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An Evaluation of Alternative Fuels and Powertrain Technologies for Canada’s Long Haul Heavy-duty Vehicle
Sector
by
Madeline Ewing
A thesis submitted in conformity with the requirements for the degree of Master of Applied Science
Department of Civil and Mineral Engineering University of Toronto
© Copyright by Madeline Ewing 2019
ii
An Evaluation of Alternative Fuels and Powertrain Technologies
for Canada’s Long Haul Heavy-duty Vehicle Sector
Madeline Ewing
Master of Applied Science
Department of Civil and Mineral Engineering
University of Toronto
2019
Abstract
Heavy-duty vehicles (HDVs) are responsible for a growing share of greenhouse gas (GHG)
emissions in Canada. Despite the near-term availability of several low GHG alternatives to
diesel, the most viable alternatives have yet to be identified. The first portion of this thesis
reports insights gathered through expert interviews in relation to the perceived barriers and
opportunities to the adoption of promising alternative technologies for long haul HDVs. Expert
insights are incorporated into frameworks for the evaluation of current or near-term alternative
technologies. The second portion of the thesis evaluates an emerging fuel, dimethyl ether
(DME), on the basis of its well-to-wheel GHG emissions when produced in Canada. It is found
that DME produced from renewable feedstocks can reduce GHG emissions by up to 60%, while
natural gas-based DME may increase GHG emissions by 20%. Insights from this thesis can
inform policies to support uptake of low GHG alternatives for HDVs.
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Acknowledgments
I would like to sincerely thank my supervisors, Daniel Posen and Heather MacLean, for all of
their support throughout my Master’s degree. The two of them provided me with valuable
insights and direction, while giving me the freedom to navigate my own route. Their positive
attitudes allowed me to feel inspired and at ease throughout the process.
Another major source of inspiration throughout my Master’s degree was the Saxe-MacLean-
Posen (SPM) research group, which included Professors MacLean and Posen, as well as
Professor Saxe, and each of their students and post-doctoral fellows. The support provided by
this group over the course of the two years I was at U of T gave me a sense of confidence and
purpose.
I would also like to extend my thanks to Mitacs for providing me with the opportunity to intern
at the Pembina Institute, as well as the funding to do so. Furthermore, I would like to extend a
big thank you to the Pembina Institute, and in particular Carolyn Kim, for their help and support
in contacting interviewees and getting me to think about the broader implications of my research.
Thank you to both Greg Peniuk and Yaser Khojasteh for allowing me to continue the work that
they started on the DME project and providing me with the resources to make it happen. Thank
you to Joanna Melnyk, as well, for her contributions to the cost benefit analysis.
Thank you to the Government of Ontario for awarding me an Ontario Graduate Scholarship
(OGS) which supported this research.
And finally, a big thank you to my friends and family for providing me with so much support
throughout the process.
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Table of Contents
Acknowledgments ........................................................................................................................ iii
Table of Contents ......................................................................................................................... iv
List of Tables ............................................................................................................................... vii
List of Figures ................................................................................................................................ x
List of Abbreviations ................................................................................................................... xi
Chapter 1 Introduction ................................................................................................................. 1
Chapter 2 Literature Review ....................................................................................................... 6
2.1 Alternative Fuel Vehicle Purchasing Considerations of the Heavy-duty Vehicle Sector.... 6
2.2 Evaluation of Alternative Fuels and Powertrain Technologies for Heavy-duty Vehicles.. 7
2.3 Multi-criteria Decision Analysis of Alternative Fuels and Powertrain Technologies ...... 10
2.4 Life Cycle Assessment of Dimethyl Ether (DME) ............................................................. 14
Chapter 3 Background ............................................................................................................... 17
3.1 Overview ........................................................................................................................... 17
3.2 Emissions from Heavy-duty Transport in Canada ............................................................ 17
3.3 Economic Importance of Freight Transport in Canada ................................................... 18
3.4 Vehicle Classification ........................................................................................................ 18
3.4.1 Vehicle Classification by Gross Vehicle Weight Rating (GVWR) .......................... 18
3.5 Emissions Standards for Class 8 Vehicles in Canada ...................................................... 21
3.6 Life Cycle Assessment (LCA) ............................................................................................ 22
3.6.1 General Concerns with LCA of Alternative Fuels .................................................... 23
3.7 Alternative Heavy-duty Vehicle Technologies for Long Haul .......................................... 25
3.7.1 Battery Electric Vehicles .......................................................................................... 25
3.7.2 Hydrogen Fuel Cell Electric Vehicles ...................................................................... 29
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3.7.3 Natural Gas Vehicles ................................................................................................ 34
3.7.4 Biodiesel ................................................................................................................... 38
3.7.5 Renewable Diesel ...................................................................................................... 40
3.7.6 DME .......................................................................................................................... 42
Chapter 4 Multi-criteria evaluation of alternative technologies for long haul trucking in
Canada: insights from expert interviews and evaluation frameworks .................................. 46
4.1 Introduction ....................................................................................................................... 46
4.2 Material and Methods ....................................................................................................... 50
4.2.1 Selection of Alternative Fuels and Powertrain Technologies ................................... 50
4.2.2 Expert Interviews ...................................................................................................... 53
4.2.3 Frameworks for Evaluation ....................................................................................... 54
4.3 Results ............................................................................................................................... 59
4.3.1 Expert Interviews ...................................................................................................... 60
4.3.2 Evaluation Frameworks ............................................................................................ 65
4.4 Discussion ......................................................................................................................... 74
4.4.1 Expert Interviews ...................................................................................................... 74
4.4.2 Evaluation Frameworks ............................................................................................ 74
4.5 Conclusion......................................................................................................................... 77
Chapter 5 Well-to-wheel greenhouse gas emissions of dimethyl ether produced from
renewable and non-renewable feedstocks in Alberta .............................................................. 79
5.1 Introduction ....................................................................................................................... 79
5.2 Background ....................................................................................................................... 81
5.2.1 Advantages of DME ................................................................................................. 82
5.2.2 Disadvantages of DME ............................................................................................. 83
5.2.3 Summary of Advantages and Disadvantages of DME ............................................. 84
5.2.4 DME Production ....................................................................................................... 85
5.3 Material and Methods ....................................................................................................... 85
5.3.1 Overview ................................................................................................................... 85
5.3.2 Upstream Activities .................................................................................................. 88
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5.3.3 DME Production Plant .............................................................................................. 91
5.3.4 Transportation and Distribution ................................................................................ 93
5.3.5 Vehicle Use ............................................................................................................... 94
5.3.6 Land Use Change (LUC) .......................................................................................... 95
5.3.7 Diesel Reference Pathway ........................................................................................ 95
5.4 Results ............................................................................................................................... 95
5.4.1 Well-to-wheel (WTW) Results ................................................................................. 95
5.4.2 Sensitivity Analysis .................................................................................................. 97
5.5 Discussion ....................................................................................................................... 101
Chapter 6 Conclusion ............................................................................................................... 104
References .................................................................................................................................. 109
Appendix A Technological Considerations with Respect to Alternative Fuel or Powertrain
Adoption ..................................................................................................................................... 125
Appendix B Fuel Cost Considerations ................................................................................... 130
Appendix C Fuel Supply Considerations ............................................................................... 135
Appendix D List of Interview Questions Used for Expert Interviews ................................ 140
Appendix E Expert Interview Responses .............................................................................. 142
Appendix F Inputs to the Frameworks Employed in Chapter 4 .......................................... 153
Appendix G Results of the Sensitivity Analysis of the Societal Cost Benefit Analysis of
Alternative Technologies for Long Haul Heavy-duty Vehicles ............................................ 163
Appendix H Select Inputs to the Well-to-wheel Assessment of DME ................................. 165
Appendix References ................................................................................................................ 166
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List of Tables
Table 3.1. Vehicle classification, make-up of Canadian on-road vehicle pool and contribution to
Canada’s on-road vehicle kilometers travelled (VKT) by weight class. ...................................... 19
Table 4.1. Number of experts interviewed by sector. ................................................................... 53
Table 4.2. Description of the attributes that are included in the multi-attribute utility analysis.. 55
Table 4.3. Summary of the methodology by which the monetary value of each attribute is
determined in the societal cost benefit analysis. ........................................................................... 59
Table 4.4. The most commonly identified opportunities and barriers to the adoption of alternative
vehicle technologies during expert interviews. ............................................................................. 60
Table 4.5. Unweighted utility of each technology for each attribute considered in the multi-
attribute utility analysis ................................................................................................................. 66
Table 4.6. Unweighted scores for each technology across all attributes considered in the
satisficing framework .................................................................................................................... 70
Table 4.7. Net present values for each attribute over a vehicle’s lifetime considered in the
societal cost benefit analysis. ........................................................................................................ 72
Table 4.8. Net present value (NPV) and ranking of technologies as determined using the societal
cost benefit analysis framework .................................................................................................... 73
Table 5.1. Properties of DME and diesel ...................................................................................... 82
Table 5.2. 100-year global warming potential values considered in this LCA ............................. 87
Table 5.3. Values of key input parameters used in upstream electricity activities ....................... 88
Table 5.4. Life cycle emission factors for upstream electricity activities .................................... 89
Table 5.5. Values of key input parameters used in upstream natural gas activities ..................... 89
Table 5.6. Values of key input parameters used in upstream biomass activities .......................... 91
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Table 5.7. The indirect DME production pathways considered in this assessment. ..................... 92
Table 5.8. Values of key input parameters used in transmission and distribution activities ........ 94
Table 5.9. Values of key input parameters used in heavy-duty vehicle use activities .................. 94
Table 5.10. The indirect DME production pathways considered in this assessment .................... 98
Table A1. Occurrence and frequency of responses to the question “What do you see as top
priorities for the trucking industry when considering an investment into an alternative fuel
vehicle?”......................................................................................................................................142
Table A2. Responses to the question “How would you rate the importance of each of the
following when investing in an alternative fuel vehicle on a scale of 1-10 with 1 being the least
important, 10 being the most important and 5 being neutral?”. .................................................. 144
Table A3. Occurrence and frequency of responses to the question “What do you think would be
the most important advantages, if any, to each of the following energy sources for long-haul
trucking in Canada?”. .................................................................................................................. 145
Table A4. Occurrence and frequency of responses to the question “What would you consider to
be the greatest obstacles to the deployment of the following energy sources for long-haul
trucking in Canada?”. .................................................................................................................. 148
Table A5. Occurrence and frequency of responses to the question “Which, if any, alternative
technologies do you predict will have at least moderate uptake in the long haul new heavy-duty
vehicle sector in Canada in the next 10 years? 20 years?” ......................................................... 151
Table A6. Occurrence and frequency of responses to the question “What do you expect to be the
dominant energy source for new long haul heavy-duty trucks in 20 years?”. ............................ 152
Table A7. Occurrence and frequency of responses to the question “Do you expect a trucking
company would be willing to pay greater lifetime costs for improved environmental performance
such as a reduction in GHG emissions or other air pollutants? If yes, how much more: a. very
little, b. somewhat, or c. much more?” ....................................................................................... 152
Table A8. Default vehicle parameters used throughout the multiple criteria decision analyses. 153
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Table A9. Inputs and assumptions used throughout the multi-attribute utility analysis, satisficing
framework and cost benefit analysis. .......................................................................................... 156
Table A10. Inputs to vehicle weight calculations. ...................................................................... 162
Table A11. Results of the societal cost benefit analysis when a low cost of NOx emissions
($300/tonne) is applied. Results are presented in thousands of dollars ($1,000s). ..................... 163
Table A12. Results of the societal cost benefit analysis when a high cost of NOx emissions
($14,00/tonne) is applied. Results are presented in thousands of dollars ($1,000s). .................. 164
Table A13. Ultimate analysis of typical wood residue and natural gas in Alberta [73–75]. ...... 165
Table A14. Values and sources of key input parameters used throughout the DME production
model. .......................................................................................................................................... 165
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List of Figures
Figure 4.1. Boxplots showing interviewee responses demonstrating the weighted importance of
various vehicle attributes .............................................................................................................. 65
Figure 4.2. Weighted utility of each technology based on each individual’s weighting of the
various attributes that have been considered ................................................................................ 67
Figure 4.3. Weighted scores for each technology as determined using a satisficing heuristic
framework with diesel as a reference ............................................................................................ 71
Figure 5.1. Chemical structure of DME ........................................................................................ 81
Figure 5.2. System boundaries of the WTW DME product system, shown simultaneously for
natural gas, wood residue and short rotation poplar feedstocks ................................................... 86
Figure 5.3. WTW GHG emissions of Aspen Plus-based models of DME in comparison to diesel.
....................................................................................................................................................... 96
Figure 5.4. WTW GHG emissions of DME produced via indirect methods versus direct methods
in comparison to a diesel baseline. ............................................................................................... 98
Figure 5.5. WTW results of the sensitivity analysis of the modelled DME pathways with respect
to changes to the grid electricity mix, feedstock transportation distance and methane emissions
from upstream natural gas ........................................................................................................... 100
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List of Abbreviations
AHP: analytical hierarchy process
ANP: analytical network process
B100: 100 percent biodiesel
B20: 20 percent biodiesel blend
BEV: battery electric vehicle
bio-DME: DME produced from biomass
CAD: Canadian dollars
CCA: Capital Cost Allowance
CFS: Clean Fuel Standard
CI: compression ignition
CNG: compressed natural gas
CO: carbon monoxide
DANP: decision-making trial and evaluation laboratory (DEMATAL)-based analytical network
process (ANP)
DEMATAL: decision-making trial and evaluation laboratory
DGE: diesel gallon equivalent
DLE: diesel litre equivalent
DME: dimethyl ether
E85: 85 percent ethanol blend
EASIUR: Estimating Air pollution Social Impact Using Regression model
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EU: European Union
FAME: fatty acid methyl esters
GDP: gross domestic product
GDP: gross domestic product
GHG: greenhouse gas
GREET: Argonne National Laboratory’s greenhouse gases, regulated emissions, and energy use
in transportation model
GTHA: Greater Toronto and Hamilton area
GVWR: gross vehicle weight rating
GWP: global warming potential
H2FCEV: hydrogen fuel cell electric vehicles
HC: hydrocarbon
HDRD: hydrogenation-derived renewable diesel
HDV: heavy-duty vehicle
ICE: internal combustion engine
LCA: life cycle assessment
LDV: light-duty vehicle
LHV: lower heating value
LNG: liquefied natural gas
LPG: liquefied petroleum gas
LUC: land use change
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MCDA: multi-criteria decision analysis
NEB: National Energy Board
NG: natural gas
NOx: nitrogen oxide
NPV: net present value
NRCAN: Natural Resources Canada
OEM: original equipment manufacturer
PEM: proton exchange membrane
PM: particulate matter
PROMETHEE: preference ranking organization method for enrichment and evaluations
RNG: renewable natural gas
ROI: return on investment
SCR: selective catalytic reduction
TOPSIS: technique for order of preference by similarity to ideal solution
ULSD: ultra-low sulfur diesel
VIKOR: multicriteria optimization and compromise solution (translated from Serbian)
VKT: vehicle kilometers travelled
WTW: well-to-wheel
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Chapter 1 Introduction
Canada must reduce emissions from its transport sector to meet its greenhouse gas (GHG)
emission reduction targets by 2030. These targets require that the country’s GHG emissions be
reduced by 30 percent below 2005 levels [1]. Though a considerable amount of work has been
put into the evaluation of potentially viable low GHG alternatives for light-duty vehicles,
comparatively less work has been into the evaluation of alternatives for heavy-duty vehicles
(HDVs). In Canada, HDVs are a broad class of vehicles weighing more than 3,856 kg and range
from pickup trucks to tractor trailers [2]. HDVs account for approximately 33 percent of GHG
emissions within Canada’s transport sector, while representing roughly 9 percent of the country’s
vehicle fleet [3]. Furthermore, GHG emissions from Canada’s HDV fleet have continued to
steadily rise over the past decade, while emissions from the passenger vehicle fleet have declined
[4]; in fact, GHG emissions from Canada’s HDV fleet are expected to surpass those of its
passenger vehicle fleet by 2050, despite representing a small proportion of the entire vehicle fleet
[5] .
A number of solutions exist to reduce GHG emissions from Canada’s HDVs including demand
reduction, mode shift, route optimization, improvements to existing vehicle efficiencies, or a
switch to alternative fuels and powertrain technologies with proven GHG emission reductions.
While I acknowledge the importance of each of these solutions, I focus on a switch to low GHG
emitting alternative fuels or powertrain technologies in this thesis.
There exist several possible alternative fuels and powertrain technologies that may be able to
mitigate GHG emissions from the HDV sector including biodiesel, renewable diesel, Fischer-
Tropsch diesel, liquefied natural gas, compressed natural gas, renewable natural gas, methanol,
dimethyl ether, hydrogen, fuel cells, hybrid electric vehicles, and battery electric vehicles.
Despite this range of options, there is no consensus over which, if any, of these alternatives can
efficiently and effectively reduce climate impacts from HDVs. In particular, there is uncertainty
surrounding the optimal low GHG alternative to diesel for long haul HDVs. These vehicles are
primarily combination tractor trailers weighing more than 15 tonnes that are responsible for the
interregional transport of goods. They operate almost exclusively using diesel and are
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responsible for approximately 56% of the vehicle kilometers travelled (VKT) by medium- and
heavy-duty vehicles in Canada [6].
The nature of the operation of long haul HDVs presents certain obstacles to the adoption of
alternative technologies. For one, long haul HDVs have particularly high energy demands due to
the heavy loads they carry and the long distances they travel [7,8]. A second obstacle is that long
haul HDVs rely heavily on public refueling infrastructure. These vehicles may take several days
to complete a single trip, and do not return to their home base for refueling each night. Third,
there is notable competition among for-hire long haul HDV carriers. These companies tend to
have limited operating margins and thus are particularly risk averse [9]. Technologies that
require additional investment or disrupt operations are unlikely to generate an enthusiastic
response from the industry. Lastly, fleet turnover rates are slow. The long lifetime of an HDV
means that the industry is committed to that technology for a considerable amount of time.
Long haul HDVs are a crucial area to tackle as a part of a GHG emissions reduction strategy.
Despite their importance, there has been little work focused on determining the most viable
alternatives to diesel. There has been no reporting on the priorities of key stakeholders of
Canada’s long haul trucking industry when it comes to investment in low GHG alternatives to
diesel. Individuals from within the clean transportation and trucking sectors must be consulted to
determine their perceptions surrounding each of the technologies; without the support of key
stakeholders, alternative technologies are unlikely to gain traction. Additionally, a multi-criteria
evaluation of alternatives to diesel has yet to be performed for Canada’s long haul HDV sector.
To date, the majority of studies have focused on evaluating alternative technologies for HDVs on
the basis of life cycle GHG emissions, lifetime costs and in some cases, criteria air pollutant
emissions [10–15]. These evaluations fail to capture the impact of alternative technologies on
other important criteria, such as cargo capacity, vehicle range, or availability of refueling
infrastructure, among others. Without comprehensive evaluations focused on the potential
impacts to operations, in particular, decision-makers are unable to identify suitable alternatives to
petroleum diesel. Finally, more work needs to be done on quantifying the life cycle GHG
emissions of emerging fuels, such as dimethyl ether (DME), a fuel that could potentially provide
a flexible path to GHG emission reductions from HDVs in Canada. Previous life cycle
assessments (LCAs) of DME performed in other jurisdictions, including East Asia [16–18],
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Thailand [19], Papua New Guinea [16], Sweden [20] and the United States [21–23] have
demonstrated that DME may have the ability to reduce life cycle GHG emissions in comparison
to a diesel baseline in other jurisdictions, but an LCA of DME produced in Canada has yet to be
performed.
The overall objectives of this thesis are three-fold:
1. Identify the concerns and priorities of the long haul trucking sector in Canada with
regards to investment in alternative fuels and powertrain technologies.
2. Identify multi-criteria decision making frameworks that can assist in the evaluation of
alternative fuels and powertrain technologies for long haul trucking which can
incorporate stakeholder insights and be easily adopted by decision makers.
3. Quantify the life cycle GHG emissions of DME produced in Canada from a variety of
feedstocks.
In line with the first objective, this thesis documents insights gathered from expert interviews
pertaining to the adoption of alternatives to diesel in the long haul trucking sector in Canada.
Interviewees were asked to comment on a set of alternative technologies either currently
available or expected to be available in the near-term, including battery electric vehicles,
hydrogen fuel cell vehicles, natural gas, biofuels and renewable diesel. Each interviewee was
asked to rate the importance of various vehicle attributes that may influence the decision to
invest in an alternative vehicle technology. These levels of importance are then incorporated into
multi-criteria decision making frameworks as per objective two. The perceived opportunities and
barriers to the adoption these alternative technologies as identified by experts are also
summarized in this thesis, as well as the expected priorities of the long haul trucking industry
when it comes to investment in an alternative fuel vehicle.
The demanding nature of long haul trucking, and the various trade-offs associated with each of
the alternative technologies make decision-making a particularly difficult task. Insights from
expert interviews informed the development of several simple decision-making frameworks for
the evaluation of various alternative fuel vehicles, including a multi-attribute utility model and a
satisficing heuristic model. These two frameworks aim to highlight some of the ways by which
expert insights can be incorporated into multi-criteria decision analysis that can be easily adopted
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by decision makers. Results from these decision-making frameworks are also compared to a
societal cost benefit analysis.
Finally, this thesis contributes to a growing body of work assessing the life cycle GHG emissions
of DME, a potential alternative to diesel. This thesis examines the advantages and disadvantages
associated with the use of DME as an HDV fuel. It also reports the results of an LCA that
examines the expected life cycle GHG emissions of various DME production pathways in
Alberta, Canada in comparison to petroleum diesel.
Insights from the thesis are expected to assist the long haul trucking sector in determining viable
low GHG alternatives to diesel. The thesis identifies both the priorities and concerns of the
trucking sector when it comes to investment in alternative fuel HDVs for long haul and presents
frameworks by which key stakeholders can evaluate alternatives. Finally, it contributes to a
growing body of work related to the assessment of DME, an emerging and promising alternative
to diesel fuel. By helping to identify viable pathways to reduce GHG emissions from the long
haul HDV transportation sector, this thesis aims to assist Canada in carving out its path to
achieving its GHG emission targets.
This thesis meets these objectives over the course of six chapters. While this first chapter has
provided an introduction to the topic, Chapter 2 will provide a detailed overview of the literature
that has previously focused on four relevant topics including the identification of alternative fuel
vehicle purchasing considerations of the HDV sector, the evaluation of alternative fuels and
powertrain technologies for HDVs, multi-criteria decision analysis of alternative fuels and
powertrain technologies, and previous LCAs of DME. In Chapter 3, relevant background
information will be provided pertaining specifically to emissions from HDVs in Canada, the
economic importance of freight transport, the gross vehicle weight rating (GVWR) vehicle
classification system, current emission standards for HDVs and an overview of LCA methods. It
will also provide an overview of each of the alternative fuels and powertrain technologies to be
discussed throughout this thesis including each of their technical limitations, availability of
vehicle models, state in Canada and expected reductions in life cycle GHG emissions. Chapters 4
and 5 will each present manuscripts that are to be submitted to peer reviewed journals. Chapter 4
will present results pertaining to objectives 1 and 2 of this thesis, namely insights from expert
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interviews and frameworks for the multi-criteria evaluation of alternative vehicle technologies
for long haul HDVs. Joanna Melnyk will also be listed as an author of this paper as she was
responsible for developing the cost benefit analysis. Meanwhile, Chapter 5 will present results
pertaining to objective 3 of this thesis, which includes a well-to-wheel LCA of DME produced in
Canada from a natural gas, wood waste and poplar. Both Greg Peniuk and Yaser Khojasteh will
be listed as authors of this paper as a result of their contributions which included developing the
initial LCA of DME produced from natural gas and wood waste and developing the chemical
process model of the DME production plant, respectively. Finally, Chapter 6 will provide a
summary of the methods used through this thesis, as well as important findings, academic
contributions, and recommendations for further assessments.
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Chapter 2 Literature Review
2.1 Alternative Fuel Vehicle Purchasing Considerations of the Heavy-duty Vehicle Sector
There is little documentation surrounding the top considerations of the on-road freight sector
when it comes to investment in alternative fuel vehicles. Anderhofstadt and Spinler (2019) used
the Delphi method of interviewing experts to identify factors affecting the purchase and
operation of alternative fuel heavy-duty trucks in Germany [24]. The authors found
that reliability, fueling/charging infrastructure, the possibility to enter low emission zones,
current fuel costs and future trend in fuel costs have the greatest influence on a decision-makers
decision to invest in alternative fuel HDVs. On the other hand, the influence of oil producers,
vehicle design, ecological impact of truck manufacturing and recycling, taxes and insurance as
well as well-to-tank emissions had the smallest impact on a decision to invest in an alternative
fuel HDV. Experts identified the availability of refueling and recharging infrastructure, as well
as vehicle purchase price as barriers to the adoption of a suite of alternative vehicle technologies
that included alternative technologies including battery electric trucks, fuel cell electric trucks,
liquefied natural gas vehicles and compressed natural gas vehicles.
Meanwhile, Klemick et al. (2015) used focus groups to identify what factors might be
responsible for the low rates of adoption of fuel saving technologies in heavy-duty trucking [25].
The authors identified a lack of information, a lack of infrastructure, as well as split incentives
between fleet owners and drivers, reliability, company-specific factors, risk and uncertainty, as
well as regulatory barriers as barriers to the adoption of fuel saving devices.
Mohamed, Ferguson and Kanaroglou (2018) recently documented the perspectives of Canadian
transit providers on the topic of factors preventing greater uptake of electric transit buses [26].
Fifty-five themes emerged within this series interviews that fall into four categories of concerns:
uncertainty surrounding the rapid technological advancement of battery electric bus technology,
the operational and financial feasibility of a battery electric bus, minimizing risk during the
decision-making process, and having a business case.
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Though not pertaining specifically to HDVs, Sierzchula (2014) performed a series of expert
interviews to determine what factors have influenced the adoption of electric vehicles within
United States and Dutch-based organization fleets [27]. Fleets generally invested in electric
vehicles to minimize their organization’s environmental impact, improve the organization’s
public perception, and make use of government grants. Motivation to make the switch to electric
vehicles, however, ranged depending on whether an organization was public or private. While
public organizations were often motivated by state regulations, private organizations were
motivated more by the desire to be an early adopter of the technology.
To date, there has been no documentation of considerations pertaining to investment in
alternative fuel vehicles specific to the long haul sector of heavy-duty trucking. In the context of
climate change, long haul heavy-duty trucking is a particularly important market to penetrate
with low GHG emission technologies due to its high petroleum diesel fuel consumption.
Furthermore, considerations of the long haul industry are likely to differ in comparison to
alternate HDV operations such as transit buses, or short-haul operations. Long haul heavy-duty
trucks carry notably heavy loads, travel distances much greater than other on-road heavy-duty
operations and rely heavily on public refueling infrastructure. Hence, unique considerations with
respect to vehicle weight, range and refueling infrastructure are expected and must be confirmed
through expert interviews.
2.2 Evaluation of Alternative Fuels and Powertrain Technologies for Heavy-duty Vehicles
A number of studies have examined the role of low GHG alternatives to diesel as a means to
reduce GHG emissions from the on-road freight sector. These studies have generally explored
alternatives to diesel on the basis of three broad categories. First, several studies have aimed to
quantify the life cycle GHG emissions of alternative technologies for HDVs, which considers the
GHG emissions associated with a product system across all stages of its life (namely resource
extraction, production, use and disposal) [10,13,28–35,36,37]. Second, a number of studies have
explored the economic feasibility of these alternatives by estimating lifetime costs
[10,30,31,33,38,39], performing a cost benefit analysis [40], or determining break even fuel costs
[15,32]. Third, a number of studies have estimated the impacts of various alternatives on local air
8
pollution [11,38,41–43]. To my knowledge, only a single study has performed a multi-criteria
analysis of on-road freight trucks [44].
Most of these studies have been conducted within the past decade and have focused on the
evaluation of alternatives within Europe [10,12,14,33–35], the United States
[10,11,13,15,31,32,36,45] and China [10]. While some of these studies have focused on zero-
emission technologies which include vehicle technologies that do not produce any harmful
tailpipe emissions, such as battery electric or hydrogen fuel cell electric vehicles [10–12,32],
others have included alternatives beyond zero-emission technologies, such as natural gas, various
biofuels or hybrid powertrains [13–15,31,33–37,45].
Due to differences in scope as well as methods employed, these studies often differ in their
conclusions. A subset of these studies concludes that zero-emission trucks, including battery
electric and/or hydrogen fuel cell electric, are expected to be the most effective pathways to
achieving GHG emission reductions [10–12]. This is concluded on the basis that zero-emission
technologies, in general, are expected to provide greater reductions in life cycle GHG emissions
than other technologies [10]. Meanwhile, another subset of studies concludes that biofuels, such
as biodiesel or renewable natural gas, will play a crucial role in reducing GHG emissions from
the sector [13,33–35,45], while den Boer et al. (2013) see the impacts of biofuels as being too
uncertain to adopt on a large scale [12]. There is also disagreement surrounding the role natural
gas can play in reducing GHG emissions; though some analyses have concluded that certain
natural gas pathways can lead to reductions in GHG emissions from on-road freight [13–
15,33,34,36], other pathways may lead to increases or negligible reductions in GHG emissions
[11,13,14,29,33,34].
Additional disagreement surrounds the economic feasibility of each of these alternatives. Certain
studies predict that zero emissions technologies, including battery electric in particular, will
eventually offer economic advantages over the current diesel baseline [10–12], with a set of
authors predicting this advantage to manifest as early as 2020 to 2030 [10,12]. Meanwhile, two
studies predict that battery electric vehicles will not offer economic advantages over a diesel
baseline [31,32]. On the other hand, Zhao, Burke and Zhu (2014) identified liquefied natural gas
9
(LNG) as the only alternative offering economic advantages over diesel among a suite of
alternatives that included LNG, LNG hybrid electric, battery electric, and fuel cell trucks [15].
While some studies that use of natural gas as a vehicle fuel in HDVs will lead to air quality
improvements stemming from reductions in certain air pollutant emissions [13], both Sen, Ercan
and Tatari (2017) and Tong et al. (2015) found that natural gas will lead to increases in air
pollutant emissions [11,29].
Adding to the challenge of understanding which technologies are expected to be the most
promising on the basis of life cycle GHG emissions, economics and air pollutant emissions is the
fact that few studies have considered impacts beyond these criteria. There are a few notable
works that evaluate the performance of alternative trucking technologies on the basis of
additional criteria. In addition to discussing their GHG emission potential and expected costs,
den Boer et al. (2013) provide an extensive review of the state-of-readiness of zero emission
trucking technologies by discussing vehicle attributes such as performance, durability, energy
storage, refueling time and vehicle weight [12]. Meanwhile, Osorio-Tejada, Llera-Sastresa and
Scarpellini (2017) analyze the performance of alternative trucking technologies on the basis of a
multi-criteria sustainability assessment, which includes criteria that span environmental
economic and social realms; however, only a very limited set of alternatives are included in the
analysis: liquefied natural gas and renewable diesel [44].
Few studies have examined the impact that a large set of alternative fuels and technologies will
have on various aspects of on-road freight operations, including long haul, and the extent to
which each fuel/technology will be able to meet the sector’s operational demands. While it is
important to evaluate the ability of alternative technologies to contribute to GHG emission
reductions, and though the cost of alternative technologies is a particularly important criteria to
be considered by the sector, the impact of alternative vehicle technologies on daily trucking
operations should not be undermined. Ultimately, the climate benefits of these alternative
technologies will only be felt if they are adopted on a large scale, and if there are limitations
uncertainty surrounding the ability of a particular alternative technology to meet the operational
needs of the sector, it is difficult to say whether or not it would ever be adopted.
10
Den Boer et al. (2013) discuss the varying attributes of zero-emissions trucking technologies
[12]; however, their analysis does not capture differences between the technologies and their
varying impacts on trucking operations within the framework of a comparative analysis. On the
other hand, Osorio-Tejada, Llera-Sastresa and Scarpellini (2017) capture various performance
metrics within a multi-criteria sustainability assessment, though their analysis considers only a
single metric related to trucking operations: reliability [44]. Their assessment, however, does
incorporate other important criteria which are discussed in more detail in Section 2.3.
In conclusion, there is a need to incorporate a greater number of criteria within frameworks
evaluating the performance of alternative trucking technologies. Moreover, due to the lack of
consensus surrounding the GHG emissions and economic benefits of each technology, a wide
range of alternatives should continue to be analyzed, including zero-emission technologies,
natural gas and biofuels. In other words, there is insufficient evidence to exclude any particular
alternative from further analysis. A detailed review of the alternative technologies considered
throughout this thesis can be found in Section 3.7, where I discuss the current state of
technology, technical limitations of each technology, expected GHG emission reductions, and
potential risks associated with the adoption of each technology.
2.3 Multi-criteria Decision Analysis of Alternative Fuels and Powertrain Technologies
Multi-criteria decision analysis (MCDA) facilitates a comparative evaluation of alternatives
across multiple criteria [46]. Application of MCDA can assist decision-makers to identify the
most preferable alternative among a suite of options. There are various MCDA methods, each of
which incorporates decision makers’ preferences in a unique way [47].
A number of MCDA methods have been employed to evaluate the performance of alternative
fuel vehicles for light-duty vehicles (LDVs) [48–52], in particular, as well as buses [52,53], and
heavy-duty trucks [44]. These methods include Analytical Hierarchy Process (AHP)
[44,48,52,54,55], the Technique For Order Of Preference By Similarity To Ideal Solution
(TOPSIS) [51,53,54], the Preference Ranking Organization Method For Enrichment And
Evaluations (PROMETHEE) [49,50], Analytical Network Process (ANP) [56], the Decision-
Making Trial And Evaluation Laboratory (DEMATAL) technique [56] and the Multi-Criteria
11
Optimization And Compromise Solution (VIKOR) [53]. Fuzzy methods have also been
incorporated into a number of frameworks to account for the uncertainty and vagueness
surrounding qualitative data [48,51,54]. Method selection depends on the availability of
information, the amount of time that stakeholders are willing to commit to the process, the
number of indicators considered and the level of accuracy desired, among others. Some of the
key methods are introduced below.
