resulted in savings
for the province of upwards of $6 billion.

1.5

Value of Research
There are three main benefits of the research contained in this dissertation: (1)

development of the SMITE model, (2) application of this model to BC, and (3), policy
perspective gained from model results to assist BC policymakers in making decisions on

12

BC ' s transportation system as it relates to the issue of climate change. Each is discussed in
turn.
Because the level of study chosen for this research was interurban transportation at
the sub-national level, and because there is a paucity of existing studies at this level, and
consequently methodologies to use at this level, it was necessary for me to devise my own
inventorying and modelling approach. The methodological approach developed for
calculating both present-day and future emissions is independent of geographical scale.
While I applied it to BC, it can serve as a template both for other jurisdictions and on
different scales.
Using the SMITE model, an in-depth, bottom-up inventory of passenger and freight
interurban transportation in BC was created. To my knowledge, there are no existing studies
that include the total emissions and area-specific EFs of each mode considered in my
research, or their geographic distribution, in BC or any other jurisdiction. I was able calculate
or estimate CO2 emissions for nine interurban transportation modes, and to calculate or
estimate specific EFs for all but one mode (marine freight).
On a practical level, the SMITE results can assist BC scholars, policymakers, and
practitioners in the transportation field in making climate change related decisions. My
research indicates that rapid reductions in interurban transportation emissions will be crucial
for achieving the province ' s legislated emission reduction target values.

1.6

Introduction to Chapters
Following this introductory chapter, the second chapter is a literature review; it

contains reviews of the literature on transportation GHG modelling and the literature on BC
transportation GHG emissions. The third chapter discusses the methodology adopted for my
13

dissertation research, while the fourth chapter contains the detailed, bottom-up inventory of
present-day (around the year 2013) interurban transportation CO2 emissions in BC. The fifth
chapter contains the results from having modelled 106 scenarios of changes to BC interurban
transportation and their impact on transportation CO 2 emissions and the 2020 and 2050
emission reduction targets, along with associated carbon offset costs. The sixth and final
chapter contains a summary of results, a discussion of examples of changes that may help BC
achieve sustained transportation emission reductions, a review of the contribution of this
research, a discussion of the limitations of this research, suggestions for further research, and
final thoughts regarding this research project.

14

CHAPTER 2: TRANSPORTATION GREENHOUSE GAS MODELLING: A
LITERATURE REVIEW

2.1

Introduction
This chapter contains a review of the literature on climate change-related

transportation modelling. The gaps in the literature and the research needs addressed by my
research are identified, which provide justification for constructing an independent
calculation model.
There are a myriad of types of transportation models with a myriad of applications.
There are cost/benefit models, network analysis models, probabilistic models, supply/demand
models, etc. that are applied to tasks such as air pollution emissions calculations, land use
coordination and infrastructure provision, safety measure recommendations, toll pricing, and
travel demand analysis for congestion reduction (Beimbom 2006, Wikibooks n.d.). The focus
of my research is emissions types of models, specifically GHG emission models, and more
specifically GHG emission models for interurban passenger and freight transportation.
Relative to these models, two literatures are reviewed in this chapter to situate the research
and highlight the knowledge gaps that the research fills : (1) the literature on transportation
modelling related to GHG emissions and to mitigation costs, and (2) the literature related to
transportation and climate change in BC. The first literature situates my work in the overall
climate change-related transportation modelling field, and the second in the realm of research
on transportation and climate change in BC.

15

2.2

Review of the literature on transportation GHG modelling

2.2.1

Types of transportation GHG models
The existing literature on transportation modelling of GHG emissions can be

distinguished by two main criteria: the scale of the model's application, and the
transportation modes covered. The scale of a model ' s application can range from global to
national to local, while models can cover any range of transportation modes from a single
mode to the totality of a transportation system in a given area. In total, I found about 30
studies related to modelling GHG emissions from the transportation sector, all of which are
discussed to one degree or another in this chapter. In each section, those studies which were
of more influence or relevance to my research are discussed first and in more detail, while
those that were of tangential relevance are discussed second and only briefly.
Within the body of transportation-related GHG emission modelling literature, there
are two main types of GHG emission models: (1) top-down and (2) bottom-up. Top-down
models use 'overarching' input data, usually aggregated fuel usage, to calculate emissions;
for instance, of all road transportation in a given region. Such models usually allocate the
aggregate fuel use or emissions to transportation subsectors or to smaller scales (hence, top
down). Bottom-up models generally use spreadsheets or other accounting software to
inventory data such as fuel usage or emissions (by vehicle type, for example), and from this
work ' bottom up' to calculate aggregated emissions. Bottom-up inventories allow for great
detail on energy use or emissions but are generally very labour intensive, whereas top-down
models are less detailed and less labour intensive-and are often more suited for large-scale
comparisons, for example between economic sectors (Becken and Patterson 2006). There is a

16

small 'meta-literature' of about a half dozen studies that analyzes and/or synthesizes results
from existing studies based on the above two model types.
Besides top-down and bottom-up model types, there are two approaches to addressing
future emissions: forecasting and backcasting. Forecasting is used to estimate future
emissions by starting from a given ' present-day' emissions value and, employing various
assumptions, attempting to derive a best-guess for emissions at a future date. Backcasting is
an opposite approach. It rests on the assumption that future targets have been met and
proceeds to work backwards to describe the means by which those targets can be achieved. In
the model developed for my research, both forecasting and backcasting approaches are used.
2.2.2

Scale of transportation GHG model application and modes covered

Global/regional scale

There is a limited body of literature on transportation GHG modelling at the
global/regional scale that directly or indirectly addresses interurban transportation. In this
section, the literature related to passenger transportation is discussed first, and then the
literature related to freight transportation. Both sections are further separated by the
transportation mode modelled. A total of 10 studies are discussed. Four out of the five pieces
of literature at the global/regional scale that address only one transportation mode analyze
aviation; the fifth analyzes freight ship emissions. The other five global level studies all
address freight transportation involving more than one mode, either in a comparative fashion
or as part of an integrated transportation system. Of the modes address in my research, I was
unable to find studies at the global/regional level for passenger bus transportation, passenger
and freight rail transportation, and passenger ship transportation.

17

Global passenger transportation: Aviation
There are a number of large-scale, top-down future emission projections for
interurban aviation transportation. (Note: Virtually all passenger aviation transportation is
interurban; only a very small fraction is intraurban, primarily helicopter travel within a city.)
One of the governing bodies of civil aviation, the International Civil Aviation
Organization (ICAO), published an Environmental Report in 2010 which reflects and
promotes cooperation among governments, industry and members of civil society and
showcases ideas and best practices that can accelerate efforts towards the goal of a
sustainable air transport industry (International Civil Aviation Organization 2010). The
report primarily covers the impacts of aviation emissions on the climate in a qualitative
manner rather than estimating their quantities; however, for the quantitative estimates
included in the report, a bottom-up approach recommended by the Intergovernmental Panel
on Climate Change (IPCC) was followed which includes surveying airline companies or
estimating aircraft movement data and standardized fuel consumption. The report highlights
that aviation currently accounts for less than 2% of global CO2 emissions, and that passenger
traffic is expected to grow at an average rate of 4.8% per year through the year 2036 but that
emissions are expected to grow at a smaller rate because of increased engine efficiencies. It
also provides projections for global aircraft fuel bum to the year 2050. The maximum fuel
bum in 2050 is estimated to be 4.5 times that of 2006. The most relevant aspect of this report
to my research was that it advocates establishing bottom-up inventories over top-down
inventories, which was the approach followed in my research, and that it provides growth
projections for aviation that I used to inform my construction of emissions scenarios for BC.

18

The IPCC published its Special Report: Aviation and the Global Atmosphere in 1999,
which contains national top-down emission inventories aggregated at the global level
(Intergovernmental Panel on Climate Change 1999). The report analyzes how subsonic and
supersonic aircraft affect climate-related properties of the atmosphere, how aviation
emissions are projected to grow in the future, and what options exist to reduce emissions and
impacts in the future. The report acknowledges that while emission growth projections can be
made with a fair amount of certainty for one to two decades based on projected passenger
growth and efficiency improvements, projections that reach beyond two decades are more
uncertain because of variables such as technological development (Intergovernmental Panel
on Climate Change 1999). The various growth scenarios up to the year 2050 contain ratios of
fuel bum between 2050 and 1990 of between 1.6 and 9.4 (Intergovernmental Panel on
Climate Change 1999, 5), meaning that emissions were expected to be between 1.6-fold and
9.4-fold those of 1990 values. The report had two main influences on my research. First,
while I did not use the emission growth ratios in my future scenario calculations, the ratios
gave me general guidelines as to what growth rates experts were projecting in the late 1990s.
Second, the large range of growth ratios indicated that future scenario calculations, especially
as we go further into the future , are subject to significant levels of uncertainty. This
reinforced my approach of modelling a comparatively large range of growth and/or reduction
rates for my future emission scenarios.
Akerman (2005) examines three ' images ' of how air travel could achieve
sustainability by the year 2050, with sustainability defined as a stabilization of atmospheric
CO2 at a concentration of 450 parts per million. His study uses backcasting. The author
concludes that radical changes are not only more likely to bring significant emission

19

reductions but also entail more risks, and that changes in people's lifestyles and travel
patterns could significantly contribute to reducing aviation emissions. Akerman's study
highlights the importance of people's travel behaviours and the difficulty in changing them.
In contrast to Akerman, Lee et al. (2009) emphasize the importance of technological change.
In their study, they quantified the contribution of aviation emissions to the radiative forcing

of climate, which while not an emission model per se directly relates to aviation CO 2
emissions. The authors project that fuel usage could increase by a factor of between 2.7 and
3.9 and radiative forcing by a factor of between 3.0 and 4.0 between 2000 and 2050, and that
significant changes in fuel usage and emissions will only be possible through the introduction
of radically different aircraft technologies or the incorporation of aviation into an emissions
trading system. Lee et al. (2010) provide an update to the 1999 IPCC report on aviation's
impact on the atmosphere. They state that aviation's contribution to radiative forcing may
increase by a factor of 3.0 to 4.0 by the year 2050 over the year 2000, and that while liquid
hydrogen and biofuels represent options for the aviation industry to reduce its emissions,
both fuel types face obstacles such as development funding and safety certifications. The
Akerman and two Lee studies were only tangentially relevant to my research; however, they
gave me insight into aviation growth rates and types of changes that could affect future
aviation emissions. The discussions of types of changes informed my discussion of examples
of how various transportation modes can achieve required annual reduction rates in the
various scenarios modelled.
Global road (passenger and freight) transportation
For road transportation, I found only one pertinent study at the global level. Barken et
al. (2007) constructed a global bottom-up inventory of road passenger and freight

20

transportation based on EFs, fuel usage, and distance driven in individual countries for eight
pollutant types (one of which was CO2 ) which were then aggregated into regional/continental
groups. The authors found that in the year 2000, the Organization for Economic Cooperation
and Development (OECD) countries (i.e., the main industrialized countries) accounted for
almost two-thirds of fuel consumption and CO 2 emissions, and that North American road
transportation in the year 2000 emitted 1,639 Mt CO 2 ( out of a global total of 4,280 Mt CO 2).
Unfortunately, while data for North America (United States and Canada) in aggregate is
presented, disaggregated data for Canada are not presented. The relevance of this study to my
research was that it advocates using a bottom-up approach using information similar to what
I used; namely, EFs, fuel consumption, and distances. However, the EFs used for this study
were likely, because of its broad geographic scope, generic, unlike the Canada-specific
private vehicle EF calculated for this research.
Global freight transportation
For freight transportation, research at the global level is sparse. Endresen et al. (2003)
compiled a bottom-up inventory of marine transport, while Cristea et al. (2013) studied trade
and GHG emissions from international freight transport using a bottom-up database to
quantify the contribution of international transport to total global CO2 emissions. Neither
study was particularly informative for my research, but both studies reinforced the approach
of compiling a bottom-up transportation emission inventory using EFs.
Regional freight transportation

Kim and Van Wee (2009) tested the hypothesis that truck and rail intermodal freight
systems are more environmentally friendly than truck-only freight systems. The authors state
that truck and rail intermodal systems are indeed more environmentally friendly than truck-

21

only systems but that the environmental benefit of rail transportation can vary significantly
depending on the type of locomotive used and the way in which the locomotive's fuel is
produced. This influenced my research because the interaction of rail and trucks through the
concept of modal shift in freight transportation was one of the main aspects I considered in
my discussion of how large annual emission reductions from the trucking sector in BC may
be able to be achieved.
Mattila and Antikainen (2011) studied how a sustainable freight system for Europe
can be achieved by the year 2050. Their study involved backcasting and using several
' futures of transportation ' scenarios to achieve an 80% reduction in GHG emissions over
2005 values (nearly the same timeframe and percentage as BC's overall emissions reduction
target). The authors conclude that there are several possible scenarios that can achieve the
targeted reductions if significant changes in transport efficiency and energy mix are utilized.
The study informed my work because it studied a very similar timeframe and emission
reduction percentage as is the case in BC. Its emphasis on efficiency improvements guided
my discussion of ways in which sustained annual emission reductions for various modes may
be able to be achieved.
Magelli et al. (2009) studied the environmental impact of exporting wood pellets
from Canada to Europe using a bottom-up approach. The limited relevance of this study for
my work lay in its methodological approach of using distances and EFs, which was also used
in my calculations where the pertinent base data were obtainable.
Global/Regional analysis of total transportation systems
Banister et al. (2011) discuss the recent history of transportation emissions and the
necessity of reducing them, along with challenges in doing so such as embedded

22

infrastructure investments, dependency on private vehicles, a lack of agreement on the global
level to which countries should reduce their emissions, and technological developments that
have not occurred as quickly as expected. The authors then discuss measures that can be used
to reduce transportation emissions (such as shifting modes, improving infrastructure, using
financial instruments, and restructuring transport governance), which generally fall in either
demand or supply management categories, but caution that demand side measures are often
neglected as they are complex. The importance of this article for my research lay in its
discussion of some of the difficulties associated with reducing transportation emissions,
which played a role in my discussion of possible examples of changes that can help various
BC transportation modes achieve sustained emission reductions.
The European Commission (2009) published a report on its version of a sustainable
future for transportation (for both passengers and freight), addressing trends such as
developing technology and changing citizens ' travel behaviour. Its relevance to my work lay
in its advocacy for a multifaceted approach that pursues not only more efficient
transportation technologies but also changes in people ' s travel behaviour, which could
include measures such as modal shift.
National/subnational scale
The majority of the work at the national level is multimodal and addresses surface
transport. Of the 14 inventories I found, six address multimodal passenger road transport
emissions, and four address multimodal freight road transport emissions. One study
addresses all transportation options at the national level, another all transportation options at
the subnational level, and three studies address freight trucking only. I was unable to find
studies at the national level that solely or extensively address passenger bus travel, passenger
or aviation freight, passenger or marine freight, or passenger rail transportation.
23

Many governments have compiled emission or fuel usage inventories at the national
or sub-national level. In Canada, there are inventories at the federal level (e.g., Environment
Canada 2013), and also at the provincial level (e.g., British Columbia Ministry of
Environment 2010). The inventory at the federal level is top-down and generally based on
fuel sales data. The provincial inventory utilizes the values from the national inventory
without performing independent calculations.
National (passenger and freight) road transportation
Buron et al. (2004) present a top-down model studying Spanish national road
transport emissions without an urban/interurban distinction and using Spain-specific EFs
(such as emissions per quantity of fuel consumed at a given speed and temperature). The
authors used the COPERT model ((COmputer Programme to calculate Emissions from Road
Transport), originally released as COPERT III, but since updated to COPERT 4), a software
program used primarily in Europe to calculate emissions from the road transport sector
(Kouridis and Ntziachristos 2000). Buron et al. (2004) acknowledge the importance (and
initial absence in their study) of disaggregated data, and conclude that while local (air
pollution) emissions have decreased because of increased environmental regulations, CO 2
emissions continue to follow an increasing, albeit slowing, trend. The relevance of this article
to my research lay in recommending to calculate EFs specific to the transportation system
being studied, which is the approach taken in my research, and also in stating that CO 2
emissions are continuing to increase, which is reflected in my research by including emission

.
.
mcrease scenanos.
Kioutsioukis et al. (2004) studied the uncertainty and sensitivity of road transport
emission estimates to changes in input parameters using Italy as a case study. In reference to

24

CO2 emissions, the authors find that emission uncertainties relate to uncertainties in
passenger car data, such as annual mileage and average trip length. Since annual mileage and
and average trip length were among the main factors in my passenger vehicle and trucking
calculations, this study' s relevance was to emphasize the possible uncertainties associated
with this approach.
Yeh et al. (2008) studied US national road transportation emissions by modelling
vehicle fuel use and corresponding emissions using the U.S. EPA ' s national MARK.et
ALiocation (MARK.AL) model technology database. The authors state that strict and systemwide CO2 reduction targets will be required to achieve significant emission reductions from
the transportation sector and suggest that policies should be informed by the transitional
nature of technology adoptions and interaction between mitigation strategies. This influenced
my research by highlighting the varied paces and successes of technology adaption and that
changes to one mitigation strategy can have impacts on other mitigation strategies, which
was tangentially relevant for my discussion of examples that may help BC achieve
transportation reductions.
Cortes, Vargas, and Corvalan (2008) studied the transportation and energy sectors in
Chile with a time horizon of ten years. While the model did not calculate CO 2 emissions, it
did calculate fuel usage, which can be converted into CO 2 emissions. The study ' s
methodology diverged from most other transportation studies in that it did not use EFs and
operational parameters for calculations but instead used activity data (vehicle-kilometres per
year) combined with changes in demographic and socio-economic factors. The relevance of
this study to my research was that it validated my approach of using activity data for
passenger vehicle and trucking calculations rather than using fuel sales data.

25

National freight transportation
For freight transportation, more studies exist at the national level than at the
global/regional level. Perez-Martinez (2010) studied freight transportation and emissions in
Spain using transportation statistics data and calculated emissions factors (amount of CO 2 per
quantity of fuel burned) in a top-down inventory model. The author states that emissions
have increased 68% between 1990 and 2007, and that by 2025 Spain could be up to 167%
above the emission levels it has committed to under the Kyoto Protocol, noting also that
emissions could be reduced 3.-3% by 2025 compared to 2007 if the average performance of
diesel vehicles in 2025 showed a 55% increase in efficiency. The emphasis on more efficient
vehicles was relevant to my research because it indicated emission reduction potential, which
reinforced my discussion of switching to more efficient vehicles as one way to reduce private
vehicle emissions.
Ang-Olson and Schroeer (2002) analyzed eight energy efficiency strategies for freight
trucking in the United States, and estimated that if 50% of trucks participated in these
measures, the maximum benefit of implementing these strategies would, by 2010, result in a
fuel usage reduction of 3.0 billion gallons and an 8.3 million metric tonne reduction (9%) of
C02e emissions. This study was only marginally relevant for my research, but it influenced
my discussion of trucking emission reductions by highlighting that not only revolutionary
changes can help to reduce emissions but also that small, currently implementable measures
can result in cumulative reductions.
Steenhof, Woudsma, and Sparling (2006) studied GHG emissions of surface freight
transport in Canada, finding that increasing cross-border trade and concurrent modal shift
towards trucks were largely responsible for increasing freight emissions. This influenced my

26

creation of scenarios in which trucking emissions are substantially and ' suddenly' reduced,
which may happen through measures such as modal shift back from trucks to trains.
Garcia-Alvarez, Perez-Martinez, and Gonzales-Franco (2012) studied fuel
consumption and CO 2 emissions in an ' intelligent' freight transportation system. A bottomup energy consumption model was used and explicitly recommended over a top-down model
because a top-down model can lead to significant errors when incorrectly applied, e.g. when
underlying assumptions are not met in a given scenario. The relevance of this article to my
research was·that it highlighted risks of using a top-down approach, which reinforced the
value of my bottom-up approach.
McKinnon and Piecyk (2009) investigated and compared various methods of
collecting data to be able to calculate CO2 emissions from road freight transportation in the
United Kingdom, including government road usage inventories and surveys of transportation
providers, both of which are also used in my study. The authors ' conclusion that data for one
activity can vary significantly if published by various sources, or even that various
government departments sometimes publish divergent data on the same activity, influenced
my research by leading me to calculate values based on traffic statistics wherever possible
rather than using ' processed ' data, such as fuel consumption or annual emissions.
The following studies form part of the literature but were of minimal relevance to my
work. Kissinger (2012) and Weber and Matthews (2008) studied the emissions associated
with food imports- Kissinger for Canada, and Weber and Matthews for the United States.
Winebrake et al. (2008) studied energy, environmental, and economic tradeoffs in intermodal
freight transportation. Three case studies for the US Eastern Seaboard revealed that while
trucking generally has a time advantage over other modes, this advantage is achieved at a

27

cost and emissions penalty. Bauer, Bekta~ and Crainic (2010) presented an approach to
intermodal transportation planning that incorporates environment-related costs into freight
transportation planning, finding that there are often trade-offs in transportation systems
between environmental costs and time costs. Demir, Bekta~ and Laporte (2011) reviewed and
numerically compared several available freight transportation vehicle emission models and
compared their outputs to data collected in the field. The models produced somewhat
different results in simulations using broadly realistic assumptions but overall were
consistent with expectations, such as fuel consumption varying with size of vehicle and speed.
National analysis of total transportation systems
I found only two studies that, like my study, include all transportation modes
(passenger and freight road transportation, rail transportation, marine transportation, and
aviation) in a single jurisdiction, though one is a country (Sweden) rather than a province as
in my research.
Akerman and Hojer (2006) utilized fuel usage data and backcasting to explore how
"sustainable transportation" could be achieved in Sweden by 2050. Sustainability is assumed
to be stabilization of atmospheric CO2 at 450 parts per million, to which end Sweden would
have to reduce its transportation emissions by 63 % compared to 2000 levels as its national
contribution from this sector. The study considered a wide range of approaches to sustainable
transportation futures, including changes in how society views and makes use of
transportation in general, and areas of high inertia, such as replacing existing infrastructure. It
informed my examples of changes that may help BC achieve transportation emission
reductions because it had the same broad approach that includes road, rail, marine and air

28

transportation modes, acknowledged the importance of societal attitudes, and highlighted that
some aspects of the transportation system are difficult to change.
Yang et al. (2009) used a spreadsheet model to study how California could reduce
transportation emissions (including sectors not covered by my work, such as agriculture)
80% below 1990 levels by 2050 and studied each transportation subsector without making an
urban/interurban distinction. The authors state that while no single strategy seems promising
for the reduction, a combined portfolio approach, including advanced vehicles and fuel as
well as travel demand reductions, could potentially yield success. The relevance of this
article was not only that it is similar in scale and scope to my work and also used a
spreadsheet-based model, but also that it advocates a multifaceted reduction strategy, which
informed my discussion of examples of how emission reductions in BC may be able to be
achieved.
Local scale
At the local (urban) level, bottom-up models seem to dominate the existing scholarly
research. All studies I found at the local level address road transportation, either for
passenger and freight transportation or just for freight transportation. I have been unable to
find any studies about urban passenger or freight transportation exclusively for the rail,
marine, and air modes. For rail, this may be a more prominent field of study in Europe or
Asia, which tend to have greater urban rail usage than North America. For marine, this is
likely because in most settings, transportation in the marine mode is either not applicable or
accounts for only limited emissions. For aviation, likely the only aspect that falls under the
urban scope would be helicopter travel, which in all probability accounts for only a very
small share of overall transportation emissions.

29

Local passenger road transportation
Borrego et al. (2003) analyzed transportation air pollution and CO 2 emissions in
Lisbon, Portugal through a bottom-up inventory utilizing speed-dependent EFs specifically
calculated for the research (amount of pollutant per distance driven at given speed on given
road segment), while Lyons et al. (2003) analyzed vehicle-kilometres in several cities across
the globe as a surrogate for vehicular emissions to estimate urban vehicle pollution. The
former article reinforced my approach of utilizing a bottom-up inventory with EFs calculated
specifically for my research, while the latter article reinforced my approach of using vehiclekilometres as a surrogate for direct emission data collection.
Local freight road transportation
For freight, I found only one urban model. Zanni and Bristow (2010) studied
emissions of CO2 from road freight transport in London through a bottom-up inventory using
traffic data and generic EFs because data to calculate specific EFs were unavailable. The
model also included projections up to the year 2050 which were carried out by calculating
average growth rates for the years for which traffic statistics were available and extrapolating
future growth rates from this and consequently basing emission projections on these values.
The authors state that there are several policies with potential to reduce emissions in the
period up to 2050 (such as low-carbon or zero carbon vehicles or packages of technological,
logistical and behavioural policy changes), but that even with optimistic policy interventions
they cannot deliver absolute reductions from 2005 levels, and instead only slow the rate of
growth. The relevance of this article lay in its approach of using various growth and
reduction rates to estimate future emissions, which was similar to mine, and in its conclusion

30

that absolute reductions may not be achieved even with emission reduction measures, which
influenced my scenario division process to also include emission growth scenarios.
Existing comparative research and analysis at the local level is sparse. I have found
only one study (N agumey 2000), which addressed paradoxes in emission reduction strategies
where perceived improvements to the transportation system can actually increase overall
emissions. This article provided impetus for me to also include emission growth scenarios in
my collection of future emission scenarios.
2.2.3

Literature on costing of transportation emission reductions
The literature on the cost of achieving transportation emission reductions that is

relevant to my research (for example, costing of measures that may reduce BC transportation
emissions, such as modal shift) is small (about half a dozen studies). None of the modelling
studies cited above include cost analyses (Yang et al. (2009), for example, explicitly state
that they excluded cost analysis because of its complexity). Most emission forecasts simply
aim to provide emission values under varying transportation scenarios. A common theme in
the studies discussed in this section is the complexity of calculating transportation GHG
reduction costs.
The International Transport Forum (2009), in a review of existing literature, studied
opportunities and costs for reducing transportation GHG emissions (without an
interurban/urban distinction) . It found that GHG mitigation should be planned on the basis of
marginal abatement costs, should focus on the most cost-effective actions, and that success
will depend on action across several fronts, such as technology and travel behaviour. In
addition, it was highlighted that regional context will play an important role in affecting
emission reductions, especially the extent to which each region ' s (country' s) geography

31

necessitates transportation and regionally varying policy approaches to both emission
standard implementation and travel behaviour. The emphasis on regional context reinforced
my focus on the sub-national level.
Azar, Lindgren and Andersson (2003) used a top-down global energy systems model
to analyze fuel choices in the transportation sector under stringent global carbon emission
constraints, specifically when it is cost-effective to carry out the transition away from
gasoline and diesel, to which fuels (including biomass, hydrogen, or solar electricity) it is
cost-effective to shift, and in which sector biomass is most cost-effectively used. They found
that oil-based fuels remain dominant in the transportation sector until approximately 2050,
that once the transition towards alternative fuels takes place, the preferred fuel is hydrogen,
and that biomass is most cost-effectively used in the heat and process heat sectors. The
relevance of this study to my research was that it deemphasizes alternative fuels as a means
of achieving significant emission reductions until after the year 2050, which is the end of the
time horizon of my study. As such, my discussion of ways of BC achieving transportation
emission reductions did not strongly emphasize alternative fuels.
Cost-related work has also been conducted in the United States using the Energy
Information Administration ' s National Energy Modeling System (NEMS) (Morrow et al.
2010). All of the policy scenarios that were modelled in this study failed to achieve a targeted
reduction of transportation GHG emissions of 14% over 2005 levels by 2020. This was
relevant for my work by leading me to emphasize technological developments or
optimizations such as modal shift over policy approaches in my discussion of ways in which
BC may be able to achieve transportation emission reductions.

32

The following two studies were of minimal influence on my work. On the national
scale, work studying the cost of reducing transportation GHG emissions (without an
interurban/urban distinction) has been conducted in Canada using a subjective evaluation
framework containing nine planning objectives that included energy conservation and
congestion reduction (Litman 2005) and highlighted that a comprehensive analysis is critical
so that improvements to one problem do not result in exacerbating another problem. In
another Canadian study, McKitrick (2012) analyzed the benefits and costs of GHG
abatement in the transportation sector using marginal abatement cost functions, finding that
the convenience and availability of the private car is a main reason why people avoid
alternatives, and that achieving a 30% reduction in GHG emissions from motor vehicles in
Canada would require taxation of about $97 5 per tonne of CO2 ( or a gasoline tax of about
$2.30 per litre), and would still result in economic deadweight losses (economic losses after
environmental benefits are accounted for) of $9.6 billion in the short-run and $2.9 billion in
the long-run.
2.2.4

Summary of literature on transportation GHG emissions and cost modelling
The body of work on modelling GHG emissions from transportation is modest, and,

relative to the research I conducted, was deficient in multiple respects. First, models typically
calculate emissions without distinguishing what activities (apart from distinguishing modes)
generate the emissions or where they are generated geographically. This is the method often
found in national emission inventories compiled by governments. Second, not all
transportation models address all transportation modes, most focus only on road
transportation. Global models generally focus on one transportation mode (e.g., aviation).
Even when models contain multiple transportation modes, not all compare the modes. Third,
33

detailed work on interurban passenger transportation GHG emissions is sparse for all model
types at all scales. Fourth, studies that calculate the cost of achieving transportation GHG
reductions are few and far between.
I concluded from the literature summarized above that there did not appear to be any
existing models that directly calculate emissions and EFs of different interurban
transportation modes in a geographically-defined area at the sub-national level. One
consequence of this is that there did not seem to exist an 'off-the-shelf model to use for my
research. I had to create my own model that would be able to address:
1. distinguish activities and geographical distribution ofregional (i.e., BC) transportation
em1ss10ns
2. include all regional (i.e., BC) transportation modes
3. focus solely on interurban transportation
4. nominally include cost.
2.3

Review of the literature on BC transportation GHG emissions
In my review of the literature on transportation GHG modelling, I found only two

academic studies at the national level that address Canada. Similarly, research on BC
transportation GHG emissions is also limited. There seem to be only about a half dozen such
studies. This section contains first a review of the literature on passenger transportation
emissions in BC and then of the literature on freight transportation emissions in BC.
Kelly and Williams (2007) constructed a bottom-up inventory for studying tourism
GHG emissions to Whistler, which assessed the relative effects of various destination
planning strategies on energy use and GHG emissions. Their study includes all GHG
emissions associated with tourism, not just transportation. It estimates transportation
emissions through a formula multiplying visitors by return distance by modal split, and then
using generic fuel efficiency and EFs (amount of C0 2 e per amount of energy used). This

34

article was relevant to my work for its BC focus and for reinforcing the approach of a
detailed bottom-up inventory in the province.
Poudenx and Merida (2007) compared the urban energy demand and GHG emissions
in the Fraser Valley from fossil fuel-based private vehicles versus electric buses and light-rail
by analyzing the modes' respective travel and emissions statistics from previously-collected
inventories. The authors state that electric trolley buses and the automated rapid transit
SkyTrain were eight times as energy efficient as private vehicles, and 100 times as emission
efficient as private vehicles in terms of.GHGs emitted per passenger-kilometre. While this
study focused on urban travel, its results were significant for my work because of the
implications for the environmental feasibility of modal shift on short-distance interurban
routes. In my research, this influenced the discussion of how transportation emissions may be
reduced on some of the shortest interurban routes in the Lower Mainland and on Vancouver
Island that have very high traffic volumes.
I was unable to find any academic studies on BC-specific freight GHG emissions.
While estimates of aggregate BC freight transportation emissions at the provincial level have
been published by the province (British Columbia Ministry of Environment 2010) (with
values that are, as discussed above, taken from the national inventory), there do not appear to
be any BC-specific studies similar to my research. These estimates list total emissions by
different vehicle types (e.g., light-duty gasoline vehicles, heavy-duty diesel vehicles), but do
not distinguish between interurban and urban emissions. There are also no BC studies that
make detailed future emission forecasts.
In summary, the existing scholarly research on BC transportation GHG emissions is
sparse. To date, it has focused almost exclusively on urban transportation emissions,

35

generally in Vancouver and surrounding areas, through bottom-up inventories. There are no
interurban studies for the entire province. Emissions data published by the province are
aggregate and top-down, using fuel usage. Interurban and urban are not distinguished, the
spatial distribution of emissions is not calculated, nor are transportation modes compared
(even though data is provided separately for different modes). My research is thus the first
extensive study of the distribution of present and future transportation GHG emissions in BC
that explicitly compares emissions between available transportation modes.

2.4

Meeting research needs and filling knowledge gaps
Much of the literature reviewed in this chapter has been relevant to my research by

(1) reinforcing the value of utilizing a bottom-up approach for the scale and scope of my
research, (2) providing insights into making emission calculations at a local level, and (3)
offering suggestions applicable to achieving emission reductions for various BC interurban
transportation modes. Four pieces were particularly relevant to guiding my research: Yang et
al. (2009), Akerman and Hojer (2006), Perez-Martinez (2010), and Steenhof, Woudsma and
Sparling (2006) .
The study conducted by Yang et al. (2009) on California transportation emission
reductions is perhaps closest to my research in scope and tirneframe, although significant
differences remain between the two geographic areas of study, such as the population density
and distances between urban areas in BC and California. Akerman and Hojer' s (2006) study
on Swedish emissions is also closely related to my study because it examines all
transportation modes in a single country (although BC, at the subnational level, is still more
than twice as large as Sweden). In terms of mitigation measures, Perez-Martinez' s (2010)
assessment of potential improvements in diesel technology is an important guideline for my

36

study of modal shift or mode efficiency improvements that provided impetus for my creation
of various scenarios in which there are 'sudden' drops in private car emissions, which may,
for example, be caused by large-scale switching to more efficient diesel vehicles (but could
also apply to other more efficient vehicles, such as hybrid cars). The study of surface freight
transportation in Canada by Steenhof, Woudsma and Sparling (2006) is directly relevant to
my research where modal shift between rail and truck may be one of the mitigation options,
especially because this study also addresses Canada. This reinforced my approach of
devising several scenarios in which there would be varying degrees of 'sudden' reductions of
trucking emissions, which may be caused, for example, by large-scale modal shift from
trucks to freight trains.
Based on my literature reviews, there are three gaps in the existing literature on GHG
transportation modelling that dictated the need to construct an independent GHG emissions
model for the specific approach and scope chosen for my research. These gaps are: (1) the
paucity of detailed transportation emission inventories at the sub-national scale (both in
general and in BC), (2) the lack of detailed comparisons between emissions from different
transportation modes and vehicle models, and (3) the absence of detailed future
transportation emission forecasts based on various scenarios of interurban transportation or
how targeted GHG reductions can be achieved.
While there are numerous transportation emission inventories as discussed in the
previous sections, none has the exact scope of my research, namely an exclusive focus on
interurban emissions of the entire passenger and freight transportation system. Existing
studies either focus on just one mode, or if they include all modes, they do not distinguish
between urban and interurban transportation except when the distinction is made by default,

37

for example in studies at the local (urban) level. The majority of transportation emission
inventories are bottom-up inventories because they can analyze the transportation sector in
more detail. Their drawback, though, is that the required level of statistical information must
be available.
The above observations provide the rationale as to why, for my research, I
constructed a spreadsheet-based, bottom-up GHG emission model and applied it to BC
interurban transportation. A bottom-up model also provided the greatest flexibility to
estimate the emission effects of future changes to BC interurban transportation (i.e., to test
various scenarios) in order to inform policy decisions. The cost of implementing emission
reduction scenarios is not included in the model. The literature review on the costing of GHG
emission reductions validates this decision because it is extremely complex and subject to too
many uncertainties, especially when long time horizons are involved. However, estimates for
offsetting excess emissions for scenarios that do not meet the targets, and the credit value of
excess reductions that exceed the targets, are included and are based on current and projected
carbon offset prices. The methodologies used to calculate current emissions and EFs of
interurban passenger and freight transportation in BC, as well as the future emission scenario
methodology, are discussed in the following chapter.

38

CHAPTER3:METHOD0LOGY
3.1

Introduction
The survey of the literature on modelling of transportation GHG emissions revealed

no studies identical to what is contained in this dissertation. Thus, there was also no
established methodology to follow for conducting my research, which compelled me to
devise my own methodological approach for calculating interurban transportation emissions
on a local scale. In this chapter, this approach is explained. I developed an Excel-based
model that I call SMITE (Simulator for Multimodal Interurban Transportation Emissions).
SMITE was used to calculate current and future emissions for BC's interurban transportation
sector. Current emissions were calculated for the year circa 2013 and future emissions for a
wide variety of scenarios up to the year 2050. The chapter is divided into two sections: the
first explains the approach for calculating current transportation emissions (which was used
to answer Research Question One), broken down by passenger and freight transportation
modes; and the second explains the approach for constructing future emission reduction
scenarios (which was used to answer Research Question Two).
3.2

Present-day emissions of passenger transportation methodology
In this section, the methodologies employed for calculating present-day passenger

transportation emissions are explained. They are discussed in order of the transportation
mode's aggregate contribution to BC's interurban transportation emissions as determined by
SMITE model calculations, thus, in the following order: private vehicles, ferries, aviation,
interurban bus, and train.

39

3 .2.1

Private vehicle methodology
For private vehicles, my method consisted of collecting private vehicle usage data,

calculating the percentage of vehicles at counting sites that were cars, and then performing
calculations in the following order: annual kilometres driven, changes in kilometres driven
between 2007 and 2013, calculating a Canada-specific highway EF, annual emissions, and
changes in emissions between 2007 and 2013.
Initially, I had planned on calculating interurban private vehicle emissions by
subtracting urban transportation emissions from total provincial road transportation
emissions, where these data would come from two different data bases. However, it turned
out that emission results using these data bases were incompatible. Urban vehicle emissions
were higher than total vehicle emissions, which is an impossibility since total road
transportation emissions are the sum of urban and interurban road transportation emissions.
The Province of BC publishes BC-wide road transportation emissions (British Columbia
Ministry of Environment 2012), and, for select years, the Community Energy and Emissions
Inventory (CEEI) (British Columbia Ministry of Environment 2014), which contains local
emission data including road transportation emissions. My plan was to subtract the CEEI
value from the overall road transportation value provided by the Province. However, as
stated above, the urban value was higher than the total value. Determining which of the two
values was correct proved impossible; they were derived using different and incompatible
methodologies. Therefore, I created an emissions inventory using a bottom-up method for
calculating emissions based on private vehicle usage. The steps to compile this inventory are
discussed in the following sections.

40

Collecting BC data for private vehicle emissions calculations
To calculate the emissions associated with interurban private vehicle transportation in
BC, the first step was to derive interurban road use statistics. The British Columbia Ministry
of Transportation counts vehicle movements at approximately 120 Permanent Count Sites as
well as more than 500 short-count (temporary) sites throughout the province. 3 Of these, 66
Permanent Count Sites as well as 13 short-count sites were chosen for my usage compilation
that cover the vast majority of primary BC interurban transportation routes as well as a small
number of secondary routes; the remaining Permanent Count Sites were excluded because
they are located within urban centres and thus likely contain a high number of urban rather
than interurban traffic, while the remaining short-count sites were excluded either because
they are also located within urban areas or because they are located on routes on which
Permanent Count Sites provide more detailed information. The vast majority of the 79 sites
chosen are located between urban areas. Data from these counting sites were input into an
Excel spreadsheet along with the route along which they are located and the distance between
the two urban areas the route connects.
The Ministry of Transportation provides various outputs for its counting sites. For my
compilation, the output called Average Annual Daily Traffic (AADT) was used for the years
2007 and 2013, the latest year for which data was available at the time of my research. This
daily value was multiplied by 365 to obtain the number of vehicles travelling past the
counting site in a given year.
Not all vehicles travel the entire distance between two urban areas. I had intended to
use a multiplier (with a value of between 0% and 100%) to reduce the AADT value so as to
account for vehicles driving only part of a route. This multiplier would have been influenced,
3

The main page for these statistics can be found at https://pub-apps.th.gov.bc.ca/tsg/.

41

for example, by the presence of towns along a route between urban areas, which may indicate
that some people only drive part of the route. However, no statistics are available that would
have allowed me to determine this multiplier in a quantitative manner. Rather than assigning
a multiplier based on a best estimate of the percentage of vehicles that would drive the entire
distance of a route, for simplicity sake, I abandoned this approach and assumed that 100% of
vehicles counted by a counting site would drive the entire distance of a given route.
Percentage of vehicles that are private vehicles (cars)
The AADT values comprise all types of vehicles that travel past a given co_unting site,
including private vehicles, trucks, buses, etc. Consequently, it is necessary to assign a
percentage for what number of the vehicles at a given site are cars. For 49 of the 79 counting
sites, this percentage was contained in the traffic statistics, and ranged from 35% to 94% of
vehicles counted. For the remaining sites, I calculated an average vehicle percentage using
the following formula:

Where,

APPV
Ss

Average percentage of vehicles counted that are private vehicles (cars)
Percentages of vehicles counted that are private vehicles at site S (there are 49
sites)

The average percentage value obtained using this method was 74%.
Private vehicle emissions calculations
The annual kilometres driven between origin and destination of a given route were
calculated using the following formula :

42

Annual distance driven on a given route R (km)
Annual traffic on route R obtained from Ministry of Transportation 's AADT (number
of vehicles)
Proportion of vehicles captured by counting site that are private vehicles for route R
(= percentage on route R I 100)
One way distance of route R between origin and destination (km), from Google Maps
The DRA values were determined for the years 2007 and 2013 . The change in kilometres
driven on route R between 2007 and 2013 was calculated using the below formula. The
percentage change values were used in the future scenario emission calculations.

Percentage changeDR =
Where,
DR 20 13
DR 2007

=

DR 2013 -DR 2007 X lOO
DR 2007

DRA kilometres driven in 2013 on route R
DRA kilometres driven in 2007 on route R

Canada average highway private vehicle emission fa ctor
Natural Resources Canada provides fuel consumption information and highway and
urban EFs for all vehicles for sale in Canada. At the time of my research, this information
was available for model years 1995 to 2013, and comprised 16,972 models (this includes
differentiations for different trim/engine options as well as manual and automatic models).
All models and emission factors from 1995 to 2013 were compiled in an Excel spreadsheet.
Next, a Canada-specific average highway private vehicle EF was calculated using the
following formula:

'J:J,6lf
EFHPVM
=-16,792
-2

ACHEFPV

Where,
ACHEFPV =
EFHPVM

Average Canadian highway EF for private vehicles (g COi/km)
Highway EF of each of 16,792 private vehicle models M for sale between
1995 and 2013 , as determined from Natural Resources Canada data

43

This yielded a value of 202.0 g CO 2/km. For comparison, the Department for
Environment, Food, and Rural Affairs (DEFRA) in the United Kingdom (UK) provides an
average vehicle EF of 201.9 g CO 2/km (DEFRA 2011). While this value is virtually identical
to my value, it includes both highway as well as less efficient urban driving, meaning that the
DEFRA value would be somewhat smaller than my value if it included only highway driving.
This is in line with my expectations given that the average vehicle in the UK is smaller than
the average vehicle in Canada and thus should have somewhat lower emissions.

Inability to calculate British Columbia average highway private vehicle
emission factor
I had originally intended to calculate a BC-specific average highway private vehicle
EF that would be reflective of the BC vehicle fleet. The following section outlines the
methodology that was intended for these calculations as well as why this approach ultimately
had to be abandoned.
Natural Resources Canada provides fuel consumption ratings for all vehicles
available for purchase in Canada. According to this information, in 2013 there were 1053
different car models available for sale in Canada (Natural Resources Canada 2014). This
number includes not only the different models by all manufacturers but also three subtypes
for these models--<lifferent trim levels, different engine sizes, and automatic or manual
transmissions. Natural Resources Canada includes annual emission values for each vehicle
model that are based on travelling an average 20,000 km per year with a mix of 55% city
driving and 45% highway driving. Since my research focuses only on interurban
transportation, interurban-specific EFs for each of the 1053 car models and subtypes, which
are for 100% highway driving, were calculated using the following formula:

44

EFH = FCH x EFF
Where,
EFH
FCH

EFF

EF highway (g COz/km)
Fuel consumption highway (L/lan) derived from Natural Resources Canada (2014)
EF fuel (2.3 kg CO 2 per L of gasoline; 2.7 kg CO 2 per L of diesel) derived from
Natural Resources Canada (2013) x 1000 g per kg

While the Natural Resources Canada information provided the kinds of vehicles
available for sale in Canada, it did not indicate how many of each vehicle are licensed in BC.
For this information, I contacted the Insurance Corporation of British Columbia (ICBC),
which is in charge of insuring and registering all vehicles in BC. ICBC provided an Excel file
of registered vehicles in 2014 that contained more than 13,000 rows of data and contained all
models for which more than 10 vehicles are insured (Lee 2014). These data, however, were
not disaggregated by model year. Thus, while the spreadsheet lists all insured models of a
particular type, it was impossible to determine the model year. Since ICBC could not provide
this information, I developed a method for determining model-specific EFs.
Models that were available in 2013 were matched with ICBC data on model types
insured. Some models could not be matched because they are no longer sold in Canada (thus,
ICBC was insuring model types that did not appear in the Natural Resources Canada (2014)
data set). Moreover, the ICBC data did not include pickup trucks, only cars and SUVs. Of the
total of 2,050,000 cars insured in BC in 2014, 1,377,000 could be allocated to Natural
Resources Canada models. Next, an aggregate EF for each model was calculated as follows:

Where,
EFM

EFH

Aggregate EF for all vehicles in BC of a given model (g COi/km), which represents the
emissions per km for the collection of all vehicles of a given model
Highway EF for a single vehicle of a given model as determined from Natural
Resources Canada data (g COi/km)
Number of vehicles insured in BC for a given model as determined from ICBC
data

45

Next, an overall, BC-specific highway EF was calculated using the following formula:
~1053 EF

EFBCH = L...M=l

VBc

Where,
EFBCH

BC-specific highway EF (g CO 2/km)
Sum of BC-specific aggregate EFs for all I 053 models M listed by Natural
Resources Canada in 2013 (g COi/km) = emissions per km in 2013 for the
collection of all vehicles for the I 053 models
Total number of vehicles insured in BC in 2014 which could be matched
with ICBC data

~1053 EF

L...M=l

M

M

Ysc

Using the above-described approach, a BC-specific highway EF of 155.9 grams of CO 2
emitted per kilometer traveled on a BC highway in 2014 was calculated.
The main issues with this approach were that nearly 700,000 vehicles could not be
matched to Natural Resources Canada fuel consumption data, that the ICBC data did not
distinguish vehicles by model years, and that pickup trucks (which are generally less fuelefficient than cars) were not included in the ICBC data. Figure 3.1 illustrates how average
private vehicle highway EFs in Canada changed between 1995 and 2013 . On average, they
have decreased.
Figure 3 .1: Average Canadian private vehicle EFs from 1995 to 2013

Average Canada Vehicle Highway EF (g COifkm)
250
200

........_,--------------~----------------

---

150
100

50
0

46

Because of the issues mentioned above, it would have been necessary to assign a
multiplier value to raise the EF of 155.9 g CO 2/km to more accurately reflect the actual BC
vehicle fleet. If, for example, a multiplier of25% were to be used, an EF of 194.9 g CO2/km
would have resulted, which is only 3.7% smaller than the Canada-specific highway EF
calculated above. However, it was impossible to develop a formula to assign such a
multiplier value. Because of these uncertainties, a Canada-specific private vehicle average
highway EF, "ACHEFPV", was calculated as previously explained and assumed to equal the
BC highway EF.

Annual BC interurban emissions
The following formula was used to calculate annual emissions for each interurban
route:
DR x ACHEFPV
EPVAR = ___1_0_6_ _
Where,

EPVAR

= Annual emissions per interurban route R (tonnes CO 2)

ACHEFPV

= BC highway EF (= 202.0 g COi/km), assume to be the same as the Canada-

DR

= Annual distance driven on interurban route R (km)

s ecific rivate vehicle avera e hi hwa EF

Total annual interurban private vehicle emissions were calculated using the following
formula:

I

79

EPVA =

EPVAR

R=l

Where,

EPVA

EPVAR

Annual emissions of private vehicles on all 79 interurban routes within BC (tonnes
CO2)
Annual emissions per interurban route R (tonnes CO2)

47

Changes in e missions 2007 - 2013
To compare emission changes between 2007 and 2013, the below formula was used.
The percentage change values were used in the future scenario emission calculations.

Percentage changeEPV =
Where,
EPV2013
EPV2001

EPV2013 - EPV2001
x 100
EPV2001

EPVAemissions in 2013
EPV Aemissions in 2007

Per-passenger emission fa ctor
One of the comparisons to be made in my research is per passenger emissions; in
other words, the amount of CO 2 emitted per passenger carried by a given mode of
transportation. For private vehicles, I made the assumption that the EF per vehicle is
equivalent to EF per passenger, thus assuming single occupancy of the car. While this will
not necessarily hold, I had no way of determining the average occupancy of private vehicles
for interurban driving. I therefore made the ' worst case' assumption. Thus, the BC-specific
highway EF of 202.0 g CO 2/km also equals the emissions per passenger-kilometre for
comparison purposes in this research.

Limitations ofprivate vehicle calculations
Private vehicle calculations were subject to five main limitations. First, some
Permanent Count Sites were not reported every year. This could be because the measuring
equipment was defective or because their use was discontinued; the Ministry of
Transportation did not indicate the reason for missing data. Short-count sites do not collect
data for extended periods as Permanent Count Sites do, so they may not have contained data
for 2007 or 2013. Where information for the years 2007 or 2013 was not available, this was
noted in the Excel spreadsheet and data from the closest suitable year substituted without
making adjustments to the values. It would have been preferable to utilize data from only
48

Permanent Count Sites, but they are not available on all routes (even some heavily travelled
routes), or on some routes they are available only for very long segments that may pass
through more than one urban area before the next Permanent Count Site. However, utilizing
short-count sites along with Permanent Count Sites allowed me to inventory traffic on more
routes, as well as to 'split up' routes so that they would in most cases only be between two
urban areas and thus more accurately represent traffic on that particular route.
Second, not all Permanent Count Sites and no short-count sites distinguish between
different sizes or types of vehicles; they count every vehicle that passes. Where Permanent
Count Sites distinguish between vehicles classes, this is only between cars and three different
lengths of trucks and only for the year 2014. The Ministry of Transportation has begun to
introduce vehicle counters that do distinguish between all vehicle types (including
motorcycles, cars, SUVs, buses, and various truck classes), but currently only five such
counters exist, which was not enough to obtain a representative value. For those sites for
which a split between cars and trucks was not available, I had to assign an average value
based on the car/truck split of the 49 sites for which these data were available.
Third, not every interurban route in BC has a traffic counter. If a route did not have a
counter, it was not included in my calculations. While the counters are strategically placed in
the province and the 79 counters I used cover the vast majority of primary BC interurban
routes and a few secondary routes, there are also routes that are not included in my research,
for which I had to effectively assume that traffic was zero. If all interurban routes (those with
counters and without) had been included, the calculated emissions would be higher.
Fourth, I had to assume that 100% of vehicles driving by a counting site travel the
entire distance of any given route because it was not possible to devise a formula to

49

quantitatively determine a limiting multiplier value. This overestimates traffic data and
consequently emissions because some people may only drive part of a given route. However,
the error introduced by assuming that 100% of vehicles drive the entire distance is likely
small for two main reasons: First, many counters are located in rural areas where there are no
towns or other points in between the urban areas to which people may travel instead of
travelling the entire distance of a route. On these routes, the percentage of people that travel a
route in its entirety is likely close to 100%. Second, it is also possible for people to travel
segments on each route that li-e on either side of the counting site but do not pass it. This kind
of travel is not captured by the counting sites. Consequently, while assuming that 100% of
vehicles travel the entirety of a route overestimates private vehicle usage, this may be
balanced out to a degree by interurban travel on a route that is not captured by the counting
sites because it occurs on either side of a counter. With the available data, it was not possible
to assess whether this limitation results in private vehicle usage being over- or
underestimated.
And fifth, the private vehicle average highway EF was not be based on the actual BC
vehicle fleet despite my best efforts. Instead a Canada-specific value was calculated.
However, it is an average of more than 16,500 EFs of vehicles sold in Canada in the 18 years
preceding my research, so it accounts for older vehicles and pickup trucks in a way that my
method for calculating a BC-specific EF could not.
3.2.2

Ferry methodology
For ferries, my method consisted of collecting data on ferry usage, calculating vessel-

specific EFs where possible, and then performing emission calculations (in the following

50

order: emissions per sailing, annual emissions, passenger-sailing EFs, and passengerkilometre EFs).

Collecting data for f erry emissions calculations
To calculate the emissions associated with ferry transportation in BC, the first step
was to create a listing of all ferry routes, their frequencies, ships used, and distances traveled.
How these data were collected is explained below.

Routes
The BC Ferries website contains a map as well as schedule of all routes served. In
total, BC Ferries lists 25 routes, which carry route identification numbers ranging from 1 to
40. It is unclear why there are more route identification numbers than routes being operated,
but one possible explanation could be that over time new routes have been added and older
ones discontinued, resulting in route identification numbers that are currently unassigned.
Some routes that include more than two ports of call are operated in more than one
combination (for example, not every sailing may stop at every port of call). All routes that
are operated were entered into an Excel spreadsheet with their respective route identification
number. These routes were further disaggregated so that every route in my listing had only
one vessel operating one itinerary (meaning that some route identification numbers appear
more than once in the schedule compilation). Because of this, my listing contained a total of
49 routes (or perhaps more accurately, ' vessel-routes'), whereas BC Ferries records 25 routes.

Frequ encies
Schedules listed on the BC Ferries website show weekly departures. These departures
can vary over a given time period (e.g., by season). The average number of weekly sailings
for each route was collected, which was then multiplied by 52 to obtain annual sailings.

51

Vess els used
The BC Ferries website contains a list of all ferries the company operates. When one
clicks on a specific vessel, information about it is displayed, which in most cases includes
engine horsepower, capacity for passengers and vehicles, and the route it typically operates
on. It appears that most vessels serve dedicated routes, while only a select few act as
' backups ' or change routes. For some routes, more than one vessel operates, especially when
there are many daily sailings. In these cases, I selected at least one representative vessel in
terms of size and capacity for the route. More than one representative vessel was selected on
routes with high frequencies where ships of markedly different sizes operate, to more
accurately reflect the vessels used in calculations.

Distances
For the majority of routes, especially longer ones, the scheduling information on the
BC ferries website lists the distance in nautical miles, which was converted to kilometres.
However, for some routes, no distances are given. This is generally the case for short
' triangular routes' where several islands are served and where the order in which they are
served varies among sailings during the day or during the week. In these cases, I used the
ArcGIS Online software ' s distance function to estimate the distance for each trip
(http://www.arcgis.com/home/webmap/viewer.html?useExisting=1).

