Final Government Distribution Chapter 10 IPCC AR6 WGIII
1 Table of Contents
2 Chapter 10: Transport .................................................................................................................. 10-1
3 Executive summary........................................................................................................................ 10-4
4 10.1 Introduction and overview ................................................................................................. 10-7
5 10.1.1 Transport and the sustainable development goals ...................................................... 10-7
6 10.1.2 Trends, drivers and the critical role of transport in GHG growth .............................. 10-9
7 10.1.3 Climate adaptation on the transport sector ............................................................... 10-12
8 10.1.4 Transport disruption and transformation .................................................................. 10-12
9 10.2 Systemic changes in the transport sector ......................................................................... 10-13
10 10.2.1 Urban form, physical geography, and transport infrastructure ................................ 10-14
11 Cross-Chapter Box 7 Urban Form: Simultaneously reducing urban transport emissions, avoiding
12 infrastructure lock-in, and providing accessible services......................................................... 10-15
13 10.2.2 Behaviour and mode choice ..................................................................................... 10-16
14 10.2.3 New demand concepts ............................................................................................. 10-18
15 10.2.4 Overall perspectives on systemic change ................................................................. 10-21
16 10.3 Transport technology innovations for decarbonisation .................................................... 10-23
17 10.3.1 Alternative fuels – an option for decarbonising internal combustion engines ......... 10-24
18 10.3.2 Electric technologies ................................................................................................ 10-30
19 10.3.3 Fuel cell technologies............................................................................................... 10-33
20 10.3.4 Refuelling and charging infrastructure..................................................................... 10-34
21 10.4 Decarbonisation of land-based transport.......................................................................... 10-38
22 10.4.1 Light-duty vehicles for passenger transport ............................................................. 10-38
23 10.4.2 Transit technologies for passenger transport............................................................ 10-46
24 10.4.3 Land-based freight transport .................................................................................... 10-52
25 10.4.4 Abatement costs ....................................................................................................... 10-57
26 10.5 Decarbonisation of aviation ............................................................................................. 10-58
27 10.5.1 Historical and current emissions from aviation ....................................................... 10-58
28 10.5.2 Short lived climate forcers and aviation................................................................... 10-59
29 10.5.3 Mitigation potential of fuels, operations, energy efficiency, and market-based measures
30 10-59
31 10.5.4 Assessment of aviation-specific projections and scenarios...................................... 10-64
32 10.5.5 Accountability and governance options ................................................................... 10-67
33 10.6 Decarbonisation of Shipping ............................................................................................ 10-67
34 10.6.1 Historical and current emissions from shipping....................................................... 10-67
35 10.6.2 Short lived climate forcers and shipping .................................................................. 10-68
36 10.6.3 Shipping in the Arctic .............................................................................................. 10-69
37 10.6.4 Mitigation potential of fuels, operations and energy efficiency............................... 10-70
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1 10.6.5 Accountability and governance options ................................................................... 10-73
2 10.6.6 Transformation trajectories for the maritime sector ................................................. 10-74
3 10.7 Scenarios from integrated, sectoral, and regional models................................................ 10-75
4 10.7.1 Transport scenario modelling................................................................................... 10-75
5 10.7.2 Global emission trajectories ..................................................................................... 10-76
6 10.7.3 Transport activity trajectories .................................................................................. 10-79
7 10.7.4 Transport modes trajectories .................................................................................... 10-83
8 10.7.5 Energy and Carbon efficiency trajectories ............................................................... 10-85
9 10.7.6 Fuel energy and technology trajectories .................................................................. 10-87
10 10.7.7 Insights from the modelling literature ...................................................................... 10-90
11 10.8 Enabling conditions ......................................................................................................... 10-91
12 10.8.1 Conclusions across the chapter ................................................................................ 10-91
13 10.8.2 Feasibility Assessment ............................................................................................. 10-94
14 10.8.3 Emerging Transport Issues....................................................................................... 10-95
15 10.8.4 Tools and Strategies to Enable Decarbonisation of the Transport Sector ................ 10-99
16 Frequently Asked Questions (FAQs) ......................................................................................... 10-103
17 References .................................................................................................................................. 10-105
18 Appendix 10.1: Data and methods for life cycle assessment ..................................................... 10-154
19 Appendix 10.2: Data and assumptions for life cycle cost analysis ............................................ 10-158
20 Appendix 10.3: Line of sight for feasibility assessment ............................................................ 10-161
21
22
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Final Government Distribution Chapter 10 IPCC AR6 WGIII
1 Executive summary
2 Meeting climate mitigation goals would require transformative changes in the transport sector
3 (high confidence). In 2019, direct greenhouse gas (GHG) emissions from the transport sector were 8.7
4 Gt CO2-eq (up from 5.0 Gt CO2-eq in 1990) and accounted for 23% of global energy-related CO2
5 emissions. 70% of direct transport emissions came from road vehicles, while 1%, 11%, and 12% came
6 from rail, shipping, and aviation, respectively. Emissions from shipping and aviation continue to grow
7 rapidly. Transport-related emissions in developing regions of the world have increased more rapidly
8 than in Europe or North America, a trend that is likely to continue in coming decades (high confidence).
9 {10.1, 10.5, 10.6}.
10 Since AR5 there has been a growing awareness of the need for demand management solutions
11 combined with new technologies, such as the rapidly growing use of electromobility for land
12 transport and the emerging options in advanced biofuels and hydrogen-based fuels for shipping
13 and aviation. There is a growing need for systemic infrastructure changes that enable behavioural
14 modifications and reductions in demand for transport services that can in turn reduce energy demand.
15 The response to the COVID-19 pandemic has also shown that behavioural interventions can reduce
16 transport-related GHG emissions. For example, COVID-19-based lockdowns have confirmed the
17 transformative value of telecommuting replacing significant numbers of work and personal journeys as
18 well as promoting local active transport. There are growing opportunities to implement strategies that
19 drive behavioural change and support the adoption of new transport technology options. {Chapter 5,
20 10.2, 10.3, 10.4, 10.8}
21 Changes in urban form, behaviour programs, the circular economy, the shared economy, and
22 digitalisation trends can support systemic changes that lead to reductions in demand for transport
23 services or expands the use of more efficient transport modes (high confidence). Cities can reduce
24 their transport-related fuel consumption by around 25% through combinations of more compact land
25 use and the provision of less car-dependent transport infrastructure. Appropriate infrastructure,
26 including protected pedestrian and bike pathways, can also support much greater localised active travel1.
27 Transport demand management incentives are expected to be necessary to support these systemic
28 changes (high confidence). There is mixed evidence of the effect of circular economy initiatives, shared
29 economy initiatives, and digitalisation on demand for transport services. For example, while
30 dematerialisation can reduce the amount of material that need to be transported to manufacturing
31 facilities, an increase in online shopping with priority delivery can increase demand for freight transport.
32 Similarly, while teleworking could reduce travel demand, increased ridesharing could increase vehicle-
33 km travelled. {Chapter 1, Chapter 5, 10.2, 10.8}
34 Battery-electric vehicles (BEVs) have lower life cycle greenhouse gas emissions than internal
35 combustion engine vehicles (ICEVs) when BEVs are charged with low carbon electricity (high
36 confidence). Electromobility is being rapidly implemented in micro-mobility (e-autorickshaws, e-
37 scooters, e-bikes), in transit systems, especially buses, and, to a lesser degree, in the electrification of
38 personal vehicles. BEVs could also have the added benefit of supporting grid operations. The
39 commercial availability of mature Lithium-Ion Batteries (LIBs) has underpinned this growth in
40 electromobility.
41 As global battery production increases, unit costs are declining. Further efforts to reduce the GHG
42 footprint of battery production, however, are essential for maximising the mitigation potential of BEVs.
43 The continued growth of electromobility for land transport would require investments in electric
44 charging and related grid infrastructure (high confidence). Electromobility powered by low-carbon
FOOTNOTE 1 Active travel is travel that requires physical effort, for example journeys made by walking or
cycling.
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1 electricity has the potential to rapidly reduce transport GHG and can be applied with multiple co-
2 benefits in the developing world’s growing cities (high confidence). {10.3, 10.4, 10.8}
3 Land-based, long-range, heavy-duty trucks can be decarbonised through battery-electric haulage
4 (including the use of Electric Road Systems), complemented by hydrogen- and biofuel-based fuels
5 in some contexts (medium confidence). These same technologies and expanded use of available
6 electric rail systems can support rail decarbonisation (medium confidence). Initial deployments of
7 battery-electric, hydrogen- and bio-based haulage are underway, and commercial operations of some of
8 these technologies are considered feasible by 2030 (medium confidence). These technologies
9 nevertheless face challenges regarding driving range, capital and operating costs, and infrastructure
10 availability. In particular, fuel cell durability, high energy consumption, and costs continue to challenge
11 the commercialisation of hydrogen-based fuel cell vehicles. Increased capacity for low-carbon
12 hydrogen production would also be essential for hydrogen-based fuels to serve as an emissions
13 reduction strategy (high confidence). {10.3, 10.4, 10.8}
14 Decarbonisation options for shipping and aviation still require R&D, though advanced biofuels,
15 ammonia, and synthetic fuels are emerging as viable options (medium confidence). Increased
16 efficiency has been insufficient to limit the emissions from shipping and aviation, and natural gas-based
17 fuels are likely inadequate to meet stringent decarbonisation goals for these segments (high confidence).
18 High energy density, low carbon fuels are required, but they have not yet reached commercial scale.
19 Advanced biofuels could provide low carbon jet fuel (medium confidence). The production of synthetic
20 fuels using low-carbon hydrogen with CO2 captured through DAC/BECCS could provide jet and marine
21 fuels but these options still require demonstration at scale (low confidence). Ammonia produced with
22 low-carbon hydrogen could also serve as a marine fuel (medium confidence). Deployment of these fuels
23 requires reductions in production costs. {10.2, 10.3, 10.4, 10.5, 10.6, 10.8}.
24 Scenarios from bottom-up and top-down models indicate that without intervention, CO2
25 emissions from transport could grow in the range of 16% and 50% by 2050 (medium confidence).
26 The scenarios literature projects continued growth in demand for freight and passenger services,
27 particularly in developing countries in Africa and Asia (high confidence). This growth is projected to
28 take place across all transport modes. Increases in demand not-withstanding, scenarios that limit
29 warming to 1.5°C degree with no or limited overshoot suggest that a 59% reduction (42-68%
30 interquartile range) in transport-related CO2 emissions by 2050, compared to modelled 2020 levels is
31 required. While many global scenarios place greater reliance on emissions reduction in sectors other
32 than transport, a quarter of the 1.5°C degree scenarios describe transport-related CO2 emissions
33 reductions in excess of 68% (relative to modelled 2020 levels) (medium confidence). Illustrative
34 mitigation pathways 1.5 REN and 1.5 LD describe emission reductions of 80% and 90% in the transport
35 sector, respectively, by 2050. Transport-related emission reductions, however, may not happen
36 uniformly across regions. For example, transport emissions from the Developed Countries, and Eastern
37 Europe and West-Central Asia (EEA) countries decrease from 2020 levels by 2050 across all scenarios
38 compatible with a 1.5°C degree goal (C1 - C2 group), but could increase in Africa, Asia and developing
39 Pacific (APC), Latin America and Caribbean, and the Middle East in some of these scenarios. {10.7}
40 The scenarios literature indicates that fuel and technology shifts are crucial to reducing carbon
41 emissions to meet temperature goals. In general terms, electrification tends to play the key role in land-
42 based transport, but biofuels and hydrogen (and derivatives) could play a role in decarbonisation of
43 freight in some contexts (high confidence). Biofuels and hydrogen (and derivatives) are likely more
44 prominent in shipping and aviation (high confidence). The shifts towards these alternative fuels must
45 occur alongside shifts towards clean technologies in other sectors (high confidence). {10.7}.
46 There is a growing awareness of the need to plan for the significant expansion of low-carbon
47 energy infrastructure, including low-carbon power generation and hydrogen production, to
48 support emissions reductions in the transport sector (high confidence). Integrated energy planning
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1 and operations that take into account energy demand and system constraints across all sectors (transport,
2 buildings, and industry) offer the opportunity to leverage sectoral synergies and avoid inefficient
3 allocation of energy resources. Integrated planning of transport and power infrastructure would be
4 particularly useful in developing countries where ‘greenfield’ development doesn’t suffer from
5 constraints imposed by legacy systems. {10.3, 10.4, 10.8}
6 The deployment of low-carbon aviation and shipping fuels that support decarbonisation of the
7 transport sector could require changes to national and international governance structures
8 (medium confidence). Currently, the Paris Agreement does not specifically cover emissions from
9 international shipping and aviation. Instead, accounting for emissions from international transport in
10 the Nationally Determined Contributions is at the discretion of each country. While the ICAO and IMO
11 have established emissions reductions targets, only strategies to improve fuel efficiency and demand
12 reductions have been pursued, and there has been minimal commitment to new technologies. Some
13 literature suggests that explicitly including international shipping and aviation under the governance of
14 the Paris Agreement could spur stronger decarbonisation efforts in these segments. {10.5, 10.6, 10.7}
15 There are growing concerns about resource availability, labour rights, non-climate
16 environmental impacts, and costs of critical minerals needed for LIBs (medium confidence).
17 Emerging national strategies on critical minerals and the requirements from major vehicle
18 manufacturers are leading to new, more geographically diverse mines. The standardisation of battery
19 modules and packaging within and across vehicle platforms, as well as increased focus on design for
20 recyclability are important. Given the high degree of potential recyclability of LIBs, a nearly closed-
21 loop system in the future could mitigate concerns about critical mineral issues (medium confidence).
22 {10.3, 10.8}
23 Legislated climate strategies are emerging at all levels of government, and, together with pledges
24 for personal choices, could spur the deployment of demand and supply-side transport mitigation
25 strategies (medium confidence). At the local level, legislation can support local transport plans that
26 include commitments or pledges from local institutions to encourage behaviour change by adopting an
27 organisational culture that motivates sustainable behaviour with inputs from the creative arts. Such
28 institution-led mechanisms could include bike-to-work campaigns, free transport passes, parking
29 charges, or eliminating car benefits. Community-based solutions like solar sharing, community
30 charging, and mobility as a service can generate new opportunities to facilitate low-carbon transport
31 futures. At the regional and national levels, legislation can include vehicle and fuel efficiency standards,
32 R&D support, and large-scale investments in low-carbon transport infrastructure. {10.8, Chapter 15}
33
34
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1 10.1 Introduction and overview
2 This chapter examines the transport sector’s role in climate change mitigation. It appraises the transport
3 system’s interactions beyond the technology of vehicles and fuels to include the full life cycle analysis
4 of mitigation options, a review of enabling conditions, and metrics that can facilitate advancing
5 transport decarbonisation goals. The chapter assesses developments in the systems of land-based
6 transport and introduces, as a new feature since AR5, two separate sections focusing on the trends and
7 challenges in aviation and shipping. The chapter assesses the future trajectories emerging from global,
8 energy, and national scenarios and concludes with a discussion on enabling conditions for
9 transformative change in the sector.
10 This section (10.1) discusses how transport relates to virtually all the Sustainable Development Goals
11 (SDGs), the trends and drivers making transport a big contributor in greenhouse gas (GHG) emissions,
12 the impacts climate change is having on transport that can be addressed as part of mitigation, and the
13 overview of emerging transport disruptions with potential to shape a low carbon transport pathway.
14
15 10.1.1 Transport and the sustainable development goals
16 The adoption of the 2030 Agenda for Sustainable Development by the United Nations (UN) has
17 renewed international efforts to pursue and accurately measure global actions towards sustainable
18 development (United Nations 2015). The 17 SDGs set out the overall goals that are further specified by
19 169 targets and 232 SDG indicators, many of which relate to transport (United Nations 2017; Lisowski
20 et al. 2020). A sustainable transport system provides safe, inclusive, affordable, and clean passenger
21 and freight mobility for current and future generations (Williams 2017; Litman 2021) so transport is
22 particularly linked to SDGs 3, 7, 8, 9, 11, 12, and 13 (Move Humanity 2018; WBA 2019; SLoCaT
23 2019; Yin 2019; IRP 2019). Table 10.1 summarises transport-related topics for these SDGs and
24 corresponding research. Section 17.3.3.7 (in Chapter 17) also provides a cross sectoral overview of
25 synergies and trade-offs between climate change mitigation and the SDGs.
26
27 Table 10.1 Main transport-related SDGs
Transport-related topics (Low carbon Transport; Active transport;
Sustainable Development Goals: Synergies and trade-offs
Advances in vehicle technology; Improved public transport system
Basic human Earth preconditions Sustainable resource Social and economic Universal values
needs use development
Electric vehicles.
- Lower air - Reduction of GHG • - Share of renewable - Role of transport for - Gender equality in
pollution emissions along the energy use. economic and human transport.
contributes to entire value chain, e.g. development.
- Energy efficiency of
positive health Well-to-Wheel (WTW).
vehicles.
outcomes. - Reduced
- Decarbonised public Inequalities.
- Further development transport rather than
- Clean and affordable
- Energy access addressing minor GHG private vehicle use.
energy off-grid.
can contribute emissions and pollutants. - Enables access to
to poverty quality education.
alleviation.
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- Transport Oriented to - Reduce material - Transport Oriented to - Partnership for the
Sustainable Development consumption during Sustainable goals.
- Transport
(TOD). production, life cycle Development (TOD).
planning a
analysis of vehicles and
major player in •
their operations
reducing
poverty in
- Circular economy •
including entire value - Sustainable transport
principle applied to chains. infrastructure and
cities.
transport. systems for cities and
rural areas.
- Close loop carbon and
• - Access to
nutrient cycle linked to - Affordability of
healthcare mobility services, this
circular economy.
can also be covered
• Diseases from air
under "universal
pollution.
access" to public
- Injuries and transport.
deaths from
traffic
accidents. - Accessibility vs.
mobility: Mobility to
opportunities;
- Reduced Transport equity;
stress level Development as
from driving. freedom.
- Links between - Positive economic
active transport growth (employment)
and good health outcomes due to
with positive resource efficiency and
effects of lower productive
walking and energy cost.
cycling.
- Role of transport
- Improving provision in accessing
road work, reconfiguration
accessibility to of social norms, as
disabled users. working from home.
- Reduce time - Transport
spent on manufacturers as key
transport/mobili employers changing
ty. role of transport-related
labour due to platform
economy, and
innovations in
autonomous vehicles.
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(Grant et al. (Farzaneh et al. 2019); (SLoCaT 2019); see (Bruun and Givoni (Hernandez 2018;
2016; Haines et see particularly following particularly following 2015; Pojani and Stead Prati 2018; Levin
al. 2017; Cheng chapters. chapters. 2015; Hensher 2017; and Faith-Ell 2019;
et al. 2018; ATAG 2018; Vecchio et al. 2020)
Nieuwenhuijsen Grzelakowski 2018;
2018; Smith et Weiss et al. 2018;
References
al. 2018; Sofiev Brussel et al. 2019;
et al. 2018; Gota et al. 2019;
Peden and Mohammadi et al.
Puvanachandra 2019; Peden and
2019; King and Puvanachandra 2019;
Krizek 2020; SLoCaT 2019; Xu et al.
Macmillan et al. 2019)
2020)
1 10.1.2 Trends, drivers and the critical role of transport in GHG growth
2 The transport sector directly emitted around 8.9 Gt Carbon dioxide equivalent (CO2eq) in 2019, up from
3 5.1 Gt CO2eq in 1990 (Figure 10.1). Global transport was the fourth largest source of GHG emissions
4 in 2019 following the power, industry, and the Agriculture, Forestry and Land Use (AFOLU) sectors.
5 In absolute terms, the transport sector accounts for roughly 15% of total greenhouse gas (GHG)
6 emissions and about 23% of global energy-related CO2 emissions (IEA 2020a). Transport GHG
7 emissions have increased fast over the last two decades, and since 2010, the sector’s emissions have
8 increased faster than for any other end-use sector, averaging +1.8% annual growth (see Section 10.7).
9 Addressing emissions from transport is crucial for GHG mitigation strategies across many countries, as
10 the sector represents the largest energy consuming sector in 40% of countries worldwide. In most
11 remaining countries, transport is the second largest energy-consuming sector, reflecting different levels
12 of urbanisation and land use patterns, speed of demographic changes and socio-economic development
13 (IEA 2012; Hasan et al. 2019; Xie et al. 2019; Gota et al. 2019).
14
15
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1
2 Figure 10.1 Global and regional transport GHG emissions trends. Indirect emissions from electricity and
3 heat consumed in transport are shown in panel (a) and are primarily linked to the electrification of rail
4 systems. These indirect emissions do not include the full life cycle emissions of transportation systems
5 (e.g., vehicle manufacturing and infrastructure), which are assessed in section 10.4. International aviation
6 and shipping are included in panel (a), but excluded from panel (b). Indirect emissions from fuel
7 production, vehicle manufacturing and infrastructure construction are not included in the sector total.
8 Source: Adapted from (Lamb et al. 2021) using data from (Minx et al. 2021).
9
10 As of 2019, the largest source of transport emissions is the movement of passengers and freight in road
11 transport (6.1 Gt CO2eq, 69% of the sector’s total). International shipping is the second largest emission
12 source, contributing 0.8 Gt CO2eq (9% of the sector’s total), and international aviation is third with 0.6
13 Gt CO2eq (7% of the sector’s total). All other transport emissions sources, including rail, have been
14 relatively trivial in comparison, totalling 1.4 Gt CO2eq in 2019. Between 2010-2019, international
15 aviation had one of the fastest growing GHG emissions among all segments (+3.4% per year), while
16 road transport remained one of the fastest growing (+1.7% per year) among all global energy using
17 sectors. Note that the COVID-19-induced economic lockdowns implemented since 2020 have had a
18 very substantial impact on transport emissions – higher than any other sector (see chapter 2).
19 Preliminary estimates from Crippa et al. (2021) suggest that global transport CO2 emissions declined to
20 7.6 GtCO2 in 2020, a reduction of 11.6% compared to 2019 (Crippa et al. 2021; Minx et al. 2021).
21 These lockdowns affected all transport segments, and particularly international aviation (estimated -
22 45% reduction in 2020 global CO2 emissions), road transport (-10%), and domestic aviation (-9.3%).
23 By comparison, aggregate CO2 emissions across all sectors are estimated to have declined by 5.1% as
24 a result of the COVID-19 pandemic (Chapter 2, section 2.2.2).
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1 Growth in transport-related GHG emissions has taken place across most world regions (see Figure
2 10.1, panel b). Between 1990 and 2019, growth in emissions was relatively slow in Europe, Asia
3 Pacific, Eurasia, and North America while it was unprecedently fast in other regions. Driven by
4 economic and population growth, the annual growth rates in East Asia, South Asia, South East Asia,
5 and Africa were 6.1%, 5.2%, 4.7%, and 4.1%, respectively. Latin America and the Middle East have
6 seen somewhat slower growth in transport-related GHG emission (annual growth rates of 2.4% and
7 3.3%, respectively) (ITF 2019; Minx et al. 2021). Section 10.7 provides a more detailed
8 comparison of global transport emissions trends with those from regional and sub-sectoral
9 studies.