AHP is one of the most frequently employed MCDA tools [46,47]. In this method, a decision
makers’ preferences are determined through pairwise comparison of alternatives. Due to its
reliance on pairwise comparisons, AHP becomes increasingly complex as an increasing number
of criteria or alternatives are considered. For this same reason, AHP also requires notable time
commitments from decision-makers. AHP is one of the most commonly applied multi-criteria
decision making tools used in the literature evaluating alternative fuel vehicles. The method is
incorporated within these analyses to determine the weights (or relative importance) of each
criteria within a decision-making analysis [48,53,54], or to determine the relative performance of
one alternative over another within a specific criterion [44,55].
TOPSIS, on the other hand, uses a ranking index to identify the best alternative as the one closest
to the ideal solution and farthest from the least desirable solution. Using the TOPSIS method, the
highest ranked alternative may not necessarily be the one closest to the ideal. One particular
criticism of TOPSIS is the fact that while being closest to the ideal solution is likely to represent
most decision makers preferences, being furthest away from the least desirable may not always
be an important consideration [53]. Moreover, the method does not incorporate weighting of the
relative importance of each criterion. TOPSIS has been employed in evaluations of alternative
vehicle technologies to determine the final ranking of alternatives [51], as a comparative
framework [53], or to validate results [54].
VIKOR is a slight variation on the TOPSIS method and differs in that it involves an aggregating
function that captures how close a particular alternative is to an ideal solution across all criteria.
Unlike TOPSIS, VIKOR does not take into account the distance of a particular alternative from
the least desirable solution. Weights are applied to each criterion in VIKOR to more accurately
reflect a decision makers’ preferences. Using the VIKOR method, the highest ranked alternative
12
is that which is closest to the ideal solution. To my knowledge, only a single study has evaluated
alternative vehicle technologies using the VIKOR method to date [53].
PROMETHEE is yet another commonly employed MCDA method that has been used to
evaluate alternative vehicle technologies and is based on an outranking principle. In this case, a
preference function is used to identify the degree to which an alternative is preferred over
another for a particular criterion. This function incorporates the degree to which one alternative
is preferred over the others, as well as the degree to which other alternatives are preferred.
Results are subsequently weighted and combined into a single score that represents the net flow.
Net flows indicate the overall preference for a particular alternative, and that with the highest net
flow is the preferred option. Both Sehatpour, Kazemi and Sehatpour [50] and Safaei,
Tichkowsky and Kumar [49] have evaluated a suite of alternative fuels using the PROMETHEE
method.
DEMATAL-based ANP (or DANP) was employed by Chang et al. (2015) to identify the relative
degree of influence of various criteria considered in the evaluation of alternative fuel vehicles
[56]. While the DEMATAL technique is used to assess causal relationships among multiple
criteria, in this case it is combined with ANP to construct an evaluation hierarchy of these
criteria. Unlike AHP, ANP methods overcome the assumption of independence among criteria.
A variety of preferred alternatives for both LDVs and HDVs have been identified using different
MCDA methods. The majority of studies evaluating alternative fuel vehicles using MCDA
methods have focused on LDVs [48–52,55,57]. Gasoline [49,52], gasoline hybrid electric
vehicles [49,51], battery electric vehicles [48], compressed natural gas (CNG) vehicles
[50,52,57], and vehicles operating using first generation biofuels [55] have all been identified as
preferred alternatives. The majority of these studies, however, have differed in their chosen
MCDA methods, geographical scope, as well as the set of criteria and alternatives that have been
considered. A single pair of studies has evaluated the same set of alternatives across the same set
of criteria. In 2009 Safaei Mohamadabadi, Tichkowsky and Kumar [49], and later in 2015
Lanjewar, Rao and Kale [52] each evaluated gasoline, gasoline hybrid electric vehicles, an 85
percent ethanol blend (E85), diesel, pure biodiesel (B100) and CNG vehicles across the
following criteria: vehicle cost, fuel cost, distance between refueling stations, number of vehicle
13
options available to consumer and GHG emissions. Using the PROMETHEE method, Safaei
Mohamadabadi, Tichkowsky and Kumar [49] identified both gasoline and gasoline hybrid
electric vehicles as the preferred alternatives, while Lanjewar, Rao and Kale [52] identified
gasoline as the preferred alternative using a novel hybrid MCDA method that combines AHP and
graph theory.
Lanjewar, Rao and Kale [52] also applied their novel hybrid MCDA method to a suite of
alternative transit buses originally considered in a study performed by Tzeng, Lin and Opricovic
in 2005 [53]. Both studies evaluated a suite of alternatives that included diesel, CNG, liquefied
petroleum gas (LPG), fuel cells, methanol, battery electric vehicles (BEVs) with opportunity
charging, BEVs with direct charging, BEVs with battery swapping, hybrid gasoline vehicles,
hybrid diesel vehicles, hybrid CNG vehicles and hybrid LPG vehicles. Their analysis considered
energy supply, energy efficiency, air pollution, noise pollution, industrial relationships, cost of
implementation, cost of maintenance, vehicle capability, road facility, speed of traffic flow and
sense of comfort. Tzeng, Lin and Opricovic (2005) identified hybrid electric buses and the
battery electric buses with battery swapping as the preferred alternatives using the VIKOR and
TOPSIS methods, respectively [53]. Similarly, Lanjewar, Rao and Kale (2015) identified battery
electric buses with battery swapping as the preferred alternative using their novel MCDA method
that combines AHP and graph theory [52].
To my knowledge, a single study to date has evaluated freight trucks using MCDA methods. In
2017, Osorio-Tejada, Llera-Sastresa and Scarpellini (2017) incorporated AHP into a multi-
criteria sustainability assessment that evaluated the use of biodiesel and LNG for on-road freight
transport in Spain [44]. Their assessment considered initial and maintenance costs, reliability,
legislation, GHG emissions, air pollutants, noise, employment, social benefits and social
acceptability. Though their method is comprehensive, there is a need to promote additional
frameworks for evaluation that can be more easily applied by decision-makers. AHP is a
computationally intensive MCDA method that requires sophisticated knowledge. Moreover,
there is a need to consider additional alternatives for freight, such as zero-emission technologies
including battery electric vehicles and hydrogen fuel cell vehicles, as well as additional criteria
related to the direct operation of each alternative technology.
14
Though to my knowledge it has not yet been applied to the evaluation of alternative vehicle
technologies, multi-attribute utility theory is a MCDA method that has been applied to evaluate
alternative energy systems more broadly [58–62]. This method combines the expected
performance of each alternative across multiple weighted attributes into a single score. Multi-
attribute utility theory holds certain advantages over other MCDA methods. In particular, the
method has ability to take into account large numbers of criteria or alternatives without
becoming too complicated, unlike MCDA methods that include pair-wise comparisons.
Moreover, unlike certain outranking methods, it provides a complete ranking of alternatives. The
method also takes into account the degree of difference between various alternatives, as opposed
to simply saying one is better than the other for a particular attribute. Finally, multi-attribute
utility theory quantifies the relationship between inputs and outputs in a less mathematically
intricate way making the method more easily understood by a decision maker. For the above
reasons, I select multi-attribute utility theory as the method of choice for the upcoming
evaluation of alternative technologies for long haul heavy-duty trucking.
2.4 Life Cycle Assessment of Dimethyl Ether (DME)
Biofuels are among a growing suite of solutions proposed to reduce GHG emissions from diesel-
fueled HDVs. One such emerging fuel, DME has garnered notable attention since Oak Ridge
National Laboratory, Volvo and Penn State University demonstrated the fuel’s promising
performance in a heavy-duty truck in 2014 [43]. DME holds promise as an alternative to diesel
for a number of reasons. First of all, its production is fairly well established seeing as the fuel has
been produced for a number of years in the chemical industry [63]. Second, the fuel has a
particularly high cetane number making it suitable for use in the compression ignition (CI)
engines commonly used by HDVs. Third, DME can eliminate particulate matter (PM) emissions
as a result of its lack of carbon-to-carbon bonds. Finally, the fuel offers a flexible pathway to
emissions reductions as it can be produced from both renewable and non-renewable feedstocks.
To be adopted as an alternative to petroleum diesel with the aim of reducing GHG emissions
from the HDV sector, the life cycle GHG emissions of DME must be established. A number of
life cycle assessments (LCAs) of DME have been performed, but they differ in their scope and/or
methods [16–23]. A number of studies have assessed DME production in jurisdictions that likely
differ too much from the Canadian context to be applicable. Several LCAs of DME have been
15
performed in East Asia, in particular [16–18], as well as Thailand [19], Papua New Guinea [16]
and Sweden [20].
Another subset of studies examined the life cycle GHG emissions of DME produced in the
United States. These studies performed LCAs of DME produced from renewable hydrogen and
captured and compressed CO2 [21]; natural gas and biogas [22]; as well as natural gas, biogas
and black liquor [23]. None of the studies performed in the United States extensively considers
the production of DME from cultivated biomass feedstocks. Matzen and Demirel [21] compare
their results to the default value for bio-DME produced from cultivated feedstocks found in
Argonne National Laboratory’s Greenhouse Gases, Regulated Emissions, and Energy Use in
Transportation (GREET) model [64], however the authors only use the value for comparative
purposes and do not discuss impacts from the cultivated biomass pathway in detail.
A number of studies have examined the life cycle GHG emissions of DME production from
waste residues [16,17,19,21–23]. DME produced from waste feedstocks is consistently expected
to have lower life cycle GHG emissions than petroleum diesel, with one study predicting GHG
emissions reductions over 100 percent as a result of co-product credits received from on-site
generation of electricity from biomass [23]. Though waste feedstocks are appealing in terms of
their low GHG emissions, their supply may be limited.
On the other hand, a few North American-based studies consider the life cycle GHG emissions
of DME produced from natural gas; however, it is unclear whether or not this pathway offers
GHG emission reductions in comparison to a petroleum diesel baseline [21–23]. While some
studies suggest that DME produced from natural gas may reduce life cycle GHG emissions by up
to 10 percent in comparison to a petroleum diesel baseline [22], others have quantified increases
in life cycle GHG emissions as high as 25 percent [21].
Due to the limited supply of waste biomass feedstocks, as well as the ability of natural gas-based
DME to produce GHG emission reductions, it is important to also consider cultivated biomass
feedstocks in the life cycle assessment of DME. Hence, this thesis completes a comparative LCA
of DME produced from poplar, wood residue and natural gas to demonstrate the differences in
GHG emission reduction potential of DME produced from dedicated energy crops, waste
biomass feedstocks and a fossil fuel. It incorporates emission factors specific to the Canadian
16
context, where possible, in order to account for differences in electricity generation mixes and
natural gas production, in particular.
17
Chapter 3 Background
3.1 Overview
To find an appropriate low GHG emission alternative to petroleum diesel for Canada’s heavy-
duty trucking sector, it is crucial to first understand the context of the problem. This chapter
provides relevant background information pertaining to the importance of the sector with respect
to its contribution to Canada’s transportation GHG emissions, as well as its economic
importance. It also provides an overview of the gross vehicle weight rating (GVWR) vehicle
classification system and justification for why long haul HDVs were selected as the focus of this
thesis. Current emission standards for HDVs in Canada will be discussed and an overview of
LCA methods presented. Lastly, an overview of each of the technologies to be discussed
throughout this thesis will be presented.
3.2 Emissions from Heavy-duty Transport in Canada
The transportation sector in Canada is responsible for 28 percent of the country’s total GHG
emissions by economic sector, second only to the oil and gas industry [3]. Within the sector,
HDVs using diesel are responsible for the greatest share of GHG emissions (33%) [3]. Moreover,
GHG emissions from the sector continue to grow as GHG emissions from other transportation
sectors decline. For instance, as GHG emissions from light-duty gasoline vehicles declined by 21
percent between 1990 and 2017, GHG emissions from HDVs grew by 245 percent over the same
period [3].
HDVs have exhibited the fastest growth in emissions within Canada’s transport sector for a
number of reasons. Simply, this growth is driven by an increase in the number of goods that
require transport, as well as the distance they are being transported. These trends stem from
population growth, economic growth, increases in ecommerce (online shopping), and an
increasingly global supply chain [5]. Additionally, freight trucks tend to have a higher carbon
intensity than other modes of freight transportation [65]. It is expected that emissions from
freight transport, driven primarily by emissions from freight trucks, will surpass those of
passenger transport by 2030 in Canada [5].
18
3.3 Economic Importance of Freight Transport in Canada
International trade plays an increasingly vital role in Canada’s economy. In the decade between
2002 and 2012, the value of Canada’s merchandise trade with both the United States and other
non-United States countries increased by 23 percent [66]. On-road transport is responsible for the
largest share of trade by transportation mode in Canada (nearly 50%) [66].
For-hire trucking companies play a major role in the success of on-road freight transport in
Canada. In 2015, these companies were responsible for the transport of 729.2 million tonnes of
goods over the course of 64 million shipments [5]. Long haul services make up an important part
of these companies, as these services were responsible for 66 percent of for-hire trucking
company revenue in 2011 [66].
The freight industry itself also provides a notable contribution to the Canadian economy.
Activities relating to transportation and warehousing of goods were responsible for 4.3 percent of
gross domestic product (GDP) in Canada in 2015 [5]. Additionally, the sector employed 892,000
people, approximately 5 percent of Canada’s workforce [5,67].
3.4 Vehicle Classification
The following section will outline why I have selected Class 8b long haul HDVs as the focus of
this thesis. It will discuss one of the primary methods of vehicle classification in Canada, gross
vehicle weight rating (GVWR), and how different vehicle classes contribute to the make-up of
Canada’s on-road vehicle pool, as well as Canada’s total vehicle kilometers travelled (VKT) by
on-road vehicles.
3.4.1 Vehicle Classification by Gross Vehicle Weight Rating (GVWR)
One of the most common ways to classify vehicles is by their gross vehicle weight rating
(GVWR). The GVWR is assigned to a vehicle by the manufacturer and dictates the maximum
weight a vehicle can carry including the net weight of the vehicle with accessories, fuel,
passengers and cargo [68]. Table 3.1 illustrates one of the most common ways by which vehicles
are classified according to their GVWR, as well as an overview of the make-up of Canada’s on-
road vehicle pool and the various contributions to the country’s total vehicle kilometers travelled
(VKT).
19
Table 3.1. Vehicle classification, make-up of Canadian on-road vehicle pool and contribution to
Canada’s on-road vehicle kilometers travelled (VKT) by weight class [6,69,70].
Vehicle
Class
Gross
Vehicle
Weight
Applications
Share of
Canadian
vehicle pool
Share of
Canadian
VKT
1 Less than
2,722 kg Passenger cars, minivans, small SUVs
96.3% 91.1% 2a 2,722 to
3,856 kg Large SUVs, standard pickups
2b 3,856 to
4,536 kg
Large pickups, utility van, minibus, step
van
3 4,536 to
6,350 kg
City delivery truck, minibus, walk-in
truck
2.1% 2.4%
4 6,350 to
7,257 kg
City delivery truck, parcel delivery truck,
large walk-in truck, bucket truck,
landscaping truck
5 7,257 to
8,845 kg
Large city delivery truck, large walk-in
truck, bucket truck
6 8,845 to
11,793 kg
Large city delivery truck, school bus,
beverage truck, rack truck, single axle
van, stake body truck
7 11,793 to
14,969 kg
City transit bus, furniture truck, tow
truck, refuse truck, home fuel truck,
cement mixers, dump truck, fire engine
8a 14,969 to
27,215 kg
Straight trucks including dump trucks,
refuse trucks, cement mixers, furniture
trucks, city transit bus, tow trucks, fire
engines 1.5% 6.4%
8b
Greater
than
27,215 kg
Combination trucks including various
configurations of tractor trailers
Medium- and heavy-duty vehicles, or vehicles with a GVWR over 4.5 tonnes, make up a very
small portion of Canada’s total vehicle pool (3.6%) [6]. Heavy-duty Class 8 vehicles alone
account for 42 percent of all medium- and heavy-duty vehicles weighing more than 4.5 tonnes,
or 1.5 percent of all on-road vehicles in Canada [6]. These vehicles tend to be responsible for the
long haul movement of goods and contribute disproportionately (6.4%) to the total VKT
travelled by on-road vehicles in Canada [6,71]. Nearly all (97.5%) of these vehicles use diesel as
a dominant fuel [6]. It is combination tractor trailers (Class 8b vehicles) that are responsible for
the largest share (78%) of VKT by heavy-duty Class 8 vehicles in Canada [6]. The
disproportionately high energy demands of Class 8b vehicles (i.e., long distances travelled in
comparison to the small number of vehicles) coupled with the projected growth in freight truck
emissions makes these vehicles of particular importance in climate change policy making in
20
Canada. The high energy demands of these vehicles makes them difficult to electrify, and though
other alternative powertrain technologies and fuels have been identified as candidates to reduce
GHG emissions from these vehicles, there is no consensus over which, if any, of the proposed
solutions can most efficiently and effectively reduce impacts.
In addition to being broken down by their GVWR, medium- and heavy-duty vehicles can be
broken down based on their application. These applications generally fall into three distinct
categories: for-hire, private or owner-operators. For-hire vehicles transport goods as their core
service, private vehicles transport goods that have been produced by their company, and owner-
operator vehicles may do a mixture of both. Nearly half (45%) of Class 8 vehicles in Canada are
for-hire, and they are responsible for 59 percent of Class 8 VKT [6]. Private vehicles represent
the second highest share of Class 8 vehicles in Canada (25%), but are responsible for only 12.2
percent of Class 8 VKT [6]. Meanwhile, owner-operator vehicles represent the smallest share of
Class 8 vehicles (20%), but represent a larger share of VKT (21%) than private vehicles [6]. This
suggests that private Class 8 vehicles are primarily used for short distance distribution purposes,
while for-hire vehicles tend to be used predominantly for long haul transport.
3.4.1.1 Class 8b Vehicles
This thesis will focus on Class 8b combination tractor trailers, which are responsible for over 78
percent of VKT by heavy-duty Class 8 vehicles, or 56 percent of all medium- and heavy-duty
VKT in Canada. Class 8b vehicles are primarily combination vehicles composed of a tractor and
any number of trailers. Combination trucks may be further categorized as short haul or long haul,
based on the average distance these vehicles are expected to travel. The aggregate VKT of
combination long haul vehicles is nearly double that of short-haul vehicles in Canada [72].
Dump trucks may also have a GVWR large enough to be considered a Class 8b vehicle, however
these are expected to represent only a small share of the Class 8 VKT in Canada [8]. As they are
expected to be responsible for the largest share of heavy-duty VKT, this thesis will focus solely
on combination long haul HDVs, categorized under the GVWR Class 8b.
3.4.1.1.1 Combination Long Haul Class 8b Vehicles
Long haul Class 8b vehicles tend to be combination vehicles that are characterized by a tractor
attached to any number of trailers. Tractors are responsible for towing trailers, which hold the
21
goods that are being transported. Tractors themselves do not carry cargo, but instead house the
driver and engine. They may also contain a sleeper cabin, which includes a bed, small kitchen,
and other amenities for the driver’s comfort on particularly long routes [8]. Tractors that do not
have a sleeper cabin are often referred to as day cabs.
Tractors may be towing either trailers or semi-trailers. While semi-trailers are attached to a
tractor by having its front end rest on the fifth wheel of the tractor, trailers rest on a dolly which
is attached to the tractor by means other than the fifth wheel [73]. Dry van semi-trailers are the
most common type of trailer for long haul Class 8b combination vehicles, but others include
refrigerated, flatbed, tank and dump trailers [8].
Class 8b combination long haul vehicles may range in configuration. These vehicles will vary in
number of trailers, as well in number of axles. The most common configuration for long haul
Class 8b combination vehicles in Canada is a tractor with a single trailer, which will typically
have five axles [6].
Combination long haul vehicles are distinguished by their range [8]. These vehicles travel long
distances between cities and do not return to their base each night [8]. Instead, these vehicles
may travel for several days in order to complete a single trip. In the United States, the average
trip length for a long haul Class 8b vehicle is just over 1,600 km, but these vehicles typically
only travel 725 to 885 km in a day [8,39].
Class 8b combination long haul vehicles are predominantly powered using diesel compression
ignition engines. These engines use the heat produced from highly compressed air to ignite fuel
that has been introduced into the combustion chamber. Class 8b combination long haul vehicles
will generally have two 473 litres (125 gallon) tanks of fuel on-board [6,39]. They are expected
to have a fuel consumption of ranging from 33 to 37 litres per 100 km driven [6,15,29].
3.5 Emissions Standards for Class 8 Vehicles in Canada
The Government of Canada released its second phase of heavy-duty vehicle and engine GHG
emission regulations in 2018 [74]. These amendments made to the 1999 Canadian
Environmental Protection Act have historically aligned with emission standards outlined by the
United States Environmental Protection Agency [75]. Emission standards are outlined for four
22
classes of HDVs: tractor trucks, vocational vehicles, heavy-duty pickup trucks, and commercial
trailers. The newest set of standards applies to new trucks and tractors of model years 2021 to
2027, and new trailers of model years 2020 to 2027 [74].
The standards require a 15 to 27 percent reduction in CO2 emissions for model year 2027 tractors
in comparison to model 2017, depending on the specifications of the tractor (i.e., GVWR, roof
height, cab type) [75]. Trailer standards, meanwhile, require a 5 to 9 percent reduction in CO2
emissions for model year 2027 trailers in comparison to model year 2017 [75]. These standards
require the use of aerodynamic add-ons, low rolling resistant tires, tire inflation monitoring
systems or trailer light weighting that will reduce the fuel consumption of the tractor [76]. Apart
from CO2 emissions, N2O and CH4 emissions have been limited to 0.1 g per BHP-hr for HDVs
older than model year 2016 [75]. Altogether, it is expected that the Phase 2 heavy-duty vehicle
and engine GHG emission regulations will reduce GHG emissions by 41 million tonnes over the
lifetime of the vehicles [75]. In comparison to the Phase 1 standards, which reduced GHG
emissions by 19.1 million tonnes, these standards are increasingly drastic.
By improving the efficiency of diesel tractor trailers and meeting the new standards, GHG
emissions from the long haul sector could be reduced by 20 to 36 percent in comparison to a
2017 baseline. Though this is substantial and may contribute significantly to meeting Canada’s
national near-term target of a 30 percent reduction in GHG emissions by 2030, it is unclear
whether or not GHG emissions from diesel vehicles could be reduced even further to achieve the
long-term goal of an 80 percent reduction in GHG emissions by 2050 [77]. Thus, it is important
to evaluate the ability of other solutions, such as the adoption of alternative fuels and powertrain
technologies, to contribute to Canada’s GHG emission reductions.
3.6 Life Cycle Assessment
Life cycle assessment (LCA) is a tool used to quantify the impacts of a product, process or
project across its entire lifetime. It will be employed in Chapters 4 and 5 to evaluate the life cycle
GHG emissions of alternative technologies for heavy-duty trucking in Canada. The International
Organization of Standardization has developed standards for LCAs, which can be found in ISO
14040 [78] and ISO 14044 [79]. Rather than simply considering the impacts associated with the
direct production or consumption of a product or process, it also considers impacts from related
23
upstream activities, transportation, and disposal. In general, a complete LCA considers five life
stages: resource extraction, manufacturing, use, transportation and distribution, and disposal.
Though LCA can be employed to quantifying a large variety of impacts, such as acidification
potential, ecotoxicity or resource use, it is commonly employed to quantify life cycle GHG
emissions of a product or process.
3.6.1 General Concerns with LCA of Alternative Fuels
There is a high degree of variance among reported life cycle GHG emissions for alternative
fuels. Whitaker et al. suggest that variation within biofuel LCAs, which can be extended to
alternative fuels in general, can be attributed to three main causes: (1) real differences that stem
from variations in production processes and fuel life cycles, (2) variation between chosen LCA
methodologies, and (3) uncertainty that stems from excluded or poorly quantified parameters
[80].
The most apparent set of variation in life cycle GHG emissions of alternative fuels stems from
differences in fuel production. Variation may arise as a result of feedstock choices [81], or fuel
production methods [82]. Biofuels, for instance, can be produced from a wide range of
feedstocks that each require variable levels of inputs. While crop-based feedstocks each require a
unique amount of fertilizer and harvesting energy, these inputs will be minimal for waste- or
residue-based feedstocks [83]. Further variability may stem from the distance a feedstock must
be transported to a production facility [84]; certain production facilities will be co-located next to
their feedstock source, while others will be located a considerable distance away. Meanwhile, at
the production facility, variability stems from differences in the methods of production of a
particular fuel (i.e., thermochemical or chemical) [85]. Geographical influences, such as the grid
electricity mix or land use, may also induce variability within results of alternative fuel LCAs
[86]. Each of these aspects will differ from one fuel to another, and from one region to the next.
This variability demands site-specific accounting of GHG emissions for fuels, but this requires
major investment. Standard values may be used to quantify the life cycle GHG emissions of a
fuel, but these neglect to capture the wide range in variations across fuel production methods and
geographic regions.
24
Chosen LCA methodologies undoubtedly have an impact on LCA results. One particularly
notable source of variation is the treatment of co-products. An individual may choose to evaluate
impacts from co-products through allocation, disaggregation or system expansion methods.
Additionally, within allocation alone, there are numerous ways by which impacts can be
allocated: mass, energy, market value, etc. The magnitude of the burden of impacts can be
shifted to a co-product at the discretion of the researcher, and this can change the outcome of the
assessment [81]. Moreover, the scope or system boundaries of an LCA impacts the final results.
For instance, a consequential LCA (one that considers the consequences of the adoption of a
particular product system) considers a larger temporal and geographical scope than an
attributional LCA (one that only considers the direct impacts of the product system being
studied) and may lead to different findings [87]. Differences stemming from the sheer number of
flows in and out of the system being considered will ultimately lead to variation in results.
Uncertainty is another major source of variability among LCAs [80]. Baker and Lepech have
identified five sources of uncertainty in LCA: database uncertainty, model uncertainty,
statistical/measurement error, uncertainty in preferences (i.e., method selection) and uncertainty
in a future system [88]. One of the notable sources of uncertainty within LCA is the
consideration of indirect land use change, or the unintended impacts a product system may have
on future global land use. Certain authors have argued that the GHG impacts of indirect land use
change may in fact outweigh the GHG benefits of biofuels [89]. Moreover, there is debate
surrounding the appropriate treatment of biogenic carbon emissions in LCA. While it is common
to treat biogenic carbon emissions as neutral, some suggest that this method may underestimate
the impact of biomass systems [90]. Though the inclusion of highly uncertain and difficult to
quantify parameters in LCA is problematic, so is the exclusion of these parameters all together.
Ultimately, there is notable variation within LCAs of alternative fuels. Though this variation may
sometimes stem from real differences in the product’s life cycle, or the imperfect nature of LCA,
it also stems from major uncertainties. This uncertainty merits caution when adopting alternative
fuels and technologies for the sake of GHG emission reductions.
25
3.7 Alternative Heavy-duty Vehicle Technologies for Long Haul
In this section, I provide an overview of each of the alternative technologies that are evaluated in
this thesis. For each technology, I discuss some of the most important technical limitations for its
use in the long haul HDV sector, the vehicle models that are either currently available or
anticipated to be available in the near term that pertain to each technology, the state of each
technological industry in Canada and the anticipated reductions in GHG emissions that stem
from each technology. Additional technological considerations, as well as considerations relating
to the predictability of future fuel costs and future fuel supply levels are discussed in detail in
Appendix A, Appendix B and Appendix C.
3.7.1 Battery Electric Vehicles
Battery electric vehicles are a “zero-emission” technology (i.e., do not produce any tailpipe
emissions), and depending on the upstream grid electricity mix, can contribute significantly to
reductions in greenhouse gas emissions from transportation [10]. Battery electric drivetrains are
also much more efficient than diesel internal combustion engine (ICE) drivetrains. Diesel ICE
engines for heavy-duty trucks typically operate an efficiency under 40 percent, while battery
electric engines can reach efficiencies of 85 percent [12,91]. Additionally, battery electric
vehicles operate at a constant engine efficiency across all driving profiles, whereas ICE engines
lose efficiency in certain driving profiles, such as hills or stop-and-start conditions [91].
Battery electric vehicles operate on a completely different drivetrain technology than the
conventional diesel ICE vehicle. Instead of deriving energy from fuel, these vehicles derive
electricity from a battery located on board the vehicle. Electricity from the battery powers an
electric motor, which then powers the wheels of the vehicle. Inverters and rectifiers are used to
convert electricity from A/C to D/C, and vice versa, and converters are used to correct the
voltage [92]. Auxiliary components such as power steering, hydraulic pump systems and
temperature control systems are electrified in battery electric vehicles [92]. Unlike ICE vehicles,
battery electric vehicles have the ability to take advantage of regenerative braking, which
captures kinetic energy from the wheels that would have otherwise been lost in braking.
26
3.7.1.1 Technical Limitations of Battery Electric Vehicles
Currently, most battery electric trucks are geared towards lighter loads and shorter ranges, and
particularly for vehicles making frequent stops and starts that can take advantage of regenerative
braking [91]. Long haul heavy-duty battery electric trucks face certain challenges before being
able to realize their full potential. These challenges stem primarily from bearing heavy loads and
travelling longer distances.
One of the largest barriers to the success of long haul heavy-duty battery electric trucks is the
range. This is a particular issue for long haul heavy-duty trucks in that they require the ability to
travel long distances without frequent refuelling. Additionally, the vehicle needs to be supplied
with enough power to transport loads sometimes weighing over 30 tonnes. In their current form,
batteries are not energy dense enough to be able to support the desired range of long haul heavy-
duty trucks. Sripad and Viswanathan (2017) estimate that a battery with a 950 km range and an
energy density of 243 Wh per kg would weigh over 16 tonnes, or close to half of the maximum
gross vehicle weight allowance of 36 tonnes that is common across many North American
regions [93]. For reference, a Class 8 heavy-duty diesel-fueled truck with dual 473 litre (125
gallon) tanks [39] would have 0.78 tonnes of diesel on board and would have a range of
approximately 2,800 km.
The battery weight will also reduce the payload capacity of these vehicles thus hindering their
ultimate return on investment (ROI). Though the battery weight is likely to drive up the curb
weight of trucks, there are certain diesel drivetrain components that drive up the curb weight of
ICE vehicles that aren’t required in battery electric vehicles (e.g., fluids, emission control
systems, exhaust systems, cooling systems) [94].
Another concern associated with battery electric trucks is the recharging time. Considering the
size of the battery required for long haul operations and the current rates of charging, it is
possible that charging times would be prohibitive to operations. For instance, it would take 400
minutes to fully charge a Tesla Semi with a fuel consumption of 1.25 kWh per km and a range of
800 km using a Tesla Supercharger (150 kW) [95].
27
3.7.1.2 Current Models of Battery Electric Vehicles
Tesla has emerged as a leader in the electric vehicle market. The company has developed a
battery electric Class 8 combination tractor trailer, or “Semi” that is currently in production.
With a range of 800 km at a gross vehicle weight of 30 tonnes and highway speed, the range of
the vehicle is over 50 percent lower than the typical range for a comparable diesel ICE heavy-
duty truck [95]. Tesla officially cites a vehicle efficiency of 1.25 kWh per km for the Semi [95].
Assuming a battery size of 1,000 kWh and a 15 percent reduction in weight from Tesla’s existing
Model S battery pack which weighs 540 kg per 90 kWh, the battery in a Tesla Semi is expected
to weigh approximately 5,100 kg [96]. Battery specifications, however, have not yet been
officially released from Tesla. Nor has the curb weight of the vehicle, which will affect how
much load the Tesla Semi will legally be allowed to carry.
Los Angeles-based start-up Xos Trucks has also emerged as a competitor to Tesla. Their battery
electric Class 8 tractor is expected to have a range of up to 480 km [97]. No other specifications
have been released to date.
Most recently, Freightliner has announced plans to release an all-electric model of their Class 8
Cascadia. Their website cites a battery capacity of 550 kWh and a vehicle range of 320 km [98].
Additionally, it is expected to be able to charge to 80 percent capacity in only an hour and a half
[98].
On the other end of the spectrum is a Class 8 HDV manufactured by BYD, a China-based
automobile manufacturer. The vehicle has a battery capacity of 207 kWh and a range up to 160
km [91]. With these specifications, the battery is only expected to weigh 1.25 tonnes, but is
unlikely to meet the range requirements of long haul vehicles. BYD does not yet manufacture a
Class 8 tractor with a sleeper cab.
The Tesla Semi is likely the only battery electric tractor trailer announced to date that would be
able to meet the performance requirements of a long haul operator. With that in mind, the Tesla
Semi has not yet officially made it to market and there are a great number of technological
improvements, including battery energy density, that would have to be made in order for the
vehicle specifications to be met.
28
3.7.1.3 Current State of Battery Electric Vehicle Industry in Canada
Quebec-based Lion Electric Company has designed a Class 8 HDV for urban applications,
though the model is not necessarily suitable for long haul [99]. Bollore Group and Electrovaya
and both involved in battery manufacturing for electric vehicles within Canada, though not
necessarily targeted at long haul heavy-duty applications [71,100]. Canada’s first commercial
electric vehicle charging station manufacturing plant opened in 2018 in Markham, Ontario [101].
3.7.1.4 Life Cycle GHG Emissions of Battery Electric Vehicles
GHG emission reductions in comparison to a diesel baseline are generally expected for battery
electric heavy-duty trucks. In a study examining medium- and heavy-duty trucks powered by
electricity produced from natural gas in the United States, Tong et al. (2015) found that battery
electric trucks reduced emissions by approximately 30 percent in comparison to a diesel baseline.
Considering the fact that nearly 80 percent of electricity generated in Canada is non-GHG
emitting, this number is likely to be lower in Canada [77]. Similarly, Sen, Ercan and Tatari
(2017) quantified emissions reductions up to 20 percent for battery electric heavy-duty trucks.
Moultak, Lutsey and Hall, meanwhile, found that battery electric heavy-duty trucks can reduce
GHG emissions of up to 48 percent in the United States [10].
There are a number of considerations, however, to keep in mind when quantifying the expected
GHG emission reductions from battery electric vehicles. GHG emissions from battery electric
vehicles stem primarily from upstream grid electricity emissions [102]. Hence, changes to the
grid electricity mix will undoubtedly impact the life cycle GHG emission intensity of electric
vehicles. Because rates of adoption of electric vehicles are unknown, it is difficult to predict how
these vehicles will impact electricity demand, and subsequent changes to the grid electricity mix.