Calculating vessel-specific emission factors
I decided to assign vessel-specific EFs where possible to reflect the diversity of the
BC Ferries fleet, which comprises some 35 vessels ranging in capacity from 133 people and
16 vehicles to 2,100 people and 4 70 vehicles (BC Ferries 2014 ), rather than using generic
ferry EFs; for example, from DEFRA. Vessel-specific EFs allow not only calculation of
more accurate total emission values but also comparison of the per-passenger EFs of specific
vessels. The methodology adopted here is a variant of that contained in the "Best Practices
52

Methodology for Quantifying Greenhouse Gas Emissions" by the BC Ministry of
Environment (2013), which is a manual designed to set out the current best practices for
quantifying and reporting GHG emissions from BC's provincial public sector organizations,
local governments, and communities.
Since emission data for BC Ferries were not available, I used ferry engine efficiency,
given in "horsepower/L/km" for select vessels (British Columbia Ministry of Environment
2013), as the basis for calculating emissions. However, engine efficiency is only available for
five routes and their associated vessels. Therefore, values were assigned to other vessels as
closely as possible, and where no appropriate matches were listed, the average efficiency
value contained in the Best Practices document was assigned.

Total CO2 emissions of BC Ferries
The engine efficiency described above was used in conjunction with fuel
consumption in order to calculate the CO2 emissions of BC Ferries. The diesel consumption
per sailing was calculated using the following formula:

Where,
FDCsRv

Diesel consumption per sailing on route R by vessel V (L); vessel is exact for
routes where only one vessel operated or representative on routes with more
than one vessel operating.
Horsepower of vessel (horsepower)
Distance of route R (km)
Engine efficiency (horsepower/L/kilometre), obtained from British Columbia
Mini st of Environment 2013

CO2 emissions per route were calculated using the following formula:

53

EFRARV =

(FDCsRv x EF0 )
106
x NAs

Where,

EFRARV

Annual CO2 emissions on a given route R by vessel V (tonnes of CO2); vessel is
exact for routes where only one vessel operated or representative on routes with
more than one vessel operating.
FDCsRv
Diesel consumption per sailing on route R by vessel V (L)
EF 0
EF for diesel fuel (2,663 g COi/L), as obtained from Environment Canada (2011)
NAs
Annual number of sailin s
Total annual BC Ferries emissions were calculated using the following formula:

I

49

EFRA =
Where,

EFRA
EFRARv

EFRARV

R=1

Annual CO2 emissions of BC Ferries (tonnes CO2) across all 49 individual routes
Annual CO2 emissions on a given route R by vessel V (tonnes CO2)

Per-passenger EFs ofBC Ferries
Having calculated the emissions produced by various BC Ferries vessels on various
routes, it was then possible to calculate per-passenger EFs for BC Ferries, which can be
compared to other per-passenger values for other transportation modes.

Load factor calculations

First, load factors (LFs)4 for BC Ferries were calculated to determine per-passenger

EFs. I diverged from the Best Practices guidelines and calculated my own LFs because their
ferry LF (average occupancy) was unrealistically high at 80%. According to my calculations,
this is not achieved on any route. For many routes, BC Ferries provides monthly embarkation
data, which I tallied in an Excel spreadsheet to determine an annual value. These data were
used to determine passenger and vehicle LFs using the following formulas :

4

The term load factor (LF) denotes the percentage of seats on a means of transportation that are occupied on
any given service.

54

Where,
LFVrR
NrR
Cr

Passenger LF for a given vessel Von a given route R in 2013
Number of passengers embarked on the vessel on route R
Maximum passenger capacity of vessel
LFvvehicleR

Where,
LFvvehicleR
NvehicleR
Cvehicle

NvehicleR

=-Cvehicle

Vehicle LF for a given vessel Von a given route R in 2013
Actual number of vehicles on the vessel on route R
Maximum vehicle ca aci of vessel

For vehicles, BC Ferries statistics do not distinguish between types of vehicles loaded
onto a ferry. Freight trucks would impact the LF if they are counted as a single vehicle
because they take up the space of several cars, for instance. BC Ferries reported that systemwide approximately 5% of vehicles travelling on BC Ferries vessels are trucks (personal
communication, Elizabeth Broadly of BC Ferries). Data for specific routes were not available.
On routes where the required information was not readily available (for instance, where
vessels travel on circular routes with various stops), BC Ferries ' overall capacity numbers
and passenger and vehicle statistics were taken from annual reports to calculate an average
LF.
The LFs calculated in this manner differed significantly from the 80% figure provided
by the Provincial Government (British Columbia Ministry of Environment 2013). According
to my calculations, the average vehicle LF for BC Ferries is 43 .1%, while for passengers it is
merely 23.4%. For those routes for which specific LFs could be calculated, the lowest
vehicle LF was 23 .6% (Earls Cove-Saltery Bay), while the highest was 100% for Haida
Gwaii sailings. In fact, for Haida Gwaii sailings the calculated LF was slightly above 100%,
which could be due to either a statistical error or incorrect vessel data from BC Ferries. For
55

passengers, the lowest LF calculated was 11.8% (Denman Island-Homby Island), while the
highest LF was 47.9% (Inside Passage).

Per-passenger EFs

Vessel-specific per-passenger EFs were calculated as follows. First, fuel consumption

per passenger per sailing and per passenger for a given vessel on a given route were
calculated using the following formulas.

FDCsRv
FCFpsRv = - - NpR
Where,
FCFrsRv

FDCsRv
NrR

Diesel consumption per passenger per sailing on route R for vessel V
(L/passenger); vessel is exact for routes where only one vessel operated or
representative on routes with more than one vessel operating
Diesel consumption per sailing on a given route R for vessel V (L)
Number of passengers on the vessel per sailing on route R

FCFPRv =
Where,
FCFPRV
FCFrsRv

FCFPSRV
DR x 100

Diesel consumption per passenger on route R for vessel V (L/passenger/km)
Diesel consumption per passenger per sailing on route R for vessel V
(L/passenger)
Distance of route R (km)

Based on the fuel consumption figures, emissions per passenger per sailing (to
compare per-trip emissions per passenger between modes) and per passenger (to compare
between modes on a per-kilometre basis) for all routes were calculated using the following
formulas. These represent per-sailing and per-passenger-kilometre EFs, respectively.

Where,
EFRrsRv =

FCFrsRv
EFo

EFRPSRV =

FCFpsRv x EFv
1000

Emissions per passenger per sailing on route R for vessel V (kg CO 2/passenger);
vessel is exact for routes where only one vessel operated or representative on routes
with more than one vessel operating.
Diesel consumption per passenger per sailing on route R for vessel V
(L/passenger)
Diesel EF (2,663 g C02/L) as obtained from Environment Canada (2011)

56

EFRPRV =

Where,
EFRrRv
FCFrRv
EFo

FCFPRv x EFv
100

Emissions per passenger-kilometre on route R for vessel V (g COi/passenger/km);
vessel is exact for routes where only one vessel operated or representative on
routes with more than one vessel operating.
Diesel consumption per passenger on route R for vessel V (L/passenger/km)
Diesel EF (2,663 C02/L) as obtained from Environment Canada 2011

Limitations offerry calculations
There are three main limitations to my ferries calculations. First, BC Ferries
schedules vary considerably between seasons. My listing is a conservative estimate of the
annual sailings for each route, and thus underestimates emissions. Second, the distances
vessels travel may not be exactly those provided by BC Ferries, or calculated by me for those
routes for which BC Ferries did not provide information. Ferries can be affected by factors
such as weather or traffic, so the distance travelled on any given sailing might be slightly
larger than that listed in my compilation. Thus, my listing underestimates emissions. Third, it
was not possible to assign a specific EF to each individual vessel because the data were not
available. Vessels were matched to EFs as closely as possible.
3.2.3

Passenger aviation methodology
For passenger aviation, my method consisted of collecting usage data, determining

plane-specific EFs, and then performing emission calculations (in the following order:
emissions per route, annual passenger aviation emissions, city-pair emissions, passengerflight EFs, and passenger-kilometre EFs).

Passenger aviation schedule listing
To calculate the emissions associated with passenger air transportation in BC, the first
step was to catalogue flights that remain entirely within the province. A list of the airlines
serving BC was compiled, along with the routes they serve and the number of flights

57

operated weekly, the flight distance for all routes, and the number of passengers that can be
carried on each flight. These input data are explained below.

Airlin es serving BC

To determine all airlines that fly within BC, two lists of airlines serving BC were

consulted (Transport Canada 2011 , Travel.be.ca 2013). The most current year for which
Transport Canada information was available at the time of my research was 2011 .

Routes and numb er of flights

To determine routes that remain entirely within BC, the websites of the airlines with

flights within BC were consulted. For those (generally larger) airlines that have International
Air Transport Association (IATA) codes and are broadly marketed, schedule information was
obtained through the KVS Availability Tool software. 5 For non-IA TA carriers, schedule
information was obtained through searches on the respective airline ' s website. Two small
float plane operators - Corilair and North Pacific Seaplanes - were excluded from the
research. Both conduct small operations with many stops that are often based on ad-hoc
demand rather than schedule.

Average week

From the schedule information, the number of flights for the week of November 1?11,

2013 was determined. This week was chosen because it is 'average' in the sense that it
contains no peak travel components such as public or school holidays. While the middle of
November is an off-peak travel season and there are likely to be more flights during peak
travel seasons, it is a reasonable assessment that the scheduled number of flights throughout
the year will be at least as high during the week chosen for this research. From this average
week, annual flights by airline by route were determined by multiplying by 52. For a few

5

The KVS Availability Tool is a subscription service containing a wealth of flight and aviation information for
"frequent fl yers" . It can be found online at w,,.,,,.,_kvstool.com.

58

routes which are not operated year-round, the actual number of weeks the flights are operated
was used to derive the number of annual flights.

Flight distances
Distances between departure and destination point for each route were obtained using
two different techniques. For most routes, distances (also known as stage lengths) were
obtained using the Great Circle Mapper website. 6 For a few routes that have no official
airport codes (mostly seaplane locations), distances between origin and destination were
estimated based on Google Maps. Once this 'shortest distance between two points' was
determined, a "diversion factor" was added to account for the fact that it is generally
impossible for an airplane to fly exactly the shortest distance between two points because of
operational considerations such as air traffic guidance and weather conditions. I was unable
to find a formula for how to calculate a diversion factor in my search of the literature. In part
this could be because the actual flight distance for any given flight will vary somewhat from
flight to flight. Even though I was unable to find such a formula, the need for a diversion
factor is obvious, so I attempted to estimate average diversion factors by comparing the
' shortest distance flown ' with the actual distance flown for various flights on the website
www .flightaware.com, which shows the flight paths of flights as well as statistics about the
respective flights, including distance travelled. Based on this, for stage lengths of less than
100 km, I estimated the diversion factor to be 10%; for stage lengths between 100 km and
200 km, I estimated the diversion factor to be 7.5%; and for stage lengths of more than 200
km, I estimated the diversion factor to be 5%. A decreasing diversion factor was chosen
because factors such as air traffic guidance and approach patterns often occur at either the
origin or destination airport and are unrelated to the flight distance (however, longer flights
6

The Great Circle Mapper lets users calculate the shortest distance between any two points in the world. It is
available online at http://www.gcmap.com/dist.

59

have a higher probability of having to fly around weather, which adds to the diversion
distance). The diversion factor was meant to add at least five kilometres to every route. The
yearly kilometres flown by a given airline on a given route and total kilometres travelled by
each airline within BC were calculated using the following formulas:

Annual distance flown by airline A on route R (km)
Distance of route R (km)
Diversion factor (0.10, 0.075, or 0.05)
Flights operated per year by airline A on route R (number of flights)

Annual distance flown per airline A on all BC-internal routes (km)
Distance flown per route R per year (km)
Number of BC-internal routes routes for airline A

Number of passengers
The number of seats available per plane was obtained either as an exact figure from
each airline ' s seat maps, or as an average when various plane types are used interchangeably.
To account for real-world operating conditions as much as possible, a LF was used. For Air
Canada and Westjet, these values are published and were used as exact values. For both
airlines, the LF is approximately 80% (Newswire 2013, Times Colonist 2013). For all other
airlines, specific LFs could not be obtained and thus a value of 80% was assumed. A LF
value does not mean that every flight will always be 80% occupied. LFs are calculated
system-wide and the average occupancy of a specific route may be higher or lower; however,
information to this degree of specificity is not publicly available.

60

Emission factors
Review of existing aviation emission factors
There are many aviation emission calculators available online for public use, offered
by a variety of providers such as environmental organizations or offsetting companies. These
calculators generally compute emissions by multiplying the distance of an individual flight
by an EF (usually expressed as grams of CO2 per passenger per kilometre (g C02/pkm)).
Generally, the EFs differ for short-haul, medium-haul, and long-haul flights (lower for longer
flights to account for the more efficient cruise phase). There are three major problems with
such emissions factors and their calculators: (1) the methodology used to arrive at the final
emission value is often unclear or even unstated, (2) the distance distinctions between the
EFs are arbitrary, and (3) the EFs assume a generic airplane. These problems are discussed in

tum.
First, some emission calculators do not describe their methodology or the EFs used.
Therefore, the user does not have any idea of how the final emissions figures were arrived at.
Second, EFs for many emission calculators are distance-based, which leads to unrealistic cutoffs. For example, DEFRA (2013) categorizes domestic flights as less than 463 km, shorthaul flights as between 463 and 1,108 km, and long-haul as greater than 1,108 km. The shorthaul EF is 41 % smaller than the domestic EF. Such a distance-dependent cut-off is
significant in BC because there are a number of flights just below or just above the distance
cut-off. This results in some longer flights having lower emissions than shorter flights, which,
all other things being equal, is logically inconsistent. Third, EFs are generally generic and do
not differ by airplane type. However, in reality, airplanes differ by size and fuel consumption
patterns, for instance. Airplanes serving BC range in size from an average passenger capacity
of four to 150.

61

Calculation of BC-sp ecific airplane emission factors
Because of the uncertainties and issues with publicly available EFs, I decided to
calculate BC-specific airplane EFs based on fuel consumption. The most accurate source of
fuel consumption data is pilot handbooks that allow pilots to plan how much fuel they will
need for a specific flight. However, this information is seldom available publicly.
Consequently, an alternative, and more complicated, methodology was devised to estimate
fuel consumption. First, basic information about the various plane types used in BC was
obtained from online sources.7 These data included the plane ' s empty weight, its maximum
takeoff weight, its fuel capacity, and its maximum range with a full payload. Second, the
actual weight of the aircraft was calculated. For a specific flight (assuming the plane would
be loaded with the exact amount of fuel needed to complete the trip), the fuel weight was
calculated using the following formula:
DRDF )
WfTF = ( RMAXp x CFp x WF

Where,

WJTF
DRDF

RMAXp
CFp
WF

Weight of total fuel for flight F (kg)
DR x DF = Stage length including diversion factor ofroute R (km)
Maximum range of plane P with full payload (km)
Fuel capacity of plane P (L)
Wei t of"et fuel (0.798 k L), as obtained from Im erial Oil n.d.

The actual take-off weight of the airplane was then calculated using the following formula:

Where,

WT
Wp

LF

WPax

Sp
WJTF

7
8

Take-off weight of airplane for flight F (kg)
Empty weight of airplane P (kg)
Load factor(%)
Average weight of passenger (kg) 8
Seat capacity of airplane P
Wei t of fuel for fli t F k

Most of the information was taken from the websites www.airlines-inform.com and www.what2tlv.com.
Average passenger weight is 84.1 kg (Federal Aviation Admini stration 2005).

62

As an intermediate step to calculate actual flight fuel consumption, the plane's fuel
consumption if it were fully loaded was calculated using the following formula:
CFp
FCFp = RMAXp
Where,
FCFr
CFr
RMAXr

Fuel consumption of plane P when fully loaded (L/lan)
Fuel capacity of plane P (L)
Maximum ran e of lane P with full ayload (Ian)

Finally, the plane's actual consumption for a specific flight was calculated using the
following formula:
FCAPF = FCFp x (-Wrp) ,
WMP

Where,
FCArF = Actual fuel consumption of plane P on flight F (L/lan)
FCFr = Fuel consumption of plane P if fully loaded (L/lan)
= Take-off weight of plane P (kg)
WTP
= Maximum take-off weight of plane P (kg)
WMP

The above-described formulas are used for calculating BC-specific airplane EFs; they
reflect real-world operating conditions as closely as possible in the absence of having access
to proprietary aircraft and airline information.
Annual CO2 emissions ofBC passenger aviation
In this section, the following sequence of calculations was used to determine annual

emissions: emissions per flight per route, emissions per route per year, emissions of each
airline for routes it operates in BC, emissions of all airlines for all routes they operate in BC,
and finally city-pair emissions.

63

Emissions on individual routes

The EF for each flight (for a given plane) was calculated using the following formula:

Where,
EFFRP
FCAFRP
EF1

EF for flight F on route R for plane P (kg COi/km)
Actual fuel consumption for flight F on route R for plane P (L/km)
EF of jet fuel (2.55 kg C0 2/L), as obtained from International Carbon Bank &
Exchange (2000)

Next, total emissions per flight were calculated using the following formula :

Where,
EAFRP
EFFRP

Emissions per flight F on route R for plane P (kg CO 2)
EF for flight F on route R for plane P (kg CO 2/km)
Stage length including diversion factor on route R (km)

DRDF

Total annual emissions were then calculated using the following formula:

Annual emissions of airline A on route R (tonnes CO 2)
Emissions per flight F on route R for plane P (kg CO 2)
Number of fli ts er year
Annual emissions for all flights by all airlines were compiled and ranked in an Excel
sheet in order of decreasing annual emissions in order to facilitate analysis of annual route
emissions. Total annual emissions by each airline were calculated using the following
formula :

I

n

£AAA=

EAAR

R=1

Where,
EAAA

Annual CO 2 emissions of airline A on all routes operated by the airline within BC
(tonnes CO2)
Annual emissions of airline A on route R (tonnes CO 2)
Number of individual routes operated by airline A within BC

64

Total annual emissions of all BC passenger aviation were calculated using the annual
emissions for each airline summed over all of its routes, as follows :

I

15

EAA =

EAAA

N=l

N

Annual CO2emissions of BC passenger aviation (tonnes CO2)
Annual CO2emissions of airline A on all routes operated by the
airline within BC (tonnes CO2)
Number of airlines operating flights within BC (=15)

City-pair emissions
When analyzing the emissions of aviation in BC and their geographical distribution, it
is important to look not only at individual routes but also at city-pairs, as several airlines
might serve the same route. A city-pair comprises the following: all airplane types an airline
operates between the same two cities; all airlines that operate between the two cities; and all
routes between the same cities (for example between various airports in Greater Vancouver
and various airports in Greater Victoria). City-pair emissions were summed using the
following formula:
EACP =

Wh ere,
EAcP

R
A

I

n

m

R=l

A=l

LEAAR

Total annual CO2emissions for all airlines serving routes between city-pair CP
(tonnes CO2/year)
Annual emissions of an airline A operating flights on route R that is part of city-pair CP
(tonnes CO2)
Number of routes for city-pair CP
Number of airlines serving a given route R for city-pair CP

65

Per-passenger EF of BC passenger aviation
Emissions per passenger per flight (used to compare per trip emissions between
modes), taking into consideration the respective LF, were calculated using the following
formula.
EFAPRPax -Flight

Where,
EF ArRPax-Flight

EAFRP
CSr
LF

EAFRP

=-CSp x LF

= EF per passenger-flight on route R for plane P (kg COi/passenger)
= Emissions per flight F on route R for plane P (kg CO 2)
= Seating capacity of plane P
= Load factor

All emissions per passenger per flight were compiled and ranked in an Excel sheet in order of
decreasing emissions.
Lastly, the per-passenger EF was calculated using the following formula:
EF APRPax -Dis tance

Where,
EF ArRPax-Distance
EArRPax-Flight
DRDF

=

EAPRPax -Flight

D

RDF

EF per passenger-distance on route R for plane P (kg COi/km)
Emissions per passenger per flight F on route R for plane P (kg CO2)
Sta e length includin diversion factor on route R (km)

All per-passenger EFs were compiled and ranked in an Excel sheet in order of
decreasing emissions per passenger-kilometre.
Limitations ofpassenger aviation calculations
The passenger aviation schedule listing and emission inventory is subject to six main
limitations. First, the inventory only includes scheduled, civil aviation flights . Private, charter,
military, and agricultural flights are not included in the inventory. Second, while most (and
all major) airlines serving BC routes are included in the inventory, a few had to be omitted
because of accounting difficulties. The emission values would be slightly higher if these
airlines had been included since they were small airline companies. Third, the inventory was

66

based on an off-peak week. The number of flights (and consequently emissions) during peak
seasons are likely slightly higher. Fourth, diversion factors were assigned that are meant to
account for real-life operating conditions in which flying the shortest distance between origin
and destination is generally not possible. The actual distances flown on any given day may be
longer or shorter than the values assumed. Fifth, it was assumed that all airlines had an 80%
LF. Actual LFs may be higher or lower, resulting in different emission values. Sixth, it was
assumed that every plane carries exactly the amount of fuel needed to complete a specific
flight. In all cases, airplanes carry more fuel, for example to be able to divert based on winter
weather conditions, or where destination airports do not offer fuel services and the airplane
carries enough fuel on the outbound flight to also operate the inbound flight. Because of the
added weight of the extra fuel, emissions would be slightly higher. Overall, the error
introduced by the above limitations should be fairly small and consequently not significantly
affect the results obtained in this research.
3 .2.4

Bus methodology
For bus travel, my method consisted of compiling usage information and then

calculating emissions per route and per year.
Bus travel schedule listing

For buses, just as for aviation, a listing of weekly and annual services was first
compiled and then aggregate emissions were calculated. Greyhound Canada is the only
company that operates interurban bus services within BC. The Greyhound website
(www.greyhound.ca) was used to identify routes and service. A route map available through
the website9 was consulted to identify which routes are serviced within BC, based on which

9

Available at http://extranet.greyhound.com/Rcvsup/schedules/sa-50.pdf.

67

31 routes were identified. Distances for each route were obtained by inputting the origin and
destination into the directions section of Google Maps. The website was then used to identify
schedules during the off-peak week of November 9th, 2013.
Weekly and annual distance travelled

The annual kilometres travelled per route were calculated as follows:

Annual distance travelled on route R (km)
Distance of route R (km)
Frequency of bus service per week ori route R

Weekly and annual emissions

I had hoped to calculate BC-specific EFs for the Greyhound fleet based on fuel
consumption data for the coaches in use but was unable to obtain such data; hence I had to
use a generic bus EF obtained from DEFRA. To calculate per passenger Greyhound
emissions, the following formula was used:

Emissions per passenger on route R (kg CO 2/passenger)
Distance of route R (km)
EF bus (0.0287 kg C02/pkm), obtained from DEFRA (2011)
The emissions per bus per route were calculated using the following formula:

Where,
EBR
EBPR

CSB

Emissions per bus trip on a given route R (kg CO 2)
Emissions per passenger on route R (kg COi/passenger)
Western Canada seating capacity of bus (average of 51), obtained from
Greyhound (2014), thus assuming the bus is 100% occupied.

68

Annual emissions per route were calculated using the following formula:

Annual bus emissions on route R (kg CO2)
Emissions per bus trip on a given route R (kg CO2)
Frequency of service per week
Total annual CO2 emissions for the Greyhound were calculated using the following formula:

I

31

EBA=

EBAR

R=l

Annual CO2 emissions of Greyhound bus service within BC (tonnes CO 2)
Annual bus emissions on route R (tonnes CO2)
Route; there are 31 routes within BC

Limitations of bus calculations
The bus schedule listing and emission inventory are subject to two main limitations.
First, an off-peak week was assumed to be reflective of the overall schedule. Actual service
frequencies may be slightly higher, but this week was chosen to obtain a conservative
estimate of bus service within BC. Second, I had to use a generic passenger-kilometre EF for
buses because I was unable to calculate a BC-specific EF. While the generic EF is based on
buses in the United Kingdom, a BC-specific EF likely would not differ much from the
generic EF because modem coaches used in Europe and North America tend to be similar in
their engine and vehicle size characteristics. Nevertheless, it may have been somewhat lower
or higher, resulting in lower or higher emissions, respectively.
3.2.5

Passenger train methodology
For passenger trains, my methodology consisted of inventorying schedule information,

and then calculating annual emissions per route and in aggregate.

69

Passenger train schedule listing
There is only one passenger rail service in BC, VIA Rail. The VIA Rail website
(www .viarail.ca) was consulted to determine train routes and schedules. There are only two
passenger train routes within BC: from the Alberta Border to Prince Rupert and from the
Alberta Border to Vancouver. Both routes were divided into two sections each (for the
Alberta Border to Prince Rupert route, to account for the train stopping in Prince George over
night, and for the Alberta Border to Vancouver route, to account for the long distance and
passengers possibly embarking or disembarking in Kamloops). The distances of each
segment were determined based on VIA Rail documents (VIA Rail 2013, 2009). Schedules
for both routes were compiled from the VIA Rail website.

Annual CO2 emissions of BC passenger trains
To calculate annual emissions, emissions per passenger per train were first
determined. Trains operating in BC are generally short, and can contain as few as two cars in
the off-season. An average occupancy of 50 passengers per train was assumed since there is
no publicly available LF. The emissions per train were calculated as follows :

Emissions per train trip on route R (tonnes CO2 I passenger)
Distance of route R (km)
EF for VIA Rail trains (117 g C02/pkm), obtained from the VIA Rail site on Wikipedia
(2014
Annual CO2 emissions for each passenger train route were then calculated:

Annual train emissions on route R (tonnes CO2)
Emissions per train trip on route R
Frequency of service on route R per week in both directions

70

Total annual train CO 2 emissions were calculated using the following formula:

Annual CO 2 emissions of passenger train service within BC (tonnes CO 2)
Annual train emissions on route R (tonnes CO 2)
Route; there are 4 routes within BC

Passenger train limitations

The passenger train schedule listing and emission calculations are subject to three
main limitations. First, an average occupancy of each train had to be assumed. Occupancy
information is not publicly available and likely differs across various regions served by VIA
Rail as well. In BC, the average occupancy likely also changes significantly with the seasons,
where trains during the summer carry tourists and have a much higher average passenger
load. Second, it was not possible to obtain a LF or estimate one. Calculating an average LF
for a train is more difficult than for other transportation modes because the capacity of the
train can easily be changed by adding or removing train cars. Third, it was not possible to
calculate a BC-specific passenger-kilometre EF because of a lack of statistics. Such an EF
may have been slightly lower or higher than the generic VIA Rail EF, resulting in slightly
lower or higher emission values.
3.3

Present-day emissions of freight transportation methodology

This section contains the methodologies employed for calculating present-day freight
transportation emissions. The methods for the various transportation modes are discussed in
the order of the respective mode's aggregate contribution to BC freight transportation
emissions, namely trucking, marine, train, and aviation.

71

3.3.1

Freight trucking methodology
My freight trucking methodology was closely based on that for private vehicles. It

involved compiling usage statistics, calculating a BC-specific tonne-kilometre EF, and then
calculating trucking emissions (in the following order: kilometres travelled, changes in
kilometres travelled between 2007 and 2013, annual emissions, and changes in emissions
between 2007 and 2013).

Collecting BC data for trucking emissions calculations
To inventory trucking data, I used the same Average Annual Daily Traffic (AADT)
data for 2007 and 2013 as for private vehicles, which was collected from 66 Permanent
Count Sites and 13 short-count sites as discussed in Section 3.2.1. This was possible because
the data from these sites tabulated all vehicles that travelled past them, including trucks. This
daily value was multiplied by 365 to obtain the number of vehicles including trucks
travelling past the counting site in a given year. Just as for private vehicles, I had to assume
that all trucks contained in the AADT values would travel the entirety of a given route.

Percentage of vehicles that are trucks
Similar to private vehicles, it was necessary to assign a percentage to the AADT
values to determine the number of vehicles that were trucks. Since the percentages included
in the traffic data only distinguish between cars and trucks (as opposed to buses, motorcycles,
etc.), the percentage of trucks for those sites for which data on the split between cars and
trucks was available was calculated using the following formula:
PT= l -PPV
Where,

PT
PPV

= Proportion of vehicles that are trucks(= percentage on route R I 100)
= Pro ortion of vehicles that are rivate vehicles cars = ercenta e on route R I 100

72

For those sites for which a split was not available, an average proportion was calculated
using the following formula:
Where,
APT
APPV =

APT= l -APPV

Average proportion of vehicles that are trucks(= percentage on route R I 100)
Average proportion of vehicles that are private vehicles (cars) (74%, see discussion in
section 3.2.1) (= percentage on route R I 100)

The average value for percentage of freight trucks was 26%.

Average freight weight of BC trucks
Because the trucking EF is per tonne-kilometre, it was necessary to calculate the
average weight of freight carried by trucks in BC (rather than the weight of the truck and its
freight, as the EF only applies to the freight). There are five traffic counting sites in BC
which are capable of measuring the weight of the vehicles that travel past them, as well as of
categorizing the vehicles into classes, including nine different types of trucks. For each site
and each truck category, total weight of trucks was calculated using the following formula:
TWTc = NT x GVW x CF

Where,

TWTc
NT
GVW
CF

Total weight of trucks in a given class C (kg)
Number of trucks
Gross vehicle weight (in pounds), obtained from traffic counting site data
Conversion factor to convert from pounds to kilograms (0.4536)

Next, the average weight across all truck classes at each specific site was calculated using the
following formula:

Li=i TWTc
AWT5 = - = - - - Li=1 NTc
Where,

AWTs

Li=i TWTc
L9= NT

= Average weight of trucks at a given counting site S (kg)
= Total weight of trucks across nine classes of trucks C (kg)
= Total number of trucks across nine classes of trucks C

73

Following this, the average weight of BC trucks across all five counting sites was
calculating using the following formula:
5

Where,
AWTBC
IL1AWT5
NS

"
AWT5
AWTBC = L..s=i
NS x 0.001

= Average weight of trucks in BC (tonnes)

= Sum of average weights of trucks at five counting sites S (kg)
= Number of sites; there are five sites in BC

The average weight for trucks obtained using this method was 24.116 tonnes. Because the
tonne-km EF is only for freight, the average weight of freight in BC was calculated using the
following formula:
AWF = AWTBC -WTractor -WTrailer
Where,
AWF
AWTBC
WTractor

WT railer

Average weight of freight (tonnes)
Average total weight of trucks in BC (tonnes)
Average weight of a truck tractor (9.07 tonnes), derived from Truckers Report
(2008)
Average weight of a truck trailer (5.90 tonnes), derived from
ShipNorthAmerica Transportation (2013)

Using this method, an average freight weight of 9.15 tonnes was calculated.
BC-specific freight trucking EF

A freight trucking tonne-kilometre EF was calculated at the national level because
statistics do not exist at the provincial level, and was assumed to hold for BC. This EF was
calculated based on data obtained from Statistics Canada (2014a). Because these data pertain
to trucking revenues, not a trucking EF, I had to follow a four-step process to calculate a
Canada trucking EF. The first step was to calculate a ratio of tonne-km travelled to weight
carried using the following formula:
TKMC
RTKMW = - - - - WTC x 0.001
Where,
RTKMW

Ratio of tonne-km to wei

t carried for a

74

ven ear in Canada

Tonne-km for trucks in Canada, obtained from Statistics Canada (2014a)
Total weight carried by trucks in Canada (kg), obtained from Statistics Canada
(2014a), then converted to tonnes

TKMC
WTC

An average of this ratio was calculated using the following formula:

I 6 - RTKMW
RATKMCW = _A-_i_ __
6

Where,
RATKMCW
RTKMW
A

Average ratio of tonne-km to weight carried in Canada between 2007
and 2012
Ratio of tonne-km to weight carried for a given year in Canada
Year total of six from 2007 to 2012

Tonne-km operated by trucks in BC were estimated using the following formula:
TKMBC = WTBC x RATKMCW

Where,
TKMBC
WTBC
RATKMCW

Tonne-km operated in BC
Weight of freight operated by trucks in BC (kg), derived from Statistics
Canada (2012c)
Average ratio of tonne-km to weight carried in Canada

A BC-specific freight trucking EF was estimated using the following formula:
ETFBC
EFTBC = TKMBC

Where,
EFT BC
ETFBC
TKMBC

EF of BC trucking (g COi/tonne-km)
Annual (urban and interurban) emissions of BC trucking (tonnes CO 2)
Tonne-km o erated b trucks in BC

Using this method, a BC trucking EF of 196 g C0 2/tkm was calculated.

Trucking emissions calculations
The annual kilometres driven between origin and destination of a given route were
calculated using the following formula:

Annual distance driven on a given route R (km)
Annual traffic on route R obtained from Ministry of Transportation's AADT (number
of vehicles)

75

PR
DR

= Proportion of vehicles captured by counting site that are trucks for route R
percentage on route R I 100)
=
One way distance between origin and destination (km), derived from Google Maps

(=

The DRA values were determined for the years 2007 and 2013. The change in
kilometres driven on route R between 2007 and 2013 was calculated using the below formula.
The percentage change values were used in the future scenario emission calculations.

Percentage change0 R =
Where,
DR2013
DR2007 =

DR 2013 -DR 2007 X lOO
DR 2007

DRA kilometres driven in 2013 on route R
DRA kilometres driven in 2007 on route R

Annual CO2 emissions of interurban truck travel in BC
The following formula was used to calculate annual emissions for each interurban
route:

Where,
ETFAR
DR
EFT8 c

Annual emissions per interurban route R (tonnes CO 2)
Annual distance driven on interurban route R (km)
EF of BC truckin (= 196 COi/tkm)

Total annual interurban trucking emissions were calculated using the following formula:

L
79

ETFA =

ETFAR

R=l

Where,
ETFA
ETFAR

Annual emissions of trucks on all 79 interurban routes within BC (tonnes CO2)
Annual emissions per interurban route R (tonnes CO 2)

Changes in emissions 2007 - 2013
To compare emission changes between 2007 and 2013, the below formula was used.
The percentage change values were used in the future scenario emission calculations.

76

Percentage changeEPV =

Where,
ETF2013
ETF2001

ETF2013 - ETF2007
x 100
ETF2007

ETF A emissions in 2013
ETFA emissions in 2007

Freight trucking limitations

Trucking calculations were subject to seven main limitations, several of which mirror
those for private vehicles as a similar methodology was employed. They are discussed in the
order in which the methodology was presented above.
First, some Permanent Count Sites are not reported every year, while short-count sites
do not collect data for extended periods as Permanent Count Sites do, so they may not have
contained data for 2007 or 2013. Where information for the years 2007 or 2013 was not
available, this was noted in the Excel spreadsheet and data from the closest suitable year
substituted without making adjustments to the values.
Second, a split between cars and trucks was not available for all counting sites, so I
had to assign an average for those sites for which this information was not available. The
actual number of trucks at these sites may be higher or lower, resulting in higher or lower
emission values.
Third, where Permanent Count Sites distinguish between cars and trucks, they
distinguish between three different length (sizes) of trucks. Because an average truck weight
was needed for calculations and because determining the empty weights of different truck
sizes proved impossible, I had to combine all types of trucks into one category. If several
truck classes and weights could have been included in the calculations, the values would
have more accurately reflected the weight of freight carried and consequently emissions. This
may have resulted in slightly lower or slightly higher emission values.

77

Fourth, not every interurban route in BC has a traffic counter. If a route did not have a
counter, it was not included in my calculations. While the counters are strategically placed in
the province and the 79 counters I used cover the vast majority of primary BC interurban
routes and a few secondary routes, there are also routes that are not included in my research,
for which I had to assume that traffic was zero. If all interurban routes (those with counters
and without) had been included, the calculated emissions would be higher.
Fifth, just as for cars I had to assume that 100% of trucks counted on a given route
travel the entirety of that route. While·this will overestimate usage and emissions, it was not
possible to quantitatively calculate an accurate percentage of trucks that do or do not travel
an entire route. If such a percentage could have been calculated for all routes, the emission
values would have been lower than my values.
Sixth, a trucking tonne-km EF, based on ratios for freight carried relative to
kilometres and tonne-kilometres relative to weight carried, was calculated based on national
data, because the relevant data does not exist at the provincial level. While the national data
does include aggregated BC data, BC-specific data may have resulted in slightly higher or
lower ratios, which would have influenced emission calculations.
Seventh, calculating emissions required the average weight of freight of BC trucks to
be calculated. Average freight weight, rather than average overall truck weight, was required
because the tonne-km EF for freight trucking applies only to the amount of freight carried,
and not the weight of the overall truck. Because I was only able to calculate one average
freight weight, the actual weight of freight on any given route and trip may be higher or
lower, resulting in higher or lower emissions. The average freight weight is comparatively
low because it is an average of loaded and empty trucks.

78

3.3.2

Marine freight methodology
For marine freight, my method involved obtaining information on the amount of

marine freight carried to be able to calculate marine freight emissions. Unlike the other
modes covered in this study, for marine freight, it was not possible to determine the
geographical distribution of emissions in BC.

Amount of marine freight carried
Data on the amount of marine freight carried within BC for a given year was obtained
from Statistics Canaqa' s series "Shipping in Canada" (Statistics Canada 2012b). This data
series, however, was discontinued in 2011. The amount of domestic freight handled in BC
and its composition in the years 2007 and 2011 were obtained from two documents (Statistics
Canada 2010, 2012a) in order to analyze whether marine freight shipments within BC had
increased or decreased in that period of time. In the report for 2007 (Statistics Canada 2010),
there are three port sites listed for Vancouver, but these were amalgamated (in name only)
into what is now Port Metro Vancouver in 2008 (Port Metro Vancouver 2014). In order to
compare values for Vancouver between years more easily, the values for the individual
Vancouver port sites for 2007 were combined.

Total CO2 emissions of marine freight in BC
It was not possible to find schedule information for BC marine freight. The Port of
Vancouver was contacted for assistance but no reply was received. Consequently, an
independent inventory of BC marine freight emissions could not be compiled and a
secondary approach had to be devised. The "Report on Energy Supply and Demand in
Canada" (Statistics Canada 2015b) lists fuel consumption by various sector of the BC
economy, including "domestic marine". Emissions of domestic marine freight in BC were
calculated by year using the following formula:

79

Where,
EMFA
EF 0
FCn
EFRA

Annual emissions from marine freight transport in BC (tonnes CO2)
EF diesel (2,663 g C02/L), as obtained from Environment Canada (2011)
Annual consumption of "domestic marine" diesel fuel in BC (L)
Annual emissions of BC Ferries (tonnes CO2) ; from ferry emission value as
calculated usin SMITE (see Section 3.2.2 above)

Marine freight limitations
The marine freight inventory is subject to three main limitations. First, it was not
possible to compile BC-specific data on marine freight routes, schedules, etc., because such
information is not publicly available. Also, it was not possible to determine the geographical
distribution of marine freight emissions because, while statistics on the amount of marine
freight handled by BC ports are available, there were no indications as to where the freight
was travelling (other than that it travelled to other destinations within BC).
Second, "Domestic marine" category in the "Report on Energy Supply and Demand
in Canada" represents Canadian-registered vessels fuelled in BC, but no information is
collected on whether the fuel is consumed in BC waters or outside the province, nor is there a
split between freight ships and pleasure boats (Ng 2015a). While this means that the
Statistics Canada data could include vessels that are fuelled in BC and then leave the
province, the emissions of which should not be included in this study, any error introduced in
this way should be minimal. Based on a search of the Transport Canada Vessel Registration
System, 15,452 vessels are registered in BC, but of the subcategories of vessels, only "cargo"
and "tanker" could be ocean-going, and there are only 52 cargo vessels and 3 tankers
registered (Transport Canada 2015). Therefore, the vast majority of fuel sold to BCregistered vessels is likely also consumed within BC waters. However, this includes fishing
vessels which do not qualify as interurban transportation, but there were no appropriate
80

statistics to disaggregate fishing vessel fuel consumption. My value for marine freight
emissions, because I am using Statistics Canada data, can be considered an upper limit. The
value for marine freight emissions listed in the BC Provincial Inventory (British Columbia
Ministry of Environment 2012) is larger than that calculated using the method outlined above.
However, this may be because it contains all vessels, where the Statistics Canada data
includes only registered vessels. There is a difference between licensing a vessel (a free
service, for example for pleasure boats) and registering a vessel (a paid service, for example
for commercial vessels) (Transport Canada 2015). Consequently, if the BC Provincial
Inventory Report (PIR) includes all boating, including pleasure boating, this would explain
why the value is larger than that derived from all fuel consumed by registered vessels in BC.
Third, it was not possible to calculate a BC-specific marine freight EF because it was
not possible to find information on fuel consumption and the overall distance travelled by
marine freight vessels in BC. Data on fuel consumption and weight of freight handled by
ports is available, but without distance statistics, a specific EF could not be calculated.
Calculating a BC-specific EF would have allowed comparisons of marine freight emissions
to other transportation modes.
3 .3 .3

Rail freight methodology
My rail freight method consisted of calculating annual CO 2 emissions and calculating

a tonne-kilometre EF.
Annual CO2 emission calculations

To calculate rail freight emissions, statistics on diesel fuel consumption by railways
in BC were obtained from Statistics Canada (2014c). Because fuel consumption was used, no

81

BC-specific freight rail EF could be determined. Emissions for both 2007 and 2012 were
calculated using the following formula:
ERFA =

Where,
ERFA
EFo
FCBCD

EFv x FCBCD
1Q6

Annual emissions of rail freight in BC (tonnes CO 2)
EF of diesel (2,663 g COi/L), as obtained from Environment Canada (2011)
Annual consumption of diesel fuel for railways in BC (L), as obtained
from Statistics Canada (2014c)

EF for rail freight calculations
A rail freight EF was calculated based on national rather than provincial statistics
because provincial level data were not available. These calculations included data for
Canada-wide railway diesel fuel consumption (Statistics Canada 2014c) and operating
statistics, including tonne-kilometre data (Statistics Canada 2014d). Annual CO2 emissions
Canada-wide were calculated using the following formula:
ERFCA =

Where,
ERFCA =
EFo
FCCD =

EFv x FCCD

106

Annual emissions of rail freight in Canada (tonnes CO2)
EF of diesel (2,663 g COi/L), as obtained from Environment Canada (2011)
Annual consumption of diesel fuel for railways in Canada (L), as obtained
from Statistics Canada (2014c)

Next, a Canada-specific EF for rail freight was calculated using the following formula:
ERFC
EFRFC= - k
A
T mA

Where,
EFRFC
ERFCA
TkmA

EF of rail freight in Canada (kg CO2/tonne-km)
Annual emissions of rail freight in Canada (tonnes CO2)
Annual tonne-kilometres travelled by Canadian rail freight

Freight train limitations
The freight train inventory is subject to two main limitations . First, Statistics Canada
data had to be relied on for emissions and usage information. It was not possible to compile

82

an independent inventory of rail freight transportation in BC. The railway emission value
listed in the BC PIR (British Columbia Ministry of Environment 2012) is significantly
smaller than that calculated using the method outlined above. However, although both
sources are ultimately from Statistics Canada, the method outlined above utilizes the data set
which comprises "common carrier railways operating in Canada that provide for-hire
passenger and freight services" and is incompatible with the BC PIR dataset which comprises
"all refiners and major distributors of refined petroleum products in Canada" (Ng 2015b ).
While it seems contradictory that railways in BC could use more fuel than what is provided
by refineries, it make sense when one considers that fuel can be loaded outside of BC and
still consumed in BC. The second limitation is that it was only possible to calculate an EF at
the national rather than provincial level due to lack of provincial-level statistics. While BC
data are part of the aggregated national data, the BC-specific value may be larger or smaller.
3.3 .4

Aviation freight methodology
For aviation freight, a methodology similar to that for passenger aviation was

developed, which included inventorying schedule information, determining plane-specific
EFs, and then calculating emissions (in the order of emissions per route, annual emissions,
emissions per flight, and tonne-kilometre Efs).
Aviation freight inventory
BC air freight operators and freight flights within BC were identified through an
online search. The website www.flightaware.com allows users to select an airport and see all
of that airport' s flight activities, including passenger and freight flights. Knowing the
destinations and names (or, for the purposes of my search, call-signs) of the airlines in
question, it was possible to catalogue the three routes with aviation freight service within BC

83

and frequencies operated during the week of October ?1\ 2014, which as far as I could
determine was an ' average ' week for transport of freight by air.

In order to calculate aviation freight emissions accurately, base data about the
respective airplanes used were compiled. The methodology employed to calculate data such
as the weight of fuel for a specific flight was identical to that employed to calculate these
values for passenger aviation, as discussed in section 3.2.3.
Emissions per flight
Emissions per flight were calculated using the following formula:
Where,
EAFFRP
EFFRP
DRDF

= Emissions per flight F on route R for plane P (kg CO 2)
= EF for flight F on route R for plane P (kg COi/km)

= Stage length including diversion factor on route R (km)

Annual emissions for each route were calculated using the following formula:

Where,
EAFAR
EAFFRP
NF

Annual emissions of airline A on route R (tonnes CO 2)
Emissions per flight F on route R for plane P (kg CO2)
Number of fli ts er ear on route R

Total emissions of BC aviation freight were calculated using the following formula:
EAFA =

I

3

EAFAR

R=1

Where,
EAFA
EAFAR
R

Annual CO 2 emissions of BC freight aviation (tonnes CO 2)
Annual emissions of airline A on route R (tonnes CO 2 )
Route; there are 3 routes

Freight-flight EFs were calculated using the following formula:

84

EAFFRP
EFAFR-weight = CFp X LF

Where,
EFAFR-weight
EAFFRP

CFp

LF

EF per unit of freight per flight F (kg CO2 )
Emissions per flight F on route R for plane P (kg CO 2)
Capacity of freight for airplane (kg)
Load factor

Freight-kilometre EFs were calculated using the following formula:
EF AFR-weight-distance

Where,
EF AFR-weight-distance
EAFR-weight
DRDF

=

EAFR-weight

D

RDF

= EF per unit of freight per kilometre in BC (kg COi/tonne-km)°

= EF per unit of freight per flight F (kg CO 2)
= Stage len

h includin diversion factor of route R (km)

Aviation freight limitations
The aviation freight schedule listing and emission inventory are subject to two main
limitations. First, only flights that are operated as all-freight operations are included. It is
possible that passenger flights also carry some cargo, such as mail, but this was not included
in my inventory because these flights are already included in the passenger aviation
inventory. Second, while the inventory is representative of the time at which it was compiled
(October 2014), it is difficult to judge without schedules if operations during the rest of the
year are the same. Moreover, it is possible that at least some freight is flown on an ad-hoc
basis rather than on a schedule. If a company spontaneously chartered a plane to carry a
given amount of cargo on a flight within BC, this should be included in my inventory;
however, it would be exceedingly difficult to catalogue ad-hoc cargo flights without direct
access to airline company data.
3.3.5

Data uncertainty assessment
In this section, data uncertainties for each mode are catalogued based on four

questions that assist in understanding the credibility of SMITE calculations. This assessment
85

can be combined with the comparison data presented in Chapter 4 to gain perspective on the
reliability of SMITE results. The four questions used in my assessment were:
What was th e nature of data availability? Availability was assessed as 'minimal BCspecific data' , ' limited BC-specific data' , or ' significant BC-specific data'.
Was it possible to create a fin e resolution inventory of the transportation mode with
the available data? The ability to create a geographically detailed inventory was assessed as
' no inventory created', 'basic inventory created ' , or 'detailed inventory created'.
Was it possible to devise calculation methods for each mode that reflect BC
operations? The ability to perform calculations that reflect operations in BC (e.g. , BCspecific EFs or using BC usage statistics) was assessed as either 'very limited BC-specific
calculations', 'limited BC -specific calculations' , or 'detailed BC -specific calculations'.
Was it possible to compare or validate results with other sources? An assessment of
' not possible ' was assigned where validation was not possible or, if it was, results were
significantly different from other sources and the differences could not be explained. An
assessment of 'possible ' was assigned where validation was possible and values were either
comparable or the differences could be explained, such as differing methodological
approaches.
Table 3.1 summarizes the answers to each question for each mode. Discussion of
validation is continued in Section 4.4 of the next chapter.

86

Table 3 .1: Data uncertainty assessment
Mode
Passenger
Private
vehicle
Ferries
Passenger
aviation
Bus
Passenger
trains
Freight
Trucking
frei!!ht
Marine
frei!!ht
Rail freight
Aviation
frei!!ht

3.4

Question 1: Data
availability

Question 2:
Inventory
creation

Question 3: BCspecific calculations

Question 4:
Data validation

Significant BCspecific data
Significant BCspecific data
Significant BCspecific data
Limited BCspecific data
Minimal BCspecific data

Detailed inventory
created
Detailed inventory
created
Detailed inventory
created
Basic inventory
created
Basic inventory
created

Detailed BC-specific
calculations
Detailed BC-specific
calculations
Detailed BC-specific
calculations
Limited BC-specific
calculations
Limited BC-specific
calculations

Possible

Significant BCspecific data
Minimal BCspecific data
Minimal BCspecific data
Limited BCspecific data

Detailed inventory
created
No inventory
created
No inventory
created
Detailed inventory
created

Detailed BC-specific
calculations
Very limited BCspecific calculations
Limited BC-specific
calculations
Detailed BC-specific
calculations

Possible
Possible
Not possible
Possible
Possible
Possible
Possible
Not possible

Future emission scenario methodology
This section outlines the methodological approach taken to answer Research Question

2 on how the province can achieve its legislated GHG emission reduction targets for 2020
and 2050. A methodology was developed for creating future emission scenarios, calculating
emissions from these scenarios, comparing these emissions to the legislated targets, and
calculating offset costs for scenarios that fail to meet the target reductions (and offset 'returns'
for scenarios that meet the targets). This methodology is the future emission scenario
component of the SMITE tool.
3.4.1

Future emission scenario component of SMITE

In addition to calculating present-day BC interurban transportation emissions, the
SMITE tool was used to study the effect of changes to BC's interurban transportation
87

emissions on meeting (or not) the province's legislated emission reduction targets. This part
of SMITE consists of a master template that can be copied to run scenarios. The master
template contains all transportation modes. For each transportation mode, there are two
columns for inputing or calculating what I call "actual/projected values" and "target values"
between the years from 2007 to 2050.
Actual values are the 'present-day' values calculated using the present-day
methodologies described in the above present-day methodology sections of this chapter.
They are the estimated yearly-emission values between 2007 and 2014. As described in the
present-day methodology sections, the year for which emissions could be calculated varies
depending on the transportation mode. Projected values were then calculated for each mode
for the years that follow the last year for which actual values could be calculated. The
projected values change depending on the scenario used. For example, if the latest calculated
actual value was for the year 2012, and a scenario dictated that emissions were to be reduced
by 1% every year in a sector, then each year's emissions were determined from 2013 to 2050
by multiplying the previous year's value by a factor of 0.99. Thus, SMITE contains or
determines annual emissions values for every year between 2007 and 2050.
Target values are the emission values that need to be obtained for the province to
meet its 2020 and 2050 targets. For each transportation mode, the 2007 target value equals
the 2007 actual value. The year 2007 is when BC's GHG reduction targets were set. Then,
for every year from 2008 to 2020, the target value is reduced by 3.03% over the previous
year, which results in the compound reduction required to obtain a 33% reduction by 2020.
Between 2020 and 2050, the annual reduction rate increases to 3.95% to obtain the overall
reduction target of 80% over 2007 values by 2050.

88

For every year and every transportation mode, the discrepancy of the actual/projected
value and the target value is calculated using the following formula:
.
Discrepancyy
=

E

- E

AP ET

T X 100

Where,

Actual/Projected emissions for a given year Y (tonnes CO 2)
where,
Actual emissions= yearly emissions values as calculated using the 'present-day
methodologies between 2007 and 2014, or latest year between these years for
which they could be calculated;
Projected emissions= yearly emissions projected for a given scenario from the
latest ' present-day' year to 2050
ET
Target emissions for a given year Y (tonnes CO 2)
Discrepancyv
discrepancy between actual/projected emissions and target emissions
for a ·ven ear Y %

EAP

SMITE contains, below every data series, a line chart that compares the projected and target
values visually to illustrate the discrepancy between the two values and whether the values
are diverging or converging over time. Figure 3.2 provides an illustrative example of a line
chart in which emissions, while decreasing over time, are unable to meet the target values,
while Figure 3.3 provides an illustrative example of a line chart in which emission reductions
exceed target values.

89

Figure 3.2: Illustrative example ofline chart in which targets are not met
10000000
9000000
8000000

- -Total freight
emissions
target (tonnes
CO2)

7000000
6000000
5000000

- -Total freight
emissions
projected
(tonnes CO2)

4000000
3000000
2000000
1000000
0
2000

2020

2040

2060

Figure 3 .3: Illustrative example of line chart in which targets are exceeded
400000 .---

- - Emissions target
(tonnes CO2)

200000
- - Emissions
projected (tonnes

150000 ...- - -

CO2)

100000 ---50000

r

0

-+

L __200_0_

2020

2040

2060

No line charts were included in Chapter 5.
Passenger and freight are calculated separately and then summed, as follows:

90

Where,
Erass
EM

Total passenger emissions for all modes for a given year Y (tonnes CO2)
Total emissions from a given mode for a given year Y (aviation, bus, private vehicle,
ferry, train) (tonnes CO2)

L
4

EFreight

Where,
EFreight
EM

=

EM

M=l

Total freight emissions for all modes for a given year Y (tonnes CO2)
Total emissions from a given mode for a given year Y (aviation, marine, train,
truck) (tonnes CO2)
ET= EPass + EFreight

Where,
ET
= Total interurban transportation emissions in BC in year Y (tonnes CO 2)
Erass
= Total passenger interurban transportation emissions in year Y (tonnes CO 2)
EFrei~ht = Total frei1mt interurban transportation emissions in year Y (tonnes CO2 )

Comparing scenarios and target values
SMITE was constructed to display in pie charts, for each scenario, the share of each
interurban transportation mode for the years 2007, 2013 (or latest year for which an actual
value could be calculated), 2020, and 2050. It determines whether the 2020 and 2050 targets
have been met for a given year for a given scenario, calculates the discrepancy, and
determines whether the targets are met within a margin of 20%. The purpose of this latter
value is to be able to rank simulations based on which ones allow BC to achieve its GHG
reduction targets, or, if the targets cannot be achieved, which scenarios come close to
achieving the mandated goals.
Furthermore, SMITE contains 'tracker' sheets that contain a list of all scenarios that
were run along with the changes incorporated into the scenario, the projected 2020 and 2050
emission values, and whether the targets have been met, or met with a margin of 20%, in

91

each scenario. Scenarios are ranked in order of increasing discrepancy from the target value
(i.e. , the greater the discrepancy, the greater the scenario misses the target value). The
scenario with the lowest overall projected emissions, and hence lowest discrepancy, is ranked
#1.