10 The rapid growth in global transport emissions is primarily a result of the fast growth in global transport
11 activity levels, which grew by 73% between 2000 and 2018. Passenger and freight activity growth have
12 outpaced energy efficiency and fuel economy improvements in this period (ITF 2019) . The global
13 increase in passenger travel activities has taken place almost entirely in non-OECD countries, often
14 starting from low motorization rates (SLoCaT 2018a). Passenger cars, two-and-three wheelers, and mini
15 buses contribute about 75% of passenger transport-related CO2 emissions, while collective transport
16 services (bus and railways) generates about 7% of the passenger transport-related CO2 emissions despite
17 covering a fifth of passenger transport globally (Rodrigue 2017; Halim et al. 2018; Sheng et al. 2018;
18 SLoCaT 2018a; Gota et al. 2019). While alternative lighter powertrains have great potential for
19 mitigating GHG emissions from cars, the trend has been towards increasing vehicle size and engine
20 power within all vehicle size classes, driven by consumer preferences towards larger sport utility
21 vehicles (SUVs) (IEA 2020a). On a global scale, SUV sales have been constantly growing in the last
22 decade, with 40% of the vehicles sold in 2019 being SUVs (IEA 2020a) – see Section 10.4, Box 10.3.
23 Indirect emissions from electricity and heat shown in Figure 10.1 account for only a small fraction of
24 current emissions from the transport sector (2%) and are associated with electrification of certain modes
25 like rail or bus transport (Lamb et al. 2021). Increasing transport electrification will affect indirect
26 emissions, especially where carbon-intense electricity grids operate.
27 Global freight transport, measured in tonne-kilometres (tkm), grew by 68% between 2000 and 2015 and
28 is projected to grow 3.3 times by 2050 (ITF 2019). If unchecked, this growth will make decarbonisation
29 of freight transport very difficult (McKinnon 2018; ITF 2019). International trade and global supply
30 chains from industries frequently involving large geographical distances are responsible for the fast
31 increase of CO2 emissions from freight transport (Yeh et al. 2017; McKinnon 2018), which are growing
32 faster than emissions from passenger transport (Lamb et al. 2021). Heavy-duty vehicles (HDVs) make
33 a disproportionate contribution to air pollution, relative to their global numbers, because of their
34 substantial emissions of particulate matter and of black carbon with high short-term warming potentials
35 (Anenberg et al. 2019).
36 On-road passenger and freight vehicles dominate global transport-related CO2 emissions and offer the
37 largest mitigation potential (Taptich et al. 2016; Halim et al. 2018). This chapter examines a wide range
38 of possible transport emission reduction strategies. These strategies can be categorised under the
39 ‘Avoid- Shift-Improve’ (ASI) framework described in Chapter 5 (Taptich et al. 2016). Avoid strategies
40 reduce total vehicle-travel. They include compact communities and other policies that minimise travel
41 distances and promote efficient transport through pricing and demand management programs. Shift
42 strategies shift travel from higher-emitting to lower-emitting modes. These strategies include more
43 multimodal planning that improves active and collective transport modes, complete streets roadway
44 design, High Occupant Vehicle (HOV) priority strategies that favour shared mode, Mobility as a Service
45 (MaaS), and multimodal navigation and payment apps. Improve strategies reduce per-kilometre
46 emission rates. These strategies include hybrid and electric vehicle incentives, lower carbon and cleaner
47 fuels, high emitting vehicle scrappage programs, and efficient driving and anti-idling campaigns
48 (Lutsey and Sperling 2012; Gota et al. 2015). These topics are assessed within the rest of this chapter
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1 including how combinations of ASI with new technologies can potentially lead from incremental
2 interventions into low carbon transformative transport improvements that include social and equity
3 benefits (see section 10.8).
4
5 10.1.3 Climate adaptation on the transport sector
6 Climate change impacts such as extremely high temperatures, intense rainfall leading to flooding, more
7 intense winds and/or storms, and sea level rise can seriously impact transport infrastructure, operations,
8 and mobility for road, rail, shipping, and aviation. Studies since AR5 confirm that serious challenges to
9 all transport infrastructures are increasing, with consequent delays or derailing (Miao et al. 2018;
10 Moretti and Loprencipe 2018; Pérez-Morales et al. 2019; Palin et al. 2021). These impacts have been
11 increasingly documented but, according to (Forzieri et al. 2018), little is known about the risks of
12 multiple climate extremes on critical infrastructures at local to continental scales. All roads, bridges,
13 rail systems, and ports are likely to be affected to some extent. Flexible pavements are particularly
14 vulnerable to extreme high temperatures that can cause permanent deformation and crumbling of
15 asphalt (Underwood et al. 2017; Qiao et al. 2019). Rail systems are also vulnerable, with a variety of
16 hazards, both meteorological and non-meteorological, affecting railway asset lifetimes. Severe impacts
17 on railway infrastructure and operations can arise from the occurrence of temperatures below freezing,
18 excess precipitation, storms and wildfires (Thaduri et al. 2020; Palin et al. 2021) as are underground
19 transport systems (Forero-Ortiz et al. 2020).
20 Most countries are examining opportunities for combined mitigation-adaptation efforts, using the need
21 to mitigate climate change through transport-related GHG emissions reductions and pollutants as the
22 basis for adaptation action (Thornbush et al. 2013; Wang et al. 2020). For example, urban sprawl
23 indirectly affects climate processes, increasing emissions and vulnerability, which worsens the potential
24 to adapt (Congedo and Munafò 2014; Macchi and Tiepolo 2014). Hence, using a range of forms of
25 rapid transit as structuring elements for urban growth can mitigate climate change-related risks as well
26 as emissions, reducing impacts on new infrastructure, often in more vulnerable areas (Newman et al.
27 2017). Such changes are increasingly seen as having economic benefit (Ha et al. 2017), especially in
28 developing nations (Chang 2016; Monioudi et al. 2018).
29 Since AR5 there has been a growing awareness of the potential and actual impacts from global sea level
30 rise due to climate change on transport systems (Dawson et al. 2016; Rasmussen et al. 2018; IPCC
31 2019; Noland et al. 2019), particularly on port facilities (Stephenson et al. 2018; Yang et al. 2018b;
32 Pérez-Morales et al. 2019). Similarly, recent studies suggest changes in global jet streams could affect
33 the aviation sector (Staples et al. 2018; Becken and Shuker 2019), and extreme weather conditions can
34 affect runways (heat buckling) and aircraft lift. Combined, climate impacts on aviation could result in
35 payload restrictions and disruptions (Coffel et al. 2017; Monioudi et al. 2018). According to (Williams
36 2017), studies have indicated that the amount of moderate-or-greater clear-air turbulence on
37 transatlantic flight routes in winter will increase significantly in the future as the climate changes. More
38 research is needed to fully understand climate induced risks to transportation systems.
39
40 10.1.4 Transport disruption and transformation
41 Available evidence suggests that transport-related CO2 emissions would need to be restricted to about
42 2 to 3 Gt in 2050 (1.5°C scenario-1.5DS, B2DS), or about 70 to 80% below 2015 levels, to meet the
43 goals set in the Paris Agreement. It also indicates that a balanced and inter-modal application of Avoid,
44 Shift, and Improve measures is capable of yielding an estimated reduction in transport emissions of
45 2.39 Gt of CO2-equivalent by 2030 and 5.74 Gt of CO2-equivalent by 2050 (IPCC 2018; Gota et al.
46 2019). Such a transformative decarbonisation of the global transport system requires, in addition to
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1 technological changes, a paradigm shift that ensures prioritisation of high-accessibility transport
2 solutions that minimise the amount of mobility required to meet people’s needs, and favours transit and
3 active transport modes (Lee and Handy 2018; SLoCaT 2021). These changes are sometimes called
4 disruptive as they are frequently surprising in how they accelerate through a technological system.
5 The assessment of transport innovations and their mitigation potentials is at the core of how this chapter
6 examines the possibilities for changing transport-related GHG trajectories. The transport technology
7 innovation literature analysed in this chapter emphasises how a mixture of mitigation technology
8 options and social changes are now converging and how, in combination, they may have potential to
9 accelerate trends toward a low carbon transport transition. Such changes are considered disruptive or
10 transformative (Sprei 2018). Of the current transport trends covered in the literature, this chapter focuses
11 on three key technology and policy areas: electro-mobility in land-based transport vehicles, new fuels
12 for ships and planes, and overall demand reductions and efficiency. These strategies are seen as being
13 necessary to integrate at all levels of governance and, in combination with the creation of fast, extensive,
14 and affordable multi-modal public transport networks, can help achieve multiple advantages in
15 accordance with SDGs
16 Electrification of passenger transport in light-duty vehicles (LDVs) is well underway as a commercial
17 process with socio-technical transformative potential and will be examined in detail in Sections 10.3
18 and 10.4. But the rapid mainstreaming of EV’s will still need enabling conditions for land transport to
19 achieve the shift away from petroleum fuels, as outlined in Chapter 3 and detailed in Section 10.8. The
20 other mitigation options reviewed in this chapter are so far only incremental and are less commercial,
21 especially shipping and aviation fuels, so stronger enabling conditions are likely, as detailed further in
22 Sections 10.5 to 10.8. The enabling conditions that would be needed for the development of an emerging
23 technological solution for such fuels are likely to be very different to electromobility, but nevertheless
24 they both will need demand and efficiency changes to ensure they are equitable and inclusive.
25 Section 10.2 sets out the transformation of transport through examining systemic changes that affect
26 demand for transport services and the efficiency of the system. Section 10.3 looks at the most promising
27 technological innovations in vehicles and fuels. The next three sections (10.4, 10.5, and 10.6) examine
28 mitigation options for land transport, aviation, and shipping. Section 10.7 describes the space of
29 solutions assessed in a range of integrated modelling and sectoral transport scenarios; Finally, Section
30 10.8 sets out what would be needed for the most transformative scenario that can manage to achieve
31 the broad goals set out in Chapter 3 and the transport goals set out in Section 10.7.
32
33 10.2 Systemic changes in the transport sector
34 Systemic change is the emergence of new organisational patterns that affect the structure of a system.
35 While much attention has been given to engine and fuel technologies to mitigate GHG emissions from
36 the transport sector, population dynamics, finance and economic systems, urban form, culture, and
37 policy also drive emissions from the sector. Thus, systemic change requires innovations in these
38 components. These systemic changes offer the opportunity to decouple transport emissions from
39 economic growth. In turn, such decoupling allows environmental improvements like reduced GHG
40 emissions without loss of economic activity (UNEP 2011, 2013; Newman et al. 2017; IPCC 2018).
41 There is evidence that suggests decoupling of transport emissions and economic growth is already
42 happening in developed and developing countries. Europe and China have shown the most dramatic
43 changes (Huizenga et al. 2015; Gao and Newman 2018; SLoCaT 2018b) and many cities are
44 demonstrating decoupling of transport-related emissions through new net zero urban economic activity
45 (Loo and Banister 2016; SLoCaT 2018a). A continued and accelerated decoupling of the growth of
46 transport-related GHG emissions from economic growth is crucial for meeting the SDGs outlined in
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1 Section 1. This section focuses on several overlapping components of systemic change in the transport
2 sector that affect the drivers of GHG emissions: Urban form, physical geography, and infrastructure;
3 behaviour and mode choice; and new demand concepts. Table 10.3, at the end of the section provides a
4 high-level summary of the effect of these systemic changes on emissions from the transport sector.
5
6 10.2.1 Urban form, physical geography, and transport infrastructure
7 The physical characteristics that make up built areas define the urban form. These physical
8 characteristics include the shape, size, density, and configuration of the human settlements. Urban form
9 is intrinsically coupled with the infrastructure that allows human settlements to operate. In the context
10 of the transport sector, urban form and urban infrastructure influence the time and cost of travel, which,
11 in turn, drive travel demand and modal choice (Marchetti and Ausubel 2004; Newman and Kenworthy
12 2015).
13 Throughout history, three main urban fabrics have developed, each with different effects on transport
14 patterns based on a fixed travel time budget of around one hour (Newman et al. 2016). The high-density
15 urban fabric developed over the past several millennia favoured walking and active transport for only a
16 few kilometres (kms). In the mid-19th century, urban settlements developed a medium density fabric
17 that favoured trains and trams traveling over 10 to 30-km corridors. Finally, since the mid-20th century,
18 urban form has favoured automobile travel, enabling mass movement between 50-60 kms. Table 10.2
19 describes the effect of these urban fabrics on GHG emissions and other well-being indicators.
20
21 Table 10.2 The systemic effect of city form and transport emissions
Annual Transport Emissions and Co- Walking Urban Transit Urban Automobile Urban
Benefits Fabric Fabric Fabric
Transport GHG 4 t/person 6 t/person 8 t/person
Health benefits from walkability High Medium Low
Equity of locational accessibility High Medium Low
Construction and household waste 0.87 t/person 1.13 t/person 1.59 t/person
Water consumption 35 kl/person 42 kl/person 70 kl/person
Land 133 m2/person 214 m2/person 547 m2/person
Economics of infrastructure and
High Medium Low
transport operations
22 Source: (Newman et al. 2016; Thomson and Newman 2018; Seto et al. 2021)
23 Since AR5, urban design has increasingly been seen as a major way to influence the GHG emissions
24 from urban transport systems. Indeed, research suggests that implementing urban form changes could
25 reduce GHG emissions from urban transport by 25% in 2050, compared with a business-as-usual
26 scenario (Creutzig et al. 2015b; Creutzig 2016). Researchers have identified a variety of variables to
27 study the relationship between urban form and transport-related GHG emissions. Three notable aspects
28 summarise these relationships: urban space utilisation, urban spatial form, and urban transportation
29 infrastructure (Tian et al. 2020). Urban density (population or employment density) and land use mix
30 define the urban space utilisation. Increases in urban density and mixed function can effectively reduce
31 per capita car use by reducing the number of trips and shortening travel distances. Similarly, the
32 continuity of urban space and the dispersion of centres reduces travel distances (Tian et al. 2020),
33 though such changes are rarely achieved without shifting transport infrastructure investments away
34 from road capacity increases (Newman and Kenworthy 2015; McIntosh et al. 2017) For example,
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1 increased investment in public transport coverage, optimal transfer plans, shorter transit travel time, and
2 improved transit travel efficiency make public transit more attractive (Heinen et al. 2017; Nugroho et
3 al. 2018a,b) and hence increase density and land values (Sharma and Newman 2020). Similarly,
4 forgoing the development of major roads for the development of pedestrian and bike pathways enhances
5 the attractiveness of active transport modes (Zahabi et al. 2016; Keall et al. 2018; Tian et al. 2020).
6 Ultimately, infrastructure investments influence the structural dependence on cars, which in turn
7 influence the lock-in or path dependency of transport options with their greenhouse emissions (Newman
8 et al. 2015b; Grieco and Urry 2016). The 21st century saw a new trend to reach peak car use in some
9 countries as a result of a revival in walking and transit use (Grieco and Urry 2016; Newman et al. 2017;
10 Gota et al. 2019). While some cities continue on a trend towards reaching peak car use on a per-capita
11 basis, for example Shanghai and Beijing (Gao and Newman 2020), there is a need for increased
12 investments in urban form strategies that can continue to reduce car-dependency around the world.
13
14 START CROSS-CHAPTER BOX HERE
15
16 Cross-Chapter Box 7 Urban Form: Simultaneously reducing urban transport emissions, avoiding
17 infrastructure lock-in, and providing accessible services
18 Authors: Felix Creutzig (Germany), Karen Seto (the United States of America), Peter Newman
19 (Australia)
20 Urban transport is responsible for about 8% of global CO2 emissions or 3 Gt CO2 per year (see Chapters
21 5 and 8). In contrast to energy supply technologies, urban transport directly interacts with mobility
22 lifestyles (see Section 5.4). Similarly, non-GHG emission externalities, such as congestion, air
23 pollution, noise, and safety, directly affect urban quality of life, and result in considerable welfare
24 losses. Low-carbon, highly accessible urban design is not only a major mitigation option, it also
25 provides for more inclusive city services related to wellbeing (Chapter 5, Sections 5.1 and 5.2). Urban
26 planning and design of cities for people are central to realise emission reductions without relying simply
27 on technologies, though the modes of transport favoured will influence the ability to overcome the lock-
28 in around automobile use (Gehl 2010; Creutzig et al. 2015b).
29 Where lock-in has occurred, other strategies may alleviate the GHG emissions burden. Urban planning
30 still plays a key role in recreating local hubs. Available land can be used to build rail-based transit,
31 made financially viable by profiting from land value captured around stations (Ratner and Goetz 2013).
32 Shared or pooled mobility can offer flexible on-demand mobility solutions that are efficient also in
33 suburbs and for integrating with longer commuting trips (ITF 2017).
34 Global emission trajectories of urban transport will be decided in rapidly urbanising Asia and Africa.
35 Urban transport-related GHG emissions are driven by incomes and car ownership but there is
36 considerable variation amongst cities with similar income and car ownership levels (Newman and
37 Kenworthy 2015). While electrification is a key strategy to decarbonise urban transport, urban
38 infrastructures can make a difference of up to a factor of 10 in energy use and induced GHG emissions
39 (Erdogan 2020). Ongoing urbanisation patterns risk future lock-in of induced demand on GHG
40 emissions, constraining lifestyles to energy intensive and high CO2-related technologies (See Section
41 5.4; 8.2.3; 10.2.1; (Erickson and Tempest 2015; Seto et al. 2016). Instead, climate solutions can be
42 locked into urban policies and infrastructures (Ürge-Vorsatz et al. 2018) especially through the
43 enhancement of the walking and transit urban fabric. Avoiding urban sprawl, associated with several
44 externalities (Dieleman and Wegener 2004), is a necessary decarbonisation condition, and can be
45 guided macro-economically by increasing fuel prices and marginal costs of motorised transport
46 (Creutzig 2014). Resulting urban forms not only reduce GHG emission from transport but also from
47 buildings, as greater compactness results in reduced thermal loss (Borck and Brueckner 2018). Health
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1 benefits from reduced car dependence are an increasing element driving this policy agenda (Section
2 10.8; (Speck 2018)).
3 Low-carbon highly accessible urban design is not only a major mitigation option, it also provides for
4 more inclusive city services related to wellbeing (Chapter 5, Sections 5.1 and 5.2). Solutions involve
5 planning cities around walkable sub-centres, where multiple destinations, such as shopping, jobs, leisure
6 activities, and others, can be accessed within a 10 minute walk or bicycle ride (Newman and Kenworthy
7 2006). Overall, the mitigation potential of urban planning is about 25% in 2050 compared with a
8 business as usual scenario (Creutzig et al. 2015a,b). Much higher levels of decarbonisation can be
9 achieved if cities take on a regenerative development approach and act as geo-engineering systems on
10 the atmosphere (Thomson and Newman 2016).
11 END CROSS-CHAPTER BOX HERE
12
13 10.2.2 Behaviour and mode choice
14 Behaviour continues to be a major source of interest in the decarbonisation of transport as it directly
15 addresses demand. Behaviour is about people’s actions based on their preferences. Chapter 5 described
16 an ‘Avoid, Shift, Improve’ process for demand-side changes that affect sectoral emissions. This section
17 discusses some of the drivers of behaviour related to the transport sector and how they link to this
18 ‘Avoid, Shift, Improve’ process.
19 Avoid - the effect of prices and income on demand: Research has shown that household income and
20 price have a strong influence on people’s preferences for transport services (Bakhat et al. 2017; Palmer
21 et al. 2018). The relationship between income and demand is defined by the income elasticity of
22 demand. For example, research suggests that in China, older and wealthier populations continued to
23 show a preference for car travel (Yang et al. 2019) while younger and low-income travellers sought
24 variety in transport modes (Song et al. 2018). Similarly, (Bergantino et al. 2018b) evaluated the income
25 elasticity of transport by mode in the UK. They found that the income elasticity for private cars is 0.714,
26 while the income elasticities of rail and bus use are 3.253 (The greater elasticity the greater the demand
27 will grow or decline, depending on income). Research has also shown a positive relationship between
28 income and demand for air travel, with income elasticities of air travel demand being positive and as
29 large as 2 (Gallet and Doucouliagos 2014; Valdes 2015; Hakim and Merkert 2016, 2019; Hanson et al.
30 2022). A survey in 98 Indian cities also showed income as the main factor influencing travel demand
31 (Ahmad and de Oliveira 2016). Thus, as incomes and wealth across the globe rise, demand for travel is
32 likely to increase as well.
33 The price elasticity of demand measures changes in demand as a result of changes in the prices of the
34 services. In a meta-analysis of the price elasticity of energy demand, (Labandeira et al. 2017) report the
35 average long-term price elasticity of demand for gasoline and diesel to be -0.773 and -0.443,
36 respectively. That is, demand will decline with increasing prices. A similar analysis of long-term data
37 in the United States (US), the United Kingdom (UK), Sweden, Australia, and Germany reports the
38 gasoline price elasticity of demand for car travel (as measured through vehicle-kilometre -vkm- per
39 capita) ranges between -0.1 and -0.4 (Bastian et al. 2016). For rail travel, the price elasticity of demand
40 has been found to range between -1.05 and -1.1 (Zeng et al. 2021). Similarly, price elasticities for air
41 travel range from -0.53 to -1.91 depending on various factors such as purpose of travel (business or
42 leisure), season, and month and day of departure (Morlotti et al. 2017). The price elasticities of demand
43 suggest that car use is inelastic to prices, while train use is relatively inelastic to the cost of using rail.
44 Conversely, consumers seem to be more responsive to the cost of flying, so that strategies that increase
45 the cost of flying are likely to contribute to some avoidance of aviation-related GHG emissions.
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1 While the literature continues to show that time, cost, and income dominate people’s travel choices
2 (Ahmad and de Oliveira 2016; Capurso et al. 2019; He et al. 2020), there is also evidence of a role for
3 personal values, and environmental values in particular, shaping choices within these structural
4 limitations (Bouman and Steg 2019). For example, individuals are more likely to drive less when they
5 care about the environment (De Groot et al. 2008; Abrahamse et al. 2009; Jakovcevic and Steg 2013;
6 Hiratsuka et al. 2018; Ünal et al. 2019). Moreover, emotional and symbolic factors affect the level of
7 car use (Steg 2005). Differences in behaviour may also result due to differences in gender, age, norms,
8 values, and social status. For example, women have been shown to be more sensitive to parking pricing
9 than men (Simićević et al. 2020).
10 Finally, structural shocks, such as a financial crisis, a pandemic, or the impacts of climate change could
11 affect the price and income elasticities of demand for transport services (van Ruijven et al. 2019).
12 COVID-19 lock-downs reduced travel demand by 19% (aviation by 32%) and some of the patterns that
13 have emerged from the lockdowns could permanently change the elasticity of demand for transport
14 (Tirachini and Cats 2020; Hendrickson and Rilett 2020; Newman 2020a; SLoCaT 2021; Hanson et al.
15 2022). In particular, the COVID-19 lock-downs have spurred two major trends: electronic
16 communications replacing many work and personal travel requirements; and, revitalised local active
17 transport and e-micro-mobility (Newman 2020a; SLoCaT 2021). The permanence of these changes
18 post- COVID-19 is uncertain but possible ((Early and Newman 2021); see Box on COVID-19, chapter
19 1). However, these changes will require growth of infrastructure for better ICT bandwidths in
20 developing countries, and better provision for micro-mobility in all cities.
21 Shift - Mode choice for urban and intercity transport: Shifting demand patterns (as opposed to
22 avoiding demand) can be particularly important in decarbonising the transport sector. As a result, the
23 cross-elasticity of demand across transport modes is of particular interest for understanding the
24 opportunities for modal shift. The cross-elasticity represents the demand effect on mode i (e.g. bus)
25 when an attribute of mode j (e.g. rail) changes marginally. Studies on the cross-elasticities of mode
26 choice for urban travel suggest that the cross-elasticity for car demand is low, but the cross-elasticities
27 of walking, bus, and rail with respect to cars are relatively large (Fearnley et al. 2017; Wardman et al.
