An Evaluation of Alternative Fuels and Powertrain Technologies for Canada’S Long Haul Heavy-Duty Vehicle Sector

An Evaluation of Alternative Fuels and Powertrain Technologies for Canada’s Long Haul Heavy-duty Vehicle Sector

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

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.

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 iv

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 v

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

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 viii

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

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

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

EU: European Union

FAME: fatty acid methyl esters

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

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],

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

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

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.

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.

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

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

(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.

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

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

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

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.

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

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

context, where possible, in order to account for differences in electricity generation mixes and natural gas production, in particular.

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].

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).

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]. Gross Share of Share of Vehicle Vehicle Applications Canadian Canadian Class Weight vehicle pool VKT Less than 1 Passenger cars, minivans, small SUVs 2,722 kg 2,722 to 2a Large SUVs, standard pickups 96.3% 91.1% 3,856 kg 3,856 to Large pickups, utility van, minibus, step 2b 4,536 kg van 4,536 to City delivery truck, minibus, walk-in 3 6,350 kg truck City delivery truck, parcel delivery truck, 6,350 to 4 large walk-in truck, bucket truck, 7,257 kg landscaping truck 7,257 to Large city delivery truck, large walk-in 5 8,845 kg truck, bucket truck 2.1% 2.4% Large city delivery truck, school bus, 8,845 to 6 beverage truck, rack truck, single axle 11,793 kg van, stake body truck City transit bus, furniture truck, tow 11,793 to 7 truck, refuse truck, home fuel truck, 14,969 kg cement mixers, dump truck, fire engine Straight trucks including dump trucks, 14,969 to refuse trucks, cement mixers, furniture 8a 27,215 kg trucks, city transit bus, tow trucks, fire engines 1.5% 6.4% Greater Combination trucks including various 8b than configurations of tractor trailers 27,215 kg

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

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

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

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

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.

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.

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.

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].

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.

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.

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].

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

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 3 volumetric terms, this would range from 2.2 to 3.7 m of gaseous hydrogen (H2) at 700 and 350 3 bar, respectively; or, 1.2 m 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].

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

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

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].

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].

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

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

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

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].

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,

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.

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

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].

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

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.

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.

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

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

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

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

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].

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

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?

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

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 2019 Includes vehicle purchase price, fuel costs and maintenance costs and ownership CAD assuming an 8% annual discount rate [182]. The weight of goods a particular vehicle technology can transport Cargo capacity kg calculated by taking into account the weight penalty incurred by certain alternative fuels or powertrain technologies. The estimated distance each vehicle technology is expected to be able Vehicle range km 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 The number of alternative refueling or high power recharging stations # of refueling available across Canada for public use at the time of the study. stations infrastructure Qualitative measure of current/future fuel availability based primarily on national production data [183]. 1 represents good availability, 2 is Fuel supply N/Aa moderate supply risk and 3 is high risk. See Appendix C for a discussion surrounding future fuel supply levels. Qualitative measure of the degree to which each alternative fuel price Fuel price N/A fluctuates based on historical price data. 1 is low volatility, 2 is volatility moderate and 3 is high. Availability of # of The number of incentives available across the country and each incentives incentives province for each alternative technology. A reflection of the industry’s experience with an alternative Industry technology within Canada’s long haul HDV sector. This is translated experience with N/A to a qualitative numeric scale whereby 0 is no experience, 1 is the technology minimal experience, 2 is some experience and 3 is lots of experience. Well-to-wheel life cycle GHG emissions for each vehicle/fuel, using GHG emissions CO eq. 2 100-year global warming potentials [184]. Other air pollutant mg/tonne- Aggregated expected tailpipe emissions, including particulate matter emissions km (PM), nitrogen oxides (NOx) and carbon monoxide (CO). aN/A = not applicable.

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.

푢(푥푘) = (푥푚푎푥 − 푥푘)/(푥푚푎푥 − 푥푚푖푛) (1)

푢(푥푘) = (푥푘 − 푥푚푖푛)/(푥푚푎푥 − 푥푚푖푛) (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.

Ix wx = n (3) ∑x=1 I

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.

n uk = ∑x=1 wxu(xk) (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.

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).

1, 𝑖푓푥 ≥ 푥 푥 = { 푘 푑 (5) 푘 0, 표푡ℎ푒푟푤𝑖푠푒

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).

푛 푦 = ∑푥=1 푤푥푥푘 (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.

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 Total cost of The net present value of the sum of vehicle purchase price, fuel costs, and ownership maintenance costs. The 2020 estimate (in 2019 CAD) of the social cost of carbon, $48/tonne

GHG emissions CO2eq. [185], is applied to the WTW GHG emissions of each alternative technology.