Cost comparison
The cost of offsetting excess emissions, or the worth of excess credits if scenarios
exceed target values, were also estimated. For this, the following carbon prices (per tonne of
CO2 ) were assumed: $5 for 2007, $10 for 2008, $15 for 2009, $20 for 2010, $25 for 2011 ,
$30 from 2012 to 2019 (all based on the actual carbon prices used by the province of BC
(British Columbia Ministry of Finance 2008)), and $100 from 2020 to 2050 (National Round
Table on the Environment and the Economy 2009). Projections for future carbon prices vary
widely in the literature, based in part on objectives and circumstances. In order to provide a
conservative value, the $100 dollar value was chosen because it was in the lower to midrange. It was assumed that if projected emissions were below the target value, the province
would be able to sell these credits on the carbon market at market value. Offset costs for each
year were calculated using the following formula:
Costsy

Where,
Costsv
ETProjected
ETTarget
Costov

= (ETProject ed - ETrarg et) X Costoy

Cost of scenario S for year Y (dollars)
Total projected emissions for year Y (tonnes CO 2)
Total target emissions for year Y (tonnes CO 2)
Cost of carbon offsets in the year Y (dollars/tonne CO 2)

Total offset costs or credit values for each scenario were then calculated as follows:

92

I

44

Costs=

Cost5 y

A=l

Where,
Costs = Total cost of scenario S (dollars)
Y
= Year between 2007 and 2050 (2050-2007+1=44 years over which to sum costs)
Costsv = Cost of scenario in a given year Y (dollars)

3.4.2

Choosing scenarios to model
There is an infinite number of possible changes to each individual transportation

modes and to the transportation system overall that could be modelled using SMITE. Since it
is not possible to model all possible scenarios, parameters for devising scenarios had to be set.
The process of scenario selection is explained in Chapter 5.

93

CHAPTER 4: INVENTORY OF BC'S INTERURBAN TRANSPORTATION CO2
EMISSIONS AND EMISSION FACTORS

4.1

Introduction
This chapter contains answers to Research Question One, What are the present-day

CO2 emissions and emission factors of interurban passenger and freight transportation in
BC? Transportation of people and freight is a vital part of BC' s economy and the lifestyle of

British Columbians, but it produces significant CO2 emissions. 10 According to SMITE model
results, in 2013, interurban transportation in BC produced approximately 11,194,000 tonnes
of CO2 . Passenger transportation produced approximately 2,443,000 tonnes of CO 2 (21.8%
of total emissions), while freight transportation produced approximately 8,751,000 tonnes of
CO2 (78.2% of total emissions). The purpose of this chapter is to present and analyze the
contribution of each interurban transportation mode in BC to these emissions totals and,
where applicable, analyze their EFs for carrying people or goods. The chapter is divided into
two major subsections: passenger and freight transportation. Within these sections,
transportation modes are discussed in the order of their contribution to overall BC
transportation CO2 emissions, as follows: for passenger transportation, (1) private vehicles,
(2) ferries, (3) passenger aviation, (4) bus travel, (5) passenger trains; and for freight
transportation, (6) freight trucking, (7) marine freight, (8) freight rail, and (9) aviation freight.

10

All values in this chapter are for CO 2, not C0 2e. Where appropriate, select C0 2e factors were converted to
CO2. Using CO 2 instead ofC0 2e does not have a significant impact on overall emissions because C0 2e values
are typically only about 1% larger than CO 2 values.

94

4.2

Passenger transportation within BC

4.2.1

Private vehicles

Introduction

Private vehicles are the most common form of daily passenger transportation in BC.
Benefits of private vehicles for interurban transportation include flexibility and access to
many locations that may either not be well or at all serviced by publicly available
transportation. In total, the 79 interurban vehicle counting sites considered in this study
captured 117,870,669 vehicle movements in 2007, and 121,201,608 in 2013. According to
SMITE calculations, total BC interurban private vehicle emissions were 1,809,667 tonnes
CO2 in 2007 and 1,917,247 tonnes CO 2 in 2013. Private vehicles were responsible for 17 .1 %
of total interurban transportation emissions and 78.4% of total passenger transportation
em1ss10ns.
In this section, private vehicle usage and emissions are discussed in the following

order: (1) total interurban private vehicle distances driven in 2007 and 2013 , (2) percentage
change of distances driven between 2007 and 2013, (3) private vehicle emissions per
kilometre of road, and (4) emissions produced by interurban private vehicle usage.
Total distance driven

According to SMITE calculations, British Columbians drove a total of 8.96 billion
interurban kilometres in 2007 and 9.49 billion interurban kilometres in 2013. The breakdown
of these distances driven by passenger vehicles on all 79 interurban routes is contained in
Table A2 .1 in Appendix 2. To illustrate these results, Table 4.1 below lists the 10 routes from
Table A2.1 with the longest distances driven in BC in 2007 and in 2013. For the 69 routes
not show in the table below, their distance values range from 211 million kilometres driven
for rank #11 to 3.9 million kilometres for rank #79. Figure 4.1 displays the geographical
95

distribution of kilometres driven in 2013. The figure is also illustrative for 2007 because the
vast majority of the routes considered did not change their kilometres-driven category on the
map.
Table 4.1: Ranking of BC routes by kilometres driven in 2007 and 2013
Rank

Route

2013 distance
driven (km)
1,120,642,345

Route

1

2007 distance
driven (km)
1,156,784,280

2

598,329,845

VancouverChilliwack
Ladysmith-Victoria

3

319,172,308

Vernon-Kelowna

345,182,544

4

314,236,267

Hope-Merritt

339,781,478

Parksville-Campbell
River
Vernon-Kelowna

5

305,851,896

327,401,725

Hope-Merritt

6

293,216,238

Parksville-Campbell
River
Parksville-Nanaimo

306,142,144

Kelowna-Penticton

7

275,043,910

Kelowna-Penticton

290,824,773

Parksville-Nanaimo

8

259,536,827

Chilliwack-Hope

267,043,271

Chilliwack-Hope

9

245,903,653

Vancouver-Squamish

250,063,982

10

234,430,740

Hope-Penticton

245,903,653

Tete Jaune CacheKamloops
Vancouver-Squamish

96

619,618,890

VancouverChilliwack
Ladysmith-Victoria

Figure 4.1: Geographical distribution of kilometres driven in BC in 2013

Leaend
2013 private vehicle
kilometres by route (km)
Brown:> 1,000,000,000

..... • Red: 200,000,000 1,000,000,000

Orange: 100,000.000 200,000,000
Yellow: 50,000,000 100,000,000
Blue: < 50,000,000
' : Route segment start/end
demarcation

.........

d

Cno k

97

The rank of the two highest counting sites in terms of total distances driven annually
did not change between 2007 and 2013. The longest distance driven was recorded at the
Vedder site (Route 1 between Vancouver and Chilliwack), with approximately 1.16 billion
kilometres driven in 2007 and 1.12 billion kilometres in 2013. The second longest distance
driven was recorded at the Hidden Hills site (Route 1 between Ladysmith and Victoria), with
approximately 598 million kilometres in 2007 and 620 million kilometres in 2013. The third
longest was at the Buckley Bay site (Route 19 between Parksville and Campbell River), with
approximately 306 million kilometres in 2007 and 345 million kilometres in 2013. By
contrast, the shortest distance driven was recorded at the Windy Point Bridge site (Route 37A
between Meziadin Junction and Stewart), with approximately 4.6 million kilometres in 2007
and 4.0 million kilometres in 2013 . The geographic distribution of distance driven is,
expectedly, linked to population density, with most kilometres driven in more densely
populated areas such as the Lower Mainland area around Vancouver and least kilometres
driven in less densely populated areas such as BC ' s northern areas.
Change in distances driven between 2007 and 2013
Comparison of traffic statistics permitted calculation of changes in traffic volumes
between 2007 and 2013 . The percentage change in distance driven by interurban passenger
vehicles is contained in Table A2.2 in Appendix 2. Out of the 79 counting sites considered
for interurban passenger vehicle traffic, 43 sites had increased vehicle numbers, five sites had
no change, and 31 sites had decreased vehicle numbers. To illustrate these results, Table 4.2
below contains the three largest and three smallest changes between 2007 and 2013. Figure
4.2 displays on which routes within BC kilometres driven have increased or decreased.

98

Table 4.2: Percentage changes in distance driven on BC routes 2007-2013
Rank
1
2
3

.. .

Route
Dawson CreekPrince George
Fort St. JohnWonowon
Salmon ArmRevel stoke

o;o

2007
distance
driven (km)
104,349,668

2013
distance
driven (km)
149,029,774

25,745 ,337

35,294,953

37.1

113,350,429

141 ,044,410

24.4

Change
42.8

77

Hope-Cache Creek

141 ,632,994

113 ,984,025

-19.5

78

Hope-Penticton

234,430,740

181 ,927,242

-22.4

79

Alexis CreekAnahim Lake

14,875 ,531

9,415 ,598

-36.7

99

Figure 4.2: Geographical distribution of percentage change in distance driven 2007-2013

Leaend
Private vehicle usage and
emission changes 2007 - 2013
Brown: >+25%
Red: +10.0% to +24.9%
Orange: +o.1%to+9.90A,
Yellow: -9.9%to0.0%

f : Route segment start/end
demarcation

SNI

Ed

100

Increases across the province in distances driven by interurban passenger vehicles
ranged from +42.8% to +0.1%. The largest increase in vehicle numbers was at the Willow
Flats counting site, which reports traffic between Dawson Creek and Prince George on Route
97. Between 2007 and 2013 , this site had an increase of 42.8%. The second highest increase
was at the Inga Lake site, which reports traffic between Fort St. John and Wonowon on
Route 97, and which had an increase of 3 7 .1 %. The third highest increase was at the
Craigellachie site, which reports traffic between Revelstoke and Salmon Arm on Route 1,
and which had an increase of24.4%. The remaining increases across BC range from 20.1%
to 0.1 %.
Five sites reported no change in traffic counts. Decreases ranged from -0.2% to
-36.7%. The third highest decrease was at the China Bar site, which counts traffic between
Hope and Cache Creek on Route 1, and which had a decrease of -19.5%. The second highest
decrease was at the Nicolum site, which counts traffic between Hope and Penticton on Route
3, and which had a decrease of -22.4%. The largest decrease in vehicle numbers was reported
at the Kleena Kleene Bridge site, which counts traffic between Alexis Creek and Anahim
Lake on Route 20. Between 2007 and 2013 , this site had a decrease in vehicles, and hence
kilometres driven, of -36.7%.

Emissions per kilometre of road
In addition to total vehicle-kilometres driven, which are directly related to the length
of a particular route on which traffic is counted, it is possible to calculate emissions
generated per kilometre of road. These values give an indication of how heavily a given route
is travelled. This is helpful in devising emission reduction scenarios because reduction
measures such as increased public transit will be more effective for higher relative traffic
volume roads. The emissions per kilometre of road per year on all routes are contained in
101

Table A2 .3 in Appendix 2. To illustrate these results, Table 4.3 below contains the three
largest and three smallest values for 2007 and 2013 , ranked by 2013 values. Figure 4.3
illustrates the emissions per kilometre of road for each route in 2013. The map is also
illustrative for 2007 because the map category of nearly all routes has not changed between
2007 and 2013.
Table 4.3: Emissions per kilometre of road for 2007 and 2013
Rank

Route

1
2
3

Vancouver-Chilli wack
Nanaimo-Lad sm ith
Parksville-Nanaim 0

77
78
79

Dease Lake-Yuko n Border
Meziadin Junction-Dease Lake
Alexis Creek-Ana him Lake

2007 emissions per km
of road
(tonnes COz/km)
2,3 37
1,591
1,559

2013 emissions per
km of road
(tonnes COz/km)
2,264
1,604
1,546

14
10
10

1I
II
9

102

Figure 4.3: Emissions per kilometre of road on BC routes in 2013

Lecend
2013 printe vehicle
emissions per kilometre of
road (tonnes CO2 per km)

Brown: > 2,000
Red: 1,000 - 2,000
Orange: 500 - 1,000
Yellow: 200 - 500
Blue: <200
• : Route segment
start/end demarcation
f
rtd

.

0,-P,-

103

In BC, the route with the highest emissions per kilometer was at the Vedder site,
which counts traffic between Vancouver and Chilliwack on Route 1, which had 2,337 tonnes
CO 2 per kilometre of road in 2007 and 2,264 tonnes CO 2 per kilometre of road in 2013. The
second highest route was at the Cassidy site, which counts traffic between Nanaimo and
Ladysmith on Route 1, and which had approximately 1,591 tonnes CO 2 per kilometre of road
in 2007 and 1,604 tonnes CO 2 per kilometre of road in 2013. The third highest route was at
the Parksville site, which counts traffic between Parksville and Nanaimo on Route 19, and
which had 1,559 tonnes CO 2 per kilometre of road in 2007 and 1,546 tonnes CO 2 per
kilometre of road in 2013.
The route with the third lowest emissions per kilometre of road was at the Cassiar
Junction site, which counts traffic between Dease Lake and the Yukon Border on Route 37,
and which had 14 tonnes CO 2 per kilometre of road in 2007 and 11 tonnes of CO 2 per
kilometre of road in 2013. The route with the second lowest emissions per kilometre of road
was at the Stikine River Bridge site, which counts traffic between Meziadin Junction and
Dease Lake on Route 37, and which had 11 tonnes CO 2 per kilometre of road in 2007 and 10
tonnes CO 2 per kilometre of road in 2013. The lowest emissions per kilometre of road in the
province were at the Kleena Kleene Bridge site, which counts traffic between Anahim Lake
and Alexis Creek on Route 20, and which had 10 tonnes CO 2 per kilometre of road in 2007
and 9 tonnes CO 2 per kilometre of road in 2013 .
Total CO2 emissions ofprivate vehicle travel in BC
Total interurban private vehicle emissions in 2007 were approximately 1,809,667
tonnes CO2, while in 2013 they were approximately 1,916,108 tonnes CO 2. The breakdown
of these emissions for all 79 interurban routes in BC is contained in Table A2.4 in Appendix
2. To illustrate these results, Table 4.4 below contains the 10 routes from Table A2.4 with the
104

highest CO 2 emissions in 2007 and 2013 . The remaining routes range in values from 42,000
tonnes CO 2 for rank #11 to 800 tonnes CO 2 for rank #79. Figure 4.4 displays the
geographical distribution of the emissions in 2013. The map is also illustrative for 2007
because the map categories of the vast majority of routes have not changed between 2007
and 2013.
Table 4.4: Private vehicle interurban CO2 emissions by route in BC
Rank

2007 emissions
(tonnes CO2)

Route

2013 emissions
(tonnes CO2)

Route

1

233 ,670

2

120,863

VancouverChilliwack
Ladysmith-Victoria

3

64,473

Vemon- Kelowna

69,727

4

63 ,476

Hope-Merritt

68,636

Parksville-Campbell
River
Vemon-Kelowna

5

61 ,782

66, 135

Hope-Merritt

6

59,230

Parksville-Campbell
River
Parksville-N anaimo

61 ,841

Kelowna-Penticton

7

55 ,559

Kelowna-Penticton

58,747

Parks vi Ile-Nanaimo

8

52,426

Chilliwack-Hope

53 ,943

Chilliwack-Hope

9

49,673

Vancouver-Squamish

50,513

10

47,355

Hope-Penticton

49,673

Tete Jaune CacheKam loops
Vancouver-Squamish

105

226,370
125, 163

VancouverChilliwack
Ladysmith-Victoria

Figure 4.4: Private vehicle interurban CO2 emissions by route in BC in 2013
Lesend

-

.

2013 private vehicle
emissions (tonnes CO2)
Brown: > 200,000
Red: 100,000- 200,000

Orange: 50.000 - 100,000
Yellow: 20.000 - 50,000
Light blue: 10.000 - 20.000
Dade blue: < 10,000

f : Route segment
start/end demarcation

,

nd

0, . ..

Pr'""

., .L•h

Et

HMUG,..,,

(., \ k

106

Emissions follow the same ranking as those values for distances driven (Table 4.1)
because the same EF was used for all private vehicle highway driving. The route with the
highest emissions is Vancouver-Chilliwack, with 226,370 tonnes CO 2. This route leads from
Vancouver, BC' s biggest city, to several of its suburbs, which likely explains the high
vehicle volume and emissions. The route with the second highest emissions is LadysmithVictoria, with 125,163 tonnes CO2. This short route leads from Victoria, one of BC's biggest
cities and its capital, north towards Nanaimo. Because the distance is comparatively short,
high emissions result from a high traffic volume. The route with the third-highest emissions
is Parksville-Campbell River, with 69,727 tonnes CO 2. This route also forms part of the
route from Victoria north, and high emissions result from a high traffic volume because the
distance is comparatively short.

Discussion
Private vehicles are an important part of the BC interurban transportation system, and
responsible for the highest share of passenger transportation emissions. Because private
vehicle usage increased between 2007 and 2013 , and because the average vehicle EF did not
significantly improve in this period, the emissions associated with private vehicles in BC
increased between 2007 and 2013. However, they should have decreased in order to be on
track to meet BC ' s GHG reduction targets.
Assuming single occupancy for private vehicles, the vehicle-specific EF is equivalent
to the per-passenger EF, since the driver is the sole passenger. At approximately 202 g
C02/pkm, the Canada-specific private vehicle EF is virtually identical to the generic car EF
provided by DEFRA. However, the value used in SMITE is only for highway driving,
whereas the DEFRA EF also includes less efficient urban driving, which indicates that the
average Canadian car is slightly less efficient than the average car considered by DEFRA.
107

Overall, two factors influence route-specific interurban passenger emissions from
private vehicles in SMITE: distance and volume of vehicles. Emissions are a product of the
distance of a route and the number of vehicles that travel it. Therefore, a long route with low
traffic volume can have similar emissions to a short route with a high traffic volume.
Determining route-specific emissions is essential for determining their geographic
distribution, such as illustrated in Figure 4.4. This information can, in tum, be used by the
public and policymakers to devise geography-specific strategies for reducing CO 2 emissions.
4.2.2

Ferries

Introduction
Ferries play an important role in BC' s transportation network because they serve
people living on islands off the coast of BC and coastal communities. Of BC ' s population of
4.5 million, approximately 780,000 or 17% live on Vancouver Island (Vancouver Island
Economy Alliance 2013), while approximately 23 ,000 or 0.5% live on the 13 main islands
that comprise the Gulf Islands between Vancouver Island and the BC mainland (Newton
n.d.). Moreover, those people living in the Sunshine Coast area, which is just northwest of
Vancouver, rely on ferries for connections to the rest of the province. Passenger ferry
services within BC are provided by BC Ferries. BC Ferries has a fleet of 35 vessels, ranging
in capacity from 13 3 people and 16 vehicles to 2, 100 people and 4 70 vehicles (BC Ferries
2014). They operated approximately 154,627 sailings on 49 routes in 2013 , travelling a total
distance of 2,514,824 km. On these sailings, they carried 7 .37 million vehicles (out of a
possible 17.97 million vehicles at full capacity) and 18.98 million passengers (out of a
possible 85.08 million passengers at full capacity), while consuming approximately 128.3
million litres of diesel fuel, which resulted in emission of 342,400 tonnes of CO 2 . Ferries

108

accounted for 3 .1 % of total interurban transportation emissions and 14.0% of passenger
transportation emissions. In this section, calculations are discussed in the following order: (1)
total BC Ferries emissions, (2) passenger-sailing EFs, and (3) passenger-kilometre EFs.
Total ferry travel CO2 emissions in BC

The total CO 2 emissions produced by BC Ferries in 2013 were approximately 342,000
tonnes of CO2 . The breakdown of these emissions for all 39 origin and destination pairs is
contained in Table A2.5 in Appendix 2. To illustrate these results, Table 4.5 below contains
the 10 most emission-intensive pairs. These 10 routes account for approximately 301 ,000
tonnes CO 2, or 88% of the total of 342,000 tonnes, and the remaining 29 routes together only
account for approximately 41 ,000 tonnes CO2, or 12% of the total. Figure 4.5 displays the
geographic distribution of BC Ferries' annual emissions on the level of the entire province
(with northern routes emphasized for improved visibility), while Figure 4.6 displays the
geographic distribution of BC Ferries' annual emissions zoomed into the southwestern comer
of the province, because this is where most BC Ferries routes are operated.
Table 4.5: Annual emissions of BC Ferries routes

1

Tsawwassen-Duke Point

Annual emissions
(tonnes CO 2)
81 ,097

2

Tsawwassen-Swartz Bay

80,686

3

Horseshoe Bay-Departure Bay

74,075

4

Horseshoe Bay-Langdale

18,561

5

Inside passage Prince Rupert-Port Hardy

10,966

6

Earls Cove- Saltery Bay

10,396

7

Haida Gwaii-Prince Rupert

6,893

8

Powell River-Comox

6,229

9

Salt Spring/Fulford- Victoria

5,664

JO

Pender Island-Swartz Bay

5,533

Rank

Route and number

109

Figure 4.5: Geographic distribution of BC Ferries annual CO 2 emissions (entire province)

~
T... _

l~ett

Lecead

.........

2013 BC Ferries
emissions by route
(tonnes C~)

Orange: 10,00020,000
Yellow: 5,000 10,000

....

110

Blue: 2,000 - 5,000
Purple: < 2,000

- .(If t,

.

Figure 4.6: Geographic distribution of BC Ferries annual CO 2 emissions (southwestern BC)

Leaend
2013 BC Ferries
emissions by route
(tonnes C{}z)
Red: > 20,000
Orange: 10.00020,000
Y cllow: 5,000 -

10,000

Blue: 2,000-5,000

Vancouver
@
Burnaby

Richmond

Clo ·OOM'

111

High emissions are attributable to three factors: distance of sailing, frequency of
sailing, and size of vessel used. The three most emission-intensive routes are all main routes
between Vancouver and Vancouver Island (Tsawwassen and Horseshoe Bay are Vancouver's
ferry ports; Swartz Bay is near Victoria; Duke Point and Departure Bay are near Nanaimo).
All three routes are comparatively long, have a high sailing frequency (as often as hourly),
and use the largest ferries in BC Ferries' fleet. The 5th and 7th ranked routes have a low
frequency but use large vessels and cover long distances, while the remaining routes in Table
4.5 are all short but with very high sailing frequencies. BC Ferries emissions are concentrated
in the province' s southwest comer between Vancouver and Vancouver Island, although the
northern ferry routes also have high emissions, mostly by virtue of their long distances.
Passenger-sailing EFs on BC Ferries routes

Passenger-sailing EFs (which can be used to compare the emissions of a given trip
between transportation modes) for all 49 routes are contained in Table A2.6 in Appendix 2.
To illustrate these results, Table 4.6 lists the 10 most emission-intensive sailings per
passenger. These routes have passenger-sailing EFs greater or equal to 25 kg CO 2. All
remaining routes have values that are below 25 kg CO2.
Table 4.6: Passenger-sailing EFs on BC Ferries routes
Rank

Route

Vessel

I

Inside Passage Prince
Rupert-Port Hardy
Haida Gwaii-Prince Rupert

Northern
Expedition
Northern
Adventure
Queen of
Chilliwack
Coastal
lnsoiration
Queen of
Alberni

2

4

Port Hardy-Bella Coola
Discovery Coast
Tsawwassen-Duke Point

5

Tsawwassen-Duke Point

3

112

Passenger-sailing
EF (kg CO2)
288
193
I 83
62

55

6
7

Day trip from Swartz Bay
(via Pender, Mayne, Galiano,
Pender)
Earls Cove- Saltery Bay

8

Satuma Is- Swartz Bay

9

Horseshoe Bay-Departure
Bay
Galiano-Swartz Bay

10

Queen of
Cumberland

51

MV Island

31

Queen of
Cumberland
Coastal
Renaissance
Queen of
Cumberland

30

Sky

26
25

The Inside Passage, from Prince Rupert to Port Hardy, has by far the highest
passenger-sailing EF at 288 kg CO 2 • However, this is also by far the longest sailing operated
by BC Ferries, at approximately 507 km. The second highest value is for the Prince RupertHaida Gwaii sailing. The passenger-sailing EF is 67% of the highest route but its distance of
172 km is only 34% of the first route, which means that the sailing is significantly more
emission-intensive on a per passenger basis. An even more drastic example of a passengersailing EF put in context is the Earls Cove-Saltery Bay route, which is ranked seventh for
passenger-sailing EF. At 17.6 km this route is approximately 3% of the distance of the Inside
Passage route, yet at 31 kg CO2 per passenger its passenger-sailing EF is almost 11 % of that
of the Inside Passage.
Passenger-kilometre EFs on BC Ferries routes
The passenger-kilometre EFs of all 49 BC Ferry routes, which were calculated using
the LFs computed for this research rather than those provided by the Provincial Government,
are contained in Table A2.7 in Appendix 2. To illustrate these results, Table 4.7 below lists
the five routes with the lowest passenger-kilometre EFs. These routes all have passengerkilometre EFs of between roughly 250 and 3 70 g C0 2/pkm. The table also contains the five
routes with the highest passenger-kilometre EFs. These routes all have passenger-kilometre
EFs of between roughly 1,000 and 1,800 g C02/pkm. Figure 4.7 displays the geographic
113

distribution of BC Ferries' passenger-kilometre EFs at the level of the entire province, while
Figure 4.8 displays the geographic distribution of BC Ferries' passenger-kilometre EFs
zoomed into the southwestern comer of the province, because this is where most BC Ferries
routes are operated.
Table 4.7: Passenger-kilometre EFs on BC Ferries routes
Ran
k

1
2
3
4
5

Vessel

Route and number

Passenger-kilometre
EF

(p C02/okm)

Chemainus-Theis Island-Penelakut Is
(20)
Tsawwassen-Swartz Bay ( 1)
Horseshoe Bay-Departure Bay (2)
Salt Spring/Long HarbourTsawwassen (9)
Pender-Tsawwassen (9)

MV Kuper

261

Spirit of British
Columbia
Queen of Oak Bay
Queen of Nanaimo

288
334
369

Queen of Nanaimo

369

".

Langdale-Keats-New BrightonLangdale ( 13)
Langdale-New BrightonEastbourne-Keats-Langdale ( 13)

Tenaka

1,007

Tenaka

1,007

47

Quadra Is-Cortes Is (24)

Tenaka

1,012

48

Haida Gwaii (11)

Northern Adventure

I, 118

49

Earls Cove-Saltery Bay (7)

MV Island Sky

1,781

45
46

114

Figure 4.7: Geographic distribution of BC Ferries passenger-kilometre EFs (province)

.........

Leaend
BC Ferries
passenger-kilometre
EFs by rout.c (g
COi/R!m}

Red: > 1,200
Orange: 1,0001,200

--

Ycllow: 600 - 1,000

.......

Blue: 400 - 600

h

Pwple: < 400

.......

,,~I

115

Figure 4.8 : Geographic distribution of BC Ferries passenger-kilometre EFs (southwestern
BC)

Le&end
BC Ferries
passenger-kilometre
EF s by route (g
C02/l?km)
Red: > 1,200
Orange: 1.000 1,200

Yellow: 600-1,000
G.,,,
I

Blue: 400 - 600

Purple: < 400

WHt

v...........
Vancouver
-,;I

S.,,ta

aa.....rlf4d

rr:,:~.~ •

116

The route with the lowest passenger-kilometre EF is Chemainus-Thetis IslandPenelak:ut Island on MV Kuper with 261 g CO2 /pkm, followed by Tsawwassen-Swartz Bay
on the Spirit of British Columbia with 288 g CO 2 /pkm. MV Kuper (built in 1985) can carry
32 vehicles and 269 passengers (BC Ferries 2014), making it one of BC Ferries' smallest
vessels, while Spirit of British Columbia (built in 1993) can carry 410 vehicles and 2,100
passengers (BC Ferries 2014), making it one of BC Ferries' largest vessels. This would seem
to indicate that neither the age nor the size of a ferry are directly related to the passengerkilometre EF. The three remaining routes of the five low-emission routes are all
comparatively long for BC Ferries routes, and are operated by mid-sized vessels with
capacities for 200-400 vehicles and 1,000-1,500 passengers.
By contrast, the highest passenger-kilometre EFs routes have emissions per
passenger-kilometre more than threefold those of MV Kuper. Three of these routes are all
sailed by the same vessel, Tenaka, and all are assigned the same generic LF. It is this low LF,
both for vehicles and passengers, coupled with an apparently fuel-inefficient vessel, that led
to a very high value. Northern Adventure travels to Haida Gwaii with very high vehicle loads
and approximately double the average system-wide passenger LF, which indicates that the
vessel itself appears to be very fuel-inefficient. The Earls Cove-Saltery Bay route has the
highest passenger-kilometre EF of all BC Ferries routes with 1,781 grams of CO 2 per
passenger-kilometre, which is almost sevenfold that of the the lowest passenger-kilometre EF
route. This route has extremely high emissions because of the apparent fuel inefficiency of
the MV Island Sky, compounded by extremely low LFs (23.6% for vehicles and 12.6% for
passengers).

117

Discussion
Ferries are an essential part of transportation in BC between mainland BC, Vancouver
Island, and the islands that lie in between. However, according to SMITE calculations, BC
Ferries accounts for a significant share of BC transportation emissions (approximately 15%
of interurban passenger emissions). The low to very low LFs indicate that BC Ferries has
excessive capacity.
DEFRA ' s average passenger-kilometre EF for a ferry is 115 g CO 2 /pkm (DEFRA
2011). MV Island Sky 's passenger-kilometr~ EF is approximately fifteen-fold this value,
while North ern Adventure' s is nearly ten-fold and Tenaka' s nearly nine-fold. Even the vessel
with the lowest passenger-kilometre EF used by BC Ferries has a value 126% higher than
DEFRA' s average value. Passenger-kilometre EFs do not seem to depend strongly on the
size or age of the vessel. MV Island Sky is a mid-size vessel built in 2008; North ern

Adventure a large vessel built in 2004; Tenaka a small vessel built in 1964; Spirit of British
Columbia a large vessel built in 1993, and MV Kuper a small vessel built in 1985 (BC Ferries
2014). Rather, passenger-kilometre EFs seem to depend on the vessel ' s engine and operating
characteristics as well as the LF on a specific sailing.
The Provincial Government pays BC Ferries a defined annual subsidy in return for
making a specified number of ferry sailings on specific routes, with a maximum total value
of about $106 million per year (British Columbia Ferry Commission 2014). This is because
ferries are the only way to access many islands and areas along the Sunshine Coast. Thus, the
province obligates BC Ferries to provide service to certain communities at a certain
frequency. The three main routes between Vancouver and Vancouver Island are selfsupporting; however, the others are subsidized. My calculations confirm that the three main
routes have LFs significantly higher than the BC Ferries average for passengers, which may
118

be high enough for them to be financially viable. While BC Ferries could likely reduce its
operating cost by introducing more fuel efficient vessels (high per-passenger EFs inevitably
are linked to high per-passenger fuel consumption), there may not be enough of a financial
incentive because of the guaranteed operating income from the province.
4.2.3

Passenger aviation
This section provides a detailed overview of the CO 2 emissions associated with BC ' s

civil aviation system around the year 2013. Passenger aviation produced 166,867 tonnes of
CO2 , which is 1.5% of total interurban transportation emissions and 6.8% of passenger
transportation emissions. The emissions of flights within BC were analyzed by airline, flight
route, and city-pair. The following order is used to discuss calculations: (1) total CO 2
emissions from civil aviation in BC, (2) CO2 emissions by airline, (3) CO2 emissions by route
for a given airline, (4) city-pair CO2 emissions, (5) passenger-flight EFs, and (6) passengerkilometre EFs.

Total CO2 emissions of civil aviation in BC
The total CO 2 emissions for BC-internal civil aviation in 2013 of approximately
167,000 tonnes CO2 were produced on 99 scheduled airline routes in BC by approximately
180,000 annual flights operated by 15 airlines that traveled almost 38,000,000 km within the
province. This value amounts to Jess than 10% of the province's estimate for domestic
aviation emissions (British Columbia Ministry of Environment 2012). However, the
province' s estimate includes in their "domestic flight" category all flights that depart from
BC to destinations either within BC or within the rest of Canada (e.g., from Vancouver to
Toronto), whereas the inventory for my research included only those flights that remain
entirely within BC.

119

CO2 emissions by airline
In total, 15 airlines were considered in this study. For each airline, the total number of
flights internal within BC (Table 4 .8) and the total emissions generated by those flights
(Table 4 .9) were calculated for the year 2013, and ranked by airline.
Table 4 .8: Ranking of airlines by annual BC-internal flights

Rank
1
2
3·
4
5
6
7
8
9
10
11
12
13
14
15

Airline
Air Canada
Harbour Air
Pacific Coastal Airlines
Seair
Central Mountain Air
Westjet
Helijet
Salt Spring Air
Tofino Air
Hawkair
KDAir
Orea Air
N orthem Thunderbird Air
AirNootka
Vancouver Island Air

Number of BCinternal flights per
year
44,720
36,608
22,932
17,992
16,900
10,192
9,152
4,576
4,368
4,004
3,952
3,328
832
312
312

% of total number

180,180

100

Annual CO2
emissions (tonnes of
CO2
69,498
26,478
25,034
24,555
12,494
3,103
2,804
1,873
448

% of total emissions

TOTAL

of flights

24.82
20.32
12.73
9.99
9.38
5.66
5.08
2.54
2.42
2.22
2.19
1.85
0.46
0.17
0.17

Table 4.9: Ranking of airlines by annual CO 2 emissions

Rank

2
3
4
5
6
7
8
9

Airline
Air Canada
Westjet
Pacific Coastal Airlines
Central Mountain Air
Hawkair
Harbour Air
Helijet
Northern Thunderbird Air
Seair

120

41.65
15.87
15.00
14.72
7.49
1.86
1.68
1.12
0.27

10
11
12
13
14
15

Orea Air
KDAir
Salt Spring Air
Tofino Air
Vancouver Island Air
AirNootka

TOTAL

195
141
104
88
35
17.8

0.12
0.08
0.06
0.05
0.02
0.01

166,868

100

Within BC, Air Canada operated the most flights and had the highest total emissions
in 2013 : 44,720 annual flights or 24.8% of total flights, and 69,500 tonnes of CO 2 or 41.7%
of total emissions. Harbour Air ranked second in terms of flights with 36,608 flights (20.3%
of total flights), but ranked sixth in terms of emissions with 3,103 tonnes of CO 2 (1.86% of
total emissions). Pacific Coastal Airlines ranked third in terms of flights with 22,932 flights
(12.7% of total flights), and also ranked third in terms of emissions with 25,034 tonnes of
CO2 (15.0% of total emissions). While Westjet only ranked sixth in terms of flights with
10,192 flights (5 .7% of total flights) , it ranked second in terms of emissions with 26,478
tonnes of CO2 (15 .9% of total emissions). This is in large part attributable to Westjet' s use of
Boeing 737 aircraft, which are the largest aircraft in operation on BC-internal routes.
Because of Air Canada' s large number of flights and Westjet's use of large aircraft, these two
airlines have the largest total annual emissions of all airlines on BC-internal flights.
CO2 emissions by route by airline

The emissions by route by airline in 2013 for all 99 internal routes in BC are
contained in Table A2 .8 in Appendix 2. To illustrate these results, Table 4.10 below contains
the top 20 route emissions ranked by total CO 2 emissions. For these 20, emissions ranged
from 11 ,300 to 2,800 tonnes CO 2 . Those routes not included in Table 4.10 range from 2,600
tonnes CO2 for rank #21 , to 18 tonnes CO 2 for rank #99.

121

Table 4.10: CO 2 emission rank by airline route
Rank

Airline

Route and aircraft used

1

AC Express

2

AC Express

3

AC Express

4

Hawkair

5

Westjet

6

AC Express

7

AC Express

8
9

Westjet
Encore
AC Express

10

AC Express

11
12

Pacific
Coastal
Airlines
AC Express

Vancouver-Fort St. John
DH4
Vancouver-Prince George
DH4
Vancouver-Terrace
DH3
Vancouver-Terrace
DH3
Vancouver-Prince George
73W
Vancouver-Kamloops
DH3
Vancouver-Prince Rupert
DH3
Vancouver-Terrace
DH4
Vancouver-Kelowna
DH3
Vancouver-Smithers
DH3
Vancouver-Cranbrook
BEl

13

AC Express

14

Westjet
Encore
Central
Mountain Air
AC Express

15
16
17
18
19
20

Westjet
Encore
Westjet
Pacific
Coastal
Airlines
Helijet

Vancouver-Castlegar
DH3
Vancouver-Victoria
DH3
Vancouver-Prince George
DH4
Vancouver-Dawson Creek
DHl
Vancouver-Cranbrook
DH3
Vancouver-Fort St. John
DH4
Vancouver-Kelowna
73W
Vancouver-Williams Lake
BEl
Vancouver-Victoria
Sikorsky S76

122

Annual
distance
with
diversion
factor
(km)
2,086,157

Annual
emissions
(tonnes CO2)

% of total
emissions

11,290

6.82

1,877,476

9,904

5.99

2,037,344

9,463

5.72

1,848,701

8,101

4.90

796,505

7,177

4.34

1,301,009

5,763

3.48

1,067,539

4,991

3.02

905,486

4,909

2.97

1,030,630

4,579

2.77

891,072

4,134

2.50

728,910

4,030

2.44

829,920

3,734

2.26

851,136

3,688

2.23

682,718

3,644

2.20

990,662

3,578

2.16

75,806

3,462

2.09

60,846

3,331

2.01

374,774

3,317

2.00

593,393

3,049

1.84

986,586

2,804

1.69

Civil aviation within BC is dominated by Air Canada. Air Canada and its subsidiary
Air Canada Express operate 10 of the 20 most emission-intensive routes in BC. Westjet and
its subsidiary Westjet Encore, despite having only a relatively small number of flights,
occupy five of the 20 most emission-intensive routes (#5, #8, #14, #17, #18). The fact that
airlines in BC employ a hub-and-spoke system in which most traffic is routed via Vancouver
is clearly reflected in the emission results. Every route in the above table is to or from
Vancouver. Rather than connecting smaller cities directly, the vast majority if traffic is
routed from spokes (the smaller cities) to the hub (Vancouver) and then connected to other
spokes (smaller destination cities). Consequently, emissions are concentrated geographically
between the hub and the spokes which receive the most frequent service by the largest
airplanes.
The ranking clearly illustrates the factors that contribute to high annual emissions: a
long flight distance, the use of medium to large aircraft, and a high frequency of flights. The
top five most emission-intensive routes in the above table all have long flight distance, use
large or medium aircraft, and have high flight frequency.

CO2 emissions of routes by city-pairs
In order to obtain a deeper understanding of the geographical distribution of
passenger aviation emissions in BC, city-pairs were considered. A city-pair includes all
airlines serving a route between two cities and all airports within the two cities. For example,
the Vancouver-Prince George route is served by Air Canada Express and Westjet, so the
city-pair includes all Air Canada Express and Westjet flights between the cities. Also, the
Greater Vancouver-Greater Victoria route includes in Greater Vancouver the airports of
Vancouver International Airport, Vancouver Heliport, Vancouver Coal Harbour, and Langley,
and in Greater Victoria the airports of Victoria International Airport, Victoria Downtown
123

Heliport, and Victoria Inner Harbour; thus the city-pair includes all flights by all airlines that
operate between any of these airports. In total, there are 60 city-pairs.
The emissions for all 60 city-pairs are contained in Table A2.9 in Appendix 2. To
illustrate these results, Table 4.11 below contains the top ten city-pairs and their annual CO 2
emissions. Emissions for the top 10 city-pairs range from approximately 22,500 to 5,500
tonnes CO2 . None of the remaining city-pairs account for more than 3% of total emissions.
Figure 4.9 displays the geographic distribution of city-pair aviation emissions.
Table 4.11 : City-pair CO 2 emissions for top 10 city-pairs
Rank

City pair

1
2
3
4
5
6
7
8
9
10

Vancouver-Terrace
Vancouver-Prince George
Vancouver-Fort St. John
Vancouver-Kelowna
Vancouver-Victoria
Vancouver-Prince Rupert
Vancouver-Cranbrook
Vancouver-Kamloops
Vancouver-Smithers
Vancouver-Williams Lake

Annual
flights
6,604
6,136
3,224
6,968
41 ,808
2,080
2,652
5,408
1,820
3,276

124

Annual
emissions
(tonnes CO2\
22,474
20,724
14,621
11 ,493
9,778
7,528
7,492
6,646
5,922
5,489

% of total

emissions

13.47
12.42
8.76
6.89
5.86
4.51
4.49
3.98
3.55
3.29

Figure 4.9: Geographic distribution of city-pair CO 2 emissions for all 60 city-pairs

Le&end
Fon-

2013 city-pair flight
emissions (tonnes
COi)
Red: > 20,000
Orange: 10.000 20,000
Yellow: 5.000 10,000
Purple: 2,000 - 5,000
Light blue: 1.0002,000
Dark blue: < 1,000

.

Whil«OUII

,1

)

\

I·

125

High city-pair emissions are attributable to the same factors as high emissions of
individual routes: long flight distances, use of medium or large aircraft, and high flight
frequencies. More than half of the top 10 city-pairs, including the three with the highest
values, have the longest flights. Moreover, these flights use medium to large aircraft, which
means that the individual flights of which the city-pairs' emissions are comprised have high
CO2 emissions. The only route among the top 10 city-pairs that is short is VancouverVictoria, which is only about 62 km. However, because of the extremely high flight
frequency (an average of 57 flights per day per direction), its emissions are high. As seen in
Figure 4.9, city-pair emissions are highest between Vancouver and the province's other
larger cities, since these receive the most frequent service flown by larger airplanes.
Flight emission factors

Two types of BC-specific emission factors were calculated: (1) emissions to carry one
passenger on one flight (referred to as passenger-flight EF), and (2) emissions to carry one
passenger one kilometre travelled on a flight (referred to as passenger-kilometre EF). The
passenger-flight EF is used for two comparison purposes: to compare emissions between
different airlines on the same route, and to compare emissions from other passenger
transportation modes that serve the same two destination points as the flights (e.g,
comparison of flights between Vancouver and Prince George to use of a private vehicle or
bus between these cities).
The passenger-flight EF for all 99 flights are contained in Table A2.10 in Appendix 2,
ranked in order from highest to lowest emissions. To illustrate these results, Table 4.12
below contains the 10 flights with the highest passenger-flight EFs and the 10 flights with the
lowest passenger-flight EFs. These 20 routes have emissions per passenger per flight of
between 269 and 3.1 kg CO2 . All values were calculated assuming a LF of 80%.
126

Table 4.12 : Passenger-flight EFs of BC aviation

Rank

Airline

Aircraft

Route

Stage
length
including
diversion
factor

Passengerflight EF
(kg CO2)

(km)

1

NTA

Prince George-Dease Lake

Beech 1900

711

269.1

2

CMA

Prince George-Fort Nelson

Beech 1900

574

221.6

3

PCA

Vancouver-Cranbrook

Beech 1900

561

203.9

4

CMA

Prince George-Kelowna

Beech 1900

517

196.3

5

PCA

Vancouver-Mas set

Saab 340

860

173.5

6

NTA

Dease Lake-Smithers

Beech 1900

457

168.7

7

CMA

Vancouver-Quesnel

Beech 1900

452

168.6

8

PCA

Vancouver-Bella Coola

Beech 1900

452

159.4

9

PCA

Vancouver-Trail

Beech 1900

427

149.8

10

CMA

Prince George-Kamloops

Beech 1900

405

149.5

--90

Harbour Air

Nanaimo-Sechelt

DHC-3 Otter

53

5.4

91

Seair

Vancouver-Saturna Is.

Cessna, Beaver

51

5.0

92

Seair

Vancouver-Salt Spring Is.

Cessna, Beaver

50

4.9

93

Seair

Vancouver-Pender Is.

Cessna, Beaver

47

4.7

94

Seair

Vancouver-Thetis Is.

Cessna, Beaver

46

4.6

95

Seair

Vancouver-Galiano Is.

Cessna, Beaver

44

4.3

96

KDAir

Qualicum Beach-Gillies Bay

Piper PA31 ,
Cessna

44

4.1

97

Seair

Vancouver-Mayne Is.

Cessna, Beaver

41

4.0

98

Tofino Air

N anaimo-Sechel t

Otter, Beaver,
Cessna

41

3.2

99

Tofino Air

Vancouver-Gabriola Is.

Otter, Beaver,
Cessna

39

3.1

Table 4.12 Legend:

CMA =
NTA =
PCA =

Central Mountain Air
Northern Thunderbird Air
Pacific Coastal Airlines

127

The information in Table 4.12 clearly illustrates that passenger-flight EFs depend
primarily on two factors: (1) the length of the flight, and (2) aircraft type used. Out of the 10
flights with the highest passenger-flight EFs, nine use Beech 1900 aircraft, which, as is also
discussed in the following section in more detail, are the least fuel efficient aircraft operated
commercially in BC. The flight with the highest passenger-flight EF (Prince George-Dease
Lake) has an extremely high value because it is operated by Beech 1900 aircraft and because
it is one of the longest BC-internal routes. The next three flights are also comparatively long
and operated by Beech 1900 aircraft, leading to high passenger-flight EFs. By contrast, the
route ranked #15 (not displayed in the table), from Vancouver to Prince Rupert, is longer
than the #1 route, but has less than half the passenger-flight EF because it does not use Beech
1900 aircraft but instead a Dash 8-300, which is much more fuel efficient. The Dash 8-300 is
an older airplane and has been superseded by the more fuel efficient Dash 8-400. As an
example, Westjet operates Dash 8-400s on the Vancouver-Fort St. John route, which is
longer than both routes discussed above but yields a passenger-flight EF of only 74.1 kg CO 2 ,
less than a quarter the passenger-flight EF of the Beech 1900 route and 37% lower than its
predecessor, the Dash 8-300, on a flight of comparable distance.
The routes with the lowest passenger-flight EFs are generally those that are very short
(such as floatplane trips between Vancouver and the islands that lie between the BC
Mainland and Vancouver Island), which are operated by small to very small aircraft.
The passenger-flight EFs can be used to compare with other modes serving the same
origin-destination points. Table 4.13 compares passenger-flight EFs and passenger-sailing
EFs of five routes.

128

Table 4.13: Comparison of passenger-flight EFs and passenger-sailing EFs on BC routes
Route

Va ncouver-Victoria
Tsawwassen (Vancouver) Swartz Bay (Victoria)
Vancouver-Nanaimo
Tsawwassen (Vancouver) Duke Point (Nanaimo)
Horses hoe Bay (Vancouver)
-De arture Bay (Nanaimo)

Passenger-flight EF (kg CO2)

5.6-7.0

Passenger-sailing EF (kg CO2)
-

5.8-6.6

-

-

17.2-21.4

-

54.8-62.1

-

18.6-25.9

For both routes, emissions per passenger travelling by ferry are much higher than
those of a passenger travelling by air, especially considering that the ferry distance is s~orter
than the flight distance because the ferry only sails from coast to coast whereas the airports
are a little further inland. Passenger-sailing EFs are especially high on the Tsawwassen-Duke
Point route because of an extremely low LF of 17.8%.

Passenger-kilometre EF
BC-specific passenger-kilometre aviation EFs are useful to compare emissions
between different aircraft types and between other transportation modes in a format that is
independent of actual trip routings and distances.
The BC passenger-kilometre EFs for all 99 flights are contained in Table A2.11 in
Appendix 2, ranked in order from highest EF to lowest EF. To illustrate these results, Table
4.13 below contains the 10 highest emission flights and 10 lowest emission flights per
passenger-kilometre. These 20 routes have passenger-kilometre EFs ranging from 386 g
C02/pkm to 74.5 g C02/pkm. All values were calculated using a LF of 80%.

129

Table 4.14: Passenger-kilometre EFs of BC aviation
Rank

1
2
3
4
5
6
7
8
9
10

CMA
CMA
NTA
CMA
NTA
CMA
CMA
PCA
CMA
CMA

Prince George-Fort Nelson
Prince George- Kelowna
Prince George- Dease Lake
Vancouver-Quesnel
Dease Lake-Smithers
Prince George-Kamloops
Vancouver-Williams Lake
Vancouver- Cranbrook
Fort Nelson-Fort St. John
Prince George-Smithers

Beech 1900
Beech 1900
Beech 1900
Beech 1900
Beech 1900
Beech 1900
Beech 1900
Beech 1900
Beech 1900
Beech 1900

Passengerkilometre
EF (g
C02/pkm
385.9
380.1
378.5
373.5
369.4
368.8
364.1
363.7
360.9
360.7

89

Westjet
Encore
Westjet
Encore
Westjet
Encore
Vancouver
Island Air
Tofino Air
Tofino Air
Orea
Airways
Westjet

Vancouver-Kelowna

Dash 8-400

84.5

Vancouver-Kamloops

Dash 8-400

84.3

Vancouver-Victoria

Dash 8-400

82.7

Campbell River-Seymour
Inlet
Nanaimo-Sechelt
Vancouver- Gabriola Is
Vancouver-Tofino

Otter, Beaver, Beech
18
Otter, Beaver, Cessna
Otter, Beaver, Cessna
Piper Navajo
Chieftain
Boeing 737 NextGeneration
Piper Navajo
Chieftain

81.1

---

90
91
92
93
94
95
96
97

98
99

Airline

Route

Aircraft

Vancouver-Prince George

Orea
Airways

Vancouver-Qualicum Beach

Orea

Abbotsford- Victoria

Airways
Westjet

Vancouver-Kelowna

Table 4.13 Legend:
CMA =
Central Mountain Air
NTA =
Northern Thunderbird Air
PCA =
Pacific Coastal Airlines

130

Piper Navajo

Chieftain
Boeing 737 NextGeneration

79.5
79.4
75.8
75 .8
75.2
75 .2

74.5

The flights with the highest passenger-kilometre EFs have one similarity: all are
operated by Beech 1900 aircraft. Values vary slightly but this is due to different initial
aircraft weights based on the amount of fuel that is needed for the specific flight. In fact, out
of the 30 highest passenger-kilometre EFs, all but one are for flights operated by Beech 1900
aircraft. By contrast, the flights with the lowest passenger-kilometre EFs are served by very
small aircraft or by large, modem aircraft. Dash 8-400 and Boeing 737 Next-Generation
aircraft have passenger-kilometre EFs that are only approximately 20% those of Beech 1900
aircraft. This suggests that while Westjet' s use of large aircraft does result in high aggregate
emissions, using these aircraft is a low-emissions way of carrying people by plane in the
province. If other aircraft, such as the Beech 1900, were used to carry the same number of
passengers, aggregate emissions would be much higher.
DEFRA, the de-facto authority on EFs, publishes a passenger-kilometre EF for
domestic flights (with a distance ofup to 463 km) of 158.6 g C02/pkm, and a passengerkilometre EF for short-haul flights (with a distance between 464 and 1108 km) of 94.0 g
C02/pkm (DEFRA 2011). Beech 1900 aircraft have a passenger-kilometre EFs that are up to
135% greater than the DEFRA domestic EF and up to 311 % greater than the DEFRA shorthaul EF. In total, 37 routes within BC have higher passenger-kilometre EFs than DEFRA ' s
value for the equivalent distance category, out of which 29 are operated by Beech 1900
aircraft, one by Dornier 38 aircraft, one by Sikorsky S-76 helicopters, and six by Saab 340
aircraft. By contrast, Dash 8-400 aircraft have passenger-kilometre EFs that are below
DEFRA ' s value for the equivalent distance category, as do Westjet's Boeing 737 jets. The
Boeing 737 jets rank 96th and 99th out of 99 routes with values that are approximately 20%
lower than DEFRA ' s average passenger-kilometre EF.

131

Discussion
Aviation is only a small contributor to BC's overall and passenger transportation
emissions at 167,000 tonnes CO 2 per year. Large airplanes, such as Westjet's Boeing 737 jets,
create some of the highest emissions per flight but they are among the lowest in terms of
passenger-kilometre EFs based on SMITE calculations. Because the passenger aviation
system in BC is based on a hub and spoke system in which most flights originate from or
arrive in Vancouver, emissions also radiate out from Vancouver, so to speak. They are
highest on those routes to larger citi~s which receive the most frequent service by the largest
airplanes. The hub-and-spoke system also means that travel within the province often results
in higher emissions because it is routed via Vancouver, compared to what emissions would
be if direct flights existed. The city-pairs with the highest aggregate emissions are those with
the most flights and the greatest distance from Vancouver. The only exception is VancouverVictoria, which is a very short route but with a very high volume of flights. The least
'emissions-friendly' aircraft used in BC are Beech 1900 series planes, which have passengerkilometre EFs of up to 386 g C0 2/pkm. By contrast, Dash 8-400 airplanes, Boeing 737 NextGenerationjets, and several small propeller airplanes have passenger-kilometre EFs between
75 g C0 2/pkm and 85 g C0 2/pkm, or only one-fifth those of the 'emissions-unfriendly'
airplanes.
4.2.4

Long-distance bus

Introduction
Only Greyhound Canada offers scheduled, interurban bus transportation within BC,
on 31 routes throughout the province. Frequency of service is moderate. In total, Greyhound
produced approximately 12,800 tonnes of CO 2 in 2013, which is 0.1 % of total interurban
transportation emissions and 0.5% of passenger transportation emissions. In this section,
132

calculations are discussed in the following order: (I) total CO 2 emissions of interurban bus
travel in BC, and (2) passenger-kilometre EF of interurban bus travel in BC.