28 2018). In practice, these cross-elasticities suggest that car drivers are not very responsive to increased
29 prices for public transit, but transit users are responsive to reductions in the cost of driving. When
30 looking at the cross-elasticities of public transit options (bus vs. metro vs. rail), research suggests that
31 consumers are particularly sensitive to in-vehicle and waiting time when choosing public transit modes
32 (Fearnley et al. 2018). These general results provide additional evidence that increasing the use of active
33 and public transport requires interventions that make car use more expensive while making public
34 transit more convenient (e.g. with smart apps that explain the exact time for transit arrival, see Box
35 10.1).
36 The literature on mode competition for intercity travel reveals that while cost of travel is a significant
37 factor (Zhang et al. 2017), sensitivity decreases with increasing income as well as when the cost of the
38 trip was paid by someone else (Capurso et al. 2019). Some research suggests little competition between
39 bus and air travel but the cross-elasticity between air and rail suggest strong interactions (Wardman et
40 al. 2018). Price reduction strategies such as discounted rail fares could enhance the switch from air
41 travel to high-speed rail. Both air fares and flight frequency impact high speed rail (HSR) usage (Zhang
42 et al. 2019b). Airline companies reduce fares on routes that are directly competing with HSR
43 (Bergantino et al. 2018a) and charge high fares on non-HSR routes (Xia and Zhang 2016). On the
44 Rome-Milan route, better frequency and connections, and low costs of HSR resulting from competition
45 between HSR companies has significantly reduced air travel and shares of buses and cars (Desmaris
46 and Croccolo 2018).
47 Finally, and as noted in Chapter 5, recent research shows that individual, social, and infrastructure
48 factors also affect people’s mode choices. For example, perceptions about common travel behaviour
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1 (what people perceive to be “normal” behaviour) influences their travel mode choice. The research
2 suggests that well-informed individuals whose personal norms match low-carbon objectives, and who
3 believe they have control over their decisions are most motivated to shift mode. Nonetheless, such
4 individual and social norms can only marginally influence mode choice unless infrastructure factors
5 can enable reasonable time and cost savings (Convery and Williams 2019; Javaid et al. 2020; Feng et
6 al. 2020; Wang et al. 2021).
7 Improve – consumer preferences for improved and alternative vehicles: While reductions in demand
8 for travel and changes in the mode choice can contribute to reducing GHG emissions from the transport
9 sector, cars are likely to continue to play a prominent role. As a result, improving the performance of
10 cars will be crucial for the decarbonisation of the transport sector. Sections 10.3 and 10.4 describe the
11 technological options available for reduced CO2 emissions from vehicles. The effectiveness in
12 deploying such technologies will partly depend on consumer preferences and their effect on adoption
13 rates. Given the expanded availability of electric vehicles, there is also a growing body of work on the
14 drivers of vehicle choice. A survey in Nanjing found women had more diverse travel purposes than
15 men, resulting in a greater acceptance of electric bikes (Lin et al. 2017). Individuals are more likely to
16 adopt an electric vehicle (EV) when they think this adoption benefits the environment or implies a
17 positive personal attribute (Noppers et al. 2014, 2015; Haustein and Jensen 2018). Other work suggests
18 that people’s preference for EVs depends upon vehicle attributes, infrastructure availability, and
19 policies that promote EV adoption, specifically, purchasing and operating costs, driving range, charging
20 duration, vehicle performance, and brand diversity (Liao et al. 2016). Behaviour change to enable
21 transport transformations will need to make the most of these factors whilst also working on the more
22 structural issues of time, space, and cost.
23
24 10.2.3 New demand concepts
25 Structural and behavioural choices that drive transport-related GHG emissions, such as time and cost
26 based on geography of freight and urban fabric, are likely to continue to be major factors. But there is
27 also a variation within each structural choice that is based around personal demand factors related to
28 values that indirectly change choices in transport. Chapter 5 identified three megatrends that affect
29 demand for services, including circular economy, the shared economy, and digitalisation. These three
30 megatrends can have specific effect on transport emissions, as described below.
31 Circular Economy: The problem of resources and their environmental impacts is driving the move to
32 a circular economy (Bleischwitz et al. 2017). Circular economy principles include increased material
33 efficiency, re-using or extending product lifetimes, recycling, and green logistics. Dematerialisation,
34 the reduction in the quantity of the materials used in the production of one unit of output, is a circular
35 economy principle that can affect the operations and emissions of the transport sector, as reductions in
36 the quantities of materials used reduces transport needs, while reductions in the weight of products
37 improves the efficiency of transporting them. Dematerialisation can occur through more efficient
38 production processes but also when a new product is developed to provide the same functionality as
39 multiple products. The best example of this trend is a smart phone, which provides the service of at
40 least 22 other former devices (Rivkin 2019). A move to declutter lifestyles can also drive
41 dematerialisation (Whitmarsh et al. 2017). Some potential for dematerialisation has been suggested due
42 to 3-D printing, which would also reduce transport emissions through localised production of product
43 components (d’Aveni 2015; UNCTAD 2018). There is evidence to suggest, however, that reductions
44 in material use resulting from more efficient product design or manufacturing are offset by increased
45 consumer demand (Kasulaitis et al. 2019). Whether or not dematerialisation can lead to reduction of
46 emissions from the transport sector is still an open questions that requires evaluating the entire product
47 ecosystem (Van Loon et al. 2014; Coroama et al. 2015; Kasulaitis et al. 2019).
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1 Shared Economy. Shared mobility is arguably the most rapidly growing and evolving sector of the
2 sharing economy and includes bike sharing, e-scooter sharing, car-sharing, and on-demand mobility
3 (Greenblatt and Shaheen 2015). The values of creating a more shared economy are related to both
4 reduced demand and greater efficiency, as well as the notion of community well-being associated with
5 the act of sharing instead of simply owning for oneself (Maginn et al. 2018; Sharp 2018). The literature
6 on shared mobility is expanding, but there is much uncertainty about the effect shared mobility will
7 have on transport demand and associated emissions (Nijland and Jordy 2017; ITF 2018a; Tikoudis et
8 al. 2021).
9 Asia represents the largest car-sharing region with 58% of worldwide membership and 43% of global
10 fleets deployed (Dhar et al. 2020). Europe accounts for 29% of worldwide members and 37% of shared
11 vehicle fleets (Shaheen et al. 2018). Ride-sourcing and carpooling systems are amongst the many new
12 entrants in the short-term shared mobility options. On-demand transport options complemented with
13 technology have enhanced the possibility of upscaling (Alonso-González et al. 2018). Car-sharing could
14 provide the same level of service as taxis, but taxis could be three times more expensive (Cuevas et al.
15 2016). The sharing economy, as an emerging economic-technological phenomenon (Kaplan and
16 Haenlein 2010), is likely to be a key driver of demand for transport of goods although data shows
17 increasing container movement due to online shopping (Suel and Polak 2018).
18 There is growing evidence that this more structured form of behavioural change through shared
19 economy practices, supported by a larger group than a single family, has a much greater potential to
20 save transport emissions, especially when complemented with decarbonised grid electricity (Greenblatt
21 and Shaheen 2015; Sharp 2018). Carpooling, for example, could result in an 11% reduction in vkm and
22 a 12% reduction in emissions, as carpooling requires less empty or non-productive passenger-
23 kilometres (pkm) (ITF 2020a,b). However, the use of local shared mobility systems such as on-demand
24 transport may create more transport emissions if there is an overall modal shift out of transit (ITF 2018a;
25 Schaller 2018). Similarly, some work suggests that commercial shared vehicle services such as Uber
26 and Lyft are leading to increased vehicle kms travelled (and associated GHG emissions) in part due to
27 deadheading (Schaller 2018; Tirachini and Gomez-Lobo 2020; Ward et al. 2021). Successful providers
28 compete by optimising personal comfort and convenience rather than enabling a sharing culture
29 (Eckhardt and Bardhi 2015), and concerns have been raised regarding the wider societal impacts of
30 these systems and for specific user groups such as older people (Fitt 2018; Marsden 2018). Concerns
31 have also been expressed over the financial viability of demand-responsive transport systems (Ryley et
32 al. 2014; Marsden 2018), how the mainstreaming of shared mobility systems can be institutionalised
33 equitably, and the operation and governance of existing systems that are only mode and operator-
34 focused (Akyelken et al. 2018; Jittrapirom et al. 2018; Pangbourne et al. 2020; Marsden 2018).
35 Digitalisation: In the context of the transport sector, digitalisation has enabled teleworking, which in
36 turn reduces travel demand. On the other hand, the prevalence of online shopping, enabled by the digital
37 economy, could have mixed effects on transport emissions (Le et al. 2021). For example, online
38 shopping could reduce vkm travelled but the move to expedited or rush delivery could mitigate some
39 benefits as they prevent consolidation of freight (Jaller and Pahwa 2020).
40 Digitalisation could also lead to systemic changes by enabling smart mobility. The smart mobility
41 paradigm refers to the process and practices of assimilation of ICTs and other sophisticated hi-
42 technology innovations into transport (Noy and Givoni 2018). Smart mobility can be used to influence
43 transport demand and efficiency (Benevolo et al. 2016). The synergies of emerging technologies (ICT,
44 IOT, Big Data) and shared economy could overcome some of the challenges facing the adoption of
45 emerging technologies (Marletto 2014; Chen et al. 2016; Weiss et al. 2018; Taiebat and Xu 2019) and
46 enable the expected large growth in emerging cities to be more sustainable (Docherty et al. 2018).
47 However, ICT, in particular IoT, could also cause more global energy demand (Hittinger and Jaramillo
48 2019). Box 10.1 summarises the main smart technologies being adopted rapidly by cities across the
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1 world and their use in transport. There is a growing body of literature about the effect of smart
2 technology (including sensors guiding vehicles) on the demand for transport services. Smart
3 technologies can improve competitiveness of transit and active transport over personal vehicle use by
4 combining the introduction of new electro-mobility that improves time and cost along with behaviour
5 change factors (Henrik et al. 2017; SLoCaT 2018a,b, 2021). However, it is unclear what will be the net
6 effect of smart technology on the GHG emissions from the transport sector (Debnath et al. 2014; Lenz
7 and Heinrichs 2017).
8
9 START BOX HERE
10 Box 10.1 Smart city technologies and transport
11 Information and Communication Technology (ICT): ICT is at the core of Smart Mobility and will
12 provide the avenue for data to be collected and shared across the mobility system. The use of ICT can
13 help cities by providing real-time information on mobility options that can inform private vehicles along
14 with transit users or those using bikes, or who are walking. ICT can help with ticketing and payment
15 for transit or for road user charges (Tafidis et al. 2017; Gössling 2018) when combined with other
16 technologies such as Blockchain (Hargroves et al. 2020).
17 Internet of Things (IoT) Sensors: Sensors can be used to collect data to improve road safety, improve
18 fuel efficiency of vehicles, and reduce CO2 emissions (Kubba and Jiang 2014; Kavitha et al. 2018).
19 Sensors can also provide data to digitally simulate transport planning options, inform the greater
20 utilisation of existing infrastructure and modal interconnections, and significantly improve disaster and
21 emergency responses (Hargroves et al. 2017). In particular, IoT sensors can be used to inform the
22 operation of fast-moving Trackless Tram and its associated last mile connectivity shuttles as part of a
23 transit activated corridor (Newman et al. 2019, 2021).
24 Mobility as a Service (MaaS): New, app-based mobility platforms will allow for the integration of
25 different transport modes (such as last mile travel, shared transit, and even micro-transit such as scooters
26 or bikes) into easy-to-use platforms. By integrating these modes, users will be able to navigate from A
27 to B to C based on which modes are most efficient with the necessary bookings and payments being
28 made through the one service. With smart city planning, these platforms can steer users towards shared
29 and rapid-transit (which should be the centre-piece of these systems), rather than encourage more people
30 to opt for the perceived convenience of booking a single-passenger ride (Becker et al. 2020). In low
31 density car-dependent cities, however, MaaS services such as the use of electric scooters/bikes are less
32 effective as the distances are too long and they do not enable the easy sharing that can happen in dense
33 station precincts (Jittrapirom et al. 2017).
34 Artificial Intelligence (AI) and Big Data Analytics: The rapidly growing level of technology enablement
35 of vehicles and urban infrastructure, combined with the growing ability to analyse larger and larger data
36 sets, presents a significant opportunity for transport planning, design, and operation in the future. These
37 technologies are used together to enable decisions about what kind of transport planning is used down
38 particular corridors. Options such as predictive congestion management of roads and freeways,
39 simulating planning options, and advanced shared transit scheduling can provide value to new and
40 existing transit systems (Toole et al. 2015; Anda et al. 2017; Hargroves et al. 2017).
41 Blockchain or Distributed Ledger Technology: Blockchain Technology provides a non-hackable
42 database that can be programmed to enable shared services like a local, solar microgrid where both solar
43 and shared electric vehicles can be managed (Green and Newman 2017). Blockchain can be used for
44 many transport-related applications including being the basis of MaaS or any local shared mobility
45 service as it facilitates shared activity without intermediary controls. Other applications include verified
46 vehicle ownership documentation, establishing identification, real-time road user pricing, congestion
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1 zone charging, vehicle generated collision information, collection of tolls and charges, enhanced freight
2 tracking and authenticity, and automated car parking and payments (Hargroves et al. 2020). This type
3 of functionality will be particularly valuable for urban regeneration along a transit-activated corridor
4 where it can be used for managing shared solar in and around station precincts as well as managing
5 shared vehicles linked to the whole transport system (Newman et al. 2021). This technology can also
6 be used for road user charging along any corridor and by businesses accessing any services and in
7 managing freight (Carter and Koh 2018; Nguyen et al. 2019; Sedlmeir et al. 2020; Hargroves et al.
8 2020).
9 END BOX HERE
10
11 Autonomous vehicles are the other emerging transport technology that have the potential to
12 significantly improve ride quality and safety. Planes and high-speed trains are already largely
13 autonomous as they are guided in all their movements, especially coming into stations and airports,
14 although that does not necessarily mean they are driverless. Automation is also being used in new on-
15 road transit systems like Trackless Trams (Ndlovu and Newman 2020)). Private vehicles are being fitted
16 with more and more levels of autonomy and many are being trialled as ‘driverless’ in cities (Aria et al.
17 2016; Skeete 2018). If autonomous systems can be used to help on-road transit become more time and
18 cost competitive with cars, then the kind of transformative and disruptive changes needed to assist
19 decarbonisation of transport become more feasible (Bösch et al. 2018; Kassens-Noor et al. 2020; Abe
20 2021). Similarly, vehicle automation could improve vehicle efficiency and reduce congestion, which
21 would in turn reduce emissions (Vahidi and Sciarretta 2018; Massar et al. 2021). On the other hand, if
22 autonomous cars make driving more convenient, they could reduce demand for transit (Auld et al. 2017;
23 Sonnleitner et al. 2021). Paradoxically, autonomous cars could provide access to marginal groups such
24 as the elderly, people with disabilities, and those who cannot drive, which could in turn increase travel
25 demand (as measured by pkm) (Harper et al. 2016).
26 Heavy haulage trucks in the mining industry are already autonomous (Gaber et al. 2021) and automation
27 of long-haul trucks may happen sooner than automation of LDVs (Hancock et al. 2019). Autonomous
28 trucks may facilitate route, speed optimisation, and reduced fuel use, which can in turn reduce emissions
29 (Nasri et al. 2018; Paddeu and Denby 2021). There is growing interest in using drones for package
30 delivery. Drones could have lower impacts than ground-based delivery and, if deployed carefully,
31 drones could reduce energy use and GHG emissions from freight transport (Stolaroff et al. 2018).
32 Overall, some commentators are optimistic that smart and autonomous technologies can transform the
33 GHG from the transport sector (Seba 2014; Rivkin 2019; Sedlmeir et al. 2020). Others are more
34 sanguine unless policy interventions can enable the technologies to be used for purposes that include
35 zero carbon and the SDGs (Faisal et al. 2019; Hancock et al. 2019).
36
37 10.2.4 Overall perspectives on systemic change
38 The interactions between systemic factors set out here and technology factors discussed in much more
39 detail in the next sections, show that there is always going to be a need to integrate both approaches.
40 Good technology that has the potential to transform transport will not be used unless it fulfils broad
41 mobility and accessibility objectives related to time, cost, and well-being. Chapter 5 has set out three
42 transport transformations based on demand-side factors with highly transformative potential. Table 10.3
43 provides a summary of these systemic changes and their likely impact on GHG emissions. Note that the
44 quantitative estimates provided in the table may not be additive and the combined effect of
45 these strategies on GHG emissions from the transport sector require additional analysis.
46 Table 10.3 Components of systemic change and their impacts on the transport sector
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Systemic Change Mechanisms through which it affects emissions in transport sector and
likely impact on emissions
Changes in urban form Denser, more compact polycentric cities with mixed land use patterns can reduce
the distance between where people live, work, and pursue leisure activities,
which can reduce travel demand. Case studies suggest that these changes in
urban form could reduce transport-related GHG emissions between 4-25%,
depending on the setting (Creutzig et al. 2015a,b; Pan et al. 2020).
Investments in transit and Improving public transit systems and building infrastructure to support active
active transport transport modes (walking and biking) could reduce car travel. Case studies
infrastructure suggest that active mobility could reduce emissions from urban transport by 2%-
10% depending on the setting (Creutzig et al. 2016; Zahabi et al. 2016; Keall et
al. 2018; Gilby et al. 2019; Neves and Brand 2019; Bagheri et al. 2020; Ivanova
et al. 2020; Brand et al. 2021). A shift to public transit modes can likely offer
significant emissions reductions, but estimates are uncertain.
Changes in economic Higher demand as a result of higher incomes could increase emissions,
structures particularly in aviation and shipping. Higher prices could have the opposite
effect and reduce emission. Structural changes associated with financial crises,
pandemics, or the impacts of climate change could affect the elasticity of
demand in uncertain ways. Thus, the effect of changes in economic structures
on the GHG emissions from the transport sectors is uncertain.
Teleworking A move towards a digital economy that allows workers to work remotely and
access information remotely could reduce travel demand. Case studies suggest
that teleworking could reduce transport emissions by 20% in some instances, but
are likely 1%, at most, across the entire transport system (Roth et al. 2008;
O’Keefe et al. 2016; Shabanpour et al. 2018; O’Brien and Aliabadi 2020).
Dematerialisation of the A reduction in goods needed due to combining multiple functions into one
economy device would reduce the need for transport. Reduced weights associated with
dematerialisation would improve the efficiency of freight transport. However,
emissions reductions from these efforts are likely dwarfed by increased
consumption of goods.
Supply chain management Supply chains could be optimised to reduce the movement or travel distance of
product components. Logistics planning could optimise the use of transport
infrastructure to increase utilization rates and decrease travel. The effect of these
strategies on the GHG emissions from the transport sector is uncertain.
e-commerce The effect of e-commerce on transport emissions is uncertain. Increased e-
commerce would reduce demand for trips to stores but could increase demand
for freight transport (particularly last-mile delivery) (Jaller and Pahwa 2020; Le
et al. 2021).
Smart mobility ICT and smart city technologies can be used to improve the efficiency of
operating the transport system. Furthermore, smart technologies can improve
competitiveness of transit and active transport over personal vehicle use by
streamlining mobility options to compete with private cars. The effect of smart
mobility on the GHG emissions from the transport sector is uncertain (Creutzig
2021).
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Shared mobility Shared mobility could increase utilisation rates of LDVs, thus improving the
efficiency of the system. However, shared mobility could also divert users from
transit systems or active transport modes. Studies on ride-sourcing have reported
both potential for reductions and increases in transport-related emissions
(Schaller 2018; Ward et al. 2021). Other case studies suggests that carpooling to
replace 20% of private car trips could result in a 12% reduction in GHG (ITF
2020a,b).Thus, the effect of shared mobility on transport-related GHG emissions
is highly uncertain.
Vehicle automation Vehicle automation could have positive or negative effects on emissions.
Improved transit operations, more efficient traffic management, and better
routing for light- and heavy-duty transport could reduce emissions (Vahidi and
Sciarretta 2018; Nasri et al. 2018; Massar et al. 2021; Paddeu and Denby 2021).
However, autonomous cars could make car travel more convenient, removing
users from transit systems and increasing access to marginalised groups, which
would in turn increase vkms travelled (Harper et al. 2016; Auld et al. 2017;
Sonnleitner et al. 2021). Drones could reduce energy use and GHG emissions
from freight transport (Stolaroff et al. 2018)
1 10.3 Transport technology innovations for decarbonisation
2 This section focuses on vehicle technology and low-carbon fuel innovations to support decarbonisation
3 of the transport sector. Figure 10.2 summarises the major pathways reviewed in this section. The
4 advancements in energy carriers described in Figure 10.2 are discussed in greater detail in Chapter 6
5 (Energy) and Chapter 11 (Industry) but the review presented in this chapter highlights their application
6 in the transport sector. This section pays attention to the advancements in alternative fuels, electric, and
7 fuel cell technologies since AR5.
8
9
10
11 Figure 10.2 Energy pathways for low-carbon transport technologies. Primary energy sources are shown
12 in the far left, while the segments of the transport system are in the far right. Energy carriers and vehicle
13 technologies are represented in the middle. Primary pathways are shown with solid lines, while dashed
14 lined represent secondary pathways.
15
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1 10.3.1 Alternative fuels – an option for decarbonising internal combustion engines
2 The average fuel consumption of new Internal Combustion Engine (ICE) vehicles has improved
3 significantly in recent years due to more stringent emission regulations. However, improvements are
4 now slowing down. The average fuel consumption of LDVs decreased by only 0.7 % between 2016 and
5 2017, reaching 7.2 litres of gasoline-equivalent (Lge) per 100 km in 2017, much slower than the 1.8 %
6 improvement per year between 2005 and 2016 (GFEI 2020). Table 10.4 summarises recent and
7 forthcoming improvements to ICE technologies and their effect on emissions from these vehicles.
8 However, these improvements are not sufficient to meet deep decarbonisation levels in the transport
9 sector. While there is significant and growing interest in electric and fuel-cell vehicles, future scenarios
10 indicate that a large number of LDV may continue to be operated by ICE in conventional, hybrid, and
11 plug-in hybrid configurations over the next 30 years (IEA 2019a) unless they are regulated away
12 through ICE vehicle sales bans (as some nations have announced) (IEA 2021a). Moreover, ICE
13 technologies are likely to remain the prevalent options for shipping and aviation. Thus, reducing CO2
14 and other emissions from ICEs through the use of low-carbon or zero-carbon fuels is essential to a
15 balanced strategy for limiting atmospheric pollutant levels. Such alternative fuels for ICE vehicles
16 include natural gas-based fuels, biofuels, Ammonia, and other synthetic fuels.