Cost Based on social costs of particulate matter 10 micrometers or less in diameter (PM ), nitrogen oxides (NO ) and carbon monoxide (CO) Criteria air pollutant 10 x tailpipe emissions from each alternative technology. Values of emissions $3,850/tonne, $8,950/tonne and $2,000/tonne, respectively, are used (all 2019 CAD) [186]. 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 Cargo revenue difference in cargo revenue in comparison to the 2016 average [187], as Benefit 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 th th [189]. I take values reflecting the 5 and 95 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.

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 Low GHG emissions Battery weight penalty Zero tailpipe emissions Battery electric vehicles Short range Lower maintenance requirements High vehicle cost and costs Limited availability of refueling Lower GHG emissions Hydrogen fuel cell electric infrastructure Fast refueling vehicles High fuel cost Long range High vehicle cost Limited availability of refueling Lower GHG emissions infrastructure Natural gas vehicles Lower total cost of ownership Uncertainty surrounding GHG Low fuel cost emission reductions Lower GHG emissions Cold weather operability issues Biodiesel Drop-in fuel Low fuel availability Renewable diesel Drop-in fuel Low fuel availability

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

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

(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].

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.

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

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 1.00 0.46 0.00 0.02 0.00 0.00 0.00 0.00 infrastructure GHG emissions 0.00 0.98 0.32 0.24 0.20 0.88 0.46 1.00 Other air pollutant 0.01 1.00 0.49 0.49 0.00 0.17 1.00 0.49 emissions 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

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

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

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.

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 0 0 0 0 0 0 0 infrastructure 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.

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

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 Life cycle PM10 NOx CO Cargo of GHG tailpipe tailpipe tailpipe revenue ownership emissions emissions emissions 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.

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

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

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

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

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

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.

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

(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

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

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

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

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].

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.

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.

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. a CH4 30 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.

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).

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.

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].

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

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. Inputs Outputs Efficiency Feedstock Description (LHV, NG Biomass Elec. DME MeOH Steam %) Synthesis of DME using electricity Natural derived from natural 1.46 - 0.06 1.00 0.00 0.05 66.0 Gas gas supplemented with grid electricity Synthesis of DME from wood residue using on- Wood site generated - 1.70 0.13 1.00 0.00 0.06 54.4 Residue electricity from biomass supplemented with grid electricity Synthesis of DME from poplar using electricity Poplar derived from biomass - 1.70 0.13 1.00 0.00 0.06 54.4 supplemented with grid electricity

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.

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

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

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.

3000 240 2000 140

1000 eq./MJ)

eq./km) 40 2 2 0

-60 (g CO (g

(g CO (g -1000

WTW GHG emissions emissions GHG WTW emissions GHG WTW -2000 -160

-3000 -260 Natural Gas Wood Poplar Diesel Residue

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

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.

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 NG Biomass Elec. DME Steam Methanol (LHV, %) 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.

1400

1200

1000

800 eq./km) 2 Indirect 600 Direct

(g CO (g 400 WTW GHG emissions emissions GHG WTW 200

0 Natural Wood Poplar Diesel Gas Residue

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.

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.

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1600

1400

1200

1000

eq./km) 800 2

600 (g CO (g

WTW GHG emissions emissions GHG WTW 400

200

Diesel Diesel Diesel

Poplar Poplar Poplar

Natural Gas Natural Natural Gas Natural Gas Natural

Wood Residue Wood Residue Wood Residue Wood Electricity Generation Feedstock Transport Methane Emissions from Distance Upstream NG

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.

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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

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[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

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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.

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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

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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.

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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).

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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-

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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.

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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

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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].

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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.

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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

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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?

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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 Total

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

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Durability 1 1 2 Price stability/ability to predict future 1 1 2 costs Refueling/recharging speed 1 1 2 Cargo capacity 1 1 2 Availability of skilled maintenance 1 1 2 workers Functionality 1 1 Resiliency 1 1 Driver training requirements 1 1 Range 1 1 Power 1 1 Availability of government programs for 1 1 subsidies 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

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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 - 10 10 10 3 9 10 8 9 9/10 9 5 10 9 9 6/7 10 5 6 ownership 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 8 8 10 9/10 9 9 8 9 8 10 10 8 10 6 9 10 6 8 8 infrastructure GHG 5 5 6 8 2 3 5 3 5 7 5/6 4 10 9 2 7/8 2 4 5 emissions Other air pollutant 4 5 6 9 2 4 5 3 6 9 6 4 7 8 5 5 2 4 4 emissions Industry 6 3/4 10 8 9 8 8 5 8 9 5 7 2 8 8 5 3 6 9/10 experience Availability of 10 10 8 8 9 6 7 7 8 10 7 6 8 10 6 6 5 4 5 incentives Fuel price 7 6 7 8/9 8 8 8 2 10 8 9 6/7 8/9 5 8 10 8 3 5 volatility Availability of skilled - - 7 8 7 7/8 7 10 9 8 8 6 7 8 10 5 10 8 maintenance workers