Total CO2 emissions of long-distance bus travel in BC
Emissions for all 31 Greyhound bus routes internal to BC are contained in Table
A2.12 in Appendix 2, ranked in order from highest to lowest annual emissions. To illustrate
these results, Table 4.15 below lists the top 10 emission-intensive bus routes. For these,
annual CO2 emissions range from 1,500 to 400 tonnes of CO 2 . None of the remaining 21
routes have annual .emissions that are greater than 400 tonnes CO2 . Figure 4.10 displays the
geographical distribution of bus emissions across the province.
Table 4.15 : Emissions of bus routes within BC
Rank

Route

Distance
(km)

1
2
3
4
5
6
7
8
9
10

Kamloops--Golden
Cache Creek-Prince George
Vancouver-Hope
Vancouver-Whistler
Prince George-Prince Rupert
Merritt -Kamloops
Hope-Merritt
Victoria-Nanaimo
Prince George-Dawson Creek
Fort St. John-Fort Nelson

360
443
155
125
718
87
124
111
404
380

133

Daily oneway trips

4
3
8
6
1
6
4
4
1
1

Annual CO2
emissions
(tonnes CO2)

1,534
1,416
1,321
799
765
556
529
474
430
405

Figure 4.10: Geographic distribution of Greyhound emissions

Leaead
2013 interurban bus
emissions (tonnes CC>i)

Red:> 1,000
Yellow: 500-1.000
Blue: 200 - 500
Purple:< 200

ALBERTA

Rt

Cal<
(

134

High annual emissions for a specific route are linearly related to the frequency of
service and the distance between origin and destination. Therefore emissions of Greyhound
routes are highest in BC's interior, which are long, and for those leading to Vancouver,
which have high frequencies. Greyhound has a relatively uniform bus fleet (i.e., most
coaches are either identical or very similar). Therefore, the type of equipment used to service
the route, unlike aviation, is only of marginal importance.

Passenger-kilometre EF of interurban bus travel in BC
Since the entire bus fleet has virtually identical emission performance, the only factor
influencing emissions per passenger carried is the LF, or percentage of seats occupied on an
average trip. It was not possible to find substantive statistics in this regard; Greyhound does
not seem to publish them. However, according to Bradley (2007), they reported North
American system-wide LFs of approximately 50% in 2007. Bertrand (2012) states that LFs
on some routes in BC are as low as 21 %. If Greyhound buses were assumed to be equivalent
to the 'average bus' used in the DEFRA database, and if the average Greyhound bus was
indeed about 50% occupied, then the passenger-kilometre EF for Canada would be
approximately 57 g C0 2/pkm (i.e., double the average DEFRA EF since DEFRA assumes
full occupancy (DEFRA 2011)). However, if occupancy on a bus was as low as 21 %, the
passenger-kilometre EF would be approximately 137 g C0 2/pkm (or approximately five-fold
the generic DEFRA bus EF), making it no more efficient a means of conveyance than
traveling on an average airplane.

Discussion
Bus travel is not a widely-used means of transportation in BC for reasons that likely
include the large distances in the province and the slow speed of bus travel compared to
airplanes or private cars. Interurban buses only contribute 13,000 tonnes CO 2 , or 0.1 % of
135

BC's total interurban transportation emissions, generated mostly on the busy corridor east of
Vancouver and several long routes in BC's interior. Despite the potential for a bus to be an
efficient means of transport when it is fully or nearly fully occupied with a passengerkilometre EF of approximately 28 g C02/pkm (DEFRA 2011), it appears that low LFs (with
estimates ranging between 21 % and 50%) mean that the bus is ultimately not as low
emissions as it could be.
4.2.5

Passenger trains

Introduction

The use of passenger trains to travel within BC is rare. There are only two scheduled
routes that passengers can travel on: from the Alberta Border to Prince Rupert or from the
Alberta Border to Vancouver on the Canadian, a train that travels from Toronto to
Vancouver. Trains on both services do not travel daily and take significantly longer to travel
from origin to destination than alternative modes of transportation. Passenger trains in 2013
produced approximately 4,500 tonnes of CO 2 , which is less than 0.1% of total interurban
transportation emissions and approximately 0.2% of passenger transportation emissions. In
this section, calculations are discussed in the following order: (1) total CO2 emissions of
passenger rail travel in BC, and (2) passenger-kilometre EF of passenger rail travel in BC.
Total CO2 emissions ofpassenger rail travel in BC

VIA Rails's operations within BC produced approximately 4,525 tonnes of CO 2 in
2013, of which 2,044 tonnes were attributable to Alberta Border-Prince Rupert operations,
and 2,480 tonnes were attributable to Alberta Border-Vancouver operations. For these
calculations, passenger numbers had to be assumed because detailed information is not
available from VIA Rails's annual reports. VIA Rail's Annual Report (VIA Rail 2015) states
that there were approximately 344 passengers per week in 2014 on the Alberta Border136

Prince Rupert route, on which there are about three trains per direction per week. The
number of passengers is higher for the Alberta Border-Vancouver route, but considering that
this train runs all the way to Toronto, more than 3,000 km east of Vancouver, it is unclear
how many passengers travel the entire voyage and how many only travel a segment of it.
Therefore, I assumed there to be approximately 50 passengers on average on each train on
both BC routes as opposed to estimating a LF.

11

Passenger-kilometre EF ofpassenger train travel in BC
At an average 117 g C0 2/pkm for VIA Rail (Wikipedia 2014) 12 the train is, per
passenger-kilometre, as emission intensive as a modern airplane. However, compared to
high-speed electric trains in Asia and Europe, which can have passenger-kilometre EFs as
low as 15 g C02/pkm (DEFRA 2011), it is much higher. Moreover, the figure of 117 g
C02/pkm for VIA Rail is presumably system-wide, including VIA's busier routes in Eastern
Canada. Based on my work in the tourism industry, I would guess that VIA's LF in western
Canada is lower than in Eastern Canada; consequently, the average passenger-kilometre EF
in Western Canada is likely somewhat higher than the 117 g C0 2/pkm value.

Discussion
Train travel in BC produced merely 4,500 tonnes CO2, which were approximately
evenly split between the two routes that are operated within BC. Despite the ability for trains
to be the most emissions-friendly passenger transportation mode, with a passenger-kilometre
EF as low as 15 g C02/pkm, trains in BC do not realize their full potential.

11

Estimating a LF for trains, especially over a one-year period, is difficult because, unlike trains or buses, cars
can be added or removed from a train based on demand and thus the number of available seats changes. Based
on personal experience working in the tourism industry, the BC trains are always longer in the summer than in
the winter. Approximately 50 passengers per train seems a reasonable estimate of its year-round average
occupancy.
12
Wikipedia was the only available source of information for this value. The page cites personal
communication as its source. I was unable to obtain a value from a verified source.

137

4.3

Freight transportation within BC

4.3.1

Freight trucking

Introduction
Freight trucking forms one of the backbones of the BC freight transportation system.
Trucks are used to distribute food, deliver goods, and move natural resources such as logs,
finished wood products, and mineral ore. There are 23,274 trucking companies in BC, of
which 90% operate between one and five vehicles (British Columbia Trucking Association
2012). Trucking produced approximately 5,431,000 tonnes of CO 2 in 2013 , which was
48.5% of total interurban transportation emissions and 62.1 % of freight transportation
emissions. Trucking is the transportation mode with the single highest annual emissions. In
this section, freight trucking usage and emissions are discussed in the following order: (1)
total interurban trucking distances driven in 2007 and 2013, (2) percentage change of
distances driven between 2007 and 2013, (3) trucking emissions per kilometre of road, and
(4) emissions produced by interurban trucking.

Total distance driven
According to SMITE calculations, trucks in BC drove a total of 2.92 billion
interurban kilometres in 2007 and 3.03 billion interurban kilometres in 2013 . The breakdown
of these distances driven by passenger vehicles on all 79 interurban routes is contained in
Table A2 .13 in Appendix 2. To illustrate these results, Table 4.16 below lists the 10 routes
from Table A2.13 with the longest distances driven in BC in 2007 and in 2013. For the 69
routes not show in the table below, their distance values range from 84 million kilometres
driven for rank #11 to 1.1 million kilometres for rank #79. Figure 4.11 displays the
geographical distribution of kilometres driven in 2013 . Because the vast majority of the

138

routes considered did not change their category on the map, the map is also illustrative for
2007.
Table 4.16: Ranking of BC routes by truck kilometres driven in 2007 and 2013
Rank
1

2007 distance
driven (km)
236,931 ,720

2

141 ,178,613

VancouverChilliwack
Hope-Merritt

3

112,141 ,622

4

2013 distance
driven (km)
229,529,155

Route

Route

147,093 ,529

VancouverChilliwack
Hope-Merritt

Vemon-Kelowna

119,382,682

Vemon-Kelowna

105,587,620

Ladysmith-Victoria

109,344,510

Ladysmith-Victoria

5

103,021 ,922

Parksville-Nanaimo

107,563 ,456

Kelowna-Penticton

6

99,898,514

105,757,071

Revelstoke-Golden

7

96,637,050

Cache CreekWilliams Lake
Kelowna-Penticton

104,756,533

8

94,907,957

9
10

102,1 8 1,677

94,421 ,996

Tete Jaune CacheKamloops
Hope-Cache Creek

Tete Jaune CacheKamloops
Parksville-Nanaimo

98 ,130,571

Kamloops-Merritt

91 ,100,314

Revelstoke-Golden

95,164,348

Cache CreekWilliams Lake

139

Figure 4.11: Geographical distribution of trucking kilometres driven in BC in 2013

Lesend
2013 trucking
kilometres by route (km)
Brown: > 200,000,000

"°"""!"L

Red: }00,000,000200,000,000
Orange: 50,000,000 100,000,000
Yellow: 20,000,000 50,000,000
Blue: < 20,000,000
• : Route segment
start/end demarcation

O<~Pr.....

Ca

140

The rank of the four highest counting sites in terms of total distances driven annually
did not change between 2007 and 2013 . The longest distance driven was recorded at the
Vedder site (Route 1 between Vancouver and Chilliwack), with approximately 23 7 million
kilometres driven in 2007 and 230 million kilometres in 2013. The second longest distance
driven was recorded at the Coquihalla site (Route 5 between Hope and Merritt), with
approximately 141 million kilometres in 2007 and 14 7 million kilometres in 2013. The third
longest was at the Oyama site (Route 97 between Vernon and Kelowna), with approximately
112 million kilometres in 2007 and 119 million kilometres in 2013. By contrast, the shortest
distance driven was recorded at the Powell River site (Route 101 between Saltery Bay ferry
terminal and Powell River), with approximately 1.3 million kilometres in 2007 and 1.1
million kilometres in 2013 . The highest percentage of vehicles on a route that were trucks
was recorded between Fort Nelson and Liard River, where 65% of vehicles were trucks. The
lowest percentage of vehicles on a route that were trucks was recorded between Gibsons and
Sechelt, where only 6% of vehicles were trucks.
The geographic distribution of distance driven is, expectedly, linked to population
density, with most kilometres driven between large urban areas in BC's southwest, and fewer
kilometres driven in the rural northern part of the province.
Change in distances driven between 2007 and 2013
Comparison of traffic statistics permitted calculation of changes in traffic volumes
between 2007 and 2013. The percentage change in distance driven by trucks is contained in
Table A2.14 in Appendix 2. Out of the 79 counting sites considered, 42 sites had increased
vehicle numbers, five sites had no change, and 32 sites had decreased vehicle numbers. To
illustrate these results, Table 4.17 below contains the three largest and three smallest changes

141

between 2007 and 2013. Figure 4.12 displays on which routes within BC kilometres driven
have increased or decreased .
Table 4.17: Percentage changes in trucking distance driven on BC routes 2007-2013
Rank
I

2
3

...

77
78
79

Route
Dawson CreekPrince George
Fort St. JohnWonowon
Salmon ArmRevel stoke
Hope-Cache Creek
Hope-Penticton
Alexis CreekAnahim Lake

2007 distance
driven (km)
56,188,283

2013 distance
driven (km)
80,246,801

Chanl!e
42 .8

37,048,168

50,790,298

37.1

63 ,759,616

79,337,480

24.4

94,421 ,996
74,030,760
5,784,929

75 ,989,350
57,450,708
3,098,295

-I 9.5
-22.4
-46.4

142

O/o

Figure 4.12: Geographical distribution of percentage change in trucking distance driven
2007-2013

I.eaend
Trucking usage and emission
changes 2007 - 2013

.

Rlonbow

Brown: > +25%
Red: +10.0% to +24.9%
Orange: +o.l % to +9.90/o
Yellow: -9.9% to 0.0%

Blue: <-10%
• : Route segment start/end
demarcation

Ed

143

Increases across the province in distances driven by trucks ranged from +42.8% to
+0.1 %. The largest increase in vehicle numbers was at the Willow Flats counting site, which
reports traffic between Dawson Creek and Prince George on Route 97. Between 2007 and
2013 , this site had an increase of 42.8%. The second highest increase was at the Inga Lake
site, which reports traffic between Fort St. John and Wonowon on Route 97, and which had
an increase of 37 .1 %. The third highest increase was at the Craigellachie site, which reports
traffic between Revelstoke and Salmon Arm on Route 1, and which had an increase of 24.4%.
The remaining increases across BC range from 20.1 % to 0.1 %.
Five sites reported no change in traffic counts. Decreases ranged from -0.2% to 36.7%. The third highest decrease was at the China Bar site, which counts traffic between
Hope and Cache Creek on Route 1, and which had a decrease of -19.5%. The second highest
decrease was at the Nicolum site, which counts traffic between Hope and Penticton on Route
3, and which had a decrease of -22.4%. The largest decrease in vehicle numbers was reported
at the Kleena Kleene Bridge site, which counts traffic between Alexis Creek and Anahim
Lake on Route 20. Between 2007 and 2013 , this site had a decrease in trucks, and hence
kilometres driven, of -46.4%.
Emissions per kilometre of road

In addition to total vehicle-kilometres driven, which are directly related to the length
of a particular route on which traffic is counted, it is possible to calculate emissions
generated per kilometre of road. This illustrates how heavily a given route is travelled which
may help in devising mitigation strategies based on the volume of traffic. The emissions per
kilometre of road per year on all routes are contained in Table A2.15 in Appendix 2. To
illustrate these results, Table 4.18 below contains the three largest and three smallest values
for 2007 and 2013 , ranked by 2013 values. Figure 4.13 illustrates the emissions per kilometre
144

of road for each route in 2013. Because the map category of nearly all routes has not changed
between 2007 and 2013 , the map is also illustrative for 2007.
Table 4.18 : Trucking emissions per kilometre of road for 2007 and 2013
Rank
1
2
3
...
77
78
79

Route
Parksville-Nanaimo
Vancouver-Chill iwack
Vemon-Kelowna

2007 emissions per km
of road (tonnes
CO2/km)
4,861
4,248
3,723

2013 emissions per
km of road (tonnes
CO2/km)
4,821
4, 115
3,964

48
31
31

33
33
26

Dease Lake-Yukon Border
Meziadin Junction-Dease Lake
Alexis Creek-Anahim Lake

145

Figure 4.13 : Trucking emissions per kilometre of road on BC routes in 2013

l..ecead
2013 trucking emissions per
kilometre of road (tonnes

COipcrkm)

Brown: > 4,000
Red: 2000-4,000
Orange: 1,000 - 2,000
Yellow: 500-1,000
Blue: < 500

f : Route segment
start/end demarcation

.

0,-Pr-

.

Wlw!ocoun

E

146

In BC, the route with the highest emissions per kilometer was at the Parksville site,
which counts traffic between Parksville and Nanaimo on Route 19, and which had 1,559
tonnes CO 2 per kilometre of road in 2007 and 1,546 tonnes CO 2 per kilometre of road in
2013 . The second highest route was at the Vedder site, which counts traffic between
Vancouver and Chilliwack on Route 1, and which had 4,248 tonnes CO2 per kilometre of
road in 2007 and 4,115 tonnes CO 2 per kilometre of road in 2013. The third highest route
was at the Oyama site, which counts traffic between Vernon and Kelowna on Route 97, and
which had approximately 3,723 tonnes CO 2 per kilometre of road in 2007 and 3,964 tonnes
CO 2 per kilometre of road in 2013.
The route with the third lowest emissions per kilometre of road was at the Cassiar
Junction site, which counts traffic between Dease Lake and the Yukon Border on Route 37,
and which had 48 tonnes CO 2 per kilometre of road in 2007 and 33 tonnes of CO 2 per
kilometre of road in 2013 . The route with the second lowest emissions per kilometre of road
was at the Stikine River Bridge site, which counts traffic between Meziadin Junction and
Dease Lake on Route 37, and which had 31 tonnes CO 2 per kilometre of road in 2007 and 33
tonnes of CO 2 per kilometre of road in 2013 . The lowest emissions per kilometre of road in
the province were at the Kleena Kleene Bridge site, which counts traffic between Anahim
Lake and Alexis Creek on Route 20, and which had 31 tonnes CO 2 per kilometre of road in
2007 and 26 tonnes of CO 2 per kilometre of road in 2013.
Total CO2 emissions of trucking travel in BC

Total interurban trucking emissions in 2007 were approximately 5,233 ,917 tonnes
CO2, while in 2013 they were approximately 5,431,451 tonnes CO 2. The breakdown ofthese
emissions for all 79 interurban routes in BC is contained in Table A2. l 6 in Appendix 2. To
illustrate these results, Table 4.19 below contains the 10 routes from Table A2 . l 6 with the
147

highest CO2 emissions in 2007 and 2013. The remaining routes range in values from 151 ,000
tonnes CO2 for rank #11 to 2,000 tonnes CO 2 for rank #79. Figure 4.14 displays the
geographical distribution of the emissions in 2013 . Because the map categories of the vast
majority of routes have not changed between 2007 and 2013 , the map is also illustrative for
2007.
Table 4.19: Trucking interurban CO 2 emissions by route in BC
Rank

2007 emissions
(tonnes CO2)

Route

2013 emissions
(tonnes CO2)

Route

1

424,796

411 ,523

2

253 , 120

VancouverChilliwack
Hope-Merritt

263 ,724

VancouverChilliwack
Hope- Merritt

3

201 ,059

Vernon-Kelowna

214,042

Vernon-Kelowna

4

189,308

Ladysmith-Victoria

196,044

Ladysmith-Victoria

5

184,708

Parksvi lle-Nanaimo

192,851

Kelowna- Penticton

6

179, 108

189,612

Revelstoke--Golden

7

173,261

Cache CreekWilliams Lake
Kelowna-Penticton

187,818

8

170, 161

183,202

9

169,289

Tete Jaune CacheKam loops
Hope-Cache Creek

Tete Jaune CacheKam loops
Parksville-Nanaimo

175,939

Kam loops- Merritt

IO

163,334

Revelstoke--Golden

170,620

Cache CreekWilliams Lake

148

Figure 4.14: Trucking interurban CO 2 emissions by route in BC in 2013

Leaend
2013 trucking emissions
(tonnesCOi)
Brown: > 400,000
Red: 200,000 - 400,000
Orange: 100,000 - 200,000
Yellow: 50,000-100,000
Light blue: 20,000 - 50,000
Dark blue: < 20,000

• : Route segment
start/end demarcation

.

•llakatla

.

Wllllec:c,,,n

149

Emissions follow the same ranking as those values for distances driven (Table 4.16)
because the same EF was used for all trucking calculations. The route with the highest
emissions is Vancouver-Chilliwack, with 411 ,523 tonnes CO 2 . This route leads from
Vancouver, BC ' s biggest city, to several of its suburbs, as well as east towards much of the
rest of BC via the Trans-Canada-Highway. The route with the second highest emissions is
Hope-Merritt, with 263 ,724 tonnes CO 2 . This route is a major part of the transportation
network that leads from Vancouver east to the Okanagan area and further towards Alberta.
The route with the third-highest emissions is Vemon-Kelowna, with 214,042 tonnes CO2 .
This route links two of the biggest cities in BC ' s Interior, and high emissions result from a
high traffic volume because the distance is comparatively short.
Discussion
Trucks form a backbone of the BC interurban transportation system, and are
responsible for the highest share of freight transportation emissions. Overall, two factors
influence route-specific interurban freight emissions from trucking in SMITE: distance and
volume of vehicles. Emissions are a product of the distance of a route and the number of
vehicles that travel it. Therefore, a long route with low traffic volume can have similar
emissions to a short route with a high traffic volume. Determining route-specific emissions is
essential for determining the geographic distribution of emissions, such as illustrated in
Figure 4.14. While it was only possible to calculate a trucking tonne-km EF at the national
level ( as explained in Chapter 3), at 196 g C02/tkm, this EF is lower than the average
trucking tonne-km EF of 232 g C0 2/tkm published by DEFRA (2009).

150

4.3.2

Marine freight

Introduction
Marine freight transport in BC is important to move goods to Vancouver Island and
other destinations on the BC coast. Information on BC marine freight appears to be very
sparse. I relied on data from Statistics Canada, especially the series "Shipping in Canada"
(Statistics Canada 2012b ). Since this series was discontinued after 2011 , the last year of
detailed data for marine freight was 2011. Marine freight produced approximately 1,883,000
tonnes of CO2 in 2011 , which was 16.8% of total interurban emissions and 21.5% of
interurban freight emissions. In this section, statistics and calculations are discussed in the
following order: (1) amount of marine freight transported within BC, (2) total CO 2 emissions
of marine freight in BC, and (3) tonne-kilometre EF of marine freight in BC.

Amount of marine freight transported within BC
Twelve of Canada' s busiest ports are located in BC, with Metro Vancouver being by
far the busiest port in all of Canada. The port of Vancouver is made up of more than one site.
For 2007, these sites were listed individually but for 2011 they were listed as one site, "Metro
Vancouver", because the sites were amalgamated in name, though not physically, as Port
Metro Vancouver in 2008 (Port Metro Vancouver 2014). The values for the individual sites
that make up Port Metro Vancouver were added for 2007 to compare with the 2011 values.
In 2011, Port Metro Vancouver handled 11 ,059,000 tonnes of domestic freight and
96,516,000 tonnes of international freight for a total of 107,575,000 tonnes, far ahead of the
second-ranked port, Saint John, which handled 31 ,469,000 tonnes (Statistics Canada 2012a).
Domestic marine freight shipped from BC is almost exclusively destined for other
ports in BC, rather than ports in other Canadian provinces because the only way to reach
non-BC Canadian ports would be to travel via the Panama Canal, which is likely prohibitive
151

both in terms of cost and time. In 2011 , only 4,600 tonnes of machinery, manufactured goods,
and fuels and basic chemicals were shipped from BC to ports in eastern Canada, while
12,900 tonnes were shipped from eastern Canada to BC. By contrast, shipments within BC
included 3,047,000 tonnes of minerals, 517,000 tonnes of coal, 11 ,500 tonnes of fuels and
basic chemicals, 8,078 ,000 tonnes of forest and wood products, 47,000 tonnes of pulp and
paper products, 500 tonnes of machinery and transportation equipment, and 572,000 tonnes
of manufactured and miscellaneous goods (Statistics Canada 2012a).
In 2007, Port Metro Vancouver handled 11 ,138,000 tonnes of domestic freight,

mainly comprised of stone, sand, gravel and crushed stone, salt, logs, and wood chips
(Statistics Canada 2010). In 2011 , it handled 11 ,059,000 tonnes of domestic freight (0.7%
decrease from 2007), mainly comprised of limestone, stone, sand, gravel and crushed stone,
salt, non-metallic metals, coal, logs and other wood in the rough, wood chips, lumber,
newsprint, cement, and non-metallic waste and scrap. By contrast, BC' s second largest
international harbour, Prince Rupert, handled no domestic marine freight at all (Statistics
Canada 2012a).
Overall, ports in BC handled 25 ,591,000 tonnes of domestic freight in 2007 (Statistics
Canada 2010), while they handled 24,524,000 tonnes of domestic freight in 2011 , a 4.2%
decrease. Of this amount, the five busiest ports in 2007, in decreasing order, were: (1) Metro
Vancouver with 11,138,000 tonnes, (2) East Coast Vancouver Island with 4,577,000 tonnes,
(3) Howe Sound with 3,713 ,000 tonnes, (4) Crofton with 1,697,000 tonnes, and (5) Beale
Cove with 1,053 ,000 tonnes (Statistics Canada 2010). The five busiest ports in 2011 , in
decreasing order, were: (1) Metro Vancouver with 11 ,059,000 tonnes, (2) East Coast
Vancouver Island with 4,422,000 tonnes, (3) Howe Sound with 3,472,000 tonnes, (4) Crofton

152

with 1,192,000 tonnes, and (5) Texada Island with 1,091,000 tonnes (Statistics Canada
2012a).
Total CO2 emissions of marine freight in BC

Based on SMITE calculations, which were based on fuel consumption, BC marine
emissions in 2013 were 1,883,007 tonnes CO 2 .
Discussion

Marine freight is an important part of the economy, and also a very large contributor
to CO2 emissions, more than five-fold those of BC Ferries. However, the paucity of
information and statistics on BC marine freight makes is exceedingly difficult to calculate
marine freight emissions. Moreover, because no appropriate statistics on BC-internal marine
shipping could be found, it was not possible to calculate a BC-specific EF of marine freight
transportation. Data on fuel consumption, shipping distances, and frequency of shipping
would be needed to calculate an EF. It was not possible to locate these data, and DEFRA also
does not provide a generic marine freight EF. The inability to calculate an EF also makes it
impossible to compare the sector's tonne-kilometre EF with other freight transportation
modes.
4.3.3

Freight trains

Introduction

Rail freight transportation is significant in BC. Rail is used to transport exports to the
ports in Vancouver and Prince Rupert for shipping to Asia, to distribute imports from Asia to
the rest of BC and the rest of the country, and to move goods, including natural resources
such as coal, grain, and mineral ore, around the province. In this section, calculations are
discussed in the following order: (1) total CO2 emissions of rail freight in BC, and (2) tonnekm EF of rail freight in BC.
153

Total CO2 emissions of rail freight in BC

Rail freight statistics are scarce, both at the provincial and federal levels. According
to SMITE calculations based on Statistics Canada (2014c) data, emissions in 2007 were
1,361 ,000 tonnes CO2 , and emissions in 2012 were 1,428,000 tonnes CO 2 . Freight trains thus
accounted for approximately 12.8% of total interurban transportation emissions and 16.3 %
of interurban freight transportation emissions.
Tonne-km EF of rail freight in BC
It was only possible to calculate a per-tonne EF of rail freight on a national level,

although the value for BC should be quite similar. According to SMITE calculations, the pertonne-km EF of rail freight in BC was 16 g CO 2/tonne-km in 2007 and 15 g CO 2/tonne-km in
2012. Based on these calculations, the emissions of trucking per tonne-kilometre are
approximately 12 times higher than those of freight trains.
Discussion

Determining total emissions and a tonne-km EF of rail freight was difficult because
of sparse statistics. There are no published schedules for freight trains, nor are there extensive,
BC-specific statistics such as weight carried by trains and distances over which it is carried,
which can be used to calculate a tonne-km EF. Because of this, a broader approach had to be
taken, which was made more difficult by two incompatible data sources (British Columbia
Ministry of Environment 2012, Statistics Canada 2014c) and no obvious indications as to
why they differ substantially.
4.3.4

Aviation freight

Introduction

Aviation freight plays a important but limited role in BC' s economy, for example by
linking BC to Canada and the rest of the world in terms of courier services or transport of
154

perishable items. Within BC, dedicated aviation freight services play only a small role in the
aviation market, with 7,228 annual flights within BC on 11 routes in 2014 operated by three
cargo airlines, compared to over 180,000 annual passenger flights within BC. The relatively
low number of cargo operations can likely be explained in part by the high cost of aviation
freight, especially compared to trucking. CO2 emissions associated with BC ' s aviation freight
system are discussed in the following order: (1) total CO2 of aviation freight in BC, and (2) a
per-tonne-km EF of BC aviation freight.

Total CO2 emissions of aviation freight in BC
Total BC-internal aviation freight emissions in 2014 were 8,882 tonnes CO 2 . Freight
aviation accounted for approximately 0.1 % of total interurban transportation emissions and
0.1 % of interurban freight transportation emissions. Table 4.20 contains a list of all 11
dedicated aviation freight services in BC in 2014 and their annual emissions.
Table 4.20: BC-internal aviation freight services and annual CO 2 emissions

Rank

Route

Operator

Flights
per
year

1

Kami oopsPrince George
KelownaVancouver
Kami oopsVancouver
KelownaVancouver
VancouverVictoria
Kami oopsVancouver
KamloopsKelowna
VancouverVictoria

Kelowna
Flightcraft
Kelowna
Flightcraft
Kelowna
Flightcraft
Skylink
Express
Kelowna
Flightcraft
Skylink
Express
Skylink
Express
Skylink
Express

520

Distance
with
diversion
factor
(km)
405 .3

572

302.4

520

270.9

520

302.4

520

69.3

520

270.9

520

120.4

1,352

69.3

2
3
4
5
6
7
8

155

Aircraft

Convair
CV-580
Convair
CV-580
Convair
CV-580
Beech
1900C
Boeing
727-200
Cessna
Caravan
Beech
1900C
Cessna
Caravan

Annual
emissions
(tonnes CO2)
2,677
2,140
1,728
760
515
310
285
189

9

10
11

VancouverVictoria
VancouverNanaimo
VancouverNanaimo

Morningstar

1,144

69.3

Skylink
Express
Morningstar

520

57.2

520

57.2

160

Cessna
Caravan
Cessna
Caravan
Cessna
Caravan

60
60

Annual emissions for aviation freight, similar to passenger aviation, depend largely
on the size of aircraft used, the distance of flights, and the frequency of flights. The highestranking flights in the table above are all comparatively long and operated by relatively large
aircraft. The Boeing 727 employed by Kelowna Flightcraft is by far the biggest all-cargo
airplane in use in BC, but because it only flies on the short Vancouver-Victoria route, its
annual emissions are comparatively low. On the other hand, the Convair CV-580 is a midsize airplane that travels on the longest all-cargo routes within BC, which explains why the
routes that use this plane have the highest aggregate emissions.

Tonn e-km EF of aviation freight in BC
Table 4.21 contains a list of the per-tonne EFs or all 11 dedicated aviation freight
services in BC in 2014.
Table 4.21 : Tonne-Km EF for BC aviation freight

Rank

Route

Operator

Aircraft

1
2
3
4
5
6
7
8
9
10
11

Karnloops-Vancouver
Vancouver-Victoria
Vancouver-Victoria
Vancouver-N anaimo
Vancouver-N anaimo
Kelowna-V ancouver
Karnloops-Kelowna
Karnloops-Prince George
Kelowna-V ancouver
Karnloops-Vancouver
Vancouver-Victoria

Skylink Express
Skylink Express
Morningstar
Skylink Express
Morningstar
Skylink Express
Skylink Express
Kelowna Flightcraft
Kelowna Flightcraft
Kelowna Flightcraft
Kelowna Flightcraft

Cessna Caravan
Cessna Caravan
Cessna Caravan
Cessna Caravan
Cessna Caravan
Beech 1900C
Beech 1900C
Convair CV-580
Convair CV-580
Convair CV-580
Boeing 727-200

156

Tonne-km
EF (g CO2
/tkm)
6,810
6,240
6,240
6,200
6,200
6,200
5,120
4,660
4,540
4,500
940

For BC-internal flights, tonne-kilometre EFs vary widely, varying largely by aircraft
type. The five highest tonne-km EFs are for flights operated by small Cessna Caravan aircraft,
followed by those operated by Beech aircraft, then Convair aircraft, and lastly flights
operated by Boeing 727 aircraft. The tonne-km EFs of the Cessna aircraft are more than six
times higher than those of the Boeing 727, indicating that while the aggregate emissions of a
flight operated by Boeing 727 aircraft are much higher than those of a Cessna Caravan, the
Boeing 727 can operate such a flight at much lower emissions per unit of freight carried than
the Cessna.
Aircraft fuel efficiency, and with it the tonne-km EFs, have improved with time
(Peeters and Schouten 2006). Cargo airplanes, however, have a tendency to be old. In fact,
many cargo airplanes start their flying careers as passenger airplanes and are later converted
for freighter operations. For instance, Kelowna Flightcraft's Convair 580 was manufactured
in 1956, while their Boeing 727s were built between 1969 and 1979 (Contrails Photography
n.d.). Given this state of affairs, it is likely that cargo aircraft emission factors will only
slowly improve.
Discussion
Aviation freight accounts for only a small share of overall emissions in BC but its
emissions are high considering the very limited extent of aviation freight transportation
within the province. High emissions are largely related to aircraft size, distance flown, and
frequency of service. Moreover, tonne-km EFs for BC aviation freight are very high, and
subject to a significant range, from 940 to 6,810 g C0 2/tkm, with the tonne-km EF depending
largely on the type of aircraft.

157

4.4

Comparison of modelling results to results from the literature
A comparison of my modelling results was possible to different degrees for the

different transportation modes. These comparisons are discussed here in the same order in
which the modes were presented in this chapter.

Passenger: private vehicles
SMITE interurban private vehicle emissions were compared to emissions derived
from fuel sale statistics. However, while there are statistics on how much fuel is sold at gas
stations i11- BC, how much of this fuel is used for interurban driving is not available. The
overall (urban and interurban) private vehicle emission value in the BC PIR (British
Columbia Ministry of Environment 2012) is approximately 8.0 million tonnes CO2 ,
compared to the SMITE interurban value of approximately 1.9 million tonnes CO 2 . If these
numbers are correct, it would mean there is an approximately 75/25 split between urban and
interurban driving. It was not possible to independently confirm if this split holds.

Passenger: ferries
BC Ferries emissions were compared to fuel consumption data. Based on its annual
report, BC Ferries spent $121 million on diesel fuel in 2013 (BC Ferries 2013). Assuming the
average diesel price in Vancouver in 2013 was $1.41 per litre (Statistics Canada 2015a), the
amount that BC Ferries spent would have purchased approximately 85 million litres of diesel
fuel, which in tum would have resulted in approximately 229,000 tonnes of CO 2 . This value
is about 33% less than the SMITE value of 342,000 tonnes of CO 2 . However, it is quite likely
that BC Ferries pays substantially less than the average Vancouver diesel price because it
purchases large quantities of fuel, which is likely discounted. Consequently, the same amount
of money would allow BC Ferries to purchase more fuel, which would have resulted in
higher emissions, which would bring the value closer to my calculated value.
158

Passenger: aviation

For passenger aviation, comparison is difficult because (1) most airlines do not
compile BC-internal data for their operations, and (2) most airlines operating in BC are small
and private, and as such do not publish annual reports. The only comparison I was able to
pursue was to compare the small BC airline Harbour Air' s emissions to those I calculated for
the airline. Harbour Air is a carbon-neutral company and publishes how much it offsets.
According to the company (Harbour Air 2015), it offsets approximately 7,500 tonnes CO 2
per year. This is significantly larger than the value of 3,100 tonnes calculated in this research;
however, Harbour Air is a completely carbon neutral company, meaning all aspects of its
operation, including employee commuting, building heating, etc., are offset. There is no
breakdown of emissions between flights and non-flight operations, though. It might be
reasonable to expect that roughly one-half of emissions were due to flights and one-half to
non-flight operations, which would suggest that the SMITE calculations for this airline are in
the right ball park.
Passenger: bus

Interurban bus emissions could not be compared with other results. Greyhound, the
operator of interurban buses in BC, was sold in 2007 to the First Group of Great Britain, who
no longer publish a stand-alone annual report. Their report (First Group 2015) mentions only
financials not operating statistics or fuel expenditures, and does so only on a Canada-wide
scale.
Passenger: rail

For passenger trains, VIA Rail ' s annual report does not explicitly state the emissions
associated with the company's operations. The average 2014 diesel price was $1.41 per litre
(Statistics Canada 2015a). VIA Rail, according to its annual report (VIA Rail 2015), spent
159

$125.6 million on train operating costs system-wide in Canada, which would have bought
approximately 89 million litres of diesel fuel assuming, for simplicity, that fuel is the only
operating cost. With this fuel, VIA Rail operated 9.856 million train-kilometres (VIA Rail
2015), of which 465,500 train-kilometres were in BC according to my calculations.
Assuming that the split of train-kilometres between Canada and BC also holds for fuel
consumption between Canada and BC, this would have resulted in 4.2 million litres of fuel
used in BC, which in tum would have produced 11,200 tonnes CO 2, compared to the SMITE
value of 4,500 tonnes CO2. While there is no specific breakdown of operating cost categories
in the annual reports, fuel is, naturally, not the only operating cost. Consequently, the actual
amount of money spent on fuel, and fuel purchased and emissions generated, would be less,
bringing the value closer to my calculated value. Moreover, trains in BC, especially from the
Alberta border to Prince Rupert, are generally short and slow. As such, they may use less fuel
than longer trains operating on higher-speed routes in eastern Canada, which would further
reduce the emission value and bring it closer to the 4,500 tonnes of CO2 figure, which would
suggest that the SMITE calculations for passenger rail are credible.
Freight: trucking
My method was to compare SMITE values to values from the BC PIR (Government
of British Columbia 2014) for total (urban and interurban) heavy-duty gasoline and heavyduty diesel vehicle emissions. According to the PIR, these emissions were 6,473,000 tonnes
CO2 in 2007 and 7,209,000 tonnes CO2 in 2013. The province's inventory contains a large
value for heavy-duty gasoline usage, but most trucks are fuelled by diesel and there are very
few heavy-duty gasoline vehicles in use in North America (United States Environmental
Protection Agency 2012). The large value for heavy-duty gasoline in the BC inventory may
be mistakenly assigned or not refer to freight vehicles, but it was assumed for this study that
160

all heavy-duty vehicles are involved in the transportation of freight. Comparing the PIR value
to my value, there was an approximate 75/25 split between interurban and urban trucking
emissions in 2013, which I was not able to verify.

Freight: marine
There are very few statistics related to marine cargo in BC, which makes comparisons
difficult. According to the province's GHG inventory (British Columbia Ministry of
Environment 2012), total marine emissions (passenger and freight) in 2012 were 2,643,518
tonnes CO2. Data for 2013 are not yet available. Using the compound growth rate of
1.0013% between the GHG inventory values for 2007 and 2012 for one additional year leads
to an estimate of approximately 2,646,897 tonnes CO2 for BC marine transportation in 2013.
Subtracting BC Ferries passenger emissions of 341,563 tonnes CO 2 from the GHG
inventory's value yields total annual marine freight emissions of 2,305,334 tonnes CO2. This
value is larger than the 1.9 million tonnes CO2 derived from my calculations, but it likely
includes licensed as well as registered vessels. While pleasure boats are usually licensed,
there is no need to register them (Transport Canada 2015). Since pleasure boating cannot be
considered interurban transportation, it should not be included in the calculations for this
research, which means that the lower value I calculated based on fuel consumption of
registered vessels (1,883,007 tonnes CO 2) should be more representative of the actual
emissions. However, since registered vessels also include fishing boats which also cannot be
considered interurban transportation, the value presented in this research should be seen as an
upper limit for marine freight emissions.

Freight: rail
According to BC ' s GHG inventory, emissions were 676,000 tonnes CO 2 in 2011, and
689,000 tonnes CO2 in 2012 (British Columbia Ministry of Environment 2012). By contrast,
161

SMITE calculations resulted in 1,361,000 tonnes CO 2 in 2007 and 1,428,000 tonnes CO2 in
2012. The (lower) provincial value is based on data that is provided by refineries on how
much fuel they sold to which sectors, while my calculation resulting in the higher value is
based on operating statistics provided by railway operators (Ng 2015b). While at first it may
not seem logical that emissions could be higher than emissions based on the amount of fuel
sold by refineries to rail companies, it is necessary to keep in mind that BC is not a closed
system. Given that fuel tends to be cheaper in Alberta and Washington State than in BC, it
would make financial sense for railway operators to fuel their trains in those jurisdictions
before proceeding into BC whenever possible. This would appear to explain why railway
operators would report higher fuel consumption values than what is provided by refineries
within the province. Consequently, the value obtained through my calculation is likely more
representative of rail freight emissions.
Freight: aviation

Comparing aviation freight emissions to other results was not possible because none
of the aviation freight operators in BC publish information on their fuel consumption or
em1ss1ons.
4.5

Summary
In this chapter, a detailed portrait of the CO2 emissions associated with interurban

transportation around the year 2013 in BC was presented. Total interurban transportation
emissions are displayed in Table 4.22 in order of total annual emissions. Figure 4.15
illustrates how each mode contributes to total interurban transportation emissions, Figure
4.16 illustrates how passenger transportation modes contribute to interurban passenger

162

transportation emissions, and Figure 4.17 illustrates how freight transportation modes
contribute to interurban freight transportation emissions.
Table 4.22: Total annual BC interurban transportation emission
Mode

Total annual
emissions
(tonnes
CO2)

Freight: Trucking

5,431 ,000

Passenger: Private vehicles

1,916,000

Freight: Marine

1,883,000

Freight: Rail

1,428,000

Passenger: Ferries

342,000

Passenger: Aviation

167,000

Passenger: Buses

13,000

Freight: Aviation

9,000

Passenger: Rail

5,000

TOTAL

11,194,000

Percent of
total
transportation
emissions
48.5
17.1
16.8
12.8
3.1
1.5
0.1
0.1
<0.1
100

Passenger-kilometre EF
(passenger transportation), or
tonne-kilometre EF (freight
transportation)
196 g COz/tkm
202 g C02/pkm
---

15 g COz/tkm
260 g C02/pkm -1,781 g C02/pkm
75g C02/pkm - 386 g COz/pkm
56 g COz/pkm* -137 g COz/pkm*
940 g C02/tkm - 6,810 g COz/tkm
117 g C02/pkm*

Table 4.22 Legend:
Pkm = Passenger-kilometre
Tkm = Tonne-kilometre
= Value obtained from alternative sources and not calculated as part of this research
*

163

Figure 4.15 : Total interurban transportation emission percentages

Total interurban transportation
emission percentages

-

• Trucking freight
• Marine freight
• Ferry
• Intercity buses
• Passenger trains

• Private vehicles
Rail freight
• Passenger aviation
• Aviation freight

Figure 4.16: Passenger interurban transportation emission percentages

Passenger interurban transportation emission percentages

• Private vehicles
• Passenger aviation
• Passenger trains

• Ferry
Intercity buses

164

Figure 4.17: Freight interurban transportation emission percentages

Freight interurban transportation
emission percentages

• Trucking freight • Marine freight
• Rail freight

Aviation freight

*Aviation freight is not visible because the value is too small.
Total interurban passenger and freight CO 2 emissions in BC in 2013 were estimated
to be 11 ,194,000 tonnes CO 2. Out of this, 48. 5% were contributed by freight trucking, 17 .1 %
by private vehicles, 16.8% by marine freight, 12.8% by rail freight, 3.1% by ferries, 1.5% by
passenger aviation, 0.1 % by buses, 0.1 % by aviation freight, and less than 0.1 % by passenger
trains. Total interurban transportation emissions produced by passenger transportation
accounted for 21 .8%, while the remaining 78.2% of emissions were produced by freight
transportation. Freight transportation emissions were thus almost four times larger than
passenger transportation emissions.
Freight trucking is the largest contributor both to BC interurban freight emissions and
to overall BC interurban transportation emissions, emitting more than 5 .4 million tonnes CO 2,
which is nearly 184% greater than the next largest sector, private vehicles. Calculating a
trucking EF was only possible at the national scale, and it is approximately 196 C0 2/tkm.
165

The geographical distribution of trucking emissions is linked to population levels; emissions
are highest between dense-populated cities and lowest in rural areas.
The second largest contributor to BC's interurban transportation emissions is private
passenger vehicles emitting more than 1.9 million tonnes CO2 , which accounts for nearly
one-fifth of BC interurban transportation emissions. Car usage increased between 2007 and
2013. The vehicle passenger-kilometre EF is 202 C0 2/pkm, calculated specifically for
Canada to provide a more accurate value for BC car usage. Private vehicles account for
approximately 78% of aH passenger transportation emissions, and emissions are 460%
greater than those of the next highest passenger transportation mode, ferries. Private vehicle
emissions correlate with population levels and densities, with aggregate emissions
concentrated around the densely-populated Vancouver, Lower Mainland, southern
Vancouver Island, and Okanagan areas.
The third largest contributor to BC's interurban transportation emissions is marine
freight emitting approximately 1.9 million tonnes CO 2 . It was not possible to calculate its
tonne-km EF. Determining the geographical distribution of marine freight emissions also was
not possible with existing data.
It was not possible to obtain detailed information on rail freight, but based on the
available material, the tonne-km rail EF is approximately 15 g C0 2/tkm, or much lower than
freight trucking. Trains are the third-largest contributor to BC freight transport emissions.
Emissions from rail freight are much higher than those of passenger rail services, but are
lower than marine freight or trucking emissions. It was not possible to determine the
geographical distribution of rail freight emissions with existing data.

166

Ferries accounted for 342,000 tonnes CO 2 . Ferries in BC operate with passengerkilometre EFs between 260 g C0 2/pkm and 1,781 g C0 2/pkm, depending on the vessel. This
means that any trip on BC Ferries, even on its lowest EF vessel, is less emissions-friendly
than a trip with the average BC car. The value of 1,781 g C02/pkm is the highest passengerkilometre EF in BC across all passenger modes. BC Ferries emissions are concentrated on
the three main routes between Greater Vancouver and southern Vancouver Island as well as
the routes to various islands between the BC Mainland and Vancouver Island.
·Passenger aviation accounted for 167,000 tonnes CO 2 , and operated with passengerkilometre EFs between 75 g C0 2/pkm and 386 g C0 2/pkm, depending on the route and
aircraft. The aggregate annual emissions value was low considering the importance of
aviation for the passenger transportation system and that aviation is often considered one of
the prime examples of a form of transportation that is harming the environment. Moreover,
the results of this research indicate that on a passenger-kilometre basis, and depending on the
aircraft used, aviation can more efficient than other transportation modes, such as ferries.
Additionally, airplanes benefit from being independent (for the most part) of terrain in how
they travel from origin to destination, so can often travel shorter distances than land-based
transportation modes and thus further reduce the emissions per person per trip. Passenger
aviation emissions follow a hub and spoke pattern that radiates out of Vancouver, since this
is where most routes depart from. Routes to cities with higher population levels tend to have
higher emissions because they receive more frequent service and by larger airplanes.
While buses have only very small aggregate emissions, their passenger-kilometre EF
of 57 g C02/pkm makes them the most efficient means of transporting passengers in BC.
This passenger-kilometre EF varies significantly with average occupancy, however, which

167

was difficult to determine for BC. Bus emissions are concentrated on the route leading
eastwards from Vancouver because it has a high bus traffic volume, and on several stretches
in BC ' s interior because oflong distances.
Aviation freight is also only responsible for very small aggregate emissions, and has
tonne-kilometre EFs between 940 g C0 2/tkm to 6,810 g C02/tkm. It is by far the most
emissions-intensive way of carrying freight in BC, with between 4.8 and 35 times higher
tonne-kilometre EFs than trucking and between 63 and 454 times higher tonne-kilometre EFs
than rail freight.
Finally, passenger trains account for the smallest aggregate amount of BC interurban
transportation emissions, and operate with a passenger-kilometre EF of 117 g C0 2/pkm. At
this value, passenger trains are more efficient than some airplanes, all ferries, and most cars,
but less efficient than buses; however, since there are only two routes, substituting travel by
rail is not possible for most destinations in the province.
For seven out of the nine interurban transportation modes included in my research, I
was able to validate my results to varying degrees. Because statistics compiled by the
government on activities such as fuel sales do not distinguish between fuel being used for
urban or interurban transportation, comparing SMITE results to other results from the
literature was a challenge. However, I was able to establish that my numbers seem
comparable to results obtained by other methods for the whole or portions of the various
modes of BC interurban transportation.

168

CHAPTER 5: FUTURE EMISSION SCENARIOS
5.1

Introduction
The purpose of this chapter is to provide answers to Research Question Two, What

changes to BC interurban transportation can help the province to achieve its legislated 2020
and 2050 emission reduction targets, and how far above target values will projected values
be if reduction rates are insufficient? Answers are provided through the development and
analysis of scenarios representing changes to the BC interurban transportation system.
These scenarios were modelled using the SMITE tool (explained in Chapter 3), and
are based on percentage changes (increases or decreases) to annual emissions from the
various transportation modes starting from current year emissions. There are essentially an
infinite number of possible changes that could be made to BC ' s interurban transportation
system that would alter its GHG emissions. For this study, two main types of scenarios were
modelled: (1) emission reduction scenarios, and (2) emission increase scenarios. Emission
reduction scenarios for this study were defined to incorporate structured, systematic annual
emission reductions that move BC ' s transportation system towards or beyond the target
levels. Emission increase scenarios were defined to incorporate emission increases for part or
all of the period of time modelled. The changes incorporated in each scenario were modelled
to begin in the year 2014. The emissions resulting from both decrease and increase scenarios
were compared with target values. If emission increases occurred, the discrepancy between
these emissions and the target values was used to estimate costs to offset the discrepancy
assuming progressively increasing carbon offset prices. This approach may be valuable to
policymakers and the general public as a proxy for demonstrating potential (financial)
consequences of various future transportation paths in BC. If emission decreases occurred,
169

excess carbon credits were calculated assuming that the province could sell these credits at
market value. Again, this is a proxy for demonstrating potential (financial) benefits of
meeting the targets. A spreadsheet-based approach was chosen over a formulaic approach
because it facilitated having different starting years for different emission increase or
decrease scenarios, as well as the ability to vary increase or decrease rates for individual
modes for any given year, for instance to model the impact of a given technology projected
to become available by a certain year. Thus, while a spreadsheet-based approach may be
more complex than a formulaic approach, it may ultimately be better suited to deal with the
varying timelines and emission change patterns involved in this research.
The scenarios developed for this research incorporated 'plausible' changes to the
interurban transportation system. Plausible in this case means that scenarios have annual
emission increases or decreases no larger than 5% because such large changes (particularly
reductions) seem unlikely on a sustained basis given past experience and current estimates of
the rate of change to transportation systems in general. There are several exceptions to my
five-percent rule. One exception was one scenario in which targets are exactly met, and
others were select scenarios based on a business-as-usual (BAU) approach. BAU is based on
each mode ' s 2007-2013 emissions trends, where for instance a BAU rate of -1 % combined
with a -5% per annum (pa) reduction would result in a -6% pa reduction for a given mode.
Examples of systemic changes to the transportation system included efficiency improvements
for new Canadian cars, which are expected to be approximately 3.46% pa between 2011 and
2025 (Environment Canada 2013). Also, the aviation industry is aiming for an annual fuel
consumption reduction of 2% pa between 2005 and 2020 (Transport Canada 2012). Plausible
changes for my scenario development also included ' sudden ' reductions for some scenarios

170

in which all or some of the modes reduce their emissions significantly at one point in time.
This may be caused, for instance, by revolutionary technological developments, which are
more likely to occur within the next 35 years (i.e., by the time of the 2050 target) than within
the next five years (i.e., by the time of the 2020 target), which is why these kinds of changes
were only modelled to take place in or after 2020. Literature reviewed in Chapter 2 indicates
that for many transportation modes, increases in emissions are expected, which led me to also
consider emission increase scenarios as plausible developments. Originally, in order to
develop my future scenarios, I had hoped to use results from surveys I had sent to
transportation providers and vehicle manufacturers asking them for their expert opinion on
likely future technological and other improvements in the transportation sector. However,
none of my surveys were returned, so I had to abandon that approach. 13 The templates for the
surveys can be found in Appendix 1.
My technique for developing the scenarios was, for a given scenario, to change the
annual emissions of each transportation mode by a fixed percentage over a fixed period of
time. For example, one scenario might model a 1% pa emissions reduction for all
transportation modes. Another scenario might model an emissions reduction of 2% pa for
some transportation modes while only a 1% pa emission reduction for other modes. Yet
another scenario might model an increase to the year 2020 for some modes and then model a
decrease to 2050. The percentage change values ranged from -5% to +5%, in 1% increments.
The exceptions to this approach were several scenarios involving BAU where an already
negative BAU rate combined with a high annual reduction resulted in reduction rates in
excess of -5%, and scenario number 6, which modelled that emission targets would be
13

I submitted surveys and interview requests to BC Ferries, Via Rail, Greyhound, 16 airlines, and 29 vehicle
manufacturers. Only BC Ferries responded, and they only provided me with details about its operations rather
than completing my survey.

171

exactly met. For this scenario, which employed backcasting, annual reduction rates depended
on the initial and target values for each mode and the compound annual reduction rates
required to meet the targets (which resulted in required annual reduction rates of up to
-8.58%). Scenarios with emission reduction values were assumed to represent technological,
regulatory policy, and/or social behaviour changes related to BC' s interurban transportation
system.
I modelled 106 scenarios. This number of scenarios, while not exhaustive, permitted
me to bracket a wide range of emission results from those that were excessively negative
( 1Os of times higher than the target values) to unrealistically positive (dramatically under the
target values, which might represent, for instance, radical changes in transportation
technology). This wide range of scenarios may be beneficial to policymakers and the general
public to enhance their perspective on the effect of various emissions changes to BC' s
transportation system.
In the following sections, a limited and representative number of the 106 scenarios

are discussed, as follows. First, a select number of scenarios (a total of 65) that failed to meet
both the 2020 and 2050 reduction targets are presented. These are used to illustrate common
characteristics for why scenarios failed to meet the targets. Second, scenarios that achieved
the 2050 emission reduction target but not the 2020 target are presented (a total of 15). (Note:
In this study, there were no scenarios that met the 2020 target but not the 2050 target.) Lastly,

scenarios that achieved both the 2020 and 2050 emission reduction targets are presented (a
total of two). Table A3 . l, in Appendix 3, contains a listing of all scenarios, parameters
changed in each scenario, discrepancies between actual (calculated) emissions and the 2020

172

and 2050 target values, and offset costs for those scenarios that overshot the target values as
well as credit values for those scenarios that exceeded the target values.

5.2

Scenarios that do not meet 2020 or 2050 emission reduction targets

5.2.1

Introduction
Most of the scenarios that were modelled in my sample of I 06 scenarios met neither

the 2020 nor the 2050 emission reduction targets. Such scenarios demonstrate the
consequences if little or no action is taken to reduce interurban tra~sportation emissions. In
this section, four categories of scenarios that failed to meet target values are discussed
(increasing emissions, scenarios involving BAU, waiting too long to make changes, and
reduction rates that are too small). All scenarios discussed in this section can be found in
TableA3.1 in Appendix 3.

Scenario with increasing emissions
Scenarios were calculated to show the variance with the 2020 and 2050 reduction
targets when there are increases in the emissions of any or all of the transportation modes (for
reasons such as population growth, for example). No scenario with increases in emissions,
whether for the whole period to 2050 or parts of it, achieved either the 2020 or the 2050
emission reduction target. These scenarios, while undesirable in their quantitive outcomes,
are nevertheless useful in illustrating just how far above the target value transportation
emissions could be if they are not reduced systematically and in a sustained manner.
Scenarios 14-18 modelled increases in emissions for all modes from 1% pa to 5% pa.
Their cost to offset and discrepancy with target values is plotted in Figure 5.1.