17
18 Table 10.4 Engine technologies to reduce emissions from light-duty ICE vehicles and their
19 implementation stage. Table nomenclature: GDI = Gasoline direct injection, VVT = Variable valve
20 technology, CDA = Cylinder deactivation, CR = compression ratio, GDCI = Gasoline direct injection
21 compression ignition, EGR = exhaust gas recirculation, RCCI = Reactivity controlled compression
22 ignition, GCI = Gasoline compression ignition. Source: (Joshi 2020)
23
Implementation CO2 Reduction
Engine Technology
Stage (%)
Implemented Baseline: GDI, turbo, stoichiometry. 0
Atkinson cycle (+ VVT) 3-5
Dynamic CDA + Mild Hybrid or Miller 10 -15
Lean-burn GDI 10 - 20
Variable CR 10
Spark assisted GCI 10
GDCI 15 - 25
Development
Water Injection 5 - 10
Pre-chamber concepts 15 - 20
Homogeneous Lean 15 - 20
Dedicated EGR 15 - 20
2-stroke opp. piston Diesel 25 - 35
RCCI 20 - 30
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1
2 Natural Gas: Natural gas could be used as an alternative fuel to replace gasoline and diesel. Natural gas
3 in vehicles can be used as compressed natural gas (CNG) and liquefied natural gas (LNG). CNG is
4 gaseous at relatively high pressure (10 to 25 MPa) and temperature (-40 to 30oC). In contrast, LNG is
5 used in liquid form at relatively low pressure (0.1 MPa) and temperature (-160oC). Therefore, CNG is
6 particularly suitable for commercial vehicles and light- to medium-duty vehicles, whereas LNG is better
7 suited to replace diesel in HDVs (Dubov et al. 2020; Dziewiatkowski et al. 2020; Yaïci and Ribberink
8 2021). CNG vehicles have been widely deployed in some regions, particularly in Asian-Pacific
9 countries. For example, there are about 6 million CNG vehicles domiciled in China, the most of any
10 country (Qin et al. 2020). However, only 20% of vehicles that operate using CNG were originally
11 designed as CNG vehicles, with the rest being gasoline-fuelled vehicles that have been converted to
12 operate with CNG (Chala et al. 2018).
13 Natural gas-based vehicles have certain advantages over conventional fuel-powered ICE vehicles,
14 including lower emissions of criteria air pollutants, no soot or particulate, low carbon to Hydrogen ratio,
15 moderate noise, a wide range of flammability limits, and high-octane numbers (Kim 2019; Bayat and
16 Ghazikhani 2020). Furthermore, the technology readiness of natural gas vehicles is very high (TRL 8-
17 9), with direct modification of existing gasoline and diesel vehicles possible (Transport and
18 Environment 2018; Peters et al. 2021; Sahoo and Srivastava 2021). On the other hand, methane
19 emissions from the natural gas supply chain and tailpipe CO2 emissions remain a significant concern
20 (Trivedi et al. 2020). As a result, natural gas as a transition transportation fuel may be limited due to
21 better alternative options being available and due to regulatory pressure to decarbonise the transport
22 sector rapidly. For example, the International Maritime Office (IMO) has set a target of 40% less carbon
23 intensity in shipping by 2030, which cannot be obtained by simply switching to natural gas.
24 Biofuels: Since AR5, the faster than anticipated adoption of electromobility, primarily for LDVs, has
25 partially shifted the debate around the primary use of biofuels from land transport to the shipping and
26 aviation sectors (Davis et al. 2018; IEA 2017a).At the same time, other studies highlight that biofuels
27 may have to complement electromobility in road transport, particularly in developing countries, offering
28 relevant mitigation opportunities in the short- and mid-term (up to 2050) (IEA 2021b). An important
29 advantage of biofuels is that they can be converted into energy carriers compatible with existing
30 technologies, including current powertrains and fuel infrastructure. Also, biofuels can diversify the
31 supply of transport fuel, raise energy self-sufficiency in many countries, and be used as a strategy to
32 diversify and strengthen the agro-industrial sector (Puricelli et al. 2021). The use of biofuels as a
33 mitigation strategy is driven by a combination of factors, including not only the costs and technology
34 readiness levels of the different biofuel conversion technologies, but also the availability and costs of
35 both biomass feedstocks and alternative mitigation options, and the relative speed and scale of the
36 energy transition in energy and transport sectors (Box 10.2).
37
38 START BOX HERE
39
40 Box 10.2 – Bridging land use and feedstock conversion footprints for biofuels
41
42 Under specific conditions, biofuels may represent an important climate mitigation strategy for the
43 transport sector (Muratori et al. 2020; Daioglou et al. 2020). Both SR1.5 and SRCCL highlighted that
44 biofuels could be associated with climate mitigation co-benefits and adverse side effects to many SDGs.
45 These side-effects depend on context-specific conditions, including deployment scale, associated land-
46 use changes and agricultural management practices (see Section 7.4.4 and Box 7.10 in Chapter 7). There
47 is broad agreement in the literature that the most important factors in determining the climate footprint
48 of biofuels are the land use and land use change characteristics associated with biofuel deployment
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1 scenarios e.g. (Elshout et al. 2015; Daioglou et al. 2020). This issue is covered in more detail in Chapter
2 7, Box 7.1. While the mitigation literature primarily focuses on the GHG-related climate forcings, note
3 that land is an integral part of the climate system through multiple geophysical and geochemical
4 mechanisms (albedo, evaporation, etc.). For example, Sections 2.2.7, 7.3.4 in the WGI report indicate
5 that geophysical aspects of historical land use change outweigh the geochemical effects, leading to a
6 net cooling effect. The land-related carbon footprints of biofuels presented in sections 10.4-10.6 are
7 adopted from Chapter 7 (See section 7.4.4 and Box 7, Figure 7.1). The results show how the land-related
8 footprint increases due to an increased outtake of biomass, as estimated with different models that rely
9 on global supply scenarios of biomass for energy and fuel of 100 EJ. The integrated assessment models
10 and scenarios used include the EMF 33 scenarios (IAM-EMF33), from partial models with constant
11 land cover (PM-CLC), and from partial models with natural regrowth (PM-NGR). These results are
12 combined with both biomass cultivation emission ranges for advanced biofuels aligned with (Edwards
13 et al. 2017; El Akkari et al. 2018; Jeswani et al. 2020; Puricelli et al. 2021) and conversion efficiencies
14 and conversion phase emissions as described in Table 10.5. The modelled footprints resulting from land
15 use changes related to delivering 100 EJ of biomass at global level are in the range of 3 – 77 gCO2eq./MJ
16 of advanced biofuel (median 38 gCO2eq./MJ) at an aggregate level for IAMs and partial models, with
17 constant land cover (Rose et al. 2020; Daioglou et al. 2020). The results for partial models with natural
18 regrowth are much higher (91-246 CO2eq./MJ advanced biofuel. The latter ranges may appear in
19 contrast with the results from the scenario literature in 10.7, where biofuels play a role in many scenarios
20 compatible low warming levels. This contrast is a result of different underlying modelling practices.
21 The general modelling approach used for the scenarios in the AR6 database accounts for the land-use
22 change and all other GHG emissions along a given transformation trajectory, enabling assessments of
23 the warming level incurred. The results labelled "EMF33" and "partial models with constant land cover"
24 are obtained with this modelling approach. The results in the category "partial models with natural
25 regrowth" attribute additional CO2 emissions to the bioenergy system, corresponding to estimated
26 uptake of CO2 in a counterfactual scenario where land is not used for bioenergy, but instead subject to
27 natural vegetation regrowth. While the partial analysis provides insights into the implications of
28 alternative land-use strategies, such analysis does not identify the actual emissions of bioenergy
29 production. As a result, the partial analysis is not compatible with the identification of warming levels
30 incurred by an individual transformation trajectory, and therefore not aligned with the general approach
31 applied for the scenarios in the AR6 database.
32 More details on land-use change impacts and the potential to deliver the projected demands of biofuels
33 at the global level are further addressed in Chapter 7. While, in general, the above results cover most of
34 the variety of GHG range intensities of biofuel options presented in the literature, the more specific
35 LCA literature should be consulted when considering specific combinations of biomass feedstock and
36 conversion technologies in specific regions.
37
38 END BOX HERE
39
40 Many studies have addressed the life cycle emissions of biofuel conversion pathways for land transport,
41 aviation, and marine applications, e.g. (Edwards et al. 2017; Staples et al. 2018; Tanzer et al. 2019).
42 Bioenergy technologies generally struggle to compete with existing fossil fuel-based ones because of
43 the higher costs involved. However, the extent of the cost gap depends critically on the availability and
44 costs of biomass feedstock (IEA 2021b). Ethanol from corn and sugarcane is commercially available in
45 countries such as Brazil and the US. Biodiesel from oil crops and hydro-processed esters and fatty acids
46 are available in various countries, notably in Europe and parts of Southeast Asia. On the infrastructure
47 side, biomethane blending is being implemented in some regions of the US and Europe, particularly in
48 Germany, with the help of policy measures (IEA 2021b). While many of these biofuel conversion
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1 technologies could also be implemented using seaweed feedstock options, these value chains are not
2 yet mature (Jiang et al. 2016).
3 Technologies to produce advanced biofuels from lignocellulosic feedstocks have suffered from slow
4 technology development and are still struggling to achieve full commercial scale. Their uptake is likely
5 to require carbon pricing and/or other regulatory measures, such as clean fuel standards in the transport
6 sector or blending mandates. Several commercial-scale advanced biofuels projects are in the pipeline
7 in many parts of the world, encompassing a wide selection of technologies and feedstock choices,
8 including carbon capture and sequestration (CCS) that supports carbon dioxide removal (CDR). The
9 success of these projects is vital to moving forward the development of advanced biofuels and bringing
10 many of the advanced biofuels' value chains closer to the market (IEA 2021b). Finally, biofuel
11 production and distribution supply chains involve notable transport and logistical challenges that need
12 to be overcome. (Mawhood et al. 2016; Skeer et al. 2016; IEA 2017a; Puricelli et al. 2021).
13
14 Table 10.5 summarises performance data for different biofuel technologies, while Figure 10.3 shows
15 the technology readiness levels, which are based on (Mawhood et al. 2016; Skeer et al. 2016; IEA
16 2017a; Puricelli et al. 2021).
17
18 Table 10.5 Ranges of efficiency, GHG emissions, and relative costs of selected biofuel conversion
19 technologies for road, marine, and aviation biofuels.
GHG emissions of Relative cost of
Conversion Energy efficiency
Main application conversion process conversion
technology of conversiona
(gCO2eq./MJfuel)b process
Lignocellulosic
Road 35%c 5d Medium
ethanol
Gasification and
Road/Aviation Fischer-Tropsch 57%e <1d High
synthesis
Ethanol from sugar
Road 60-70%f 1 – 31d Low
and starch
Biodiesel from oil
Road 95%g 12 - 30 d Low
crops
Upgraded pyrolysis
Marine 30 - 61%h 1-4h Medium
oil
Hydro-processed
Aviation/Marine esters and fatty 80%i 3i Medium
acids
Aviation Alcohol to jet 90%j <1k High
Biomethane from
Road/Marine 60%l n.a. Low
residues
Hydrothermal
Marine/Aviation 35-69%h <1h High
liquefaction
Sugars to
Aviation 65%m 15m High
hydrocarbons
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Gasification and
Road syngas 40%n 30-40n High
fermentation
1 Notes: aCalculated as liquid fuels output divided by energy in feedstock entering the conversion plant; bGHG
2 emission here refers only the conversion process. Impacts form the different biomass options are not included
3 here as they are addressed in Chapter 7; c(Olofsson et al. 2017); d(Edwards et al. 2017); e(Simell et al. 2014); f(de
4 Souza Dias et al. 2015); g(Castanheira et al. 2015); h(Tanzer et al. 2019); i(Klein et al. 2018); j(Narula et al. 2017);
5 k
(de Jong et al. 2017); l(Salman et al. 2017); m(Moreira et al. 2014; Roy et al. 2015; Handler et al. 2016)
6
7
8 Figure 10.3 Commercialisation status of selected biofuels conversion technologies. The grey boxes
9 represent the current TRL of each conversion technology.
10
11 Within the aviation sector, jet fuels produced from biomass resources (so-called sustainable aviation
12 fuels, SAF) could offer significant climate mitigation opportunities under the right policy circumstances.
13 Despite the growing interest in aviation biofuels, demand and production volumes remain negligible
14 compared to conventional fossil aviation fuels. Nearly all flights powered by biofuels have used fuels
15 derived from vegetable oils and fats, and the blending level of biofuels into conventional aviation fuels
16 for testing is up to 50% today (Mawhood et al. 2016). To date, only one facility in the US is regularly
17 producing sustainable aviation fuels based on waste oil feedstocks. The potential to scale up bio-based
18 SAF volumes is severely restricted by the lack of low cost and sustainable feedstock options (see
19 Chapter 7). Lignocellulosic feedstocks are considered to have a great potential for the production of
20 financially competitive bio-based SAF in many regions. However, production facilities involve
21 significant capital investment and estimated levelised costs are typically more than twice the selling
22 price of conventional jet fuel. In some cases (notably for vegetable oils), the feedstock price is already
23 higher than that of the fossil jet fuel (Mawhood et al. 2016). Some promising technological routes for
24 producing SAF from lignocellulosic feedstocks are below technology readiness level (TRL) 6 (pilot
25 scale) with just a few players involved in the development of these technologies. Although it would be
26 physically possible to address the mid-century projections for substantial use of biofuels in the aviation
27 sector (from IEA and other sectoral organisations (ICAO 2017)), this fuel deployment scale could only
28 be achieved with very large capital investments in bi-based SAF production infrastructure, and
29 substantial policy support.
30 In comparison to the aviation sector, the prospects for technology deployment are better in the shipping
31 sector. The advantage of shipping fuels is that marine engines have a much higher operational flexibility
32 on a mix of fuels, and shipping fuels do not need to undergo as extensive refining processes as road and
33 aviation fuels to be considered drop-in. However, biofuels in marine engines have only been tested at
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1 an experimental or demonstration stage, leaving open the question about the scalability of the
2 operations, including logistics issues. Similar to the aviation sector, securing a reliable, sustainable
3 biomass feedstock supply and mature processing technologies to produce price-competitive biofuels at
4 a large scale remains a challenge for the shipping sector (Hsieh and Felby 2017). Other drawbacks
5 include industry concerns about oxidation, storage, and microbial stability for less purified or more
6 crude biofuels. Assuming that biofuels are technically developed and available for the shipping sector
7 in large quantities, a wider initial introduction of biofuels in the sector is likely to depend upon increased
8 environmental regulation of particulate and GHG emissions. Biofuels may also may offer a significant
9 advantage in meeting ambitious sulphur emission reduction targets set by the sectoral organisations.
10 More extensive use of marine biofuels will most likely be first implemented in inner-city waterways,
11 inland river freight routes, and coastal green zones. Given the high efficiency of the diesel engine, a
12 large-scale switch to a different standard marine propulsion method in the near to medium-term future
13 seems unlikely. Thus, much of the effort has been placed on developing biofuels compatible with diesel
14 engines. So far, biodiesel blends look promising, as used in land transport. Hydrotreated vegetable oil
15 (HVO) is also a technically good alternative and is compatible with current engines and supply chains,
16 while the introduction of multifuel engines may open the market for ethanol fuels (Hsieh and Felby
17 2017).
18 Ammonia: At room temperature and atmospheric pressure, Ammonia is a colourless gas with a distinct
19 odour. Due to relatively mild conditions for liquefaction, Ammonia is transferred and stored as a
20 liquefied or compressed gas and has been used as an essential industrial chemical resource for many
21 products. In addition, since the chemical structure of Ammonia is without carbon molecules, Ammonia
22 has attracted attention as a carbon-neutral fuel that can also improve combustion efficiency (Gill et al.
23 2012). Furthermore, Ammonia could also serve as a Hydrogen carrier and used in fuel cells. These
24 characteristics have driven increased interest in the low-carbon production of Ammonia, which would
25 have to be coupled to low-carbon Hydrogen production (with low-carbon electricity providing the
26 needed energy or with CCS).
27 For conventional internal combustion engines, the use of Ammonia remains challenging due to the
28 relatively low burning velocity and high ignition temperature. Therefore, Frigo and Gentili (2014) have
29 suggested a dual-fuelled spark-ignition engine operated by liquid Ammonia and Hydrogen, where
30 Hydrogen is generated from Ammonia using the thermal energy of exhaust gas. On the other hand, the
31 high-octane number of Ammonia means good knocking resistance of spark ignition engines and is
32 promising for improving thermal efficiency. For compression ignition engines, the high-ignition
33 temperature of Ammonia requires a high compression ratio causing an increase in mechanical friction.
34 Since Gray et al. (1966), many studies have shown that the compression ratio can be reduced by mixing
35 combustion with secondary fuels such as diesel and Hydrogen with low self-ignition temperatures, as
36 summarised by Dimitriou and Javaid (2020). Using a secondary fuel with a high cetane number and the
37 adoption of a suitable fuel injection timing has enabled highly efficient combustion of compression
38 ignition engines in the dual fuel mode with Ammonia ratios up to 95% (Dimitriou and Javaid 2020).
39 One major challenge for realising an Ammonia-fuelled engine is the reduction of unburned Ammonia,
40 as described in Section 6.4.5. (Reiter and Kong 2011). Processes being examined include the use of
41 exhaust gas recirculation (EGR) (Pochet et al. 2017) and after treatment systems. However, these
42 processes require space, which is a constraint for LDVs and air transport but more practical for ships.
43 Shipbuilders are developing an Ammonia engine based on the existing diesel dual-fuel engine to launch
44 a service in 2025 (Brown 2019; MAN-ES 2019). Ammonia could therefore contribute significantly to
45 decarbonisation in the shipping sector (as expanded in section 10.6) with potential niche applications
46 elsewhere.
47 Synthetic fuels: Synthetic fuels can contribute to transport decarbonisation through synthesis from
48 electrolytic Hydrogen produced with low carbon electricity or Hydrogen produced with CCS, and
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1 captured CO2 using the Fischer-Tropsch process (Liu et al. 2020a). Due to similar properties of synthetic
2 fuels to those of fossil fuels, synthetic fuels can reduce GHG emissions in both existing and new
3 vehicles without significant changes to the engine design. While the Fischer-Tropsch process is a well-
4 established technology (Liu et al. 2020a), low-carbon synthetic fuel production is still in the
5 demonstration stage. Even though their production costs are expected to decline in the future due to
6 lower renewable electricity prices, increased scale of production, and learning effects, synthetic fuels
7 are still up to 3 times more expensive than conventional fossil fuels (Section 6.6.2.4). Furthermore,
8 since the production of synthetic fuels involves thermodynamic conversion loss, there is a concern that
9 the total energy efficiency is lower than that of electric vehicles (Soler 2019). Given these high costs
10 and limited scales, the adoption of synthetic fuels will likely focus on the aviation, shipping, and long-
11 distance road transport segments, where decarbonisation by electrification is more challenging. In
12 particular, synthetic fuels are considered promising as an aviation fuel (as expanded in section 10.5).
13
14 10.3.2 Electric technologies
15 Widespread electrification of the transport sector is likely crucial for reducing transport emissions and
16 depends on appropriate energy storage systems (EES). However, large-scale diffusion of EES depends
17 on improvements in energy density (energy stored per unit volume), specific energy (energy stored per
18 unit weight), and costs (Cano et al. 2018). Recent trends suggest EES-enabled vehicles are on a path of
19 becoming the leading technology for LDVs, but their contribution to heavy-duty freight is more
20 uncertain.
21 Electrochemical storage of light and medium-duty vehicles: Electrochemical storage, i.e., batteries, are
22 one of the most promising forms of energy storage for the transport sector and have dramatically
23 improved in their commerciality since AR5. Rechargeable batteries are of primary interest for
24 applications within the transport sector, with a range of mature and emerging chemistries able to support
25 the electrification of vehicles. The most significant change since AR5 and SPR1.5 is the dramatic rise
26 in lithium-ion batteries (LIB), which has enabled electromobility to become a major feature of
27 decarbonisation.
28 Before the recent growth in market share of LIBs, lead-acid batteries, nickel batteries, high-temperature
29 sodium batteries, and redox flow batteries were of particular interest for the transport sector (Placke et
30 al. 2017). Due to their low costs, lead-acid batteries have been used in smaller automotive vehicles, e.g.
31 e-scooters and e-rickshaws (Dhar et al. 2017). However, their application in electric vehicles will be
32 limited due to their low specific energy (Andwari et al. 2017). Nickel-metal hydride (NiMH) batteries
33 have a better energy density than lead-acid batteries and have been well-optimised for regenerative
34 braking (Cano et al. 2018). As a result, NiMH batteries were the battery of choice for hybrid electric
35 vehicles (HEVs). Ni-Cadmium (NiCd) batteries have energy densities lower than NiMH batteries and
36 cost around ten times more than lead-acid batteries (Table 6.5, Chapter 6). For this reason, NiCd
37 batteries do not have major prospects within automotive applications. There are also no examples of
38 high-temperature sodium or redox flow batteries being used within automotive applications.
39 Commercial application of LIBs in automotive applications started around 2000 when the price of LIBs
40 was more than 1,000 USD per kWh (Schmidt et al. 2017). By 2020, the battery manufacturing capacity
41 for automotive applications was around 300 GWh per year (IEA 2021a). Furthermore, by 2020, the
42 average battery pack cost had come down to 137 USD per kWh, a reduction of 89% in real terms since
43 2010 (Henze 2020). Further improvements in specific energy, energy density (Nykvist et al. 2015;
44 Placke et al. 2017) and battery service life (Liu et al. 2017) of LIBs are expected through additional
45 design optimisation (Table 6.5, Chapter 6). These advancements are expected to lead to EVs with even
46 longer driving ranges, further supporting the uptake of LIBs for transport applications (Cano et al.
47 2018). However, the performance of LIBs under freezing and high temperatures is a concern (Liu et al.
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1 2017) for reliability. Auto manufacturers have some pre-heating systems for batteries to see that they
2 perform well in very cold conditions (Wu et al. 2020).
3 For EVs sold in 2018, the material demand was about 11 kilotonnes (kt) of optimised lithium, 15 kt of
4 cobalt, 11 kt of manganese, and 34 kt of nickel (IEA 2019a, 2021a). IEA projections for 2030 in the EV
5 30@30 scenario show that the demand for these materials would increase by 30 times for lithium and
6 around 25 times for cobalt. While there are efforts to move away from expensive materials such as
7 cobalt (IEA 2019a, 2021a), dependence on lithium will remain, which may be a cause of concern
8 (Olivetti et al. 2017; You and Manthiram 2018). A more detailed discussion on resource constraints for
9 lithium is provided in Box 10.6 on critical materials.
10 Externalities from resource extraction are another concern, though current volumes of lithium are much
11 smaller than other metals (steel, aluminium). As a result, lithium was not even mentioned in the global
12 resource outlook of UNEP (IRP 2019). Nonetheless, it is essential to manage demand and limit
13 externalities since the demand for lithium is going to increase many times in the future. Reuse of LIBs
14 used in EVs for stationary energy applications can help in reducing the demand for LIBs. However, the
15 main challenges are the difficulty in accessing the information on the health of batteries to be recycled
16 and technical problems in remanufacturing the batteries for their second life (Ahmadi et al. 2017).
17 Recycling lithium from used batteries could be another possible supply source (Winslow et al. 2018).
18 While further R&D is required for commercialisation (Ling et al., 2018), recent efforts at recycling
19 LIBs are very encouraging (Ma et al. 2021). The standardisation of battery modules and packaging
20 within and across vehicle platforms, increased focus on design for recyclability, and supportive
21 regulation are important to enable higher recycling rates for LIBs (Harper et al. 2019).