Charging patterns may similarly impact the GHG emission intensity of electric vehicles.
Excessive charging during peak hours imposes an extra load on the electrical grid. To satisfy this
extra demand, certain power plants will temporarily increase their generation capacity. This
temporary increase in generation is often referred to as marginal generation. The facilities that
satisfy marginal demand tend to be those that can easily be turned “on” or “off”, such as coal or
natural gas plants. Recent work on marginal emission factors highlights the variability in GHG
emissions from electricity generation stemming from patterns in consumption. Tamayao et al.
29
found that life cycle emissions differ by up to 50 percent when considering marginal emission
factors as opposed to average emission factors in an assessment of battery electric and hybrid
passenger vehicles in the United States [103]. However, life cycle GHG emissions were
consistently lower than average ICE vehicle emissions across all scenarios [103]. On the other
hand, Gai et al. quantified expected GHG emission reductions from electric passenger vehicle
deployment in the greater Toronto and Hamilton Area (GTHA) using marginal emission factors
and found that reductions on the order of 80 percent were expected with a 30 percent deployment
rate [104].
It is difficult to predict the extent to which battery electric long haul HDVs will contribute to
Canadian GHG emission reduction targets due to the difficulty associated with predicting future
changes to the grid and quantifying marginal emission factors, but it is expected that some
degree of reduction will occur, particularly in Canada where the carbon intensity or life cycle
CO2eq. emissions produced per unit of electricity generation is on average very low. Kennedy
(2015) suggests that battery electric vehicles will offer life cycle benefits over diesel or gasoline
in countries with a grid electricity that has a carbon intensity below 600 tonnes CO2eq. per GWh.
Canada, meanwhile, has an average carbon intensity of grid electricity of 167 tonnes of CO2eq.
per GWh [105]. Keeping in mind that the carbon intensity of grid electricity in Canada is only
expected to decrease in line with GHG emission reduction targets [77], a reduction in carbon
intensity is expected for long haul heavy-duty battery electric vehicles in comparison to diesel.
3.7.2 Hydrogen Fuel Cell Electric Vehicles
Like battery electric vehicles, hydrogen fuel cell electric vehicles produce zero tailpipe
emissions. Furthermore, depending on the source of hydrogen (i.e., when produced from
renewable sources), hydrogen fuel cell electric vehicles can contribute to life cycle reductions in
GHG emissions in comparison to conventional fossil-based fuels [106]. In comparison to battery
electric vehicles, hydrogen fuel cell electric vehicles offer increased range and decreased
refuelling time (similar refuelling times to diesel ICE vehicles) [15,39,107]. Though fuel cell
electric vehicles have a lower efficiency (50-60%) than battery electric vehicles (85%), hydrogen
is more energy dense than current batteries, and thus more fuel can be stored on board with a
lower weight penalty [12,39,91,108].
30
Fuel cells create electricity through a reaction between hydrogen and oxygen. A fuel cell
contains two electrodes, as well as an anode and a cathode that are separated by a membrane.
Hydrogen entering the electrolyte is separated into electrons and protons. Protons pass through
the membrane to the cathode, while electrons move to the anode and generate electricity [109].
Electrons eventually reach the cathode where they recombine with protons, as well as oxygen
from the air to form water [109].
There are two main types of configurations for hydrogen fuel cell electric vehicles: battery-
dominant and fuel cell-dominant. Battery-dominant fuel cell electric vehicles rely primarily on
electricity from a battery, while the fuel cell acts as a range extender [39,107]. Meanwhile, fuel
cell-dominant vehicles are powered mostly by electricity derived from the hydrogen fuel cell, but
have a smaller battery system to capture energy from regenerative braking, and to assist with
start-up, hill climbing and powering auxiliaries [107,109]. In both configurations, electricity is
used to power an electric motor, which subsequently powers the wheels of the vehicle as well as
any auxiliary components [39]. Regenerative braking is possible in both configurations.
Hydrogen can be produced from a variety of different feedstocks and methods. Common
feedstocks include natural gas, water or biomass. Hydrogen is most commonly produced through
steam methane reformation of natural gas [106,109], but hydrogen can similarly be produced
from biogas, a renewable alternative similar in chemical composition to natural gas [109].
Hydrogen production from water electrolysis using renewable energy has gained attention in
recent years in its ability to contribute to greater reductions in life cycle emissions, though is
expected to incur higher costs [39,110]. Other notable hydrogen production pathways include
nuclear hydrogen production, proton exchange membrane (PEM) electrolysis using various
forms of electricity (grid, solar, wind), biomass gasification followed by pressure swing
adsorption or steam methane reformation of biogas, reformation of biomass-derived liquids, and
thermochemical water splitting [110].
3.7.2.1 Technical Limitations of Hydrogen Fuel Cell Electric Vehicles
Storage of hydrogen in a fuel cell electric vehicle presents a trade-off between increased range,
and increased volume or weight. There are a number of different methods by which hydrogen
can be stored on board a vehicle, the dominant two being liquid and compressed. Cryogenic
31
storage of hydrogen, as well as storage based on physical or chemical adsorption are currently
being tested, but require further development [12].
Liquid hydrogen is contained within highly insulated stainless steel tanks at temperatures below -
250°C [12]. Liquefaction of hydrogen gas, however, requires very high inputs of energy (30 to
40% of the energy content of hydrogen itself) [12]. Additionally, evaporation of the liquid
hydrogen occurs when heat is added, subsequently leading to an increase in pressure within the
storage system [12,109]. Hydrogen must be “blown off” in order to bring the pressure back down
to acceptable levels [12,109].
Compressed hydrogen has been developed in response to the aforementioned storage issues
associated with liquid hydrogen. In this case, gaseous hydrogen is stored on board vehicles at
high pressures (350 or 700 bar) [12]. Density of the hydrogen increases with increasing pressure.
Storage at 700 bar has been developed and offers improvements to density (23 kg/m3 or 3,260
MJ/kg) over 350 bar storage tanks (16 kg/m3 or 2,270 MJ/kg), though at higher costs [39]. The
density of compressed hydrogen is over 40 percent lower than the density of liquid hydrogen
storage (40 kg/m3 or 5,675 MJ/kg); however, the energy required to compress hydrogen is much
lower (15%) in comparison to liquefaction (30 to 40%) [12].
Like batteries, the additional volume of hydrogen stored on-board a vehicle presents certain
limitations. The weight of hydrogen needed to supply a long haul heavy-truck with 1,000 km of
travel at an efficiency of 292 kWh per 100 km is expected to be approximately 88 kg [12]. In
volumetric terms, this would range from 2.2 to 3.7 m3 of gaseous hydrogen (H2) at 700 and 350
bar, respectively; or, 1.2 m3 of liquid H2 [12]. At a pressure of 700 bar, hydrogen storage
volumes for long haul heavy-duty applications are on the order of eight times larger than their
diesel counterparts [12]. The weight of the fuel storage tank is also limiting. While a 400 L
diesel tank is expected to weigh 80 kg, an equivalent hydrogen storage tank would weigh 1622
kg, 2503 kg or 1460 kg for 700 bar, 350 bar and liquid storage tanks, respectively [12].
The durability of automotive fuel cells is another notable concern. Current fuel cell systems are
expected to have a lifetime of 10,000 hours [39]. For long haul heavy-duty applications, this
translates to a replacement schedule of 3 to 6 years [39].
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3.7.2.2 Current Models of Hydrogen Fuel Cell Electric Vehicles
US-based Nikola Motor Company has developed a Class 8 fuel cell electric vehicle, Nikola One.
It has a fuel cell-dominant powertrain configuration [39]. The vehicle is expected to have a range
as high as 1,600 km and a fuel consumption as low as 125 kWh per 100 km [111]. The vehicle
has a dry weight (i.e., excluding consumables, drivers or fuel) of 8 to 9.5 tonnes and a payload
capacity of 30 tonnes [111]. The vehicle is powered by a 300 kWh fuel cell and a 240 to 320
kWh battery [111]. The size of the hydrogen tank is undisclosed; however, at least 100 kg of
hydrogen would be required to supply a range of 1,600 km [39]. The vehicle has not yet fully
been brought to market.
For what the company has called “Project Portal”, Toyota developed two Class 8 hydrogen fuel
cell electric vehicles that operate as drayage trucks (for short distance ground freight transport) in
the Long Beach, California area. The second model, referred to internally as “Beta”, has a gross
vehicle weight allowance of 36 tonnes and a range of 500 km, 50 percent higher than the
previous model [112]. The vehicle features a 12 kWh battery and two-114 kW fuel cell stacks
[112]. Though Beta would not be suitable for long haul freight operations, it highlights the
current state of hydrogen fuel cell electric vehicle technology.
3.7.2.3 Current state of Hydrogen Fuel Cell Electric Vehicle Industry in Canada
Currently, there are no hydrogen fuel cell Class 8 heavy-duty long haul vehicles being operated
or sold commercially within Canada. Hydrogen refuelling infrastructure in the country is
extremely limited; there are currently only two public hydrogen refuelling stations [113,114].
There, however, are a number of corporations involved in hydrogen fuel cell vehicle
development within Canada, particularly in Vancouver. Ballard is one such company and is
considered a world leader in development and manufacturing of proton exchange membrane fuel
cells. They currently have fuel cell stacks ranging in capacity from 60 to 100 kW for application
in HDVs [115]. Though their headquarters are located in Canada, they’re biggest markets appear
to be China and Europe. Loop, also located in Vancouver, has developed hydrogen fuel cell
engines for class 6 to 8 vehicles [116]. Their products are mainly exported to China and the
United States. A Daimler plant located in Burnaby, BC is host to Mercedez-Benz Canada’s fuel
33
cell division where fuel cell stacks are manufactured for the company’s fuel cell models [117].
Palcan, meanwhile, is focused on methanol fuel cell vehicles using proton exchange membranes.
The company has developed a range extender for electric vehicles, which are mainly being used
in buses in China where there is existing methanol refuelling infrastructure [118]. Hydrogenics,
located in Mississauga, is also developing proton exchange membrane fuel cells for application
in HDVs. Like many of the aforementioned corporations, the company mainly exports to Europe
and the United States [119].
There are a few notable companies in Canada focused on hydrogen fuel production and
development of refuelling infrastructure for vehicles. Powertech Labs is involved in hydrogen
infrastructure development, but primarily for projects in California [120]. The Vancouver-based
Hydrogen Technology and Energy Corporation (HTEC) is currently producing most of their
hydrogen using natural gas via steam methane reformation, but are planning on producing a
greater share of their hydrogen via electrolysis using Vancouver’s hydropower in the future
[114]. They have been involved in developing Canada’s first hydrogen refuelling stations [121].
Air Liquide, an industrial gas producer located in Quebec, is experienced in hydrogen production
and the development of hydrogen refueling infrastructure. Their most recent project includes a
hydrogen production facility in western United States that will produce enough hydrogen to
power 35,000 fuel cell vehicles [122].
None of the aforementioned companies are marketing their products to long haul heavy-duty
applications, but instead are mostly focused on urban applications, including passenger vehicles,
busses or refuse trucks. Additionally, approximately 90 percent of Canadian hydrogen and fuel
cell technology is exported [117]. This suggests there is still limited application of hydrogen fuel
cell technologies for long range freight transport.
3.7.2.4 Life Cycle GHG Emissions of Hydrogen Fuel Cell Electric Vehicles
Hydrogen fuel cell vehicles produce no GHG emissions while in operation, and as such, it is
hydrogen production that has the biggest influence on life cycle GHG emissions. Hydrogen
production may occur through steam reformation, gasification, electrolysis, or thermochemical
means. On the other hand, possible feedstocks range from coal or natural gas, to water or wind
34
electricity. Depending on the feedstock and production process, Ozbilen, Diner and Rosen [123]
conclude that GHG emissions of hydrogen production may vary from approximately 412 to
12,000 kg CO2eq. per kg H2.
Considering the full life cycle of a hydrogen fuel cell vehicle, there is uncertainty surrounding
the vehicle’s ability to reduce GHG emissions in comparison to its petroleum diesel counterpart.
There are a wide range of life cycle GHG emission estimates for hydrogen fuel cell electric
vehicles. In some scenarios, the vehicles are expected to reduce GHG emissions in comparison to
their diesel counterpart [15,107], while in others, life cycle GHG emissions are expected to be
higher for hydrogen fuel cell electric vehicles [10,29,107].
Though results are dependent on a host of variables, life cycle emissions are typically higher for
hydrogen fuel cell electric vehicles when the hydrogen is produced from a fossil fuel, namely
natural gas [29,107,124], and when hydrogen is liquified [107,124]. When hydrogen is produced
using renewable sources and is not liquified, life cycle GHG emissions tend to be lower
[107,124].
3.7.3 Natural Gas Vehicles
Natural gas vehicles are attractive in that they may offer reduced costs in comparison to their
diesel counterparts [125]. These vehicles are particularly appealing for countries with large
domestic natural gas reserves who are looking to reduce their reliance on foreign oil imports. For
instance, recent shale gas discoveries in the United States have prompted a resurgence in interest
in natural gas vehicles [126]. Natural gas vehicles also offer potential reductions in life cycle
GHG emissions in comparison to their diesel counterparts. While certain studies have quantified
reductions as large as 14 percent, others have predicted negligible reductions, or in some cases,
slight increases (less than 1%) [13,127].
Natural gas vehicles can be used in engines designed specifically for natural gas, or in
conventional spark ignition (SI) or compression ignition (CI) engines that have been modified to
accommodate natural gas. These engines may differ in their compression ratio, air-fuel ratio and
the method by which the fuel is ignited [126]. SI natural gas vehicles are the most common and
mature of all natural gas vehicle technologies [125].
35
Fuel is stored on board vehicles either in the form of compressed natural gas (CNG) or liquefied
natural gas (LNG). CNG is typically stored at 250 bar and 27°C in a specially designed
container, while LNG is stored in a cryogenic container at -162°C [125]. CNG and LNG increase
the density of natural gas by approximately 200 and 600 times, respectively, in comparison to
natural gas at standard temperature and pressure [125]. The higher density of LNG (394 kg/m3 or
18,000 MJ/m3) makes it better suited for long haul applications in comparison to CNG (198
kg/m3 or 9,033 MJ/m3) [125].
3.7.3.1 Renewable Natural Gas
Renewable natural gas (RNG) is similar in chemical composition to conventional natural gas
derived from fossil-fuels but is instead derived from renewable sources. The fuel has notable
GHG emission reduction benefits [33,34]. RNG is produced through the gasification or
anaerobic digestion of a carbon-rich feedstock. It is wet feedstocks that typically undergo
anaerobic digestion to produce RNG. In anaerobic digestion, microbes breakdown organic
material in the absence of oxygen to produce biogas, a methane-rich gas [128]. On the other
hand, gasification is employed to produce RNG from relatively dry materials. In gasification,
high temperatures (>500°C) produce synthesis gas (syngas) from organic material in the
presence of oxygen or steam [128]. Syngas is then cleaned-up and converted to RNG through
methanation. Gasification typically results in higher yields of methane and can tolerate a greater
variety of non-homogenous feedstocks [128]. When upgraded or cleaned-up, RNG can be used
in existing natural gas infrastructure, including pipelines and vehicles.
The Canadian Gas Association, which represents the country’s natural gas transmission and
distribution companies, has set a target of 10 percent RNG in Canada’s natural gas stream by
2030 [129]. As of 2017, Canada had eleven RNG production facilities either in operation or
development. These are located primarily in Ontario, Quebec and British Columbia [130].
Canada is expected to have enough waste feedstocks available to supply almost half of the
country’s natural gas consumption in 2014 with RNG [131]. The largest availability of feedstock
across the country comes from forest and agricultural waste (85%) [131]. For Canada’s RNG
potential to be realized, technological improvements need to be made, and competition with
other industries such as pelletization need to be addressed [131].
36
3.7.3.2 Technical Limitations of Natural Gas Vehicles
The biggest drawback of natural gas vehicles is the increased fuel storage capacity required and
the additional weight. Due to its lower energy density, approximately two to four times the
storage volume is required for LNG and CNG vehicles, respectively, in comparison to diesel
[125]. Natural gas vehicles also have a higher vehicle curb weight. CNG fuel systems with a
capacity of 200 diesel gallon equivalent (DGE) are expected to incur a weight penalty of 1,270
kg, while LNG fuel systems with a capacity of 240 DGE are expected to incur a weight penalty
of 570 kg [125]. These figures take into account the fact that natural gas engines are expected to
weigh approximately 180 kg less than an equivalent diesel engine [125]. Heavier curb weights
not only decrease the amount of cargo that’s able to be stored on-board a vehicle but may also
contribute to higher rates of fuel consumption.
The bulky nature of storage tanks also contributes to an increase in fuel consumption for natural
gas vehicles by increasing the drag coefficient [125]. This in combination with higher curb
weights results in natural gas vehicles having a fuel economy 5 to 15 percent lower than their
diesel counterparts [125,127]. The difference in fuel economy is greater at lighter vehicle
weights, thereby making the penalty lower for heavier HDVs [127].
Natural gas is a colourless, odourless gas. The compound mercaptan, which gives off a rotten
egg smell, is typically added to natural gas products so that it can be detected in the event of a
leak [132]. Though CNG can accommodate mercaptan, LNG cannot as a result of the low
temperature of the fuel, and so methane detectors must be located on board the vehicle [132].
3.7.3.3 Current Models of Natural Gas Vehicles
Cummins Westport has developed natural gas spark-ignition engines for application in HDVs,
notably the 400 HP ISX12 G [133]. The engine can accommodate both CNG and LNG and can
power vehicles weighing up to 36.3 tonnes [133]. It is estimated that 80 to 85 percent of all
trucks in Ontario carry loads under 36.3 tonnes [134]. Thus, the Quebec-Ontario freight corridor
is likely to accommodate a switch to natural gas vehicles [134]. Limitations exist however, when
considering heavier vehicle loads, as well as freight movement across mountainous corridors that
require more vehicle power, such as in western Canada [134]. The current natural gas engine
offerings cannot accommodate the needs of HDVs operating under more demanding conditions
37
that require higher horsepower ratings [125]. The lack of high-power natural gas engine
availability in Canada was noted by two interviewees in the expert interviews that I conducted
(see Table A4).
3.7.3.4 Current State of Natural Gas Vehicle Industry in Canada
Canada is the world’s fifth largest producer of natural gas [135]. The industry is well-established
and host to over a dozen companies involved in accelerating the deployment of natural gas
vehicles in Canada [136].
There are currently 12,745 natural gas vehicles in-use in Canada, only 2 percent of which are
HDVs [125]. 40 percent of new medium- and heavy-duty natural gas vehicles, however, are
highway tractor trailers, or Class 8b vehicles [125].
The majority (almost 90%) of refuelling stations within Canada are for CNG, as opposed to LNG
[125]. Most of these CNG refuelling stations are designed to be fast-fill for light-duty vehicles.
In December 2018, the construction of three CNG refuelling stations designed for use by freight
vehicles along Ontario’s 401 highway was completed [137]. One of the stations located in
London, will offer RNG [137].
3.7.3.5 Life Cycle GHG Emissions of Natural Gas Vehicles
There is uncertainty as to whether or not natural gas (CNG or LNG) can reduce GHG emissions
from conventional petroleum diesel-fueled HDVs. While some reports cite GHG emission
reductions as high as 30 percent [15,33,36,136,138], others have cited increases relative to
petroleum diesel [29,139,140].
Recent investigations into rates of methane leakage from North American natural gas systems
suggest that official estimates may underestimate actual levels of emissions by as much as 100
percent [141,142]. This recent finding alongside variance in the literature point to considerable
uncertainty surrounding whether or not natural gas can reliably reduce GHG emissions relative
to petroleum diesel.
Another factor that bears consideration is the shorter atmospheric lifetime of CH4 emissions in
comparison to CO2. Due to the greater share of CH4 emissions coming from natural gas in
38
comparison to petroleum diesel, the fuel may have stronger adverse climate impacts in the near-
term [140]. There is considerable risk associated with the adoption of natural gas vehicles for
climate mitigation purposes.
On the other hand, RNG is primarily produced using waste feedstocks, including municipal solid
waste, wastewater, or unused crop or forest residues. By making use of waste feedstocks, the fuel
receives a credit for offsetting the GHG emissions that would have otherwise been released
through their decomposition. RNG may, however, also be made from cultivated crops. Use of
RNG in heavy-duty trucks is expected to produce GHG emissions reductions ranging from 43 to
90 percent in comparison to a diesel baseline [33,34].
3.7.4 Biodiesel
Biodiesel is Canada’s most popular renewable alternative to diesel [143]. It can be used in CI
engines with little to no modifications [13]. It is a liquid fuel produced from the
transesterification of fats and oils, primarily canola and soy, and to a lesser extent recycled
vegetable oils and animal fats [143]. The resulting compound is fatty acid methyl esters (FAME).
Biodiesel can be used on its own (B100), but is most typically blended with petroleum diesel as a
result of limits imposed by vehicle manufacturers warranties [13].
Biodiesel holds a number of advantages over petroleum diesel. Unlike petroleum which relies on
the extraction of fossil fuels, biodiesel is made from renewable sources, such as dedicated energy
crops or waste residues. When biodiesel is produced from waste residues, such as used restaurant
cooking oil, the fuel can make use of feedstocks that would otherwise be destined for landfill
[144]. From an air quality perspective, it may contribute to a reduction in certain air pollutant
emissions [13,144]. Biodiesel also has vastly improved lubricity and a higher cetane number,
leading to improved performance over petroleum diesel in a CI engine [143]. Finally, biodiesel
has reduced ecological impacts when released to the environment as a result of being
biodegradable and less toxic than petroleum diesel [143,144].
3.7.4.1 Technical Limitations of Biodiesel
Perhaps the biggest issue with biodiesel is its poor cold flow properties making it unsuitable for
certain Canadian winter conditions. The cloud point of biodiesel ranges from -3 to 15°C, and
39
after this point certain compounds begin to crystallize [145]. This leads to fuel gelling within the
engine. Numerous factors contribute to this undesirable property including carbon chain length,
degree of saturation, degree of isomerization, presence and position of double bonds, and degree
of chain branching [146]. Research is underway to improve these properties, and thereby make
biodiesel suitable in colder conditions. For instance, Anwar and Garforth have identified the use
of heterogeneous catalysis as a means to improve the composition of biodiesel [146].
In addition to this, biodiesel has poor thermal stability [143]. In other words, the fuel is
susceptible to deterioration at high temperatures. The combustion of biodiesel in CI engines is
also associated with slight increases in NOx emissions, which contribute to the formation of
smog and subsequent adverse impacts on human health [144]. Despite its applicability in CI
engines, biodiesel cannot be easily integrated with existing petroleum infrastructure, namely
pipelines [143]. There are concerns associated with the contamination of certain petroleum
products such as jet fuel. Lastly, biodiesel has an energy content that is nearly 10 percent lower
than diesel [143].
3.7.4.2 Current Vehicle Models That Can Accommodate Biodiesel
Biodiesel can be used in existing vehicles equipped with CI engines. Blend levels may, however,
be limited by original equipment manufacturer (OEM) warranties [147].
3.7.4.3 Current State of Biodiesel Industry in Canada
Canada has a two percent renewable fuel content requirement for diesel across most provinces,
and both Ontario and British Columbia have adopted a four percent requirement [143]. This
federal regulation, in addition to some more ambitious provincial regulations, led to the
consumption of a total of 474 million litres of biodiesel in 2015 [143]. Biodiesel is currently the
only domestically produced alternative fuel being used to satisfy Canada’s renewable fuel
standards [143].
There are currently eleven commercial biodiesel plants operating in Canada. Archer Daniels
Midland is currently the largest producer with an annual capacity of 265 million litres, followed
by Atlantic Biodiesel at 170 million litres and BIOX at 116 million litres [143].
40
Gains in efficiency and cost-effectiveness are ongoing in biofuel production. SBI Bioenergy in
Edmonton, Alberta recently received funding for the development of a new method of biodiesel
production that occurs in a single reactor and is expected to reduce capital and operating costs by
62 percent and 12 percent, respectively, in comparison to conventional biodiesel [143]. The
company is targeting a capacity of 10 million litres per year once the plant is in operation [143].
Beneful Inc. has also recently received financing for a commercial-scale biodiesel plant in
Sarnia, Ontario [148]. The plant is expected to have a capacity of over 75 million litres per year
[149]. Like SBI Bioenergy, it will make use of single-step biodiesel production technology
[148].
3.7.4.4 Life Cycle GHG Emissions of Biodiesel
Meyer et al. (2011) quantified GHG emissions reductions on the order of 10 percent for heavy-
duty trucks operating using 20 percent biodiesel (B20) blends derived from soy [13]. On the
other hand, Fulton and Miller (2015) found that GHG emissions could be reduced by up to 75
percent when pure biodiesel (B100) produced from soy is used in a heavy-duty truck [45]. It
appears as though biodiesel can positively contribute to Canada’s climate targets, though the
magnitude of this contribution may change depending on the feedstock, as well as the blending
limits. For instance, MacLean et al. (2019) estimated that the life cycle GHG emissions of
biodiesel range from 24.9 g CO2eq. per MJ for biodiesel produced from soy to -7.5 g CO2eq. per
MJ for biodiesel produced from tallow [147].
It is important to note, however, that there is a lack of consensus over the magnitude of impacts
stemming from indirect land use change associated with biofuel production. These effects are
particularly difficult to quantify and are the subject of much debate [89,150,151]. Hence, this
remains a major source of uncertainty associated with biofuel production.
3.7.5 Renewable Diesel
Hydrogenation-derived renewable diesel (HDRD or renewable diesel) is another alternative fuel
to petroleum diesel. Renewable diesel is nearly identical in chemical composition to petroleum
diesel but is produced through the hydrogenation of triglycerides found in biomass. This
similarity to petroleum diesel allows renewable diesel to act as a “drop-in” fuel. In other words,
41
it can be produced in existing petroleum-based facilities, transported using the same
infrastructure, and combusted in CI engines. While most engine manufacturers place limits on
the amount of biodiesel used, this is not the case for renewable diesel [152].
Though renewable diesel is most commonly made from similar feedstocks to biodiesel, such as
vegetable oils and animal fats, it can take advantage of a wider range of feedstocks, including
lignocellulosics [143,153]. Though lignocellulosics can be used as a feedstock, they must first be
converted to bio-oil via pyrolysis [143]. Additionally, no industrial-scale facility is currently
using lignocellulosics as a primary feedstock for renewable diesel [153].
This ability to use a wider range of feedstocks than biodiesel stems from the chemical
composition of renewable diesel. First of all, while free fatty acids found in biodiesel react with
catalysts to form soaps, they become paraffins in renewable diesel [153]. Secondly, biodiesel
produces oxygenated methyl esters with varying degrees of saturations depending on the
feedstock [153]. Renewable diesel, on the other hand, produces fully-saturated paraffinic
hydrocarbons, which are not susceptible to the oxidative instability that unsaturated methyl esters
are [153]. The superior oxidative stability of renewable diesel is also particularly important for
storage and handling.
Importantly for Canada, the cloud point of renewable diesel can be easily altered using an
isomerization unit in order to improve the fuel’s cold flow properties [143]. Additional benefits
for renewable diesel include a higher cetane number than both petroleum diesel and biodiesel,
lower life cycle GHG emissions, and a similar energy content to petroleum diesel (43 to 44
MJ/kg for petroleum diesel and renewable diesel versus 39 MJ/kg for biodiesel) [143,153].
3.7.5.1 Technical Limitations of Renewable Diesel
There are no major technical limitations to the use of renewable diesel. One small issue is the
lower lubricity of renewable diesel in comparison to petroleum diesel. This, however, is easily
fixed by using chemical additives [147,153].
3.7.5.2 Current Models That Can Accommodate Renewable Diesel
Renewable diesel is a “drop-in” fuel that is compatible with existing diesel engines [147]. Thus,
any HDV equipped with a standard CI engine is suitable for use with renewable diesel.
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3.7.5.3 Current state of Renewable Diesel Industry in Canada
The major disadvantage of renewable diesel is that there are no existing industrial-scale
renewable diesel plants in Canada. As of 2012, renewable diesel was only being produced in
Europe, Southeast Asia and the United States [153]. As an imported fuel, it is costlier than
biodiesel. That being said, 149 million litres of renewable diesel were blended into Canada’s
petroleum diesel in 2015, and its usage since 2010 has tripled [143].
In December 2018, Canadian-based Forge Hydrocarbons received funding to construct their first
commercial lipid-to-hydrocarbon production plant [154]. The plant is expected to be located in
Sombra, Ontario and will produce drop-in fuels at a capacity of 19 million litres per year [154].
The lipid-to-hydrocarbon process will use extreme pressure and temperature, in the absence of
any hydrogen or catalysts, to produce both renewable diesel and naphtha (biojet) from low value
oils [155].
3.7.5.4 Life Cycle GHG Emissions of Renewable Diesel
Estimates for the life cycle GHG emissions of renewable diesel are similar to those of biodiesel,
though tend to be a bit higher for renewable diesel due higher processing requirements. Fulton
and Miller (2015) quantify GHG emissions reductions of up to 70 percent for a heavy-duty truck
operating using pure renewable diesel produced from switchgrass [45]. Meanwhile, Romare and
Hanarp (2017) estimate that pure renewable diesel produced from palm oil and tallow can reduce
GHG emissions by 45 and 85 percent, respectively, in comparison to a petroleum diesel baseline
in the European Union (EU) [35]. Like biodiesel, the GHG emission intensity of renewable
diesel is expected to vary considerably depending on the feedstock. MacLean et al. (2019)
estimate that the GHG emission intensity of renewable diesel will range from -3.1 to 31.3 g
CO2eq. per MJ when produced from tallow and soy, respectively. Renewable diesel appears to
be able to reliably offer GHG emission reductions in comparison to diesel at a similar level to
biodiesel but faces similar uncertainty as biodiesel with respect to indirect land use change.
3.7.6 DME
DME is an alternative diesel fuel that can be produced through the dehydration of methanol.
Though gaseous at standard temperature and pressure, it becomes a liquid when subject to slight
43
pressure or cooling [63]. DME is particularly well-suite for use in CI engines due to its high
cetane number (55 to 60) [156]. Due to its lack of carbon-carbon bonds, the combustion of DME
does not produce particulate (PM) emissions. Additionally, the fuel is expected to produce fewer
hydrocarbon (HC), carbon monoxide (CO) and, in some cases nitrogen oxide (NOx) emissions,
as well [156–161].
DME has historically been produced through the dehydration of methanol derived from syngas
[162]. A direct method of DME production from syngas is also possible using bi-functional
catalysts in a single reactor [163]. Due to the unselective nature of the gasification process often
used to produce syngas, DME can be produced from essentially any carbon-rich feedstock.
Production processes for both indirect (from methanol) and direct (from syngas) production of
DME are well-established [164].
DME has very few notable safety issues. Its high volatility restricts it from having any major
impacts on land or water; it is non-toxic, non-carcinogenic, non-mutagenic and non-teratogenic;
and it is thermally stable and burns with a visible flame [165]. DME is, however, flammable, and
its volatility may present certain issues [165]. Additionally, it is prone to a build-up of static
charge [165].
DME is already being produced in significant quantities, particularly in Asia-Pacific. Global
annual DME production is approximately 10 million tonnes, and is concentrated in China [165].
Though there is increasing interest in the application of DME as a diesel alternative fuel, it is
most commonly used as a blend stock for LPG and as an aerosol propellant [166]. As a result of
sharing certain properties, DME can be integrated into existing LPG infrastructure [63,165].
There have been two notable demonstrations that have successfully explored the applicability of
DME in CI engines [157,161,167]. Landalv et al. worked in partnership with Chemrec, Haldor
Topsoe and Volvo to develop a bioDME fueled heavy-duty trucks [161]. The trucks successfully
operated using DME for over one million km [161]. Hansen et al. in partnership with Haldor
Topsoe, Volvo and Statoil successfully demonstrated the applicability of DME in city busses
[157]. Meanwhile, Oberon Fuels are working with the New York City Department of Sanitation
to demonstrate a DME-fueled Class 8 Mack truck [167].
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3.7.6.1 Technical Limitations of DME
Park & Lee (2014) performed an extensive investigation into the applicability of DME in CI
engines [156]. The authors found that the low viscosity and lubricity of DME in comparison to
diesel, results in potential fuel leakage and increased wear and tear of moving engine parts [156].
This, however, is likely to be overcome by using additives such as Lubrizol or biodiesel [156].
DME has an energy density approximately 30 percent lower than that of diesel [156]. This
suggests that vehicles using DME will bear additional weight and volume constraints as a result
of a larger fuel tank size necessary to satisfy adequate range; however, additional space will be
available due to the lack of a particulate filter [63]. Additionally, the lower energy density means
that the fuel injection rate of DME must be greater than that of diesel [156]. Park & Lee (2014)
also identified that the spray characteristics of DME differ from those of diesel primarily as a
result of the specific vaporization properties of DME [156]. Thus, it is expected that the injection
pressure will not need to be so high for engines using DME [156]. Ultimately, there will be
minor engine modifications required for use of DME in CI engines.
3.7.6.2 Current Models That Can Accommodate DME
There are currently no dedicated DME Class 8 heavy-duty combination tractor trailers available
on the market in Canada.
3.7.6.3 Current State of DME Industry in Canada
Enerkem Inc., who are producing methanol in Edmonton, have begun to test the production of
bio-DME at their Innovation Centre in Westbury, Quebec [168]. They group has successfully
produced DME from carbon-rich municipal solid waste at a plant that has been in operation for
over 1,000 hours [168].
3.7.6.4 Life Cycle GHG Emissions of DME
Life cycle GHG emissions from DME vary considerably depending on the feedstock. Life cycle
emissions from DME produced from natural gas, one of the cheapest and most abundant
potential feedstocks, are expected to be somewhat higher than those of petroleum diesel [21,23].
On the other hand, DME produced from renewable sources is expected to have considerably
lower life cycle GHG emissions than petroleum diesel [17,19,21,23]. Like the other biofuels
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considered in this assessment, the GHG emission intensity of DME is expected to be
considerably dependent on the feedstock. The GHG emission reduction potential of DME in
Canada will be explored in more detail in Chapter 5.