173

Figure 5.1: Graphic illustration of Scenarios 14-18

Scenarios 14-18
$120,000,000,000

3500.0

$100,000,000,000

3000.0
2500.0

$80,000,000,000

2000 .0

$60,000,000,000

1500.0

$40,000,000,000

1000.0

$20,000,000,000

500 .0
0.0

$0
+1%

+2%

+3%

+4%

+5%

Annual emissions percentage change
- - Cost to offset($)

- - Discrepancy with 2050 target value

Increases of 1% pa yield a 2050 value that is six-fold above target; increases of 2% pa
yield a value that is more than nine-fold above target; increases of 3% pa yield a value that is
nearly 14-fold above target; increases of 4% pa yield a value that is nearly 20-fold above
target; and, finally, increases of 5% pa yield a 2050 value that is nearly 30-fold above target.
Offsetting the excess emissions to meet the legislated emission reduction targets would cost,
between 2007 and 2050, $29. 7 billion for Scenario 14 to $97 .3 billion for Scenario 18.
Scenario 18 had the costliest results of all scenarios that were modelled. Figure 5.1 illustrates
that the cost of offsetting relative to the discrepancy with target values decreases the higher
the discrepancy with target values becomes. Though not modelled, if this correlation
continued above 5% annual emission increases, it would imply that small annual increases
are relatively more costly to offset than larger annual increases relative to the target values.
Scenarios 40 and 77-90 modelled an emissions increase of 1% to 5% pa for each
mode, up to a given point in time (2020, 2025 , or 2030), after which they would decrease at
the same rate (1 % to 5% pa). None of these scenarios met the 2050 targets. Scenario 85 came
174

the 2050 target. The other scenarios, up to BAU -3% pa, failed to meet the targets and
yielded 2050 values between 46 % and 211 % above target, resulting in total offset costs
between 2007 and 2050 from $3.8 billion for Scenario 94 to $12.9 billion for Scenario 92.
Figure 5.2 illustrates that the cost to offset nearly directly correlates with the
discrepancy with 2050 targets. This may indicate that in terms of selecting scenarios based
on BAU minus a reduction rate, choosing a higher reduction scenario is not inherently
cheaper or more expensive in terms of offsetting excess emissions, although the cost of
implementing the scenarios may vary.

Waiting too long to make changes
Waiting too long before implementing serious and sustained emission reduction rates
means that targets cannot be met. For Scenarios 21-25 and 30-39, there are no changes in
emissions until 2020, 2030, or 2040 (i.e., the emissions remain steady at their 2013 values),
after which all modes would reduce emissions at rates between 1% pa and 5% pa, depending
on the scenario. None of the scenarios were able to achieve the reduction targets. If no
changes are made until 2020, the 2050 values are between 4.6% and 260% above target,
resulting in offset costs from $4.6 billion for Scenario 34 to $16.4 billion for Scenario 30. If
no changes are made until 2030, the 2050 values are between 75% and 299% above target,
resulting in offset costs from $12.5 billion for Scenario 25 to $18 .9 billion for Scenario 21. If
no changes are made until 2040, the 2050 values are between 192% and 341 % above target,
resulting in offset costs from $18.4 billion for Scenario 39 to $20.5 billion for Scenario 35.
Allowing modes to continue their 2007-2013 BAU trends until 2020, 2025, or 2030
before achieving reductions of 1% pa and 5% pa for each mode also failed to meet target
values. These are modelled in Scenarios 41-55. If BAU trends are followed until 2020, the
2050 values are between 4.3% and 253% above target, resulting in offset costs between $4.4
177

billion for Scenario 55 to $15.8 billion for Scenario 51. If BAU trends are followed until
2025, the 2050 values are between 33% and 266% above target, resulting in offset costs
between $8.2 billion for Scenario 50 to $16.7 billion for Scenario 46. If BAU trends are
followed until 2030, the 2050 values are between 69% and 280% above targets, resulting in
offset costs between $11.6 billion for Scenario 45 to $17.5 billion for Scenario 41.
The scenarios above indicate that an approach of continuing what is currently done
before significantly reducing emissions will not allow the 2050 target to be met unless the
change resulting in reductions comes within the next few years and is then implemented at a
rate of approximately -5% pa.
Reduction rates too small
Reduction rates lower than 4% pa are too small to meet the 80% emission reduction
target for 2050. A reduction of 80% over the span of 43 years requires a compound annual
reduction rate of -3.67%. Therefore, all scenarios which modelled reductions between 1%
and 3% pa were unable to meet the 2050 reduction targets. Even Scenario 4, which modelled
a reduction of 4% pa, yielded a 2050 value that was 7.0% above target. This is because the
annual 4% reduction would have had to start in 2007, but since the calculations started with
the year 2014, up to which reduction rates of 4% pa had not been achieved for most modes,
the 2050 value still could not be met.
All scenarios that involve reduction rates between 1% and 3% pa require some form
of 'sudden' or radical changes to meet the targets . These changes could include technologies
that allow for zero emission transportation, wide-sweeping modal shift, or revolutionary
technologies that allow emissions for certain modes to be cut by high rates such as 50%. As
examples of such sudden change, Scenarios 103-105 modelled huge reductions in trucking
emissions. Freight trucking is by far the largest contributor to present-day emissions as
178

calculated by SMITE. In these scenarios, all sectors except trucking would reduce emissions
by 1% pa. Trucking emissions would reduce by 1% pa until 2025, at which point 25% of
trucking emissions would be eliminated 'suddenly' in Scenario 103 (for example because of
modal shift to trains), 50% of trucking emissions in Scenario 104, and 75% of trucking
emissions in Scenario 105. For simplicity's sake, it was assumed that freight train emissions
would not increase despite the additional freight carried. Projected 2050 total interurban
transportation emissions were between 125% and 208% above target values, resulting in total
offset costs from $7.2 billion for Scenario 105 to $12.8 billion for Scenario 103.
5.2.2

Discussion of scenarios
Most scenarios in my sample of 106 scenarios were unable to meet the 2020 and 2050

emission reduction targets. In those that were modelled, the four main characteristics that
lead to failure were having increases in emissions, continuing with BAU trends too long,
waiting too long before implementing radical changes, and having reduction rates that are too
small. The results of the scenarios discussed in this section reinforce not only that increases
in emissions from current levels will result in values that are significantly above the target
values, but also that small to moderate reduction rates may ultimately not be able to achieve
the reduction targets.
5.3

Scenarios that meet 2050, but not 2020, emission reduction targets

5.3.1

Introduction
In this section, the scenarios that meet only the 2050 emission reduction targets are

discussed. This selection of scenarios illustrates that even strong initial and sustained
reduction rates cannot meet the 2020 emission reduction targets, and that if 2020 targets are

179

not met, meeting 2050 emission reduction targets will generally require significant and
' sudden ' reductions for at least some sectors on top of strict and sustained reduction rates.
However, the requirements to achieve only the 2050 reduction targets are less stringent than
those to achieve both the 2020 and 2050 emission reduction targets, which are discussed in
the next section. A total of 15 scenarios in my sample of 106 scenarios met the 2050 but not
the 2020 emission reduction targets. Table 5 .1 lists these scenarios, their overall projected
emissions and discrepancy with the 2050 target value, changes that were modelled in each
scenario, and estimated value of excess carbon credits. Scenarios are listed in order of
increasing total projected 2050 emissions (i .e., the scenario with the lowest overall projected
emissions is discussed first) .
Table 5.1 : Scenarios that meet only 2050 emission reduction targets
Seen

2020

2050

29

35.4

-100.0

•
•

76

0.9

-69.0

•
•
•
le

le

71

0.9

-66.3

le
le

le

le
le

65

0.9

-61.8

le
le

Passenger transportation
parameters
(% pa)
All modes:
•
-1 % pa through 2030, then •
all modes Oemissions
Aviation: -5% pa
•
Bus: -5% pa
•
Cars: -5% pa 2020, then
•
instant 50% reduction, then
-5% pa
Ferries: -5% pa
•
Trains: -5% pa to 2030,
then O emissions because
of electric trains
Aviation: -5% pa
•
Bus: -5% pa
•
Cars: -5% pa to 2020, then •
instant 30% reduction, then
-5% pa
Ferries: -5% pa
•
Trains: -5% pa to 2030,
then Oemissions because
of electric trains
All modes:
•
-5% pa to 2030, then
•
180

Freight transportation
parameters
(% oa)
All modes:
-1 % pa through 2030, then
all modes Oemissions
Aviation: -5% pa
Marine: -5% pa
Train: -5% pa to 2030, then
0 emissions because of
electric trains
Truck: -5% pa to 2025,
then 75% reduction, then 5% pa

Offset
(bn $)
2.41
5.84

Aviation: -5% pa
Marine: -5% pa
Train: -5% pa to 2030, then
0 emissions because of
electric trains
Truck: -5% pa to 2025,
then 75% reduction, then 5% pa

5.4:

All modes:
-5% pa to 2030, then

3.88

75

8.7

-54.6

le

•
•

•
•

70

8.7

-50.7

•
•
•
•
•

64

8.7

-44.3

74

17.1

-33.7

•
•
•

le

le
le

•

69

17.1

-28 .1

•
•
•
le

•
5
58

0.9
4.7

-27.4
-26.3

•
•
•

63

17.1

-18 .9

•

le

halved, then -5% pa
halved, then -5% pa
Aviation: -4% pa
• Aviation: -4% pa
Bus: -4% pa
• Marine: -4% pa
Cars: -4% pa to 2020, then • Train: -4% pa to 2030, then
50% reduction, then -4%
0 emissions because of
electric trains
pa
Ferries: -4% pa
• Truck: -4% pa to 2025,
then 75% reduction, then Trains: -4% pa to 2030,
4%pa
then O emissions because
of electric trains
Aviation: -4% pa
• Aviation: -4% pa
Bus: -4% pa
• Marine: -4% pa
Cars: -4% pa to 2020, then • Train: -4% pa efficiency to
30% reduction, then -4%
2030, then O emissions
because of electric trains
pa
Ferries: -4% pa
• Truck: -4% pa to 2025,
then 75% reduction, then Trains: -4% pa to 2030,
4%pa
then O emissions because
of electric trains
All modes:
• All modes:
-4% pa to 2030, then
• -4% pa to 2030, then
halved, then -4% pa
halved, then -4% pa
Aviation: -3% pa
• Aviation: -3% pa
Bus: -3% pa
• Marine: -3% pa
Cars: -3% pa to 2020, then • Train: -3% pa to 2030, then
0 emissions because of
50%, then -3% pa
electric trains
Ferries: -3% pa
Trains: -3% pa to 2030,
• Truck: -3% pa to 2025,
then 75% reduction, then then O emissions because
3%pa
of electric trains
Aviation: -3% pa
• Aviation: -3% pa
Bus: -3% pa
• Marine: -3% pa
Cars: -3% pa to 2020, then • Train: -3% pa to 2030, then
30%, then -3% pa
0 emissions because of
electric trains
Ferries: -3% pa
Trains: -3% pa to 2030,
• Truck: -3% pa to 2025,
then 75% reduction, then then O emissions because
3%pa
of electric trains
All modes: -5% pa
All
modes: -5% pa
•
All modes:
All
modes:
•
10% over 2007 reduction • 10% over 2007 reduction
by 2015, 20% over 2015
by 2015, 20% over 2015
by 2020, 30% over 2020
by 2020, 30% over 2020
by 2030, 40% over 2030
by 2030, 40% over 2030
by 2040, 50% over 2040
by 2040, 50% over 2040
by 2050.
by 2050.
All modes:
• All modes:
-3% pa to 2030, then
• -3% pa to 2030, then
halved, then -3% pa
halved, then -3% pa

181

4.42

3.92

2.07

2.71

2.0S

1.23
0.51

0.11

73

25 .9

-3.6

•
•

•
•
•
95
26

6.3
0.9

-1.0
-0.8

•
•
•

Aviation: -2% pa
•
Buses: -2% pa
•
Cars: -2% pa to 2020, then •
instant 50% reduction, then
-2% pa
Ferries: -2% pa
•
Trains : -2% pa to 2030,
then O emissions because
of electric trains
All modes: BAU - 4% pa •
All modes:
•
-5% pa to 2030, then -4% •
pa to 2040, then -3% pa to
2050.

Aviation: -2% pa
Marine: -2% pa
Train: -2% pa to 2030, then
0 emissions because of
electric trains
Trucks: -2% pa to 2025 ,
then instant 75% reduction,
then -2% pa
All modes: BAU -4% pa
All modes:
-5% pa to 2030, then -4%
pa to 2040, then -3% pa to
2050.

0.62

0.6S
0.55

Table 5.2 Legend
Seen=
Scenario number in Table A3.1 in Appendix 3
2020 =
Discrepancy with 2020 target(%), where a negative value represents the percent
by which the scenario emissions are under the 2020 target
2050 =
Discrepancy with 2050 target(%), where a negative value represents the percent
by which the scenario emissions are under the 2050 target
Offset = Value of excess offset credits ($ billions)
BAU=
Business-as-usual (no changes made to current emission trends of mode)
5.3.2

Discussion of scenarios
Strong and sustained annual emission reductions alone are not sufficient for meeting

the 2020 emission reduction targets, but they may be able to meet the 2050 targets. Even
scenarios involving 5% pa reductions (76, 71 , 65 , 5, and 26), which are the highest annual
reductions modelled apart from the backcasting scenario and certain BAU-based scenarios,
failed to meet the 2020 emission reduction target, even though the projected values are only
minimally above the target value.
Eleven of the 15 scenarios in this section modelled some kind of dramatic and
' sudden ' change to part or all of the transportation system at some point beyond 2020 that
would significantly reduce emissions and after which emissions would either be zero for one
or all modes, or after which modes would continue reducing their emissions at an annual rate.

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These changes may be caused, for example, by various kinds of modal shift, for example
from trucks to trains, by shifts within a mode (such as to smaller and more efficient private
vehicles), or by the wide-scale adoption of revolutionary technologies such as electric
vehicles. The scenarios show that varying degrees of such changes (for example, both a
sudden 30% and 50% reduction of car emissions) can meet the target values. Because these
scenarios resulted in emissions that are below the target value, sellable credits ranged from a
total of $111 million to $5. 8 billion. (Note: As previously stated the development and
implementation costs of such sudden changes are not incorporated into SMITE. These may
well be more than the amounts gained by selling credits.)
Because the required annual reduction rate is higher than -4%, Scenario 5, modelling
-5% pa reductions, was able to meet the 2050 emission reduction target. Scenario 4 (not
listed in the table above), was not able to meet the target even though its reduction rate of 4% pa was above the required annual compound rate because it requires that these reductions
begin in 2007 and not 2013, which was the base year for the future scenario calculations.
Scenario 58 modelled a 10% reduction of 2007 values by 2015, 20% reduction of
2015 values by 2020, 30% reduction of 2020 values by 2030, 40% reduction of 2030 values
by 2040, and 50% reduction of 2040 values by 2050. This yielded a 2020 value 4.7 % above
target, and a 2050 value 26.3% below target without a change in the modal composition of
emissions. Because the scenario results in emissions that are below target, sellable credits are
approximately $510 million. To achieve the reduction rates in this scenario, revolutionary
developments of some sort would have to occur. However, unlike the 'sudden' reduction
scenarios discussed above, this scenario ' s reductions do not occur all at once, meaning that
there is more time for changes to be planned and implemented (for example, through

183

successive cycles of product development). As well, a slight failure to accomplish one 'step'
may be balanced out by overachieving on one of the previous or following steps.
Scenario 95 modelled that all modes reduce their emissions by a rate equal to their
BAU trend from 2012 to 2013 minus 4%. This resulted in higher reduction rates for those
modes which already reduced their emissions between 2007 and 2013, while it meant that if a
mode's BAU trend was between 0% and +4%, this rate would change from growth to
shrinkage. Although this scenario may not be feasible to implement, especially for those
modes which already experienced emission reductions and would thus have to decrease their
emissions even further annually, it may be a feasible option in that it allows the province to
just meet its 2050 targets.
Scenario 26 modelled that all modes reduce their emissions by 5% pa until 2030, then
by 4% pa between 2030 and 2040, and by 3% pa between 2040 and 2050. The slowing
reduction rate may be caused, for example, by increased transportation usage because of
population growth. This scenario illustrates that easing reduction rates can still meet the 2050
target (even if just), but that reductions must start at a high annual reduction rate and can then
only gradually decrease after 2030.
While 15 scenarios were able to meet or exceed the 2050 emission reduction targets,
none of these scenarios would likely be easy to implement. Focusing on 2050, instead of
2020, does however have the advantage that there is a much greater chance of the
development of revolutionary technologies or other radical changes, for some or all of the
modes in question, within the next 35 years rather than within the next five years. These
revolutionary developments may then contribute to achieving the required annual emission
reductions.

184

5.4

Scenarios that meet 2020 and 2050 emission reduction targets

5.4.1

Introduction
In this section, the scenarios that met both the 2020 and 2050 emission reduction

targets are presented. Only two scenarios out of 106 were able to meet both reduction targets,
which illustrates the magnitude of changes required to meet the targets. These two scenarios
are listed in Table 5.2 along with their overall projected emissions and discrepancy with the
2020 and 2050 target values, changes that were modelled in each scenario, and estimated
value of excess carbon credits. Scenarios are discussed in order of decreasing 2050 emission
reductions (i.e., the scenario with the lowest overall projected emissions is introduced first) .
Table 5.2: Scenarios that meet both 2020 and 2050 emission reduction targets
Seen

2020
(%)

2050
(%)

96

-1.4

-33 .0 •

6

-2.3

-2.3 •

•
•

•
•

Passenger transportation
parameters
(%pa)
All modes follow BAU
(growth/shrink rate 20072013) -5% pa
Aviation: +0.54% pa to
2020, then -3 .95% pa
Bus: -8.5 8% to 2020, then
-3 .95% pa
Cars: -6.33% to 2020, then
-3.95% pa
Ferries: -4.35% to 2020,
then -3.95% pa
Trains: -5.56% to 2020,
then -3 .95% pa

•

•

•
•
•

Freight transportation
parameters
(% pa)
All modes follow BAU
(growth/shrink rate 20072013) -5% pa
Aviation: -6.46% to 2020,
then -3.95% pa
Marine: -3.10% to 2020,
then -3 .95% pa
Train: -5.45% to 2020,
then -3.95% pa
Truck: -6.05% to 2020,
then -3 .95% pa

Offset
(bn $)
1.85

0.06

Table 5.1 Legend
Seen=
Scenario number in Table A3.1 in Appendix 3
2020 =
Discrepancy with 2020 target(%), where a negative value represents the percent
by which the scenario emissions are under the 2020 target
2050 =
Discrepancy with 2050 target(%), where a negative value represents the percent
by which the scenario emissions are under the 2050 target
Offset= Value of excess offset credits($ billions)
BAU=
Business-as-usual (no changes made to current emission trends of mode)

185

5.4.2

Discussion of scenarios
Scenario 96 modelled that all modes reduce their emissions by a rate equal to their

BAU rate from 2007 to 2013, minus 5%. For the three modes that already reduced their
emissions between 2007 and 2013 (passenger aviation, ferries, and marine freight), this
would mean that even more stringent annual reductions need to occur. For the remaining six
modes which had increases in their emissions between 2007 and 2013 or whose emissions
were steady, it turned them into shrinkage rates. This scenario shows that it is possible to
meet both the 2020 and 2050 reduction targets, but it would require significant changes soon,
for example rapid deployment of new technologies such as hydrogen-powered cars and/or
sweeping behavioural changes such as widespread use of public transportation. For modes
which have already reduced their emissions from 2007 to 2013, reducing emissions by an
additional 5% pa would likely be difficult because some aspects that may help achieve these
rates, such as technological changes, may have already been taken advantage of. For modes
which have not reduced their emissions between 2007 and 2013, reducing emissions by 5%
pa may be due to more systemic issues (such as infrastructure investment) that have
prevented or discouraged these modes from reducing their emissions so far. As such,
accomplishing a BAU -5% pa scenario would likely require a multifaceted approach that
pays close attention not only to which modes have reduced their emissions and which have
increased theirs, but also to why certain modes have been able to reduce their emissions
while others have not. While this scenario is significantly (33%) below the 2050 target value,
it is only 1.4% below the 2020 target value. This highlights that accomplishing the 2020
target is very much linked to the point in time at which sustained reductions begin, and that
with every year that passes without embarking on systemic reductions to transportation

186

emissions, meeting the 2020 targets and eventually the 2050 targets becomes increasingly
difficult. Moreover, the excess offset value of $1.85 billion could very likely by surpassed by
the cost of implementing this scenario.
Scenario 6 was the only scenario modelled that utilized backcasting, namely
assuming that both 2020 and 2050 targets would be met by each mode individually and
calculating the rates that allowed each mode to accomplish these reductions. Passenger
aviation was an exceptional case in this scenario because its emissions decreased
significantly between 2007 and 2013, to the point where they were below the target value.
This may have been caused, in part, by the introduction of newer and more fuel-efficient
aircraft in BC or by schedule consolidation. Because passenger aviation emissions decreased
at more than 7% pa between 2007 and 2013, meeting the 2020 target value would actually
allow passenger aviation to increase its emissions by 0.53% pa between 2013 and 2020.
Buses would have to reduce their emissions by -8.58% pa, private vehicles by -6.32% pa,
ferries by -4.35% pa, passenger trains by -5.56% pa, aviation freight by -6.46% pa, marine
freight by-3.10% pa, freight trains by-5.45% pa, and freight trucks by -6.06% pa. These are
some of the highest reduction rates modelled, and for all modes except ferries and marine
freight, they exceed my self-imposed maximum modelled value of -5% pa. Between 2020
and 2050, all modes would then, having achieved their 2020 target value, reduce their
emissions -3.95% pa to achieve the 80% reduction over 2007 values by 2050. In theory, this
scenario should result in just meeting the target values and have a net offset cost/credit value
of zero.
Only the most ambitious of scenarios are able to achieve both the 2020 and 2050
emission reduction targets. Scenario 6 is a baseline that illustrates what annual reduction

187

rates are required in order to exactly meet the 2020 and 2050 reduction targets. All other
scenarios, ambitious though their reduction rates may be, require some sort of ' sudden'
change in order to meet the 2050 targets. Since these kinds of changes are not likely to occur
within the next five years and were thus not modelled to happen before 2020, only two
scenarios were able to meet both targets.
What makes achieving the 2020 targets even more difficult is that more than half of
the time from implementation of the law to reduce emissions (2007) to 2020 has already
passed, and to date most sectors have achieved little or no emission reductions. Consequently,
there are only about six years from 2014 (the starting year for my projections) in which to
achieve a 33% emission reduction. Thus, the scenarios illustrate that not only do drastic steps
have to be taken in order to meet the 2020 and 2050 emission reductions, but their sustained
implementation needs to happen sooner rather than later.

5.5

Summary
In this chapter, a representative sample of the 106 scenarios was presented in order to

develop perspective on the ability of British Columbians and the provincial government to
meet their legislated emission reduction targets. Analysis of these scenarios for BC's
interurban transportation system demonstrates promising paths for meeting the emission
reduction targets, as well as 'unpromising paths' that will ensure the targets are not met.
Figure 5.3 illustrates the cost to offset excess emissions relative to annual emission
percentage changes ranging from -5% to +5% (not all of these scenarios were discussed
individually).

188

Figure 5.3: Cost to offset relative to annual emission percentage changes.

Cost to offset($) relative to annual
emission percentage changes
$120,000,000,000
$100,000,000,000
$80,000,000,000
$60,000,000,000
$40,000,000,000
$20,000,000,000
$0
-$20,000,000,000

-5% -4% -3% -2% -1% 0% +1% +2% +3% +4% +5%

Figure 5.3 indicates that the cost to offset excess emissions increases exponentially
the higher the annual emission percentage increase is. This may indicate that while there
might only be a limited financial return to larger annual reduction rates (such as from -4% to
-5% ), the cost of inaction resulting in emission increases rises exponentially the higher the
mcreases are.
The majority of scenarios modelled failed to meet both 2020 and 2050 reduction
targets. Scenarios that failed to meet the targets can be broadly grouped into (1) scenarios
with emission increases, (2) scenarios involving BAU, (3) waiting too long to make changes,
and (4) reduction rates that are too small. Some scenarios were not expected to meet the
targets because they modelled increases in emissions, but others, with reductions up to 3% pa,
were also unable to achieve significant enough reductions. This indicates the difficulty in
achieving BC' s legislated GHG reduction goals, since even sustained reductions of 3% pa are,
with current technologies, something that would be quite difficult to achieve. However, when
it comes to planning for the future, examination of the characteristics of scenarios that failed
to meet the reduction targets provides valuable perspective for policymakers and the general

189

public. They illustrate that reductions will have to be substantial and sustained over most or
all of the period until 2050 in order for the emission reduction targets to be met.
Meeting only 2050 targets requires strict annual emission reductions for all modes, as
well as, in most successful scenarios, ' sudden' changes happening at some point beyond the
year 2020 that allow one or more modes to significantly reduce their emissions. All of these
scenarios would likely be a major challenge to implement. Scenarios focusing on 2050 have
the advantage of having time on their side in terms of the development of revolutionary
technological, political, or behavioural change. Scenario 58 (mandated 10% reduction over
2007 emissions by 2015, 20% over 2015 emissions by 2020, 30% over 2020 emissions by
2030, 40% over 2030 emissions by 2040, and 50% over 2040 emissions by 2050) and
Scenario 74 (-3% pa for all modes, then ' sudden ' emission reductions for cars and trucks in
the 2020s) are among the most promising options in this category. Scenario 58 is promising
because it requires that reduction rates are gradually increased. This means that there is time
for ways of achieving these reduction rates. Scenario 74 is promising in that it requires only a
moderate -3% pa reduction, but it also requires a ' sudden ' reduction in the near future, for
example a modal shift in freight transport and rail electrification. Modal shift and rail
electrification are steps that can already be implemented, but modal shift would require
systemic changes to transportation while rail electrification is hindered mostly by the
extremely high cost.
In this chapter, a variety of scenarios that modelled changes to BC' s transportation
system have been discussed. My research points to the fact that only scenarios with the most
drastic changes (i.e., the strictest reductions) were able to achieve both the 2020 and 2050
emission reduction targets. These would, in all probability, be very difficult to implement

190

because they require significant and sustained reduction rates that would, most likely, involve
revolutionary technological developments, overcoming social inertia, and high costs. My
research indicates not only that drastic changes are required but also that they have to occur
sooner rather than later. Delaying change will only mean that it will then need to be all the
more radical. To end on an optimistic note, there seem to be a variety of paths the province
could choose to set BC on a course towards achieving the emission reduction targets; the
caveat is that none seem to be easy to implement.

191

CHAPTER 6: CONCLUSION
The purpose of this final chapter is multifold. First, results of the research are
summarized. Next, examples of how BC may be able to achieve significant emission
reductions for various transportation modes are discussed. After this, the contribution of this
research to existing knowledge as well as limitations of this research are highlighted. Next,
suggestions for future research are discussed, which is followed by final thoughts about this
research project.
6.1

Summary of results
The goal of this research was to try to provide answers to two main research

questions. The first question is: What are the present-day total CO2 emissions and EFs of
interurban passenger and freight transportation in BC? Subsidiary questions include: What
did each mode contribute towards its sectoral (passenger or freight) emissions totals? How do
transportation modes compare to each other in terms ofEFs to carry one passenger or one
unit of freight over one unit of distance? To provide answers to these questions, the
Simulator for Multimodal Interurban Transportation Emissions (SMITE) tool was developed,
which is a spreadsheet-based inventory and scenario calculation tool. The inventory function
was used to compile emission inventories of BC interurban passenger and freight
transportation for each of nine modes (private vehicles, ferries, passenger aviation, interurban
buses, passenger trains, trucking freight, marine freight, train freight, and aviation freight).
Emission totals and BC-specific per-person or per-tonne EFs were then calculated in the
SMITE tool using EFs (specifically calculated for this research if possible, or taken from the
literature if not) and usage statistics at the local, provincial, and/or national level. The results
were displayed in tables and maps.
192

The second research question is: What changes to BC interurban transportation can
help the province to achieve its legislated 2020 and 2050 emission reduction targets, and
how far above target values will projected values be if reduction rates are insufficient?
Subsidiary questions include: What combinations of changes to the BC interurban
transportation system can help the province to achieve its emission reduction targets? What
are the costs for offsetting excess emissions in those scenarios that do not meet the reduction
targets? To answer these questions, the future scenario function of the SMITE tool was used,
which allows users to enter parameters for each mode and then calculate projected future
emission values for each transportation mode and the system as a whole, as well as whether
2020 and 2050 targets are met, and the cost of offsetting excess emissions or the value of
offset credits. For most scenarios modelled, the rate of change ranged from -5% to +5% per
year. The scenarios do not, in most cases, represent specific types of change to the
transportation system but rates of change in the modes of the system, which may be affected
by, for example, technological, political, and behavioural factors. A total of 106 scenarios,
representing a broad spectrum of changes, were modelled and analyzed.
6.1.1

Summary of results for Research Question One
What are the total CO 2 emissions and EFs of interurban passenger and freight

transportation in BC? According to the calculations performed using the SMITE tool, the
CO 2 emissions of BC interurban transportation around 2013 were 11.19 million tonnes CO 2 .
Table 6.1 (identical to Table 1.1 in chapter 1) summarizes the individual interurban
transportation modes, their total annual emissions and respective percentage of overall
interurban transportation emissions and passenger or freight sector, and their EFs.

193

Table 6.1 : Summary of BC interurban transportation emissions and EFs by mode
Transportation
mode

Emissions
by mode
(tonnes
CO2)

Passenger transportation

Percentage
of
total
interurban
transportation
emissions

Percentage
of
passenger
interurban
transportation
emissions

Percentage
of
freight
interurban
transportation
emissions

EF
(g C02/pkm for
passengers; g
COi/tkm for
freight) (range
where available
or average)

Private vehicles

1,916,000

17.1

78.4

202

Ferry

342,000

3.1

14.0

260- 1,781

Passenger aviation

167,000

1.5

6.8

75 - 386

Intercity buses

13,000

0.1

0.5

57* - 137*

Passenger trains

5,000

<0.1

0.2

117*

Trucking freight

5,431,000

48 .5

62.1

196

Marine freight

1,883,000

16.8

21.5

n/a

Rail freight

1,428,000

12.8

16.3

15

9,000

0.1

0.1

940-6,810

11,194,000

100%

Freight transportation

Aviation freight
Total

100%

100%

Table 6.1 Legend:
pkm = Passenger-kilometre
tkm
= Tonne-kilometre
= Value could not be calculated independently and was taken from the literature.
*
Below, each of the nine modes is discussed in order of their contribution to BC
interurban transportation emissions.
Trucking freight is the greatest contributor to BC interurban transportation emissions
with 5,431,000 tonnes of CO 2 , or 48.5% of total emissions. Trucking emissions are
concentrated geographically between major urban centres and especially on the busy
highways in densely-populated southwestern BC. BC trucking has an EF of 196 g C0 2/tkm.
This compares to an EF of 122 g C0 2/tkm by DEFRA (2012), and an EF of between 100 g
194

C02/tkm and 200 g C02/tkm by Chapman (2007), where values vary based on the size and
weight of the truck.
Passenger vehicles accounted for 1,916,000 tonnes of CO 2 , or 17 .1 % of total
emissions. The EF of the average Canadian vehicle, based on highway EF information for
more than 16,500 individual models sold in Canada between 1995 and 2013, is 202.0 g
C02/pkm. DEFRA (2012) publishes an EF of 202 g CO 2/km, while Chapman (2007) lists an
EF of approximately 240 g CO2/km. Both of these EFs are a combination of both less
efficient city driving and more efficient highway driving. The fact that the car EF calculated
in this research is as high as that provided by DEFRA, even though it only includes more
efficient highway driving, stands to reason because the average Canadian car tends to be
larger than the average car globally. Private vehicle emissions are closely related to
population density levels: in BC, they are highest around Vancouver and the denselypopulated Lower Mainland, southern Vancouver Island, and Okanagan areas, and lowest in
rural and northern areas of BC.
Marine freight is the third greatest contributor to BC interurban transportation
emissions with 1,883,000 tonnes of CO 2 , or 16.5% of total emissions. The geographic
distribution of these emissions could not be determined because schedule and routing
information were not available. Moreover, a BC-specific EF could not be calculated because
of a lack of relevant statistics. Chapman (2007) lists an EF of approximately 40 g C02/tkm,
but it is unclear whether this EF is for ocean-shipping or domestic shipping, or how EFs for
each of these would vary.
Rail freight accounted for 1,428,000 tonnes of CO2 , or 12.8% of total emissions. The
EF of rail freight is 15 g C0 2/tkm. Trains therefore have a tonne-kilometre EF 92% lower

195

than trucks. DEFRA (2012) lists a rail freight EF of 28 g C02/tkm, while Chapman (2007)
lists an EF of approximately 30 g C0 2/tkm. The lower value for BC may be caused by trains
in BC generally consisting of many cars (more than an average European train), where
carrying more cars may make the train more efficient than a shorter train per unit of freight
carried. Determining the geographic distribution of emissions was not possible because
statistical information on route usage was not available.
Ferries accounted for only 342,000 tonnes of CO 2 or 3 .1 % of total emissions, despite
high passenger-kilometre EFs and the low LFs observed on many of its routes. BC Ferries
operated 155,000 sailings travelling a total of 2.5 million kilometres. Aggregate ferry
emissions are concentrated between Greater Vancouver and southern Vancouver Island,
since this is where most sailings take place and the largest vessels are used. The geographic
distribution of passenger-kilometre EFs depends mostly on the vessel used and its average
LF, with the highest EF (1,781 g C0 2/pkm) found on the Sunshine Coast near Vancouver,
and the lowest EF (261 g C02/pkm) found on a route serving several small islands off
Vancouver Island. At an average 696 g C0 2/pkm, the BC passenger-kilometre ferry EF is
more than 3.5 times higher than that of driving the average BC vehicle as a single occupant.
DEFRA (2012) lists a ferry EF of 115 g C02/pkm. This significantly lower value may be due
to ferries in Europe generally carrying higher percentages of foot passengers. Ferries in BC
also have the ability to carry these foot passengers, but more difficult public transit access to
many ports may mean that less people choose to travel as foot passengers, which increases
the per-passenger EF of those passengers who do travel.
Passenger aviation accounted for 167,000 tonnes CO 2, or 1.5% of total emissions. A
total of 15 airlines operated 180,000 flights that travelled 38 million kilometres within BC.

196

The geographic distribution is mostly between Vancouver and the other, larger cities around
the province, since these receive the most frequent air service by the largest airplanes.
Passenger-kilometre EFs of passenger aviation in BC range from 74.5 g C02/pkm on
Westjet' s Boeing 737 jets to 385 .9 g C02/pkm using CMA ' s Beech 1900 series airplanes. On
certain flights, Westjet thus achieves lower passenger-kilometre EFs than what is, according
to my calculations, the most fuel-efficient vehicle for sale in BC, the Toyota Prius, which
achieves a highway passenger-kilometre EF of 92 g C02/pkm assuming single occupancy. At
an average 184.5 g C0 2/pkm, passenger aviation has a slightly lower passenger-kilometre EF
than using the average BC vehicle as a single occupant, and less than one-third that of ferries .
DEFRA (2012) lists an EF of approximately 167 g C0 2/pkm. This value, although only
somewhat lower than that calculated in this research, may be explained by more efficient
aircraft or higher LFs.
Buses accounted for approximately 13 ,000 tonnes of CO 2 or 0.1 % of total emissions.
Emissions are geographically centred on longer routes in BC ' s interior and in the area around
Vancouver which sees the highest service frequencies. Estimates for a bus passengerkilometre EF range from 57g C02/pkm to 137 g C02/pkm, depending on average occupancy.
Thus, bus emissions could be the lowest of available interurban transportation modes in BC.
However, when occupancy numbers are low, they may be only somewhat lower than
averages for aviation and private vehicles. The bus EF can be compared to a bus EF of 28 g
C02/pkm provided by DEFRA (2012), which may be lower because of higher LFs in Europe.
Aviation freight accounted for approximately 9,000 tonnes CO 2, or 0.1 % of total
emissions. These emissions are generated on the select few routes that see regular all-freight
flights , such as Vancouver to Victoria, Prince George, and Kelowna. The tonne-kilometre EF

197

of aviation freight is, depending on the airplane type used, between 940 g C0 2/tkm and 6,810
g C02/tkm. Aviation freight thus has, by far, the highest tonne-kilometre EFs within BC,
with EFs that are 4.8 to 35 times higher than trucking and 63 to 454 times higher than rail
freight. DEFRA (2012) provides an aviation freight EF of 2,044 g C02/tkm, while Chapman
(2007) provides an EF of approximately 1,430 g C02/tkm. These lower values as averages
are comparable with the results ofthis research, especially considering that Cessna aircraft,
which had the highest EFs of aviation freight in BC, are so small that they may not be used
for aviation freight in other parts of the world, such as Europe.
Passenger trains accounted for approximately 5,000 tonnes CO 2 , or less than 0.1 % of
total emissions. It has the lowest aggregate emissions because only two routes are operated,
and these have low traffic volumes. The passenger-kilometre EF of passenger trains in BC is
approximately 117 g C0 2/pkm. This means that while BC trains have higher EFs than their
electric, high-speed counterparts, they still have lower passenger-kilometre EFs than cars,
airplanes, and ferries. Chapman (2007) provides a passenger train EF of approximately 50 g
C02/pkm, while DEFRA (2012) lists an EF of 55 g C02/pkm. These lower values can likely
be explained by much higher train LFs in Europe, along with at least partial electrification
resulting in lower emissions.
6.1.2

Summary of results for Research Question Two
A total of 106 scenarios of changes to the BC interurban transportation system were

modelled using the SMITE tool. The parameters in each scenario were chosen to reflect
' plausible ' changes to each transportation mode.
The majority of the scenarios modelled were unable to meet either the 2020 or 2050
emission reduction targets. Scenarios that failed to meet the targets could be slotted into four
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categories: (1) scenarios that incorporated increases in emissions for either all or part of the
time studied, (2) scenarios that continued business-as-usual trends for too long before making
systemic changes to reduce emissions, (3) scenarios that kept emissions steady too long
before making systemic changes to reduce emissions, and (4) scenarios that incorporated
reduction rates that were too small.
A total of 15 of the 106 scenarios met the 2050 target but not the 2020 target. Apart
from requiring significant annual reduction rates from all modes ( either sustained or
increasing with time), 11 out of the 15 scenarios also required ' sudden ' or drastic reductions
to take place at some point beyond 2020, that would allow one or more modes to
significantly reduce their emissions in a single step. Since all of these scenarios exceeded the
2050 reduction target, sellable offsets ranged from $111 million to $5 .84 billion. Only two
scenarios meet or exceed both the 2020 and 2050 reduction targets. One of these modelled
reductions of the 2007-2013 BAU rates minus 5%, while the other used backcasting to
calculate the exact rates that allow each mode to meet its emission targets. Although both
scenarios featured some of the highest reduction rates modelled, they both only just managed
to meet the 2020 targets, which highlighted that meeting the 2020 targets in particular
critically depends on sustained and significant annual reduction rates to be implemented
sooner than later. Sellable offsets ranged from $60 million to $1 .85 billion.

6.2

Examples of changes that may help accomplish CO 2 reductions
Based on my analysis of the current aggregate CO 2 emissions and EFs of BC

interurban transportation, as well as the scenarios of future changes, I put forth six concrete
examples of how emission reductions in the BC interurban transportation system may be
achieved. These examples also serve to illustrate the policy value or capability of the SMITE
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model. They are ordered more or less according to their importance for achieving the 2020
and 2050 emission reduction targets.
Example 1: Reducing trucking emissions through modal shift to freight trains and
through small-scale truck efficiency improvements
Freight trucking has the highest total CO 2 emissions of all interurban transportation in
BC, and at 196 g CO2/tonne-km, its tonne-kilometre EF is more than 13 times higher than the
15 g CO2/tonne-km produced by rail freight. Some of the 'sudden' reductions to trucking
emissions which were modelled in various scenarios discussed in Chapter 5 could be
delivered by a modal shift for freight from trucks to trains. In scenarios where all modes
reduce their emissions by 1% per year, reducing trucking emissions 25% through modal shift
to trains by 2025 (and assuming that railway emissions would not increase because of their
higher efficiency and that there is at least partial railway electrification) results in 2050
emissions that are 208% above target values; reducing trucking emissions 50% results in
2050 emissions that are 166% above target values; and reducing trucking emissions 75%
results in 2050 emissions that are 125% above target values. This compares to 2050
emissions that are 23 7% above target values if there is no sudden reduction of trucking
emissions and all modes reduce their emissions 1% per year, in which case total projected
2050 emissions are 7.6 Mt CO2 . By comparison, total projected emissions are 7.0 Mt CO2 in
the 25% reduction scenario, 6.0 Mt CO 2 in the 50% reduction scenario, and 5.1 Mt CO 2 in
the 75% reduction scenario. In the 75% trucking emission reduction scenario, the cost of
offsetting excess emissions is $7.2 billion below that of the non-modal shift scenario (the
cost is halved). In short, SMITE shows significant emission reductions could result from a
modal shift from truck to rail.

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This shift may be particularly attractive because the technology and infrastructure
needed already exist. Rail freight is, however, not a perfect substitute for freight trucking,
because trains are naturally bound by the limitations of the rail network and are unable to
travel to as many places are freight trucks, especially in rural and remote parts of the
province where building rail access if it is currently not available may not be feasible. Thus,
there are positives and negatives of a truck-to-rail modal shift; however, the SMITE model
demonstrates that climate benefits are one of the positives.
Modal shift to freight trains is not the only way in which BC could reduce its freight
trucking emissions, though. Small-scale efficiency improvements to trucks, such as to their
aerodynamics and the use oflow-friction lubricants, may reduce fuel consumption in trucks
by as much as 33.2% (Ang-Olson and Schroeer 2002). If through such measures BC's tonnekilometre EF was reduced by 33.2% from 196 g C0 2/tkm to 131 g C02/tkm by 2020,
emissions would be 3,630,000 tonnes CO 2, assuming 2013 trucking usage (kilometres and
route driven). This value is only 3.5% larger than the 2020 trucking target value of 3,507,000
tonnes CO2 , meaning that wide-scale adoption of small-scale efficiency improvements to
trucks may allow BC to nearly meet its 2020 trucking emission reduction target without any
reduction in trucking usage.
Example 2: Reduce passenger-kilometre EFs ofprivate vehicles below 100 g CO2/km

Private vehicles generate by far the highest emissions of interurban passenger
transportation in BC. As such, accomplishing any reductions in this mode is vital to reducing
overall transportation emissions. Private cars account for nearly one-fifth of all interurban
transportation emissions in BC, which are directly related to the distances traveled each year
and to the EFs of the vehicles used. Emissions from private cars can be reduced substantially

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by switching to vehicles with lower passenger-kilometre EFs, even without reducing travel
usage. In my research, I calculated a Canada-specific car EF of 202 g CO2/km. Based on an
analysis of the vehicles available for sale in Canada in 2013 (Natural Resources Canada
2014), the vehicle with the lowest EF was the Toyota Prius, with an EF of 92 g CO2/km. Of
the more than 1,000 vehicle models available in 2013, more than 100 have EFs below 130 g
CO2/km. Ifby 2020 all vehicles were replaced by models that had the EF of the 2013 Prius,
and assuming that car usage (kilometres and routes driven) remain at 2013 levels, private
vehicle emissions in 2020 would be 873,000 tonnes CO2. The 2020 target value for private
vehicles is 1,212,000 tonnes CO 2, meaning that emissions would be 339,000 tonnes CO2 or
28.0% below target. The 2050 emission target value for private vehicles is 362,000 tonnes
CO2, which means that maintaining a fleet-wide EF of a 2013 Prius will not be sufficient to
meet the 2050 target value, as the emissions of 873,000 tonnes CO 2 would be 141 % above
target, assuming 2013 usage (kilometres and routes driven). Thus, converting all vehicles by
2020 to an average EF that is equivalent to the 2013 Prius EF means BC's 2020 private
vehicle target could be met but its 2050 target could not. The private vehicle target and the
'Prius EF' total emissions value become identical in 2028 (at approximately 878,000 tonnes
CO2). Therefore, if all cars were switched to 2013 Prius models by 2020, it would allow eight
years for new vehicle technologies to be developed with even lower EFs that could then lead
to reducing emissions below target values between 2028 and 2050. The example in this
section illustrates two key points. First, it illustrates the significant degree of improvement
required in the automobile fleet to meet the 2020 target (for private vehicles)-a drop in EF
of about 100 g CO2/km, or approximately 50%. Second, it illustrates the value oflowering
vehicle EFs sooner rather than later.

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Example 3: Reduce private vehicle emissions through modal shift
Promoting modal shift of passenger transportation from private vehicles to trains and
buses could be viable in those areas of BC that have the highest population densities and
passenger vehicle traffic volumes. Two areas are of especial interest: the Lower Mainland
east of Vancouver, and the region between Victoria and Nanaimo.
For private vehicles, the Vancouver to Chilliwack route currently has the highest
emissions of all BC roads, with 226,000 tonnes CO 2 in 2013 . We can compare emission
reductions if light rail or bus replaces private vehicles on this route. In the Lower Mainland,
the West Coast Express rail service links Vancouver to Mission (Translink 2015), a distance
of approximately 65 km. Assume it is extended approximately 40 km to Chilliwack, the last
of the large cities in the Lower Mainland east of Vancouver. Light rail has a passengerkilometre EF of 67 g (DEFRA 2012), which is 67% smaller than the private vehicle EF of
202 g CO2/km calculated in this research. If (unrealistically) all private vehicle traffic on this
route were replaced by trains with an EF of 67 g C0 2/pkm, emissions could be reduced to as
little as 75,000 tonnes CO 2. This is a 151 ,000 tonnes CO 2 drop, which equals approximately
7.9% of all 2013 interurban private vehicle emissions.
Alternatively, express buses, which perhaps could use the dedicated high-occupancyvehicle lanes, could also be used at little initial expense to reduce congestion and emissions.
Assuming a coach EF as low as 28 g C02/pkm (DEFRA 2012) and full occupancy (the EF is
86% smaller than that for cars), then (unrealistically) switching all private vehicle traffic on
this route to buses could reduce emissions to as little as 32,000 tonnes CO2. This 194,000
tonnes CO2 reduction equals approximately 10.1 % of all 2013 interurban private vehicle
emissions. Modal replacement from private vehicles to rail and/or bus service may be

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feasible on other high emission private vehicle routes such as Victoria-N anaimo (a distance
of 100 km), or the Vancouver-Hope-Kamloops/Kelowna loop routes.
There are two main take-home lessons from this example of private vehicle-torail/bus modal shifts. First, the SMITE model calculations demonstrate significant emission
reductions from a modal shift, thus indicating such modal shifts are a worthwhile topic of
policy discussion. Second, the calculations are an example of the value of developing a
localized model such as SMITE. SMITE determines the geographic distribution of usage and
emissions that can be put to use for making calculations and comparisons on a route by route
basis. This illustrates the policy value of a localized model.

Example 4: Reductions of rail freight emissions
Rail freight has a significantly lower tonne-kilometre EF than trucking, but it still
produces significant emissions. Currently, it accounts for 12.8% of BC ' s interurban
transportation emissions and, if the province were to engage in large-scale modal shift from
truck to rail transport, this percentage would likely rise. Improvements in diesel locomotive
technology may allow railway companies to reduce their emissions slightly each year.
However, to significantly reduce railway emissions, radical changes, such as electrification
of the BC railway system, would be needed. Much ofBC ' s electricity is produced by
hydroelectric dams, which means that BC ' s electricity supply is basically ' clean ' and has
minimal GHG emissions.
SMITE can be deployed to compare emission reductions and costs in the
electrification of BC ' s rail network. According to SMITE, full electrification of the BC rail
network could reduce emissions by up to 1.4 Mt CO2 from 2013 levels, or approximately
12.8% of total BC interurban transportation emissions in 2013 . For SMITE cost calculations,

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I assumed that offsetting a tonne of CO2 would cost $100 between 2020 and 2050, and that a
credit for an excess tonne of CO2 reduced could be sold on the offset market for $100. IfBC
were to electrify its entire rail network by 2020 (and since the electricity would come from
hydroelectric power, there are nominally no additional GHGs from electricity generation),
between 2020 and 2050 BC would be able to sell offset credits for 16.5 million tonnes CO 2
compared to the target values for that time span, resulting in potential revenues of $1 .65
billion.
This can now be compared to the cost of infrastructure investment to build an allelectric rail system. Using data from the UK, the budget for electrifying a mere 300 km of
rail from London to Swansea was estimated at approximately $1.85 billion (RailwayTechnology.com 2010). This proposal did not include upgrading the railway tracks to
accommodate high-speed trains, which is significantly more expensive. Assuming, based on
the British case, that the cost of electrification is approximately $6.2 million per kilometre of
rail, electrifying nearly all 10,000 km ofrailway in BC (Statistics Canada 2014b) would cost
upwards of $62 billion, or 3,700% more than what BC could, under ideal circumstances, earn
by selling excess CO 2 credits. Based on a British study (Railway-Technology.com 2010), the
cost savings of electric trains over diesel trains are approximately 82 cents (Canadian) per
kilometre. Thus, for electrification to pay for itself (excluding any potential revenue from
carbon offsets), trains would have to travel 75 .6 billion kilometres. Statistics are only
available at the national level on train operating statistics, but since BC accounts for 26.3%
of all train fuel consumption in Canada (Statistics Canada 2014c ), it should account for a
roughly equal percentage of total freight train-kilometres (105 ,473 ,695) (Statistics Canada
2014e, f) , or approximately 27,340,000 train-kilometres pa. At this rate, electrification would

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take a staggering 2,725 years to pay for itself. However, it is likely that significant emission
reductions could be achieved at much lower cost if only the main railway arteries are
electrified, such as Vancouver to Prince George or from the Lower Mainland towards the
Alberta border.
This example illustrates two main points. First, electrification of BC ' s rail system is
not a panacea; there are significant financial obstacles to this method of emission reduction.
And second, the SMITE model is capable of generating rough cost comparisons that might
be beneficial to policymakers and the general public in discussions of financial feasibility.
Example 5: Improve f erry EFs to below 300 g C02/pkm
BC Ferries is a vital part of the BC interurban transportation system, but its average
passenger-kilometre EF is 696 g C02/pkm (the highest value is 1,781 g C0 2/pkm), which is
by far the highest average EF of all BC interurban passenger transportation modes. The
lowest BC Ferries EF is 261 g C02/pkm, which is still higher than the average EFs for all
other passenger transportation modes. If the average ferry EF was reduced to 300 g C0 2/pkm
by 2020, assuming 2013 usage, emissions in 2020 would be 198,000 tonnes CO2 instead of
342,000 tonnes CO2, which equates to a 42% reduction. This would bring emissions 21 %
below the 2020 target value of 250,000 tonnes CO2. If by 2020 an average EF of 300 g
C02/pkm was only achieved on the main routes linking Vancouver to Vancouver Island,
emissions in 2020 would still be reduced by 77,000 tonnes CO 2, or 22% of 2013 ferry
emissions. This value of 265 ,000 tonnes CO2 would then only be 6% above the 2020 target
value. Measures to reduce ferry EFs could include buying new and more fuel-efficient
vessels, increasing load factors, and dropping or consolidating routes. The case here
illustrates, first, that it is difficult to achieve significant emission reductions in BC ' s ferry

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service, and second, that the SMITE model again is useful for providing geographically
detailed (i.e., route specific) results.

Example 6: Improve airplane passenger-kilometre EFs to below 100 g C02/pkm
The average passenger-kilometre EF of airplanes in BC is 184.5 g C0 2/pkm. The
results in Chapter 4 show that there are significant differences in the EFs of different aircraft
models. In particular, the Beech 1900 series aircraft have the highest EFs in BC, with values
ofup to 385.9 g C02/pkm, which is approximately five-fold those of Boeing 737 jets (which
are as low as 74.5 g C0 2/pkm). Ifby 2020 all planes had the EF of Boeing 737 jets,
emissions at 2013 usage levels would be 98,000 tonnes CO2. This results in a 69,000 tonnes
CO2 drop and equates to reducing passenger aviation emissions by 41 %, or 43% below the
2020 target value of 173,000 tonnes CO2. Recognizing that the Boeing 737 is not suitable for
all BC routes because of its large size, ifby 2020 the average EF was reduced to, say, 100 g
C02/pkm, emissions at 2013 usage levels would be 132,000 tonnes CO2. This results in a
37,000 tonnes CO2 reduction and still equates to an overall passenger aviation emission
reduction of 21.0%, or 24% below the 2020 target value of 173,000 tonnes CO 2. While fleet
changeovers that result in lower EFs are costly, high passenger-kilometre EFs are directly
related to high fuel consumption, so airlines would be able to reduce their operating costs by
introducing more efficient aircraft.
This example illustrates two main points: First, significant emission reductions can be
achieved in the passenger aviation sector if EFs are lowered across the fleet to be closer to
those of the planes with the lowest passenger-kilometre EFs. Second, it highlights the value
of SMITE's high degree of geographical resolution, which identifies usage and emissions by
individual routes. This again illustrates SMITE's policy value.

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Summary

The six examples discussed above show that there are changes (often requiring only
wider adoption of existing technologies rather than revolutionary technologies and systemic
changes) that can help various interurban transportation modes in BC to reduce their
emissions, in several cases below the 2020 emission target values. The examples also
highlight the value of SMITE, which, as a localized, bottom-up model, can analyze usage and
emissions for individual routes rather than the entire interurban transportation sector. This
makes it an important policymaking tool.
6.3

Contribution of research

In brief, for my research, I devised a novel inventorying and scenario projection
methodology, and applied it to British Columbia. My study is a contribution to existing
knowledge both on a practical and theoretical level. On the practical level, it contributes a
detailed, bottom-up inventory of interurban passenger and freight transportation in BC, along
with its geographical distribution where possible. To my knowledge, this is the first such
detailed inventory in BC. Furthermore, the results of my future scenario calculations provide
perspective on how BC needs to change its interurban transportation system to achieve its
mandated emission reduction targets. Together, the inventory and future scenario calculations
provide practical data and calculations for policy decisions regarding interurban
transportation emissions in BC.
At the theoretical level, my study contributes a more or less novel collection of
spreadsheet-based techniques for inventorying transportation emissions and projecting future
emissions. This collection of techniques is what I called the SMITE model. It uses placespecific rather than generic EFs to more accurately reflect transportation fleets at a local

208

level; contains fine grain geographical resolution that may make it 'policy-friendly' for local
policymakers; addresses only interurban transportation rather than the entirety of the
transportation system, thus focusing on a portion of the transportation system that is often
overlooked; and is capable of comparing projected emissions to target values and
determining (offset) costs of achieving or not achieving the targets. SMITE was developed as
a generic tool that can be applied to other jurisdictions or on other geographical scales, even
though in my research it was applied only to BC.