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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

2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 Total

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

BEV Ability to market environmental 1 1 stewardship Versatility of fuel (for 1 microgeneration and distributive 1 generation) Availability of refueling 1 1 infrastructure Ease of use 1 1 Reduced noise pollution 1 1

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 1 1 1 1 4

FCEV Zero tailpipe emissions

2 H Higher payload capacity than 1 1 BEVs Efficient cabin heating (using 1 1 waste heat from fuel cell)

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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 1 1 2 pollutant emissions CNGLNG Availability of refueling 1 1 2 infrastructure Availability of technology 1 1 2

Flexibility of bi-fuel engine 1 1

Maintenance familiarity 1 1

Domestic source of energy 1 1

Low GHG emissions 1 1 2

Reduced landfill volume 1 1 RNG Circularity of production process 1 1

Low GHG emissions 1 1 1 1 1 1 6 Drop-in fuel 1 1 1 3

Less disruptive technology 1 1 2 1 1 Renewable resource

B20 Can make use of waste residues 1 1

Promotes economic development in 1 1 other sectors (like agriculture) Market ready 1 1

Knowledge of fuel 1 1

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Compliance with a clean fuel 1 1 standard 1 1 1 1 1 5 Drop-in fuel Low GHG emissions 1 1 2

HDRD Potential for higher blend levels 1 1 2

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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 Tot

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 1 1 2

technicians BEV 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

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

FCEV 1 1 1 1 1 5

Vehicle cost 2 H Cost 1 1 1 3

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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 1 1 from electrolysis 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 CNG/LNG Disruption to operations 1 1 Lifetime cost 1 1 Energy intensive fuel production process 1 1 Availability to expand 1 1

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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

Fuel availability 1 1 2

Infrastructure availability 1 1 RNG Cost 1 1 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 B20 1 1 2 respective GHG emissions 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

Fuel availability 1 1 1 1 4

Fuel cost 1 1 2

HDRD Food versus fuel 1 1

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

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 10 years HDRD 1 1 1 3 RNG 1 1 Biofuels 1 1 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 20 years HDRD 1 1 Natural gas hybrids 1 1

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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

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

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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

Annual VKTa 155,556 km/year [44] Average VKT for Canadian tractors

Lifetime 5 years [44] Average tractor ownership length in Canada VKT Lifetime VKT 780,000 km Calculated using annual VKT and lifetime 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

FuelConsumption B20 34 L/100km [10] HDRD 34 L/100km Based on similar volumetric energy densities for biodiesel and HDRD

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

Energy Density HDRD 36.5 MJ/L [49] LHV aVKT=vehicle kilometres travelled, bDLE=diesel litre equivalent

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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

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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.

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Table A9. Inputs and assumptions used throughout the multi-attribute utility analysis, satisficing framework and cost benefit analysis.

Value Units Source Notes 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 Incremental cost natural gas vehicle, assumed to be the low-end estimate from CNG $264,530 CAD [60] source Incremental cost natural gas vehicle, assumed to be the high-end estimate from LNG $311,170 CAD [60] source RNG $264,530 CAD Same as a CNG vehicle

Vehicle Vehicle Purchase Price B20 $206,240 CAD Same as a diesel vehicle HDRD $206,240 CAD Same as a diesel vehicle Diesel $1.12 CAD/L [19] Average Canadian retail diesel price between 2015 and 2018 Average industrial Canadian electricity price, including taxes, by taking into BEV $0.09 CAD/kWh [61] 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] FuelCosts RNG $3.88 CAD/DLE [29] B20 $1.34 CAD/L [30] HDRD $1.48 CAD/L [30] 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

Maintenance Costs 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

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Diesel $508,922 CAD BEV $314,930 CAD H2 FCEV $1,026,290 CAD CNG $529,740 CAD Calculated by taking into account vehicle purchase price, fuel costs and LNG $592,550 CAD maintenance costs. RNG $1,273,400 CAD B20 $517,200 CAD

TotalCost of Ownership HDRD $532,210 CAD Diesel 27,790 kg [47,63] Based on average values for a diesel powertrain and diesel fuel Based on the Tesla Semi’s reported range of 800 km, efficiency of 1.25 kWh/km

BEV 24,780 kg [48,50,58] 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 Cargo Capacity B20 27,790 kg Assumed to be the same as diesel HDRD 27,790 kg Assumed to be the same as diesel 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 Vehicle Vehicle Range 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