The next chapter of this thesis will focus on the more mature set of technologies described in this
section, including battery electric vehicles, hydrogen fuel cell electric vehicles, natural gas
vehicles, biodiesel and renewable diesel. It will incorporate the considerations outlined in this
chapter into multi-criteria frameworks for analyses which can help long haul trucking companies
in Canada identify the most promising alternative technologies.
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Chapter 4 Multi-criteria evaluation of alternative technologies for long
haul trucking in Canada: insights from expert interviews and evaluation frameworks
4.1 Introduction
To meet Canada’s greenhouse gas (GHG) emission reduction targets of 30 percent below 2005
levels by 2030 [3], major changes will have to take place in the country’s most GHG intensive
economic sectors. Second only to industry including oil and gas, transportation accounts for
nearly one quarter of Canada’s GHG emissions [65]. One of the most demanding sectors of road
transport is freight trucks, which are primarily diesel-fueled heavy-duty vehicles (HDVs) that are
responsible for movement of goods in Canada [65].
This study focuses on the long haul on-road movement of goods in Canada, which is dominated
by combination tractor trailers. These vehicles are responsible for nearly 60 percent of vehicle
kilometers travelled by Canada’s heavy-duty sector and operate almost exclusively using
petroleum diesel [6]. Identifying an attractive low GHG alternative to diesel for use in long haul
HDVs presents certain challenges due to the sector’s unique characteristics. First, these vehicles
transport heavy loads often weighing over 15 tonnes [169], and travel long distances often
exceeding 1,600 km per trip [8]. Second, long haul HDVs typically operate along multi-day
routes preventing them from returning to a home base after each shift. As such, these vehicles
rely heavily on public refueling infrastructure. Finally, the long haul trucking industry is highly
competitive and profit margins tend to be low [9], making the industry notably risk averse. These
characteristics must be considered when exploring low GHG alternatives to diesel fuel.
Various tools have been employed to identify viable alternatives to diesel for HDVs. Life cycle
assessment (LCA), a tool aimed at identifying the environmental impacts of a product across all
stages of its life cycle. It has been applied to examine the ability of alternative fuels and
powertrain technologies to reduce GHG emissions from HDVs in comparison to conventional
diesel HDVs [10,13,36,45,28–35], recently synthesized by Cañete Vela et al. [37]. Several
prominent LCA models such as GREET [64] and GHGenius [170] also include results for
HDVs.
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In addition to LCAs, other studies explore the economic feasibility of alternative technologies by
estimating lifetime costs [10,30,31,33,38,39,45], performing a cost-benefit analysis [40], or
determining break even fuel costs [15,32]. Meanwhile, other studies have examined alternatives
to diesel through air quality modelling or tailpipe emissions testing [38,41–43,171]. The
aforementioned studies generally only explore one or two dimensions of alternative technologies
and none consider the potential impacts of alternative technologies on daily trucking operations.
The evaluation of diesel alternatives has most commonly occurred within specific subsets of the
broader HDV fleet, including transit buses [31,32,38,40,172], vocational vehicles such as
delivery or distribution trucks [28,35] and refuse trucks [173,174]. Few studies evaluate low
GHG alternatives for Class 8 tractor trailers specifically [10,13,45], and there is only a single
study focused on long haul [45]. It is difficult to compare results across existing studies that
examine alternatives for Class 8 tractor trailers not only because they evaluate alternatives using
different metrics, but they each also consider a different set of alternative technologies. While
Meyer, Green and Corbett [13] evaluated a biodiesel blend, an e-diesel blend, Fischer-Tropsch
diesel, compressed natural gas (CNG) and liquefied natural gas (LNG), Fulton and Miller [45]
evaluated diesel, diesel hybrid vehicles, CNG, LNG, biodiesel, advanced biofuels, hydrogen fuel
cell electric vehicles and battery electric vehicles, and Moultak, Lutsey and Hall [10] evaluated
diesel, diesel hybrid vehicles, CNG, LNG, fuel cell electric vehicles and battery electric vehicles.
Certain patterns, however, arise across these studies. For instance, both Meyer, Green and
Corbett [13] and Fulton and Miller [45] identified biofuels as the most promising low GHG
emission alternative to diesel. Moreover, battery electric and fuel cell electric vehicles are
consistently expected to be the most expensive alternative technologies in the near term [10,45].
While previous research has identified the most promising technologies in terms of life cycle
GHG emissions, air pollutant emissions or lifetime costs, it is unclear which of these alternative
technologies are likely to be favoured and adopted by the long haul trucking industry. Being able
to identify the most promising technologies from the industry perspective has important
implications on, for instance, refueling/recharging infrastructure build-out and developing
incentives for increased low GHG alternative fuel production. In order to determine the most
promising alternative technologies for the long haul trucking sector, I suggest evaluating
alternatives on the basis of a multi-criteria analysis that includes operational considerations, as
48
these are expected to be some of the most important considerations of the industry. Importantly,
this analysis should incorporate expert insights that highlight the long haul trucking sector’s most
important considerations with respect to investment in an alternative vehicle technology.
To date, only a few studies have reported on the top considerations of various HDV sectors with
respect to investment in an alternative fuel HDV [24–26,175]. Mohamed, Ferguson and
Kanaroglou [26] documented the perspectives of Canadian transit operators with respect to what
factors might be preventing greater uptake of battery electric transit buses. The authors found
that uncertainty surrounding the pace of future technological advancements, the operational and
financial feasibility of a battery electric bus, minimizing risk during the decision-making process,
and having a business case were some of the biggest barriers. Klemick et al. investigated the
investment decisions of trucking firms with respect to the adoption of fuel saving devices [25],
however the interviews did not cover perceptions surrounding alternative fuels or powertrain
technologies. Finally, though not HDVs, Sierzchula [175] interviewed a number of US- and
Dutch-based company fleets to determine what motivated these companies to invest in electric
vehicles. In general, fleets did so to improve environmental impact and public perception, as well
as to make use of government grants. Perhaps most relevant is a study conducted by
Anderhofstadt and Spinler [24] in which the authors employed the Delphi method of
interviewing experts to determine the most important factors affecting the adoption of heavy-
duty trucks in Germany. The authors identified a truck’s reliability, the availability of refueling
infrastructure, the ability to enter low emission zones, as well as future fuel costs. Though each
of the aforementioned studies effectively reports on expert insights within specific sectors
[26,175], or more generally [24,25], there are gaps in the literature regarding the priorities of the
long haul heavy-duty trucking sector, specifically, that should be addressed to more effectively
evaluate alternative technologies.
Multi-criteria analysis of alternative vehicle technologies has previously been applied to
alternative vehicle technologies. A number of multi-criteria decision analysis (MCDA) methods
have been employed including analytical hierarchy process (AHP) [44,48,52,54,55], TOPSIS
[51,53,54], preference ranking organization method for enrichment and evaluations
(PROMETHEE) [49,50], analytical network process (ANP) [56], decision-making trial and
evaluation laboratory (DEMATAL) technique [56] and VIKOR [53]. Fuzzy methodologies have
49
also been incorporated into a number of frameworks to account for the uncertainty and
vagueness surrounding qualitative data [48,51,54]. The majority of these studies have focused on
LDVs [49–52,176], as well as buses [52,53]. Only a single study to date has evaluated HDVs
using MCDA methods. Osorio-Tejada, Llera-Sastresa and Scarpellini [44] incorporated AHP
into a multi-criteria sustainability assessment that evaluated the use of biodiesel and LNG for
freight transport in Spain. Their assessment considered initial and maintenance costs, reliability,
legislation, GHG emissions, air pollutants, noise, employment, social benefits and social
acceptability. There is a need to consider additional technologies in the evaluation of alternatives
for long haul transport, including zero-emission technologies such as battery electric and
hydrogen fuel cell electric, as well as other biofuels. Moreover, while the methods of Osorio-
Tejada, Llera-Sastresa and Scarpellini [44] are comprehensive, there is a need to develop
additional frameworks that are less mathematically complex and can more easily be adopted by
decision-makers. There is also a need to include additional criteria related to vehicle operation.
This research proposes several frameworks for the evaluation of alternative long haul trucking
technologies, which can incorporate expert insights and be easily adopted by decision-makers.
To aid in the development of these frameworks, this research first identifies the priorities of the
long haul trucking industry in Canada with regards to investment in an alternative vehicle
technology, as well as the perceived opportunities and barriers to the adoption of each
technology. These insights build upon the limited public information on the views of industry
stakeholders surrounding the adoption of alternative technologies within the long haul sector.
Importantly, I also ask interviewees to value the importance of various vehicle attributes that
might be considered when deciding to invest in a new technology. These expert insights are then
incorporated into multiple frameworks for the evaluation of alternative technologies in their
current or near-term state using the best available data for each technology at the time this study
was carried out. The primary method of evaluation that I employ is a multi-attribute utility
analysis [177], a specific type of multi-criteria decision analysis whereby the relative
performance of alternatives is evaluated across multiple attributes and combined to generate a
single score and ranking for each technology. Results from the analysis are compared to those of
a satisficing heuristic framework and a simplified societal cost benefit analysis. Comparisons
across frameworks are made to highlight that preferences for alternative technologies may
change depending on the type of decision-making methods that are employed. Results from the
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analysis have the potential to inform areas for future research and development, as well as ways
in which the government may direct policy to support the uptake of low GHG technologies.
4.2 Material and Methods
4.2.1 Selection of Alternative Fuels and Powertrain Technologies
There are a wide range of alternatives to diesel fuel and compression ignition engines that are
being explored for the HDV sector. This analysis focuses specifically on commercial and near-
commercial technologies: battery electric vehicles, hydrogen fuel cell vehicles, natural gas
(compressed, liquefied and renewable), a 20 percent biodiesel blend (B20), and 100 percent
renewable diesel (HDRD). Each of the alternative technologies selected for analysis either have
tractor trailer models or engines available at least for presale [95,97,111,133], or in the case of
fuels, are the maximum operational blend level of the fuel being utilized domestically [143]. As
such, in this assessment I consider the current or near-term state of technology at the time this
study was carried out. More detail surrounding the current state of and limitations of each
alternative technology can be found in Section 3.7.
4.2.1.1 Battery Electric Vehicles
Battery electric vehicles operate using an electric engine and offer superior efficiency in
comparison to vehicles that operate using an internal combustion engine. While the vehicle
efficiency of a heavy-duty truck using an internal combustion engine is typically under 40
percent, battery electric vehicles offer reliable efficiencies as high as 85 percent [12,91]. The
battery electric vehicle produces no tailpipe emissions thereby contributing to improvements to
air quality [10]. Moreover, depending on the source of electricity used, battery electric vehicles
may offer lower life cycle GHG emissions in comparison to a conventional diesel vehicle [10].
The current energy density of batteries, however, imparts certain limitations on range and cargo
capacity. As long haul HDVs transport heavy loads and travel long distances, they require a
significant amount of energy storage on-board. As such, there is a balance that needs to be struck
between storing additional energy on board to satisfy range requirements and limiting battery
size to maximize cargo capacity. Moreover, in order to be able to recharge during a brief rest
stop, long haul heavy-duty trucks require especially high power charging infrastructure that is
51
not currently available on the market [178]. Hence, current charging times for long haul heavy-
duty battery electric vehicles may be prohibitively long for certain operators.
4.2.1.2 Hydrogen Fuel Cell Electric Vehicles
In hydrogen fuel cell electric vehicles, hydrogen stored on board the vehicle passes through fuel
cells to generate electricity, which is then supplied to the electric motor. A battery is stored on
board, which may supply additional electricity. In comparison to battery electric vehicles,
hydrogen fuel cell electric vehicles offer increased range and decreased refueling time. Other
than water vapour, these vehicles generate no tailpipe emissions [109]. Depending on the
feedstock used to produce the hydrogen, these vehicles may also reduce life cycle GHG
emissions in comparison to a petroleum diesel baseline [106].
Hydrogen may be stored in a liquid or gaseous state, though the latter is more common [107].
Storage of gaseous hydrogen requires heavy-duty tanks that weigh more than a standard diesel
fuel tank and also take up additional space [12]. Hence, there is a slight penalty to the cargo
capacity of these vehicles. Finally, hydrogen for transportation is currently being produced in
North America in very limited quantities, and is also notably more expensive than diesel fuel
[179].
4.2.1.3 Natural Gas Vehicles
Natural gas may be used in engines designed specifically for natural gas, or in a diesel engine
that has been modified to accommodate natural gas. In this assessment I focus specifically on a
dedicated natural gas vehicle. Natural gas is stored on board a vehicle either in compressed or
liquefied form. Though compressed natural gas (CNG) tends to be cheaper [179], liquefied
natural gas (LNG) has a higher energy density [125] and thus offers greater range with a reduced
impact on cargo capacity. There is some speculation surrounding the ability of conventional
natural gas to reduce life cycle GHG emissions in comparison to petroleum diesel [13,127],
however, renewable natural gas (RNG) can be blended into the natural gas stream thereby
reducing the fuel’s life cycle GHG emissions [33,34], owing to assumptions made surrounding
biogenic carbon. Heavy-duty natural gas vehicles are also expected to offer certain air quality
benefits compared to diesel, including reduced particulate matter (PM), nitrogen oxides (NOx),
carbon monoxide (CO) and hydrocarbon (HC) emissions [13,126].
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As a result of the fuel’s lower energy density in comparison to petroleum diesel, weight and
volume penalties are incurred by the fuel storage tanks on board natural gas vehicles [125]. As
previously mentioned, these penalties are lower for LNG.
4.2.1.4 Biodiesel
Biodiesel is a liquid fuel derived from renewable feedstocks that can be blended into the
petroleum diesel pool and used in compression ignition engines. Petroleum diesel in Canada is
required to meet a minimum two percent renewable content and this is typically met through the
addition of biodiesel [143]. As biodiesel is produced from renewable feedstocks, it tends to
produce lower life cycle GHG emissions in comparison to petroleum diesel as a result of
assumptions surrounding the neutrality of biogenic carbon emissions [13]. Additionally, the
biodiesel blends generally result in reduced PM, CO and HC emissions relative to pure
petroleum diesel [143].
Biodiesel differs in chemical composition from petroleum diesel. In particular, biodiesel
generally has a higher cloud point than petroleum diesel, which can affect its usage in cold
weather [147]. As a result, the percentage of biodiesel blended into the petroleum diesel pool
tends to be limited by its cloud point, as well as limitations imposed by OEM warranties and cost
[147]. A 20 percent biodiesel blend (B20) is the highest blend level that is currently used in
Canada, but this blend is not used in winter months [147]. There is, however, evidence to suggest
that a B20 blend could be successfully used in cold Canadian winters with cloud point correction
[180]. The energy density of pure biodiesel is slightly lower than that of petroleum diesel and
thus vehicles operating using a 20 percent biodiesel blend may experience slightly higher (1-2%)
fuel consumption [145].
4.2.1.5 Renewable Diesel
Hydrogenation derived renewable diesel (HDRD), is a renewable fuel that is almost chemically
identical to petroleum diesel. Renewable diesel is considered a “drop-in” fuel that can be used in
compression ignition engines in place of petroleum diesel and does not have the same blending
limits as biodiesel. Renewable diesel benefits from lower life cycle GHG emissions than
petroleum diesel because, like biodiesel, it is made from renewable feedstocks. The fuel is
currently being imported and used to satisfy Canada’s two percent renewable content
53
requirement for the diesel pool alongside biodiesel [143]. Though it is not yet produced in
Canada, a facility in Ontario is expected to be in operation by late 2019 [154]. Renewable diesel,
however, is costly to produce in comparison to both biodiesel and petroleum diesel. Specifically,
it is estimated to cost 20 to 30 percent more to produce than petroleum diesel [181].
4.2.2 Expert Interviews
To inform the evaluation of alternative trucking technologies for long haul, I conducted expert
interviews to identify the expected industry priorities and considerations for investment in
alternative vehicle technologies. I received approval from the University of Toronto Research
Ethics Board on April 14th, 2019 for these interviews. I interviewed nineteen individuals with
demonstrated knowledge of alternative fuel technologies. Interviews were conducted over the
phone between April and July 2019 and lasted approximately one hour in length. I obtained the
contact information of prospective participants through referrals from peers. Interviewee
backgrounds span several relevant sectors (see Table 4.1).
Table 4.1. Number of experts interviewed by sector.
Sector Number of Interviewees
Trucking Companies 3
Trucking Associations 3
Fuels/Technology Associations 3
Research Institutes 3
Government 2
Clean Energy Consultants 2
Manufacturers 2
Trucking Magazine Editor 1
Total 19
Participants were asked a pre-determined set of questions to gather data related to three main
research questions (the full list of interview questions can be found in Appendix D):
1. What are the perceived barriers and opportunities associated with each alternative vehicle
technology in relation to long haul trucking operations?
2. What are the top priorities of the long haul trucking industry when considering
investment in an alternative vehicle technology?
3. How do stakeholders of the long haul trucking sector value different vehicle or
technological attributes when considering investment in a new alternative vehicle
technology?
54
Participants were not sent information related to the alternatives prior to the interview; instead, I
relied on each interviewee’s demonstrated knowledge of the topic. Interviewees were asked to
comment on each of the aforementioned topics with respect to the current state of technology.
Responses related to questions one and two were manually analyzed by reviewing interview
notes and counting the number of times various responses appeared.
To determine how the sector might value various attributes during decision-making practices (as
per question three), interviewees are asked to value the following attributes on a scale of 1 to 10
with 1 being the least important and 10 being the most important: vehicle lifetime costs, vehicle
purchase price, fuel cost, fuel price volatility, fuel supply, availability of refueling infrastructure,
refueling time, vehicle range, cargo capacity, availability of skilled maintenance workers,
greenhouse gas emissions, other air pollutant emissions, availability of government incentives,
and industry experience with the technology. I selected attributes based on my knowledge of the
industry and expectations surrounding what long haul carriers would be most likely to consider
in the decision to invest in an alternative vehicle fuel or powertrain technology; however,
interviewees were also given the chance to provide values for additional attributes outside of
those that are listed. Interviewees were permitted to provide the same score to multiple attributes.
These attributes and their varying level of importance are incorporated in the multiple
frameworks for evaluation of alternative long haul HDV technologies.
4.2.3 Frameworks for Evaluation
4.2.3.1 Multi-Attribute Utility Theory
The primary framework that I employ to evaluate alternative technologies for long haul trucking
is the multi-attribute utility method [177]. This method was selected for its ability to account for
stakeholder preferences using a simple expression under conditions of uncertainty. Notably, the
framework is simple enough that it can be understood and replicated by decision makers.
The first step in the development of this framework is the selection of attributes to be included. I
base these off of the same set of attributes that I ask interviewees to value the importance of in
expert interviews (see Section 4.2.2), however some attributes are excluded from the framework
due to interdependencies with other attributes, or due to a lack of data. The five guiding
principles for attribute selection – systemic, consistency, independency, measurability and
55
comparability – as identified by Wang et al. were considered [46]. Under the systemic principle,
I assume that evaluating the alternative technologies on the basis on multiple attributes can
produce better results than evaluation on the basis of a single attribute [46]. Under the
consistency principle, I ensure that the attributes are consistent with the objective, which is the
evaluation of alternative technologies for long haul operations. Under the independency
principle, I ensure that each of the selected attributes evaluates alternatives using exclusive
metrics. Under the measurability principle, I ensure that the selected attributes can be measured
using quantitative or qualitative methods. Finally, under the comparability principle, I normalize
attributes in order to compare values across technologies. The final set of attributes included the
analysis are described in Table 4.2 and described in more detail in Appendix F.
Table 4.2. Description of the attributes that are included in the multi-attribute utility analysis.
Additional details can be found in Appendix F.
Attribute Unit Description
Total cost of
ownership
2019
CAD
Includes vehicle purchase price, fuel costs and maintenance costs and
assuming an 8% annual discount rate [182].
Cargo capacity kg
The weight of goods a particular vehicle technology can transport
calculated by taking into account the weight penalty incurred by
certain alternative fuels or powertrain technologies.
Vehicle range km The estimated distance each vehicle technology is expected to be able
to travel on one tank of fuel or charge.
Refueling time mins The number of minutes it takes to refuel or recharge a tractor trailer.
Availability of
refueling
infrastructure
# of
stations
The number of alternative refueling or high power recharging stations
available across Canada for public use at the time of the study.
Fuel supply N/Aa
Qualitative measure of current/future fuel availability based primarily
on national production data [183]. 1 represents good availability, 2 is
moderate supply risk and 3 is high risk. See Appendix C for a
discussion surrounding future fuel supply levels.
Fuel price
volatility N/A
Qualitative measure of the degree to which each alternative fuel price
fluctuates based on historical price data. 1 is low volatility, 2 is
moderate and 3 is high.
Availability of
incentives
# of
incentives
The number of incentives available across the country and each
province for each alternative technology.
Industry
experience with
the technology
N/A
A reflection of the industry’s experience with an alternative
technology within Canada’s long haul HDV sector. This is translated
to a qualitative numeric scale whereby 0 is no experience, 1 is
minimal experience, 2 is some experience and 3 is lots of experience.
GHG emissions CO2eq. Well-to-wheel life cycle GHG emissions for each vehicle/fuel, using
100-year global warming potentials [184].
Other air pollutant
emissions
mg/tonne-
km
Aggregated expected tailpipe emissions, including particulate matter
(PM), nitrogen oxides (NOx) and carbon monoxide (CO). aN/A = not applicable.
56
With the attributes selected, values that reflect the current or near-term state of the technology
are identified for each attribute across each of the various technologies (e.g., the best estimate for
the total cost of a battery electric tractor trailer). These values are obtained through a literature
review that includes peer reviewed journal articles, as well as government and industry reports,
and the Canada-based LCA model GHGenius [170] (see Appendix F for a detailed list of these
values and their respective sources). All values are highly approximate and should be interpreted
as illustrative only. As many alternative technologies are on the cutting edge (e.g., battery
electric vehicles, hydrogen fuel cell vehicles) and have yet to be commercially tested, there is
notable uncertainty associated with many of the inputs to the framework. Further study is
required to produce more definitive values and explore parameter sensitivity.
I next determine the relative utility of each technology for a particular attribute. I base my
methods off of those of Maxim (2014) who evaluates electricity generation technologies using
multi-attribute utility analysis [62]. The utility, in this case, represents the level of usefulness of a
technology in comparison to the other alternatives within a particular attribute (e.g., how much
longer a diesel vehicle can travel in comparison to a battery electric vehicle). To determine
utility, values are normalized across each attribute. A min-max normalization method was used
to produce a utility value ranging from 0 to 1 whereby the utility of attribute x for technology k is
determined for attributes that are inversely correlated with utility using equation 1 and for
attributes that are directly correlated with utility using equation 2 whereby u(xk) is the utility of
technology k for attribute x, xk is the value of attribute x for technology k, xmin is the lowest value
and xmax is the highest value for attribute x across all technologies.
𝑢(𝑥𝑘) = (𝑥𝑚𝑎𝑥 − 𝑥𝑘)/(𝑥𝑚𝑎𝑥 − 𝑥𝑚𝑖𝑛) (1)
𝑢(𝑥𝑘) = (𝑥𝑘 − 𝑥𝑚𝑖𝑛)/(𝑥𝑚𝑎𝑥 − 𝑥𝑚𝑖𝑛) (2)
As the expert interviews reveal to us the varying degrees of importance among attributes, the
utilities for each attribute are weighted. As mentioned in Section 4.2.2, nineteen experts with
demonstrated knowledge of alternative technologies and the long haul trucking industry were
asked to value the importance of multiple vehicle attributes on a scale of 1 to 10. Normalized
weights (w) for each attribute (x) are determined using equation 3 whereby wx is the weighted
importance of an attribute and I is the expert-identified degree of importance of each attribute
ranging from 1 to 10.
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wx =Ix
∑ Inx=1
(3)
Final scores for each technology (uk) are determined using equation 4 whereby the utility u(xk) of
each attribute (x) for each technology (k) is multiplied by the corresponding weight for that
attribute (wx) and summed up across all attributes for each technology.
uk = ∑ wxu(xk)nx=1 (4)
A higher score, or higher overall utility, implies that a particular alternative technology is
preferred. Final rankings of technologies are generated for each interviewee to highlight the
expected variability in rankings of technologies across interviewees.
4.2.3.2 Comparator Frameworks
Two additional frameworks are explored to demonstrate the variability of outcomes that can be
produced when different frameworks are applied. Tzeng, Lin and Opricovic [53] have previously
demonstrated the variability in outcomes of a multi-criteria analysis of alternative fuel transit
buses when using two different methodologies, VIKOR and TOPSIS. To highlight potential
differences in technology rankings, I present results from both a satisficing heuristic framework,
as well as a societal cost benefit analysis alongside the multi-attribute utility framework. The
satisficing framework evaluates alternative technologies on the basis of their performance
relative to the current petroleum diesel baseline. Meanwhile, the simplified societal cost benefit
analysis evaluates alternatives on their ability to produce the greatest profit, while minimizing
costs and detrimental externalities. On the other hand, the function of the multi-attribute utility
analysis is to rank technologies on the basis of their relative performance across a number of
attributes in comparison to other alternatives. By comparing rankings of alternative technologies
across these three frameworks, I can elucidate how different methods of decision-making will
impact the ranking of alternative technologies.
Though these frameworks have been applied to the evaluation of alternative technologies for
long haul HDVs in Canada, it is a simplified application and as such, the results presented are for
illustrative purposes only. In other words, it is the application of these frameworks that is the
focus of this analysis, as opposed to the ranking of technologies.
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4.2.3.2.1 Satisficing Framework
I employ a satisficing heuristic framework to evaluate the alternative technologies based on their
performance relative to diesel fuel baseline [47]. This method of evaluation is selected as I
expect certain companies may be open to investment in alternative technologies if they meet the
current performance standard set by diesel fuel across some of the most important attributes.
In this framework, for each attribute (x), a technology (k) receives a point for having a
performance that is better than or equal to a diesel baseline (d) (equation 5).
𝑥𝑘 = {1, 𝑖𝑓𝑥𝑘 ≥ 𝑥𝑑
0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (5)
The same attributes (see Table 4.2) and expert-identified values for each attribute (see Section
4.2.2) that were included in the multi-attribute utility analysis are considered in the satisficing
framework. Total scores (y) for each technology (k) are determined by summing up each
technology’s weighted score (wxxk) across each attribute (x) (equation 6).
𝑦 = ∑ 𝑤𝑥𝑥𝑘𝑛𝑥=1 (6)
4.2.3.2.2 Societal Cost Benefit Analysis
I expect that cost benefit analysis may be one of the primary methods used to inform decision-
making within long haul trucking companies. I propose use of a societal cost benefit analysis, to
capture some of the externalities imposed on society by long haul trucking. In this framework, I
assign a monetary value (2019 CAD) to each of the attributes being considered, and the
technologies are ranked according to their net present value (NPV) with a discount factor of 8%
per year across the vehicle’s initial five year period of ownership. Not all of the previously
considered attributes are included within the societal cost benefit analysis due to challenges
associated with monetization. Attributes that are monetized are total cost of ownership, GHG
emissions, other air pollutant emissions and cargo revenue. The framework currently only
includes negative externalities, though there are also positive ones, such as improved quality of
life. Table 4.3 describes these attributes and how they are each assigned a monetary value.
Final scores for each technology are calculated by subtracting the costs from the benefits of each
technology to determine the net present value. Those with a higher NPV are considered to be
preferred.
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Table 4.3. Summary of the methodology by which the monetary value of each attribute is
determined in the societal cost benefit analysis (a detailed breakdown of these attributes and their
values can be found in Appendix F).
Attribute Methodology
Co
st
Total cost of
ownership
The net present value of the sum of vehicle purchase price, fuel costs, and
maintenance costs.
GHG emissions
The 2020 estimate (in 2019 CAD) of the social cost of carbon, $48/tonne
CO2eq. [185], is applied to the WTW GHG emissions of each alternative
technology.
Criteria air pollutant
emissions
Based on social costs of particulate matter 10 micrometers or less in
diameter (PM10), nitrogen oxides (NOx) and carbon monoxide (CO)
tailpipe emissions from each alternative technology. Values of
$3,850/tonne, $8,950/tonne and $2,000/tonne, respectively, are used (all
2019 CAD) [186].
Ben
efit
Cargo revenue
A function of cargo capacity, vehicle range and refueling time, this
attribute takes into account the expected loss or gain in revenue compared
to a diesel baseline associated with the transport of goods for each
alternative technology. It is calculated by taking into account the
difference in cargo revenue in comparison to the 2016 average [187], as
well as the amount spent on driver salary as a result of extra time spent
refueling the vehicle using the most recent recommended hourly earning
rate for truck drivers [188].
There is notable debate surrounding the method by which the social costs of emissions should be
accounted for. As an example of the uncertainty associated with this method, I demonstrate
sensitivity of the societal cost benefit analysis to NOx emissions, which is a particularly
influential parameter. To apply this method in an actual decision-making context, additional
sensitivity analysis is recommended. Values are taken from the Estimating Air pollution Social
Impact Using Regression (EASIUR) model developed by researchers at Carnegie Mellon
University, which predicts the marginal social cost of air pollutant emissions in the United States
[189]. I take values reflecting the 5th and 95th percentile values of the annual social costs of NOx
emissions, which are $300 to $14,000 per tonne (2019 CAD), respectively.
4.3 Results
This section presents results from the expert interviews and the three frameworks I employ to
evaluate the alternative vehicle technologies.
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4.3.1 Expert Interviews
In the expert interviews, information was collected related to the perceived opportunities and
barriers to the adoption of each alternative technology, the top priorities of the trucking industry
when considering investment in an alternative fuel vehicle, as well as the relative importance of
various attributes that might be considered in decision-making practices. Responses provide
insights on the priorities and operational demands of the trucking industry and how these might
be met across different alternative vehicle technologies.
4.3.1.1 Opportunities and Barriers to the Adoption of Alternative Vehicle Technologies
When asked what they perceive to be the most notable opportunities and barriers to the adoption
of each alternative vehicle technology, interviewees responded with considerations that span
environmental, financial and operational realms. The most frequently identified opportunities
and barriers to the adoption of each alternative can be found in Table 4.4. A detailed summary of
these responses by interviewee can be found in Appendix E. As the table illustrates, cost,
fuel/infrastructure availability, and GHG emissions are salient features, though each technology
is also presented with its own idiosyncratic considerations.
Table 4.4. The most commonly identified opportunities and barriers to the adoption of
alternative vehicle technologies during expert interviews.
Opportunities Barriers
Battery electric vehicles
Low GHG emissions
Zero tailpipe emissions
Lower maintenance requirements
and costs
Battery weight penalty
Short range
High vehicle cost
Hydrogen fuel cell electric
vehicles
Lower GHG emissions
Fast refueling
Long range
Limited availability of refueling
infrastructure
High fuel cost
High vehicle cost
Natural gas vehicles
Lower GHG emissions
Lower total cost of ownership
Low fuel cost
Limited availability of refueling
infrastructure
Uncertainty surrounding GHG
emission reductions
Biodiesel Lower GHG emissions
Drop-in fuel
Cold weather operability issues
Low fuel availability
Renewable diesel Drop-in fuel Low fuel availability
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4.3.1.1.1 Opportunities
Across each of the alternative vehicle technologies, the ability to reduce GHG emissions in
comparison to a petroleum diesel baseline was one of the most frequently cited opportunities.
This suggests that many of the interviewees agree that alternative fuels or vehicle technologies
could play a role in mitigating greenhouse gas emissions from the long haul trucking sector. In
addition to greenhouse gas emissions, eight interviewees collectively saw the ability of battery
electric, hydrogen fuel cell electric and natural gas vehicles to reduce or entirely eliminate
criteria air pollutant emissions as yet another opportunity that would benefit the environment.
A number of operational benefits were cited with respect to both battery electric and hydrogen
fuel cell vehicles, in particular. Four interviewees noted that they expect battery electric
technologies to offer improved vehicle performance, such as superior torque. For hydrogen fuel
cell vehicles, eleven interviewees noted the long range and fast refueling time of the vehicles as a
particular advantage. On the other hand, five interviewees noted the “drop-in” properties of
biodiesel and HDRD as a particular advantage. As these fuels are able to be used at certain blend
levels in existing compression ignition engines with little to no modifications, they are expected
to cause less disruption to operations.
Based on interviewee responses, financial opportunities are only perceived to be associated with
battery electric and natural gas vehicles. Eight interviewees in total cited reduced maintenance
and fuel costs as opportunities associated with battery electric vehicles, while eleven
interviewees cited the low total cost of ownership and/or fuel costs as advantages of natural gas
vehicles.
4.3.1.1.2 Barriers
The lack of availability of refueling infrastructure was cited as a major barrier to the adoption of
almost all of the alternative vehicle technologies. Fifteen interviewees in total identified this as a
barrier. In particular, this was by far the most cited barrier to the adoption of both hydrogen fuel
cell electric and natural gas vehicles. Not only does this affect the ability of carriers to operate
these vehicles over certain distances, but it also affects their revenue. If drivers are forced off
their route to seek out alternative refueling stations, the total trip length is longer and takes up
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time that could have been spent completing another potentially profitable trip. Moreover, there
are sizeable penalties for late delivery and competition for quick delivery.
Financial concerns were also some of the most commonly identified barriers to the adoption of
each of the alternative vehicle technologies. Vehicle costs were identified by thirteen individuals
in total as a barrier for alternative technologies that require a new vehicle or engine to be
purchased, whereas fuel costs were identified by six individuals as a barrier for alternative fuel
technologies that can be used in existing internal combustion engine vehicles (i.e., biodiesel and
renewable diesel). For hydrogen fuel cell electric vehicles both vehicle costs and fuel costs were
among the most commonly cited barriers to adoption as identified by seven individuals. This
technology not only requires a fuel cell electric vehicle to be purchased, but also hydrogen fuel,
which may be prohibitively expensive to some carriers.
Operational considerations were noted mainly for battery electric vehicles and biodiesel. Fifteen
individuals collectively cited vehicle range, battery weight penalty and recharging time as major
barriers to the successful adoption of battery electric vehicles. On the other hand, cold weather
operability was the most commonly identified barrier to the adoption of biodiesel.
The availability of fuel was cited by eight interviewees as a major barrier to the adoption of
biofuels, including biodiesel and HDRD. Though there is a mandatory renewable fuel content
blended into Canada’s diesel pool, availability of the fuel outside of these low blend levels is
perceived as limited.