22 Several next-generation battery chemistries are often referred to as post-LIBs (Placke et al. 2017). These
23 chemistries include metal-sulphur, metal-air, metal-ion (besides Li) and all-solid-state batteries
24 (ASSB). The long development cycles of the automotive industry (Cano et al. 2018) and the advantages
25 of LIBs in terms of energy density and cycle life (Table 6.5, Chapter 6) mean that it is unlikely that
26 post-LIB technologies will replace LIBs in the next decade. However, lithium-sulphur, lithium-air, and
27 zinc-air have emerged as potential alternatives for LIBs. These emerging chemistries may also be used
28 to supplement LIBs in dual-battery configurations, to extend the driving range at lower costs or with
29 higher energy density (Cano et al. 2018). Lithium-sulphur (Li-S) batteries have a lithium metal anode
30 with a higher theoretical capacity than lithium-ion anodes and much lower cost sulphur cathodes relative
31 to typical Li-ion insertion cathodes (Manthiram et al. 2014). As a result, Li-S batteries are much cheaper
32 than LIB to manufacture and have a higher energy density (Table 6.5, Chapter 6). Conversely, these
33 batteries face challenges from sulphur cathodes, such as low conductivity of the sulphur and lithium
34 sulphide phases, and the relatively high solubility of sulphur species in common lithium battery
35 electrolytes, leading to low cycle life (Cano et al. 2018). Lithium-air batteries offer a further
36 improvement in specific energy and energy density above Li–S batteries owing to their use of
37 atmospheric oxygen as a cathode in place of sulphur. However, their demonstrated cycle-life is much
38 lower (Table 6.5, Chapter 6). Lithium-air batteries also have low specific power. Therefore, lithium-air
39 require an extra battery for practical applications (Cano et al. 2018). Finally, zinc–air batteries could
40 more likely be used in future EVs because of their more advanced technology status and higher
41 practically achievable energy density (Fu et al. 2017). Like Li-air batteries, their poor specific power
42 and energy efficiency will probably prevent zinc-air batteries from being used as a primary energy
43 source for EVs. Still, they could be promising when used in a dual-battery configuration (Cano et al.
44 2018).
45 The technological readiness of batteries is a crucial parameter in the advancement of EVs (Manzetti
46 and Mariasiu 2015). Energy density, power density, cycle life, calendar life, and the cost per kWh are
47 the pertinent parameters for comparing the technological readiness of various battery technologies
48 (Manzetti and Mariasiu 2015; Andwari et al. 2017; Lajunen et al. 2018). Table 6.5 in Chapter 6 provides
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1 a summary of the values of these parameters for alternative battery technologies. LIBs comprehensively
2 dominate the other battery types and are at a readiness level where they can be applied for land transport
3 applications (cars, scooters, electrically assisted cycles) and at battery pack costs below 150 USD per
4 kWh, making EVs cost-competitive with conventional vehicles (Nykvist et al. 2019). In 2020 the stock
5 of battery-electric LDVs had crossed the 10 million mark (IEA 2021a). (Schmidt et al. 2017) project
6 that the cost of a battery pack for LIBs will reach 100 USD per kWh by 2030, but more recent trends
7 show this could happen much earlier. For example, according to IEA, battery pack costs could be as
8 low as 80 USD per kWh by 2030 (IEA 2019a). In addition, there are clear trends that now vehicle
9 manufacturers are offering vehicles with bigger batteries, greater driving ranges, higher top speeds,
10 faster acceleration, and all size categories (Nykvist et al. 2019). In 2020 there were over 600,000
11 battery-electric buses and over 31,000 battery-electric trucks operating globally (IEA 2021a).
12 LIBs are not currently envisaged to be suitable for long-haul transport. However, several battery
13 technologies are under development (Table 6.5, Chapter 6), which could further enhance the
14 competitiveness of EVs and expand their applicability to very short-haul aviation and ships, especially
15 smaller vehicles. Li-S, Li-air, and Zn-air hold the highest potential for these segments (Cano et al.
16 2018). All three of these technologies rely on making use of relatively inexpensive elements, which can
17 help bring down battery costs (Cano et al. 2018). The main challenge these technologies face is in terms
18 of the cycle life. Out of the three, Li-S has already been used for applications in unmanned aerial
19 vehicles (Fotouhi et al., 2017) due to relatively high specific energy (almost double the state of art
20 LIBs). However, even with low cycle life, Li-air and Zn-air hold good prospects for commercialisation
21 as range extender batteries for long-range road transport and with vehicles that are typically used for
22 city driving (Cano et al. 2018).
23 Alternative electricity storage technologies for heavy-duty transport: While LIBs described in the
24 previous section are driving the electrification of LDVs, their application to railways, aviation, ships,
25 and large vehicles faces challenges due to the higher power requirements of these applications. The use
26 of a capacitor with a higher power density than LIBs could be suitable for the electrification of such
27 vehicles. It is one of the solutions for regenerating large and instantaneous energy from regenerative
28 brakes. Classical capacitors generally show more attractive characteristics in power density (8,000-
29 10,000 W/kg) than batteries. However, the energy density is poor (1-4 Wh/kg) compared to batteries,
30 and there is an issue of self-discharge (González et al. 2016; Poonam et al. 2019). To improve the energy
31 density, electrochemical double layer capacitors (EDLCs; supercapacitor) and hybrid capacitors (10-24
32 Wh/kg, 900-9000 W/kg in the product-level) such as Li-ion capacitors (LICs) have been developed.
33 The highest energy density of the LIC system (100-140 Wh/kg in the research stage) are approaching
34 that of the Li-ion battery systems (80-240 Wh/kg in the product stage) (Naoi et al. 2012; Panja et al.
35 2020). Examples of effective use of capacitors include a 12 tonne truck with a capacitor-based kinetic
36 energy recovery system (KERS) that has been reported to save up to 32% of the fuel use of standard
37 truck (Kamdar 2017). Similarly, an EDLC bank applied to electric railway systems has been shown to
38 result in a 10% reduction in power consumption per day (Takahashi et al. 2017). Finally, systems in
39 which capacitors are mounted on an electric bus for charging at a stop have been put into practical use,
40 e.g., Trackless Tram (Newman et al. 2019). At the bus stop, the capacitor is charged at 600 kW for 10
41 ~ 40 seconds, which provides enough power for 5 ~ 10 km (Newman et al. 2019). In addition, more
42 durable capacitors can achieve a longer life than LIB systems (ADB 2018).
43 Hybrid energy storage (HES) systems, which combine a capacitor and a battery, achieve both high
44 power and high energy, solving problems such as capacity loss of the battery and self-discharge of the
45 capacitor. In these systems, the capacitor absorbs the steeper power, while the LIB handles the steady
46 power, thereby reducing the power loss of the EV to half. Furthermore, since the in-rush current of the
47 battery is suppressed, there is an improvement in the reliability of the LIB (Noumi et al., 2014). In a
48 hybrid diesel train, 8.2% of the regenerative energy is lost due to batteries' limited charge-discharge
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1 performance; however, using an EDLC with batteries can save this energy (Takahashi et al. 2017;
2 Mayrink et al. 2020)
3 The development of power storage devices and advanced integrated system approaches, including
4 power electronics circuits such as HES and their control technologies, are important for the
5 electrification of mobility. These technologies are solutions that could promote the electrification of
6 systems, reduce costs, and contribute to the social environment through multiple outcomes in the
7 decarbonisation agenda.
8
9 10.3.3 Fuel cell technologies
10 In harder-to-electrify transport segments, such as heavy-duty vehicles, shipping, and aviation,
11 Hydrogen holds significant promise for delivering emissions reductions if it is produced using low-
12 carbon energy sources. In particular, Hydrogen fuel cells are seen as an emerging option to power larger
13 vehicles for land-based transport (Tokimatsu et al. 2016; IPCC 2018; IEA 2019b). Despite this
14 potential, further advancements in technological and economic maturity will be required in order for
15 Hydrogen fuel cells to play a greater role. While this section focuses primarily on Hydrogen fuel cells,
16 Ammonia and Methanol fuel cells may also emerge as options for low power applications.
17 During the last decade, Hydrogen fuel cell vehicles (HFCVs) have attracted growing attention, with
18 fuel cell technology improving through research and development. Fuel cell systems cost 80 to 95 per
19 cent less than they did in the early 2000s, at approximately $50 per kW for light-duty (80 kW) and $100
20 per kW for medium-heavy duty (160 kW). These costs are approaching the US Department of Energy's
21 (US DOE) goal of $40 per kW in 2025 at a production target of 500,000 systems per year(IEA 2019c).
22 In addition to cost reductions, the power density of fuel cell stacks has now reached around 3.0 kW/L,
23 and average durability has improved to approximately 2,000-3,000 hours (Jouin et al. 2016; Kurtz et al.
24 2019). Despite these improvements, fuel cell systems are not yet mature for many commercial
25 applications. For example, the US DOE has outlined that for Hydrogen fuel cell articulated trucks (semi-
26 trailers) to compete with diesel vehicles, fuel cell durability will need to reach 30,000 hours (US DOE
27 2019). While some fuel cell buses have demonstrated durability close to these targets (Eudy and Post
28 2018a), another review of light fuel cell vehicles found maximum durability of 4,000 hours (Kurtz et
29 al. 2019). As more fuel cell vehicles are trialled, it is expected that further real-world data will become
30 available to track ongoing fuel cell durability improvements.
31 Ammonia and Methanol fuel cells are considered to be less mature than Hydrogen fuel cells. However,
32 they offer the benefit of using a more easily transported fuel that can be directly used without converting
33 to Hydrogen (Zhao et al. 2019). Conversely, both Methanol and Ammonia are toxic, and in the case of
34 Methanol fuel cells, carbon dioxide is released as a by-product of generating electricity with the fuel
35 cell (Zhao et al. 2019). Due to the lower power output, Methanol and Ammonia fuel cells are also not
36 well-suited to heavy duty vehicles (Jeerh et al. 2021). They are therefore unlikely to compete with
37 Hydrogen fuel cells. However, Ammonia and Methanol could be converted at refuelling stations to
38 Hydrogen as an alternative to being directly used in fuel cells (Zhao et al. 2019).
39 Several FCV-related technologies are fully ready for demonstration and early market deployment,
40 however, further research and development will be required to achieve full-scale commercialisation,
41 likely from 2030 onwards (Staffell et al. 2019; Energy Transitions Commission 2020; IEA 2021b).
42 Some reports argue that it may be possible to achieve serial production of fuel cell heavy-duty trucks
43 in the late 2020s, with comparable costs to diesel vehicles achieved after 2030 (Jordbakker et al. 2018).
44 Over the next decade or so, Hydrogen FCVs could become cost-competitive for various transport
45 applications, potentially including long-haul trucks, marine ships, and aviation (FCHEA 2019; FCHJU
46 2019; BloombergNEF 2020; Hydrogen Council 2017, 2020). The speed of fuel cell system cost
47 reduction is a key factor for achieving widespread uptake. Yet, experts disagree on the relationship
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1 between the scale of fuel cell demand, cost, and performance improvements (Cano et al. 2018). Costs
2 of light-, medium-, and heavy-duty fuel cell powertrains have decreased by orders of magnitude with
3 further reductions of a factor of two expected with continued technological progress (Whiston et al.
4 2019). For example, the costs of platinum for fuel cell stacks have decreased by an order of magnitude
5 (Staffell et al. 2019); current generation FCVs use approximately 0.25 g/kW Pt and a further reduction
6 of 50-80% is expected by 2030 (Hao et al. 2019).
7 Hydrogen is likely to take diverse roles in the future energy system: as a fuel in industry and buildings,
8 as well as transport, and as energy storage for variable renewable electricity. Further research is required
9 to understand better how a Hydrogen transport fuel supply system fits within the larger Hydrogen
10 energy system, especially in terms of integration within existing infrastructure, such as the electricity
11 grid and the natural gas pipeline system (IEA 2015).
12 Strong and durable policies would be needed to enable widespread use of Hydrogen as a transport fuel
13 and to sustain momentum during a multi-decade transition period for Hydrogen FCVs to become cost-
14 competitive with electric vehicles (IEA 2019c; FCHEA 2019; FCHJU 2019; BNEF 2020; Hydrogen
15 Council 2017, 2020). The analysis suggests that Hydrogen is likely to have strategic and niche roles in
16 transport, particularly in long-haul shipping and aviation. With continuing improvements, Hydrogen
17 and electrification will likely play a role in decarbonising heavy-duty road and rail vehicles.
18
19 10.3.4 Refuelling and charging infrastructure
20 The transport sector relies on liquid gasoline, and diesel for land-based transport, jet fuel for aviation,
21 and heavy fuel oil for shipping. Extensive infrastructure for refuelling liquid fossil fuels already exists.
22 Ammonia, synthetic fuels, and biofuels have emerged as alternative fuels for powering combustion
23 engines and turbines used in land, shipping, and aviation (Figure 10.2). Synthetic fuels such as e-
24 Methanol and Fischer-Tropsch liquids have similar physical properties and could be used with existing
25 fossil fuel infrastructure (Soler, 2019). Similarly, biofuels have been used in several countries together
26 with fossil fuels (Panoutsou et al. 2021). Ammonia is a liquid, but only under pressure, and therefore
27 will not be compatible with liquid fossil fuel refuelling infrastructure. Ammonia is, however, widely
28 used as a fertiliser and chemical raw material and 10% of annual Ammonia production is transported
29 via sea (Gallucci 2021). As such, a number of port facilities include Ammonia storage and transport
30 infrastructure and the shipping industry has experience in handling Ammonia (Gallucci 2021). This
31 infrastructure would likely need to be extended in order to support the use of Ammonia as a fuel for
32 shipping and therefore ports are likely to be the primary sites for these new refuelling facilities.
33 EVs and HFCV require separate infrastructure than liquid fuels. The successful diffusion of new vehicle
34 technologies is dependent on the preceding deployment of infrastructure (Leibowicz 2018), so that the
35 deployment of new charging and refuelling infrastructure will be critical for supporting the uptake of
36 emerging transport technologies like EVs and HFCVs, where it makes sense for each to be deployed.
37 As a result, there is likely a need for the simultaneous investment in both infrastructure and vehicle
38 technologies to accelerate decarbonisation of the transport sector.
39 Charging infrastructure: Charging infrastructure is important for a number of key reasons. From a
40 consumer perspective, robust and reliable charging infrastructure networks are required to build
41 confidence in the technology and overcome the often-cited barrier of 'range anxiety' (She et al. 2017).
42 Range anxiety is where consumers do not have confidence that an EV will meet their driving range
43 requirements. For LDVs, the majority of charging (75-90%) has been reported to take place at or near
44 homes (Figenbaum 2017; Webb et al. 2019; Wenig et al. 2019). Charging at home is a particularly
45 significant factor in the adoption of EVs as consumers are less willing to purchase an EV without home
46 charging (Berkeley et al. 2017; Funke and Plötz 2017; Nicholas et al. 2017). However, home charging
47 may not be an option for all consumers. For example, apartment dwellers may face specific challenges
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1 in installing charging infrastructure (Hall and Lutsey 2020). Thus, the provision of public charging
2 infrastructure is another avenue for alleviating range anxiety, facilitating longer distance travel in EVs,
3 and in turn, encouraging adoption (Hall and Lutsey 2017; Melliger et al. 2018; Narassimhan and
4 Johnson 2018; Melton et al. 2020). Currently, approximately 10% of charging occurs at public
5 locations, roughly split equally between AC (slower) and DC (fast) charging (Figenbaum 2017; Webb
6 et al. 2019; Wenig et al. 2019). Deploying charging infrastructure at workplaces and commuter car
7 parks is also important, particularly as these vehicles are parked at these locations for many hours.
8 Indeed, around 15-30% of EV charging currently occurs at these locations (Figenbaum 2017; Webb et
9 al. 2019; Wenig et al. 2019). It has been suggested that automakers and utilities could provide support
10 for the installation of home charging infrastructure (Hardman et al. 2018), while policy-makers can
11 provide support for public charging. Such support could come via supportive planning policy, building
12 regulations, and financial support. Policy support could also incentivise the deployment of charging
13 stations at workplaces and commuter car parks. Charging at these locations would have the added
14 benefit of using excess solar energy generated during the day (Hardman et al. 2018; Webb et al. 2019).
15 While charging infrastructure is of high importance for the electrification of light-duty vehicles,
16 arguably, it is even more important for heavy-duty vehicles given the costs of high-power charging
17 infrastructure. It is estimated that the installed cost of fast-charging hardware can vary between
18 approximately USD 45,000 to 200,000 per charger, depending on the charging rate, the number of
19 chargers per site, and other site conditions (Nicholas 2019; Hall and Lutsey 2019; Nelder and Rogers
20 2019). Deployment of shared charging infrastructure at key transport hubs, such as bus and truck depots,
21 freight distribution centres, marine shipping ports and airports, can encourage a transition to electric
22 vehicles across the heavy transport segments. Furthermore, if charging infrastructure sites are designed
23 to cater for both light and heavy-duty vehicles, infrastructure costs could decrease by increasing
24 utilisation across multiple applications and/or fleets (Nelder and Rogers 2019).
25 There are two types of charging infrastructure for electric vehicles: conductive charging involving a
26 physical connection and wireless/induction charging. The majority of charging infrastructure deployed
27 today for light and heavy-duty vehicles is conductive. However, wireless charging technologies are
28 beginning to emerge – particularly for applications like bus rapid transit – with vehicles able to charge
29 autonomously while parked and/or in motion (IRENA 2019). For road vehicles, electric road systems,
30 or road electrification, is also emerging as an alternative form of conductive charging infrastructure that
31 replaces a physical plug (Ainalis et al. 2020; Hill et al. 2020). This type of charging infrastructure is
32 particularly relevant for road freight where load demand is higher. Road electrification can take the
33 form of a charging rail built into the road pavement, run along the side of the road, through overhead
34 catenary power lines - similar to electrical infrastructure used for rail - or at recharging facilities at
35 stations along the route. This infrastructure can also be used to directly power other electrified
36 powertrains, such as hybrid and HFCV (Hardman et al. 2018; Hill et al. 2020).
37 Charging infrastructure also varies in terms of the level of charging power. For light vehicles, charging
38 infrastructure is generally up to 350 kW, which provide approximately 350 kilometres for every 10
39 minutes of charging. For larger vehicles, like buses and trucks, charging infrastructure is generally up
40 to 600 kW, providing around 50-100 km for every 10 minutes of charging (depending on the size of the
41 bus/truck). Finally, even higher power charging infrastructure is currently being developed at rates
42 greater than 1 MW, particularly for long-haul trucks and for short-haul marine shipping and aviation.
43 For example, one of the largest electric ferries in the world, currently operating in Denmark, uses a 4.4
44 MW charger (Heinemann et al. 2020).
45 Finally, there are several different charging standards, varying across transport segments and across
46 geographical locations. Like electrical appliances, different EV charging connectors and sockets have
47 emerged in different regions, e.g. CCS2 in Europe (ECA 2021), GB/T in China (Hove and Sandalow
48 2019). Achieving interoperability between charging stations is seen as another important issue for
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1 policy-makers to address to provide transparent data to the market on where EV chargers are located
2 and a consistent approach to paying for charging sessions (van der Kam and Bekkers 2020).
3 Interoperability could also play an important role in enabling smart charging infrastructure (Neaimeh
4 and Andersen 2020).
5 Smart charging - electric vehicle-grid integration strategies: EVs provide several opportunities for
6 supporting electricity grids if appropriately integrated. Conversely, a lack of integration could
7 negatively affect the grid, particularly if several vehicles are charged in parallel at higher charging rates
8 during peak demand periods (Webb et al. 2019; Jochem et al. 2021). There are three primary approaches
9 to EV charging. In unmanaged charging, EVs are charged ad-hoc, whenever connected, regardless of
10 conditions on the broader electricity grid (Webb et al. 2019; Jochem et al. 2021). Second, in managed
11 charging, EVs are charged during periods beneficial to the grid, e.g. high renewable generation and/or
12 low demand periods. Managed charging also allows utilities to regulate the rate of charge and can thus
13 provide frequency and regulation services to the grid (Weis et al. 2014). Finally, in bidirectional
14 charging or vehicle-to-grid (V2G), EVs are generally subject to managed charging, but an extension
15 provides the ability to export electricity from the vehicle's battery back to the building and/or wider
16 electricity grid (Ercan et al. 2016; Noel et al. 2019; Jochem et al. 2021). The term 'smart charging' has
17 become an umbrella term to encompass both managed charging (often referred to as a V1G) and
18 vehicle-to-grid (V2G). For electric utilities, smart charging strategies can provide backup power,
19 support load balancing, reduce peak loads (Zhuk et al. 2016; Noel et al. 2019; Jochem et al. 2021),
20 reduce the uncertainty in forecasts of daily and hourly electrical loads (Peng et al. 2012), and allow
21 greater utilisation of generation capacity (Hajimiragha et al. 2010; Madzharov et al. 2014).
22 Smart charging strategies can also enhance the climate benefits of EVs (Yuan et al. 2021). Controlled
23 charging can help avoid high carbon electricity sources, decarbonisation of the ancillary service
24 markets, or peak shaving of high carbon electricity sources (Jochem et al. 2021). V2G-capable EVs can
25 result in even lower total emissions, particularly when compared to other alternatives (Reddy et al.
26 2016). Noel et al.(2019) analysed V2G pathways in Denmark and noted that at a penetration rate of
27 75% by 2030, $34 billion in social benefits could be accrued (through things like displaced pollution).
28 These social benefits translate to $1,200 per vehicle. V2G-capable EVs were found to have the potential
29 to reduce carbon emissions compared to a conventional gasoline vehicle by up to 59%, assuming
30 optimised charging schedules (Hoehne and Chester 2016).
31 Projections of energy storage suggest smart charging strategies will come to play a significant role in
32 future energy systems. Assessment of different energy storage technologies for Europe showed that
33 V2G offered the most storage potential compared to other options and could account for 200 GW of
34 installed capacity by 2060, whereas utility-scale batteries and pumped hydro storage could provide 160
35 GW of storage capacity (Després et al., 2017). Another study found that EVs with controlled charging
36 (V1G) could provide similar services to stationary storage but at a far lower cost (Coignard et al. 2018).
37 While most deployments of smart charging strategies are still at the pilot stage, the number of projects
38 continues to expand, with the V2G Hub documenting at least 90 V2G projects across 22 countries in
39 2021 (Vehicle to Grid (VG) 2021). Policymakers have an important role in facilitating collaboration
40 between vehicle manufacturers, electricity utilities, infrastructure providers, and consumers to enable
41 smart charging strategies and ensure EVs can support grid stability and the uptake of renewable energy.
42 This is a critical part of decarbonising transport.
43 Hydrogen infrastructure: HFCVs are reliant on the development of widespread and convenient
44 Hydrogen refuelling stations (FCHEA 2019; IEA 2019c; BNEF 2020). Globally, there are around 540
45 Hydrogen refuelling stations, with the majority located in North America, Europe, Japan, and China
46 (IEA 2021a). Approximately 70% of these refuelling stations are open to the public (Coignard et al.
47 2018). Typical refuelling stations currently have a refuelling capacity of 100 to 350 kg/day (CARB
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1 2019, 2020; H2 Tools 2020; AFDC 2021). At most, current Hydrogen refuelling stations have daily
2 capacities under 500 kg/day (Liu et al. 2020b).
3 The design of Hydrogen refuelling stations depends on the choice of methods for Hydrogen supply and
4 delivery, compression and storage, and the dispensing strategy. Hydrogen supply could happen via on-
5 site production or via transport and delivery of Hydrogen produced off-site. At the compression stage,
6 Hydrogen is compressed to achieve the pressure needed for economic stationery and vehicle storage.
7 This pressure depends on the storage strategy. Hydrogen can be stored as a liquid or a gas. Hydrogen
8 can also be dispensed to the vehicles as a gas or a liquid, depending on the design of the vehicles (though
9 it tests the extremes of temperature range and storage capacity for an industrial product). The
10 technological and economic development of each of these components continues to be researched.