6.4

Limitations of research
The CO2 emissions calculations in Chapter 4 and future emission projections in

Chapter 5 are subject to a number of limitations. For the CO2 emission calculations in
Chapter 4, uncertainties such as difficulty in establishing the exact number of services
operated on a certain route, or difficulty in calculating representative EFs, were among the
main challenges and applied to most transportation modes covered. For other modes
(especially marine freight), a dearth of statistical information further complicated research
efforts. The limitations applicable to each specific transportation mode were discussed in
Chapter 3 following the description of the methodological approach for that mode. However,
despite the limitations ofmy Chapter 4 calculations, I am reasonably confident in my results,
as outlined in the comparison section of that chapter. I was able to validate my results to
varying degrees for seven of the nine modes covered in my research; the two modes for
which comparisons were not possible account for only 0.1 % of emissions each.
The future emission scenarios in Chapter 5 are also subject to a number of limitations.
The most significant is that the starting values for each scenario, for years between 2007 and
2014, are directly based on the results of Chapter 4. If values in Chapter 4 contain errors,
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these errors transfer to the future scenario calculations. Moreover, since it was impossible to
calculate all possible parameter changes, I limited the number of changes to model. These,
depending on the mode, may not accurately reflect realistically achievable values. I had
hoped to interview transportation providers and car manufacturers in order to obtain a better
understanding of achievable emission reduction values, but because of the lack of
participation in my survey, this was not possible.

6.5

Suggestions for further research
There are two main suggested areas for further research: (1) improving SMITE, and

(2) expanding the application of SMITE.
First, in terms of improving SMITE, one important step would be to obtain improved
transportation usage statistics. For instance, for marine freight, one of the largest contributors
to BC interurban transportation emissions in total terms, information on distances travelled or
tonnage carried from origins to destinations is very sparse. Compiling the required data
should be possible. Companies should be aware of how much cargo they carry over which
distances, since this is most likely how they bill their clients. This information could possibly
be collected (anonymously if there are competition issues) and aggregated. A reintroduction
of some of the data series on marine traffic that have been discontinued would also alleviate
some of the paucity of statistical information. Another important step would be to improve
the detail of statistical information on BC fleets for most modes (e.g., through surveying
transportation companies or vehicle manufacturers) . This would allow more accurate and
representative EFs to be calculated, which in tum would improve the accuracy of emission
calculations. This would also allow more accurate comparisons between transportation

210

modes, and enable comparisons to include those modes which so far could not be compared
because of lacking information (such as marine freight).
Second, building on the research in this dissertation, the application of SMITE could
be expanded. In its current scope, which addresses only interurban transportation, SMITE
could be expanded to the national (e.g. , Canada) or even supra-national scale, as the methods
developed for this research allow for such an expansion. A second direction to build is to
expand SMITE to combine (BC's) urban and interurban transportation systems. Combining
both components would facilitate a better understanding of the GHG emission contributions
of each part of the system, and how changes can help to reduce emissions. This expanded
model could then also be applied at various geographic scales, such as different provinces or
the entirety of Canada.
6.6

Final thoughts
Transportation of people and freight has been a cornerstone of societies for millennia,

and there are no indications that the importance of transportation will lessen in the future. On
the contrary, increasing global interconnectivity has resulted in emissions, of both passenger
and freight transportation, steadily rising across the globe. In addition, awareness of the
contribution of transportation sector GHG emissions to negative climate impacts has also
been increasing globally. As such, there is a distinct need to reconcile the importance of
transportation, both interurban and urban, with its climate and environmental impacts.
The first step in reducing GHG emissions, not just from transportation but from other
economic sectors as well, is to obtain a clear insight into emissions levels and the activities
that generate them, which can be accomplished through detailed usage and emission
inventories. Conducting research for this dissertation has illustrated very clearly just how
211

difficult and complex it is to accurately inventory transportation emissions. This complexity
is caused not only by the lack of statistical information but also by the difficulty of
establishing methodologies. Various approaches to quantify the same aspect of transportation
emissions (such as annual emissions) may, depending on their scope and methods, result in
entirely different values, as was the case for BC rail freight emissions. Also, there are a
multitude of stakeholders involved in the transportation system, who may have divergent
interests in terms of transportation's path for the future. Accurately quantifying
transportation' s environmental impact and plotting paths for the future will require
consultation and agreement among its many stakeholders and a streamlining of the
inventorying process that is transparent, fair, and accountable.
Finally, the time to start acting on reducing transportation GHG emissions is now.
Many of the options for reducing transportation emissions already exist, and simply need to
be disseminated more widely and more rapidly. Revolutionary technological developments
may make the transition to a lower-carbon transportation system easier, but waiting for such
developments to occur distracts from beginning to reduce transportation emissions through
those measures already at our disposal. My research has demonstrated that for the BC
interurban transportation sector to achieve BC's ambitious GHG reduction targets, systematic
changes to the transportation sector are required and that they need to be initiated as soon as
possible, otherwise achieving the legislated reduction targets will become more difficult with
each passing year.

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222

APPENDIX 1: Interview Questions
British Columbia Vehicle Manufacturer Questionnaire
Purpose of Questionnaire: The purpose of this questionnaire is to gather information on
whether manufacturers of vehicles sold in British Columbia are influenced by environmental
considerations and what likely future improvements in vehicle efficiency will be.
For those questions which ask you to rank your opinion, please use the following scale:
1: Strong disagree
2: Disagree
3: Neutral/does not apply
4: Agree
5: Strongly agree

C omoanv orofilI e
1.

2.
3.
4.
5.

6.

7.

D

My company's vehicles sold in markets other than North
America are generally more fuel-efficient than those sold
in North America.
Customers in North America value vehicle performance
over efficiency.

D

D
1

D

1

D
1
D

1

My company produces diesel vehicles.

10.

11.

section.
Diesel vehicles are more efficient than gasoline vehicles
of similar engine size.
Diesel engines in my company's vehicles are designed to
consume a similar amount of fuel as a gasoline engine
but provide more performance.
Diesel engines in my company's vehicles are designed to

223

D

1

Ifyou answered "no " to Question 8, please skip this
9.

3

4

5

D
3
D
3
D
3
D

D
4
D
4
D
4
D

D
5
D
5
D
5
D

Yes

Diesel en ines
8.

2
D
2
D
2
D
2
D

1

Fuel consumption and greenhouse gas (GHG) emissions
are central concerns when we design vehicles.
Our customers demand vehicles that are more fuel
efficient.
We strive to go above meeting environmental legislation
when designing vehicles.
Competition with other vehicle manufacturers has
provided a greater incentive for improving vehicle
efficiency than environmental legislation.
My company produces vehicles for markets other than
North America.
Ifyou answered "no " to Question 5, please skip this
question.

No

D

2
D

3

4

5

D

D

D

2
D

3
D

4
D

5
D

Yes

No

D

D
3

4

5

D
1
D

2
D
2
D

D
3
D

D
4
D

D
5
D

1

2

3

4

5

1

12.
13.
14.
15.
16.

provide a similar performance to a gasoline engine but
use less fuel to do so.
My company is working on building diesel engines that
are more efficient than current models.
If you answered "yes" to Question 12, please elaborate
on these measures:
In your opinion, what is the maximum fuel consumption
reduction (as a percentage) that is feasible for diesel
engines by 2020 compared to 2013?
In your opinion, what is the maximum fuel consumption
reduction (as a percentage) that is feasible for diesel
engines by 2050 compared to 2013?
Making diesel engines more efficient will significantly
increase the cost of the vehicles.

D

18.
19.
20.
21.
22.
23.

My company only produces gasoline engines because
there is no demand for diesel vehicles.
My company is considering introducing diesel engines.

My company is working on building gasoline engines
that are more efficient than current models.
If you answered "yes" to Question 19, please elaborate
on these measures:
In your opinion, what is the maximum fuel consumption
reduction (as a percentage) that is feasible for gasoline
engines by 2020 compared to 2013?
In your opinion, what is the maximum fuel consumption
reduction (percentage) that is feasible for gasoline
engines by 2050 compared to 2013?
Making gasoline engines more efficient will significantly
increase the cost of the vehicles.

25.
26.
27.
28.

I

D

No
D

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1
D

I

1
D

I D I D I D I D

2
D

I

2

3
D

I

3

4
D

I

4

Yes
D
Yes
D
Click here to enter text.

5
D

5

No
D
No
D

Click here to enter text.
Click here to enter text.

My company produces vehicles that are neither gasoline nor
diesel powered.
Ifyou answered "yes" to Question 24, please complete
Questions 26-31.
Ifyou answered "no" to Question 24, please complete
Question 2 5 only.
My company is considering building alternative fuel
vehicles in the future.
My company builds the following types of alternative fuel
vehicles:
The performance of alternative-fuel vehicles is comparable
to fossil-fuel powered vehicles.
Alternative fuel vehicles are significantly more expensive
than fossil-fuel vehicles.

224

I

D

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1
D

I

Alternative fuel vehicles

24.

I

D

Yes
D
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Gasorme em!lnes
17.

I

D

2
D

I

3
D

I

4
D

Yes
D

No
D

Yes
D
Click here to enter text.
1
D
1
D

I

5
D

2
D
2
D

3
D
3
D

No
D
4
D
4
D

5
D
5
D

29.

Prices for alternative fuel vehicles will drop and become
closer to fossil-fuel vehicles.

30.

Year by which price of fossil fuel and alternative fuel
vehicle could be comparable:
My company hopes to shift a greater share of its business to
alternative fuel vehicles in the future.

31.

2
4
1
3
D I D I D I D
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1
D

I

2
D

I

3
D

I

4
D

British Columbia Interurban Transportation Provider Questionnaire
Purpose of Questionnaire: The purpose of this questionnaire is to gather information on
British Columbia interurban transportation providers, their perceptions regarding the BC
Carbon Tax, and fuel usage and emission reduction measures they have engaged in or may
engage in in the future .
For those questions which ask you to rank your opinion, please use the following scale:
1: Strong disagree
2: Disagree
3: Neutral/does not apply
4: Agree
5: Strongly agree

C omoanv orofilI e
1.

2.
3.
4.
5.

Is your company aware of the 2007 BC Greenhouse Gas Reduction
Targets Act, which requires emissions to be reduced 33% by 2020 over
2007 levels and to be reduced 80% by 2050 over 2007 levels?
Do you consider the transportation sector to be a strong contributor to
overall fuel consumption and greenhouse gas (GHG) emission creation?
Has your company calculated its GHG emissions?

Has your company considered or implemented measures to reduce its fuel
consumption and associated GHG emissions?
Transportation is an important part of the BC economy and lifestyle
because of the province' s size. However, transportation contributes 37%
of overall BC GHG emissions, compared to an average of 20% globally.
Despite this, do you think exemptions to environmental legislation should
be made to BC transportation because of its importance to the province?

225

Yes
D

No
D

Yes
D
Yes
D
Yes

No
D
No
D
No

D

D

Yes
D

No
D

I

5
D

I

5
D

BC Carbon Tax

6.

7.
8
9.
10.
11.
12.
13.
14.

15.

The BC Carbon Tax has had a financial impact on my
company.
The cost burden of the BC Carbon Tax is absorbed by
my company.
The cost burden of the BC Carbon Tax is passed on to
customers.
The BC Carbon Tax has created an incentive for my
company to change how it operates in order to save fuel.
If you answered "agree" or "strongly agree" to Question
9, what measures have you taken, and to what reductions
in fuel usage have they lead?
If the BC Carbon Tax was increased further, this would
create an incentive/more of an incentive for my company
to adjust its operations.
The BC Carbon Tax is transparent in how it is applied
and what the monies collected are used for.
The BC Carbon Tax is effective in achieving its intended
goals.
If you answered "disagree" or "strongly disagree" to
Question 13, how would you, as a transportation
provider, prefer to encourage transportation stakeholders
to reduce their emissions?
Other comments regarding the BC Carbon Tax:

F ut ure fueI usa2e an d emission re d ucti ons
16. Reducing fuel consumption will not only reduce
emissions but also save my company money.
17. My company is aware of how we can reduce emissions
but implementing these measures is too expensive.
18. My company knows that reducing fuel consumption and
associated emissions would reduce operating expenses
but does not have the expertise to carry out such
reductions.
19. My company will be able to reduce fuel consumption
and associated GHG emissions by 33% by the year 2020
and still be able to offer the same level of transportation
service
20. If you answered "agree" or " strongly agree" to
Question 19, how would this likely be achieved
(e.g., energy efficiency measures, alternative fuels,
etc.)?
21. If you answered "disagree" or " strongly disagree" to
Question 19, what kinds of measures would such
reductions require (e.g., new technologies etc.)?
22. My company will be able to reduce fuel consumption
and associated GHG emissions by 80% by the year 2050
and still be able to offer the same level of transportation

226

1
D
1
D
1
D
1

0

2
D
2
D
2
D
2

3
D
3
D
3

4
D
4
D
4

5
D
5
D
5

3

4

5

D

0

1

0

0

0

0

0

2

3

4

5

Click here to enter text.
1

0

0

2

0

3

0

4

0

5

0

0

0

0

0

0

0

0

5
D

4

5

1

2

3

Click here to enter text.

4

0

Click here to enter text.

1

2

3

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

2

3

4

5

1

1

0

2

2

0

3

3

0

4

4

5

5

0

0

Click here to enter text.

Click here to enter text.

1

2

3

4

0

0

0

5

0

0

23.
24.
25.
26.
27.
28.
29.

service.
If you answered "agree" or "strongly agree" to Question
19, how would this likely be achieved (e.g., energy
efficiency measures, alternative fuels, etc.)?
If you answered "disagree" or "strongly disagree" to
Question 19, what kinds of measures would such
reductions require (e.g., new technologies etc.)?
What do you think are the greatest fuel consumption
reductions (as a percentage) that can realistically be
achieved by the year 2020?
What do you think are the greatest fuel consumption
reductions (as a percentage) that can realistically be
achieved by the year 2050?
Costs for implementing measures that reduce emissions,
such as new technology or infrastructure, should be
borne by transportation providers.
Costs for implementing measures that reduce emissions,
such as new technology or infrastructure, should be
borne by transportation users.
Other comments regarding future fuel consumption
reductions:

227

Click here to enter text.
Click here to enter text.
Click here to enter text.
Click here to enter text.
1
D

2
D

3
D

4
D

5
D

1

2
D

3
D

4
D

5
D

D

Click here to enter text.

APPENDIX 2: Calculation Data
Table A2.1: Ranking of BC routes by kilometres driven in 2007 and 2013

1

2007 distance
driven (km)
1,156,784,280

2
3

598,329,845
319,172,308

4
5

314,236,267
305,851 ,896

6
7
8
9

293,216,238
275,043,910
259,536,827
245,903 ,654

10
11

234,430,740
211 ,246,743

12
13

201,294,945
193,920,646

14
15

177,411 ,374
174,539,700

16
17
18

169,186,297
168,922,701
163,463,571

19

161 ,491 ,056

20

141 ,632,994

21

139,723 ,570

22

137,558 ,718

23

127,476,002

24

119,098 ,704

25
26

118,108,014
115,550,897

Rank

Route
VancouverChilliwack
Ladysmith-Victoria
Vernon-Kelowna
Hope-Merritt
Parksville-Campbell
River
Parksville-Nanaimo
Kelowna-Penticton
Chilliwack-Hope
Vancouver-Squamish

2013 distance
driven (km)
1,120,642,345
619,618,890
345,182,544
339,781 ,478
327,401 ,726

Route
VancouverChilliwack
Ladysmith-Victoria
Parksville-Campbell
River
Vernon-Kelowna
Hope-Merritt

Hope-Penticton
Tete Jaune CacheKamloops
Vernon-Salmon Arm
Cache CreekWilliams Lake
Kamloops-Merritt
Monte Creek-Salmon
Arm
Revelstoke-Golden
Kelowna-Merritt
Squamish-Whisler

245,903 ,654
208,527,464

Kelowna-Penticton
Parksville-Nanaimo
Chilliwack-Hope
Tete Jaune CacheKamloops
Vancouver-Squamish
Kamloops-Merritt

206,553 ,573
197,410,688

Vernon-Salmon Arm
Kelowna-Merritt

196,405 ,989
184,730,792

Whistler-Cache
Creek/Pemberton
Hope-Cache Creek

154,272,743

Revelstoke-Golden
Cache CreekWilliams Lake
Hope-Penticton
Squamish-Whisler
Monte Creek-Salmon
Arm
Kamloops-Cache
Creek
Whistler-Cache
Creek/Pemberton
Dawson CreekPrince George
Salmon ArmRevels toke
Parksville-Campbell
River
Penticton-Osoyoos

Kamloops-Cache
Creek
Parksville-Campbell
River
Penticton-Osoyoos
Golden-Radium Hot
Springs
N anaimo-Ladysmith
Prince GeorgeQuesnel

228

306,142,144
290,824,773
267,043 ,271
250,063 ,982

181 ,927,242
181 ,217,025
179,440,935

153,857,081
149,029,774
141,044,410
136,866,240
127,876,115
119,134,467
119,098 ,704

Nanaimo-Ladysmith
Golden-Radium Hot
Springs

27

113,350,429

28
29

105,527,953
105,461,443

30

104,548 ,147

31

104,349,668

32

93 ,902,309

33

90,190,398

34

87,169,446

35

86,589,534

36

86,503,306

37

84,398,220

38

81,381 ,670

39

81 ,286,595

40

81,004,538

41

75 ,087,800

42

73,777,946

43

73 ,368,504

44

62,906,290

45

62,610,531

46

Salmon ArmRevelstoke
Monte Creek-Vernon
Prince GeorgeVanderhoof
Cran brook-Fairmont
Hot Springs
Dawson CreekPrince George
Golden-Alberta
Border

115,332,700
113,984,025
108,422,345
105,461,443
104,694,001
103,404,179

Alberta/BC
Boundary-Highway
93 Junction
Cranbrook-Cresfon

99,757,814

Port Hardy-Campbell
River
Kamloops-Monte
Creek
Dawson Creek-Ft. St.
John
Rock CreekCastlegar
Cranbrook-Highway
93 Junction
Quesnel-Williams
Lake
Ucluelet JunctionParksville
Tete Jaune CachePrince George
Gibsons-Sechelt

90,415,975

96,914,070

88 ,757,021
88 ,388 ,984
83 ,8 85,760
78,446,216
73 ,991 ,734
73 ,981 ,470
72,077,762
68 ,517,618

58 ,906,795

55,284,068

Kelowna-Rock
Creek
Radium Hot SpringsFairmont Hot Springs
Nelson-Kaslo

47

55 ,041,153

Castlegar-Trail

56,464,405

48
49

53,777,990
52,850,723

52,826,158
50,930,056

50
51

52,738,047
50,930,056

Houston-Smithers
Tete Jaune CacheAlta border
Sechelt-ferry
Bums Lake-Houston

229

59,871 ,987

57,214,024

50,502,933
47,269,310

Prince GeorgeQuesnel
Hope-Cache Creek
Monte Creek-Vernon
Prince GeorgeVanderhoof
Cranbrook-Fairmont
Hot Springs
Alberta/BC
Boundary-Highway
93 Junction
Golden-Alberta
Border
Dawson Creek-Ft. St.
John
Cranbrook-Highway
93 Junction
Kamloops-Monte
Creek
Port Hardy-Campbell
River
Cranbrook-Creston
Quesnel-Williams
Lake
Rock CreekCastlegar
Tete Jaune CachePrince George
Gibsons-Sechelt
Ucluelet JunctionParksville
Kelowna-Rock
Creek
Castlegar-Trail
Tete Jaune CacheAlta border
Radium Hot SpringsFairmont Hot Springs
Nelson-Kaslo
Bums Lake-Houston
Sechelt-ferry
Prince RupertTerrace

52

48,105,496

Creston-Castlegar

44,945,180

53

43,729,920

44,570,690

54

42,184,218

44,529,766

Houston-Smithers

55

39,356,928

42,436,221

Kitwanga-Terrace

56

38,960,845

41 ,506,807

57

37,885,993

Castlegar-Christina
Lake
Williams LakeAlexis Creek
Prince RupertTerrace
Vanderhoof-Fraser
Lake
N akusp-Castlegar

Vanderhoof-Fraser
Lake
Creston-Castlegar

40,505,693

58

37,355,640

Kitwanga-Terrace

39,496,723

59

36,320,347

38,356,361

60

34,636,003

Fraser Lake-Bums
Lake
Vemon-Nakusp

Williams LakeAlexis Creek
Castlegar-Christina
Lake
Fraser Lake-Bums
Lake
Vemon-Nakusp

61
62

34,497,172
33,375,162

63
64
65
66
67
68
69
70
71
72
73
74

Terrace-Kitimat
Dawson CreekAlberta Border
31 ,976,599 Smithers-New
Hazelton
27,235,661 Fort Nelson-Liard
River
25 ,745,337 Fort St. JohnWonowon
22,389,969 Hope-Agassiz
21 ,391,920 Ucluelet JunctionTofino
21 ,121 ,309 WonowonBuckincllorse River
19,753,493 Kitwanga-Meziadin
Junction
18,224,457 Liard River-Lower
Post
16,092,558 Meziadin JunctionDease Lake
15,563 ,162 Kitwanga-New
Hazelton
14,875,531 Alexis CreekAnahimLake
11 ,796,435 1 km north of Prophet
River-Fort Nelson

75

11 ,404,702

76

11 ,089,459

Dease Lake-Yukon
Border
Buckinghorse River-

230

38,029,350

Dawson CreekAlberta Border
36,506,716 Terrace-Kitimat
35,526,311 Nakusp-Castlegar
Fort St. JohnWonowon
31 ,976,599 Smithers-New
Hazelton
29,885,196 Fort Nelson-Liard
River
23,380,148 WonowonBuckinghorse River
20,925 ,187 Ucluelet JunctionTofino
20,586,471 Hope-Agassiz
35,294,953

19,794,819

Kitwanga-Meziadin
Junction
18,020,262 Liard River-Lower
Post
17,344,201 Meziadin JunctionDease Lake
17,142,707 Kitwanga-N ew
Hazelton
12,281 ,987 Dease Lake-Yukon
Border
10,988,383 Buckinghorse River1 km north of Prophet
River
10,513,314 1 km north of Prophet
River-Fort Nelson
9,415,598 Alexis Creek-

77

9,337,284

78

4,889,890

79

4,646,260

Total

8,964,072,902

1 km north of Prophet
River
Saltery Bay ferry
terminal-Powell
River
Ucluelet JunctionUcluelet
Meziadin JunctionStewart

AnahimLake
8,325,504
4,665,167
3,954,264

Saltery Bay ferry
terminal-Powell
River
Ucluelet JunctionUcluelet
Meziadin JunctionStewart

9,491 ,321 ,413

Table A~.2: Percentage changes in distance driven on BC routes 2007-2013
Rank

Route

1

Dawson CreekPrince George
Fort St. JohnWonowon
Salmon ArmRevels toke
Prince RupertTerrace
Tete Jaune CacheKami oops
Kami oops-Merritt
Kelowna-Merritt
Revelstoke-Golden
Vanderhoof-Fraser
Lake
Dawson Creek-Ft. St.
John
Alberta/BC
Boundary-Highway
93 Junction
Dawson CreekAlberta Border
Kitwanga-Terrace
Parksville-Campbell
River
Kelowna-Penticton
Cranbrook-Highway
93 Junction
Squamish-Whistler
Vernon-N akusp
Wonowon-

2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

2007 distance
driven (km)
104,349,668

2013 distance
driven (km)
149,029,774

O/o
Chan~e
42 .8

25,745,337

35,294,953

37.1

113,350,429

141 ,044,410

24.4

39,356,928

47,269,310

20.1

211 ,246,743

250,063,982

18.4

177,411 ,374
168,922,701
169,186,297
38 ,960,845

208 ,527,464
197,410,688
196,405,989
44,945 ,180

17.5
16.9
16.1
15.4

84,398,220

96,914,070

14.8

90,190,398

103,404,179

14.7

33 ,375,162

38 ,029,350

13 .9

37,355,640
305,851,896

42,436,221
345,182,544

13.6
12.9

275 ,043 ,910
81,286,595

306,142,144
90,415,975

11.3
11.2

163,463,571
34,636,003
21 ,121 ,309

181 ,217,025
38 ,356,361
23,380,148

10.9
10.7
10.7

231

20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

Buckinghorse River
Kamloops-Cache
Creek
Kitwanga-N ew
Hazelton
Fort Nelson-Liard
River
Fraser Lake-Bums
Lake
Tete Jaune CacheAlta border
Meziadin JunctionDease Lake
Dease Lake-Yukon
Border
Castlegar-Trail
Vemon-Kelowna
Golden-Alberta
Border
Terrace-Kitimat
Hope-Merritt
Ladysmith-Victoria
Chilliwack-Hope
Monte Creek-Salmon
Arm
Monte Creek-Vernon
Vernon-Salmon Arm
Kamloops-Monte
Creek
Port Hardy-Campbell
River
N anaimo--Ladysmi th
Penticton--Osoyoos
Tete Jaune CachePrince George
Kitwanga-Meziadin
Junction
Cranbrook-Fairrnont
Hot Springs
Vancouver-Squamish
Prince GeorgeVanderhoof
Bums Lake-Houston
Smithers-New
Hazelton
Golden-Radium Hot
Springs
Prince GeorgeQuesnel

139,723 ,570

154,272,743

10.4

15,563,162

17,142,707

10.1

27,235,661

29,885,196

9.7

36,320,347

39,496,723

8.7

52,850,723

57,214,024

8.3

16,092,558

17,344,201

7.8

11,404,702

12,281,987

7.7

55,041 ,153
319,172,308
93 ,902,309

58,906,795
339,781 ,478
99,757,814

7.0
6.5
6.2

34,497,172
314,236,267
598,329,845
259,536,827
174,539,700

36,506,716
327,401 ,726
619,618 ,890
267,043 ,271
179,440,935

5.8
4.2
3.6
2.9
2.8

105,527,953
201,294,945
86,503,306

108,422,345
206,553,573
88 ,757,021

2.7
2.6
2.6

86,589,534

88 ,388 ,984

2.1

118,108,014
127,476,002
73 ,777,946

119,134,467
127,876,115
73 ,981,470

0.9
0.3
0.3

19,753,493

19,794,819

0.2

104,548 ,147

104,694,001

0.1

245 ,903,654
105,461 ,443

245,903 ,654
105,461 ,443

0.0
0.0

50,930,056
31 ,976,599

50,930,056
31 ,976,599

0.0
0.0

119,098 ,704

119,098,704

0.0

115,550,897

115,332,700

-0.2

232

50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79

Parksville-Campbell
River
Parksville-Nanaimo
Buckinghorse River1 km north of Prophet
River
Liard River-Lower
Post
Williams LakeAlexis Creek
Gibsons-Sechelt
Ucluelet JunctionTotino
VancouverChilliwack
Quesnel-Williams
Lake
Cranbrook-Creston
Sechelt-ferry
Nelson-Kaslo
Ucluelet JunctionUcluelet
Whistler-Cache
Creek/Pemberton
Cache CreekWilliams Lake
Kelowna-Rock
Creek
Nakusp--Castlegar
Creston-Castlegar
Castlegar-Christina
Lake
Hope-Agassiz
Ucluelet JunctionParksville
Rock CreekCastlegar
Radium Hot SpringsFairmont Hot Springs
Saltery Bay ferry
terminal-Powell
River
1 km north of Prophet
River-Fort Nelson
Meziadin JunctionStewart
Houston-Smithers
Hope-Cache Creek
Hope-Penticton
Alexis Creek-

137,558,718

136,866,240

-0.5

293,216,238
11,089,459

290,824,773
10,988 ,383

-0.8
-0.9

18,224,457

18,020,262

-1.1

42,184,218

41 ,506,807

-1.6

73,368,504
21 ,391 ,920

72,077,762
20,925,187

-1.8
-2.2

1,156,784,280

1,120,642,345

-3 .1

81 ,004,538

78,446,216

-3 .2

87,169,446
52,738,047
55 ,284,068
4,889,890

83 ,885,760
50,502,933
52,826,158
4,665,167

-3.8
-4.2
-4.4
-4.6

161,491 ,056

153,857,081

-4.7

193,920,646

184,730,792

-4.7

62,906,290

59,871 ,987

-4.8

37,885 ,993
48 ,105,496
43 ,729,920

35 ,526,311
44,570,690
40,505,693

-6.2
-7.3
-7.4

22,389,969
75,087,800

20,586,471
68 ,517,618

-8 .1
-8.8

81 ,381 ,670

73,991 ,734

-9.1

62,610,531

56,464,405

-9.8

9,337,284

8,325,504

-10.8

11,796,435

10,513 ,314

-10.9

4,646,260

3,954,264

-14.9

53,777,990
141 ,632,994
234,430,740
14,875,531

44,529,766
113,984,025
181 ,927,242
9,415,598

-17.2
-19.5
-22.4
-36.7

233

I

IAnahim Lake

Table A2.3: Emissions per kilometre of road for 2007 and 2013
Rank

Route

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

Vancouver-Chilliwack
Nanaimo-Ladysrnith
Parksville-Nanaimo
Ladysmith-Victoria
Vernon-Kelowna
Chilliwack-Hope
Kelowna-Penticton
Vancouver-Squarnish
Vernon-Salmon Ann
Gibsons-Sechelt
Kamloops-Monte Creek
Squarnish-Whisler
Hope-Merritt
Parksville-Campbell River
Kamloops-Merri tt
Monte Creek-Salmon Ann
Penticton-Osoyoos
Castlegar-Trail
Radium Hot Springs-Fairmont
Hot Springs
Kamloops-Cache Creek
Kelowna-Merritt
Whistler-Cache
Creek/Pemberton
Golden- Alberta Border
Cranbrook-Highway 93
Junction
Golden-Radium Hot Springs
Parksville-Campbell River
Revelstoke-Golden
Dawson Creek-Ft. St. John
Monte Creek-Vernon
Alberta/BC BoundaryHighway 93 Junction
Salmon Arm-Revelstoke
Prince George-Vanderhoof
Sechelt-ferry
Cranbrook-Fairmont Hot
Springs

20
21
22
23
24
25
26
27
28
29
30
31
32
33
34

2007 emissions per km
of road (tonnes
COz/km)
2,337
1,591
1,559
1,358
1,194
953
868
730
678
674
624
560
516
515
412
410
409
383
342

2013 emissions per
km of road (tonnes
COz/km)
2,264
1,604
1,546
1,406
1,271
981
966
730
695
662
640
620
581
538
484
421
410
410
375

340
267
261

312
308
281

260

234

277

253

276

234
232
229
227
227
225

266
261
258
249
234
233

222
215
197
196

230
215
196
192

35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79

Cache Creek-Williams Lake
Prince George-Quesnel
Hope-Penticton
Houston-Smithers
Dawson Creek-Alberta Border
Cranbrook-Creston
Nelson-Kaslo
Hope-Cache Creek
Tete Jaune Cache-Alta border
Quesnel-Williams Lake
Hope-Agassiz
Vanderhoof -Fraser Lake
Ucluelet Junction-Totino
Bums Lake-Houston
Tete Jaune Cache-Kamloops
Ucluelet Junction-Ucluelet
Castlegar-Christina Lake
Terrace-Kitimat
Ucluelet Junction-Parksville
Fraser Lake-Bums Lake
Rock Creek-Castlegar
Smithers-New Hazelton
Kelowna-Rock Creek
Creston-Castlegar
Kitwanga-Terrace
Port Hardv-Camobell River
Williams Lake-Alexis Creek
Kitwanga-New Hazelton
Saltery Bay ferry terminalPowell River
Fort St. John-Wonowon
Prince Rupert-Terrace
Tete Jaune Cache-Prince
George
Nakuso-Castlegar
Dawson Creek-Prince George
Wonowon-Buckinghorse River
Vemon-Nakusp
1 km north of Prophet RiverFort Nelson
Kitwanga-Meziadin Junction
Buckinghorse River-1 km
north of Prophet River
Liard River-Lower Post
Fort Nelson-Liard River
Meziadin Junction-Stewart
Alexis Creek-Anahim Lake
Dease Lake-Yukon Border
Meziadin Junction-Dease Lake

235

192
191
175
170
169
168
160
150
142
138
137
136
135
129
126
123
113
112
109
105
96
95
93
78
76
75
75
73
63

191
189
183
161
157
154
152
149
141
136
133
132
129
126
121
119
118
114
105
100
95
88
87
87
81
80
77
74
74

58
55
55

73
66
56

52
52
37
36
26

55
49
41
40
26

26
26

26
24

19
18
15
14
10
10

20
19
13
11
11
9

Table A2.4: Private vehicle interurban CO 2 emissions by route in BC

1

2007 emissions
(tonnes CO2)
233,670

2
3

120,863
64,473

4
5

63 ,476
61,782

6
7
8
9

59,230
55,559
52,426
49,673

10
11

47,355
42,672

12
13

40,662
39,172

14
15

35,837
35 ,257

16
17
18

34,176
34,122
33 ,020

19

32,621

20

28 ,610

21

28 ,224

22

27,787

23

25,750

24

24,058

25
26

23,858
23 ,341

27

22,897

28

21 ,317

Rank

Route
VancouverChilliwack
Ladysmith-Victoria
Vernon-Kelowna
Hope-Merritt
Parksville-Campbell
River
Parksville-N anaimo
Kelowna-Penticton
Chilliwack-Hope
Vancouver-Squamish

2013 emissions
(tonnes CO2)
226,370
125,163
69,727
68 ,636
66,135

VancouverChilliwack
Ladysmith-Victoria
Parksville-Campbell
River
Vernon-Kelowna
Hope-Merritt

Hope-Penticton
Tete Jaune CacheKamloops
Vernon-Salmon Arm
Cache CreekWilliams Lake
Kamloops-Merri tt
Monte Creek-Salmon
Arm
Revelstoke-Golden
Kelowna-Merritt
Squamish-Whisler

49,673
42,123

Kelowna-Penticton
Parksville-N anaimo
Chilliwack-Hope .
Tete Jaune CacheKamloops
Vancouver-Squamish
Kamloops-Merritt

41 ,724
39,877

Vernon-Salmon Arm
Kelowna-Merritt

39,674
37,316

Whistler-Cache
Creek/Pemberton
Hope-Cache Creek

31,163

Revelstoke-Golden
Cache CreekWilliams Lake
Hope-Penticton
Squamish-Whisler
Monte Creek-Salmon
Arm
Kamloops-Cache
Creek
Whistler-Cache
Creek/Pemberton
Dawson CreekPrince George
Salmon ArmRevels toke
Parksville-Campbell
River
Penticton-Osoyoos

Kamloops-Cache
Creek
Parksville-Campbell
River
Penticton-Osoyoos
Golden-Radium Hot
Springs
N anaimo-Ladysmith
Prince GeorgeQuesnel
Salmon ArmRevels toke
Monte Creek-Vernon

236

61,841
58 ,747
53 ,943
50,513

Route

36,749
36,606
36,247

31,079
30,104
28,491
27,647
25 ,831
24,065
24,058
23,297
23 ,025

Nanaimo-Ladysmith
Golden-Radium Hot
Springs
Prince GeorgeQuesnel
Hope-Cache Creek

29

21,303

Prince GeorgeVanderhoof
Cranbrook-Fairmont
Hot Springs
Dawson CreekPrince George
Golden-Alberta
Border

30

21,119

31

21,079

32

18,968

33

18,218

34

17,608

35

17,491

36

17,474

37

17,048

38

16,439

39

16,420

40

16,363

41

15,168

42

14,903

43

14,820

44

12,707

45

12,647

46

11 ,167

Kelowna-Rock
Creek
Radium Hot SpringsFairmont Hot Springs
Nelson-Kaslo

47

11 ,118

Castlegar-Trail

48
49

10,863
10,676

50
51

10,653
10,288

Houston-Smithers
Tete Jaune CacheAlta border
Sechel t-ferry
Bums Lake-Houston

52

9,717

Creston--Castlegar

21,901

Monte Creek-Vernon

21,303

Prince GeorgeVanderhoof
Cranbrook-Fairmont
Hot Springs
Alberta/BC
Boundary-Highway
93 Junction
Golden-Alberta
Border

21,148
20,888

Alberta/BC
Boundary-Highway
93 Junction
Cranbrook-Creston

20,151

Port Hardy-Campbell
River
Ka ml oops-Monte
Creek
Dawson Creek-Ft. St.
John
Rock CreekCastlegar
Cranbrook-Highway
93 Junction
Quesnel-Williams
Lake
Ucluelet JunctionParksville
Tete Jaune CachePrince George
Gibsons-Sechelt

18,264

19,577

17,929
17,855
16,945
15,846
14,946
14,944
14,560
13,841
12,094
11,899

Quesnel-Williams
Lake
Rock CreekCastlegar
Tete Jaune CachePrince George
Gibsons-Sechelt
Ucluelet JunctionParksville
Kelowna-Rock
Creek
Castlegar-Trail

11,557

Tete Jaune CacheAlta border
11,406 Radium Hot SpringsFairmont Hot Springs
10,671 N elson-Kaslo
10,288 Bums Lake-Houston
10,202
9,548
9,079

237

Dawson Creek-Ft. St.
John
Cranbrook-Highway
·93 Junction
Kamloops-Monte
Creek
Port Hardy-Campbell
River
Cranbrook-Creston

Sechel t-ferry
Prince RupertTerrace
Vanderhoof -Fraser
Lake

9,003

Creston-Castlegar

8,995

Houston-Smithers

8,572

Kitwanga-Terrace

8,384

Williams LakeAlexis Creek
Castlegar-Christina
Lake
Fraser Lake-Burns
Lake
Vernon-Nakusp

57

Castlegar-Christina
Lake
8,521 Williams LakeAlexis Creek
7,950 Prince RupertTerrace
7,870 Vanderhoof -Fraser
Lake
7,653 N akusp-Castlegar

58

7,546

Kitwanga-Terrace

7,978

59

7,337

7,748

60

6,996

Fraser Lake-Burns
Lake
Vernon-N akusp

61
62

6,968
6,742

63

6,459

64

5,502

65

5,201

66

4,523

67

Ucluelet JunctionTofino
4,267 WonowonBuckin!tliorse River
3,990 Kitwanga-Meziadin
Junction
3,681 Liard River-Lower
Post
3,251 Meziadin JunctionDease Lake
3,144 Kitwanga-N ew
Hazelton
3,005 Alexis CreekAnahimLake
2,383 1 km north of Prophet
River-Fort Nelson

53
54
55
56

68
69
70
71

72
73
74

8,833

Terrace-Kitimat
Dawson CreekAlberta Border
Smithers-New
Hazelton
Fort Nelson-Liard
River
Fort St. JohnWonowon
Hope-Agassiz

2,304

76

2,240

Dawson CreekAlberta Border
7,374 Terrace-Kitimat
7,176 Nakusp-Castlegar

7,682

7,130
6,459
6,037
4,723

4,321

75

8,182

Dease Lake-Yukon
Border
Buckinghorse River1 km north of Prophet
River

238

4,227
4,158
3,999
3,640
3,504
3,463
2,481
2,220
2,124
1,902

Fort St. JohnWonowon
Smithers- New
Hazelton
Fort Nelson-Liard
River
WonowonBuckin!tliorse River
Ucluelet JunctionTofino
Hope-Agassiz
Kitwanga-Meziadin
Junction
Liard River-Lower
Post
Meziadin JunctionDease Lake
Kitwanga-New
Hazelton
Dease Lake-Yukon
Border
Buckinghorse River1 km north of Prophet
River
1 km north of Prophet
River-Fort Nelson
Alexis CreekAnahimLake

77

1,886

78

988

79

939

Total

1,682

Saltery Bay ferry
tenninal-Powell
River
Ucluelet JunctionUcluelet
Meziadin JunctionStewart

942
799

Saltery Bay ferry
tenninal-Powell
River
Ucluelet JunctionUcluelet
Meziadin JunctionStewart

1,917,247

1,860,644

Table A2.5: Annual emissions of BC Ferries routes
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28

Annual emissions (tonnes

Route

CO2)

Tsawwassen-Duke Point (30)
Tsawwassen-Swartz Bay (1)
Horseshoe Bay-Departure Bay (2)
Horseshoe Bay-Langdale (3)
Inside passage Prince Rupert-Port Hardy (10)
Earls Cove-Saltery Bay (7)
Haida Gwaii ( 11)
Powell River-Comox (17)
Salt Spring/Fulford-Victoria (4)
Pender-Swartz Bay (5)
Snug Cove-Horseshoe Bay (8)
Mayne-Swartz Bay
Port McNeill-Alert Bay-Sointula (25)
Nanaimo Harbour-Gabriola (19)
Satuma Island-Swartz Bay (5)
Galiano--Tsawwassen (9)
Day trip from Swartz Bay (via Pender, Mayne, Galiano,
Pender)
Campbell River-Quadra Island (23)
Galiano-Swartz Bay (5)
Salt Spring/Long Harbour-Tsawwassen (9)
Langdale-Keats/Gambier
Powell River-Texada Island (18)
Port Hardy-Bella Cool a Discovery Coast (40)
Salt Spring/Vesuvius-Crofton (6)
Chemainus-Theis Island-Penelakut Is (20)
Quadra Island-Cortes Is (24)
Galiano Island-Mayne Island
Mayne Island-Pender Island

239

81,097
80,686
74,075
18,561
10,966
10,396
6,893
6,229
5,664
5,533
4,042
3,904
3,208
2,900
2,872
2,544
2,261
2,131
1,655
1,600
1,391
1,350
1,271
1,171
1,159
1,126
1,088
886

29
30
31
32
33
34
35
36
37
38
39

Mayne-Tsawwassen (9)
Pender Island-Salt Spring Island Long Harbour
Buckley Bay-Denman Island (21)
Galiano Island-Pender Island
Mayne-Saturna Island Lyall Hrbr
Pender-Satuma
Brentwood Bay-Mill Bay (12)
Haida Gwaii Skidegate-Alliford Bay (26)
Denman Island-Homby Island (22)
Pender-Tsawwassen (9)
Mayne-Salt Spring IS Long Harbour

837
790
743
602
537
365
360
271
226
148
28

Total

341,563

Table A2.6: Passenger-sailing EFs on BC Ferries routes
Rank

Route and number

Vessel

1

Northern Expedition
Northern Adventure
Queen of Chilliwack

193
183

9
10

Horseshoe Bay-Departure Bay (2)

Coastal Inspiration
Queen of Alberni
Queen of
Cumberland
MV Island Sky
Queen of
Cumberland
Coastal Renaissance

62
55
51

7
8

Inside passage Prince Rupert-Port Hardy
(10)
Haida Gwaii ( 11)
Port Hardy-Bella Coola Discovery Coast
(40)
Tsawwassen-Duke Point (30)
Tsawwassen-Duke Point (30)
Day trip from Swartz Bay (via Pender,
Mayne, Galiano, Pender)
Earls Cove-Saltery Bay (7)
Satuma Island -Swartz Bay (5)

Passenger-sailing EF
(kg CO2)
288

Galiano-Swartz Bay ( 5)

Queen of

2
3
4
5
6

13

Langdale-New Brighton-KeatsEastbourne-Langdale (13)
Langdale-New Brighton-EastbourneKeats-Langdale (13)
Tsawwassen-Swartz Bay (1)

14

Mayne-Swartz Bay

15
16

Horseshoe Bay-Departure Bay (2)
Langdale-Eastbourne-Keats-Langdale
(13)
Powell River-Comox (17)

11

12

17

26
25

Cumberland
Tenaka

24

Tenaka

22

Queen of New
Westminster
Queen of
Cumberland
Queen of Oak Bay
Tenaka

21

Queen of Burnaby

240

31
30

20
19
18
18

22

Tsawwassen-Swartz Bay (1)
Langdale-New Brighton-EastbourneLangdale (13)
Port McNeill-Alert Bay-Sointula (25)
Langdale-Keats-New Brighton-Langdale
(13)
Pender-Swartz Bay (5)

23

Tsawwassen-Swartz Bay ( 1)

24
25
26
27
28
29
30
31

Pender-Tsawwassen (9)
Salt Spring/Long Harbour-Tsawwassen (9)
Quadra Island-Cortes Island (24)
Langdale-Eastbourne-Langdale ( 13)
Mayne-Tsawwassen (9)
Pender-Satuma
Salt Spring/Fulford-Victoria (4)
Powell River-Texada Island (18)

32
33

Galiano Island-Pender Island
Pender Island-Salt Spring Island Long
Harbour
Galiano-Tsawwassen (9)
Horseshoe Bay-Langdale (3)
Mayne-Satuma Island Lyall Hrbr
Langdale-New Brighton-Langdale (13)
Mayne-Salt Spring Island Long Harbour
Chemainus-Theis Island-Penelakut Island
(20)
Galiano Island-Mayne Island
Nanaimo Harbour-Gabriola (19)
Mayne Island-Pender Island
Snug Cove-Horseshoe Bay (8)
Haida Gwaii Skidegate-Alliford Bay (26)
Campbell River-Quadra Island (23)
Salt Spring/Vesuvius-Crofton (6)
Brentwood Bay-Mill Bay (12)
Buckley Bay-Denman Island (21)
Denman Island-Homby Island (22)

18
19
20
21

34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

Coastal Celebration
Tenaka

17
16

Quadra Queen II
Tenaka

15
14

Queen of
Cumberland
Spirit of British
Columbia
Queen ofNanaimo
Queen of Nanaimo
Tenaka
Tenaka
Queen ofNanaimo
Bowen Queen
Skeena Queen
North Island
Princess
Bowen Queen
Bowen Queen

14
13
12
12
12
11
10
9
9
8
8
8

Queen ofNanaimo
Queen of Coquitlam
Bowen Queen
Tenaka
Bowen Queen
MVKuper

7
7
7
6
6
5

Bowen Queen
Quinsam
Bowen Queen
Queen of Capilano
Kwuna
Powell River Queen
Howe Sound Queen
Klitsa
Quinitsa
Kahloke

4
4
4
4
3
3
2
2
2
1

Table A2.7: Passenger-kilometre EFs on BC Ferries routes
Rank
1

Route and number

Vessel

Earls Cove-Salterv Bay (7)

MV Island Skv

241

Passenger-kilometre
EF (2 C0 2/pkm)
1,781

2

Haida Gwaii (11)

3
4

11

Quadra Island-Cortes Island (24)
Langdale-New Brighton-EastbourneKeats-Langdale (13)
Langdale-Keats-New BrightonLangdale (13)
Langdale-New Brighton-Langdale
(13)
Langdale-Eastbourne-Langdale (13)
Langdale-New Brighton-EastbourneLangdale (13)
Langdale-Eastbourne-Keats-Langdale
(13)
Langdale-New Brighton-KeatsEastbourne-Langdale (13)
Galiano-Swartz Bay (5)

12

Mayne-Swartz Bay

13

Pender-Swartz Bay (5)

14

Saturna Island-Swartz Bay (5)

15
16
17
18

Day trip from Swartz Bay (via Pender,
Mayne, Galiano, Pender)
Salt Spring/Fulford-Victoria (4)
Tsawwassen-Duke Point (30)
Powell River-Texada Island (18)

19

Campbell River-Quadra Island (23)

20
21

Tsawwassen-Duke Point (30)
Port Hardy-Bella Coola Discovery
Coast (40)
Buckley Bay-Denman Island (21)
Snug Cove-Horseshoe Bay (8)
Inside passage Prince Rupert-Port
Hardy (10)
Powell River-Comox (17)
Galiano Island-Mayne Island
Galiano Island-Pender Island
Mayne Islan-Pender Island
Mayne-Salt Spring Island Long
Harbour
Mayne-Saturna Island Lyall Hrbr
Pender Island-Salt Spring Island Long
Harbour
Pender-Saturna

5
6
7
8
9
10

22
23
24

25
26
27
28
29
30
31
32

242

Northern
Adventure
Tenaka
Tenaka

1,118

Tenaka

1,007

Tenaka

1,007

Tenaka
Tenaka

1,007
1,007

Tenaka

1,007

Tenaka

1,007

1,012
1,007

Queen of
Cumberland
Queen of
Cumberland
Queen of
Cumberland
Queen of
Cumberland
Queen of
Cumberland
Skeena Queen
Coastal Inspiration
North Island
Princess
Powell River
Queen
Queen of Alberni
Queen of
Chilliwack
Quinitsa
Queen of Capilano
Northern
Expedition
Queen of Burnaby
Bowen Queen
Bowen Queen
Bowen Queen
Bowen Queen

982

561
551
551
551
551

Bowen Queen
Bowen Queen

551
551

Bowen Queen

551

982
982
982
982
923
883
838
814
778
734
721
642
567

33
34
35

Nanaimo Harbour-Gabriola (19)
Denman Island-Homby Island (22)
Tsawwassen-Swartz Bay (1)

36
37

Haida Gwaii Skidegate-Alliford Bay
(26)
Salt Spring/Vesuvius-Crofton (6)

38

Horseshoe Bay-Departure Bay (2)

39
40

Brentwood Bay-Mill Bay (12)
Horseshoe Bay-Langdale (3)

41
42
43
44
45
46

Port McNeill-Alert Bay-Sointula (25)
Tsawwassen-Swartz Bay (1)
Galiano-Tsawwassen (9)
Mayne-Tsawwassen (9)
Pender-Tsawwassen (9)
Salt Spring/Long HarbourTsawwassen (9)
Horseshoe Bay-Departure Bay (2)
Tsawwassen-Swartz Bay (1)

47
48
49

Chemainus-Theis Island-Penelakut
Island (20)

Quinsam
Kahloke
Queen of New
W estrninster
Kwuna

548
487
482

Howe Sound
Queen
Coastal
Renaissance
Klitsa
Queen of
Coquitlam
Quadra Queen II
Coastal Celebration
Queen ofNanaimo
Queen ofNanaimo
Queen ofNanaimo
Queen ofNanaimo

472

479

466
420
413
407
387
369
369
369
369

Queen of Oak Bay
Spirit of British
Columbia
MVKuper

334
288
261

Average

696

Table A2.8: CO 2 emission rank by airline route
Ran
k

Airline

Route

1

AC Express

2

AC Express

3

AC Express

4

Hawkair

5

Westjet

6

AC Express

7

AC Express

Vancouver-Fort
St. John
VancouverPrince George
VancouverTerrace
VancouverTerrace
VancouverPrince George
VancouverKamloops
VancouverPrince Rupert

Aircraft

DH4

Annual
kilometres with
diversion factor
(km)
2,086,157

Annual
emissions
(tonnes
CO2)
11,290

%of
total
emis
sions
6.82

DH4

1,877,476

9,904

5.99

DH3

2,037,344

9,463

5.72

DH3

1,848,701

8,101

4.90

73W

796,505

7,177

4.34

DH3

1,301,009

5,763

3.48

DH3

1,067,539

4,991

3.02

243

9

Westjet
Encore
AC Express

10

AC Express

11
12

Pacific
Coastal
Airlines
AC Express

13

AC Express

14

Westjet
Encore
Central
Mountain Air
AC Express

8

15
16
17
18
19
20
21
22
23

Westjet
Encore
Westjet
Pacific
Coastal
Airlines
Helijet
Pacific
Coastal
Airlines
Hawkair

24

Central
Mountain Air
AC Express

25

AC Express

26

AC Express

27

Central
Mountain Air
Central
Mountain Air
Central
Mountain Air
Central
Mountain Air
Hawkair

28
29
30
31

DH4

905,486

4,909

2.97

DH3

1,030,630

4,579

2.77

DH3

891,072

4,134

2.50

BEl

728,910

4,030

2.44

DH3

829,920

3,734

2.26

DH3

851,136

3,688

2.23

DH4

682,718

3,644

2.20

DHl

990,662

3,578

2.16

DH3

758,066

3,462

2.09

DH4

608,462

3,331

2.01

73W

374,774

3,317

2.00

BEl

593,393

3,049

1.84

VancouverVictoria
VancouverMasset

Sikorsky
S76
Saab 340

986,586

2,804

1.69

536,609

2,598

1.57

VancouverPrince Rupert
VancouverWilliams Lake
VancouverPenticton
VancouverSandspit
VancouverKelowna
VancouverComox
VancouverCampbell River
Prince GeorgeKelowna
Fort Nelson-Fort
St. John
Vancouver-

DH3

574,829

2,536

1.53

BEH

465,465

2,440

1.47

DH3

537,373

2,380

1.44

DH3

490,090

2,290

1.38

DH4

437,237

2,255

1.36

BEH

451,840

2,230

1.35

BEH

423,051

2,111

1.28

BEH

376,085

2,058

1.24

D38

237,728

1,950

1.18

DH3

408,408

1,787

1.08

VancouverTerrace
VancouverKelowna
VancouverSmithers
VancouverCranbrook
VancouverCastlegar
VancouverVictoria
VancouverPrince George
VancouverDawson Creek
VancouverCranbrook
Vancouver-Fort
St. John
VancouverKelowna
VancouverWilliams Lake

244

32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51

Pacific
Coastal
Airlines
Pacific
Coastal
Airlines
Central
Mountain Air
Pacific
Coastal
Airlines
Central
Mountain Air
Pacific
Coastal
Airlines
Pacific
Coastal
Airlines
Pacific
Coastal
Airlines
Central
Mountain Air
Westjet
Encore
Pacific
Coastal
Airlines
AC Express
Pacific
Coastal
Airlines
Central
Mountain Air
Westjet
Encore
Central
Mountain Air
Pacific
Coastal
Airlines
Central
Mountain Air
Westjet
Encore
Central
Mountain Air

Smithers
Vancouver-Port
Hardy

BEl

337,100

1,762

1.07

VancouverPowell River

BEl

345 ,909

1,688

1.02

VancouverQuesnel
Vancouver-Port
Hardy

BEH

305,214

1,642

0.99

Saab 340

355,828

1,596

0.96

Prince GeorgeFort St. John
Vancouver-Trail

BEH

285 ,012

1,472

0.89

BEl

266,666

1,421

0.86

Vancouver-Trail

Saab 340

311 ,111

1,410

0.85

Vancouver-Bella
Cool a

BEl

258 ,258

1,386

0.84

Prince GeorgeKarnloops
VictoriaKelowna
VancouverVictoria

BEH

252,907

1,343

0.81

DH4

250,723

1,314

0.79

BEl

262,434

1,257

0.76

VancouverNanaimo
VancouverCampbell River

DH3

290,347

1,257

0.76

BEl

249,985

1,241

0.75

DH3

256,183

1,148

0.69

DH4

218 ,618

1,141

0.69

BEH

202,457

1,051

0.64

BEl

199,116

977

0.59

DHI

264,755

920

0.56

DH4

169,697

884

0.53

BEH

169,806

882

0.53

Prince GeorgeTerrace
VancouverKelowna
Prince GeorgeSmithers
VancouverComox
Fort NelsonDawson Creek
VancouverKarnloops
Fort Nelson-Fort
St. John