Few obstacles related to the environmental impacts of these alternative technologies emerged,
however one in particular stands out. Five interviewees expressed concerns surrounding the
ability of natural gas to reduce GHG emissions with respect to a petroleum diesel baseline. This
suggests that there is skepticism surrounding the effectiveness of the fuel for the purpose of
climate change mitigation in the heavy-duty long haul trucking sector.
4.3.1.2 Considerations for Investment in Alternative Vehicle Technologies
Seventeen interviewees cited financial considerations when asked about the top priorities of the
long haul trucking industry when considering investment in a new alternative vehicle technology
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(for a full list of responses see Table A1). Among these considerations were costs (in general),
fuel costs, maintenance costs, vehicle resale value, vehicle purchase price, payback period, price
stability, depreciation, and impact on return on investment (ROI). As various costs, including
vehicle costs and fuel costs, were also cited as some of the greatest obstacles to the adoption of
most of the alternative technologies, this suggests that alternative vehicle technologies are
unlikely to be widely adopted unless they were to reach a financial parity with diesel.
The availability of refueling infrastructure was another one of the most commonly cited priorities
of the long haul trucking industry when considering investment in an alternative vehicle
technology. It was identified as a consideration by nine individuals. This stems largely from the
long haul trucking sector’s reliance on public refueling infrastructure. Long haul trucks operate
along routes that may take several days to complete, and hence do not return to base after each
shift. These vehicles do not carry enough fuel on board for multi-day trips, and so without public
refueling stations along their route, these vehicles would be unable to operate as usual.
Seven interviewees expressed concerns surrounding the reliability of alternative technologies.
Seven interviewees also referred to the level of experience with or how proven a technology is,
or the importance of a technology’s durability or resiliency as being a top priority. In this same
vein, four interviewees cited operability and functionality as major considerations.
Three interviewees also noted the importance of the driver’s needs when considering investment
in an alternative vehicle technology. The long haul trucking sector is facing a driver shortage,
and hence, driver attraction and retention are of particular concern.
A number of these considerations are consistent with those identified by Klemick et al. [25] who
performed interviews to identify factors affecting the uptake of fuel saving devices for HDVs. In
their study, upfront costs, fuel economy, maintenance issues and reliability were identified by
fleet managers as top considerations in tractor purchase decisions. Similarly, Anderhofstadt and
Spinler identified reliability, the availability of refueling or recharging infrastructure and fuel
costs as some of the most important factors to consider when purchasing an alternative fuel
heavy-duty truck through a series of stakeholder interviews [24].
64
4.3.1.3 Weighting the Importance of Vehicle Attributes
Interviewees were asked to weight the importance of various vehicle attributes on a scale of one
to ten in order to determine how each might be considered in the decision to invest in an
alternative long haul vehicle technology (refer to Table 4.2 for a list of attributes). Interviewees
were given the opportunity to comment on any additional attributes, however, the majority of
interviewees agreed that the provided set of attributes were sufficient. Four additional attributes
were mentioned including reliability, driver experience, resale value and properly executed
demonstration projects. These attributes were excluded from further analysis due to a limited
number of data points but may warrant consideration in future decision-making frameworks.
Aggregated responses are presented in Figure 4.1, and detailed responses in Table A2.
Ultimately, the availability of refueling infrastructure, cargo capacity and total cost of ownership
were rated most important, on average. On the other hand, industry experience with the
technology, GHG emissions and other air pollutant emissions were rated least important, on
average.
There tends to be a greater level of agreement between interviewees with respect to the attributes
that were weighted with a higher importance, on average. This includes cargo capacity, total cost
of ownership and fuel costs. Meanwhile, there are higher levels of disagreement associated with
attributes that were weighted with a lower importance. In particular, interviewees exhibited high
levels of disagreement with respect to the importance of industry experience with the technology
and GHG emissions. There are differences in the ways in which various organizations will
approach decision-making, and this will be reflected in the variety of solutions that are proposed.
65
Figure 4.1. Boxplots showing interviewee responses demonstrating the weighted importance of
various vehicle attributes, with 10 being the most and 1 being the least important. Boxplots show
ranking by individual expert and are arranged by median with tails representing a 95 percent
confidence interval. Outliers are represented by dots.
4.3.2 Evaluation Frameworks
The values of each vehicle attribute as identified by experts and outlined in Section 4.3.1.3 are
incorporated into three decision-making frameworks. By incorporating expert insights, I am able
to identify the attractiveness of various alternatives with the expected priorities of the long haul
trucking industry in mind. Not all experts interviewed come directly from the trucking sector
(see Section 4.2.2 for a list of the sectors that interviewees come from) and as a result, results
only reflect the anticipated priorities of the sector. The different frameworks that I employ in the
66
following sections were used to highlight the variety of results that can be achieved depending
on how an organization decides to approach decision-making.
4.3.2.1 Multi-Attribute Utility Theory Framework
Unweighted utilities and preliminary rankings for each technology are listed in Table 4.5.
Technologies are ordered by unweighted utility. Higher utility values indicate superior
performance.
Table 4.5. Unweighted ranking results from summing a technology’s utility across attributes,
without assigning a differential importance to each of the attributes. A score of 1 indicates that a
technology has the most desirable outcome for that particular attribute, while a score of 0
indicates that a technology has the least desirable expected outcome with respect to each of the
other alternatives.
Diesel BEV LNG CNG B20 HDRD H2 FCEV RNG
Total cost of ownership 0.84 1.00 0.71 0.78 0.83 0.75 0.26 0.00
Cargo capacity 1.00 0.00 0.83 0.69 1.00 1.00 0.45 0.69
Refueling time 1.00 0.00 0.99 0.99 1.00 1.00 0.99 0.99
Vehicle range 1.00 0.00 0.20 0.09 0.98 1.00 0.39 0.09
Fuel supply 1.00 1.00 0.50 0.50 0.50 0.00 0.00 0.00
Availability of refueling
infrastructure 1.00 0.46 0.00 0.02 0.00 0.00 0.00 0.00
GHG emissions 0.00 0.98 0.32 0.24 0.20 0.88 0.46 1.00
Other air pollutant
emissions 0.01 1.00 0.49 0.49 0.00 0.17 1.00 0.49
Industry experience 1.00 0.00 0.67 0.67 0.67 0.33 0.00 0.00
Availability of incentives 0.00 1.00 1.00 1.00 0.50 0.25 0.75 1.00
Fuel price volatility 0.00 1.00 0.50 0.50 0.00 0.00 0.50 0.50
Unweighted utility 6.8 6.4 6.2 6.0 5.7 5.4 4.8 4.8
Figure 4.2 shows the range in scores obtained by taking into account the varying levels of
importance of different attributes, as identified by the interviewed experts and outlined in Section
4.3.1.3. Though the ranking of technologies varied across interviewees depending on their
unique set of weights, our application of the multi-attribute utility framework consistently ranks
diesel as the top choice. This can be attributed to the fact that petroleum diesel is expected to
exhibit the best performance across largest share of attributes, including total cost of ownership,
cargo capacity, refueling time, vehicle range, fuel supply, availability of refueling infrastructure
67
and industry experience, many of which were weighted with high levels of importance. Diesel
was expected to exhibit the worst performance across most of the remaining attributes, including
GHG emissions, other air pollutant emissions, availability of incentives and fuel price volatility,
however, these attributes were weighted with a lower level of importance, on average.
Figure 4.2. Weighted utility of each technology based on each individual’s weighting of the
various attributes that have been considered. Boxplots show ranking by individual expert and are
arranged by median with tails representing a 95 percent confidence interval. Outliers are
represented by dots.
This exercise, however, is aimed at highlighting opportunities associated with low GHG
alternatives to diesel. On average, LNG and battery electric vehicles (BEVs) are the highest
68
scoring alternative to petroleum diesel. Battery electric vehicles (BEVs), CNG and biodiesel
(B20) follow closely behind LNG and, on average, share fairly similar scores.
LNG vehicles rank favourably as a result of their high cargo capacity, long vehicle range and
some industry experience with the technology. In comparison to the other natural gas-based
technologies, CNG and RNG, LNG has superior cargo capacity and vehicle range, which can
both be attributed to its comparatively higher energy density. Additionally, though RNG benefits
from particularly low GHG emissions, LNG and CNG hold the advantage of having some
industry experience with the technology. On the other hand, RNG is penalized for a high total
cost of ownership, a notably low fuel supply and a lack of industry experience with the
technology. Despite scoring relatively high, it is unclear whether or not Canada could meet its
GHG emission reduction targets using LNG [190].
Battery electric vehicles also produce relatively favourable scores, which stem in particular from
the relative abundance of the fuel supply, the high availability of charging infrastructure, the
number of government incentives to offset vehicle costs and encourage the development of
charging infrastructure, and also the low price volatility of electricity. On the other hand, because
battery electric vehicles exhibit favourable performance across some of the attributes that were
typically weighted with a low importance, such as GHG emissions and other air pollutant
emissions, battery electric vehicles have a lower ranking when weights are applied (see Table
4.5). It is worth noting the uncertainty associated with many inputs to the evaluation of battery
electric vehicles, however. Though Canada’s National Energy Board [183] predicts that
increases to electricity generation are expected to outpace growing demand for electricity, there
is uncertainty surrounding the effects of electrification of heavy-duty transportation modes, such
as road freight, on electricity demand (see Appendix C for a discussion surrounding risk to
Canadian electricity supply). Additionally, although there is growing availability of high-power
charging infrastructure across Canada, it is uncertain whether or not each of these chargers
would be suitable for use by a heavy-duty tractor trailer. Moreover, it is likely that many carriers
would find the current estimated charging time of 400 minutes prohibitive to their operations.
Meanwhile, biodiesel (B20) shares many of the same properties as diesel, including a relatively
low total cost of ownership, a high cargo capacity, fast refueling time and long range. Where
69
biodiesel falls short, however, is with respect to its very limited availability of refueling
infrastructure for B20 blends, as well as its ability to meaningfully reduce both GHG and other
air pollutant emissions at a blend level of only 20 percent biodiesel. Moreover biodiesel suffers
from notably volatile fuel prices that tend to track petroleum diesel prices [147].
Renewable diesel (HDRD) shares from many of the same attributes as biodiesel and petroleum
diesel, however, renewable diesel is particularly limited by its fuel supply. There are currently no
renewable diesel fuel producers in Canada [143]. The fuel is also notably expensive in
comparison to both biodiesel and petroleum diesel [147]. Though renewable diesel is penalized
somewhat for its minimal industry experience with the technology, this is not expected to be a
particularly limiting factor due to the “drop-in” nature of the fuel. Unlike biodiesel, renewable
diesel benefits from the ability to be used at higher blend levels and as such, renewable diesel
may also able to produce more meaningful GHG emission reductions in comparison to biodiesel.
Hydrogen fuel cell electric vehicles (H2 FCEVs) benefit from a moderately sized cargo capacity,
as well as a fast refueling time and long range. On the other hand, the technology suffers from a
low fuel supply and lack of availability of refueling infrastructure, as well as a lack of industry
experience with the technology. Additionally, most of the hydrogen currently being produced in
Canada is derived from natural gas, and hence, does not produce as notable GHG emission
benefits as its renewable counterpart.
4.3.2.2 Satisficing Framework
The unweighted ranking of technologies when evaluated using the satisficing framework are
listed in Table 4.6. The total score indicates the number of attributes by which each technology is
expected to exhibit the same performance as diesel fuel.
Weighted scores for each technology are illustrated in Figure 4.3. Weighting has less of an effect
on the rankings of alternatives using the satisficing framework. The ranking of alternatives is, on
average, the same as when scores were unweighted. This stems primarily from the fact that
attributes are assigned a score of zero when their performance was not expected to meet a
petroleum diesel baseline, and hence, are not affected by weighting.
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Table 4.6. Unweighted scores for each technology across all attributes considered in the
satisficing framework. A score of 1 indicates that a technology meets or exceeds the performance
of petroleum diesel for that particular attribute, while 0 indicates that it does not. Ranking
indicates the degree of preference for an alternative based on the total score in descending order.
HDRD BEV B20 H2FCEV LNG CNG RNG
Total cost of ownership 0 1 0 0 0 0 0
Cargo capacity 1 0 1 0 0 0 0
Refueling time 1 0 1 0 0 0 0
Vehicle range 1 0 0 0 0 0 0
Fuel supply 0 1 0 0 0 0 0
Availability of refueling
infrastructure 0 0 0 0 0 0 0
GHG emissions 1 1 1 1 1 1 1
Other air pollutant emissions 1 1 0 1 1 1 1
Industry experience 0 0 0 0 0 0 0
Availability of incentives 1 1 1 1 1 1 1
Fuel price volatility 1 1 1 1 1 1 1
Total Score 7 6 5 4 4 4 4
Renewable diesel (HDRD) is on average the most preferable alternative technology when
alternatives are evaluated using the satisficing framework. In this framework, alternative
technologies are rewarded for having performance that is equal to or better than a petroleum
diesel baseline. Because renewable diesel is almost chemically identical to petroleum diesel, it
also shares many of the same properties including cargo capacity, vehicle range and refueling
time. Moreover, renewable diesel is rewarded for having lower life cycle GHG emissions and
reduced air pollutant emissions, as well as a higher availability of incentives in comparison to
petroleum diesel. In total, renewable diesel is expected to meet or exceed the performance of
petroleum diesel across seven of the eleven attributes included in the framework.
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Figure 4.3. Weighted scores for each technology as determined using a satisficing heuristic
framework with diesel as a reference. Attributes within each score have been weighted according
to each interviewees’ designated weight. Boxplots show ranking by individual expert and are
arranged by mean with tails representing a 95 percent confidence interval. Outliers are
represented by dots.
Battery electric vehicles (BEVs) are expected to exhibit performance equal to or greater than a
petroleum diesel baseline across five attributes, namely total cost of ownership, fuel supply,
GHG emissions, air pollutant emissions, the availability of incentives and fuel price volatility.
Meanwhile, biodiesel (B20) is rewarded for exhibiting performance similar or better than
petroleum diesel across five of the seven same attributes as renewable diesel, with vehicle range
and industry experience being the one exception. As biodiesel has a slightly lower energy
density, it also has a slightly lower range than petroleum diesel. Moreover, biodiesel is more
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ubiquitous in Canada in comparison to renewable diesel. Hydrogen fuel cell electric vehicles (H2
FCEV), LNG, CNG and RNG were each rewarded across the same attributes as battery electric
vehicles, with the exception of fuel supply.
4.3.2.3 Societal Cost Benefit Analysis
Final inputs to the societal cost benefit analysis can be found in Table 4.7 and final results and
rankings in Table 4.8. Ultimately, battery electric vehicles (BEVs) are shown to be the most
attractive alternative when the societal cost benefit framework is applied. Though I have
provided the rankings associated with the application of this framework to the evaluation of
alternative technologies for long haul HDVs, it is important to note that this analysis represents a
simplified application of the societal cost benefit analysis and that results are presented for
illustrative purposes only.
Table 4.7. Net present values for each attribute considered in the societal cost benefit analysis
over the course of a vehicle’s initial five years of ownership. All values are in thousands of
dollars ($1,000s).
Total cost
of
ownership
Cargo
revenue
Life cycle
GHG
emissions
PM10
tailpipe
emissions
NOx
tailpipe
emissions
CO
tailpipe
emissions Diesel 509 4,094 10 57 12,480 1,348
BEV 315 3,206 2 0 0 0
H2 FCEV 1,026 3,850 6 0 0 0
LNG 593 4,016 8 11 8,990 86
CNG 530 3,956 9 11 8,855 85
RNG 1,270 3,956 2 11 8,855 85
B20 517 4,094 9 57 13,011 1,260
HDRD 532 4,094 3 28 7,950 877
This framework considers the impact of externalities including GHG emissions and other air
pollutant emissions. Nitrogen oxide emissions, in particular, have the largest impact on results.
Impacts stemming from particulate matter (PM) emissions may be underestimated due to the fact
that I have not separated out the PM2.5 portion of PM emissions, which are expected to produce
more detrimental impacts on human health than PM emissions with a larger diameter and are
typically associated with a higher social cost [186]. Moreover, the chosen values for cargo
revenue and total cost of ownership may be underestimates, as these values are particularly
uncertain due to low levels of technological uptake.
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Table 4.8. Net present value (NPV) and rank of technologies as determined using the societal
cost benefit analysis framework. Rank of 1 is most preferred. All values are presented in
thousands of dollars ($1,000).
NPV Ranking
BEV 2,889 1
H2 FCEV 2,817 2
HDRD -5,296 3
CNG -5,534 4
LNG -5,671 5
RNG -6,271 6
Diesel -10,310 7
Biodiesel -10,760 8
Despite having particularly long refueling times and low cargo capacity, battery electric vehicles
do not produce any tailpipe emissions, have relatively low life cycle GHG emissions in the
Canadian context and also have a low total cost of ownership as a result of low maintenance
requirements and fuel costs. Moreover, though hydrogen fuel cell vehicles are expected to have
the highest total cost of ownership, as well as the second lowest cargo capacity, these vehicles
are still ranked second among the alternative technologies considered. This highlights the
importance of externalities including the air pollutant and GHG emissions within this analysis, as
this is ultimately what has led the hydrogen fuel cell electric vehicle to its favourable ranking.
To highlight the sensitivity of results to chosen social costs of emissions, I generate results for a
range of social costs of NOx emissions. Detailed results of this sensitivity analysis can be found
in Appendix G. Ultimately, when a lower value for the social cost of NOx emissions is assumed,
the rankings of alternatives changes as the expected difference in impacts stemming from zero
emission (e.g., battery electric) and non-zero emission (e.g., natural gas) technologies is reduced.
Whereas battery electric and hydrogen fuel cell electric vehicles were originally the preferred
technologies, CNG and LNG are the most preferred technologies with a low social cost of NOx
emissions (Table A11). Moreover, each of the technologies is expected to produce net societal
benefits, while the majority of technologies were expected to result in a net cost to society under
the original scenario. On the other hand, as the value of the social cost of NOx emissions is
increased, the difference in impacts between zero- and non-zero emission technologies is further
exacerbated and preference for the zero-emission technologies is even stronger. Battery electric
and hydrogen fuel cell electric vehicles continue to be the preferred technology when a high
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social cost of NOx emissions is applied, and the cost of the non-zero emission technologies to
society is increased (Table A12).
4.4 Discussion
4.4.1 Expert Interviews
It is crucial that experts and key stakeholders continue to be consulted in order to effectively
promote the adoption of low GHG technologies for on-road heavy-duty transport. This study
consulted a relatively small set of individuals and would benefit from further insights to
determine whether the trends I have identified hold true across a larger sample size. Moreover,
this study relied heavily on the expertise and reputation of each interviewee. To increase the
level of confidence in the insights gathered through these expert interviews, it would be
beneficial to provide experts with key information prior to conducting the interview. Moreover,
future surveys may could expand on results by being more quantitative in nature. Though I
required interviewees to quantify the relative importance of various vehicle attributes, responses
to each of the other questions were qualitative in nature. Nonetheless, with key considerations
and barriers to the adoption of alternative technologies identified, future work should move
forward in identifying ways by which these barriers can be overcome in order to promote future
widespread adoption of promising technologies.
4.4.2 Evaluation Frameworks
Each of the frameworks presented in this evaluation has its strengths and limitations. For
instance, the number of attributes included in the multi-attribute utility analysis has a strong
influence on results. The greater the number of attributes that are included in a framework, the
less important each individual attribute becomes. Conversely, if multiple attributes are included
which measure the same metric (e.g., if each air pollutant was its own attribute), this would skew
results in favour of the technology with the lowest level of air pollutant emissions. Moreover, the
multi-attribute utility framework is somewhat insensitive to outliers, such as the 400 minute
recharging time of the battery electric HDV, since the utilities are normalized to a value between
0 and 1. Ultimately, this range in values can only capture so much detail when the number of
significant digits considered is limited. Meanwhile, the satisficing heuristic framework is almost
completely insensitive to magnitudes in differences between technologies. Instead, technologies
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are simply evaluated on the basis of whether or not they meet a baseline (i.e., yes or no). Slight
magnitudes in preferences for certain technologies are captured when weights are applied,
however, only among technologies that meet the baseline and in this framework are assigned a
value of 1. Of all the frameworks presented, the societal cost benefit analysis is most sensitive to
magnitudes in differences between technologies. While this may be beneficial in cases where
there is higher level of certainty in the inputs to the analysis, it may be problematic in cases
where uncertainty is high.
Ultimately, none of the alternative technologies are expected to perform as well as petroleum
diesel when evaluated using the multi-attribute utility theory and satisficing frameworks. Low
scores and inferior performance across some of the attributes that are most important to carriers
(i.e., cargo capacity, total cost of ownership and availability of refueling infrastructure) indicate
that none of the alternative technologies in their current or near-term state are likely to meet the
performance requirements of long haul carriers.
Differences in rankings of technologies both within the evaluation frameworks where expert-
identified weights were applied, as well as differences in rankings between the various
evaluation frameworks highlight the fact that there may not be a universal low GHG solution for
Canada’s long haul HDV sector. Depending on the priorities of a particular decision maker, the
preferred low GHG alternative to petroleum diesel may vary. This has important implications on
the coordination of infrastructure build-out in Canada, for instance.
While in the societal cost benefit analysis the two zero-emission technologies, including battery
electric vehicles and hydrogen fuel cell electric vehicles, are ranked higher than diesel long haul
HDVs, it is important to acknowledge some limitations of this specific analysis. First, though I
propose that it is vital to consider externalities including GHG emissions and other air pollutant
emissions, it is unclear whether or not these factors would be considered in a cost benefit
analysis performed by a trucking company due to these being costs imposed on society, as
opposed to the trucking company itself. Second, values for the social cost of carbon and other air
pollutant emissions were taken directly from literature, but there is widespread uncertainty and
disagreement surrounding these values [191,192]. Third, I have not captured the time spent
locating an alternative refueling station in this analysis. Most of the alternative vehicle
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technologies have relatively few or in some cases no alternative refueling/recharging stations
located across the country and hence, these vehicles would not be able to operate as normal and
generate revenue from goods movement.
What these different frameworks do not highlight is the fact that when each of these alternative
technologies officially comes to market, they are unlikely to be adopted by long haul trucking
companies without extensive development of refueling/recharging station networks across the
country. Long haul trucking companies rely heavily on the availability of public refueling
infrastructure as they tend to operate along multi-day routes. Moreover, expert interviews
highlighted that the availability of refueling infrastructure is expected to be of the upmost
importance to long haul companies. Though I have considered the total number of refueling
stations for each technology, I have not considered their geographical placement and the
limitations this might impose on long haul trucking operations. For instance, there is currently
only a single hydrogen refueling station, and as such, it is unlikely that a long haul trucking
company would be able to operate as normal along the majority of its routes.
Furthermore, it is important to remember that while each of these technologies are either in the
commercialization or development stage, not all have fully entered the market or have been
tested or piloted on-road. Hence, inputs to the assessment are particularly uncertain. For instance,
there is notable skepticism surrounding the future purchase price of a battery electric tractor
trailer. While I have taken the estimate for this input directly from Tesla, who have listed the
expected price of their tractor trailer “Semi” at $230,000 (CAD) [95], this price point seems
particularly low considering the cost differential of an ICE and electric vehicle in other sectors,
such as transit buses [38]. Additionally, I was unable to find any data related to the expected
difference in weight between an electric and internal combustion engine on a tractor trailer. As
such, I may be underestimating the cargo capacity of a battery electric vehicle.
Moreover, once commercialized, vehicle improvements are likely to be on-going, potentially
resulting in the improved performance of certain alternative vehicle technologies. For instance,
we may see rapid improvement in battery energy density thereby making the battery electric
vehicle a more attractive alternative. Because of this uncertainty, I am not suggesting investment
in any particular alternative technology. Instead, I am proposing the use of different frameworks
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for assessment which may benefit decision makers in the future when there is more certainty
surrounding the performance of alternative vehicle technologies.
These types of assessments of emerging alternative technologies for long haul will help guide
decision makers and provide them with increased confidence. In particular, these evaluations will
help guide long haul carriers who may be considering investment in an alternative fuel vehicle,
as well as government and industry who aim to provide support. As previously mentioned, long
haul carriers are particularly risk averse and are unlikely to adopt unproven or unevaluated
technologies. Moreover, the alternative technologies that promise great reductions in GHG
emissions tend to be notably more expensive than the current petroleum diesel baseline. As such,
long haul trucking companies may require financial support, and these types of evaluations will
help guide government and other organizations to determine where support is needed.
Though the frameworks proposed in this study are suitable means of evaluation of alternative
technologies, it does not necessarily mean that they provide the most accurate or comprehensive
methods of assessment. There is a wide range in evaluation methods used to assess alternative
technologies for transportation and energy systems, and little evidence to suggest which is
optimal. Though multi-attribute utility theory, satisficing heuristic methods and a societal cost
benefit analysis were chosen in this case to incorporate the desired levels of stakeholder
engagement, for ease of understanding by decision makers or to replicate the expected decision-
making practices of carriers, other methods of evaluation should also be explored.
4.5 Conclusion
As the impacts of climate change become increasingly severe, it is important to transition the
country’s most GHG intense sectors to lower emitting modes. To do this efficiently, there must
be a coordinated effort among decision makers. This work has identified barriers to the adoption
of potentially lower emitting modes for long haul on-road heavy-duty transportation in Canada.
Through expert interviews, financial considerations, the availability of refueling/recharging
infrastructure, reliability and driver retention were identified as the greatest considerations of
long haul trucking companies when considering investment in an alternative vehicle or fuel
technology. Similar factors, however, were also identified the greatest barriers to the adoption of
a suite of low GHG alternative technologies. It is important for government and industry to work
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towards overcoming these barriers in order to promote widespread adoption of low GHG
technologies. Using expert-identified weights for a range of vehicle attributes, battery electric
vehicles, hydrogen fuel cell electric vehicles, natural gas vehicles, biodiesel and renewable diesel
were evaluated in comparison to petroleum diesel using the multi-attribute utility method of
multiple criteria decision making. This method illustrates one of the ways by which alternative
vehicle technologies can be evaluated by decision makers. Though results are particularly
uncertain due to low technology maturity, LNG vehicles are identified as the most attractive
alternative to petroleum diesel fuel in their current state. When evaluated using a satisficing
framework on the basis of whether or not an alternative technology meets the baseline set by
diesel, renewable diesel is currently the most preferable alternative. Finally, when technologies
are evaluated through the lens of a societal cost benefit analysis that considers the social costs of
carbon and other air pollutant emissions, battery electric vehicles are the preferred alternative.
The difference in outcomes of each of these frameworks that were chosen for their ability to
represent the possible decision making behaviour of long haul heavy-duty trucking companies
highlights that there may not be a universal low GHG solution for the long haul trucking
industry. Moreover, the fact that petroleum diesel vehicles were identified as the preferred
technology using the multi-attribute utility framework highlights the need for government and
industry interventions to bring particularly low emitting alternative technologies, such as battery
electric or renewable hydrogen fuel cell vehicles, to operational or financial parity alongside
petroleum diesel vehicles.
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Chapter 5 Well-to-wheel greenhouse gas emissions of dimethyl ether produced from renewable and non-renewable feedstocks
in Alberta
5.1 Introduction
As a means to meet Canada’s climate target of a 30 percent reduction in greenhouse gas (GHG)
emissions by 2030 relative to a 2005 baseline, it is important to look to the country’s most GHG
intensive sectors. Heavy-duty vehicles (HDVs) account for over 35 percent of GHG emissions
from transportation in Canada, and GHG emissions from this sector have steadily increased over
the past decade even as passenger vehicle GHG emissions decline [5,65]. Vehicles in the on-road
HDV sector predominantly use petroleum diesel as a fuel [6]. Dimethyl ether (DME) is a
promising alternative fuel for HDVs that, when produced using renewable feedstocks, has the
potential to lower GHG emissions. To quantify the GHG emissions reduction potential of the
fuel, this study aims to evaluate the life cycle GHG emissions of DME produced in Canada.
Although there are clear and viable alternative fuels for the light-duty vehicle fleet (e.g., ethanol,
electrification), a viable replacement for diesel within the heavy-duty fleet is less clear. Several
alternatives have been proposed including electrification, hydrogen, natural gas, biodiesel,
renewable diesel and DME; however many of the aforementioned alternatives still have
technological challenges to overcome, or require major changes to infrastructure or engines
[65,136,153]. Electrification of vehicles is occurring at a fairly substantial rate within the
transport sector; however, batteries in their current form are unlikely to be able to provide the
energy storage or range necessary for long haul heavy-duty trucking [193]. Storage and
transportation of hydrogen is challenging as a result of a low energy density [194,195]. Natural
gas requires high pressures (for compressed natural gas), or cryogenic conditions (for liquid
natural gas) [195]. Furthermore, major engine modifications or dedicated engines are required
for use in the heavy-duty fleet [195]. Biodiesel produced from the transesterification of vegetable
oils or animal fats, as well as hydrogenation derived renewable diesel (HDRD) are increasingly
being blended with conventional diesel in some locations around the world, including Canada
[143]. However, while biodiesel can reduce particulate matter (PM), carbon monoxide (CO) and
hydrocarbon (HC) tailpipe emissions, it may in some cases lead to increases in nitrogen oxide
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(NOx) emissions [147,153,196–198]. On the other hand, HDRD presents limitations in terms of
its high production costs [153]. DME is a potentially viable HDV fuel that promises
improvements to air emissions from the heavy-duty transport sector that has so far garnered little
attention. It is a suitable alternative to diesel as it can be used in compression ignition engines
with minor engine modifications, it can be produced from both renewable and non-renewable
sources, and it produces no PM and low NOx, CO and HC emissions upon combustion [157–
161]. Hence, DME may also have the ability to reduce some of the negative health impacts
associated with diesel exhaust.
To date, a number of life cycle assessments (LCAs) have evaluated the life cycle GHG emissions
of DME as a vehicle fuel, though each differs in their scope and methods. As a result of the
fuel’s popularity in East Asia, a number of LCAs of DME have been performed to assess the use
and production of DME in this region [16–18]. Other LCAs have been performed in the context
of Thailand [19], Papua New Guinea [16] and Sweden [20]. These results are not easily
extrapolated to the Canadian context due to differences such as input supply chains and
feedstock availability.
A number of LCAs of DME have been performed in the context of the United States (US), which
mirrors the Canadian context a little more closely. These studies have evaluated the life cycle
GHG emissions of DME produced from renewable hydrogen and captured and compressed CO2
[21]; natural gas and biogas [22]; as well as natural gas, biogas and black liquor [23]. Each of
these studies have focused on renewable waste residues or natural gas from the United States.
Despite producing demonstrated GHG emission reductions, it is unclear whether or not waste
residues will be able to meet demand levels for DME. Hence, it is also important to explore the
impacts of DME produced from cultivated biomass which can more reliably produce adequate
supply levels, and which has shown compelling results as a low carbon feedstock for DME in
other jurisdictions. To my knowledge, there is no North American-based LCA that extensively
explores the life cycle GHG emissions of DME produced from cultivated biomass.
This chapter puts forth a comparative evaluation of DME produced from natural gas, wood
residue and poplar to highlight the differences in results expected for fossil fuel, waste residues
and cultivated biomass feedstocks. Alberta is be used as a case study within Canada as a result of
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its high concentration of energy industry and large availability of biomass resources [199].
Results are modeled for use of DME in a heavy-duty truck and are be compared to that of a
reference petroleum diesel-fueled heavy-duty truck. Ultimately, this study aims to inform key
stakeholders, including trucking companies, policy makers and the chemical manufacturing
industry, who may be seeking to aid in the reduction of life cycle GHG emissions from HDVs in
Canada.
5.2 Background
DME has been manufactured for years as a fuel for residential heating and cooking, an aerosol
propellant, a solvent and a chemical feedstock [200]. It is estimated that 45 million tonnes of
DME are produced each year, mainly in China, Japan and Korea [200,201]. China is responsible
for nearly 90 percent of global DME demand where it is used as a domestic fuel for heating and
cooking [201]. DME is not currently being manufactured commercially as a transportation fuel,
and there is no commercially available DME HDV. The only production of DME in Canada is
occurring lab-scale at Enerkem Inc.’s Innovation facility in Westbury, Quebec [168].
DME is a simple organic compound with the formula CH3OCH3. Its chemical structure is
illustrated in Figure 5.1. The properties of DME vary considerably from those of diesel. Some of
these differences work in favour of DME as a diesel replacement, while others do not. These
properties are summarized below in Table 5.1.
Figure 5.1. Chemical structure of DME [202].
DME is a gaseous fuel at atmospheric pressures, and must be pressurized over 0.5 MPa into a
liquid state in order to be used as a vehicle fuel [203]. DME offers advantages over diesel as a
fuel in HDVs as a result of its high oxygen content, lack of carbon to carbon (C-C) bonds, low
carbon to hydrogen (C:H) ratio, high cetane number, high latent heat, and low boiling point. On
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the other hand, the low lubricity and viscosity of DME, its low lower heating value, and high
compressibility pose some issues with regards to the fuel’s ability to be an effective diesel
replacement. DME shares many of the same properties as liquefied propane gas (LPG) and can
be handled in many of the same ways [203].
5.2.1 Advantages of DME
5.2.1.1 High Oxygen Content
The oxygen content of a fuel influences the occurrence of a combustion reaction, as well as the
formation of soot and PM. While oxygen promotes combustion, it hinders the formation of PM.
PM produced during combustion can be oxidized, thereby reducing emissions. At 34.8 percent
and 0 percent oxygen content by weight for DME and diesel, respectively, DME holds a
significant advantage. The high oxygen content of DME, in combination with its lack of C-C
bonds results in a near smokeless combustion and low rates of formation and high rates of
oxidation of PM [202]. As a result of these properties, DME-fueled vehicles are unlikely to
require a particulate filter, and may also be able to meet emissions standards without a selective
catalytic reduction (SCR) emission control system [203].
Table 5.1. Properties of DME and diesel [156,202].