11 If Hydrogen is produced off site in a large centralised plant, it must be stored and delivered to refuelling
12 stations. The cost of Hydrogen delivery depends on the amount of Hydrogen delivered, the delivery
13 distance, the storage method (compressed gas or cryogenic liquid), and the delivery mode (truck vs.
14 pipeline). Table 10.6 describes the three primary options for Hydrogen delivery. Most Hydrogen
15 refuelling stations today are supplied by trucks and, very occasionally, Hydrogen pipelines. Gaseous
16 tube trailers could also be used to deliver Hydrogen in the near term, or over shorter distances, due to
17 the low fixed cost (although the variable cost is high). Both liquefied truck trailers and pipelines are
18 recognised as options in the medium to long-term as they have higher capacities and lower costs over
19 longer distances (FCHJU 2019; Li et al. 2020; EU 2021).Alternatively, Hydrogen can be produced on
20 site using a small-scale onsite electrolyser or steam methane reforming unit combined with CCS.
21 Hydrogen is generally dispensed to vehicles as a compressed gas at pressures 350 or 700 bar, or as
22 liquified Hydrogen at – 253oC (Hydrogen Council 2020).
23
24 Table 10.6 Overview of three transport technologies for Hydrogen delivery in the transport sector
25 showing relative differences. Source: (IEA 2019c)
Delivery Variable Deployment
Capacity Energy loss Fixed costs
distance costs phase
Gaseous tube
Low Low Low Low High Near term
trailers
Liquefied Medium to
Medium High High Medium Medium
truck trailers long term
Hydrogen Medium to
High High Low High Low
pipelines long term
26
27 The costs for Hydrogen refuelling stations vary widely and remain uncertain for the future (IEA 2019c).
28 The IEA reports that the investment cost for one Hydrogen refuelling station ranges between USD 0.6–
29 2 million for Hydrogen at a pressure of 700 bar and a delivery capacity of 1,300 kg per day. The
30 investment cost of Hydrogen refuelling stations with lower refuelling capacities (~50 kg H2 per day)
31 delivered at lower pressure (350 bar) range between USD 0.15–1.6 million. A separate estimate by the
32 International Council for Clean Transport suggests that at a capacity of 600 kg of Hydrogen per day,
33 the capital cost of a single refuelling station would be approximately USD 1.8 million (ICCT 2017).
34 Given the high investment costs for Hydrogen refuelling stations, low utilisation can translate into a
35 high price for delivered Hydrogen. In Europe, most pumps operate at less than 10% capacity. For small
36 refuelling stations with a capacity of 50 kg H2 per day, this utilisation rate translates to a high price of
37 around USD 15–25 per kg H2 – in line with current retail prices (IEA 2019c). The dispensed cost of
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1 Hydrogen is also highly correlated with the cost of electricity, when H2 is produced using electrolysis,
2 which is required to produce low-carbon Hydrogen.
3
4 10.4 Decarbonisation of land-based transport
5 10.4.1 Light-duty vehicles for passenger transport
6 LDVs represent the main mode of transport for private citizens (ITF 2019) and currently represent the
7 largest share of transport emissions globally (IEA 2019d). Currently, powertrains depending on gasoline
8 and diesel fuels remain the dominant technology in the LDV segment (IEA 2019d). HEVs, and fully
9 battery electric vehicles (BEVs), however, have become increasingly popular in recent years (IEA
10 2021a). Correspondingly, the number of life cycle assessment (LCA) studies investigating HEVs,
11 BEVs, and fuel cell vehicles have increased. While historically the focus has been on the tailpipe
12 emissions of LDVs, LCA studies demonstrate the importance of including emissions from the entire
13 vehicle value chain, particularly for alternative powertrain technologies.
14 Figure 10.4 presents the cumulative life cycle emissions for selected powertrain technologies and fuel
15 chain combinations for compact and mid-sized LDV. This figure summarizes the harmonized findings
16 from the academic literature reviewed and the data submitted through an IPCC data collection effort,
17 as described in Appendix 10.1 (Cusenza et al. 2019; Hawkins et al. 2013; Tong et al. 2015b; Bauer et
18 al. 2015; Gao et al. 2016; Ellingsen et al. 2016; Kim and Wallington 2016; Cai et al. 2017; Ke et al.
19 2017; Lombardi et al. 2017; Miotti et al. 2017; Evangelisti et al. 2017; Valente et al. 2017; de Souza et
20 al. 2018; Elgowainy et al. 2018; Luk et al. 2018; Bekel and Pauliuk 2019; Messagie et al. 2014; Hoque
21 et al. 2019; IEA 2019a; Rosenfeld et al. 2019; Shen et al. 2019; Wang et al. 2019; Wu et al. 2019;
22 Benajes et al. 2020; Ambrose et al. 2020; Hill et al. 2020; Knobloch et al. 2020; JEC 2020; Qiao et al.
23 2020; Cox et al. 2018; Sacchi 2021; Zheng et al. 2020; Wolfram et al. 2020; Valente et al. 2021). The
24 values in the figure (and the remaining figures in this section) depend on the 100-year GWP used in
25 each study, which may differ from the recent GWP updates from WGI. However, it is unlikely that the
26 qualitative insights gained from the figures in this section would change using the update 100-year
27 GWP values.
28 Furthermore, note that the carbon footprint of biofuels used in Figure 10.4 are aggregate numbers not
29 specific to any individual value chain or fuel type. They are derived by combining land use-related
30 carbon emissions from Chapter 7 with conversion efficiencies and emissions as described in Section
31 10.3. Specifically, land use footprints derived from the three modelling approaches employed here are:
32 1) Integrated Assessment Models – Energy Modelling Forum 33 (IAM EMF33); 2) Partial models
33 assuming constant land cover (CLC), and, 3) Partial models using natural regrowth (NRG). The
34 emissions factors used here correspond to scenarios where global production of biomass for energy
35 purposes are 100 EJ/year, with lower emissions factors expected at lower levels of consumption and
36 vice-versa. Further details are available in Box 10.2 and Chapter 7.
37
38
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1
2 Figure 10.4 Life cycle GHG emissions intensities for mid-sized light-duty vehicle and fuel technologies
3 from the literature. The primary x-axis reports units in g CO2-eq vkm-1, assuming a vehicle life of 180,000
4 km. The secondary x-axis uses units of g CO2-eq pkm-1, assuming a 1.5 occupancy rate. The values in the
5 figure rely on the 100-year GWP value embedded in the source data, which may differ slightly with the
6 updated 100-year GWP values from WGI. The shaded area represents the interquartile range for
7 combined vehicle manufacturing and end-of-life phases. The length of the box and whiskers represent the
8 interquartile range of the operation phase for different fuel chains, while their placement on the x-axis
9 represents the absolute life cycle climate intensity, that is, includes manufacturing and end-of-life phases.
10 Each individual marker indicates a data point. ‘Adv. Biofuels’ i.e., advanced biofuels, refers to the use of
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1 second-generation biofuels and their respective conversion and cultivation emission factors. ‘IAM
2 EMF33’ refers to emissions factors for advanced biofuels derived from simulation results from the
3 integrated assessment models EMF33 scenarios. ‘PM’ refers to partial models, where ‘CLC’ is with
4 constant land cover and ‘NRG’ is with natural regrowth. ‘Hydrogen, low-carbon electricity’ is produced
5 via electrolysis using low-carbon electricity. ‘Hydrogen, natural gas SMR’ refers to fuels produced via
6 steam methane reforming of natural gas.
7
8 The tailpipe emissions and fuel consumption reported in the literature generally do not use empirical
9 emissions data. Rather, they tend to report fuel efficiency using driving cycles such as New European
10 Driving Cycle (NEDC) or the US EPA Federal Test Procedure. As a result, depending on the driving
11 cycle used, operating emissions reported in literature are possibly underestimated by as much as 15-
12 38%, in comparison to actual real driving emissions (Fontaras et al. 2017; Tsiakmakis et al. 2017;
13 Triantafyllopoulos et al. 2019). The extent of these underestimations, however, vary between
14 powertrain types, engine sizes, driving behaviour and environment.
15 Current average life cycle impacts of mid-size ICEVs span from approximately 65 g CO2-eq pkm-1 to
16 210 g CO2-eq pkm-1, with both values stemming from ICEVs running on biofuels. Between this range
17 of values, the current reference technologies are found, with diesel-powered ICEVs having total median
18 life cycle impacts of 130 g CO2-eq pkm-1 and gasoline-fuelled vehicles with 160 g CO2-eq pkm-1. Fuel
19 consumption dominates the life cycle emissions of ICEVs, with approximately 75% of these emissions
20 arising from the tailpipe and fuel chain.
21 HEVs and plug-in HEVs (PHEVs) vary in terms of degree of powertrain electrification. HEVs mainly
22 rely on regenerative braking for charging the battery. PHEVs combine regenerative braking with
23 external power sources for charging the battery. Operating emissions intensity is highly dependent on
24 the degree to which electrified driving is performed, which in turn is user- and route-dependent. For
25 PHEVs, emissions intensity is also dependent on the source of the electricity for charging. HEV and
26 PHEV production impacts are comparable to the emissions generated for producing ICEVs as the
27 batteries are generally small compared to those of BEVs. Current HEVs may reduce emissions
28 compared to ICEVs by up to 30%, depending on the fuel, yielding median life cycle intensities varying
29 between 60 g CO2-eq pkm-1 (biofuels, EMF33) and 165-170 g CO2-eq pkm-1 (biofuels, partial models
30 NRG). Within this wide range, all the combinations of electric/fossil driving can be found, as well as
31 the life cycle intensity for driving 100% on fossil fuel. Because HEVs rely on combustion as the main
32 energy conversion process, they offer limited mitigation opportunities. However, HEVs represent a
33 suitable temporary solution, yielding a moderate mitigation potential, in areas where the electricity mix
34 is currently so carbon intensive that the use of PHEVs and BEVs is not an effective mitigation solution
35 (Wolfram and Wiedmann 2017; Wu et al. 2019).
36 In contrast to HEVs, PHEVs may provide greater opportunities for use-phase emissions reductions for
37 LDVs. These increased potential benefits are due to the ability to charge the battery with low-carbon
38 electricity and the longer full-electric range in comparison to HEVs (Laberteaux et al. 2019). Consumer
39 behaviour (e.g., utility factor (UF) and charging patterns), manufacturer settings, and access to
40 renewable electricity for charging strongly influence the total operational impacts (Wu et al, 2019). The
41 UF is a weighting of the percentage of distance covered using the electric charge (charge depleting (CD)
42 stage) versus the distance covered using the internal combustion engine (charge sustaining (CS) stage)
43 (Paffumi et al. 2018). When the PHEV operates in CS mode, the internal combustion engine is used for
44 propulsion and to maintain the state of charge of the battery within a certain range, together with
45 regenerative braking (Plötz et al. 2018; Raghavan and Tal 2020). When running in CS mode, PHEVs
46 have a reduced mitigation potential and have impacts comparable to those of HEVs. On the other hand,
47 when the PHEV operates in CD mode, the battery alone provides the required propulsion energy (Plötz
48 et al. 2018; Raghavan and Tal 2020). Thus, in CD mode, PHEVs hold potential for higher mitigation
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1 potential, due to the possibility of charging the battery with low carbon electricity sources.
2 Consequently, the UF greatly influences the life cycle emissions of PHEVs. The current peer-reviewed
3 literature presents a wide range of UFs mainly due to varying testing protocols applied for estimating
4 the fuel efficiency and user behaviour (Pavlovic et al. 2017; Paffumi et al. 2018; Plötz et al. 2018, 2020;
5 Raghavan and Tal 2020; Hao et al. 2021). These factors make it difficult to harmonize and compare
6 impacts across PHEV studies. Due to the low number of appropriate PHEV studies relative to the other
7 LDV technologies and the complications in harmonizing available PHEV results, this technology is
8 omitted from Figure 10.4. However, due to the dual operating nature of PHEV vehicles, one can expect
9 that the life cycle GHG emissions intensities for these vehicles will lie between those of their ICEV and
10 BEV counterparts of similar size and performance.
11 Currently, BEVs have higher manufacturing emissions than equivalently sized ICEVs, with median
12 emissions of 14 t CO2-eq/vehicle against approximately 10 t CO2-eq/vehicle of their mid-sized fossil-
13 fuelled counterparts. These higher production emissions of BEVs are largely attributed to the battery
14 pack manufacturing and to the additional power electronics required. As manufacturing technology and
15 capacity utilization improve and globalizes to regions with low-carbon electricity, battery
16 manufacturing emissions will likely decrease. Due to the higher energy efficiency of the electric
17 powertrain, BEVs may compensate for these higher production emissions in the driving phase.
18 However, the mitigation ability of this technology relative to ICEVs is highly dependent on the
19 electricity mix used to charge the vehicle. As a consequence of the variety of energy sources available
20 today, current BEVs have a wide range of potential average life cycle impacts, ranging between 60 and
21 180 g CO2-eq pkm-1 with electricity generated from wind and coal, respectively. The ability to achieve
22 large carbon reductions via vehicle electrification is thus highly dependent on the generation of low-
23 carbon electricity, with the greatest mitigation effects achieved when charging the battery with low-
24 carbon electricity. The literature suggests that current BEVs, if manufactured on low carbon electricity
25 as well as operated on low carbon electricity would have footprints as low 22 g CO2-eq pkm-1 for a
26 compact sized car (Ellingsen et al. 2014, 2016). This value suggests a reduction potential of around
27 85% compared to similarly sized fossil fuel vehicles (median values). Furthermore, BEVs have a co-
28 benefit of reducing local air pollutants that are responsible for human health complications, particularly
29 in densely populated areas (Hawkins et al. 2013; Ke et al. 2017).
30 As with BEVs, current HFCVs have higher production emissions than similarly sized ICEVs and BEVs,
31 generating on average approximately 15 t CO2-eq/vehicle. As with BEVs, the life cycle impacts of
32 FCVs are highly dependent on the fuel chain. To date, the most common method of Hydrogen
33 production is steam methane reforming from natural gas (Khojasteh Salkuyeh et al. 2017), which is
34 relatively carbon intensive, resulting in life cycle emissions of approximately 88 g CO2-eq pkm-1.
35 Current literature covering life cycle impacts of the FCVs show that vehicles fuelled with Hydrogen
36 produced from steam methane reforming through natural gas offer little or no mitigation potential over
37 ICEVs. Other available Hydrogen fuel chains vary widely in carbon intensity, depending on the
38 synthesis method and the energy source used (electrolysis or steam methane reforming; fossil fuels vs.
39 renewables). The least carbon-intensive Hydrogen pathways rely on electrolysis powered by low-
40 carbon electricity. Compared to ICEVs and BEVs, FCVs for LDVs are at a lower technology readiness
41 level as discussed in section 10.3.
42
43 START BOX HERE
44
45 Box 10.3 – Vehicle size trends and implications on the fuel efficiency of LDVs
46 Vehicle size trends: On a global scale, SUV sales have been constantly growing in the last decade, with
47 39% of the vehicles sold in 2018 being SUVs (IEA 2019d). If the trend towards increasing vehicle size
48 and engine power continues, it may result in higher overall emissions from the LDV fleet (relatively to
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1 smaller vehicles with the same powertrain technology). The magnitude of the influence vehicle mass
2 has on fuel efficiency varies with the powertrain, which have different efficiencies. Box 10.3 Figure 1
3 highlights this relationship using data from the same literature used to create Figure 10.4. Higher
4 powertrain efficiency results in lower energy losses in operation, and thus requires less energy input to
5 move a given mass than a powertrain of lower efficiency. This pattern is illustrated by the more gradual
6 slope of BEVs in Box 10.3 Figure 1. The trend towards bigger and heavier vehicles with consequently
7 higher use phase emissions can be somewhat offset by improvements in powertrain design, fuel
8 efficiency, light weighting, and aerodynamics (Gargoloff et al. 2018; Wolfram et al. 2020). The
9 potential improvements provided by these strategies are case-specific and not thoroughly evaluated in
10 the literature, either individually or as a combination of multiple strategies.
11 Light weighting: There is an increasing use of advanced materials such as high-strength steel,
12 aluminium, carbon fibre, and polymer composites for vehicle light weighting (Hottle et al. 2017). These
13 materials reduce the mass of the vehicle and thereby also reduce the fuel or energy required to drive.
14 Light-weighted components often have higher production emissions than the components they replace
15 due to the advanced materials used (Kim and Wallington 2016). Despite these higher production
16 emissions, some studies suggest that the reduced fuel consumption over the lifetime of the light-
17 weighted vehicle may provide a net mitigation effect in comparison to the non-light-weighted vehicle
18 (Hottle et al. 2017; Kim and Wallington 2013; Upadhyayula et al. 2019; Milovanoff et al. 2019;
19 Wolfram et al. 2020). However, multiple recent publications have found that in some cases, depending
20 on, for example, vehicle size and carbon intensity of the light weighting materials employed, the GHG
21 emissions avoided due to improved fuel efficiency do not offset the higher manufacturing emissions of
22 the vehicle (Luk et al. 2018; Wu et al. 2019). In addition, these advanced materials may be challenging
23 to recycle in a way that retains their high technical performance (Meng et al. 2017).
24 Co-effects on particulate matter: Light weighting may also alleviate the particulate matter (PM)
25 emissions arising from road and brake wear. BEVs are generally heavier than their ICEV counterparts,
26 which may potentially cause higher stress on the road surfaces and tires, with consequently higher PM
27 emissions per kilometre driven (Timmers and Achten 2016). Regenerative braking in HEVs, BEVs and
28 FCVs, however, reduces the mechanical braking required, and therefore may compensate for the higher
29 brake wear emissions from these heavier vehicle types. In addition, BEVs have no tailpipe emissions,
30 which further offsets the increased PM emissions from road and tire wear. Therefore, light-weighting
31 strategies may offer a carbon and particulates mitigation effect; however, in some cases, other
32 technological options may reduce CO2 emissions even further.
33
34
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1
2 Box 10.3, Figure 1 Illustration of energy consumption as a function of vehicle size (using mass as a proxy)
3 and powertrain technology. FCVs omitted due to lacking data.
4
5 END BOX HERE
6 Two-wheelers, consisting mainly of lower-powered mopeds and higher-powered motorcycles, are
7 popular for personal transport in densely populated cities, especially in developing countries. LCA
8 studies for this class of vehicle are relatively uncommon compared to four-wheeled LDVs. In the
9 available results, however, two-wheelers exhibit similar trends for the different powertrain technologies
10 as the LDVs, with electric powertrains having higher production emissions, but usually lower operating
11 emissions. The life cycle emissions intensity for two-wheelers is also generally lower than four-wheeled
12 LDVs on a vehicle-kilometre basis. However, two-wheelers generally cannot carry as many passengers
13 as four-wheeled LDVs. Thus, on a passenger-kilometre basis, a fully occupied passenger vehicle may
14 still have lower emissions than a fully occupied two-wheeler. However, today, most passenger vehicles
15 have relatively low occupancy and thus have a correspondingly high emissions intensity on a pkm basis.
16 This points to the importance of utilization of passenger vehicles at higher occupancies to reduce the
17 life cycle intensity of LDVs on a pkm basis. For example, the median emissions intensity of a gasoline
18 passenger vehicle is 222 g CO2-eq vkm-1, and 160 g CO2-eq vkm-1 for a gasoline two-wheeler (Cox and
19 Mutel 2018). At a maximum occupancy factor of four and two passengers, respectively, the transport
20 emissions intensity for these vehicles are 55 and 80 g CO2-eq pkm-1. Under the same occupancy rates
21 assumption, BEV two-wheelers recharged on the average European electricity mix, achieve lower life
22 cycle GHG intensities than BEV four-wheeled LDVs. On the other hand, FCV two-wheelers with
23 Hydrogen produced via steam methane reforming present higher GHG intensity than their four-wheeled
24 counterparts, when compared on a pkm basis at high occupancy rates.
25 ICEV, HEV, and PHEV technologies, which are powered using combustion engines, have limited
26 potential for deep reduction of GHG emissions. Biofuels offer good mitigation potential if low land use
27 change emissions are incurred (e.g., the IAM EMF33 and partial models, CLC biofuels pathways shown
28 in Figure 10.4). The literature shows large variability, depending on the method of calculating
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1 associated land use changes. Resolving these apparent methodological differences is important to
2 consolidating the role biofuels may play in mitigation, as well as the issues raised in Chapter 7 about
3 the conflicts over land use. The mitigation potential of battery and fuel cell vehicles is strongly
4 dependent on the carbon intensity of their production and the energy carriers used in operation.
5 However, these technologies likely offer the highest potential for reducing emissions from LDVs. Prior
6 work on the diffusion dynamics of transport technologies suggests that “the diffusion of infrastructure
7 precedes the adoption of vehicles, which precedes the expansion of travel” (Leibowicz 2018). These
8 dynamics reinforce the argument for strong investments in both the energy infrastructure and the vehicle
9 technologies.
10 To successfully transition towards LDVs utilizing low-carbon fuels or energy sources, the technologies
11 need to be accessible to as many people as possible, which requires competitive costs compared to
12 conventional diesel and gasoline vehicles. The life cycle costs (LCCs) of LDVs depend on the
13 purchasing costs of the vehicles, their efficiency, the fuel costs, and the discount rate. Figure 10.5 shows
14 the results of a parametric analysis of LCC for diesel LDVs, BEVs, and FCVs. The range of vehicle
15 efficiencies captured in Figure 10.5 are the same as the ranges used for Figure 10.4, while the ranges
16 for fuel costs and vehicle purchase prices come from the literature. The assumed discount rate for this
17 parametric analysis is 3%. Appendix 10.2 includes the details about the method and underlying data
18 used to create this figure.
19
20
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1
2
3
4 Figure 10.5 LCC for light-duty ICEVs, BEVs, and HFCVs. The results for ICEVs represent the LCC of a
5 vehicle running on gasoline. However, these values are also representative for ICEVs running on diesel as
6 the costs ranges in the literature for these two solutions are similar. The secondary y-axis depicts the cost
7 of the different of the energy carriers normalized in USD/GJ for easier cross-comparability.
8
9 Figure 10.5 shows the range of LCC, in USD per pkm, for different powertrain technologies, and the
10 influence of vehicle efficiency (low or high), vehicle purchase price, and fuel/electricity cost on the
11 overall LCC. For consistency with Figure 10.4, an occupancy rate of 1.5 is assumed. Mid-sized ICEVs
12 have a purchase price of USD 20,000-40,000, and average fuel costs are in the range of 1-1.5 USD/L.
13 With these conditions, the LCC of fossil-fuelled LDVs span between 0.22-0.35 USD per pkm or
14 between 0.17-0.28 USD per pkm, for low and high efficiency ICEVs respectively (Figure 10.5).
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1 BEVs have higher purchase prices than ICEVs, though a sharp decline has been observed since AR5.
2 Due to the rapid development of the lithium-ion battery technology over the years (Schmidt et al. 2017)
3 and the introduction of subsidies in several countries, BEVs are quickly reaching cost parity with
4 ICEVs. Mid-sized BEVs average purchase prices are in the range of USD 30,000-50,000 but the
5 levelised cost of electricity shows a larger spread (65-200 USD/MWh) depending on the geographical
6 location and the technology (see Chapter 6). Therefore, assuming purchase price parity between ICEVs
7 and BEVs, BEVs show lower LCC (Figure 10.5) due to higher efficiency and the lower cost of
8 electricity compared to fossil fuels on a per-GJ basis (secondary y-axis on Figure 10.5).