245

52

Harbour Air

53

Northern
Thunderbird
Air
Westjet
Encore
Pacific
Coastal
Airlines
Central
Mountain Air
Harbour Air

54
55
56
57
58
59
60
61
62
63
64
65
66
67
68

Pacific
Coastal
Airlines
Northern
Thunderbird
Air
Central
Mountain Air
Pacific
Coastal
Airlines
Harbour Air
Pacific
Coastal
Airlines
Northern
Thunderbird
Air
Pacific
Coastal
Airlines
Harbour Air
Central
Mountain Air
Harbour Air

70

Central
Mountain Air
Harbour Air

71

AC Express

72

Northern
Thunderbird

69

VancouverVictoria
Prince GeorgeDease Lake

DHC-3
Otter
Beech
1900

702,187

864

0.52

147,857

851

0.51

VancouverVictoria
KelownaCran brook

DH4

148,949

761

0.46

BEl

142,506

727

0.44

QuesnelWilliams Lake
VancouverNanaimo
Port HardyBella Bella

BEH

140,140

684

0.41

DHC-3
Otter
Saab 340

516,402

636

0.38

133,825

583

0.35

Dease LakeSmithers

Beech
1900

95,004

505

0.31

Prince GeorgeFort Nelson
VancouverComox

BEH

89,599

498

0.30

Saab 340

91 ,900

398

0.24

VancouverComox
VancouverAnahimLake

DHC-3
Otter
BEl

291 ,015

363

0.22

64,373

342

0.21

Smithers-Bob
Quinn

Beech
1900

66,394

339

0.21

Campbell RiverComox

BEl

71 ,386

339

0.21

VancouverNanaimo
Fort Nelson-Fort
St. John
VancouverVictoria
Campbell RiverComox
NanaimoSechelt
VancouverKelowna
Bob QuinnDease Lake

DHC-3
Otter
DH3

260,718

320

0.19

67,922

302

0.18

DHC-3
Otter
BEH

241 ,155

297

0.18

51 ,308

245

0.15

DHC-3
Otter
CRJ

181,210

223

0.13

31,231

201

0.12

Beech
1900

36,223

177

0.11

246

73

Air
Harbour Air

74

Harbour Air

75
76

Pacific
Coastal
Airlines
KDAir

77

Seair

78

AC Express

79

Harbour Air

80

Seair

81

Orea Airways

82
83

Pacific
Coastal
Airlines
Orea Airways

84

Hawkair

85

Salt Spring
Air
Seair

86
87

88

Salt Spring

Air

Seair

89

Orea Airways

90

Seair

91

Seair

92

Tofino Air

93

Tofino Air

VancouverMaple Bay
VancouverSechelt
Campbell RiverComox

DHC-3
Otter
DHC-3
Otter
Saab 340

139,110

171

0.10

103,074

127

0.08

26,770

114

0.07

VancouverQualicum Beach

Piper
PA31,
Cessna
Cessna,
Beaver
DH4

247,104

112

0.07

230,287

109

0.07

21,278

107

0.06

DHC-3
Otter
Cessna,
Beaver
Piper
Navajo
Chieftain
BEl

83,283

102

0.06

169,770

80

0.05

154,440

74

0.04

15,101

73

0.04

Piper
Navajo
Chieftain
DH3

148,694

72

0.04

16,8 17

69

0.04

Float
plane
Cessna,
Beaver
Float

120,120

53

0.03

110,510

52

0.03

118,404

52

0.03

Cessna,
Beaver
Piper
Navajo
Chieftain
Cessna,
Beaver
Cessna,
Beaver
Otter,
Beaver,
Cessna
Otter,
Beaver,

108,108

51

0.03

101,816

49

0.03

103,303

49

0.03

100,901

48

0.03

88,889

45

0.03

84,084

43

0.03

VancouverNanaimo
VancouverVictoria
VancouverSechelt
VancouverNanaimo
VancouverQualicum Beach
Anahim LakeBella Coola
VancouverTofino
SmithersTerrace
Vancouver-Salt
Spring Is
VancouverSatuma Is
Vancouver-Salt
Spring Is

Vancouver-Salt
Spring Is
AbbotsfordVictoria
VancouverPender Is
VancouverThetis Is
NanaimoSechelt
VancouverGabriola Is

plane

247

Pacific
Coastal
Airlines
Vancouver
Island Air

Bella BellaKlem tu

96

Se air

97

KDAir

98

Seair

99

AirNootka

VancouverGaliano Is
Qualicum
Beach-Gillies
Bay
VancouverMayne Is
Gold RiverKyuquot

94

95

Campbell RiverSeymour Inlet

Cessna
Beaver

Otter,
Beaver,
Beech 18
Cessna,
Beaver
Piper
PA31,
Cessna
Cessna,
Beaver
Float
plane

Tot
al

92,893

41

0.02

67,080

35

0.02

64,064

30

0.02

64,064

29

0.02

59,259

28

0.02

40,248

18

0.01

37,688,164

166,867

100

Table A2.9: City-pair CO 2 emissions
Rank

City pair

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

Vancouver-Terrace
Vancouver-Prince George
Vancouver-Fort St. John
Vancouver-Kelowna
Vancouver-Victoria
Vancouver-Prince Rupert
Vancouver-Cranbrook
Vancouver-Kamloops
Vancouver-Smithers
Vancouver-Williams Lake
Vancouver-Comox
Vancouver-Castlegar
Vancouver-Dawson Creek
Vancouver-Port Hardy
Vancouver-Campbell River
Fort Nelson-Fort St. John
Vancouver-Trail
Vancouver-Mas set
Vancouver-Nanaimo
Vancouver-Penticton
Vancouver-Sandspit
Prince George-Kelowna
Vancouver-Powell River

Annual
flights
6,604
6,136
3,224
6,968
41,808
2,080
2,652
5,408
1,820
3,276
7,020
1,976
1,248
1,924
3,640
1,456
1,352
624
20,644
1,976
624
728
2,704

248

Annual
emissions
(tonnes CO2)
22,474
20,724
14,621
11,493
9,778
7,528
7,492
6,646
5,922
5,489
3,968
3,734
3,578
3,358
3,353
3,134
2,831
2,598
2,395
2,380
2,290
2,058
1,688

% of total
emissions

13.47
12.42
8.76
6.89
5.86
4.51
4.49
3.98
3.55
3.29
2.38
2.24
2.14
2.01
2.01
1.88
1.70
1.56
1.44
1.43
1.37
1.23
1.01

24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60

Vancouver-Quesnel
Prince George-Fort St. John
Vancouver-Bella Coola
Prince George-Kamloops
Victoria-Kelowna
Prince George-Terrace
Prince George-Smithers
Fort Nelson-Dawson Creek
Prince George-Dease Lake
Kelowna-Cranbrook
Campbell River-Comox
Quesnel-Williams Lake
Port Hardy-Bella Bella
Dease Lake-Smithers
Prince George-Fort Nelson
Vancouver-Anafum Lake
Smithers-Bob Quinn
Nanaimo-Sechelt
Vancouver-Sechelt
Vancouver-Qualicum Beach
Bob Quinn-Dease Lake
Vancouver-Maple Bay
Vancouver-Salt Spring Is
Vancouver-Totino
Anahim Lake-Bella Coola
Smithers-Terrace
Abbotsford-Victoria
Vancouver-Satuma Is
Vancouver-Pender Is
Vancouver-Thetis Is
Vancouver--Gabriola Is
Bella Bella-Klemtu
Campbell River-Seymour
Inlet
Qualicum Beach-Gillies Bay
Vancouver-Galiano Is
Vancouver-Mayne Is
Gold River-Kyuquot

Total

676
936
572
624
728
624
624
676
208
520
5,3 56
1,300
728
208
156
156
208
5,616
2,080
4,680
208
1,976
5,408
1,560
728
156
2,184
2,392
2,184
2,184
2,184
1,456
312

1,642
1,472
1,386
1,343
1,314
1,148
1,051
920
851
727
697
684
583
505
498
342
339
268
216
207
177
171
152
74
72
69
53
52
49
48
43
41
35

0.98
0.88
0.83
0.80
0.79
0.69
0.63
0.55
0.51
0.44
0.42
0.41
0.35
0.30
0.30
0.20
0.20
0.16
0.13
0.12
0.11
0.10
0.09
0.04
0.04
0.04
0.03
0.03
0.03
0.03
0.03
0.02
0.02

1,456
1,456
1,456
312

30
29
28
18

0.02
0.02
0.02
0.01

180,180

166,867

100

Table A2.10: Passenger-flight EFs of BC aviation
Rank

Airline

Route

1

Northern

Prince George-

Aircraft

Beech 1900

249

Stage length
including
diversion factor
711

Passengerflight EF (kg

CO2)

269.1

2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Thunderbird
Air
Central
Mountain Air
Pacific Coastal
Airlines
Central
Mountain Air
Pacific Coastal
Airlines
Northern
Thunderbird
Air
Central
Mountain Air
Pacific Coastal
Airlines
Pacific Coastal
Airlines
Central
Mountain Air
Pacific Coastal
Airlines
Central
Mountain Air
Pacific Coastal
Airlines
Central
Mountain Air
Hawkair

20

Central
Mountain Air
Northern
Thunderbird
Air
Central
Mountain Air
Central
Mountain Air
Hawkair

21

Hawkair

22

Pacific Coastal
Airlines
Central
Mountain Air
Pacific Coastal
Airlines

17
18
19

23
24

Dease Lake
Prince GeorgeFort Nelson
VancouverCranbrook
Prince GeorgeKelowna
VancouverMasset
Dease LakeSmithers
VancouverQuesnel
Vancouver-Bella
Cool a
Vancouver-Trail
Prince GeorgeKamloops
VancouverAnahimLake
VancouverWilliams Lake
Vancouver-Port
Hardy
Fort Nelson-Fort
St. John
VancouverPrince Rupert
Prince GeorgeSmithers
Smithers-Bob
Quinn
Fort Nelson-Fort
St. John
Prince GeorgeFort St. John
VancouverTerrace
VancouverSmithers
VancouverWilliams Lake
VancouverDawson Creek
KelownaCran brook

BEH

574

221.6

BEl

561

203.9

BEH

517

196.3

Saab 340

860

173.5

Beech 1900

457

168.7

BEH

452

168.6

BEl

452

159.4

BEl

427

149.8

BEH

405

149.5

BEl

413

144.1

BEH

358

130.4

BEl

360

123.9

BEH

327

117.8

DH3

790

117.7

BEH

324

117.0

Beech 1900

319

113.3

D38

327

111.6

BEH

305

109.2

DH3

726

107.4

DH3

714

105.6

BEl

300

101.5

DHl

794

96.9

BEl

274

91.9

250

25

AC Express

26

AC Express

27

AC Express

28

AC Express

29
30

Pacific Coastal
Airlines
AC Express

31

Westjet Encore

32

34

Pacific Coastal
Airlines
Central
Mountain Air
W estj et Encore

35

AC Express

36

Pacific Coastal
Airlines
Northern
Thunderbird
Air
Central
Mountain Air
AC Express

33

37
38
39
40
41

Pacific Coastal
Airlines
AC Express

42

W estj et Encore

43

AC Express

44

Central
Mountain Air
Central
Mountain Air
Westjet

45
46
47
48
49

Pacific Coastal
Airlines
Central
Mountain Air
Central

VancouverPrince Rupert
VancouverSandspit
VancouverTerrace
VancouverSmithers
Vancouver-Trail
Vancouver-Fort
St. John
Vancouver-Fort
St. John
Vancouver-Port
Hardy
VancouverCampbell River
VancouverTerrace
VancouverCranbrook
VancouverCampbell River
Bob QuinnDease Lake
VancouverComox
VancouverPrince George
VancouverComox
VancouverKelowna
VancouverPrince George
VancouverCastlegar
Prince GeorgeTerrace
Fort NelsonDawson Creek
VancouverPrince George
VancouverPowell River
QuesnelWilliams Lake
Fort Nelson-Fort

DH3

790

90.8

DH3

785

90.3

DH3

726

82.9

DH3

714

81.5

Saab 340

427

80.7

DH4

836

77.3

DH4

836

74.1

Saab 340

360

67.3

BEH

185

64.1

DH4

726

63.7

DH3

561

63.0

BEl

185

60.4

Beech 1900

174

59.2

BEH

147

50.5

DH4

547

49.3

BEl

147

47.6

CRJ

300

47.4

DH4

547

47.3

DH3

420

46.5

DH3

411

46.0

DHl

392

46.0

73W

547

41.5

BEl

128

41.1

BEH

108

36.5

DH3

327

36.2

251

50
51
52

Mountain Air
Pacific Coastal
Airlines
AC Express

53

Pacific Coastal
Airlines
AC Express

54

AC Express

55

W estj et Encore

56

Helijet

57
58

Pacific Coastal
Airlines
AC Express

59

Westjet Encore

60

Westjet Encore

61

Westjet

62

Pacific Coastal
Airlines
AirNootka

63
64

Vancouver
Island Air

65

Orea Airways

66

Harbour Air

67
68

Hawkair
Central
Mountain Air
Pacific Coastal
Airlines
KDAir

69
70
71

73

Pacific Coastal
Airlines
Pacific Coastal
Airlines
Salt Spring Air

74

Orea Airways

72

St. John
Port Hardy-Bella
Bella
VancouverKelowna
Anahim LakeBella Coola
VancouverKamlooos
VancouverPenticton
VictoriaKelowna
VancouverVictoria
VancouverComox
VancouverKelowna
VancouverKelowna
VancouverKamlooos
VancouverKelowna
VancouverVictoria
Gold RiverKyuquot
Campbell RiverSeymour Inlet
VancouverTofino
VancouverComox
Smithers-Terrace
Campbell RiverComox
Campbell RiverComox
VancouverQualicum Beach
Bella BellaKlemtu
Campbell RiverComox
Vancouver-Salt
Spring Is
Vancouver-

Saab 340

184

33.4

DH3

300

32.8

BEl

97

30.8

DH3

272

29.6

DH3

272

29.6

DH4

344

29.2

Sikorsky S76

108

27.4

Saab 340

147

26.6

DH4

300

26.5

DH4

300

25.4

DH4

272

22.9

73W

300

22.4

BEl

68

21.5

Float plane

129

17.8

Otter,
Beaver,
Beech 18
Piper Navajo
Chieftain
DHC-3 Otter

215

17.4

204

15.5

147

15.3

DH3
BEH

108
43

14.9
14.3

BEl

43

13.4

Piper PA31,
Cessna
Beaver

99

9.4

64

8.7

Saab 340

43

7.6

Float plane

55

7.5

Piper Navajo

99

7.4

252

75

Orea Airways

76

AC Express

77

Harbour Air

78

Harbour Air

79

Harbour Air

80

Harbour Air

81

Salt Spring Air

82

Seair

83

AC Express

84

Harbour Air

85

Harbour Air

86

AC Express

87

Harbour Air

88

Seair

89

Westjet Encore

90
91

Harbour Air
Seair

92

Seair

93

Seair

94

Seair

95

Seair

96

KDAir

97

Seair

98

Tofino Air

99

Tofino Air

Qualicum Beach
Chieftain
AbbotsfordPiper Navajo
Chieftain
Victoria
VancouverDH3
Victoria
DHC-3 Otter
VancouverMaple Bay
DHC-3 Otter
VancouverVictoria
VancouverDHC-3 Otter
Victoria
DHC-3 Otter
VancouverNanaimo
Vancouver-Salt
Float plane
Spring Is
VancouverCessna,
Beaver
Nanaimo
VancouverDH3
Nanaimo
DHC-3 Otter
VancouverNanaimo
DHC-3 Otter
VancouverSechelt
VancouverDH4
Victoria
DHC-3 Otter
VancouverSechelt
VancouverCessna,
Beaver
Nanaimo
VancouverDH4
Victoria
Nanaimo-Sechelt DHC-3 Otter
VancouverCessna,
Beaver
Satuma Is
Vancouver-Salt
Cessna,
Spring Is
Beaver
VancouverCessna,
Beaver
Pender Is
VancouverCessna,
Beaver
Thetis Is
VancouverCessna,
Galiano Is
Beaver
Qualicum Beach- Piper PA31,
Gillies Bay
Cessna
VancouverCessna,
Mayne Is
Beaver
Nanaimo-Sechelt Otter,
Beaver,
Cessna
VancouverOtter,

253

98

7.4

68

7.3

70

7.2

68

7.0

68

7.0

67

6.9

50

6.8

67

6.6

59

6.3

58

6.0

58

6.0

68

5.9

57

5.9

58

5.8

68

5.6

53
51

5.4
5.0

50

4.9

47

4.7

46

4.6

44

4.3

44

4.1

41

4.0

41

3.2

39

3.1

I Gabriola Is

I Beaver,
Cessna

Table A2.11: Passenger-kilometre EFs of BC aviation
Rank

Airline

Route

1

Central Mountain
Air
Central Mountain
Air
Northern
Thunderbird Air
Central Mountain
Air
Northern
Thunderbird Air
Central Mountain
Air
Central Mountain
Air
Pacific Coastal
Airlines
Central Mountain
Air
Central Mountain
Air
Central Mountain
Air
Northern
Thunderbird Air
Pacific Coastal
Airlines
Pacific Coastal
Airlines
Pacific Coastal
Airlines
Central Mountain
Air
Pacific Coastal
Airlines
Central Mountain
Air
Central Mountain
Air
Northern
Thunderbird Air
Central Mountain

Prince George-Fort
Nelson
Prince GeorgeKelowna
Prince George-Dease
Lake
Vancouver-Quesnel

2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

Aircraft
BEH

Passenger-kilometre
EF(2 COzfpkm
385.9

BEH

380.1

Beech 1900

378.5

BEH

373.5

Dease Lake-Smithers

Beech 1900

369.4

Prince GeorgeKamloops
Vancouver-Williams
Lake
VancouverCranbrook
Fort Nelson-Fort St.
John
Prince GeorgeSmithers
Prince George-Fort
St. John
Smithers-Bob Quinn

BEH

368.8

BEH

364.1

BEl

363.7

BEH

360.9

BEH

360.7

BEH

358.7

Beech 1900

355.1

Vancouver-Bella
Coo la
Vancouver-Trail

BEl

353.0

BEl

350.6

Vancouver-Anahim
Lake
Vancouver-Campbell
River
Vancouver-Port
Hardy
Vancouver-Comox

BEl

349.1

BEH

346.6

BEl

344.0

BEH

342.8

D38

341.7

Beech 1900

340.0

BEH

338.8

Fort Nelson-Fort St.
John
Bob Quinn-Dease
Lake
Quesnel-Williams

254

22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39

Air
Pacific Coastal
Airlines
Pacific Coastal
Airlines
Central Mountain
Air
Pacific Coastal
Airlines
Pacific Coastal
Airlines
Pacific Coastal
Airlines
Pacific Coastal
Airlines
Pacific Coastal
Airlines
Pacific Coastal
Airlines
Helijet
Pacific Coastal
Airlines
Pacific Coastal
Airlines
Pacific Coastal
Airlines
Pacific Coastal
Airlines
Pacific Coastal
Airlines
Pacific Coastal
Airlines
AC Express
Hawkair

45

Hawkair
Hawkair
AirNootka
Hawkair
Pacific Coastal
Airlines
Salt Spring Air

46

Salt Spring Air

47

Central Mountain
Air
Central Mountain
Air
AC Express

40
41
42
43
44

48
49

Lake
Vancouver-Williams
Lake
Kelowna-Cranbrook

BEl

338.1

BEl

335.5

Campbell RiverComox
Vancouver-Campbell
River
Vancouver-Comox

BEH

332.2

BEl

326.7

BEl

323.0

Vancouver-Powell
River
Anahim Lake-Bella
Cool a
Vancouver-Victoria

BEl

321.1

BEl

318.0

BEl

315.2

Campbell RiverComox
Vancouver-Victoria
Vancouver-Masset

BEl

312.7

Sikorsky S76
Saab 340

253.7
201 .7

Vancouver-Trail

Saab 340

188.9

Vancouver-Port
Hardy
Port Hardy-Bella
Bella
Vancouver-Comox

Saab 340

186.9

Saab 340

181.7

Saab 340

180.6

Saab 340

177.5

CRJ
DH3

158.0
149.1

DH3
DH3
Float plane
DH3
Beaver

148.0
147.9
138.2
138.1
136.9

Float plane

136.8

Float plane

136.7

DHl

122.0

DHl

117.4

DH3

115.0

Campbell RiverComox
Vancouver-Kelowna
Vancouver-Prince
Rupert
Vancouver-Terrace
Vancouver-Smithers
Gold River-Kyuquot
Smithers-Terrace
Bella Bella-Klemtu
Vancouver-Salt
Spring Is
Vancouver-Salt
Spring Is
Vancouver-Dawson
Creek
Fort Nelson-Dawson
Creek
Vancouver-Prince

255

50
51
52
53

AC Express
AC Express
AC Express
AC Express

54

56
57
58

Central Mountain
Air
Central Mountain
Air
AC Express
AC Express
AC Express

59
60
61
62
63

AC Express
AC Express
AC Express
Harbour Air
Harbour Air

64
65
66
67
68
69
70
71
72
73

Harbour Air
Harbour Air
Harbour Air
Harbour Air
Harbour Air
Harbour Air
Harbour Air
Seair
Seair
Seair

74

Seair

75
76

77

Seair
Seair
Seair

78
79

Seair
KDAir

80

KDAir

81

AC Express

82

AC Express

83

Westjet Encore

84

AC Express

55

Rupert
Vancouver-Sandspit
Vancouver-Terrace
Vancouver-Smithers
VancouverCranbrook
Prince GeorgeTerrace
Fort Nelson-Fort St.
John
Vancouver--Castlegar
Vancouver-Kelowna
VancouverKamloops
Vancouver-Penticton
Vancouver-Victoria
Vancouver-N anaimo
Vancouver--Comox
Vancouver-Maple
Bay
Vancouver-Victoria
Vancouver-Victoria
Vancouver-Nanaimo
Vancouver-N anaimo
Vancouver-Sechelt
Vancouver-Sechelt
N anaimo-Sechelt
Vancouver-N anaimo
Vancouver-Nanaimo
Vancouver-Satuma
Is
Vancouver-Salt
Spring Is
Vancouver-Pender Is
Vancouver-Thetis Is
Vancouver-Galiano
Is
Vancouver-Mayne Is
Vancouver--Qualicum
Beach
Qualicum BeachGillies Bay
Vancouver-Fort St.
John
Vancouver-Prince
George
Vancouver-Fort St.
John
Vancouver-Kelowna

256

DH3
DH3
DH3
DH3

115.0
114.3
114.1
112.3

DH3

112.0

DH3

111.0

DH3
DH3
DH3

110.7
109.3
109.0

DH3
DH3
DH3
DHC-3 Otter
DHC-3 Otter

109.0
106.6
106.5
103.8
102.6

DHC-3 Otter
DHC-3 Otter
DHC-3 Otter
DHC-3 Otter
DHC-3 Otter
DHC-3 Otter
DHC-3 Otter
Cessna, Beaver
Cessna, Beaver
Cessna, Beaver

102.6
102.6
102.6
102.4
102.4
102.4
102.3
98 .8
98.7
98.6

Cessna, Beaver

98 .6

Cessna, Beaver
Cessna, Beaver
Cessna, Beaver

98.6
98.5
98.5

Cessna, Beaver
Piper PA31,
Cessna
Piper PA31 ,
Cessna
DH4

98.5
94.7

92.5

DH4

90.1

DH4

88.6

DH4

88.1

94.2

Vancouver-Terrace
Vancouver-Prince
George
Vancouver-Victoria
Victoria-Kelowna
Vancouver-Kelowna
VancouverKamloops
Vancouver-Victoria
Campbell RiverSeymour Inlet
Nanaimo-Sechelt

85
86

Westjet Encore
Westjet Encore

87
88
89
90

AC Express
Westjet Encore
Westjet Encore
Westjet Encore

91
92
93

Westjet Encore
Vancouver Island
Air
Tofino Air

94

Tofino Air

95

Orea Airways

96

Westjet

97

Orea Airways

98

Orea Airways

Vancouver-Prince
George
Vancouver-Qualicum
Beach
Abbotsford-Victoria

99

Westiet

Vancouver-Kelowna

Vancouver-Gabriola
Is
Vancouver-Tofino

DH4
DH4

87.8
86.4

DH4
DH4
DH4
DH4

86.2
84.8
84.5
84.3

DH4
Otter, Beaver,
Beech 18
Otter, Beaver,
Cessna
Otter, Beaver,
Cessna
Piper Navajo
Chieftain
73W

82.7
81.1
79.5
79.4
75.8
75.8

Piper Navajo
Chieftain
Piper Navajo
Chieftain
73W

75.2
75 .2
74.5

Avera2e

184.5

Table A2.12: Emissions of bus routes within BC
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Route

Distance
(km)

Kami oops-Golden
Cache Creek-Prince George
Vancouver-Hope
Vancouver-Whistler
Prince George-Prince Rupert
Merritt-Kami oops
Hope-Merritt
Victoria-Nanaimo
Prince George-Dawson Creek
Fort St. John-Fort Nelson
Parksville-Port Hardy
Valemount-Kamloops
Golden-Alberta Border (for Banff)
Prince George-Valemount
Kelowna-Merritt
Hope-Osoyoos

360
443
155
125
718
87
124
111
404
380
352
322
74
292
128
251

257

Daily oneway trips
4
3
8
6
1
6
4
4
1
1
1
1
4
1
2
1

Annual CO2
emissions (tonnes
CO2)

1,534
1,416
1,321
799
765
556
529
474
431
405
375
343
315
311
273
268

Cranbrook---C,olden
Castlegar--Cranbrook
Osoyoos--Castlegar
Kelowna-Vemon
Valemount-Alberta Border (for
Jasper)
Hope-Cache Creek
Fort Nelson-Toad River
Parksville-Tofino
Kamloops--Cache Creek
N anaimo-Parksville
Cranbrook-Alberta Border (for Fort
Macleod)
Whistler-Pemberton
Osoyoos-Kelowna
Dawson Creek-Fort St. John·
Vanderhoof-Fort. St. James

17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

246
229
222
54
97

1
1
1
4
2

262
244
237
230
207

191
188
172
83
38
146

1
1
1
2
4
1

204
200
184
177
162
156

33
125
75
61

4
1
1
1

141
133
80
65

12,795

Total

Table A2.13: Ranking of BC routes by truck kilometres driven in 2007 and 2013
Rank

1

2007 distance
driven (km)
236,931 ,720

Route

2

141,178,613

VancouverChilliwack
Hope-Merritt

3

112,141,622

4

2013 distance
driven (km)
229,529,155

Route

147,093 ,529

VancouverChilliwack
Hope-Merritt

Vemon-Kelowna

119,382,682

Vemon-Kelowna

105,587,620

Ladysmith-Victoria

109,344,510

Ladysmith-Victoria

5

103,021,922

Parksville-N anaimo

107,563,456

Kelowna-Penticton

6

99,898,514

105,757,071

Revelstoke---C,olden

7

96,637,050

Cache CreekWilliams Lake
Kelowna-Penticton

104,756,533

8

94,907,957

9

102,181,677

94,421,996

Tete Jaune CacheKarol oops
Hope-Cache Creek

Tete Jaune CacheKamloops
Parksville-Nanaimo

98 ,130,571

Kami oops-Merritt

10

91,100,314

Revelstoke---C,olden

95 ,164,348

11

83,487,706

Kami oops-Merritt

84,329,454

Cache CreekWilliams Lake
Chilliwack-Hope

12

81,958,998

Chilliwack-Hope

80,246,801

13

74,030,760

Hope-Penticton

79,337,480

14

63,759,616

Salmon ArmRevel stoke

75,989,350

258

Dawson CreekPrince George
Salmon ArmRevels toke
Hope-Cache Creek

15

61,324,760

16

59,351,219

17

58,257,504

Monte Creek-Salmon
Arm
Kelowna-Merritt

27

Parksville-Camp bell
River
56,188,283 Dawson CreekPrince George
50,580,514 Fort Nelson-Liard
River
49,647,942 Quesnel-Williams
Lake
49,521,813 Prince GeorgeQuesnel
46,250,391 Golden-Alberta
Border
41,845,491 Golden-Radium Hot
Springs
39,006,287 Prince GeorgeVanderhoof
37,048,168 Fort St. JohnWonowon
36,733,133 Cranbrook-Fairmont
Hot Springs
33,899,229 Cranbrook-Creston

28

33,324,617

Monte Creek-Vernon

29

33,146,614

30

32,821,530

31

30,393,054

32

30,078,555

Tete Jaune CachePrince George
Dawson Creek-Ft. St.
John
Kamloops-Monte
Creek
Vernon-Salmon Arm

33

28,593,560

34

28,560,155

35

26,382,200

36

25,438,317

37

24,657,101

38

23,744,528

18
19
20
21
22
23
24
25
26

Rock CreekCastlegar
Cranbrook-Highway
93 Junction
Ucluelet JunctionParksville
Alberta/BC
Boundary-Highway
93 Junction
Kamloops-Cache
Creek
Tete Jaune CacheAlta border

259

69,360,512

Kelowna-Merritt

65,749,056

Parksville-Campbell
River
Monte Creek-Salmon
Arm
Hope-Penticton

63,046,815
57,450,708
55,501,079

Fort Nelson-Liard
River
50,790,298 Fort St. JohnWonowon
49,428,300 Prince GeorgeQuesnel
49,134,446 Golden-Alberta
Border
48,079,939 Quesnel-Williams
Lake
41,845,491 Golden-Radium Hot
Springs
39,006,287 Prince GeorgeVanderhoof
37,688,805 Dawson Creek-Ft. St.
John
36,784,379 Cranbrook-Fairmont
Hot Springs
34,238,635 Monte Creek-Vernon
32,622,240

Cranbrook-Creston

31,767,775

Cranbrook-Highway
93 Junction
Kamloops-Monte
Creek
Tete Jaune CachePrince George
Vernon-Salmon Arm

31,184,899
30,992,238
30,864,327
29,165,281
27,224,602
25,997,096
25,704,851
24,073,758

Alberta/BC
Boundary-Highway
93 Junction
Kamloops-Cache
Creek
Rock CreekCastlegar
Tete Jaune CacheAlta border
Ucluelet JunctionParksville

39

22,102,210

40

21,998,295

41

21,382,926

42

19,959,569

43

Kelowna-Rock
Creek
Radium Hot SpringsFairmont Hot Springs
Vancouver-Squatnish

21,382,926

Vancouver-Squatnish

21,036,103

Kelowna-Rock
Creek
Squatnish-Whisler

20,135,225
19,838,845

19,648,724

Whistler-Cache
Creek/Pemberton
Creston-Castlegar

44

19,424,132

Nelson-Kaslo

19,129,212

45

19,226,886

Nanaimo-Ladysmith

19,107,925

46

19,048,138

Penticton--Osoyoos

47

18,894,970

Houston-Stni thers

19,016,044 Whistler-Cache
Creek/Pemberton
18,560,542 Nelson-Kaslo

48

18,162,619

Squatnish-Whisler

18,204,930

Creston-Castlegar

49

17,894,344

Burns Lake-Houston

17,894,344

Burns Lake-Houston

50

17,085,650

Dawson CreekAlberta Border
Vanderhoof-Fraser
Lake
Houston-Stnithers

57

WonowonBuckinghorse River
16,290,315 1 km north of Prophet
River-Fort Nelson
15,280,506 Port Hardy-Campbell
River
14,994,638 Dawson CreekAlberta Border
14,821 ,482 Williams LakeAlexis Creek
14,576,640 Castlegar-Christina
Lake
13,688,945 Vanderhoof-Fraser
Lake
13,124,955 Kitwanga-Terrace

58

13,018,061

59

12,761,203

60

12,169,407

Buckinghorse River1 km north of Prophet
River
Fraser Lake-Burns
Lake
V ernon-N akusp

61

12,120,628

Terrace-Ki tima t

12,899,407

62

11 ,235,021

12,826,684

63

11 ,100,672

64

10,353,882

Stni thers- N ew
Hazelton
Prince RupertTerrace
Parksville-Campbell

51
52
53
54
55
56

17,281,071

260

19,393,983

15,791,550
15,645,594
15,598,056
14,910,024

Radium Hot SpringsFairmont Hot Springs
Nanaimo-Ladystnith
WonowonBuckinghorse River
Penticton--Osoyoos

Port Hardy-Campbell
River
Kitwanga-Terrace

14,583,473

Williams LakeAlexis Creek
14,518,386 1 km north of Prophet
River-Fort Nelson
13,877,227 Fraser Lake-Burns
Lake
13,501 ,898 Castlegar-Christina
Lake
13,476,559

Vernon-N akusp

13,332,370

Prince RupertTerrace
Buckinghorse River1 km north of Prophet
River
Terrace-Ki tima t

11 ,235,021
10,301 ,760

Stnithers-New
Hazelton
Parksville-Campbell

River

River
65

10,059,261

Hope-Agassiz

9,248,994

Hope-Agassiz

66

8,316,437

Nakusp-Castlegar

8,032,745

Castlegar-Trail

67

7,516,080

7,798 ,459

Nakusp-Castlegar

68

7,505,612

Ucluelet JunctionTofino
Castlegar-Trail

7,352,093

69

6,940,417

70

6,403,188

71

5,859,783

72

5,784,929

73

5,654,142

5,611,437

Ucluelet JunctionTofino
Kitwanga-Meziadin
Junction
Liard River-Lower
Post
Meziadin JunctionDease Lake
Kitwanga-New
Hazelton
Sechelt-ferry

74

5,468,138

4,600,708

Gibsons-Sechelt

75

4,683,096

4,315,293

76

4,007,058

77

1,718,070

78

1,632,470

79

1,273 ,266

Dease Lake-Yukon
Border
Alexis CreekAnahimLake
Ucluelet JunctionUcluelet
Meziadin JunctionStewart
Saltery Bay ferry
terminal-Powell
River

Total

2,919,241 ,552

Kitwanga-Meziadin
Junction
Liard River-Lower
Post
Sechelt-ferry
Alexis CreekAnahimLake
Meziadin JunctionDease Lake
Kitwanga-New
Hazelton
Gibsons-Sechelt
Dease Lake-Yukon
Border
Ucluelet JunctionUcluelet
Meziadin JunctionStewart
Saltery Bay ferry
terminal-Powell
River

6,954,936
6,331 ,443
6,093 ,909
6,023 ,113

3,098,295
1,639,113
1,389,336
1,135,296

3,029,417,338

Table A2.14: Percentage changes in trucking distance driven on BC routes 2007-2013
Rank
1
2
3
4
5

Route
Dawson CreekPrince George
Fort St. JohnWonowon
Salmon ArmRevelstoke
Prince RupertTerrace
Kami oops-Merritt

2007 distance
driven (km)
56,188,283

2013 distance
driven (km)
80,246,801

Chan2e
42.8

37,048,168

50,790,298

37.1

63 ,759,616

79,337,480

24.4

11,100,672

13,332,370

20.1

83,487,706

98 ,130,571

17.5

261

O/o

6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

26
27
28
29
30
31
32
33
34
35
36

Kelowna-Merritt
Revelstoke-Golden
Vanderhoof-Fraser
Lake
Dawson Creek-Ft. St.
John
Alberta/BC
Boundary-Highway
93 Junction
Dawson CreekAlberta Border
Kitwanga-Terrace
Parksville-Campbell
River
Kelowna-Penticton
Cranbrook-Highway
93 Junction
Squarnish-Whisler
Vernon-Nakusp
WonowonBuckinghorse River
Kamloops-Cache
Creek
Tete Jaune CacheKamloops
Kitwanga-New
Hazelton
Fort Nelson-Liard
River
Fraser Lake-Bums
Lake
Tete Jaune CacheAlta border
Meziadin JunctionDease Lake
Dease Lake-Yukon
Border
Castlegar-Trail
Vernon-Kelowna
Golden-Alberta
Border
Terrace-Kitimat
Hope-Merritt
Ladysmith-Victoria
Chilliwack-Hope
Monte Creek-Salmon
Arm
Monte Creek-Vernon
Vernon-Salmon Arm

59,351 ,219
91 ,100,314
13,688 ,945

69,360,512
105,757,071
15,791 ,550

16.9
16.1
15.4

32,821 ,530

37,688,805

14.8

25 ,438,317

29,165,281

14.7

14,994,638

17,085,650

13.9

13,124,955
58,257,504

14,910,024
65,749,056

13.6
12.9

96,637,050
28,560,155

107,563,456
31 ,767,775

11.3
11.2

18, 162,619
12,169,407
17,281 ,071

20,135,225
13,476,559
19,129,212

10.9
10.7
10.7

24,657,101

27,224,602

10.4

94,907,957

104,756,533

10.4

5,468,138

6,023,113

10.1

50,580,514

55 ,501 ,079

9.7

12,761,203

13,877,227

8.7

23 ,744,528

25 ,704,851

8.3

5,654,142

6,093,909

7.8

4,007,058

4,315 ,293

7.7

7,505,612
112,141,622
46,250,391

8,032,745
119,382,682
49,134,446

7.0
6.5
6.2

12,120,628
141 ,178,613
105,587,620
81,958 ,998
61 ,324,760

12,826,684
147,093,529
109,344,510
84,329,454
63 ,046,815

5.8
4.2
3.6
2.9
2.8

33 ,324,617
30,078,555

34,238,635
30,864,327

2.7
2.6

262

37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57

58
59
60
61
62
63
64
65
66

Kamloops-Monte
Creek
Port Hardy-Campbell
River
N anaimo-Ladysmi th
Penticton--Osoyoos
Kitwanga-Meziadin
Junction
Cranbrook-Fairmont
Hot Springs
Vancouver-Squamish
Prince GeorgeVanderhoof
Bums Lake-Houston
Smithers-New
Hazelton
Golden-Radium Hot
Springs
Prince GeorgeQuesnel
Parksville-Campbell
River
Parksville--Nanaimo
Buckinghorse River1 km north of Prophet
River
Liard River-Lower
Post
Williams LakeAlexis Creek
Gibsons-Sechelt
Ucluelet JunctionTofino
VancouverChilliwack
Quesnel-Williams
Lake
Cranbrook-Creston
Sechelt-ferry
Nelson-Kaslo
Ucluelet JunctionUcluelet
Whistler-Cache
Creek/Pemberton
Cache CreekWilliams Lake
Kelowna-Rock
Creek
Nakusp--Castlegar
Tete Jaune Cache-

30,393,054

31 ,184,899

2.6

15,280,506

15,598,056

2.1

19,226,886
19,048 ,138
6,940,417

19,393,983
19,107,925
6,954,936

0.9
0.3
0.2

36,733,133

36,784,379

0.1

21 ,382,926
39,006,287

21 ,382,926
39,006,287

0.0
0.0

17,894,344
11,235,021

17,894,344
11 ,235,021

0.0
0.0

41,845,491

41 ,845,491

0.0

49,521 ,813

49,428,300

-0.2

10,353 ,882

10,301 ,760

-0.5

103,021 ,922
13,018,061

102,181,677
12,899,407

-0.8
-0.9

6,403,188

6,331,443

-1.1

14,821,482

14,583 ,473

-1.6

4,683,096
7,516,080

4,600,708
7,352,093

-1.8
-2.2

236,931 ,720

229,529,155

-3.1

49,647,942

48 ,079,939

-3.2

33 ,899,229
5,859,783
19,424,132
1,718,070

32,622,240
5,611 ,437
18,560,542
1,639,113

-3.8
-4.2
-4.4
-4.6

19,959,569

19,016,044

-4.7

99,898 ,514

95 ,164,348

-4.7

22,102,210

21 ,036,103

-4.8

8,316,437
33 ,146,614

7,798,459
30,992,238

-6.2
-6.5

263

Prince George
Creston-Castlegar
Castlegar-Christina
Lake
Hope-Agassiz
Ucluelet JunctionParksville
Rock CreekCastlegar
Radium Hot SpringsFairmont Hot Springs
Saltery Bay ferry
terminal-Powell
River
1 km north of Prophet
River-Fort Nelson
Meziadin JunctionStewart
Houston-Smithers
Hope-Cache Creek
Hope-Penticton
Alexis CreekAnahimLake

67
68
69
70
71
72
73
74
75
76
77
78
79

19,648,724
14,576,640

18,204,930
13,501,898

-7.3
-7.4

10,059,261
26,382,200

9,248 ,994
24,073 ,758

-8.1
-8 .8

28 ,593 ,560

25 ,997,096

-9.1

21,998,295

19,838,845

-9.8

1,273,266

1,135,296

-10.8

16,290,315

14,518,386

-10.9

1,632,470

1,389,336

-14.9

18,894,970
94,421 ,996
74,030,760
5,784,929

15,645,594
75 ,989,350
57,450,708
3,098,295

-17.2
-19.5
-22.4
-46.4

Average

2.4

Table A2.15: Trucking emissions per kilometre of road for 2007 and 2013
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

Route
Parksville-Nanaimo
Vancouver-Chilliwack
Vemon-Kelowna
Kelowna-Penticton
Chilliwack-Hope
N anaimo-Ladysmith
Ladysmith-Victoria
Hope-Merritt
Kamloops-Merritt
Kamloops-Monte Creek
Salmon Arm-Revelstoke
Monte Creek-Salmon Arm
Revelstoke-Golden
Golden-Alberta Border
Fort St. John-Wonowon
Parksville-Campbell River
Kelowna-Merritt

2007 emissions per km
of road (tonnes
CO2/km)
4,861
4,248
3,723
2,707
2,672
2,298
2,127
2,058
1,946
1,721
1,278
1,136
1,110
1,096
1,066
899
886

264

2013 emissions per
km of road (tonnes
CO2/km)
4,821
4,115
3,964
3,013
2,749
2,318
2,203
2,144
2,022
1,997
1,381
1,314
1,273
1,207
1,023
982
972

18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60

Radium Hot Springs-Fairmont
Hot Springs
Vernon-Salmon Arm
Dawson Creek-Ft. St. John
Cranbrook-Highway 93
Junction
Cache Creek-Williams Lake
Dawson Creek-Alberta Border
Golden-Radium Hot Springs
Prince George-Quesnel
Quesnel-Williams Lake
Hope-Cache Creek
Prince George-Vanderhoof
Monte Creek-Vernon
Alberta/BC BoundaryHighway 93 Junction
Tete Jaune Cache-Alta border
Squamish-Whisler
Cranbrook-Fairmont Hot
Springs
Kamloops-Cache Creek
Vancouver-Squamish
Cranbrook-Creston
Tete Jaune Cache-Kamloops
Penticton-Osoyoos
Hope-J\.gassiz
Castlegar-Trail
Vanderhoof-Fraser Lake
Nelson-Kaslo
Houston-Smithers
Ucluelet Junction-Tofino
Bums Lake-Houston
Hope-Penticton
Gibsons-Sechelt
Terrace-Kitimat
Ucluelet Junction-Ucluelet
Fraser Lake-Bums Lake
Dawson Creek-Prince George
Fort Nelson-Liard River
Ucluelet Junction-Parksville
Castlegar-Christina Lake
Smithers-New Hazelton
W onowon-Buckirnmorse River
1 km north of Prophet RiverFort Nelson
Kelowna-Rock Creek
Whistler-Cache
Creek/Pemberton
Rock Creek-Castlegar

265

878

961

870
831
788

922
901
876

785
748
746
728
728
706
672
636
610

836
766
728
726
724
713
706
653
646

579
568
564

614
612
611

563
552
547
542
533
529
500
498
492
464
423
421
401
385
382
351
340
335
327
325
300
297
296
289

588
564
557
552
544
503
497
488
475
438
412
401
381
375
371
367
355
355
326
311
310
296
296
289

286
284

275
273

271

273

61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79

Kitwanga-Terrace
Buckinghorse River-1 km
north of Prophet River
Creston-Castlegar
Kitwanga-New Hazelton
Williams Lake-Alexis Creek
Tete Jaune Cache-Prince
George
Sechelt-ferry
Prince Rupert-Terrace
Parksville-Campbell River
Vemon-Nakusp
Port Hardy-Campbell River
N akusp-Castlegar
Kitwanga-Meziadin Junction
Saltery Bay ferry terminalPowell River
Liard River-Lower Post
Meziadin Junction-Stewart
Dease Lake-Yukon Border
Meziadin Junction-Dease Lake
Alexis Creek-Anahim Lake

267
249

270
269

238
233
228
218

263
251
229
204

195
155
138
118
112
102
81
76

186
166
154
125
121
96
82
68

61
49
48
31
31

60
41
33
33
26

Table A2.16: Trucking interurban CO 2 emissions by route in BC
Rank
1

2007 emissions
(tonnes CO2)
424,796

Route

2

253,120

VancouverChilliwack
Hope-Merritt

3

201,059

4

2013 emissions
{tonnes CO2)
411 ,523

Route

263,724

VancouverChilliwack
Hope-Merritt

Vemon-Kelowna

214,042

Vemon-Kelowna

189,308

Ladysmith-Victoria

196,044

Ladysmith-Victoria

5

184,708

Parksville-Nanaimo

192,851

Kelowna-Penticton

6

179,108

189,612

Revelstoke-Golden

7

173,261

Cache CreekWilliams Lake
Kelowna-Penticton

187,8 18

8

170,161

9

183,202

169,289

Tete Jaune CacheKamloops
Hope-Cache Creek

Tete Jaune CacheKamloops
Parksville-Nanaimo

175,939

Kami oops-Merritt

10

163,334

Revelstoke-Golden

170,620

11

149,685

Kami oops-Merritt

151,195

Cache CreekWilliams Lake
Chilliwack-Hope

12

146,945

Chilliwack-Hope

143,875

266

Dawson CreekPrince George

13

132,730

Hope-Penticton

142,244

14

114,315

136,242

15

109,949

124,357

Kelowna-Merritt

16

106,411

Salmon ArmRevels toke
Monte Creek-Salmon
Arm
Kelowna-Merri tt

Salmon ArmRevel stoke
Hope-Cache Creek

17

104,450

18

100,740

19

90,686

20

89,014

21

88,788

22

82,922

23

75 ,025

24

69,934

25

66,424

26

65 ,859

27

60,778

Parksville-Campbell
River
Dawson CreekPrince George
Fort Nelson-Liard
River
Quesnel-Williams
Lake
Prince GeorgeQuesnel
Golden-Alberta
Border
Golden-Radium Hot
Springs
Prince GeorgeVanderhoof
Fort St. JohnWonowon
Cranbrook-Fairmont
Hot Springs
Cranbrook-Creston

28

59,748

Monte Creek-Vernon

29

59,429

30

58 ,846

31

54,492

32

53 ,928

Tete Jaune CachePrince George
Dawson Creek-Ft. St.
John
Kami oops-Monte
Creek
Vernon-Salmon Arm

33

51,265

34

51 ,206

35

47,301

36

45,608

117,882

Parksville-Campbell
River
113,037 Monte Creek-Salmon
Arm
103,004 Hope-Penticton
99,508

Fort Nelson-Liard
River
91 ,062 Fort St. JohnWonowon
88,620 Prince GeorgeQuesnel
88,093 Golden-Alberta
Border
86,203 Quesnel-Williams
Lake
75,025 Golden-Radium Hot
Springs
69,934 Prince GeorgeVanderhoof
67,572 Dawson Creek-Ft. St.
John
65 ,951 Cranbrook-Fairmont
Hot Springs
61 ,387 Monte Creek-Vernon
58,489

Cranbrook-Creston

56,957

Cranbrook-Highway
93 Junction
Kami oops-Monte
Creek
Tete Jaune CachePrince George
Vernon-Salmon Arm

55,911
55 ,566

Rock CreekCastlegar
Cranbrook-Highway
93 Junction

55 ,337

Ucluelet JunctionParksville
Alberta/BC
Boundary-Highway
93 Junction

48 ,811

267

52,291

46,610

Alberta/BC
Boundary-Highway
93 Junction
Kami oops-Cache
Creek
Rock CreekCastlegar

37

44,208

38

42,572

39

39,627

40

39,441

41

38,338

42

35,786

43

Kamloops-Cache
Creek
Tete Jaune CacheAlta border
Kelowna-Rock
Creek
Radium Hot SpringsFairmont Hot Springs
Vancouver-Squamish

46,086
43 ,162
38,338
37,716
36,101

Kelowna-Rock
Creek
Squamish-Whisler
Radium Hot SpringsFairmont Hot Springs
Nanaimo-Ladysmith

35,228

Whistler-Cache
Creek/Pemberton
Creston-Castlegar

34,772

44

34,826

Nelson-Kaslo

34,297

45

34,472. Nanaimo-Ladysmith

34,259

46

34,151

Penticton---Osoyoos

34,094

47

33,877

Houston-Smithers

Whistler-Cache
Creek/Pemberton
33,277 Nelson-Kaslo

48

32,564

Squamish-Whisler

32,640

Creston-Castlegar

49

32,083

Burns Lake-Houston

32,083

Burns Lake-Houston

50

30,983

30,633

51

29,207

52

27,396

Dawson CreekAlberta Border
Vanderhoof-Fraser
Lake
Houston-Smithers

53

26,884

54

26,573

55

26,135

56

24,543

57

23,532

WonowonBuckinghorse River
1 km north of Prophet
River-Fort Nelson
Port Hardy-Campbell
River
Dawson CreekAlberta Border
Williams LakeAlexis Creek
Castlegar-Christina
Lake
Vanderhoof-Fraser
Lake
Kitwanga-Terrace

58

23 ,340

59

22,880

60

21 ,819

Buckinghorse River1 km north of Prophet
River
Fraser Lake-Burns
Lake
Vernon-N akusp

61

21 ,731

Terrace-Kitimat

62

20,143

Smithers-New
Hazelton

268

35,569

Tete Jaune CacheAlta border
Ucluelet JunctionParksville
Vancouver-Squamish

28 ,313
28,051
27,966
26,732
26,147
26,030
24,881
24,208
24,162
23,904

WonowonBuckincllorse River
Penticton---Osoyoos

Port Hardy-Campbell
River
Kitwanga-Terrace
Williams LakeAlexis Creek
1 km north of Prophet
River-Fort Nelson
Fraser Lake-Burns
Lake
Castlegar-Christina
Lake
Vernon-Nakusp

Prince RupertTerrace
23 ,127 Buckinghorse River1 km north of Prophet
River
22,997 Terrace-Kitimat

20,143

18,035

Prince RupertTerrace
Parksville-Campbell
River
Hope-Agassiz

16,583

Smithers-New
Hazelton
Parksville-Campbell
River
Hope-Agassiz

66

14,911

N akusp-Castlegar

14,402

Castlegar-Trail

67

13,476

13,982

N akusp-Castlegar

68

13,457

Ucluelet JunctionTotino
Castlegar-Trail

13,182

69

12,443

12,470

70

11,480

71

10,506

Kitwanga-Meziadin
Junction
Liard River-Lower
Post
Sechelt-ferry

72

10,372

73

10,137

10,061

Ucluelet JunctionTotino
Kitwanga-Meziadin
Junction
Liard River-Lower
Post
Meziadin JunctionDease Lake
Kitwanga-New
Hazelton
Sechelt-ferry

74

9,804

8,249

Gibsons-Sechelt

75

8,396

7,737

76

7,184

77

3,080

78

2,927

79

2,283

Dease Lake-Yukon
Border
Alexis CreekAnahimLake
Ucluelet JunctionUcluelet
Meziadin JunctionStewart
Saltery Bay ferry
terminal-Powell
River

63

19,902

64

18,564

65

Total

18,470

11,352
10,926

Alexis CreekAnahim Lake
Meziadin JunctionDease Lake
Kitwanga-New
Hazelton
Gibsons-Sechelt

10,799

Dease Lake-Yukon
Border
Ucluelet JunctionUcluelet
Meziadin JunctionStewart
Saltery Bay ferry
terminal-Powell
River

5,555
2,939
2,491
2,035

5,431,451

5,233,917

269

APPENDIX 3: SMITE future scenarios
Table A3.1: SMITE future scenarios
Legend:
Seen
%2020
%2050
Cost to offset ($bn)

PA

PB

PC

PF

PT
AF
MF

TF
FT

BAU

= Scenario number
= Discrepancy to 2020 target(%)
= Discrepancy to 2050 target(%)
= Estimated offset cost (positive values) or excess credit value (negative values) (billions of dollars)
= Changes to passenger aviation
= Changes to passenger bus
= Changes to passenger cars
= Changes to passenger ferries
= Changes to passenger trains
= Changes to aviation freight
= Changes to marine freight
= Changes to train freight
= Changes to freight trucks
= Business-as-usual (no changes made to current emission trends of mode)

2020

2050

%

Cost to
offset
($bn)

PA

PB

PC

PF

PT

AF

MF

TF

FT

I

35.8

236.5

14.36

-!%pa

-1% pa

-1% pa

-1% pa

-1% pa

-1% pa

-!%pa

-1% pa

-!%pa

2

25.9

130.1

8.99

-2%pa

-2%pa

-2% pa

-2% pa

-2%pa

-2% pa

-2%pa

-2% pa

-2% pa

-3%pa

-3% pa

-3% pa

Seen

O/o

3

17.1

57.3

4.78

-3% pa

-3%pa

-3%pa

-3% pa

-3%pa

-3% pa

4

8.7

7.0

1.44

-4% pa

-4%pa

-4%pa

-4% pa

-4%pa

-4% pa

-4% pa

-4% pa

-4% pa

5

0.9

-27.4

-1.23

-5%pa

-5%pa

-5% pa

-5%pa

-5% pa

-5% pa

-5%pa

-5%pa

-5%pa

270

6

-2.3

-2 .3

-0.07

7

35.4

177.1

11.40

8

28.9

103.6

7.70

9

35.4

81.7

9.16

+0.54% pa
to 2020 to
reach
target,
then 3.82995 pa
-)%pa
reduction
until 2030,
then 5%
pa
reduction,
e.g.
because of
revolution
ary
technology
-1%pa
reduction
to 2025
e.g.
efficiency,
then 5%
pa
reduction
revolution
arv tech
!%pa
reduction
to 2020,
2% pa to
2030, 3%
pa to 2040,
4% pa to
2050

-8 .58% to
2020, then
- 3.95

-6.33% pa
to 2020,
then -3.95

-4.35% pa
to 2020,
then -3.95

-5 .56% pa
to 2020,
then -3 .95

-6.46% pa
to 2020,
then -3 .95

-3 .10% pa
to 2020,
then -3 .95

-5.45% pa
from 2013
to 2020,
then -3 .95

-6%pa

-)%pa
reduction
through to
2050, e.g.
because of
efficiency
gains

-1% pa
reduction
to 2030,
e.g.
because of
efficiency,
then all
cars
electric
with 0
emissions
-)%pa
reduction
to 2030
e.g.
efficiency,
then all
cars
electric/hy
drogen, 0
emissions
!%pa
reduction
to 2020,
2% pa to
2030, 3%
pa to 2040,
4% pa to
2050

-1% pa
reduction
through to
2050, e.g.
because of
efficiency
gains

-)%pa
reduction
through to
2050, e.g.
because of
efficiency
gains

-1% pa
reduction
through to
2050, e.g.
because of
efficiency
gains

-1%pa
reduction
through to
2050, e.g.
because of
efficiency
gains

-1% pa
reduction
through to
2050, e.g.
because of
efficiency
gains

-1% pa
reduction
through to
2050, e.g.
because of
efficiency
gains

-2%pa
reduction,
e.g.
because of
efficiency,
route
consolidati
on etc.