Property Unit DME Diesel fuel
Chemical formula CH3OCH3 C8H18 to C25H52
Molar mass g/mol 46.07 96-170
Liquid density kg/m3 667 831
Carbon content mass % 52.2 86
Hydrogen content mass % 13 14
Oxygen content mass % 34.8 0
C:H ratio 0.337 0.516
Critical temperature K 500 708
Cetane number 55-60 40-50
Auto-ignition temperature K 508 523
Boiling point (at 1 atm) K 248.1 450-643
Enthalpy of vapourization kJ/kg 467.14 250-300
Lower heating value MJ/kg 27.6 42.5
Modulus of elasticity N/m2 6.37E+08 14.86E+08
Kinematic viscosity of liquid cSt <0.1 3
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5.2.1.2 High Cetane Number
The cetane number is the inverse of a fuel’s ignition delay in a compression ignition (CI) engine.
The higher the cetane number, the shorter the delay between fuel injection and combustion of the
fuel. This delay plays a role in rates of combustion, as well as emission characteristics. In CI
engines, a higher cetane number is desirable. At 55-60 and 40-50, respectively for DME and
diesel, DME has a notably higher cetane number making it ideal for use in a CI engine [156]. In
addition to a higher cetane number, DME also has a lower auto-ignition temperature than diesel
[156].
5.2.1.3 Low Boiling Point and High Latent Heat
The boiling point of a fuel effects its evaporation upon injection into an engine cylinder [202].
The low boiling point of DME (248.1 K at 1 atm) versus diesel (450-643 K at 1 atm) means that
there will be quick vaporization of liquid DME spray when it is injected into the combustion
chamber [156,202]. Additionally, the latent heat, or enthalpy of vaporization, of DME is much
larger than that of diesel. This means that DME will consume more heat during the vaporization
process, thereby dropping the temperature in the combustion chamber. The fast vaporization of
DME and subsequent drop in temperature in the combustion chamber results in reduced
formation of NOx during combustion [156].
5.2.1.4 Safety Features
In addition to the properties mentioned above, DME also has a number of safety features. First of
all, DME is chemically stable and only reacts under severe conditions [156]. Second, DME burns
with a visible blue flame, an important safety feature of fuels [156]. DME also has a distinct
sweet ether-like odour [159]. Lastly, DME has no toxic effects associated with its regular use
and has low acute and sub-chronic inhalation toxicity [156]. It is also non-carcinogenic, non-
teratogenic and non-mutagenic [159].
5.2.2 Disadvantages of DME
5.2.2.1 Lower Heating Value
The lower heating value of DME is 27.6 MJ/kg, while that of diesel is 42.5 MJ/kg [202]. This
means that the amount of energy released as heat upon combustion of DME will be much lower
84
than that of diesel. Consequently, to deliver the same amount of energy provided by diesel, a
larger volume of DME must be combusted. This has implications on the size of the fuel tank
required in a DME-fueled vehicle.
5.2.2.2 Low Viscosity and Lubricity
The viscosity and lubricity of DME are notably lower than that of diesel. Low viscosity and
lubricity can have very negative effects on a CI engine. Viscosity of a fuel is important in that it
allows a fuel to pass readily through the engine, while lubricity prevents degradation of moving
parts [203]. A lack of viscosity and lubricity may also induce fuel leakage within the fuel supply
system [202,203]. While the viscosity of diesel is approximately 3 cSt, that of DME can be lower
than 0.1 cSt [202]. While this is problematic, it has been suggested that DME be blended with
additives such as biodiesel to increase its lubricity and viscosity [156].
5.2.2.3 High Compressibility
The modulus of elasticity of DME is considerably lower than that of diesel [202]. Therefore, the
compressibility of DME is higher than that of diesel, and the compression work of the fuel pump
will be higher for DME, too [202,203].
5.2.3 Summary of Advantages and Disadvantages of DME
Certain properties of DME demand minor modifications be made to a standard compression
ignition engine that stem in particular from the fuel’s low lubricity and viscosity, high
compressibility and low energy density. DME-fueled vehicles will likely require modified fuel
tanks, fuel lines, fuel pumps, fuel injectors, more durable seals and gaskets and updated software
to time the fuel injection system [203]. Despite these shortcomings, the high oxygen content,
high cetane number and low boiling point of DME offer significant benefits over diesel. These
properties lead to inherently lower levels of criteria air pollutant emissions produced during
combustion, as well as a shorter ignition delay. Moreover, the added weight of pressurized fuel
tanks may be offset by reduced emission control requirements [203].
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5.2.4 DME Production
The production of DME is conceptually simple and typically involves two major steps. First, the
feedstock is converted to synthesis gas (syngas), a mixture of hydrogen, carbon monoxide and
carbon dioxide, through either gasification or steam reformation. The second major step involves
the synthesis of DME either through indirect or direct methods. In traditional indirect synthesis,
syngas is first converted to methanol (MeOH) in a well-established process using copper-based
catalysts [204]. Then, the methanol is purified and passed over alumina- or zeolite-based
catalysts to produce DME. Direct synthesis of DME takes place in a single reactor using bi-
functional catalysts. As a result of using a single reactor, the direct synthesis method is typically
a more cost effective method of production [204]. The complexity of the reaction, however, is
much greater than the indirect method and leads to difficulties in achieving a high purity product
[204].
5.3 Material and Methods
5.3.1 Overview
The purpose of an LCA of an alternative fuel is to quantify the environmental impacts of all
stages of the fuel’s life cycle. Guidelines for LCA have been developed by the International
Organization for Standardization (ISO) [78]. Typically, fuel LCAs consider resource extraction,
transportation, processing, distribution, and use of the fuel in a vehicle, and are commonly
referred to as well-to-wheel (WTW) assessments, or fuel cycle LCAs. It does not include the
infrastructure required for producing or transporting fuels, or the life cycle of the HDV itself.
Figure 5.2 illustrates the system boundary considered in this particular assessment.
Life cycle assessments are commonly performed to quantify a range of environmental impacts
such as global warming potential (GWP), eutrophication, acidification, ozone depletion, among
others. Many LCAs, particularly those evaluating transportation fuels, limit their scope to
assessing the GHG emission intensity of a product system [205]. This LCA also limits its scope
to assessing the life cycle GHG emission intensity of DME, while acknowledging the importance
of other impact categories as part of future policy assessments. A primary functional unit of one
kilometer travelled will be used but results for the core pathways will also be presented on the
basis of one megajoule (MJ) of fuel.
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Three core pathways of DME production are considered in this assessment. A single pathway is
modelled for each feedstock: natural gas, wood residue and poplar. Each of these pathways
considers a conversion process that was modelled using Aspen Plus chemical process modelling
software. The system boundaries considered in this analysis are illustrated in Figure 5.2.
Activities related to feedstock production, feedstock transportation, DME production, DME
distribution and DME combustion in an HDV are considered. All three pathways considered
produce DME using indirect methods as this has been the predominant method of DME
production, to date [203]. There is, however, growing interest in direct methods of DME
production as a result of its higher efficiency, which would contribute to even greater reductions
in life cycle GHG emissions. Impacts of direct production pathways will be explored in a
sensitivity analysis.
Figure 5.2. System boundaries of the WTW DME product system, shown simultaneously for
natural gas, wood residue and short rotation poplar feedstocks. Main life cycle stages are shown
along the center line in blue, with key parameters shown above (below) if they contribute to
GHG emissions or sequestration in the LCA accounting.
87
LCAs are developed for DME produced in Alberta using inputs compiled from government
reports, journal articles, GHGenius and GREET. The latter two data sources are life cycle
models developed to evaluate the life cycle emissions of vehicles and transportation fuels in
Canada and the US, respectively. Focusing on Alberta allows for specific input assumptions to
be used rather than broader geographic averages, such as the province’s average electricity mix
which was obtained from Canada’s National Energy Board [199] and upstream emissions from
natural gas which were compiled using Alberta Energy Regulator ST3 report on gas, GHGenius,
Canada’s National Inventory Report and Statistics Canada [206–210]. Where inputs are not
expected to differ notably between Alberta and the broader North American context, inputs from
GREET are relied upon as a result of the model’s widespread use among LCAs of transport
fuels.
The concepts of global warming potential (GWP) and carbon dioxide equivalent (CO2eq.) are
important for quantifying the overall effect of a process on climate change. Climate active
species, including methane and nitrous oxide, are assigned a GWP to express their impact on the
climate relative to carbon dioxide. GWPs vary depending on the timescale, and different
methodologies produce slightly different values. The 100-year GWP values without climate
carbon feedback from the IPCC’s Fifth Assessment Report will be used for this assessment and
can be found in Table 5.2 [211].
Table 5.2. 100-year global warming potential values considered in the LCA [211].
Global Warming Potentials (100 year)
CO2 1 g CO2eq.
CH4 30a g CO2eq.
N2O 265 g CO2eq. afossil methane
Input parameter uncertainty is a challenge with all LCAs. This study addresses uncertainty
through a targeted sensitivity analysis. Parameters that are both uncertain and impactful on the
analysis are varied across a reasonable range to quantify the potential effects on the results of the
LCA.
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5.3.2 Upstream Activities
5.3.2.1 Electricity Generation
GHG emissions associated with electricity production are calculated by taking into account a
weighted average of upstream fuel emissions and direct plant emissions for six broad classes of
electricity generation technologies. Transmission and distribution losses of 6.5 percent are
incorporated into these three emission factors [64].
The electricity generation mix for Alberta is taken from the National Energy Board’s Provincial
Energy Profile for Alberta for 2016 (see Table 5.3) [199]. At the time of writing this, nearly 90
percent of the electricity supply in Alberta is derived from fossil fuels, with more than half of
that coming from coal and coke-fired power plants, thus making the province’s grid electricity
relatively GHG-intensive [199].
Table 5.3. Values of key input parameters used in upstream electricity activities [64,199].
Parameter Value
Alberta Electricity Generation Mix (2016)
Residual Oil-Fired Power Plants 0%
Natural Gas-Fired Power Plants 40%
Coal-Fired Power Plants 47%
Biomass Power Plants 3%
Nuclear Power Plants 0%
Other Power Plants (hydro, wind, geothermal, etc.) 10%
Upstream Electrical Efficiency (LHV)
Residual Oil-Fired Power Plants 35%
Natural Gas-Fired Power Plants 50%
Coal-Fired Power Plants 36%
Biomass Power Plants 22%
Nuclear Power Plants 100%
Other Power Plants (hydro, wind, geothermal, etc.) 100%
Electricity Line Losses 6.5%
Emission factors related to electricity generation are taken from GREET, with the exception of
upstream emissions for natural gas which are updated to reflect the context of Alberta (see
section 5.3.2.2 for a description of these modifications and Table 5.4 for a list of emission
factors).
89
Table 5.4. Life cycle emission factors for upstream electricity activities (g CO2eq./GJ) (LHV)
[64].
Fuel Upstream Plant
Residual Oil 41,337 237,398
Natural Gas 28,507 153,726
Coal 16,312 282,971
Biomass 11,971 9,120
Nuclear N/A N/A
Misc. Renewable 0 0
5.3.2.2 Natural Gas Production
Upstream emissions from natural gas supply are estimated by taking into account emissions from
fuels consumed during natural gas extraction, direct emissions (i.e., fugitive or venting) of GHGs
during natural gas extraction and emissions resulting from gas transportation. Emissions are
proportional to the distance travelled, and are raised slightly by a loss factor, accounting for gas
leakage during transmission. Details related to these activities can be found in Table 5.5.
Table 5.5. Values of key input parameters used in upstream natural gas activities [206,208,212–
214].
Parameter Value Unit
Natural Gas Recovery Energy
Recovery Efficiency 96 %
Electric Input 1 %
Natural Gas Input 86 %
Natural Gas Processing Energy
Processing Efficiency 97 %
Electric Input 3 %
Natural Gas Input 96 %
Methane Venting and Leakage for Natural Gas
Recovery – Completion 0.0005 g CH4/MJ NG
Recovery – Workover 0 g CH4/MJ NG
Recovery – Liquid Unloading 0.009 g CH4/MJ NG
Well Equipment 0.02 g CH4/MJ NG
Processing 0.0005 g CH4/MJ NG
Transmission and Storage 0.007 g CH4/MJ NG
Recovery - Flaring 8705 J NG/MJ NG
Natural Gas Transmission Loss
Energy Loss 33.2 J/MJ NG
For gas production in Alberta, statistics from the Alberta Energy Regulator are used to determine
the flaring rate and production energy is taken from a recent update to GHGenius [206,212].
Intentional and fugitive GHG emissions occur throughout natural gas recovery and processing.
90
For the baseline of this analysis, values from GHGenius are used for direct GHG emissions. No
changes are made to the assumptions for direct CO2 emissions, which are mostly due to the
removal and venting of naturally occurring CO2 during the processing of raw gas. There is some
disagreement between industry stakeholders and Environment Canada on methane leakage
during upstream natural gas production [212]. A sensitivity analysis on upstream methane
emissions will be performed in light of the conflicting statistics.
It is assumed that emissions stemming from transmission are proportional to both the length of
the pipeline and the amount of gas transmitted. Albertan estimates for this analysis stem from
Canada’s National Inventory Report and data on natural gas transmission from Statistics Canada
[208,213,214]. A loss factor of 0.0056 percent is calculated by taking into account the loss per
mile, distance transported and methane mass fraction of natural gas. A distance of 160 km (100
miles) is assumed, which is also the default in GREET [64].
5.3.2.3 Wood Residue Collection
Wood residue is modelled as mix of logging residue and forest thinnings. Upstream energy
requirements take into account both collection and transportation energy (Table 5.6) for a
breakdown of inputs for these activities). Transportation energy is proportional to the total
distance travelled as well as the moisture content of the biomass, with default assumptions of 145
km (90 miles) and 30 percent, respectively [64]. All GHG emissions are from diesel combustion,
the single source of fuel for this activity. Assumptions are unchanged from values presented in
GREET but are expected to be reasonable for the Albertan context. Moreover, the collection of
wood residue represents a relatively small fraction of total life cycle energy and GHG emissions.
5.3.2.4 Poplar Production
Total upstream energy requirements for poplar are modelled as the sum of farming energy
requirements including energy embedded in the farming equipment, fertilizer and chemical use,
as well as transportation energy requirements (refer to Table 5.6 for a more detailed breakdown).
Transportation is proportional to distance travelled from farm to refinery, which is assumed to be
80 km (50 miles) [64]. It is assumed that all farming and transportation energy is derived from
diesel. Energy and fertilizer requirements for poplar production have been sourced from GREET
[64].
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Table 5.6. Values of key input parameters used in upstream biomass activities [64].
Parameter Value Unit
Biomass Moisture Content (During Transport)
Forest Residue 30 %
Poplar 30 %
Upstream Energy Required to Collect and Process Forest Residue (Diesel)
Logging residues 137 MJ/dry tonne
Forest thinnings 170 MJ/dry tonne
Weighted average 154 MJ/dry tonne
Upstream Energy Poplar
Farming Energy Use 312 MJ/dry tonne
Diesel Stationary 62 MJ/dry tonne
Diesel Off-road 250 MJ/dry tonne
Collection Energy 2 MJ/tonne-km, round trip
Fertilizer/Pesticide Use
Nitrogen 2172 g/dry tonne
P2O5 650 g/dry tonne
K2O 573 g/dry tonne
CaCO3 25618 g/dry tonne
Herbicide 66 g/dry tonne
Insecticide 11 g/dry tonne
5.3.3 DME Production Plant
5.3.3.1 Aspen Plus Model
A process model that uses biomass or natural gas for DME production is developed by colleague
Yaser Khojasteh in Aspen Plus. Using this integrated system which can accommodate both
feedstocks can help apply some synergies which are not available in conventional DME
production processes. By taking the advantages of natural gas as a relatively inexpensive
feedstock, with high hydrogen-to-carbon ratio, it may be possible to improve the
technoeconomic performance of the DME plant and make it financially competitive
with traditional transportation fuel production, such as diesel. On the other hand, by using
biomass as the plant’s co-feedstock, we can reduce the lifecycle greenhouse gas emissions of the
process and exploit the environmental benefits of this renewable feedstock. More details
surrounding the Aspen Plus modelling work are expected to be released in an upcoming
publication.
The Aspen Plus simulation results show that the thermal efficiency of this proposed DME
process varies between 44 percent and 71 percent (LHV), depending on the DME synthesis
92
process and feedstock type. The thermal efficiency of the plant is found to be higher using
natural gas as a feedstock. Furthermore, the direct production approach tends to be more efficient
as its thermal efficiency is 5 to 10 percent higher than the efficiency of the indirect approach
with the various feedstocks, which results in lower GHG emissions. In this analysis, the life
cycle GHG emissions for indirect pathways are assessed as this represents the predominant
method of production of DME [203], and because it will illustrate the upper end of emissions for
each feedstock.
5.3.3.2 DME Production
Three DME production pathways developed using Aspen Plus chemical modelling software are
highlighted in this assessment (Table 5.7). DME is the only product of each pathway. Although
electricity is produced as a result of excess steam during DME production, it is kept within the
closed loop production process. Any additional electricity requirements are supplemented with
electricity imported from the grid. CO2 emissions from the production plant are calculated using
a carbon balance.
Table 5.7. The indirect DME production pathways considered in this assessment. Inputs are on a
MJ/MJ of fuel basis. All pathways have an output of 1.00 MJ of DME. Thermal efficiencies
represent the input-output ratio of each production pathway.
Feedstock Description
Inputs Outputs Efficiency
(LHV,
%) NG Biomass Elec. DME MeOH Steam
Natural
Gas
Synthesis of DME
using electricity
derived from natural
gas supplemented with
grid electricity
1.46 - 0.06 1.00 0.00 0.05 66.0
Wood
Residue
Synthesis of DME from
wood residue using on-
site generated
electricity from
biomass supplemented
with grid electricity
- 1.70 0.13 1.00 0.00 0.06 54.4
Poplar
Synthesis of DME from
poplar using electricity
derived from biomass
supplemented with grid
electricity
- 1.70 0.13 1.00 0.00 0.06 54.4
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The production efficiency for bio-DME is consistently lower than natural gas-based DME. The
differences between the two pathways lie in the conversion from feedstock to syngas. DME
production requires syngas with impurities removed and an H:C ratio between 2 and 4. However,
dry wood typically only has an H:C ratio of about 1.4, while that of Albertan natural gas is closer
to 4 (Table A13 for ultimate analyses of wood species and Alberta natural gas).
Generating usable syngas, thus, is more energetically favourable when using natural gas as a
feedstock compared to biomass. To correct this problem, a water gas shift reaction is employed.
The requirement to adjust the H:C ratio of wood-based syngas imposes financial and energy
costs that are not present in the natural gas pathway.
A credit is included in the WTW results for bio-DME pathways in order to offset the biogenic
carbon emissions associated with combustion of biomass during DME production. The credit is
proportional to the share of fuel requirements that are derived from biomass during the
production stage. In other words, all CO2 released as a result of biomass combustion is included
in this credit.
5.3.4 Transportation and Distribution
The delivery of fuel from the production plant to customers is independent of the choice of DME
production pathway. This assessment assumes the same distribution methods of DME as
proposed in GREET, which includes a total transportation distance of 3,010 km (1,870 miles)
from the plant to the bulk terminal [64]. Heavy-duty diesel trucks perform the final distribution
of the fuel over a distance of 48 km (30 miles) [64]. With these assumptions, DME produced
near Edmonton could be transported as far as Ottawa. See Table 5.8 for a detailed breakdown of
these activities.
Given the higher costs and emissions associated with gathering biomass feedstock or
transporting natural gas, life cycle GHG emissions are minimized when the hypothetical DME
plant is located as close as possible to its feedstock source. This holds even if the DME must be
transported a longer distance to consumers.
94
Table 5.8. Values of key input parameters used in transmission and distribution activities [64].
Parameter Value
Feedstock Transmission Distances (km)
NG to DME Plant 160
NG to Diesel Refinery 1090
NG to Power Generation 600
Forest Residue 140
Poplar (Farm to Refinery) 80
DME Transmission and Distribution (km, one-way)
Transmission
Barge 840
Pipeline 880
Rail 1290
Distribution
Truck 50
Diesel Transmission and Distribution (km, one-way)
Transmission
Ocean Tanker 2090
Barge 320
Pipeline 180
Rail 790
Distribution
Truck 50
5.3.5 Vehicle Use
Vehicle parameters used in this assessment can be found in Table 5.9. The vehicle use stage
considers emissions associated with fuel combustion in a heavy-duty truck and were also taken
from GREET for fleet year 2015 [64]. It is assumed that the diesel and DME-fueled vehicles
share the same efficiency [215]. However, because DME has more energy per unit carbon than
diesel, it creates less CO2 even at the same fuel efficiency. A credit is given to offset all of the
biogenic CO2 emissions associated with the combustion of bio-DME in vehicles.
Table 5.9. Values of key input parameters used in heavy-duty vehicle use activities [64].
Parameter Value Unit
Diesel Truck Fuel Use 32 L/100 km
Diesel Truck Payload 21 tonnes
DME Truck Fuel Use 32 L/100 km
DME Truck Payload 21 tonnes
95
5.3.6 Land Use Change (LUC)
Land use change (LUC) emissions account for fluxes in GHG emissions that occur as a result of
land use conversion. These changes in emissions stem from factors such as changes in soil
carbon storage, release of N2O from fertilizers, and methane sinks associated with peatlands. An
estimate for LUC is included in the WTW emissions of DME produced from poplar. This
estimate was taken directly from a publication by GREET developers, who quantified the direct
LUC emissions associated with the conversion of cropland into poplar as ranging from -0.28 to
0.04 tonnes C/ha/year [216]. The midpoint of these values, -0.12 tonnes C/ha/year was used in
this assessment to quantify the potential LUC emissions from poplar plantations. The default
estimate for poplar yield found in GREET, 12 tonnes/ha/year, is used [64]. Ultimately, this
represents a very small portion of the fuel’s final life cycle emissions.
5.3.7 Diesel Reference Pathway
LCA results from the three DME pathways will be compared to a diesel reference pathway.
Diesel has been chosen as a reference due to the fact that it is currently the most widely used
heavy-duty truck fuel. Data related to the fuel’s life cycle GHG emissions are taken directly from
GREET, with slight updates to natural gas and electricity inputs to ensure its applicability in
Alberta [64]. The same life stages that were considered for DME are considered for diesel,
namely upstream resource extraction, production, transportation and distribution, and use.
5.4 Results
5.4.1 Well-to-wheel (WTW) Results
Results of the WTW assessment for the Aspen Plus models of DME produced from natural gas,
wood residue and poplar in Alberta are presented in Figure 5.3 alongside the diesel reference
pathway. As DME and diesel vehicles are expected to have the same efficiency [215], WTW
results on an energy basis as well as distance basis have been presented on the same graph.
Results for each pathway are broken down by life stage. The upstream life stage includes all
activities related to natural gas processing, electricity generation and biomass production or
collection. Out of the three production pathways, DME produced from wood residue emits the
96
lowest WTW GHG emissions at 440 g CO2eq. per km or 40 g CO2eq. per MJ, representing a 59
percent reduction relative to diesel (1,080 g CO2eq./km).
On the other hand, the WTW GHG emissions of natural gas-based DME (1,270 g CO2eq./km)
are 10 percent higher than those of diesel. Losses during the upstream processing of natural gas
account for most of the difference between the two pathways. Although natural gas-based DME
may offer some local air quality benefits over diesel, it does not appear to be a compelling fuel
for achieving reductions in WTW GHG emissions.
Figure 5.3. WTW GHG emissions of Aspen Plus-based models of DME in comparison to diesel.
Accounting for biogenic carbon credits, the net WTW GHG emissions of DME produced from
wood residue (440 g CO2eq./km) and poplar (460 g CO2eq./km) are on the order of 57 to 65
percent lower than those of either natural gas-based DME or diesel. Differences between the
results of these two biomass feedstocks can be attributed primarily to upstream emissions from
farming.
Results for the bio-DME, however, are highly dependent on the credit received from biogenic
carbon, which relies on assumptions made regarding the carbon neutrality of biogenic CO2
emissions from biomass combustion. Because the carbon contained in both wood residue and
poplar is considered biogenic, it receives a credit when combusted. In effect, all CO2 emissions
from vehicle use and biomass used for electricity generation during production are not
-260
-160
-60
40
140
240
-3000
-2000
-1000
0
1000
2000
3000
Natural Gas WoodResidue
Poplar Diesel
WT
W G
HG
em
issio
ns
(g C
O2e
q./
MJ)
WT
W G
HG
em
issio
ns
(g C
O2e
q./
km
)
97
considered in the net WTW assessment. What remains is the upstream GHG emissions
associated with farming or collecting biomass, grid electricity generation, and DME distribution.
When biogenic CO2 credits are treated as net emission sources, GHG emissions from all biomass
to DME pathways are much higher than natural gas-based DME pathways. DME production
from wood is less efficient meaning that it has a larger energy input-to-output ratio as a result of
its lower H:C ratio in comparison to natural gas.
The largest contributors to upstream emissions in the natural gas pathway include conventional
natural gas recovery, as well as combustion during processing. Meanwhile, upstream emissions
from the wood residue-based DME pathway stem primarily from direct power plant emissions,
in addition to biomass transportation. Upstream emissions from the poplar pathways are
dominated by farming-related activities, as well as some direct power plant emissions.
5.4.2 Sensitivity Analysis
A sensitivity analysis was performed to highlight differences between direct and indirect
methods of DME production. A separate analysis was performed to highlight the relative
importance of the electricity generation mix, feedstock transport distance and methane emissions
from natural gas. Results from these analyses can be found in Figure 5.4 and Figure 5.5. The
motivation for these analyses stems from the fact that the grid electricity mix in Alberta is
becoming more heavily reliant on renewables [199], feedstock transmission distance will vary
largely based on availability and thus may vary in location, and concerns about the reliability of
methane emission factors have been raised [141,217]. Moreover, it is possible that DME
production will transition from indirect to direct methods as a result of the processes’ higher
thermal efficiency.
5.4.2.1 Direct versus indirect production pathways
Aspen Plus modelling not only mapped out the indirect production of DME as described
throughout this analysis, but also the direct production of DME. While the indirect DME
production pathway produces DME from methanol, there is no methanol intermediary step in the
direct method. Results of the Aspen Plus modelling for the direct DME pathway can be found in
Table 5.10.
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Table 5.10. The indirect DME production pathways considered in this assessment. Inputs and
outputs are on a MJ/MJ of DME basis. All pathways have an output of 1.00 MJ of DME.
Thermal efficiencies represent the input-output ratio of each production pathway.
Feedstock Inputs Outputs Efficiency
(LHV, %) NG Biomass Elec. DME Steam Methanol
Natural Gas 1.56 - 0.07 1.00 0.16 0.048 70.7
Wood Residue - 1.65 0.12 1.00 0.04 0.06 54.4
Poplar - 1.65 0.12 1.00 0.04 0.06 54.4
Using outputs from the Aspen Plus model, the life cycle GHG emissions of the direct DME
production pathways were calculated and are compared to the indirect DME production and
petroleum diesel reference pathways in Figure 5.4. To account for the methanol co-product in the
direct production pathway, life cycle GHG emission impacts have been allocated on an energy
basis.
Figure 5.4. WTW GHG emissions of DME produced via indirect methods versus direct methods
in comparison to a petroleum diesel baseline.
Ultimately, the direct method of DME production is expected to produce slightly lower WTW
GHG emissions than the indirect method across all feedstocks. Despite this, natural gas based
DME is still expected to have a higher GHG intensity than petroleum diesel.
0
200
400
600
800
1000
1200
1400
NaturalGas
WoodResidue
Poplar Diesel
WT
W G
HG
em
issio
ns
(g C
O2e
q./
km
)
Indirect
Direct
99
5.4.2.2 Impact of grid electricity mix, feedstock transport distance and upstream emissions from natural gas
The electricity generation mix in Alberta is evolving as coal power is phased out and replaced
with natural gas and renewable energy [199]. In the longer term, electricity networks in Alberta
may approach 100 percent non-emitting fuel sources (e.g., nuclear and wind). The range
displayed in the left hand panel of Figure 5.5 represents the WTW life cycle GHG emissions for
two extreme bounding scenarios: a 100 percent non-emitting grid electricity on the low end and
100 percent coal-fired generation on the high end. Of all of the parameters assessed in this
sensitivity analysis, electricity generation had the greatest impact on the WTW results. A 100
percent renewable grid in Alberta is expected to reduce the GHG intensity of wood residue- and
poplar-based DME by 81 percent and 86 percent, respectively. Across all electricity generation
scenarios, natural gas-derived DME is consistently expected to have higher life cycle GHG
emissions than diesel. As Alberta already derives a considerable share of its electricity from coal
and other fossil-based sources, a switch to a 100 percent coal-based grid has less of a pronounced
effect on all pathways.
Feedstock transport distances are altered to reflect the possibility of a DME production plant co-
located next to its major feedstock source (i.e., no transportation as a lower bound), and that of
transporting feedstocks from the northern region of Alberta to a DME production plant located
near a major population center in the south. Feedstock transport distance has a particularly large
impact on the bio-based pathways. By increasing the transport distance to 800 km (the distance
from Fort McMurray to Calgary), the GHG intensity of wood residue- and poplar-based DME
are expected to increase by 51 percent and 37 percent, respectively. Conversely, by eliminating
the feedstock transport entirely, the GHG intensity decreases by 11 percent and 8 percent for
wood residue- and poplar-based DME, respectively. Meanwhile, the GHG intensity of natural
gas-based DME is reduced by 37 percent when the production plant is co-located next to a
population center, while it increases by 8 percent when transport distance increases to 800 km.
The diesel life cycle is insensitive to feedstock transport distance due to the fact that this activity
represents a much smaller portion of its total impacts.
100
Figure 5.5. WTW results of the sensitivity analysis of the modelled DME pathways with respect
to changes to the grid electricity mix, feedstock transportation distance and methane emissions
from upstream natural gas. Electricity generation mixes were changed to reflect (1) a 100% coal-
based grid and (2) a 100% renewables-based grid; feedstock transport distance was altered to
reflect (1) 800km and (2) 0km; and methane emissions from natural gas were altered to reflect
(1) twice the estimated upstream emissions of conventional natural gas in the United States and
(2) zero upstream emissions.
There are many concerns about the reliability of methane emission factors for natural gas
[141,217]. Although methane leakage in Alberta may be lower than in the United States for
structural reasons, official estimates in both jurisdictions are likely lower than actual emissions
[141]. As such, the five DME production pathways are shown in Figure 5.5 with no methane
emissions in the low case and with double the official United States estimates in the high case
[64]. As expected, the natural gas-based DME pathway is particularly sensitive to changes in
upstream methane emissions from natural gas production. By changing upstream emissions to
reflect double those of conventional natural gas production in the United States, the GHG
intensity of natural gas-based DME produced in Alberta was projected to increase by 14 percent.
0
200
400
600
800
1000
1200
1400
1600
Na
tura
l G
as
Wo
od R
esid
ue
Po
pla
r
Die
se
l
Na
tura
l G
as
Wo
od R
esid
ue
Po
pla
r
Die
se
l
Natu
ral G
as
Wo
od R
esid
ue
Popla
r
Die
se
l
Electricity Generation Feedstock TransportDistance
Methane Emissions fromUpstream NG
WT
W G
HG
em
issio
ns
(g C
O2e
q./
km
)
101
Because upstream emissions from natural gas produced in Alberta are already expected to be
relatively low, changing the upstream methane emissions to zero had less of an effect on the
GHG intensity of natural gas-based DME (-2%). Changes to the upstream methane emissions
from natural gas production also had a notable effect on the poplar and wood residue pathways
as a result of the fact that these pathways supplement their electricity requirements with
electricity imported from the grid and a notable portion of electricity in Alberta is derived from
natural gas. Increases to upstream methane emissions resulted in increases to the GHG intensity
of wood residue- and poplar-based DME by 22 percent and 15 percent, respectively.
Across all scenarios considered in the sensitivity analysis, the bio-based pathways reliably have
the lowest expected GHG intensity. Additionally, the GHG intensity of natural gas-based DME
generally remains higher than that of diesel. The GHG intensity of diesel was impacted very little
by most of the parameters assessed in this sensitivity analysis. Bio-DME shows the greatest
potential to reduce GHG emissions from the heavy-duty transport sector in Alberta, so long as
assumptions made regarding the neutrality of biogenic carbon emissions hold up. It is worth
noting, however, that adoption of any biofuel for heavy-duty transportation will ultimately
depend on feedstock supply availability.
5.5 Discussion
The emissions reduction potential of bio-DME is fairly substantial compared to other fuels. In a
widely cited study, Beer et al. [139] estimate the life cycle GHG emissions of compressed
natural gas, liquified natural gas, wood-based ethanol (E95), a biodiesel blend (B35) and
biodiesel (B100) in comparison to diesel. Certain pathways, including the liquified natural gas
and biodiesel blend, are estimated to increase life cycle GHG emissions by 10 to 15 percent,
while compressed natural gas offers slight reductions (10%) and the wood-based ethanol (E95)
and biodiesel (B100) are estimated to reduce life cycle GHG emissions by 45 percent and 55
percent, respectively. Meanwhile, wood residue- and poplar-based DME offer GHG emissions
reductions on the order of 57 to 59 percent in comparison to diesel.
Though bio-DME offers promising reductions in life cycle GHG emissions, adoption of any
biofuel will depend on a multitude of factors. An important consideration with regards to bio-
based fuels is producing an adequate supply of feedstock to achieve large-scale GHG reductions
102
[218]. If there is limited availability of the desired feedstock, the WTW results presented in this
assessment cannot be achieved. For DME derived from wood residue, production will be limited
by the availability of forest residues located within close proximity to the hypothetical DME
production plant. Estimates of wood residue availability in Alberta range from 150,00 to 200,000
bone dry tonnes [219,220]. On the lower end, this is equivalent to approximately 141 million
vehicle kilometers travelled (VKT) using wood residue-based DME, or less than one percent of
Canada’s total heavy-duty VKT in 2009 [221].
For crop-based feedstocks such as poplar, feedstock availability is highly dependent on the
availability of suitable land. On one hand, the land must be of a high enough quality that it is
able to support the cultivation of feedstocks, but on the other hand, it will ideally not be of such a
high quality that it is displacing prime agricultural land for human food production. To transition
fully to poplar DME, Canada would need to set aside close to 2.5 million acres (or 1 million ha)
of land per year for cultivation [221].