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Diesel 11 minutes [65] Average for medium/heavy-duty vehicles Time it would take to charge a Telsa Semi with an estimated fuel consumption of BEV 400 minutes [3] 1.25kWh/km using a Tesla Supercharger (150kW)

H2 FCEV 15 minutes [46] CNG 15 minutes [66] LNG 15 minutes [67]

Refueling Time 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 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

infrastructure Number of stations supplying blend levels of 20% and above for vehicle classes B20 2 # of stations [8] 6-8 Availability of refueling Number of stations supplying blend levels of 20% and above for vehicle classes HDRD 0 # of stations [8] 6-8 Diesel 3

BEV 3

H2 FCEV 1 CNG 2 1, 2 and 3 correspond to low, moderate and high levels of fuel supply in Canada, LNG 2 respectively

FuelSupply RNG 1 B20 2 [38,39] HDRD 1

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Diesel 3 [52]

BEV 1 [52] Assuming dependence on natural gas commodity price as most of Canadian H2 FCEV 2 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

Fuelprice volatility B20 3 [52] Assuming the same price volatility as biodiesel due to dependence on similar HDRD 3 commodity prices 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) Availability of incentives B20 2 [69,70] CFS (BC); Écocamionnage (QC) HDRD 1 [69] CFS (BC) Diesel 3 BEV 0

H2 FCEV 0 CNG 2 Based on author’s knowledge of rate of uptake of each technology from LNG 2 stakeholder interviews RNG 0

B20 2 Industry Experience HDRD 1

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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 GHG Emissions B20 1160 g CO2eq./km [53] Biodiesel produced from canola HDRD 360 g CO2eq./km [53] Renewable diesel produced from canola Diesel 3.42 mg/tonne-km [55] BEV 0.00 mg/tonne-km Electric engines produce zero tailpipe emissions Electric engines produce zero tailpipe emissions H2 FCEV 0.00 mg/tonne-km CNG 0.68 mg/tonne-km [55]

emissions LNG 0.68 mg/tonne-km [55]

10 RNG 0.68 mg/tonne-km [55] PM B20 3.42 mg/tonne-km [55] Based on the low-end estimate of HDRD tailpipe emission reductions in a HDRD 2.47 mg/tonne-km [56] heavy-duty truck in comparison to petroleum diesel 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]

emissions x RNG 236.31 mg/tonne-km [55] NO B20 335.62 mg/tonne-km [55] Based on the low end estimate of HDRD tailpipe emission reductions in a heavy- HDRD 299.39 mg/tonne-km [56] duty truck in comparison to petroleum diesel

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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] CO emissions B20 147.26 mg/tonne-km [55] Based on the low end estimate of HDRD tailpipe emission reductions in a heavy- HDRD 102.51 mg/tonne-km [56] duty truck in comparison to petroleum diesel Diesel 482.89 mg/tonne-km [55,56] BEV 0.00 mg/tonne-km [55,56]

H2 FCEV 0.00 mg/tonne-km [55,56] 247.27 mg/tonne-km CNG [55,56] Aggregated PM, NOx and CO tailpipe air pollutant emissions for each LNG 247.27 mg/tonne-km [55,56] technology

Emissions RNG 247.27 mg/tonne-km [55,56]

Other Air Pollutant B20 486.31 mg/tonne-km [55,56] HDRD 404.37 mg/tonne-km [55,56]

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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

Diesel Powertrain 1,851 kg [63] Engine and cooling system, transmission and accessories

Diesel Diesel Fuel 785 kg [47] Two 473 L (125 gallon) tanks Based on 800 km range and 1.25k Wh/km efficiency of Tesla Semi, and Tesla Semi Battery Estimate 4,000 kg [50,58] advanced battery density of 0.25 kWh/kg

BEV Weight reduction from no after -204 kg [48] treatment 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 FCEV

Based on 300 kWh expected in Nikola One and an advanced battery density of 2 Battery 75 kg [50,64]

H 0.25 kWh/kg Weight reduction from no after -204 kg [48] treatment Weight reduction from no after

-204 kg [48] treatment

Reduction in engine weight -85 kg [48] CNG Fuel system weight increase 1,216 kg [48] Weight reduction from no after

-204 kg [48] treatment

Reduction in engine weight -85 kg [48] LNG

Fuel system weight increase 807 kg [48]

Biodiesel Fuel 797 kg Calculated B20

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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 Life cycle PM Cargo 10 NO tailpipe CO tailpipe CBA of GHG tailpipe x Rank revenue emissions emissions difference ownership emissions emissions 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

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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 Life cycle PM Cargo 10 NO tailpipe CO tailpipe CBA of GHG tailpipe x Rank revenue emissions emissions difference ownership emissions emissions 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

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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 Natural Gas (ash- and moisture-free) (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

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