9 FCVs represent the most expensive solution for LDV, mainly due to the currently higher purchase price
10 of the vehicle itself. However, given the lower technology readiness level of FCVs and the current
11 efforts in the research and development of this technology, FCVs could become a viable technology for
12 LDVs in the coming years. The issues regarding the extra energy involved in creating the Hydrogen
13 and its delivery to refuelling sites remain, however. The levelized cost of Hydrogen on a per GJ basis
14 is lower than conventional fossil fuels but higher than electricity. In addition, within the levelized cost
15 of Hydrogen, there are significant cost differences between the Hydrogen producing technologies.
16 Conventional technologies such as coal gasification and steam methane reforming from natural gas,
17 both with and without carbon capture and storage, represent the cheapest options (Bekel and Pauliuk
18 2019; Parkinson et al. 2019; Khzouz et al. 2020; Al-Qahtani et al. 2021). Hydrogen produced via
19 electrolysis is currently the most expensive technology, but with significant potential cost reductions
20 due to the current technology readiness level.
21
22 10.4.2 Transit technologies for passenger transport
23 Buses provide urban and peri-urban transport services to millions of people around the world and a
24 growing number of transport agencies are exploring alternative-fuelled buses. Alternative technologies
25 to conventional diesel-powered buses include buses powered with CNG, LNG, synthetic fuels, and
26 biofuels (e.g., biodiesel, renewable diesel, dimethyl ether); diesel hybrid-electric buses; battery electric
27 buses; electric catenary buses, and Hydrogen fuel cell buses. Rail is an alternative mode of transit that
28 could support decarbonisation of land-based passenger mobility. Electric rail systems can provide urban
29 services (light rail and metro systems), as well as longer distance transport. Indeed, many cities of the
30 world already have extensive metro systems, and regions like China, Japan and Europe have a robust
31 high-speed inter-city railway network. Intercity rail transport can be powered with electricity; however,
32 fossil fuels are still prevalent for long-distance rail passenger transport in some regions. Battery electric
33 long-distance trains may be a future option for these areas.
34 Figure 10.6 shows the life cycle GHG emissions from different powertrain and fuel technologies for
35 buses and passenger rail. The data in each panel came from a number of relevant scientific studies (IEA
36 2019e; Tong et al. 2015a; Dimoula et al. 2016; de Bortoli et al. 2017; Meynerts et al. 2018; Cai et al.
37 2015; de Bortoli and Christoforou 2020; Hill et al. 2020; Liu et al. 2020a; Valente et al. 2021, 2017).
38 The width of the bar represents the variability in available estimates, which is primarily driven by
39 variability in reported vehicle efficiency, size, or drive cycle. While some bars overlap, the figure may
40 not fully capture correlations between results. For example, low efficiency associated with aggressive
41 drive cycles may drive the upper end of the emission ranges for multiple technologies; thus, an overlap
42 does not necessarily suggest uncertainty regarding which vehicle type would have lower emissions for
43 a comparable trip. Additionally, reported life cycle emissions do not include embodied GHG emissions
44 associated with infrastructure construction and maintenance. These embodied emissions are potentially
45 a larger fraction of life cycle emissions for rail than for other transport modes (Chester and Horvath
46 2012; Chester et al. 2013). One study reported values ranging from 10-25 g CO2 per passenger-
47 kilometre (International Union of Railways 2016), although embodied emissions from rail are known
48 to vary widely across case studies (Olugbenga et al. 2019). These caveats are also applicable to the
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1 other figures in this section.
2
3
4
5
6
7 Figure 10.6 Life cycle GHG intensity of land-based bus and rail technologies. Each bar represents the
8 range of the life cycle estimates, bounded by minimum and maximum energy use per pkm, as reported
9 for each fuel/powertrain combination. The ranges are driven by differences in vehicle characteristics and
10 operating efficiency. For energy sources with highly variable upstream emissions low, medium and/or
11 high representative values are shown as separate rows. The primary x-axis shows life cycle GHG
12 emissions, in g CO2-eq per pkm, assuming 80% occupancy; the secondary x-axis assumes 20%
13 occupancy. The values in the figure rely on the 100-year GWP value embedded in the source data, which
14 may differ slightly with the updated 100-year GWP values from WGI. For buses, the main bars show full
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1 life cycle, with vertical bars disaggregating the vehicle cycle. ‘Diesel, high’ references emissions factors for
2 diesel from oil sands. ‘Adv. Biofuels’ i.e., advanced biofuels, refers to the use of second-generation
3 biofuels and their respective conversion and cultivation emission factors. ‘IAM EMF33’ refers to
4 emissions factors for advanced biofuels derived from simulation results from the integrated assessment
5 models EMF33 scenarios. ‘PM’ refers to partial models, where ‘CLC’ is with constant land cover and
6 ‘NRG’ is with natural regrowth. ‘DAC FT-Diesel, wind electricity’ refers to Fischer-Tropsch diesel
7 produced via a CO2 direct air capture process that uses wind electricity. ‘Hydrogen, low-carbon
8 renewable’ refers to fuels produced via electrolysis using low-carbon electricity. ‘Hydrogen, natural gas
9 SMR’ refers to fuels produced via steam methane reforming of natural gas. Results for ICEVs with ‘high
10 emissions DAC FT-Diesel from natural gas’ are not included here since the life cycle emissions are
11 estimated to be substantially higher than petroleum diesel ICEVs.
12
13 Figure 10.6 highlights that BEV and FCV buses and passenger rail powered with low carbon electricity
14 or low carbon Hydrogen, could offer reductions in GHG emissions compared to diesel-powered buses
15 or diesel-powered passenger rail. However, and not surprisingly, these technologies would offer only
16 little emissions reductions if power generation and Hydrogen production rely on fossil fuels. While
17 buses powered with CNG and LNG could offer some reductions compared to diesel-powered buses,
18 these reductions are unlikely to be sufficient to contribute to deep decarbonisation of the transport sector
19 and they may slow down conversion to low or zero-carbon options already commercially available.
20 Biodiesel and renewable diesel fuels (from sources with low upstream emissions and low risk of induced
21 land use change) could offer important near-term reductions for buses and passenger rail, as these fuels
22 can often be used with existing vehicle infrastructure. They could also be used for long haul trucks and
23 trains, shipping and aviation as discussed below and in later sections.
24 There has been growing interest in the production of synthetic fuels from CO2 produced by direct air
25 capture (DAC) processes. Figure 10.6 includes the life cycle GHG emissions from buses and passenger
26 rail powered with synthetic diesel produced through a DAC system paired with a Fischer-Tropsch (FT)
27 process based on (Liu et al. 2020a). This process requires the use of Hydrogen (as shown in Figure 10.2
28 in section 10.3), so the emissions factors of the resulting fuel depend on the emissions intensity of
29 Hydrogen production. An electricity emissions factor less than 140 g CO2-eq kWh-1 would be required
30 for this pathway to achieve lower emissions than petroleum diesel (Liu et al. 2020a); e.g., this would
31 be equivalent to 75% wind and 25% natural gas electricity mix (see Appendix 10.1). If the process
32 relied on steam methane reforming for Hydrogen production or fossil-based power generation, synthetic
33 diesel from the DAC-FT process would not provide GHG emissions reductions compared to
34 conventional diesel. DAC-FT from low-carbon energy sources appears to be promising from an
35 emissions standpoint and could warrant the R&D and demonstration attention outlined in the rest of the
36 chapter, but it cannot be contemplated as a decarbonisation strategy without the availability of low-
37 carbon Hydrogen.
38 At high occupancy, both bus and rail transport offer substantial GHG reduction potential per pkm, even
39 compared with the lowest-emitting private vehicle options. Even at 20% occupancy, bus and rail may
40 still offer emission reductions compared to passenger cars, especially notable when comparing BEVs
41 with low-carbon electricity (the lowest emission option for all technologies) across the three modes.
42 Only when comparing a fossil fuel-powered bus at low occupancy with a low-carbon powered car at
43 high occupancy is this conclusion reversed. Use of public transit systems, especially those that rely on
44 buses and passenger rail fuelled with the low carbon fuels previously described would thus support
45 efforts to decarbonise the transport sector. Use of these public transit systems will depend on urban
46 design and consumer preferences (as described in Section 10.2 and Chapters 5 and 8), which in turn
47 depend on time, costs, and behavioural choices.
48 Figure 10.7 shows the results of a parametric analysis of the LCCs of transit technologies with the
49 highest potential for GHG emissions reductions. As with Figure 10.5, the vehicle efficiency ranges are
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1 the same as those from the LCA estimates (80% occupancy). Vehicle, fuel, and maintenance costs
2 represent ranges in the literature (Eudy and Post 2018b; IEA 2019e; Argonne National Laboratory 2020;
3 BNEF 2020; Eudy and Post 2020; Hydrogen Council 2020; IEA 2020b,c; IRENA 2020; Johnson et al.
4 2020; Burnham et al. 2021; IEA 2021c,d; U.S. Energy Information Administration 2021), and the
5 discount rate is 3% where applicable. Appendix 10.2 of the chapter provides the details behind these
6 estimates. The panels for the ICEV can represent buses and passenger trains powered with any form of
7 diesel, whether derived from petroleum, synthetic hydrocarbons, or biofuels. For reference, global
8 average automotive diesel prices from 2015-2020 fluctuated around 1 USD/L, and the 2019 world
9 average industrial electricity price was approximately 100 USD/MWh (IEA 2021d). Retail Hydrogen
10 prices in excess of 13 USD/kg have been observed (Eudy and Post 2018a; Argonne National Laboratory
11 2020; Burnham et al. 2021) though current production cost estimates for Hydrogen produced from
12 electrolysis are far lower ((IRENA 2020), and as reported in Chapter 6), at around 5-7 USD/kg with
13 future forecasts as low as 1 USD/kg ((IRENA 2020; BNEF 2020; Hydrogen Council 2020), and as
14 reported in Chapter 6).
15 Under most parameter combinations, rail is the most cost-effective option, followed by buses, both of
16 which are an order of magnitude cheaper than passenger vehicles. Note that costs per pkm are strongly
17 influenced by occupancy assumptions; at low occupancy (e.g., <20% for buses and <10% for rail), the
18 cost of transit approaches the LCC for passenger cars. For diesel rail and buses, cost ranges are driven
19 by fuel costs, whereas vehicle are both important drivers for electric or Hydrogen modes due to high
20 costs (but also large projected improvements) associated with batteries and fuel cell stacks. Whereas
21 the current state of ICEV technologies is best represented by cheap vehicles and low fuel costs for diesel
22 (top left of each panel), these costs are likely to rise in future due to stronger emission/efficiency
23 regulations and rising crude oil prices. On the contrary, the current status of alternative fuels is better
24 represented by high capital costs and mid-to-high fuel costs (right side of each panel; mid-to-bottom
25 rows), but technology costs are anticipated to fall with increasing experience, research, and
26 development. Thus, while electric rail is already competitive with diesel rail, and electric buses are
27 competitive with diesel buses in the low efficiency case, improvements are still required in battery costs
28 to compete against modern diesel buses on high efficiency routes, at current diesel costs. Similarly,
29 improvements to both vehicle cost and fuel costs are required for Hydrogen vehicles to become cost
30 effective compared to their diesel or electric counterparts. At either the upper end of the diesel cost
31 range (bottom row of ICEV panels), or within the 2030-2050 projections for battery costs, fuel cell
32 costs and Hydrogen costs (top left of BEV and FCV panels) – both battery and Hydrogen powered
33 vehicles become financially attractive.
34
35
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5 Figure 10.7 Life cycle costs for internal combustion engine vehicles ICEV, BEV, and HFCV for buses and
6 passenger rail. The range of efficiencies for each vehicle type are consistent with the range of efficiencies
7 in Figure 10.6 (80% occupancy). The results for the ICEV can be used to evaluate the life cycle costs of
8 ICE buses and passenger rail operated with any form of diesel, whether from petroleum, synthetic
9 hydrocarbons, or biofuel, as the range of efficiencies of vehicles operating with all these fuels is similar.
10 The secondary y-axis depicts the cost of the different energy carriers normalized in USD/GJ for easier
11 cross-comparability.
12
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1 10.4.3 Land-based freight transport
2 As is the case with passenger transport, there is growing interest in alternative fuels that could reduce
3 GHG emissions from freight transport. Natural gas-based fuels (e.g., CNG, LNG) are an example,
4 however these may not lead to drastic reductions in GHG emissions compared to diesel. Natural gas-
5 powered vehicles have been discussed as a means to mitigate air quality impacts (Khan et al. 2015; Pan
6 et al. 2020; Cai et al. 2017) but those impacts are not the focus of this review. Decarbonisation of
7 medium and heavy-duty trucks would likely require the use of low-carbon electricity in battery-electric
8 trucks, low-carbon Hydrogen or Ammonia in fuel-cell trucks, or bio-based fuels (from sources with low
9 upstream emissions and low risk of induced land use change) used in ICE trucks.
10 Freight rail is also a major mode for the inland movement of goods. Trains are more energy efficient
11 (per tkmm) than trucks, so expanded use of rail systems (particularly in developing countries where
12 demand for goods could grow exponentially) could provide carbon abatement opportunities. While
13 diesel-based locomotives are still a major propulsion used in freight rail, interest in low-carbon
14 propulsion technologies is growing. Electricity already powers freight rail in many European countries
15 using overhead catenaries. Other low-carbon technologies for rail may include advanced storage
16 technologies, biofuels, synthetic fuels, Ammonia, or Hydrogen.
17
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4 Figure 10.8 Life cycle GHG intensity of land-based freight technologies and fuel types. Each bar
5 represents the range of the life cycle estimates, bounded by minimum and maximum energy use per tkm,
6 as reported for each fuel/powertrain combination. The ranges are driven by differences in vehicle
7 characteristics and operating efficiency. For energy sources with highly variable upstream emissions, low,
8 medium and/or high representative values are shown as separate rows. For trucks, the primary x-axis
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1 shows life cycle GHG emissions, in g CO2-eq per tkm, assuming 100% payload; the secondary x-axis
2 assumes 50% payload. The values in the figure rely on the 100-year GWP value embedded in the source
3 data, which may differ slightly with the updated 100-year GWP values from WGI. For rail, values
4 represent average payloads. For trucks, main bars show full life cycle, with vertical bars disaggregating
5 the vehicle cycle. ‘Diesel, high’ references emissions factors for diesel from oil sands. ‘Adv. Biofuels’
6 refers to the use of second-generation biofuels and their respective conversion and cultivation emission
7 factors. ‘IAM EMF33’ refers to emissions factors for advanced biofuels derived from simulation results
8 from the EMF33 scenarios. ‘PM’ refers to partial models, where ‘CLC’ is with constant land cover and
9 ‘NRG’ is with natural regrowth. DAC FT-Diesel, wind electricity refers to Fischer-Tropsch diesel
10 produced via a CO2 direct air capture process that uses wind electricity. ‘Ammonia and Hydrogen, low-
11 carbon renewable’ refers to fuels produced via electrolysis using low-carbon electricity. ‘Ammonia and
12 Hydrogen, natural gas SMR’ refers to fuels produced via steam methane reforming of natural gas.
13
14 Figure 10.8 presents a review of life cycle GHG emissions from land-based freight technologies (heavy
15 and medium-duty trucks, and rail). Each panel within the figure represents data in GHG emissions per
16 tkm of freight transported by different technology and/or fuel types, as indicated by the labels to the
17 left. The data in each panel came from a number of relevant scientific studies (Merchan et al. 2020;
18 Frattini et al. 2016; Zhao et al. 2016; CE Delft 2017; Isaac and Fulton 2017; Song et al. 2017; Cooper
19 and Balcombe 2019; S. Mojtaba et al. 2019; Nahlik et al. 2016; Prussi et al. 2020; Hill et al. 2020; Liu
20 et al. 2020a; Valente et al. 2021; Gray et al. 2021; Valente et al. 2017; Tong et al. 2015a). Similar to
21 the results for buses, technologies that offer substantial emission reductions for freight include: ICEV
22 trucks powered with the low carbon variants for biofuels, Ammonia or synthetic diesel; BEVs charged
23 with low carbon electricity; and FCVs powered with renewable-based electrolytic Hydrogen, or
24 Ammonia. Since Ammonia and Fischer-Tropsch diesel are produced from Hydrogen, their emissions
25 are higher than the source Hydrogen, but their logistical advantages over Hydrogen are also a
26 consideration (as discussed in Section 10.3).
27 Trucks exhibit economies of scale in fuel consumption, with heavy duty trucks generally showing lower
28 emissions per tkm than medium duty trucks. Comparing the life cycle GHG emissions from trucks and
29 rail, it is clear that rail using internal combustion engines is more carbon efficient than using internal
30 combustion trucks. Note that the rail emissions are reported for an average representative payload, while
31 the trucks are presented at 50% and 100% payload, based on available data. The comparison between
32 trucks and rail powered with electricity or Hydrogen is less clear – especially considering that these
33 values omit embodied GHG from infrastructure construction. One study reported embodied rail
34 infrastructure emissions of 15 g CO2 per tonne-kilometre for rail (International Union of Railways
35 2016), although such embodied emissions from rail are known to vary widely across case studies
36 (Olugbenga et al. 2019). Regardless, trucks and rail with low carbon electricity or low-carbon Hydrogen
37 have substantially lower emissions than incumbent technologies.
38 For trucks, Figure 10.8 includes two x-axes representing two different assumptions about their payload,
39 which substantially influence emissions per tonne-kilometre. These results highlight the importance of
40 truckload planning as an emissions reduction mechanism, for example, as also shown in (Kaack et al.
41 2018). Several studies also point to improvements in vehicle efficiency as an important mechanism to
42 reduce emissions from freight transport (Taptich et al. 2016; Kaack et al. 2018). However, projections
43 for diesel vehicles using such efficiencies beyond 2030 are promising, but still far higher emitting than
44 vehicles powered with low carbon sources.
45 Figure 10.9 shows the results of a parametric analysis of the LCC of trucks and freight rail technologies
46 with the highest potential for deep GHG reductions. As with Figure 10.8, the vehicle efficiency ranges
47 are the same as those from the LCA estimates (80% payload for trucks; effective payload as reported
48 by original studies for rail). Vehicle, fuel and maintenance costs represent ranges in the literature
49 (Moultak et al. 2017; Eudy and Post 2018b; IEA 2019e; Argonne National Laboratory 2020; BNEF
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1 2020; IRENA 2020; Burnham et al. 2021; IEA 2021c), and the discount rate is 3% where applicable
2 (details are in Appendix 10.2). The panels for the ICEV can represent trucks and freight trains powered
3 with any form of diesel, whether derived from petroleum, synthetic hydrocarbons, or biofuels. See
4 discussion preceding Figure 10.7 for additional details about current global fuel costs. Under most
5 parameter combinations, rail is the more cost-effective option, but the high efficiency case for trucks
6 (representing fuel efficient vehicles, favourable drive cycles and high payload) can be more cost-
7 effective than the low efficiency case for rail (representing systems with higher fuel consumption and
8 lower payload). For BEV trucks, cost ranges are driven by vehicle purchase price due to the large
9 batteries required and the associated wide range between their current high costs and anticipated future
10 cost reductions. For all other truck and rail technologies, fuel cost ranges play a larger role. Similar to
11 transit technologies, the current state of freight ICEV technologies is best represented by cheap vehicles
12 and low fuel costs for diesel (top left of each panel), and the current status of alternative fuels is better
13 represented by high capital costs and mid-to-high fuel costs (right side of each panel; mid-to-bottom
14 rows), with expected future increases in ICEV LCC and decreases in alternative fuel vehicle LCC.
15 Electric and Hydrogen freight rail are potentially already competitive with diesel rail (especially electric
16 catenary (IEA 2019e)), but low data availability (especially for Hydrogen efficiency ranges) and wide
17 ranges for reported diesel rail efficiency (likely encompassing low capacity utilization) makes this
18 comparison challenging. Alternative fuel trucks are currently more expensive than diesel trucks, but
19 future increases in diesel costs or a respective decrease in Hydrogen costs or in BEV capital costs
20 (especially the battery) would enable either alternative fuel technology to become financially attractive.
21 These results are largely consistent with raw results reported in existing literature, which suggest
22 ambiguity over whether BEV trucks are already competitive, but more consistency that Hydrogen is
23 not yet competitive, but could be in future (Zhao et al. 2016; White and Sintov 2017; Moultak et al.
24 2017; Sen et al. 2017; Zhou et al. 2017; Mareev et al. 2018; Yang et al. 2018a; El Hannach et al. 2019;
25 S. Mojtaba et al. 2019; Tanco et al. 2019; Burke and Sinha 2020; Jones et al. 2020). There is limited
26 data available on the LCC for freight rail, but at least one study IEA (2019g) suggests that electric
27 catenary rail is likely to have similar costs as diesel rail, while battery electric trains remain more
28 expensive and Hydrogen rail could become cheaper under forward-looking cost reduction scenarios.
29
30
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3 Figure 10.9 Life cycle costs for ICEV, BEV, and HFCV for heavy-duty trucks and freight rail. The range
4 of efficiencies for each vehicle type are consistent with the range of efficiencies in Figure 10.8. The results
5 for the ICEV can be used to evaluate the life cycle costs of ICE trucks and freight rail operated with any
6 form of diesel, whether from petroleum, synthetic hydrocarbons, or biofuels, as the range of efficiencies
7 of vehicles operating with all these fuels is similar. The secondary y-axis depicts the cost of the different of
8 the energy carriers normalized in USD/GJ for easier cross-comparability.
9
10 10.4.4 Abatement costs
11 Taken together, the results in this section suggest a range of cost-effective opportunities to reduce GHG
12 emissions from land-based transport. Mode shift from cars to passenger transit (bus or rail) can reduce
13 GHG emissions while also reducing LCCs, resulting in a negative abatement cost. Likewise, increasing
14 the utilization of vehicles (i.e., % occupancy for passenger vehicles or % payload for freight vehicles)
15 simultaneously decreases emissions and costs per pkm or per tkm, respectively. Within a given mode,
16 alternative fuel sources also show strong potential to reduce emissions at minimal added costs. For
17 LDVs, BEVs can offer emission reductions with LCCs that are already approaching that for
18 conventional ICEVs. For transit and freight, near-term abatement costs for the low-carbon BEV and
19 FCV options relative to their diesel counterparts range from near 0 USD/tonne CO2-eq (e.g., BEV buses
20 and BEV passenger rail) into the hundreds or even low thousands of dollars per tonne CO2-eq (e.g., for
21 heavy duty BEV and FCV trucks at current vehicle and fuel costs). With projected future declines in
22 storage, fuel cell, and low-carbon Hydrogen fuel costs, however, both BEV and FCV technologies can
23 likewise offer GHG reductions at negative abatement costs across all land-transport modes in 2030 and
24 beyond. Further information about costs and potentials is available in Chapter 12.
25
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1 10.5 Decarbonisation of aviation
2 This section addresses the potential for reducing GHG emissions from aviation. The overriding
3 constraint on developments in technology and energy efficiency for this sector is safety. Governance is
4 complex in that international aviation comes under the International Civil Aviation Organization
5 (ICAO), a specialised UN agency. The measures to reduce GHG emissions that are considered include
6 both in-sector (technology, operations, fuels) and out of sector (market-based measures, high-speed rail
7 modal shift/substitution). Demand management is not explicitly considered in this section, as it was
8 discussed in 10.2. A limited range of scenarios to 2050 and beyond are available and assessed at the
9 end of the section.
10
11 10.5.1 Historical and current emissions from aviation
12 Aviation is widely recognised as a ‘hard-to-decarbonise’ sector (Gota et al. 2019) having a strong
13 dependency on liquid fossil fuels and an infrastructure that has long ‘lock-in’ timescales, resulting in
14 slow fleet turnover times. The principal GHG emitted is CO2 from the combustion of fossil fuel aviation
15 kerosene (‘JET-A’), although its non-CO2 emissions can also affect climate (see section 10.5.2).