Another important consideration to make when it comes to biofuels is cost. As a result of
intensive pretreatment requirements and upstream production costs (except in the case of waste
products), biofuels tend to be more expensive than their fossil-based counterparts. Additionally,
there are no existing plants producing DME as a vehicle fuel on a large scale. Consequently,
there are large uncertainties associated with the cost of DME production, in general. A
technoeconomic assessment of DME production from both renewable and non-renewable
feedstocks would be beneficial in order to better establish the economic viability of the results
outlined in this assessment. Furthermore, assessments detailing the impacts of other
sustainability metrics such as water use, energy use, or human health would be beneficial.
One particularly notable limitation of this study is that these results are based on models and lab-
scale work. Though the production of DME is a well-established process, there are no large-scale
commercial plants in Canada that are producing DME as an alternative fuel from either fossil- or
bio-based feedstocks. Thus, production efficiencies, emission factors and input-output ratios are
particularly uncertain.
Despite these considerations, a pilot project testing DME’s applicability in HDV fleets in the EU
in partnership with Volvo was proven successful [161]. The fuel’s success in vehicle fleet tests
103
alongside WTW GHG emissions reductions up to 59 percent in comparison to diesel suggest that
DME could play a major role in combating climate change in Canada. By satisfying all of
Canada’s heavy-duty travel needs with bio-DME, GHG emissions on the order of 13,141 to
13,528 tonnes CO2eq. would be avoided [6].
Furthermore, it is worth highlighting once again the ability of DME to reduce emissions of
certain criteria air pollutants, namely particulate matter, nitrogen oxides, carbon monoxide and
hydrocarbons, in comparison to diesel [65,153,193–195]. The combustion of diesel, the
dominant HDV fuel, leads to considerably higher levels of criteria air pollutant emissions, which
can have serious effects on human health. These impacts on human health have in some cases led
to the ban of certain diesel vehicles in densely populated city centers [222]. The ability of DME
to act as a diesel alternative fuel in existing compression ignition engines with only minor engine
modifications coupled with its ability to reduce criteria air pollutant emissions suggests that
DME should be seriously considered as an alternative to diesel.
104
Chapter 6 Conclusion
The increasingly dire nature of climate change demands that society take action to reduce GHG
emissions from the most polluting sectors. This thesis has explored ways by which Canada can
reduce GHG emissions from its heavy-duty on-road transport sector, which is responsible for a
rapidly increasing proportion of the country’s total transport emissions. It has provided its
readers with an overview of the benefits and limitations of some of the most promising
alternatives to petroleum diesel for HDVs, expert-identified considerations surrounding the
uptake of alternative technologies, decision-making frameworks to aid in the identification of
suitable alternatives for long haul on-road movement of goods in Canada, and a quantification of
the life cycle GHG emissions of dimethyl ether produced from both renewable and non-
renewable feedstocks in Canada.
Below I discuss the methods used, results and contributions of each of the three objectives of this
thesis:
1. Identify the concerns and priorities of the long haul trucking sector in Canada with
regards to investment in alternative fuels and powertrain technologies.
Interviews were carried out with nineteen experts to determine the perceived opportunities and
barriers to the adoption of current or near-term alternative technologies for long haul trucking.
The greatest considerations with respect to investment in an alternative fuel or powertrain
technology were discussed. Experts were also asked to comment on how they might weight the
importance of various vehicle attributes when considering investment in an alternative vehicle
technology, which were then incorporated into the multi-criteria evaluation of alternative
technologies as per objective two.
Expert interviews revealed that the greatest considerations associated with the adoption of
alternative technologies for long haul heavy-duty trucking are higher costs, the availability of
refueling/recharging infrastructure, reliability of the technology, and how each technology might
contribute to driver retention. Many of these concerns were also cited as the major barriers to the
105
adoption of alternative long haul trucking technologies suggesting that many technologies are not
yet expected to meet the operational needs of the long haul trucking sector in Canada.
While a small number of studies have focused on identifying the top considerations of certain
vehicle sectors with respect to investment in low GHG technologies [24–27], these assessments
have typically been limited to a specific fleet, none of which have been long haul HDVs. Hence,
this thesis has not only contributed to a growing body of work aimed at identifying barriers to the
adoption of low GHG technologies but it has also reported on novel data with respect to the
unique considerations of the long haul heavy-duty trucking sector.
By identifying specific barriers to the adoption of alternative fuels and powertrain technologies,
government and industry can more effectively create targeted policy programs centered around
making low or zero emission alternative fuel vehicle technologies more attractive. Research into
which support measures would be most effective at overcoming the major barriers identified in
this thesis should be carried out to complement this work.
2. Identify multi-criteria decision making frameworks that can assist in the evaluation of
alternative fuels and powertrain technologies for long haul which can incorporate
stakeholder insights and be easily adopted by decision makers.
I incorporated the expert-identified weights of various vehicle attributes (as per objective one)
into three multi-criteria decision making frameworks which were used to highlight how
stakeholder concerns might be incorporated into the evaluation of alternative technologies for
long haul trucking. The first and primary proposed method of evaluation was a multi-attribute
utility analysis which evaluates the relative performance various alternative technologies across
multiple weighted attributes using simple mathematical expressions. The second proposed
method of evaluation was a satisficing heuristic framework whereby technologies were evaluated
across multiple weighted attributes on their performance relative to a petroleum diesel baseline.
Lastly, a simplified societal cost benefit analysis was proposed as one of the ways by which
companies could evaluate alternative technologies with a greater emphasis on externalities. Due
to the low technological maturity of certain alternatives, the results of these various evaluation
frameworks are not meant to be prescriptive but are rather aimed at highlighting some of the
different methods of evaluation that can be easily adopted by decision makers.
106
By employing these different frameworks to the evaluation of alternative technologies for long
haul heavy-duty trucking, this thesis highlighted the various strengths and limitations of each
method of evaluation. In particular, this relates to the level of detail captured by each framework.
The satisficing framework is not particularly effective at capturing even large differences in the
performance of technologies across attributes, whereas the multi-attribute utility framework
captures a moderate level of detail, and finally, the societal cost benefit analysis captures a
notable level of detail. The differences in the rankings of alternative technologies both across the
frameworks as well as within the frameworks that incorporate expert-identified weights suggests
that the optimal alternative technology may change depending on the decision making behaviour
and preferences of a particular company. This suggests that there may not be a universally
preferred alternative to diesel for long haul trucking.
Multi-criteria decision analysis has been widely applied to the evaluation of alternative fuels and
powertrain technologies. These past analyses, however, have most commonly evaluated
alternative technologies for light-duty vehicles [48–52,55,57], as well as transit buses [52,53],
while only a single study has evaluated a limited set of alternatives for heavy-duty trucks [44].
The multi-criteria decision analyses presented in this thesis are the first to evaluate a
comprehensive suite of alternative technologies for long haul HDVs, specifically. Moreover, it
presents frameworks which can be easily employed by decision makers within the long haul
trucking sector.
Though I have proposed the use of three separate frameworks in this thesis, more research should
go into the development of additional frameworks. Furthermore, the most effective methods of
evaluation, as well as those most representative of the priorities and decision making behaviour
of the long haul trucking sector should be identified. Finally, evaluations of alternative
technologies should be carried out under conditions of higher certainty (i.e., when there is a
higher level of experience with each of the alternative technologies in the context of the long
haul HDV sector).
107
3. Quantify the life cycle GHG emissions of DME produced in Canada from a variety of
feedstocks.
The third objective of this thesis focused on the evaluation of DME, an emerging alternative to
petroleum diesel. I discuss the advantages and disadvantages of the fuel and evaluate its well-to-
wheel (WTW) GHG emissions when produced from natural gas, wood residue and poplar in
Alberta, Canada. I focus in particular on the differences in natural gas production and electricity
generation in Canada which make it difficult to extrapolate results from assessments of DME
performed in other jurisdictions. Impacts of methane emissions from upstream natural gas
production, feedstock transportation distance and grid electricity mix, are explored in a
sensitivity analysis.
Results from the WTW assessment of DME demonstrate that the fuel could play an important
role in reducing GHG emissions from heavy-duty transport when produced from biomass. WTW
GHG emissions reductions up to 60 percent are expected for DME produced from poplar or
wood residue in Canada in comparison to a petroleum diesel baseline. On the other hand, DME
produced from natural gas is expected to result in a 10 percent increase in WTW GHG
emissions. Ultimately, DME may offer a compelling pathway to GHG emission reductions, but
this is highly dependent on feedstock choice.
Though there have been previous assessments of DME, the majority of these have been
performed in jurisdictions that differ too much from the Canadian context for results to be
extrapolated [16–20]. And although assessments of DME have been performed within the
context of the United States which mirrors the Canadian context a little more closely [21–23],
there has yet to be a comprehensive evaluation of variation in GHG emissions stemming from
waste residues, cultivated energy crops and fossil fuels. As such, this thesis provided the first
Canada-specific life cycle assessment of DME, as well as an exploration into the differences in
impacts expected from various categories of feedstocks.
In order to explore the economic feasibility of low GHG bio-DME production, a technoeconomic
assessment of DME produced from biomass in Canada should be performed. Moreover, it is
crucial to perform a comprehensive assessment of feedstock availability paying particular
attention to location as a long transport distance can negate some of the positive impacts of bio-
108
DME. Finally, more work needs to go into the climate impacts of biogenic carbon emissions as
the WTW GHG emissions from bio-DME may in fact be higher than those of petroleum diesel in
the event that biogenic carbon emissions are not climate neutral.
Evaluation of alternative and emerging transportation systems is vital to reducing GHG
emissions from Canada’s transport sector and potentially improving the country’s energy
independence. By conferring with key stakeholders, key barriers to the adoption of the most
promising technologies can be identified and solutions proposed. Without support from key
sectors, like the on-road freight sector, technologies are unlikely to be adopted. Ultimately, the
climate benefits of alternative technologies will not be felt unless adoption occurs. Continued
evaluation of the most promising low GHG alternatives and their barriers to success will help
carve out some of the most viable pathways to meeting Canada’s climate target of 30% below
2005 levels by 2030.
109
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Appendix
Appendix A
Technological Considerations with Respect to Alternative
Fuel or Powertrain Adoption
In this section, I will discuss additional technological considerations for each alternative fuel or
powertrain technology discussed throughout this thesis, as well as considerations with respect to
the predictability of future fuel costs and fuel supply levels. I choose to discuss these
considerations for the following reasons. First, alternative fuels and powertrain technologies
must reliably meet the technological performance requirements of a trucking company or may
otherwise compromise their operations. Second, fuel costs are particularly important as they are
one of the highest costs to trucking companies, second only to driver wages [1]. I focus on
evaluating the volatility of fuel prices, specifically, as predictability is an important feature for
planning and risk. Lastly, alternative fuels and powertrain technologies will not be able to
operate without adequate fuel supply.
Battery Electric Vehicles
The adoption of battery electric heavy-duty trucks for long haul applications requires the
development of charging infrastructure along major travel corridors. As a result of the lengthy
nature of their routes, long haul heavy-duty vehicles do not typically return to a home base for
refueling. Instead, they depend on the availability of fueling infrastructure along their routes. In
order for battery electric trucks to be successfully adopted, public charging infrastructure,
including overnight charging stations, must exist.
Charging times for battery electric vehicles must be able to suit the unique needs and driving
patterns of long haul drivers. Commercial truck drivers in Canada drive no longer than 13 hours
in a day with 2 hours of breaks and 8 hours between shifts [2]. Breaks must be taken in
increments no less than 30 minutes [2]. Hence, fast battery charging of a long haul battery
electric truck would ideally occur during brief mid-shift breaks, and slow charging overnight.
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As a result of their heavier weight and larger battery storage requirements, battery electric heavy-
duty trucks will require more power for charging than a passenger vehicle. For instance, a battery
electric heavy-duty truck weighing 40,000 kg and travelling at an average speed of 80 km per
hour is expected to require 480 to 720 kWh of energy for 4.5 hours (or 360 km) of travel [3]. To
charge a vehicle with this configuration at a rest stop in under 45 minutes, a charging station with
a power output of 640 to 960 kW is required. Current models of fast, or level 3, charging stations
in Canada have a maximum power output of 120 kW [4]. With this power, the same heavy-duty
truck would take between four and six hours to charge. Hence, charging stations in their current
form will not accommodate the complete fast charging of long haul heavy-duty battery electric
trucks during short breaks.
Current fast charging stations may, however, be able to accommodate overnight charging of long
haul trucks. An eight-hour rest would be enough to satisfy charging requirements from driving,
as well as the use of climate control and other accessories in the sleeper cabin. Lutsey et al.
estimate that the power demand of a sleeper cabin peaks at 3 kW [5]. For an eight-hour rest, this
is an additional demand of 24 kWh, or 12 minutes of charging time at 120 kW.
As of 2017, Canada had 5,841 public charging stations installed, of which only 673 were fast
charging [6]. This equates to approximately one fast charging station per 15,000 km2. Though
these stations are concentrated around major population centers along the southern Canadian
border, they also tend to be located in city centers as opposed to highway rest stops making them
inconvenient to access [7]. Moreover, it is difficult to say where or not these charging stations
would be able to accommodate heavy-duty tractor trailers.
Ultimately, lack of accessibility and suitability of charging infrastructure should be a notable
consideration of trucking companies prior to the adoption of battery electric heavy-duty trucks
for long haul applications. Without access to suitable public charging stations, these vehicles will
not be able to satisfy their range requirements.
Hydrogen Fuel Cell Vehicles
Like battery electric vehicles, long haul hydrogen fuel cell heavy-duty vehicles rely on the
installation of new refueling infrastructure along major freight corridors. Without access to
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refueling stations, these vehicles will not have the range required to complete long haul trips.
There is little development of hydrogen refueling infrastructure in Canada; there are currently
only two public refueling stations, in Vancouver and Mississauga, with plans for an additional
five in the greater Vancouver area [8,9]. Lack of access to hydrogen refueling stations,
particularly outside the greater Vancouver area, poses a notable threat to the successful adoption
of hydrogen fuel cell vehicles.
Natural Gas Vehicles
Like battery electric and hydrogen fuel cell vehicles, natural gas vehicles require unique
refueling infrastructure. Presently, there are 41 public CNG refueling stations, and five public
LNG refueling stations across Canada [8]. They are concentrated primarily in southern Ontario,
Alberta and Vancouver [8]. As long haul freight movement relies on public refueling
infrastructure along major travel corridors, a greater number of CNG and LNG refueling stations
must be developed prior to the adoption of natural gas vehicles for long haul applications.
Biodiesel
The suitability of use of biodiesel in a compression ignition engine has been widely studied. In
particular, the high cloud point of the fuel raises concerns. At temperatures as high as -3 to 15
degrees Celsius, pure biodiesel (B100) may begin to crystallize [10]. These crystals threaten
engine operability. Blending biodiesel with ultra-low sulfur diesel (ULSD) lowers the cloud
point and improves the cold flow properties of the fuel. This positive effect tends to decrease
with increasing levels of biodiesel [10]. Cold temperatures in Canada could potentially threaten
the suitability of high-level blends of biodiesel as a diesel alternative.
The applicability of diesel blends with up to 20 percent biodiesel (B20) were explored in the
BIOBUS project in Montreal from 2002 to 2003 [11]. Over the course of the year, biodiesel
blends were used to fuel transit buses, including B20 blends during winter conditions with
temperatures as low as -30 degrees Celsius. Buses were stored in garages at a temperature of 15
degrees Celsius when not in use. No major problems were reported for a number of the buses,
particularly older models with 25 µm fuel filters, as well as later models with electronic fuel
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injection systems; however, models with mechanical fuel injection experienced filter clogging
[11]. There were also issues associated with refueling pump filter clogging [11].
There are also concerns surrounding biodiesel’s impact on CI engines at higher blend levels.
Some original equipment manufacturers (OEMs) impose low biodiesel blend limits. These limits
arise over concern for biodiesel compatibility with engine parts or are simply a conservative
approach to minimize risk [12]. Adverse impacts are not expected for blends lower than B20
[11,13], particularly when users follow the strict, but voluntary, standards set out by the
Canadian General Standards Board (CGSB). Despite this, use of biodiesel blends at levels higher
than those outlined in an OEM may invalidate a warranty. High-level biodiesel blends should
ultimately be used with caution in Canada to prevent issues with cold flow properties, and to
ensure coverage under warranty. Low-level biodiesel blends present fewer technological risks.
Renewable Diesel
Renewable diesel is considered a “drop-in” fuel meaning it can be used in place of petroleum
diesel in existing CI engines without the need for any modifications. The chemical composition
of renewable diesel is similar enough to petroleum diesel that it is subject to the same fuel
standards as petroleum-derived ultra-low sulfur diesel (ULSD) in Canada [14]. Like other fuels,
the cloud point of renewable diesel in the winter months should be closely monitored. Drop-in
biofuels like renewable diesel, however, ultimately face few technological considerations as they
can be adopted and used in place of diesel in existing refueling infrastructure and heavy-duty
vehicle engines.
DME
Unlike renewable diesel, DME is not a “drop-in” fuel. There has been little experience with
DME as there exist no commercial DME plants globally. Hence, the only vehicles that have
operated using DME have been prototypes. This lack of experience presents a risk to the success
of DME as an alternative fuel for long haul HDVs.
As a result of its lower energy density in comparison to petroleum diesel, DME is expected to
require a larger fueling tank, and a modified fuel injection system. Though these modifications
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may seem minimal, they are very specific for use with DME. In other words, once a vehicle has
been retrofit to suit the needs of DME, that vehicle will no longer be able to accommodate other
fuels [15].
DME also requires its own refueling infrastructure. This infrastructure, however, is not expected
to be as costly as that required by other fuels which require highly pressurized or cryogenic
conditions. Until DME technology including vehicles and infrastructure are widely available, the
fuel will not be able to act as an alternative to petroleum diesel for Canada’s long haul heavy-
duty needs.
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Appendix B
Fuel Cost Considerations
Diesel Prices
The cost of crude oil is one of the largest costs to diesel producers, and as such, changes to crude
oil prices will often be reflected in the price of diesel [16]. Canada’s National Energy Board
predicts future changes to Canada’s fuel prices under four scenarios: (1) a business-as-usual
reference case, (2) high oil and natural gas prices, (3) low oil and natural gas prices, and (4) a
technology case that reflects major technological changes stemming from a global shift to a low
carbon economy [17]. In their reference case, the organization predicts that Brent prices, which
are used as a representation of global crude prices, will remain at an average price of US$68 per
barrel over the next three years, but will begin to increase in price in 2022, reaching US$75 per
barrel by 2027 and remaining stagnant thereafter [17]. High- and low-price case scenarios
suggest that Brent prices could be as low as US$40 per barrel, or as high as US$120 per barrel
from 2025 into 2040 [17]. The Western Canadian Select price index for Canadian heavy crude
differs from the Brent price index in order to reflect differences in quality and transportation
costs. It is expected to follow the same price trends as the Brent index, but will maintain an
average price differential of approximately US$17 less per barrel between 2023 and 2040 [17].
Considering the range in prices expected under the various scenarios, there is moderate risk
associated with diesel prices in the near future.
Diesel will also be impacted by carbon pricing in Canada. The federal government has advocated
for an increase in the price of carbon by $10 per year until it eventually reaches $50 per tonne in
2020 [18]. A $50 per tonne price on carbon will result in an additional cost of approximately 8.6
cents per liter of diesel [17]. Considering an average diesel price of 112 cents per liter Canada
since 2015, this represents a 6 percent increase in total cost [19]. Moreover, the government of
Canada has announced plans to release a Clean Fuel Standard (CFS), which aims to reduce GHG
emissions from both transportation and stationary fuels by 30 megatonnes by 2030 [20]. Fuels
that do not comply with this standard will be penalized.
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Electricity Prices
Between 2007 and 2017, retail electricity rates increased faster than the rise of inflation in
Canada. Each province in Canada is responsible for its own electricity sector, including the rates
charged to consumers. These rates vary quite considerably from province to province. Most
provinces have experienced rises in electricity prices over the past decade, with the exception of
Alberta. In fact, on average, Canadian electricity prices are rising faster than the rate of inflation
[21]. Despite this, refueling costs are expected to be considerably lower for battery electric
heavy-duty vehicles in comparison to diesel-fueled heavy-duty vehicles [22]. It’s estimated that
fuel costs for battery electric HDVs will be approximately one quarter that of diesel HDVs (see
Appendix F Table A9).
There are, however, difficulties in forecasting electricity prices, particularly with increasing rates
of electrification. Predicting changes to electrical loads is challenging, and this can have notable
implications on electricity prices. In Canada, electrical load is seasonal and largely influenced by
climate. Particularly cool or warm days place an additional load on the electrical supply in order
to satisfy heating and cooling demands. The difficulty in predicting weather patterns and
subsequent impacts on electrical loads makes it difficult to forecast long-term electricity prices.
Typically, 30-year historical averages are used in forecasting future electrical loads [23], but a
rise in extreme weather as a result of global climate change impacts the applicability of these
averages.
Additionally, electrical loads may be impacted by the introduction of a large pool of electric
vehicles. The rate of uptake of electric vehicles is difficult to predict as a result of the influence
of consumer behaviour. A rapid surge in the use of electric vehicles will result in additional load
and increased demand for electricity, overall, thereby increasing prices. Furthermore, demand
pricing adopted by certain provinces will be affected by the average timing of vehicles charging.
Finally, certain methods of electricity generation may not be in compliance with the proposed
CFS [20]. Ultimately, the future of electricity prices in Canada is difficult to predict, however, it
is expected that battery electric heavy-duty vehicles will present fuel cost savings in comparison
to diesel due to the notably low price of electricity in comparison to diesel in Canada, on
average.
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Hydrogen Prices
Fuel costs are expected to make up a high portion of lifetime costs for hydrogen fuel cell
vehicles [24,25]. As such, reliable fuel costs will play a role in determining the robustness of the
technology. Presently, hydrogen costs vary considerably depending on methods of production
and feedstock. Hydrogen produced from steam reformation of natural gas costs much less to
produce than hydrogen produced through electrolysis of renewable energy, such as wind. More
recently, Layzell and Lof estimated that the wholesale cost of hydrogen produced from natural
gas is expected to range from $2.50 to $3.00 per kilogram, representing up to a 625 percent
increase in cost in comparison to diesel [26]. Costs are expected to be considerably higher for
hydrogen produced from renewables [27], however renewable hydrogen is likely to exude more
benefits under the proposed CFS in comparison to natural gas-based hydrogen [20].
Like electricity, hydrogen prices will be impacted by individual fuel costs. Two pathways that
are particularly sensitive to feedstock cost include hydrogen produced from the steam
reformation of natural gas and any electrolysis-based pathways. Feedstock costs for these two
pathways represent up to 40 percent and 55 percent of total production costs, respectively [27].
Changes to natural gas or electricity prices will dictate the future affordability of these hydrogen
production methods.
Hydrogen prices in Canada are particularly uncertain and difficult to forecast due to the lack of
existing demand for hydrogen. With increasing demand and subsequent supply of hydrogen, it is
expected that the cost of hydrogen will decrease. But with no way to accurately predict future
demand, changes to future hydrogen price are notably uncertain.
Natural Gas Prices
Natural gas prices are volatile and respond quickly to changes in supply and demand. Hence,
consumers may experience significant fluctuations in refueling prices. Over the past decade,
natural gas prices have notably declined. Average annual Henry Hub prices for natural gas
reached a 10-year peak in 2008 at US$8.86 per MMBtu but dropped by 55 percent in 2009 [28].
Since the peak in 2008, prices have averaged US$3.29 per MMBtu [28]. Canada, however, is
facing pressure from recent expansions to natural gas production in the United States, some of
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which is being sold below Henry Hub prices. As a result of this, as well as low pipeline capacity
and increasing revenue from co-products, some Canadian producers are selling their natural gas
at low or even negative prices [17]. The price differential between Albertan natural gas prices
(Nova Inventory Transfer) and Henry Hub prices averaged US$0.78 per MMBtu between 2005
and 2015 but rose to over US$1.75 per MMBtu for the first half of 2018 [17].
In their reference case, the National Energy Board predicts that Henry Hub natural gas prices
will remain low (around US$3.00/MMBtu) into 2025 as a result of oversupply [17]. Prices are
expected to grow thereafter due to the emergence of new markets, reaching US$4.16 per MMBtu
by 2040 [17]. However, high- and low-price cases suggest that prices could be as high as
US$5.26 per MMBtu or as low as $2.92 per MMBtu by 2040 [17]. Even at high price estimates,
natural gas is expected to offer cost savings over diesel.
It is important to also consider the fact that natural gas prices will reflect changes in carbon
pricing. As the price of carbon increases, so too will the cost to refuel a natural gas vehicle.
Carbon prices of $30 and $50 per tonne are expected to result in additional costs of $1.50 and
$2.49 per GJ of natural gas, respectively [17]. For reference, the average natural gas price over
the past decade has been $3.10 per GJ, so these additional costs may prove substantial.
Moreover, natural gas may not be in compliance with the proposed CFS [20]. Diesel, however,
would similarly be penalized under the carbon tax and proposed CFS, hence negating the
impacts on the price differential of the two fuels. Ultimately, it appears unlikely that the price of
natural gas will exceed that of diesel in the near future.
Renewable Natural Gas Prices
Vehicles opting to refuel using RNG may dramatically reduce their GHG emissions but are
expected to face higher costs than those refueling using conventional natural gas. Andrews
suggests that RNG fuel prices may be as high as three times those of diesel per kilometer
travelled [29]. Unlike fossil-based natural gas, RNG will not likely be penalized under the carbon
tax and is likely to be in compliance with the proposed CFS. Thus, the fuel will not be subject to
the uncertainty of long-term carbon pricing. The uncertainty facing future renewable natural gas
prices, however, is associated with supply. Without adequate supply levels, the price of RNG is
likely to remain high.
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Biodiesel Prices
Between 2009 and 2018 biodiesel prices ranged from 0.85 to 1.51 CAD per liter, while diesel
prices ranged from 0.49 to 1.03 CAD [30]. It is important to note that biodiesel prices also
include the value of a renewable identification number (RIN) which is used to track biofuel
purchases and narrow the price gap between biofuels and their petroleum-based counterparts in
the United States contributing to compliance for the U.S. Renewable Fuel Standard. Unless
offset by similar incentives in Canada, the required market selling price will need to capture this
additional value in order to have biodiesel producers be willing to sell into the Canadian market.
Like RNG, biofuels under a certain carbon intensity will not be affected by the federal carbon
pricing system and are expected to be incompliance with the proposed CFS, thereby eliminating
a major source of uncertainty associated with future biodiesel prices.
Renewable Diesel Prices
Information on renewable diesel prices is sparse. It is estimated that, on average, renewable
diesel costs 20 percent to 30 percent more to produce than its petroleum-based counterpart [31].
Renewable diesel production costs are dependent on a variety of factors including feedstock
types and costs, as well as capital and operational costs, among others. Production costs,
however, have begun to drop and are expected to drop even more so in the near future as
additional production facilities are built [31]. Renewable diesel isn’t expected to be penalized
under the carbon tax and is expected to be in compliance with the proposed CFS.
DME Prices
DME is not commercially available in Canada, making it difficult to predict future costs to
consumers. Production costs are expected to be variable, reflecting differences in production
methods and feedstocks [15]. A technoeconomic assessment of DME produced in Canada is
needed. Uncertainty surrounding future DME prices makes investment in this technology
particularly risky.
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Appendix C
Fuel Supply Considerations
Diesel Supply
The National Energy Board’s report on Canada’s Energy Future predicts changes to Canada’s
future fuel production based on the same four scenarios used to predict future changes to fuel
prices, namely (1) a reference case, (2) high oil and natural gas prices, (3) low oil and natural gas
prices, and (4) a technology case that reflects major technological changes stemming from a
global shift to a low carbon economy [17]. Though it requires refining to ultimately become
diesel, I look to future trends in crude oil production and demand in order to project future
changes to diesel supply. Crude oil production is expected to increase in Canada into 2040 under
all scenarios except for the low-price scenario, which disincentivizes production expansion due
to low financial returns. Production increases by 58 percent in the reference case scenario, and
by 88 percent and 43 percent in the high price and technology scenarios, respectively [17].
Production is expected to decline only slightly below 2017 levels by 2040 in the low-price
scenario [17].
It is also important to consider future changes to crude oil demand in Canada. In general, the
growth rate of primary energy demand is expected to slow in future years, largely as a result of
improvements made to energy efficiency. Energy demand has historically grown alongside
population and GDP, but the NEB predicts a decoupling of these trends before 2040 [17].
Although the organization predicts annual GDP and population growth rates of 0.8 percent and
1.8 percent, respectively, energy demand is expected to grow by only 0.3 percent per year in the
reference case.
Demand growth rates are expected to be even slower for fossil fuels. Under the reference
scenario, modest growth of 4.7 percent between 2017 and 2040 (or 0.2% per year) is expected,
with most of this growth stemming from natural gas [17]. Demand for fossil fuels is expected to
decrease under the technology scenario by 30 percent by 2040 [17]. This reduced demand is
driven primarily by coal power plant retirements and improvements to energy efficiency.
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Demand for crude oil, specifically, is expected to decline by approximately 2 percent in the
reference case or by 25 percent in the technology case between 2005 and 2040 [17]. Reduced
demand for crude oil coupled with likely increases in production suggests diesel supply levels
could remain high into the future.
Electricity Generation
Canadian electricity generation is expected to increase by 78 TWh or 12 percent between 2017
and 2040 under the National Energy Board’s reference case [17]. The majority of additional
capacity will stem from natural gas, hydro, wind and solar. The NEB predicts a similar increase
in electricity generation for the technology case [17].
Meanwhile, electricity demand is expected to grow modestly in both the reference and
technology cases [17]. In the technology case, increased demand incurred by higher rates of
electrification is offset by improved efficiency. Though electricity demand is expected to
increase alongside population and GDP growth in the NEB’s scenarios, increases to electricity
generation are expected to outpace this growing demand. With these trends in mind, it is possible
that Canada’s electricity supply will be able to support the adoption of long haul battery electric
heavy-duty vehicles under the NEB’s reference or technology scenarios.
Hydrogen Supply
There is notable uncertainty surrounding the future of hydrogen supply in Canada. Though
Canada is considered a world leader in hydrogen production, most of this hydrogen is consumed
by the oil and gas industry [32]. There are few notable companies producing hydrogen for
transportation at the commercial scale, including HTEC, Air Liquide, Praxair and Air Products.
Little data exists surrounding Canada’s hydrogen production capacity. In 2005, it was estimated
that Canada produced 3.8 million tonnes per year of hydrogen [33]. It is unclear what percentage
of Canadian hydrogen is produced for transportation applications. The development of necessary
production facilities would cause notable delay in the adoption of these vehicles. Air Liquide
recently invested over US$150 million into a liquid hydrogen plant in California that is expected
to produce enough hydrogen to fuel 35,000 fuel cell electric passenger vehicles [34].
Construction of the plant is expected to take three years, signaling a need to begin fuel
137
production infrastructure development in Canada if these vehicles are to be adopted in the near-
term. An inability to project future supply levels of hydrogen in Canada poses a notable threat to
the successful adoption of hydrogen fuel cell vehicles.
Natural Gas Supply
The National Energy Board predicts increases to natural gas production into 2040 in both the
reference and high price scenarios by 35 percent and 70 percent, respectively, relative to 2017
levels [17]. On the other hand, production is expected to decrease by 10 percent and 20 percent
under the technology and low-price scenarios, respectively [17]. The variability among NEB
scenarios suggests that the future of Canadian natural gas production is somewhat uncertain.
Additional uncertainty stems from projections to future demand for natural gas. Under the NEB’s
reference scenario, natural gas demand is expected to increase by up to 40 percent between 2005
and 2040, while it is expected to decline by approximately 10 percent under the technology
scenario [17]. There is moderate risk to Canada’s future natural gas supply under both the
reference and technology scenarios. Increased production coupled with increased demand in the
reference scenario, and vice versa for the technology scenario make it difficult to predict the
future availability of natural gas for long haul heavy-duty vehicles.
Renewable Natural Gas Supply
As of 2017, there were eleven RNG production facilities in operation across Canada [35]. There
is notable potential for further RNG development in Canada. The Canadian Gas Association
estimates that Canada could supply half of the country’s natural gas demands with RNG due to
its vast quantity of renewable waste feedstocks [36]. Meanwhile, Abboud et al. estimate that 130
percent of residential and commercial natural gas demands in Canada could be satisfied using
RNG [37]. The supply of RNG in Canada is ultimately expected to be limited by the number of
production facilities, and more broadly, the high capital costs associated with establishing a new
facility. Due to the lack of established production facilities, it is difficult to predict future supply
levels of RNG in Canada.
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Biofuels Supply
To fulfill the requirements of the proposed CFS, Wolinetz et al. project that biodiesel or
renewable diesel demand is expected to reach 3.7 billion liters per year [38]. For reference, there
are currently no commercial renewable diesel production plants in Canada, and biodiesel
production capacity was approximately 650 million liters in 2018 [39]. However, not all
renewable fuel to satisfy Canada’s CFS is expected to be produced in Canada – imports will play
a crucial role. Wolinetz et al. predict that approximately 56 percent of Canada’s renewable fuel
needs will be imported [38]. Remaining needs will be filled through domestic biodiesel and
renewable diesel production capacity that is expected to increase to 2.7 billion liters by 2030,
with a portion of domestic production being exported [38].
For Canadian biodiesel capacity to reach levels projected by Wolinetz et al., an additional
capacity of at least two billion liters would need to be installed in the next 10 years [38]. As of
2018, Canada’s largest biodiesel plant had a capacity of 265 million liters per year [39].
Approximately eight additional plants of an equivalent size would need to be constructed in
order to satisfy the requirements of the upcoming CFS. The average biodiesel plant capacity in
Canada is just below 60 million liters per year [39]. Canada’s largest biodiesel plant, run by
Archer Daniels Midland, took approximately four years to construct [40,41]. Construction of
such a large number of plants over this short time period is ambitious and unlikely.
It is also imperative to consider the availability of feedstocks in Canada. In general, Canada has
no shortage of biomass availability, ranging from lignocellulosics such as wood residue, to
agricultural crops. Biodiesel has traditionally been produced in North America using soy and
canola crops [39]. Other major feedstocks include sunflower oil, palm oil, beef tallow and used
cooking oils. With this in mind, it is assumed that oilseed crops, alongside some minor quantities
of waste feedstocks, will be used to fill Canada’s biodiesel and renewable diesel feedstock
demand into 2030. It is estimated that the 2.7 million liters of biodiesel or renewable diesel
would require approximately nine megatonnes of oilseed per year, which equates to roughly 34
percent of total Canadian canola and soy production [38]. Approximately one third of North
American oil seed production currently goes to biofuels [38].