-]%pa
reduction,
e.g.
because of
efficiency

-] %pa
reduction,
e.g.
because of
efficiency

-2% pa
reduction,
e.g.
because of
efficiency,
consolidati
on

-1%pa
reduction,
e.g.
because of
efficiency

-2%pa
reduction,
e.g.
because of
efficiency
and modal
shift to rail

!%pa
reduction
to 2020,
2% pa to
2030, 3%
pa to 2040,
4% pa to
2050

]%pa
reduction
to 2020,
2% pa to
2030, 3%
pa to 2040,
4% pa to
2050

1% pa
reduction
to 2020,
2% pa to
2030, 3%
pa to 2040,
4% pa to
2050

!% pa
reduction
to 2020,
2% pa to
2030, 3%
pa to 2040,
4% pa to
2050

!%pa
reduction
to 2020 ,
2% pa to
2030, 3%
pa to 2040,
4% pa to
2050

!%pa
reduction
to 2020,
2% pa to
2030, 3%
pa to 2040,
4% pa to
2050

-! % pa
reduction
to 2030
e.g.
efficiency,
then all
buses
electric/hy
drogen, 0
em1ss1ons
!%pa
reduction
to 2020,
2% pa to
2030, 3%
pa to 2040,
4% pa to
2050

271

10

25.9

130.0

8.98

2%pa
reduction

2%pa
reduction

11

24.6

65.6

5.58

-!%pa
e.g.
efficiency

12

14.8

31.5

1.82

-2% pa
e.g.
efficiency
until 2030,
then
revolution
ary tech to
halve
emissions,
then
steady
because
increase
from ferry
and usage
-2% pa to
2025 , e.g.
because of
efficiency,
then
revolution
ary tech
halves
emissions,
then 2%
pa

-2%pa
e.g.
efficiency

-4.396%
pa up to
2026 and
continued
( efficiency
projection
from
GHGenius
software
Vehicular
emissions
section)
-2% pa
efficiency
until 2030,
then
revolution
ary tech to
halve
emissions,
then
steady

-2% pa
e.g.
efficiency
to 2025 ,
then 0
emission
cars

0

2%pa
reduction

2%pa
reduction

2%pa
reduction

2%pa
reduction

2%pa
reduction

-2% pa
efficiency
until 2030,
then 4%
because of
efficiency
and people
switching
to lower
emissions
plane

-1% pa
e.g.
efficiency

-2% pa
e.g.
efficiency
until 2040,
then
revolution
ary tech to
halve
emissions,
then
steady

-2%pa
e.g.
efficiency

-1% pa
e.g.
efficiency
but
increase
from truck
switch

-3%pa
e.g.
between
efficiency
and modal
shift to
train

-3% pa,
e.g.
because of
efficiency
and
consolidati
on

Steady e.g.
efficiency
gains but
higher
usage

-2% pa to
2035 , e.g.
because of
efficiency,
then
revolution
ary tech
halves
emissions,
then 2%
oa

-3% pa,
e.g.
because of
efficiency
and
consolidati
on

Steady e.g.
efficiency
but higher
usage from
truck
modal
shift

-5% pa
e.g. from
modal
shift to
train

272

efficiency

13

45 .5

387.3

21.08

14

56.1

605 .0

29.74

15

67.5

916.5

40 .85

No
changes
e.g.
emissions
steady
between
increased
usage but
higher
efficiency.
+ !% pa
e.g.
because of
higher
usage
despite
efficiency
improYem
ents
+2% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

efficiency

No
changes
e.g.
emissions
steady
between
increased
usage but
higher
efficiency.
+ !%pa
e.g.
because of
higher
usage
despite
efficiency
improyem
ents
+2%pa
e.g.
because of
higher
usage
despite
efficiency
improYem
ents

No
changes
e.g.
emissions
steady
between
increased
usage but
higher
efficiency.
+ !%pa
e.g.
because of
higher
usage
despite
efficiency
improYem
ents
+2%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

No
changes
e.g.
emissions
steady
between
increased
usage but
higher
efficiency.
+ !%pa
e.g.
because of
higher
usage
despite
efficiency
improYem
ents
+2% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

273

No
changes
e.g.
emissions
steady
between
increased
usage but
higher
efficiency.
+l%pa
e.g.
because of
higher
usage
despite
efficiency
improYem
ents
+2%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

No
changes
e.g.
emissions
steady
between
increased
usage but
higher
efficiency.
+ !% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+2% pa
e.g.
because of
higher
usage
despite
efficiency
improYem
ents

No
changes
e.g.
emissions
steady
between
increased
usage but
higher
efficiency.
+ !%pa
e.g.
because of
higher
usage
despite
efficiency
tmproYem
ents
+2%pa
e.g.
because of
higher
usage
despite
efficiency
improYem
ents

No
changes
e.g.
em1ss10ns
steady
between
increased
usage but
higher
efficiency.
+ !% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+2% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

No
changes
e.g.
emissions
steady
between
increased
usage but
higher
efficiency.
+ !%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+2%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

16

79.6

1360.
2

55.17

17

92.4

1990.
5

73 .70

18

106.0

2882.
4

97.73

+3%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+4%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+5% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

+3%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+4% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+5% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

+3%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+4%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+5%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

+3%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+4%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+5% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

274

+3% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+4% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+5%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

+3% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+4%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+5%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

+3% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+4%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+5% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

+3% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+4%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+5%pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

+3% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+4% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents
+5% pa
e.g.
because of
higher
usage
despite
efficiency
improvem
ents

19

24.6

67.7

5.64

20

24.6

24.6

5.53

21

45.5

298.5

18 .9 1

22

45.5

225.3

16.99

10% e.g.
immediate
reduction
from
efficiency
and
consolidati
on. Then
1% pa to
2030, then
revolution
ary tech
halves
emissions,
then 1%
pa e.g.
efficiency.
-1% pa
e.g.
efficiency
through to
2030, then
0 emission
planes
No
changes to
2030, then
-1% pa
e.g.
efficiency
No
changes to
2030, then
-2% pa.
e.g.
efficiency

-1% pa to

2030, then
0 emission
buses

-1% pa to
2030, then
0 emission
cars

-2% pa,
e.g.
because of
efficiency
and
consolidati
on

Steady e.g.
because of
higher
usage

-1%pa
e.g.
efficiency
through to
2030, then
50% cut
from
revolution
ary tech
and modal
shift, then
l%pa
efficiency

-2% pa,
e.g.
because of
efficiency
and
consolidati
on

-1% pa

-3%pa
e.g.
efficiency,
e.g.
because of
efficiency
and modal
shift

-!%pa
e.g.
efficiency
through to
2030, then
0 emission
buses
No
changes to
2030, then
-1% pa
e.g.
efficiency
No
changes to
2030, then
-2% pa.
e.g.
efficiency

-l%pa
e.g.
efficiency
through to
2030, then
0 emission
cars
No
changes to
2030, then
-1% pa
e.g.
efficiency
No
changes to
2030, then
-2% pa.
e.g.
efficiency

-2%pa
e.g.
efficiency
and
consolidati
on

-1%pa
e.g.
efficiency

-2%pa
e.g.
efficiency
and
consolidati
on

-1% pa
e.g.
efficiency
despite
higher
usage

-3% pa
e.g.
efficiency
and modal
shift

No
changes to
2030, then
-)%pa
e.g.
efficiency
No
changes to
2030, then
-2% pa.
e.g.
efficiency

No
changes to
2030, then
-l%pa
e.g.
efficiency
No
changes to
2030, then
-2% pa.
e.g.
efficiency

-1%pa
e.g.
efficiency
through to
2030, then
0 emission
planes
No
changes to
2030, then
-1% pa
e.g.
efficiencv
No
changes to
2030, then
-2% pa.
e.g.
efficiency

No
changes to
2030, then
-1% pa
e.g.
efficiencv
No
changes to
2030, then
-2% pa.
e.g.
efficiency

No
changes to
2030, then
-1% pa
e.g.
efficiencv
No
changes to
2030, then
-2% pa.
e.g.
efficiency

No
changes to
2030, then
-l%pa
e.g.
efficiencv
No
changes to
2030, then
-2% pa.
e.g.
efficiency

275

e.g.
efficiency
despite
increased
usage

23

45.5

165.0

15 .3 0

24

45.5

115.4

13 .80

25

45.5

74.7

12.48

26

0.9

-0.8

-0.55

No
changes to
2030, then
-3% pa.
e.g.
efficiency
No
changes to
2030, then
-4% pa.
e.g.
efficiency
No
changes to
2030, then
-5% pa.
e.g.
efficiency
-5% pa.
e.g. to
2030
efficiency,
then -4%
pa to 2040,
then -3%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

No
changes to
2030, then
-3% pa.
e.g.
efficiency
No
changes to
2030, then
-4%pa.
e.g.
efficiency
No
changes to
2030, then
-5% pa.
e.g.
efficiency
-5% pa.
e.g. to
2030
efficiency,
then -4%
pa to 2040,
then -3%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

No
changes to
2030, then
-3% pa.
e.g.
efficiency
No
changes to
2030, then
-4% pa.
e.g.
efficiency
No
changes to
2030, then
-5% pa.
e.g.
efficiency
-5% pa.
e.g. to
2030
efficiency,
then -4%
pa to 2040,
then -3%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

No
changes to
2030, then
-3% pa.
e.g.
efficiency
No
changes to
2030, then
-4% pa.
e.g.
efficiency
No
changes to
2030, then
-5% pa.
e.g.
efficiency
-5% pa.
e.g. to
2030
efficiency,
then -4%
pa to 2040,
then -3%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

276

No
changes to
2030, then
-3% pa.
e.g.
efficiency
No
changes to
2030, then
-4% pa.
e.g.
efficiency
No
changes to
2030, then
-5% pa.
e.g.
efficiency
-5% pa.
e.g. to
2030
efficiency,
then -4%
pa to 2040,
then -3%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

No
changes to
2030, then
-3% pa.
e.g.
efficiency
No
changes to
2030, then
-4% pa.
e.g.
efficiency
No
changes to
2030, then
-5 % pa.
e.g.
efficiency
-5% pa.
e.g. to
2030
efficiency,
then -4%
pa to'2040,
then -3%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

No
changes to
2030, then
-3% pa.
e.g.
efficiency
No
changes to
2030, then
-4% pa.
e.g.
efficiency
No
changes to
2030 , then
-5% pa.
e.g.
efficiency
-5% pa.
e.g. to
2030
efficiency,
then -4%
pa to 2040 ,
then -3%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

No
changes to
2030, then
-3% pa.
e.g.
efficiency
No
changes to
2030, then
-4% pa.
e.g.
efficiency
No
changes to
2030, then
-5% pa.
e.g.
efficiency
-5% pa.
e.g. to
2030
efficiency,
then -4%
pa to 2040,
then -3%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

No
changes to
2030, then
-3% pa.
e.g.
efficiency
No
changes to
2030, then
-4%
efficiency
No
changes to
2030, then
-5% pa.
e.g.
efficiency
-5% pa.
e.g. to
2030
efficiency,
then -4%
pa to 2040,
then -3%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

27

8.7

45 .9

2.37

-4% pa.
e.g. to
2030
efficiency,
then -3%
pa to 2040,
then -2%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

-4%pa.
e.g. to
2030
efficiency,
then -3%
pa to 2040,
then -2%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

-4% pa.
e.g. to
2030
efficiency,
then -3%
pa to 2040,
then -2%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

28

17. 1

113.7

6.06

-3% pa.
e.g. to
2030
efficiency,
then -2%
pa to 2040,
then -1%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

-3% pa.
e.g. to
2030
efficiency,
then -2%
pa to 2040,
then -1 %
pa to 2050 .
Slowdown
because of
population
growth
and higher
usage.

29

35.4

-

-2.41

-1% pa.
e.g.
efficiency
through to
2030, then
because of
revolution
ary tech all

-!%pa.
e.g.
efficiency
through to
2030, then
because of
revolution
ary tech all

-3% pa.
e.g. to
2030
efficiency,
then -2%
pa. to
2040, then
-1% pa to
2050.
Slowdown
because of
population
growth
and higher
usage.
-!%pa.
e.g.
efficiency
through to
2030, then
because of
revolution
ary tech all

100.0

-4% pa.
e.g. to
2030
efficiency,
then -3%
pa. to
2040, then
-2% pa to
2050.
Slowdown
because of
population
growth
and higher
usage .
-3% pa.
e.g. to
2030
efficiency,
then -2%
pa to 2040,
then -1%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.
-!%pa.
e.g.
efficiency
through to
2030, then
because of
revolution
ary tech all

277

-4% pa.
e.g. to
2030
efficiency,
then -3%
pa to 2040,
then -2%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

-4% pa.
e.g. to
2030
efficiency,
then -3%
pa to 2040,
then -2%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

-4% pa.
e.g. to
2030
efficiency,
then -3%
pa to 2040,
then -2%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

-4% pa.
e.g. to
2030
efficiency,
then -3%
pa to 2040,
then -2%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

-4% pa.
e.g. to
2030
efficiency,
then -3%
pa to 2040,
then -2%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

-3% pa.
e.g. to
2030
efficiency,
then -2%
pa to 2040,
then -1 %
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

-3% pa.
e.g. to
2030
efficiency,
then -2%
pa to 2040,
then -1%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

-3 % pa.
e.g. to
2030
efficiency,
then -2%
pa to 2040,
then -1 %
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

-3% pa.
e.g. to
2030
efficiency,
then -2%
pa to 2040,
then -1%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

-3% pa.
e.g. to
2030
efficiency,
then -2%
pa to 2040,
then -1%
pa to 2050.
Slowdown
because of
population
growth
and higher
usage.

-1% pa.

-! % pa.
e.g.
efficiency
through to
2030, then
because of
revolution
ary tech all

-1 % pa.

-1% pa.
e.g.
efficiency
through to
2030, then
because of
revolution
ary tech all

-1% pa.

e.g.
efficiency
through to
2030, then
because of
revolution
ary tech all

e.g.
efficiency
through to
2030, then
because of
revolution
ary tech all

e.g.
efficiency
through to
2030, then
because of
revolution
ary tech all

30

45 .5

260.4

16.42

31

45.5

165 .8

12.57

32

45 .5

95.4

9.37

33

45.5

43 .2

6.70

34

45 .5

4.6

4.47

modes 0
emissions

modes 0
emissions

modes 0
emissions

modes 0
emissions

modes 0
emissions

modes 0
em1ss1ons

modes 0
emissions

modes 0
em1ss10ns

modes 0
emissions

No
changes to
2020, then
-1% pa.
e.g.
efficiency
No
changes to
2020, then
-2% pa.
e.g.
efficiency
No
changes to
2020, then
-3% pa.
e.g.
efficiency
No
changes to
2020, then
-4% pa.
e.g.
efficiency
No
changes to
2020, then
-5% pa.
e.g.
efficiency

No
changes to
2020, then
-)%pa.
e.g.
efficiency
No
changes to
2020, then
-2% pa.
e.g.
efficiency
No
changes to
2020, then
-3% pa.
e.g.
efficiency
No
changes to
2020, then
-4% pa.
e.g.
efficiency
No
changes to
2020, then
-5% pa.
e.g.
efficiency

No
changes to
2020, then
-)%pa.
e.g.
efficiency
No
changes to
2020, then
-2% pa.
e.g.
efficiency
No
changes to
2020, then
-3% pa.
e.g.
efficiency
No
changes to
2020, then
-4% pa.
e.g.
efficiency
No
changes to
2020, then
-5% pa.
e.g.
efficiency

No
changes to
2020, then
-1% pa.
e.g.
efficiency
No
changes to
2020, then
-2% pa.
e.g.
efficiency
No
changes to
2020, then
-3% pa.
e.g.
efficiency
No
changes to
2020, then
-4% pa.
e.g.
efficiency
No
changes to
2020, then
-5% pa.
e.g.
efficiency

No
changes to
2020, then
-1% pa.
e.g.
efficiency
No
changes to
2020, then
-2% pa.
e.g.
efficiency
No
changes to
2020, then
-3% pa.
e.g.
efficiency
No
changes to
2020, then
-4% pa.
e.g.
efficiency
No
changes to
2020, then
-5% pa.
e.g.
efficiency

No .
changes to
2020, then
-1% pa.
e.g.
efficiency
No
changes to
2020, then
-2% pa.
e.g.
efficiency
No
changes to
2020, then
-3% pa.
e.g.
efficiency
No
changes to
2020, then
-4% pa.
e.g.
efficiency
No
changes to
2020, then
-5% pa.
e.g.
efficiency

No
changes to
2020, then
-1%pa.
e.g.
efficiency
No
changes to
2020, then
-2% pa.
e.g.
efficiency
No
changes to
2020, then
-3% pa.
e.g.
efficiency
No
changes to
2020, then
-4% pa.
e.g.
efficiency
No
changes to
2020, then
-5% pa.
e.g.
efficiency

No
changes to
2020, then
-1% pa.
e.g.
efficiency
No
changes to
2020, then
-2% pa.
e.g.
efficiency
No
changes to
2020, then
-3% pa.
e.g.
efficiency
No
changes to
2020, then
-4% pa.
e.g.
efficiency
No
changes to
2020, then
-5% pa.
e.g.
efficiency

No
changes to
2020, then
-1% pa.
e.g.
efficiency
No
changes to
2020 , then
-2% pa.
e.g.
efficiency
No
changes to
2020, then
-3% pa.
e.g.
efficiency
No
changes to
2020, then
-4% pa.
e.g.
efficiency
No
changes to
2020, then
-5% pa.
e.g.
efficiency

278

35

45.5

340.7

20.49

36

45.5

298.1

19.94

37

45.5

259.3

19.42

38

45 .5

223 .9

18.93

39

45.5

191.7

18.46

40

55 .9

372.0

24. 18

No
changes to
2040, then
-1% pa.
e.g.
efficiency
No
changes to
2040, then
-2% pa.
e.g.
efficiency
No
changes to
2040, then
-3% pa.
e.g.
efficiency
No
changes to
2040, then
-4% pa.
e.g.
efficiency
No
changes to
2040 , then
-5% pa.
e.g.
efficiency
1% pa. e.g.
growth
because of
higher
usage to
2030, then
-1% pa.
e.g.

No
changes to
2040, then
-!%pa.
e.g.
efficiency
No
changes to
2040, then
-2% pa.
e.g.
efficiency
No
changes to
2040, then
-3% pa.
e.g.
efficiency
No
changes to
2040, then
-4% pa.
e.g.
efficiency
No
changes to
2040, then
-5% pa.
e.g.
efficiency
1% pa. e.g.
growth
because of
higher
usage to
2030, then
-!%pa.
e.g.

No
changes to
2040, then
-!%pa.
e.g.
efficiency
No
changes to
2040, then
-2% pa.
e.g.
efficiency
No
changes to
2040, then
-3% pa.
e.g.
efficiency
No
changes to
2040, then
-4% pa.
e.g.
efficiency
No
changes to
2040, then
-5% pa.
e.g.
efficiency
1% pa. e.g.
growth
because of
higher
usage to
2030, then
-!%pa.
e.g.

No
changes to
2040, then
-1% pa.
e.g.
efficiency
No
changes to
2040, then
-2% pa.
e.g.
efficiency
No
changes to
2040, then
-3% pa.
e.g.
efficiency
No
changes to
2040, then
-4% pa.
e.g.
efficiency
No
changes to
2040, then
-5% pa.
e.g.
efficiency
1% pa. e.g.
growth
because of
higher
usage to
2030, then
-1% pa.
e.g.

279

No
changes to
2040, then
-!%pa.
e.g.
efficiency
No
changes to
2040, then
-2% pa.
e.g.
efficiency
No
changes to
2040, then
-3% pa.
e.g.
efficiency
No
changes to
2040, then
-4% pa.
e.g.
efficiency
No
changes to
2040, then
-5% pa.
e.g.
efficiency
1% pa. e.g.
growth
because of
higher
usage to
2030, then
-1% pa.
e.g.

No
changes to
2040, then
-1% pa.
e.g.
efficiency
No
changes to
2040, then
-2% pa.
e.g.
efficiency
No
changes to
2040, then
-3% pa.
e.g.
efficiency
No
changes to
2040, then
-4% pa.
e.g.
efficiency
No
changes to
2040, then
-5% pa.
e.g.
efficiency
1% pa. e.g.
growth
because of
higher
usage to
2030, then
-!%pa.
e.g.

No
changes to
2040, then
-!%pa.
e.g.
efficiency
No
changes to
2040, then
-2% pa.
e.g.
efficiency
No
changes to
2040, then
-3% pa.
e.g.
efficiency
No
changes to
2040, then
-4% pa.
e.g.
efficiency
No
changes to
2040 , then
-5% pa.
e.g.
efficiency
1% pa. e.g.
growth
because of
higher
usage to
2030, then
-1% pa.
e.g.

No
changes to
2040, then
-1% pa.
e.g.
efficiency
No
changes to
2040, then
-2% pa.
e.g.
efficiency
No
changes to
2040, then
-3% pa.
e.g.
efficiency
No
changes to
2040, then
-4% pa.
e.g.
efficiency
No
changes to
2040, then
-5% pa.
e.g.
efficiency
1% pa. e.g.
growth
because of
higher
usage to
2030, then
-1% pa.
e.g.

No
changes to
2040, then
-1 % pa.
e.g.
efficiency
No
changes to
2040, then
-2% pa.
e.g.
efficiency
No
changes to
2040, then
-3% pa.
e.g.
efficiency
No
changes to
2040 , then
-4% pa.
e.g.
efficiency
No
changes to
2040, then
-5% pa.
e.g.
efficiency
1% pa. e.g.
growth
because of
higher
usage to
2030 , then
-!%pa.
e.g.

41

42.3

280.0

17.55

42

42.3

211.3

15.78

43

42.3

154.7

14.23

44

42.3

108.0

12.85

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

Each mode
follows
2007-201 3
trend to
2030, then
mandated !%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2030, then
mandated !%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2030, then
mandated lo/opa
Each mode
follows
2007-2013
trend to
2030, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2030, then
mandated !%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 3% pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2030, then
mandated 1% pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2030, then
mandated lo/ooa
Each mode
follows
2007-2013
trend to
2030, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 3% pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2030, then
mandated 1%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2030, then
mandated lo/opa
Each mode
follows
2007-2013
trend to
2030, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2030, then
mandated lo/opa
Each mode
follows
2007-2013
trend to
2030, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2030 , then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2030, then
mandated 4%pa

280

45

42.3

69.4

11 .63

46

42.3

265.5

16.70

47

42.3

184.8

14.07

48

42.3

121.4

11.81

49

42.3

71.7

9.87

Each mode
follows
2007-2013
trend to
2030, then
mandated 5%pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated !%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 3% pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 4%oa

Each mode
follows
2007-2013
trend to
2030, then
mandated 5% pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 1%oa
Each mode
follows
2007-2013
trend to
2025, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2030, then
mandated 5%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated !%pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2030, then
mandated 5% pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated !%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 4%pa

281

Each mode
follows
2007-2013
trend to
2030, then
mandated 5%pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 1%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2030, then
mandated 5%pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated )%pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 3% pa
Each mode
follows
2007-2013
trend to
2025, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2030, then
mandated 5%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated !%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated 3% pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2030, then
mandated 5% pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated )%pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2030, then
mandated 5%pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 1%pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2025, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2025 , then
mandated 4%pa

50

42.3

32.8

8.19

51

42.3

252.7

15.77

52

42 .3

161.1

12.09

53

42.3

92.9

9.05

54

42.3

41.1

6.39

Each mode
follows
2007-2013
trend to
2025 , then
mandated5% pa
Each mode
follows
2007-2013
trend to
2020, then
mandated!% pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2025, then
mandated 5%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated ! % pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 4%pa

Each mode
follows
2007-201 3
trend to
2025, then
mandated 5%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated ! % pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2025 , then
mandated 5%pa
Each mode
follows
2007-2013
trend to
2020 , then
mandated !% pa
Each mode
follows
2007-201 3
trend to
2020, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 4%pa

282

Each mode
follows
2007-2013
trend to
2025 , then
mandated 5%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated !%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2020 , then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2025, then
mandated 5%oa
Each mode
follows
2007-2013
trend to
2020 , then
mandated !% pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 4% pa

Each mode
follows
2007-2013
trend to
2025 , then
mandated 5% pa
Each mode
follows
2007-2013
trend to
2020, then
mandated ! % pa
Each mode
follows
2007-201 3
trend to
2020, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 4% pa

Each mode
follows
2007-2013
trend to
2025, then
mandated 5% pa
Each mode
follows
2007-2013
trend to
2020, then
mandated !%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 4%pa

Each mode
follows
2007-2013
trend to
2025 , then
mandated 5% pa
Each mode
follows
2007-2013
trend to
2020, then
mandated ! % pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 2%pa
Each mode
follows
2007-2013
trend to
2020, then
mandated 3%pa
Each mode
follows
2007-2013
trend to
2020 , then
mandated 4%pa

55

42.3

4.3

4.38

56

24.7

68.2

5.85

57

30.9

47.3

7.34

Each mode
follows
2007-2013
trend to
2020, then
mandated 5%pa
-2% pa.
e.g.
efficiency

Each mode
follows
2007-2013
trend to
2020, then
mandated 5%pa
-1% pa.
e.g.
efficiency

Each mode
follows
2007-2013
trend to
2020, then
mandated 5%pa
-2% pa.
e.g.
efficiency

Each mode
follows
2007-2013
trend to
2020, then
mandated 5%pa
-1% pa.
e.g.
efficiency

Each mode
follows
2007-2013
trend to
2020, then
mandated 5%pa
-1% pa.
e.g.
efficiency
to 2030,
then 0
emissions
because of
electric
trains

Each mode
follows
2007-2013
trend to
2020, then
mandated 5%pa
-2% pa.
e.g.
efficiency

Each mode
follows
2007-2013
trend to
2020, then
mandated 5%pa
-2% pa.
e.g.
efficiency

Mandated
10% over
2007
reduction
by 2020,
20% over
2020 by
2030, 30%
over 2030
by 2040,
40% over
2040 by
2050

Mandated
10% over
2007
reduction
by 2020,
20% over
2020 by
2030, 30%
over 2030
by 2040,
40% over
2040 by
2050

Mandated
10% over
2007
reduction
by 2020,
20% over
2020 by
2030, 30%
over 2030
by 2040,
40% over
2040 by
2050

Mandated
10% over
2007
reduction
by 2020,
20% over
2020 by
2030, 30%
over 2030
by 2040,
40% over
2040 by
2050

Mandated
10% over
2007
reduction
by 2020 ,
20% over
2020 by
2030, 30%
over 2030
by 2040 ,
40% over
2040 by
2050

Mandated
10% over
2007
reduction
by 2020,
20% over
2020 by
2030, 30%
over 2030
by 2040,
40% over
2040 by
2050

Mandated
10% over
2007
reduction
by 2020,
20% over
2020 by
2030, 30%
over 2030
by 2040,
40% over
2040 by
2050

283

Each mode
follows
2007-2013
trend to
2020, then
mandated 5%pa
Steady to
2030
because of
modal
shift from
truck, then
0
em1ss1ons
because of
electric
trains
Mandated
10% over
2007
reduction
by 2020,
20% over
2020 by
2030, 30%
over 2030
by 2040,
40% over
2040 by
2050

Each mode
follows
2007-2013
trend to
2020, then
mandated 5%pa
-3% pa.
e.g.
efficiency
and modal
shift to
train

Mandated
10% over
2007
reduction
by 2020,
20% over
2020 by
2030, 30%
over 2030
by 2040,
40% over
2040 by
2050

58

4.7

-26.3

0.51

59

21.5

103.9

7.23

60

21.5

103 .7

7.22

Mandated
10% over
2007
reduction
by 2015,
20% over
2015 by
2020, 30%
over 2020
by 2030,
40% over
2030 by
2040, 50%
over 2040
by 2050.
-2% pa.
e.g.
because of
efficiency

Mandated
10% over
2007
reduction
by 2015,
20% over
2015 by
2020, 30%
over 2020
by 2030,
40% over
2030 by
2040, 50%
over 2040
by 2050.
-1% pa.
e.g.
because of
efficiency

-2% pa.
e.g.
because of
efficiency

-1% pa.
e.g.
because of
efficiency

Mandated
10% over
2007
reduction
by 2015,
20% over
2015 by
2020, 30%
over 2020
by 2030,
40% over
2030 by
2040, 50%
over 2040
by 2050.
30% lower
by 2025
modal
shift, then
-2% pa.
e.g.
because of
efficiency
30% lower
by 2025
modal
shift, then
-2% pa.
e.g.
because of
efficiency

Mandated
10% over
2007
reduction
by 2015,
20% over
2015 by
2020, 30%
over 2020
by 2030,
40% over
2030 by
2040, 50%
over 2040
by 2050.
-2% pa.
e.g.
because of
efficiency
and
consolidati
on

Mandated
10% over
2007
reduction
by 2015 ,
20% over
2015 by
2020, 30%
over 2020
by 2030,
40% over
2030 by
2040, 50%
over 2040
by 2050.
Steady e.g.
because of
efficiency
despite
higher
near-urban
use

Mandated
10% over
2007
reduction
by 2015 ,
20% over
2015 by
2020, 30%
over 2020
by 2030,
40% over
2030 by
2040, 50%
over 2040
by 2050 .
-2% pa.
e.g.
because of
efficiency

Mandated
10% over
2007
reduction
by 2015 ,
20% over
2015 by
2020, 30%
over 2020
by 2030,
40% over
2030 by
2040, 50%
over 2040
by 2050.
-2% pa.
e.g.
because of
efficiency

Mandated
10% over
2007
reduction
by 2015 ,
20% over
2015 by
2020, 30%
over 2020
by 2030,
40% over
2030 by
2040, 50%
over 2040
by 2050.
-1% pa.
e.g.
because of
despite
higher
usage

Mandated
10% over
2007
reduction
by 2015 ,
20% over
2015 by
2020, 30%
over 2020
by 2030,
40% over
2030 by
2040, 50%
over 2040
by 2050.
-3% pa.
e.g.
because of
efficiency
and modal
shift to
train

-2% pa.
e.g.
because of
efficiency
and
consolidati
on

Steady to
2030 e.g.
because of
efficiency
despite
higher
near-urban
use, then 0
emissions
because of
electric
trains

-2% pa.
e.g.
because of
efficiency

-2% pa.
e.g.
because of
efficiency

-1% pa.
e.g.
because of
despite
higher
usage to
2030, then
0
emissions
because of
electric
trains

-3% pa.
e.g.
because of
efficiency
and modal
shift to
train

284

61

35.4

69.4

6.04

62

25 .9

17.4

2.77

63

17.1

-18.9

0. 11

-1% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -1 %
pa. e.g.
because of
efficiency
-2% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -2%
pa. e.g.
because of
efficiency
-3% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,

-1%pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -1 %
pa. e.g.
because of
efficiency
-2% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -2%
pa. e.g.
because of
efficiency
-3% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,

-1%pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -1%
pa. e.g.
because of
efficiency
-2% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -2%
pa. e.g.
because of
efficiency
-3% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,

-1% pa.

e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -1%
pa. e.g.
because of
efficiency
-2% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -2%
pa. e.g.
because of
efficiency
-3% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,

285

-1% pa.

e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -1 %
pa. e.g.
because of
efficiency
-2% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -2%
pa. e.g.
because of
efficiency
-3% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,

-1% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -1%
pa. e.g.
because of
efficiency
-2% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -2%
pa. e.g.
because of
efficiency
-3% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,

-)%pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -1 %
pa. e.g.
because of
efficiency
-2% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -2%
pa. e.g.
because of
efficiency
-3% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,

-1% pa.

e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -1%
pa. e.g.
because of
efficiency
-2% pa.
e.g.
because of
efficiency
to 2030 ,
then
halved
because of
revolution
ary tech,
then -2%
pa. e.g.
because of
efficiency
-3% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,

-1% pa.

e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -1%
pa. e.g.
because of
efficiency
-2% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -2%
pa. e.g.
because of
efficiency
-3% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,

64

8.7

-44.3

-2.07

65

0.9

-61.8

-3 .8 8

then -3%
pa. e.g.
because of
efficiency

then -3%
pa. e.g.
because of
efficiency

then -3%
pa. e.g.
because of
efficiency

then -3%
pa. e.g.
because of
efficiency

then -3%
pa. e.g.
because of
efficiency

then -3%
pa. e.g.
because of
efficiency

then -3%
pa. e.g.
because of
efficiency

then -3%
pa. e.g.
because of
efficiency

then -3%
pa. e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -4%
pa. e.g.
because of
efficiency
-5% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -5%
pa. e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -4%
pa. e.g.
because of
efficiency
-5% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -5%
pa. e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -4%
pa. e.g.
because of
efficiency
-5% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -5%
pa. e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency
to 2030 ,
then
halved
because of
revolution
ary tech,
then -4%
pa. e.g.
because of
efficiency
-5% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -5%
pa. e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -4%
pa. e.g.
because of
efficiency
-5% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -5 %
pa. e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -4%
pa. e:g.
because of
efficiency
-5% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -5%
pa. e.g.
because of
efficiencv

-4% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -4%
pa. e.g.
because of
efficiency
-5% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -5%
pa. e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -4%
pa. e.g.
because of
efficiency
-5% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -5%
pa. e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -4%
pa. e.g.
because of
efficiency
-5% pa.
e.g.
because of
efficiency
to 2030,
then
halved
because of
revolution
ary tech,
then -5%
pa. e.g.
because of
efficiency

286

66

35.4

218.4

12.96

-1% pa.

-lo/opa.
e.g.
because of
efficiency

e.g.
because of
efficiency

67

28 .5

10.0

2.75

-1% pa.

e.g.
because of
efficiency

-1%pa.
e.g.
because of
efficiency

68

25 .9

4.5

0.14

-2% pa.
e.g.
because of
efficiency

-2% pa.
e.g.
because of
efficiency

-)%pa.
e.g.
because of
efficiency
to 2020,
then 30%
reduction
because of
switch to
diesels,
then -1%
pa. e.g.
because of
efficiency
-1% pa.
e.g.
because of
efficiency
to 2020,
then 30%
reduction
because of
switch to
diesels,
then -1 %
pa. e.g.
because of
efficiency

-] % pa.
e.g.
because of
efficiency

-lo/opa.
e.g.
because of
efficiency

-1% pa.
e.g.
because of
efficiency

-!%pa.
e.g.
because of
efficiency

-1%pa.
e.g.
because of
efficiency

-1% pa.

-1 % pa.
e.g.
because of
efficiency

-1%pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

-1% pa.
e.g.
because of
efficiency

-1 % pa.
e.g.
because of
efficiency

-1% pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

-2% pa.
e.g.
because of
efficiency
to 2020,
then 30%
reduction
because of
switch to

-2% pa.
e.g.
because of
efficiency

-2% pa.
e.g.
because of
efficiency
to 2030,
then 0
em1ss1ons
because of
electric

-2% pa.
e.g.
because of
efficiency

-2% pa.
e.g.
because of
efficiency

-2% pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric

-)%pa.
e.g.
because of
efficiency
to 2025 ,
then 75%
reduction
because of
modal
shift to
trains, then
-1%pa.
e.g.
because of
efficiency
-2% pa.
e.g.
because of
efficiency
to 2025 ,
then 75%
reduction
because of
modal

287

e.g.
because of
efficiency

diesels,
then -2%
pa. e.g.
because of
efficiency

trains

trains

shift to
trains, then
-2% pa.
e.g.
because of
efficiency

-3% pa.
e.g.
because of
efficiency
to 2025 ,
then 75 %
reduction
because of
modal
shift to
trains, then
-3% pa.
e.g.
because of
efficiency
-4% pa.
e.g.
because of
efficiency
to 2025,
then 75%
reduction
because of
modal
shift to
trains, then
-4% pa.
e.g.
because of
efficiency

69

17.1

-28 . 1

-2.09

-3% pa.
e.g.
because of
efficiency

-3% pa.
e.g.
because of
efficiency

-3% pa.
e.g.
because of
efficiency
to 2020,
then 30%
reduction
because of
switch to
diesels,
then -3 %
pa. e.g.
because of
efficiency

-3 % pa.
e.g.
because of
efficiency

-3% pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

-3 % pa.
e.g.
because of
efficiency

-3 % pa.
e.g.
because of
efficiency

-3% pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

70

8.7

-50.7

-3.92

-4% pa.
e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency
to 2020,
then 30%
reduction
because of
switch to
diesels,
then -4%
pa. e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

-4% pa.
e.g.
because of
efficiency

-:% pa.
e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency
to 2030,
then 0
em1ss10ns
because of
electric
trains

288

71

0.9

-66 .3

-5.42

-5% pa.
e.g.
because of
efficiency

-5 % pa.
e.g.
because of
efficiency

-5% pa.
e.g.
because of
efficiency
to 2020,
then 30%
reduction
because of
switch to
diesels,
then -5%
pa. e.g.
because of
efficiency

-5% pa.
e.g.
because of
efficiency

-5% pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

-5% pa.
e.g.
because of
efficiency

-5% pa.
e.g.
because of
efficiency

-5% pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

72

35.4

81.4

4.64

-1 % pa.
e.g.
because of
efficiency

-lo/opa.
e.g.
because of
efficiency

-1 % pa.
e.g.
because of
efficiency
to 2020,
then 50%
reduction
because of
switch to
diesels and
modal
shift, then
-1% pa.
e.g.
because of
efficiency

-1 % pa.
e.g.
because of
efficiency

-1% pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

-1 % pa.
e.g.
because of
efficiency

-l o/o pa.
e.g.
because of
efficiency

-1 % pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

289

-5% pa.
e.g.
because of
efficiency
to 2025 ,
then 75%
reduction
because of
modal
shift to
trains, then
-5% pa.
e.g.
because of
efficiency
-1% pa.
e.g.
because of
efficiency
to 2025,
then 75%
reduction
because of
modal
shift to
trains, then
-)%pa.
e.g.
because of
efficiency

73

25.9

-3.6

-0.62

-2% pa.
e.g.
because of
efficiency

-2% pa.
e.g.
because of
efficiency

74

17. 1

-33 .7

-2.71

-3% pa.
e.g.
because of
efficiency

-3% pa.
e.g.
because of
efficiency

-2% pa.
e.g.
because of
efficiency
to 2020,
then 50%
reduction
because of
switch to
diesels and
modal
shift, then
-2% pa.
e.g.
because of
efficiency
-3% pa.
e.g.
because of
efficiency
to 2020,
then 50%
reduction
because of
switch to
diesels and
modal
shift, then
-3% pa.
e.g.
because of
efficiency

-2% pa.
e.g.
because of
efficiency

-2% pa.
e.g.
because of
efficiency
to 2030,
then 0
em1ss1ons
because of
electric
trains

-2% pa.
e.g.
because of
efficiency

-2%pa.
e.g.
because of
efficiency

-2% pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

-2% pa.
e.g.
because of
efficiency
to 2025 ,
then 75%
reduction
because of
modal
shift to
trains, then
-2% pa.
e.g.
because of
efficiency

-3 % pa.
e.g.
because of
efficiency

-3% pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

-3 % pa.
e.g.
because of
efficiency

-3 % pa.
e.g.
because of
efficiency

-3% pa.
e.g.
because of
efficiency
to 2030,
then 0
em1ss1ons
because of
electric
trains

-3 % pa.
e.g.
because of
efficiency
to 2025,
then 75%
reduction
because of
modal
shift to
trains, then
-3% pa.
e.g.
because of
efficiency

290

75

8.7

-54.6

-4.43

-4% pa.
e.g.
because of
efficiency

-:%pa.
e.g.
because of
efficiency

76

0.9

-69.0

-5 .84

-5% pa.
e.g.
because of
efficiency

-5% pa.
e.g.
because of
efficiency

77

67.5

356.7

27.71

2% pa. e.g.
because of
growth
because of
higher
usage to

2% pa. e.g.
because of
growth
because of
higher
usage to

-4% pa.
e.g.
because of
efficiency
to 2020,
then 50%
reduction
because of
switch to
diesels and
modal
shift, then
-4% pa.
e.g.
because of
efficiency
-5% pa.
e.g.
because of
efficiency
to 2020,
then 50%
reduction
because of
switch to
diesels and
modal
shift, then
-5% pa.
e.g.
because of
efficiency
2% pa. e.g.
because of
growth
because of
higher
usage to

-4% pa.
e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

-4% pa.
e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency

-4% pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

-4% pa.
e.g.
because of
efficiency
to 2025 ,
then 75%
reduction
because of
modal
shift to
trains, then
-4% pa.
e.g.
because of
efficiency

-5% pa.
e.g.
because of
efficiency

-5% pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

-5% pa.
e.g.
because of
efficiency

-5% pa.
e.g.
because of
efficiency

-5% pa.
e.g.
because of
efficiency
to 2030,
then 0
emissions
because of
electric
trains

-5% pa.
e.g.
because of
efficiency
to 2025 ,
then 75%
reduction
because of
modal
shift to
trains, then
-5% pa.
e.g.
because of
efficiency

2% pa. e.g.
because of
growth
because of
higher
usage to

2% pa. e.g.
because of
growth
because of
higher
usage to

2% pa. e.g.
because of
growth
because of
higher
usage to

2% pa. e.g.
because of
growth
because of
higher
usage to

2% pa. e.g.
because of
growth
because of
higher
usage to

2% pa. e.g.
because of
growth
because of
higher
usage to

291

78

79.6

339.7

31 .54

79

92.4

321.7

35 .78

80

106.0

303 .0

40.47

2030, then
-2% pa.
e.g.
because of
efficiency

2030, then
-2% pa.
e.g.
because of
efficiency

2030, then
-2% pa.
e.g.
because of
efficiency

2030, then
-2% pa.
e.g.
because of
efficiency

2030, then
-2% pa.
e.g.
because of
efficiency

2030, then
-2% pa.
e.g.
because of
efficiency

2030, then
-2% pa.
e.g.
because of
efficiency

2030, then
-2% pa.
e.g.
because of
efficiency

2030, then
-2% pa.
e.g.
because of
efficiency

3% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-5% pa.
e.g.
because of

3% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-5% pa.
e.g.
because of

3% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-5% pa.
e.g.
because of

3% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-5% pa.
e.g.
because of

3% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-5% pa.
e.g.
because of

3% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-5% pa.
e.g.
because of

3% pa. e.g.
because of
growth
because of
higher
usage to
2030 , then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2030 , then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-5% pa.
e.g.
because of

3% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-5% pa.
e.g.
because of

3% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2030, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2030 , then
-5% pa.
e.g.
because of

292

81

56.1

286.9

18 .66

82

67.5

206 . 1

16.61

83

79.6

141 .2

14.86

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

1% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-1% pa.
e.g.
because of
efficiency
2% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-3% pa.
e.g.
because of

1% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-1 % pa.
e.g.
because of
efficiency
2% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-3% pa.
e.g.
because of

1% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-1% pa.
e.g.
because of
efficiency
2% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-3% pa.
e.g.
because of

1% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-1% pa.
e.g.
because of
efficiency
2% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-3% pa.
e.g.
because of

1% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-1%pa.
e.g.
because of
efficiency
2% pa. e.g.
because of
growth
because of
higher
usage to
2020 , then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-3% pa.
e.g.
because of

1% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-1% pa.
e.g.
because of
efficiency
2% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-3% pa.
e.g . .
because of

1% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-)%pa.
e.g.
because of
efficiency
2% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-3% pa.
e.g.
because of

1% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-1% pa.
e.g.
because of
efficiency
2% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-3% pa.
e.g.
because of

1% pa. e.g.
because of
growth
because of
higher
usage to
2020 , then
-1% pa.
e.g.
because of
efficiency
2% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-3% pa.
e.g.
because of

293

84

92.4

89.4

13 .3 9

85

106.0

48.1

12.17

86

56. 1

327.6

21.62

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

4% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-5% pa.
e.g.
because of
efficiency
1% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-1% pa.
e.g.
because of

4% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-5 % pa.
e.g.
because of
efficiency
1% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-!%pa.
e.g.
because of

4% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-5% pa.
e.g.
because of
efficiency
1% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-!%pa.
e.g.
because of

4% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-5% pa.
e.g.
because of
efficiency
1% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-1 % pa.
e.g.
because of

4% pa. e.g.
because of
growth
because of
higher
usage to
2020 , then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-5% pa.
e.g.
because of
efficiency
1% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-1% pa.
e.g.
because of

4% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2020 , then
-5 % pa.
e.g.
because of
efficiency
1% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-1% pa.
e.g.
because of

4% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-5% pa.
e.g.
because of
efficiency
1% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-1% pa.
e.g.
because of

4% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-5% pa.
e.g.
because of
efficiency
1% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-1% pa.
e.g.
because of

4% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-4% pa.
e.g.
because of
efficiency
5% pa. e.g.
because of
growth
because of
higher
usage to
2020, then
-5% pa.
e.g.
because of
efficiency
1% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-!%pa.
e.g.
because of

294

87

67.5

303.6

23.32

88

79.6

225 .7

23 . 13

89

92.4

182.6

24.12

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

2% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-4% pa.
e.g.
because of

2% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-4% pa.
e.g.
because of

2% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-4% pa.
e.g.
because of

2% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-4% pa.
e.g.
because of

2% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-4% pa.
e.g.
because of

2% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-4% pa.
e.g.
because of

2% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-4% pa.
e.g.
because of

2% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-4% pa.
e.g.
because of

2% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-2% pa.
e.g.
because of
efficiency
3% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-3% pa.
e.g.
because of
efficiency
4% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-4% pa.
e.g.
because of

295

90

106.0

144.3

25 .28

91

42.3

352 .2

19.27

92

32.4

211.1

12.87

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

efficiency

5% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-5% pa.
e.g.
because of
efficiency
All modes
follow
BAU
(growth/sh
rink rate
20072013)
All modes
follow
BAU
(growth/sh
rink rate
20072013)-1%
pa

5% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-5% pa.
e.g.
because of
efficiency
All modes
follow
BAU
(growth/sh
rink rate
20072013)
All modes
follow
BAU
(growth/sh
rink rate
20072013)-1%
pa

5% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-5% pa.
e.g.
because of
efficiency
All modes
follow
BAU
(growth/sh
rink rate
20072013)
All modes
follow
BAU
(growth/sh
rink rate
20072013)-1%
pa

5% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-5% pa.
e.g.
because of
efficiency
All modes
follow
BAU
(growth/sh
rink rate
20072013)
All modes
follow
BAU
(growth/sh
rink rate
20072013)-1%
pa

5% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-5% pa.
e.g.
because of
efficiency
All modes
follow
BAU
(growth/sh
rink rate
20072013)
All modes
follow
BAU
(growth/sh
rink rate
20072013)-1%
pa

5% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-5% pa.
e.g.
because of
efficiencv
All modes
follow
BAU
(growth/sh
rink rate
20072013)
All modes
follow
BAU
(growth/sh
rink rate
20072013)-1%
pa

5% pa. e.g.
because of
growth
because of
higher
usage to
2025 , then
-5% pa.
e.g.
because of
efficiency
All modes
follow
BAU
(growth/sh
rink rate
20072013)
All modes
follow
BAU
(growth/sh
rink rate
20072013)-1%
pa

5% pa. e.g.
because of
growth
because of
higher
usage to
202(, then
-5% pa.
e.g.
because of
efficiency
All modes
follow
BAU
(growth/sh
rink rate
20072013)
All modes
follow
BAU
(growth/sh
rink rate
20072013)-1%
pa

5% pa. e.g.
because of
growth
because of
higher
usage to
2025, then
-5% pa.
e.g.
because of
efficiency
All modes
follow
BAU
(growth/sh
rink rate
20072013)
All modes
follow
BAU
(growth/sh
rink rate
20072013)-1%
pa

296

93

23 .2

113.2

7.83

94

14.4

45 .6

3.85

95

6.3

-1.0

0.69

96

-1.4

-33.0

-1 .85

97

52.8

554.9

27.44

All modes
follow
BAU
(growth/sh
rink rate
20072013)-2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-4%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-5%
pa
All modes
follow
BAU
(growth/sh
rink rate
2007-

All modes
follow
BAU
(growth/sh
rink rate
20072013)-2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) -4%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-5%
pa
All modes
follow
BAU
(growth/sh
rink rate
2007-

All modes
follow
BAU
(growth/sh
rink rate
20072013)-2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-4%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-5%
pa
All modes
follow
BAU
(growth/sh
rink rate
2007-

All modes
follow
BAU
(growth/sh
rink rate
20072013)-2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-4%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-5%
pa
All modes
follow
BAU
(growth/sh
rink rate
2007-

297

All modes
follow
BAU
(growth/sh
rink rate
20072013)-2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-4%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-5%
pa
All modes
follow
BAU
(growth/sh
rink rate
2007-

All modes
follow
BAU
(growth/sh
rink rate
20072013)-2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-4%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) -5%
pa
All modes
follow
BAU
(growth/sh
rink rate
2007-

All modes
follow
BAU
(growth/sh
rink rate
20072013)-2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-4%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-5%
pa
All modes
follow
BAU
(growth/sh
rink rate
2007-

All modes
follow
BAU
(growth/sh
rink rate
20072013)-2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) -3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-4%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-5%
pa
All modes
follow
BAU
(growth/sh
rink rate
2007-

All modes
follow
BAU
(growth/sh
rink rate
20072013)-2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-4%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)-5%
pa
All modes
follow
BAU
(growth/sh
rink rate
2007-

98

64.0

845 .0

37.94

99

75.9

1258.
8

51.46

100

88.5

1847.
0

68.95

2013)
+ )%pa

2013) +1%
pa

2013) + 1%
pa

2013) + )%
pa

2013) + 1%
pa

2013) +1%
pa

2013) + )%
pa

2013) +1%
pa

2013) + )%
pa

All modes
follow
BAU
(growth/sh
rink rate
20072013)
+2%pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)
+3% pa
All modes
follow
BAU
(growth/sh
rink rate
20072013)
+4%pa

All modes
follow
BAU
(growth/sh
rink rate
20072013) +2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +4%
pa

All modes
follow
BAU
(growth/sh
rink rate
20072013) +2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +4%
pa

All modes
follow
BAU
(growth/sh
rink rate
20072013) +2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +4%
pa

All modes
follow
BAU
(growth/sh
rink rate
20072013) +2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +4%
pa

All modes
follow
BAU
(growth/sh
rink rate
20072013) +2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +4%
pa

All modes
follow
BAU
(growth/sh
rink rate
20072013) +2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +3%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +4%
pa

All modes
follow
BAU
(growth/sh
rink rate
20072013) +2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +3%
oa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +4%
pa

All modes
follow
BAU
(growth/sh
rink rate
20072013) +2%
pa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +3%
oa
All modes
follow
BAU
(growth/sh
rink rate
20072013) +4%
pa

298

101

28 .2

217.7

12.96

-1% pa

-!%pa

102

28.2

204.0

12.24

-1% pa

-1%pa

By 2020,
all cars
improved
to
efficiency
of Prius
(53%
improvem
ent from
current
average
EF), then 1% pa
'By 2020,
all cars
improved
to
efficiency
of Prius
(53%
improvem
ent from
current
average
EF), then 1% until
2030, then
50%
reduction
more
technology
, then -1%
pa

-!%pa

-!%pa

-!%pa

-!%pa

-1% pa

-!%pa

-1% pa

-!%pa

-1% pa

-!%pa

-1% pa

-1%pa

299

103

40.6

208.2

12.79

-lo/opa

-lo/opa

-!%pa

-1% pa

-lo/opa

-1% pa

-lo/opa

104

40 .6

166.4

9.99

-1% pa

-!%pa

-lo/opa

-lo/opa

-1% pa

-1% pa

-lo/opa

105

40 .6

124.6

7. 19

-1% pa

-1% pa

-1% pa

-1% pa

-1% pa

-1% pa

-lo/opa

300

-1% pa.
e.g.
despite
higher
volume
from
modal
shift
because of
efficiency
and
electrificat
ion
-Io/opa
e.g.
despite
higher
volume
from
modal
shift
because of
efficiency
and
electrificat
ion
-1% pa
e.g.
despite
higher
volume
from
modal
shift
because of
efficiency
and
electrificat

-1% pa
until 2025 ,
then 25%
e.g. modal
shift to
train

-1% pa
until 2025,
then 50%
e.g. modal
shift to
train

-1% pa
until 2025 ,
then 75%
e.g. modal
shift to
train

10n

106

17.1

15 . 1

3.71

-3% pa to
2030, -4%
2030 to
2040, -5%
2040 to
2050.

-3% pa to
2030, -4%
2030 to
2040, -5%
2040 to
2050.

-3% pa to
2030, -4%
2030 to
2040, -5%
2040 to
2050.

-3% pa to
2030, -4%
2030 to
2040, -5%
2040 to
2050 .

301

-3% pa to
2030, -4%
2030 to
2040, -5%
2040 to
2050.

-3% pa to
2030, -4%
2030 to
2040, -5%
2040 to
2050.

-3% pa to
2030 , -4%
2030 to
2040, -5%
2040 to
2050 .

-3% pa to
2030, -4%
2030 to
2040, -5%
2040 to
2050.

-3% pa to
2030, -4%
2030 to
2040, -5%
2040 to
2050.