139
There is also the possibility of meeting requirements of the CFS using ICE vehicles fueled with
alternatives to biodiesel and renewable diesel. DME uses gasification to produce syngas in the
production process, and due to the unselective nature of gasification, the fuel could take
advantage of a wider range of feedstocks. Importantly, DME could make use of Canada’s
extensive availability of lignocellulosic feedstocks, including crop and forest residues, municipal
solid waste, short rotation woody crops and herbaceous energy crops. Use of these feedstocks
would open an additional 64 million green tonnes to 561 million dry tonnes of supply already
available in Canada [42]. There is, however, limited to no experience associated with the
production of DME in Canada, and as such, many barriers and uncertainties associated with its
success.
If exports were eliminated, the country’s current biodiesel capacity of 650 million liters per year
would be able to satisfy a three percent blending limit when considering Canada’s on-road diesel
consumption of 739.4 PJ in 2016 [43]. As CI engines become more efficient, it is possible that
the share of biofuels blended into the diesel pool could increase due to reduced fuel
consumption, in general. Without immediate planning and construction of additional diesel-
displacing biofuel production facilities in Canada, it is unlikely that there will be the domestic
capacity to increase the renewable fuel content of diesel in the near-term.
140
Appendix D
List of Interview Questions Used for Expert Interviews
1. Have you or your company examined potential use of alternative fuels and powertrain
technologies for long haul heavy-duty trucking in Canada in the past 10 years? If so, have
you considered adopting alternative fuel vehicles, of providing infrastructure/support to
aid in their adoption?
2. What alternative fuels or vehicles do you think the long haul trucking industry is most
interested in and why?
3. What do you see as the most important considerations for the long haul trucking industry
when investing in new vehicles?
4. What do you see as top priorities for the trucking industry when considering an
investment into an alternative fuel vehicle?
5. How would you rate the importance of each of the following when investing in an
alternative fuel vehicle on a scale of 1-10 with 1 being the least important, 10 being the
most important and 5 being neutral:
a. Vehicle lifetime costs
b. Vehicle purchase price
c. Fuel cost
d. Fuel price volatility
e. Fuel supply
f. Availability of refueling infrastructure
g. Refueling time
h. Vehicle range
i. Cargo capacity
j. Availability of skilled maintenance workers
k. Greenhouse gas emissions
l. Other air pollutant emissions
m. Availability of government incentives
n. Industry experience with the technology
o. Any other items that were raised in Q2
6. What do you think would be the most important advantages, if any, to each of the
following energy sources for long haul trucking in Canada:
a. Battery electric vehicles
b. Hydrogen fuel cell electric vehicles
c. Natural gas vehicles
141
d. Biodiesel
e. Other biofuels
7. What would you consider to be the greatest obstacles to the deployment of the following
energy sources for long haul trucking in Canada:
a. Battery electric vehicles
b. Hydrogen fuel cell electric vehicles
c. Natural gas vehicles
d. Biodiesel
e. Other biofuels
8. What do you expect to be the dominant energy source for new long haul heavy-duty
trucks in 20 years?
9. Which, if any, alternative technologies do you predict will have at least moderate uptake
in the long haul new heavy-duty vehicle sector in Canada in the next 10 years? 20 years?
10. If a trucking company were to make the switch to an alternative fuel vehicle, who in the
company would be involved in that decision?
11. Imagine that a trucking company has switched the majority of its fleet to an alternative
fuel or vehicle technology. What do you imagine was required for that company to feel
confident in its investment (i.e., what conditions do you expect were met before that
decision was made)?
12. Do you expect a trucking company would be willing to pay more for a vehicle if it meant
lower anticipated lifetime costs? If yes, how much more:
a. Very little
b. Somewhat
c. Much more
13. Do you expect a trucking company would be willing to pay greater lifetime costs for
improved environmental performance such as a reduction in GHG emissions or other air
pollutants? If yes, how much more:
a. Very little
b. Somewhat
c. Much more
14. Are there any other important considerations you’d like to discuss with us that we have
not captured in our questions?
142
Appendix E
Expert Interview Responses
In this section, I provide a summary of a responses to the questions found in Appendix D that I deemed most pertinent and easily
summarized.
Table A1. Occurrence and frequency of responses to the question “What do you see as top priorities for the trucking industry when
considering an investment into an alternative fuel vehicle?”.
Interviewee Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
To
tal
Cost 1 1 1 1 1 1 1 1 1 9
Availability of refueling infrastructure 1 1 1 1 1 1 1 1 1 9
Reliability 1 1 1 1 1 1 1 7
Maintenance requirements/costs 1 1 1 1 1 1 6
Fuel costs 1 1 1 1 1 5
Payback period 1 1 1 1 1 5
Maintenance training requirements 1 1 1 1 4
Vehicle resale value 1 1 1 1 4
Vehicle purchase price 1 1 1 1 4
Proven technology 1 1 1 1 4
Risk 1 1 1 1 4
GHG emissions 1 1 1 3
Attractiveness to drivers 1 1 1 3
Service disruption/change to operations 1 1 1 3
Operability 1 1 1 3
143
Durability 1 1 2
Price stability/ability to predict future
costs 1 1 2
Refueling/recharging speed 1 1 2
Cargo capacity 1 1 2
Availability of skilled maintenance
workers 1 1 2
Functionality 1 1
Resiliency 1 1
Driver training requirements 1 1
Range 1 1
Power 1 1
Availability of government programs for
subsidies 1 1
Conformation with fuel standards 1 1
Depreciation 1 1
Fuel supply 1 1
Impact on return on investment 1 1
Familiarity with technology 1 1
Ability to market green technology 1 1
144
Table A2. Responses to the question “How would you rate the importance of each of the following when investing in an alternative
fuel vehicle on a scale of 1-10 with 1 being the least important, 10 being the most important and 5 being neutral?”.
Interviewee Number
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Purchase price 10 10 8 8 8 9 10 8 4 10 8 9 10 6 10 5 10 8 5
Fuel cost 8 9 7 7 8 9 8 8 8 10 8 9 10 7 8 10 8 4 7
Total cost of
ownership - 10 10 10 3 9 10 8 9 9/10 9 5 10 9 9 6/7 10 5 6
Cargo capacity 9 10 8 9 9 8 8 9 10 10 9 8 10 8 8 10 9 10 7
Refueling time 8 8 8 8/9 8 7 6 5 8 10 8 5 7 8 7 7 6 8 6
Vehicle range 8 9 9 8 8 7 9 10 8 7 8/9 8 9 10 5 8 8
Fuel supply 7 8 7 9/10 9 8 10 9 10 8 10 8 10 5 10 8/9 8 6 8
Availability of
refueling
infrastructure
8 8 10 9/10 9 9 8 9 8 10 10 8 10 6 9 10 6 8 8
GHG
emissions 5 5 6 8 2 3 5 3 5 7 5/6 4 10 9 2 7/8 2 4 5
Other air
pollutant
emissions
4 5 6 9 2 4 5 3 6 9 6 4 7 8 5 5 2 4 4
Industry
experience 6 3/4 10 8 9 8 8 5 8 9 5 7 2 8 8 5 3 6 9/10
Availability of
incentives 10 10 8 8 9 6 7 7 8 10 7 6 8 10 6 6 5 4 5
Fuel price
volatility 7 6 7 8/9 8 8 8 2 10 8 9 6/7 8/9 5 8 10 8 3 5
Availability of
skilled
maintenance
workers
- - 7 8 7 7/8 7 10 9 8 8 6 7 8 10 5 10 8
145
Table A3. Occurrence and frequency of responses to the question “What do you think would be the most important advantages, if any,
to each of the following energy sources for long haul trucking in Canada?”. Interviewee Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
To
tal
BE
V
Low GHG emissions 1 1 1 1 1 1 1 1 1 1 10
Reduced maintenance requirements
1
1 1
1
1 1 1 1 8
Zero tailpipe emissions
1
1 1 1 1
1
1 1 8
Low fuel cost
1
1
1
1 1 1 6
Improved vehicle performance
1 1
1
1 4
Lower total cost of ownership
1
1
1
3
Low fuel price volatility
1
1 2
Ability to market environmental
stewardship
1 1
Versatility of fuel (for
microgeneration and distributive
generation)
1
1
Availability of refueling
infrastructure
1 1
Ease of use
1 1
Reduced noise pollution 1 1
H2 F
CE
V
Long range
1
1
1 1
1 1
1 1 1 9
Low GHG emissions
1
1
1 1
1
1 6
Fast refueling
1
1
1
1 1
1 6
Zero tailpipe emissions 1 1 1 1 4
Higher payload capacity than
BEVs
1
1
Efficient cabin heating (using
waste heat from fuel cell)
1
1
146
C
NG
LN
G
Low GHG emissions
1 1 1
1
1
1
1 7
Lower total cost of ownership 1 1 1 1 1 1 6
Low fuel cost 1 1 1 1 1 5
Less disruptive technology
1
1
1 3
Industry experience
1 1
1 3
Fuel availability 1 1 1 3
Reductions in some criteria air
pollutant emissions
1
1 2
Availability of refueling
infrastructure
1 1
2
Availability of technology
1
1 2
Flexibility of bi-fuel engine
1
1
Maintenance familiarity
1
1
Domestic source of energy
1 1
RN
G Low GHG emissions 1 1 2
Reduced landfill volume 1 1
Circularity of production process 1 1
B2
0
Low GHG emissions
1 1
1
1 1
1
6
Drop-in fuel 1 1 1 3
Less disruptive technology
1
1
2
Renewable resource 1 1
Can make use of waste residues
1
1
Promotes economic development in
other sectors (like agriculture)
1
1
Market ready
1
1
Knowledge of fuel
1
1
147
Compliance with a clean fuel
standard
1 1 H
DR
D Drop-in fuel 1 1 1 1 1 5
Low GHG emissions 1 1 2
Potential for higher blend levels 1 1 2
148
Table A4. Occurrence and frequency of responses to the question “What would you consider to be the greatest obstacles to the
deployment of the following energy sources for long haul trucking in Canada?”.
Interviewee Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
To
t-
al
BE
V
Battery weight penalty 1 1 1 1 1 1 1 1 1 1 10
Vehicle range 1 1 1 1 1 1 1 1 1 9
Vehicle cost 1 1 1 1 1 1 1 1 1 9
Charging time 1 1 1 1 1 1 1 7
Availability of refueling infrastructure 1 1 1 1 1 1 6
Grid capacity 1 1 1 3
Lack of industry experience 1 1 1 3
Durability in Canadian climates 1 1 1 3
Training required for operators and
technicians 1 1 2
No market-ready vehicles available 1 1 2
Battery cost 1 1
Disruption to operations 1 1
Battery supply 1 1
Standardization 1 1
Resale uncertainty 1 1
Electricity costs 1 1
Impact on marginal electricity generation 1 1
H2 F
CE
V
Availability of refueling infrastructure 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14
Fuel costs 1 1 1 1 1 1 6
Vehicle cost 1 1 1 1 1 5
Cost 1 1 1 3
149
Fuel availability 1 1 2
Safety 1 1 2
Concern from drivers sharing the road 1 1 2
Lack of industry experience 1 1 2
Disruption to operations 1 1
Competition with BEVs 1 1
Overheating of fuel cells 1 1
Training requirements 1 1
Payload capacity 1 1
Vehicle availability 1 1
Cost of infrastructure 1 1
Reliability 1 1
Hydrogen distribution 1 1
Electricity demand of hydrogen produced
from electrolysis 1 1
CN
G/L
NG
Availability of refueling infrastructure 1 1 1 1 1 1 1 1 1 1 1 11
GHG emissions 1 1 1 1 1 5
Vehicle cost 1 1 1 1 4
Fossil-based 1 1 1 1 4
Safety 1 1 2
Reliability 1 1 2
High power engine availability 1 1 2
Disruption to operations 1 1
Lifetime cost 1 1
Energy intensive fuel production process 1 1
Availability to expand 1 1
150
Modest payload penalty 1 1
Modest range penalty 1 1
Comfort 1 1
Uncertain value proposition 1 1
Use of an internal combustion engine 1 1
RN
G
Fuel availability 1 1 2
Infrastructure availability 1 1
Cost 1 1
B2
0
Cold weather operability 1 1 1 1 1 1 1 1 1 9
Fuel availability 1 1 1 1 1 1 6
Fuel cost 1 1 1 1 1 5
Availability of higher blend levels 1 1 2
Vehicle maintenance 1 1 2
Food versus fuel 1 1 2
OEM warranties 1 1 2
Validating source of biodiesel and
respective GHG emissions 1 1 2
Perceptions 1 1
Fuel quality 1 1
Uncertainty surrounding land use impacts 1 1
Availability of refueling infrastructure 1 1
Land requirements 1 1
Reliability 1 1
HD
RD
Fuel availability 1 1 1 1 4
Fuel cost 1 1 2
Food versus fuel 1 1
151
Table A5. Occurrence and frequency of responses to the question “Which, if any, alternative technologies do you predict will have at
least moderate uptake in the long haul new heavy-duty vehicle sector in Canada in the next 10 years? 20 years?”
Interviewee Number
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Total
10
yea
rs
Natural gas 1 1 1 1 1 1 1 1 1 1 10
Hydrogen fuel cell 1 1 1 1 1 1 6
Battery electric 1 1 1 1 4
Biodiesel 1 1 1 1 4
Natural gas hybrids 1 1 1 3
HDRD 1 1 1 3
RNG 1 1
Biofuels 1 1
20
yea
rs
Hydrogen fuel cell 1 1 1 1 1 1 1 1 8
Battery electric 1 1 1 1 4
Natural gas 1 1 1 3
Biodiesel 1 1
HDRD 1 1
Natural gas hybrids 1 1
152
Table A6. Occurrence and frequency of responses to the question “What do you expect to be the dominant energy source for new long
haul heavy-duty trucks in 20 years?”.
Interviewee Number
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Total
Diesel 1 1 1 1 1 1 1 1 8
BEV 1 1 1 1 1 1 1 7
Hydrogen fuel cell 1 1 1 1 4
Natural gas 1 1
No dominant fuel 1 1
Table A7. Occurrence and frequency of responses to the question “Do you expect a trucking company would be willing to pay greater
lifetime costs for improved environmental performance such as a reduction in GHG emissions or other air pollutants? If yes, how
much more: a. very little, b. somewhat, or c. much more?”
Interviewee Number
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Total
Somewhat 1 1 1 1 1 1 1 1 8
Very little 1 1 1 1 1 1 1 7
Not at all 1 1 1 1 1 1 1 7
Much more 1 1
153
Appendix F
Inputs to the Frameworks Employed in Chapter 4
Table A8. Default vehicle parameters used throughout the multiple criteria decision analyses.
Value Units Source Notes
VK
T Annual VKTa 155,556 km/year [44] Average VKT for Canadian tractors
Lifetime 5 years [44] Average tractor ownership length in Canada
Lifetime VKT 780,000 km Calculated using annual VKT and lifetime
Fu
el C
on
sum
pti
on
Diesel 33 L/100km [45]
BEV 125 kWh/100km [46]
H2 FCEV 175 kWh/100km [47]
CNG 40 DLEb/100km [48]
LNG 40 DLE/100km [48]
RNG 40 DLE/100km Assumed to be the same as CNG
B20 34 L/100km [10]
HDRD 34 L/100km Based on similar volumetric energy densities for biodiesel and HDRD
En
erg
y D
ensi
ty Diesel 38.7 MJ/L [49] Lower heating value (LHV)
Battery 0.25 kWh/kg [50] Advanced battery density
Hydrogen 33 kWh/kg [50] LHV
B100 35.4 MJ/L [49] LHV
HDRD 36.5 MJ/L [49] LHV
aVKT=vehicle kilometres travelled, bDLE=diesel litre equivalent
154
Table A9 outlines the inputs to the multi-attribute utility and satisficing heuristic frameworks, as
well as some of the inputs to the societal cost benefit analysis described in Sections 4.2.3.1,
4.2.3.2.1 and 4.2.3.2.2, respectively. Below are a list of attributes and the methodology by which
their inputs have been calculated:
Total cost of ownership: calculated by taking into account vehicle purchase price, fuel costs and
maintenance costs. A vehicle ownership lifetime of 5 years [78] is assumed and that the battery
electric vehicle and hydrogen fuel cell electric vehicle are sold prior to them needing their
battery and fuel cell replaced, respectively. It’s assumed that the salvage value for each vehicle is
negligible in comparison to operational costs and hence exclude it from the analysis. The Capital
Cost Allowance (CCA) Class Depreciation has a provision to discount freight trucks weighing
over 11,788 kg by 40 percent per year. This suggests that rapid deterioration is likely.
Cargo capacity: calculated by taking into account the difference between a vehicle’s curb
weight and a gross vehicle weight limit of 36,000 kg. Across all vehicle technologies, I consider
differences in fuel weight, fuel storage system weight, engine weight and exhaust after
treatments (see Table A10 for a list of inputs to vehicle weight calculations).
Vehicle range: calculated by taking into account the fuel consumption and the amount of fuel
expected to be stored on board each vehicle technology.
Refueling time: reported based on industry averages in all cases except for the battery electric
vehicle, which I calculated based on the reported range and fuel consumption of the Tesla Semi
and the charging speed of a Tesla Supercharger.
Availability of refueling infrastructure: I consult NRCAN’s Electric Charging and Alternative
Fueling Stations Locator [8] to estimate the number of public alternative recharging/refueling
stations available for class 6 to 8 vehicles across the country. For diesel vehicles, I consult the
Canadian Fuels Association [51] to determine the number of diesel cardlock stations.
Fuel supply: I assign technologies a numeric value of 1, 2 or 3 which correspond to low,
moderate and high, respectively, to represent current supply levels. I base this level of risk off of
155
my own understanding of the current fuel markets in Canada, as well as data published by the
National Energy Board [17], Littlejohns et al. [39] and Wolinetz et al. [38].
Fuel price volatility: I estimate fuel price volatility based on historical fuel price data found in
the United States Department of Energy’s most recent Clean Cities Alternative Fuel Price Report
[52], and where data is not available, based on my own understanding of fuel prices.
Availability of incentives: calculated by summing up all federal and provincial incentives
available for each vehicle technology.
Industry experience with the technology: technologies are assigned a numeric value of 0, 1, 2
or 3 which correspond to no experience, little experience, some experience and lots of
experience, respectively. These values are assigned based on my knowledge of the level of
penetration of each technology within the long haul trucking industry.
GHG emissions: The well-to-wheel GHG emissions (which omits the end-of-life stage of a
vehicle technology) for each vehicle technology are calculated using GHGenius, a Canada-based
LCA model for fuels and vehicles [53]. Results are generated for Class 8 trucks in target year
2018 using updated vehicle ranges and a 31 percent fraction of kilometers driven in the city [54].
The most likely feedstocks and fuel production pathways for each fuel are considered, namely
the average Canadian electricity grid mix for the battery electric vehicle, steam reformed natural
gas for hydrogen fuel cell electric vehicles, renewable natural gas produced from wood waste,
and biodiesel and renewable diesel produced from canola.
Other air pollutant emissions: Estimates from Meyer et al. [55] and Aatola et al. [56] are used
to determine levels of particulate matter under 10 micrometers (PM10), nitrogen oxides (NOx)
and carbon monoxide (CO) tailpipe emissions for each alternative technology. Emissions are
aggregated to determine a single value for each technology. Sulfur oxide (SOx) emissions are
excluded due to negligible levels of emissions across all technologies. Only tailpipe emissions
are accounted for as its assumed that any upstream emissions are isolated from major populated
centers and hence, will have a less pronounced effect on human health. It is unclear what
assumptions were made with regards to the emission control systems employed for each vehicle
technology, which presents a certain limitation to the estimates reported.
156
Table A9. Inputs and assumptions used throughout the multi-attribute utility analysis, satisficing framework and cost benefit analysis.
Value Units Source Notes
Veh
icle
Pu
rch
ase
Pri
ce
Diesel $206,240 CAD [57] Estimated price of a 2020 Freightliner New Cascadia model
BEV $230,000 CAD [58] Estimated price of a Tesla Semi
H2 FCEV $499,250 CAD [59] Estimated price of a Nikola One
CNG $264,530 CAD [60] Incremental cost natural gas vehicle, assumed to be the low-end estimate from
source
LNG $311,170 CAD [60] Incremental cost natural gas vehicle, assumed to be the high-end estimate from
source
RNG $264,530 CAD Same as a CNG vehicle
B20 $206,240 CAD Same as a diesel vehicle
HDRD $206,240 CAD Same as a diesel vehicle
Fu
el C
ost
s
Diesel $1.12 CAD/L [19] Average Canadian retail diesel price between 2015 and 2018
BEV $0.09 CAD/kWh [61] Average industrial Canadian electricity price, including taxes, by taking into
account one city per province for April 2018
H2 FCEV $0.47 CAD/kWh [52]
CNG $0.90 CAD/DLE* [52]
LNG $0.96 CAD/DLE [52]
RNG $3.88 CAD/DLE [29]
B20 $1.34 CAD/L [30]
HDRD $1.48 CAD/L [30]
Ma
inte
na
nce
Co
sts
Diesel $0.05 CAD/km [47]
BEV $0.02 CAD/km [47]
H2 FCEV $0.03 CAD/km [47]
CNG $0.07 CAD/km [62]
LNG $0.07 CAD/km [62]
RNG $0.07 CAD/km Assumed to be the same as CNG
B20 $0.05 CAD/km [11] No added maintenance costs for buses running on B20
HDRD $0.05 CAD/km Maintenance costs assumed to be the same as petroleum diesel
*DLE = diesel litre equivalent
157
To
tal
Co
st o
f O
wn
ersh
ip Diesel $508,922 CAD
Calculated by taking into account vehicle purchase price, fuel costs and
maintenance costs.
BEV $314,930 CAD
H2 FCEV $1,026,290 CAD
CNG $529,740 CAD
LNG $592,550 CAD
RNG $1,273,400 CAD
B20 $517,200 CAD
HDRD $532,210 CAD
Ca
rgo
Ca
pa
city
Diesel 27,790 kg [47,63] Based on average values for a diesel powertrain and diesel fuel
BEV 24,780 kg [48,50,58] Based on the Tesla Semi’s reported range of 800 km, efficiency of 1.25 kWh/km
and advanced battery density of 0.25 kWh/kg
H2 FCEV 26,140 kg [50]
CNG 26,860 kg [48]
LNG 27,270 kg [48]
RNG 26,860 kg Assumed to be the same as CNG
B20 27,790 kg Assumed to be the same as diesel
HDRD 27,790 kg Assumed to be the same as diesel
Veh
icle
Ra
ng
e
Diesel 2830 km Based on 2-150 gallon fuel tanks
BEV 800 km [58]
H2 FCEV 1600 km [64]
CNG 980 km [48,60]
LNG 1200 km [48,60]
RNG 980 km Assumed to be the same as CNG
B20 2800 km Based on 2-150 gallon fuel tanks and the lower energy density of biodiesel
HDRD 2830 km Based on 2-150 gallon fuel tanks
158
R
efu
elin
g T
ime
Diesel 11 minutes [65] Average for medium/heavy-duty vehicles
BEV 400 minutes [3] Time it would take to charge a Telsa Semi with an estimated fuel consumption of
1.25kWh/km using a Tesla Supercharger (150kW)
H2 FCEV 15 minutes [46]
CNG 15 minutes [66]
LNG 15 minutes [67]
RNG 15 minutes Assumed to be same as CNG
B20 11 minutes Assumed to be same as diesel
HDRD 11 minutes Assumed to be same as diesel
Av
ail
ab
ilit
y o
f re
fuel
ing
infr
ast
ruct
ure
Diesel 1265 # of stations [51] Number of cardlock stations in Canada
BEV 588 # of stations [8] Number of publicly accessible level 3 charging stations for vehicle classes 6-8
H2 FCEV 1 # of stations [8] Number of publicly accessible stations for vehicle classes 6-8
CNG 31 # of stations [8] Number of publicly accessible stations for vehicle classes 6-8
LNG 5 # of stations [8] Number of publicly accessible stations for vehicle classes 6-8
RNG 0 # of stations
B20 2 # of stations [8] Number of stations supplying blend levels of 20% and above for vehicle classes
6-8
HDRD 0 # of stations [8] Number of stations supplying blend levels of 20% and above for vehicle classes
6-8
Fu
el S
up
ply
Diesel 3
1, 2 and 3 correspond to low, moderate and high levels of fuel supply in Canada,
respectively
BEV 3
H2 FCEV 1
CNG 2
LNG 2
RNG 1
B20 2 [38,39]
HDRD 1
159
F
uel
pri
ce v
ola
tili
ty
Diesel 3 [52]
BEV 1 [52]
H2 FCEV 2 Assuming dependence on natural gas commodity price as most of Canadian
hydrogen is currently being produced from natural gas
CNG 2 [52]
LNG 2 Assuming the same as CNG
RNG 2 Assuming the same as CNG
B20 3 [52]
HDRD 3 Assuming the same price volatility as biodiesel due to dependence on similar
commodity prices
Av
ail
ab
ilit
y o
f in
cen
tiv
es
Diesel 0
BEV 4 [68–71] Electric Vehicle and Alternative Fuel Infrastructure Deployment Initiative
(EVAFIDI) (CAD); Clean Fuel Standard (CFS) (BC); Écocamionnage (QC);
Zero-Emission Vehicle Infrastructure Program (CAD)
H2 FCEV 3 [68,69,71] EVAFIDI; CFS (BC); Zero-Emission Vehicle Infrastructure Program (CAD)
CNG 4 [68–70,72] EVAFIDI; CFS (BC); Natural gas for transportation (FortisBC), Écocamionnage
(QC)
LNG 4 [68–70,72] EVAFIDI; CFS (BC); Natural gas for transportation (FortisBC), Écocamionnage
(QC)
RNG 4 [68–70,72] EVAFIDI; CFS (BC); Natural gas for transportation (FortisBC), Écocamionnage
(QC)
B20 2 [69,70] CFS (BC); Écocamionnage (QC)
HDRD 1 [69] CFS (BC)
Ind
ust
ry E
xp
erie
nce
Diesel 3
Based on author’s knowledge of rate of uptake of each technology from
stakeholder interviews
BEV 0
H2 FCEV 0
CNG 2
LNG 2
RNG 0
B20 2
HDRD 1
160
GH
G E
mis
sio
ns
Diesel 1390 g CO2eq./km [53]
BEV 240 g CO2eq./km [53]
H2 FCEV 840 g CO2eq./km [53] Hydrogen produced from natural gas
CNG 1110 g CO2eq./km [53]
LNG 1010 g CO2eq./km [53]
RNG 210 g CO2eq./km [53] RNG produced from wood waste
B20 1160 g CO2eq./km [53] Biodiesel produced from canola
HDRD 360 g CO2eq./km [53] Renewable diesel produced from canola
PM
10 e
mis
sio
ns
Diesel 3.42 mg/tonne-km [55]
BEV 0.00 mg/tonne-km Electric engines produce zero tailpipe emissions
H2 FCEV 0.00 mg/tonne-km Electric engines produce zero tailpipe emissions
CNG 0.68 mg/tonne-km [55]
LNG 0.68 mg/tonne-km [55]
RNG 0.68 mg/tonne-km [55]
B20 3.42 mg/tonne-km [55]
HDRD 2.47 mg/tonne-km [56] Based on the low-end estimate of HDRD tailpipe emission reductions in a
heavy-duty truck in comparison to petroleum diesel
NO
x e
mis
sio
ns
Diesel 321.92 mg/tonne-km [55]
BEV 0.00 mg/tonne-km Electric engines produce zero tailpipe emissions
H2 FCEV 0.00 mg/tonne-km Electric engines produce zero tailpipe emissions
CNG 236.31 mg/tonne-km [55]
LNG 236.31 mg/tonne-km [55]
RNG 236.31 mg/tonne-km [55]
B20 335.62 mg/tonne-km [55]
HDRD 299.39 mg/tonne-km [56] Based on the low end estimate of HDRD tailpipe emission reductions in a heavy-
duty truck in comparison to petroleum diesel
161
CO
em
issi
on
s Diesel 157.54 mg/tonne-km [55]
BEV 0.00 mg/tonne-km Electric engines produce zero tailpipe emissions
H2 FCEV 0.00 mg/tonne-km Electric engines produce zero tailpipe emissions
CNG 10.27 mg/tonne-km [55]
LNG 10.27 mg/tonne-km [55]
RNG 10.27 mg/tonne-km [55]
B20 147.26 mg/tonne-km [55]
HDRD 102.51 mg/tonne-km [56] Based on the low end estimate of HDRD tailpipe emission reductions in a heavy-
duty truck in comparison to petroleum diesel
Oth
er A
ir P
oll
uta
nt
Em
issi
on
s
Diesel 482.89 mg/tonne-km [55,56]
Aggregated PM, NOx and CO tailpipe air pollutant emissions for each
technology
BEV 0.00 mg/tonne-km [55,56]
H2 FCEV 0.00 mg/tonne-km [55,56]
CNG 247.27 mg/tonne-km [55,56]
LNG 247.27 mg/tonne-km [55,56]
RNG 247.27 mg/tonne-km [55,56]
B20 486.31 mg/tonne-km [55,56]
HDRD 404.37 mg/tonne-km [55,56]
162
Table A10. Inputs to vehicle weight calculations. Component Weight Units Source Notes
Wheels and Tires 771 kg [63] 10 aluminum wheels and tires
Chassis/Frame 925 kg [63] Frame rails and cross members, fifth wheel and brackets
Drivetrain & Suspension 1,311 kg [63] Drive axles, steer axle, and suspension system
Misc. Accessories/Systems 1,388 kg [63] Batteries, fuel system and exhaust hardware
Truck Body Structure 1,465 kg [63] Cab-in-white, sleeper unit, hood and fairings, interior and glass
Die
sel
Diesel Powertrain 1,851 kg [63] Engine and cooling system, transmission and accessories
Diesel Fuel 785 kg [47] Two 473 L (125 gallon) tanks
BE
V Tesla Semi Battery Estimate 4,000 kg [50,58] Based on 800 km range and 1.25k Wh/km efficiency of Tesla Semi, and
advanced battery density of 0.25 kWh/kg
Weight reduction from no after
treatment -204 kg [48]
H2 F
CE
V
Hydrogen Fuel 88 kg [50] For 1000 km of travel at efficiency of 292 kWh/100 km
Hydrogen Storage Tank 1,622 kg [50] For a 700 bar storage tank
Fuel Cell System 857 kg [50] For a 300 kW fuel cell system
Battery 75 kg [50,64] Based on 300 kWh expected in Nikola One and an advanced battery density of
0.25 kWh/kg
Weight reduction from no after
treatment -204 kg [48]
CN
G
Weight reduction from no after
treatment -204 kg [48]
Reduction in engine weight -85 kg [48]
Fuel system weight increase 1,216 kg [48]
LN
G
Weight reduction from no after
treatment -204 kg [48]
Reduction in engine weight -85 kg [48]
Fuel system weight increase 807 kg [48]
B2
0
Biodiesel Fuel 797 kg Calculated
163
Appendix G
Results of the Sensitivity Analysis of the Societal Cost Benefit Analysis of
Alternative Technologies for Long Haul Heavy-duty Vehicles
Table A11. Results of the societal cost benefit analysis when a low cost of NOx emissions ($300/tonne) is applied. Results are
presented in thousands of dollars ($1,000s).
Total cost
of
ownership
Cargo
revenue
Life cycle
GHG
emissions
PM10
tailpipe
emissions
NOx tailpipe
emissions
CO tailpipe
emissions
CBA
difference Rank
CNG 530 3,956 9 11 340 85 2,981 1
LNG 593 4,016 8 11 345 86 2,973 2
BEV 315 3,206 2 0 0 0 2,889 3
H2 FCEV 1,026 3,850 6 0 0 0 2,817 4
RNG 1,270 3,956 2 11 340 85 2,245 5
HDRD 532 4,094 3 28 446 877 2,177 6
B20 517 4,094 9 57 500 1,260 1,786 7
Diesel 509 4,094 10 57 479 1,348 1,733 8
164
Table A12. Results of the societal cost benefit analysis when a high cost of NOx emissions ($14,00/tonne) is applied. Results are
presented in thousands of dollars ($1,000s).
Total cost
of
ownership
Cargo
revenue
Life cycle
GHG
emissions
PM10
tailpipe
emissions
NOx tailpipe
emissions
CO tailpipe
emissions
CBA
difference Rank
BEV 315 3,206 2 0 0 0 2,890 1
H2 FCEV 1,026 3,850 6 0 0 0 2,817 2
CNG 530 3,956 9 11 13,413 85 -10,092 3
LNG 593 4,016 8 11 13,617 86 -10,299 4
RNG 1,270 3,956 2 11 13,413 85 -10,829 5
HDRD 532 4,094 3 28 17,580 877 -14,958 6
Diesel 509 4,094 10 57 18,903 1,348 -16,691 7
B20 517 4,094 9 57 19,708 1,260 -17,422 8
165
Appendix H
Select Inputs to the Well-to-wheel Assessment of DME
Table A13. Ultimate analysis of typical wood residue and natural gas in Alberta [73–75].
Average Wood
(ash- and moisture-free)
Natural Gas
(moisture-free)
Wt. % Mole % Wt. % Mole %
C 48.33 31.88 73.32 20.27
H 5.83 45.80 24.03 79.13
O 44.83 22.19 0.99 0.20
N 0.22 0.13 1.66 0.39
S 0.03 0.01 trace trace
Table A14. Values and sources of key input parameters used throughout the DME production
model.
Parameter Value Unit
Fuel and Feedstock Lower Heating Values (LHV)
Diesel 40.39 BTU/g
DME 27.47 BTU/g
Natural Gas 44.7 BTU/g
Wood Residue 19.1 BTU/g
Poplar 17.6 BTU/g
Fuel and Feedstock Carbon Intensities
Diesel 0.022 g C/BTU
DME 0.019 g C/BTU
Natural Gas 0.016 g C/BTU
Wood Residue 0.026 g C/BTU
Poplar 0.030 g C/BTU
166
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