16 International emissions of CO2 are about 65% of the total emissions from aviation (Fleming and de
17 Lépinay 2019), which totalled approximately 1 Gt of CO2 in 2018. Emissions from this segment of the
18 transport sector have been steadily increasing at rates of around 2.5% per year over the last two decades
19 (see Figure 10.10), although for the period 2010 to 2018 the rate increased to roughly 4% per year. The
20 latest available data (2018) indicate that aviation is responsible for approximately 2.4% of total
21 anthropogenic emissions of CO2 (including land use change) on an annual basis (using IEA data, IATA
22 data and global emissions data of Le Quéré et al., 2018).
23
24
25
26 Figure 10.10 Historical global emissions of CO2 from aviation, along with capacity and transport work
27 (given in available seat kilometres, ASK; revenue passenger kilometres, RPK), Adapted from Lee et al.
28 (2021) using IEA and other data
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1
2 10.5.2 Short lived climate forcers and aviation
3 Aviation’s net warming effect results from its historical and current emissions of CO 2, and non-CO2
4 emissions of water vapour, soot, sulphur dioxide (from sulphur in the fuel), and nitrogen oxides (NOx,
5 = NO + NO2) (Penner et al. 1999; Lee et al. 2021; Naik et al. 2021). Although the effective radiative
6 forcing (ERF) of CO2 from historic aviation emissions is not currently the largest forcing term, it is
7 difficult to address because of the sector’s current dependency on fossil-based hydrocarbon fuels and
8 the longevity of CO2. A residual of emissions of CO2 today will still have a warming effect in many
9 thousands of years (Archer et al. 2009; Canadell et al. 2021) whereas water vapour, soot, and NOx
10 emissions will have long ceased to contribute to warming after some decades. As a result, CO2
11 mitigation of aviation to ‘net zero’ levels, as required in 1.5 ºC emission scenarios, requires fundamental
12 shifts in technology, fuel types, or changes of behaviour or demand.
13 The non-CO2 effects of aviation on climate fall into the category of short-lived climate forcers (SLCFs).
14 Emissions of NOx currently result in net positive warming from the formation of short-term ozone
15 (warming) and the destruction of ambient methane (cooling). If the conditions are suitable, emissions
16 of soot and water vapour can trigger the formation of contrails (Kärcher 2018), which can spread to
17 form extensive contrail-cirrus cloud coverage. Such cloud coverage is estimated to have a combined
18 ERF that is ~57% of the current net ERF of global aviation (Lee et al. 2021), although a comparison of
19 cirrus cloud observations under pre- and post-COVID-19 pandemic conditions suggest that this forcing
20 could be smaller (Digby et al. 2021). Additional effects from aviation from aerosol-cloud interactions
21 on high-level ice clouds through soot (Chen and Gettelman 2013; Zhou and Penner 2014; Penner et al.
22 2018), and lower-level warm clouds through Sulphur (Righi et al. 2013; Kapadia et al. 2016) are highly
23 uncertain, with no best estimates available (Lee et al. 2021). In total, the net ERF from aviation’s non-
24 CO2 SLCFs is estimated to be approximately 66% of aviation’s current total forcing. It is important to
25 note that the fraction of non-CO2 forcing to total forcing is not a fixed quantity and is dependent on the
26 recent history of growth (or otherwise) of CO2 emissions (Klöwer et al. 2021) The non-CO2 effects
27 from aviation are the subject of discussion for mitigation options (e.g., (Arrowsmith et al. 2020)).
28 However, the issues are complex, potentially involving technological and operational trade-offs with
29 CO2.
30
31 10.5.3 Mitigation potential of fuels, operations, energy efficiency, and market-based
32 measures
33 Technology options for engine and airframe: For every kg of jet fuel combusted, 3.16 kg CO2 is emitted.
34 Engine and airframe manufacturers’ primary objective, after safety issues, is to reduce direct operating
35 costs, which are highly dependent on fuel burn. Large investments have gone into engine technology
36 and aircraft aerodynamics to improve fuel burn per km (Cumpsty et al. 2019). There have been major
37 step changes in engine technology over time, from early turbojet engines, to larger turbofan engines.
38 However, the basic configuration of an aircraft has remained more or less the same for decades and will
39 likely remain at least to 2037 (Cumpsty et al. 2019). Airframes performance has improved over the
40 years with better wing design, but large incremental gains have become much harder as the technology
41 has matured. For twin-aisle aircraft, generally used for long ranges, fuel-burn is a pressing concern and
42 there have been several all-new aircraft designs with improvements in their lift-to-drag ratio (Cumpsty
43 et al. 2019). The principal opportunities for fuel reduction come from improvements in aerodynamic
44 efficiency, aircraft mass reduction, and propulsion system improvements. In the future, Cumpsty et al.
45 (2019) suggest that the highest rate of fuel burn reduction achievable for new aircraft is likely to be no
46 more than about 1.3% per year, which is well short of ICAO’s aspirational goal of 2% global annual
47 average fuel efficiency improvement. Radically different aircraft shapes, like the blended wing body
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1 (where the wings are not distinct from the fuselage) are likely to use about 10% less fuel than future
2 advanced aircraft of conventional form (Cumpsty et al. 2019). Such improvements would be “one-off”
3 gains, do not compensate for growth in emissions of CO2 expected to be in excess of 2% per annum,
4 and would take a decade or more to penetrate the fleet completely. Thus, the literature does not support
5 the idea that there are large improvements to be made in the energy efficiency of aviation that keep pace
6 with the projected growth in air transport.
7 Operational improvements for navigation: From a global perspective, aircraft navigation is relatively
8 efficient, with many long-haul routes travelling close to great circle trajectories, and avoiding
9 headwinds that increase fuel consumption. The ICAO estimates that flight inefficiencies on a global
10 basis are currently of the order 2–6% (ICAO 2019), while (Fleming and de Lépinay 2019) project
11 operational improvements (air traffic management) of up to 13% on a regional basis by 2050.
12 ‘Intermediate stop operations’ have been suggested, whereby longer-distance travel is broken into flight
13 legs, obviating the need to carry fuel for the whole mission. (Linke et al. 2017) modelled this operational
14 behaviour on a global basis and calculated a fuel savings of 4.8% over a base case in which normal fuel
15 loads were carried. However, this approach increases the number of landing/take-off cycles at airports.
16 ‘Formation flying’, which has the potential to reduce fuel burn on feasible routes has also been proposed
17 (Xu et al. 2014; Marks et al. 2021).
18 Alternative biofuels, synthetic fuels, and liquid Hydrogen: As noted above, the scope for reducing CO2
19 emissions from aviation through improved airplane technology or operations is limited and unable to
20 keep up with the projected growth, let alone reduce beyond the present emission rate at projected levels
21 of demand (assuming post-pandemic recovery of traffic). Thus, the literature outlined here suggests that
22 the only way for demand for aviation to continue to grow without increasing CO2 emissions is to employ
23 alternative lower-carbon bio- or synthetic aviation fuels (Klöwer et al. 2021). For shorter ranges, flights
24 of light planes carrying up to 50 passengers may be able to use electric power (Sahoo et al. 2020) but
25 these planes are a small proportion of the global aviation fleet (Epstein and O’Flarity 2019; Langford
26 and Hall 2020) and account for less than 12% of current aviation CO2 emissions. Alternative lower-
27 carbon footprint fuels have been certified for use over recent years, principally from bio-feedstocks, but
28 are not yet widely available at economic prices (Kandaramath Hari et al. 2015; Capaz et al. 2021a). In
29 addition, alternative fuels from bio-feedstocks have variable carbon footprints because of different life
30 cycle emissions associated with various production methods and associated land-use change (de Jong
31 et al. 2017; Staples et al. 2018; Capaz et al. 2021b; Zhao et al. 2021).
32 The development of ‘sustainable aviation fuels’ (referred to as ‘SAFs’) that can reduce aviation’s carbon
33 footprint is a growing area of interest and research. Alternative aviation fuels to replace fossil-based
34 kerosene have to be certified to an equivalent standard as Jet-A for a variety of parameters associated
35 with safety issues. Currently, the organisation responsible for aviation fuel standards, ASTM
36 International, has certified seven different types of sustainable aviation fuels with maximum blends
37 ranging from 10% to 50% (Chiaramonti 2019). Effectively, these blend requirements limit the amount
38 of non-hydrocarbon fuel (e.g., Methanol) that can be added at present. While there currently is a
39 minimum level of aromatic hydrocarbon contained in jet fuel to prevent ‘O-ring’ shrinkage in the fuel
40 seals (Khandelwal et al. 2018), this minimum level can likely be lower in the medium- to long- term,
41 with the added benefits of reduced soot formation and reduced contrail cirrus formation (Bier et al.
42 2017; Bier and Burkhardt 2019).
43 Bio-based fuels can be produced using a variety of feedstocks including cultivated feedstock crops, crop
44 residues, municipal solid waste, waste fats, oils and greases, wood products and forestry residues
45 (Staples et al. 2018). Each of these different sources can have different associated life cycle emissions,
46 such that they are not net zero-CO2 emissions but have associated emissions of CO2 or other GHGs
47 from their production and distribution (see Section 10.3 and Box 10.2). In addition, associated land use
48 change emissions of CO2 represent a constraint in climate change mitigation potential with biofuel
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1 (Staples et al. 2017) and has inherent large uncertainties (Plevin et al. 2010). Other sustainability issues
2 include food vs. fuel arguments, water resource use, and impacts on biodiversity. Cost-effective
3 production, feedstock availability, and certification costs are also relevant (Kandaramath Hari et al.
4 2015). Nonetheless, bio-based SAFs have been estimated to achieve life cycle emissions reductions
5 ranging between approximately 2% and 70% under a wide range of scenarios (Staples et al. 2018). For
6 a set of European aviation demand scenarios, Kousoulidou and Lonza (2016) estimated that the fuel
7 demand in 2030 would be ~100 Mtoe and biokerosene (HEFA/HVO) penetration would provide around
8 2% of the total fuel demand at that date. Several issues limit the expansion of biokerosene for aviation,
9 the primary one being the current cost of fossil fuel compared to the costs SAF production (Capaz et al.
10 2021a). Other hybrid pathways e.g., the Hydrogenation of biofuels (the Hydrogen assumed to be
11 generated with low carbon energy), could increase the output and improve the economic feasibility of
12 bio-based SAF (Hannula 2016; Albrecht et al. 2017).
13 Costs remain a major barrier for bio-SAF, which cost around three times the price of kerosene by
14 (Kandaramath Hari et al. 2015). Clearly, for SAFs to be economically competitive, large adjustments
15 in prices of fossil fuels or the introduction of policies is required. Staples et al. (2018) estimated that in
16 order to introduce bio-SAFs that reduce life cycle GHG emissions by at least 50% by 2050, prices and
17 policies were necessary for incentivization. They estimate the need for 268 new biorefineries per year
18 and capital investments of approximately USD 22 to 88 billion (2015 prices) per year between 2020
19 and 2050. Wise et al. (2017) suggest that carbon prices would help leverage production and availability.
20 Various pathways have been discussed for the production of non-bio SAFs such as power-to-liquid
21 pathways (Schmidt et al. 2018), sometimes termed ‘electro-fuels’ (Goldmann et al. 2018), or more
22 generalised power to ‘x’ pathways (Kober and Bauer 2019). This process would involve the use of low
23 carbon energy electricity, CO2, and water to synthesise jet fuel through the Fischer-Tropsch process or
24 Methanol synthesis. Hydrogen would be produced via an electrochemical process, powered by low
25 carbon energy and combined with CO2 captured directly from the atmosphere or through BECCS. The
26 energy requirement from photovoltaics has been estimated to be of the order 14 – 20 EJ to phase out
27 aviation fossil fuel by 2050 (Gössling et al. 2021a). These synthetic fuels have potential for large life
28 cycle emission reductions (Schmidt et al. 2016). In comparison to bio-SAF production, the
29 implementation of the processes is in its infancy. However, assuming availability of low carbon energy
30 electricity, these fuels have much smaller land and water requirements than bio-SAF. Low carbon
31 energy supply, scalable technology, and therefore costs represent barriers. (Scheelhaase et al. 2019)
32 review current estimates of costs, which are estimated to be approximately 4 to 6 times the price of
33 fossil kerosene.
34 Liquid Hydrogen (LH2) as a fuel has been discussed for aeronautical applications since the 1950s
35 (Brewer 1991) and a few experimental aircraft have flown using such a fuel. Experimental, small
36 aircraft have also flown using Hydrogen fuel cells. Although the fuel has an energy density per unit
37 mass about 3 times greater than kerosene, it has a much lower energy density per unit volume
38 (approximately factor 4, (McKinsey 2020)). The increased volume requirement makes the fuel less
39 attractive for aviation since it would require the wings to be thickened or else fuel to take up space in
40 the fuselage. Bicer and Dincer (2017) found that LH2-powered aircraft compared favourably to
41 conventional kerosene-powered aircraft on a life cycle basis, providing that the LH2 was generated from
42 low carbon energy sources (0.014 kg CO2 per tonne km cf 1.03 kg CO2 per tonne km, unspecified
43 passenger aircraft). However, Ramos Pereira et al. (2014) also made a life cycle comparison and found
44 much smaller benefits of LH2-powered aircraft (manufactured from low carbon energy) compared with
45 conventional fossil-kerosene. The two studies expose the sensitivities of boundaries and assumptions
46 in the analyses. (Shreyas Harsha 2014; Rondinelli et al. 2017) conclude that there are many
47 infrastructural barriers but that the environmental benefits of low carbon-based LH2 could be
48 considerable. Khandelwal et al. (2013) take a more optimistic view of the prospect of LH2-powered
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1 aircraft but envisage them within a Hydrogen-oriented energy economy. A recently commissioned
2 study by the European Union (EU)’s ‘Clean Sky’ (McKinsey 2020) addresses many of the aspects of
3 the opportunities and obstacles in developing LH2 powered aircrafts. The report provides an optimistic
4 view of the feasibility of developing such aircraft for short to medium haul but makes clear that new
5 aircraft designs (such as blended-wing body aircraft) would be needed for longer distances.
6 The non-CO2 impacts of LH2-powered aircrafts remain poorly understood. The emission index of water
7 vapour would be much larger (estimated to be 2.6 times greater by Ström and Gierens (2002)) than for
8 conventional fuels, and the occurrence of contrails may increase but have lower ERF because of the
9 lower optical depth (Marquart et al. 2005). Moreover, contrails primarily form on soot particles from
10 kerosene-powered aircraft, which would be absent from LH2 exhaust (Kärcher 2018). The overall effect
11 is currently unknown as there are no measurements. Potentially, NOx emissions could be lower with
12 combustor redesign (Khandelwal et al. 2013).
13 In conclusion, there are favourable arguments for LH2-powered aircraft both on an efficiency basis
14 (Verstraete 2013) and an overall reduction in GHG emissions, even on an life cycle basis. However,
15 LH2 requires redesign of the aircraft, particularly for long-haul operations. Similarly, there would be a
16 need for expanded infrastructure for fuel manufacture, storage, and distribution at airports, which is
17 likely to be more easily overcome if there is a more general move towards a Hydrogen-based energy
18 economy.
19 Technological and operational trade-offs between CO2 and non-CO2 effects: Since aviation has
20 additional non-CO2 warming effects, there has been some discussion as to whether these can be
21 addressed by either technological or operational means. For example, improved fuel efficiency has
22 resulted from high overall pressure ratio engines with large bypass ratios. This improvement has
23 increased pressure and temperature at the combustor inlet, with a resultant tendency to increase thermal
24 NOx formation in the combustor. Combustor technology aims to reduce this increase, but it represents
25 a potential technology trade-off whereby NOx control may be at the expense of extra fuel efficiency.
26 Estimating the benefits or disbenefits of CO2 (proportional to fuel burned) vs. NOx in terms of climate
27 is complex (Freeman et al. 2018).
28 Any GWP/GTP type emissions equivalency calculation always involves the user selection of a time
29 horizon over which the calculation is made, which is a subjective choice (Fuglestvedt et al. 2010). In
30 general, the longer the time horizon, the more important CO2 becomes in comparison with a short-lived
31 climate forcing agent. So, for example, a net (overall) aviation GWP for a 20-year time horizon is 4.0
32 times that of CO2 alone, but only 1.7 over a 100-year time horizon. Correspondingly, a GTP for a 20-
33 year time horizon is 1.3, but it is 1.1 for 100 years (Lee et al. 2021).
34 A widely discussed opportunity mitigation of non-CO2 emissions from aviation is the avoidance of
35 persistent contrails that can form contrail cirrus. Contrails only form in ice-supersaturated air below a
36 critical temperature threshold (Kärcher 2018). It is therefore feasible to alter flight trajectories to avoid
37 such areas conducive to contrail formation, since ice-supersaturated areas tend to be 10s to 100s of km
38 in the horizontal and only a few 100 metres in the vertical extent (Gierens et al. 1997). Theoretical
39 approaches show that avoidance is possible on a flight-by-flight basis (Matthes et al. 2017; Teoh et al.
40 2020). Case studies have shown that flight planning according to trajectories with minimal climate
41 impact can substantially (up to 50%) reduce the aircraft net climate impacts despite small additional
42 CO2 emissions (e.g., (Niklaß et al. 2019)). However, any estimate of the net benefit or disbenefit
43 depends firstly on the assumed magnitude of the contrail cirrus ERF effect (itself rather uncertain,
44 assessed with a low confidence level;) and upon the choice of metric and time-horizon applied. While
45 this is a potentially feasible mitigation option, notwithstanding the CO2 percontrail trade-off question,
46 meteorological models cannot currently predict the formation of persistent contrails with sufficient
47 accuracy in time and space (Gierens et al. 2020) such that this mitigation option is speculated to take of
48 the order of up to a decade to mature (Arrowsmith et al. 2020)
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1 Market-based offsetting measures: The EU introduced aviation into its CO2 emissions trading scheme
2 (ETS) in 2012. Currently, the EU-ETS for aviation includes all flights within the EU as well as to and
3 from EEA states. Globally, ICAO agreed in 2016 to commence, in 2020, the ‘Carbon Offsetting and
4 Reduction Scheme for International Aviation’ (CORSIA). The pandemic subsequently resulted in the
5 baseline being changed to 2019.
6 CORSIA has a phased implementation, with an initial pilot phase (2021–2023) and a first phase (2024–
7 2026) in which states will participate voluntarily. The second phase will then start in 2026–2035, and
8 all states will participate unless exempted. States may be exempted if they have lower aviation activity
9 levels or based on their UN development status. As of September 2021, 109 ICAO Member States will
10 voluntarily be participating in CORSIA starting in 2022. In terms of routes, only those where both States
11 are participating are included. There will be a special review of CORSIA by the end of 2032 to
12 determine the termination of the scheme, its extension, or any other changes to the scheme beyond
13 2035.
14 By its nature, CORSIA does not lead to a reduction in in-sector emissions from aviation since the
15 program deals mostly in approved offsets. At its best, CORSIA is a transition arrangement to allow
16 aviation to reduce its impact in a more meaningful way later. From 2021 onwards, operators can reduce
17 their CORSIA offsetting requirements by claiming emissions reductions from ‘CORSIA Eligible Fuels’
18 that have demonstrably reduced life cycle emissions. These fuels are currently available at greater costs
19 than the offsets (Capaz et al. 2021a). As a result, most currently approved CORSIA offsets are avoided
20 emissions, which raises the issue of additionality (Warnecke et al. 2019) The nature of ‘avoided
21 emissions’ is to prevent an emission that was otherwise considered to be going to occur, e.g. prevented
22 deforestation. Avoided emissions are ‘reductions’ (over a counterfactual) and purchased from other
23 sectors that withhold from an intended emission (Becken and Mackey 2017), such that if additionality
24 were established, a maximum of 50% of the intended emissions are avoided. Some researchers suggest
25 that avoided deforestation offsets are not a meaningful reduction, since deforestation continues to be a
26 net source of CO2 emissions (Mackey et al. 2013; Friedlingstein et al. 2020).
27 Modal shift to High-Speed Rail: Due to the limitations of the current suite of aviation mitigation
28 strategies, the potential for high-speed rail (HSR) is of increasing interest (Givoni and Banister 2006;
29 Chen 2017; Bi et al. 2019). The IEA’s Net Zero by 2050 roadmap suggests significant behavioural
30 change with more regional flights shifting to HSR in the NZE pathway (IEA 2021e). For HSR services
31 to be highly competitive with air travel, the optimal distance between the departure and arrival points
32 has been found to be in the approximate range of 400-800 km (Bows et al. 2008; Rothengatter 2010),
33 although in the case of China’s HSR operations, this range can be extended out to 1,000 km with
34 corresponding air services having experienced significant demand reduction upon HSR service
35 commencement (Lawrence et al. 2019). In some instances, negative effects on air traffic, air fare, and
36 flight frequency have occurred at medium-haul distances such as HSR services in China on the Wuhan-
37 Guangzhou route (1,069 km) and the Beijing-Shanghai route (1,318 km) (Fu et al. 2015; Zhang and
38 Zhang 2016; Chen 2017; Li et al. 2019; Ma et al. 2019). This competition at medium-haul distances is
39 contrary to that which has been experienced in European and other markets and may be attributable to
40 China having developed a comprehensive network with hub stations, higher average speeds, and an
41 integrated domestic market with strong patronage (Zhang et al. 2019a).
42 The LCA literature suggests that the GHG emissions associated with HSR vary depending on spatial,
43 temporal, and operational specifics (Åkerman 2011; Baron et al. 2011; Chester and Horvath 2012; Yue
44 et al. 2015; Hoyos et al. 2016; Jones et al. 2017; Robertson 2016, 2018; Lin et al. 2019). These studies
45 found a wide range of approximately 10 - 110 grams CO2 per pkm for HSR. This range is principally
46 attributable to the sensitivity of operational parameters such as the HSR passenger seating capacity,
47 load factor, composition of renewable and non-renewable energy sources in electricity production,
48 rolling stock energy efficiency and patronage (i.e. ridership both actual and forecast), and line-haul
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1 infrastructure specifics (e.g. tunnelling and aerial structure requirements for a particular corridor)
2 (Åkerman 2011; Chester and Horvath 2012; Yue et al. 2015; Newman et al. 2018; Robertson 2018) The
3 prospect for HSR services providing freight carriage (especially on-line purchases) is also growing
4 rapidly (Strale 2016; Bi et al. 2019; Liang and Tan 2019) with a demonstrated emission reduction
5 potential from such operations (Hoffrichter et al. 2012). However, additional supportive policies will
6 most likely be required (Strale 2016; Watson et al. 2019). Limiting emissions avoidance assessments
7 for HSR modal substitution to account only for CO2 emissions ignores aviation’s non-CO2 effects (see
8 Section 10.5.2), and likely results in an under-representation of the climate benefits of HSR replacing
9 flights.
10 HSR modal substitution can generate a contra-effect if the air traffic departure and arrival slots that
11 become available as the result of the modal shift are simply reallocated to additional air services (Givoni
12 and Banister 2006; Givoni and Dobruszkes 2013; Jiang and Zhang 2016; Cornet et al. 2018; Zhang et
13 al. 2019a). Furthermore, HSR services have the potential to increase air traffic at a hub airport through
14 improved networks but this effect can vary based on the distance of the HSR stations to airports (Jiang
15 and Zhang 2014; Xia and Zhang 2016; Zhang et al. 2019b; Liu et al. 2019). Such rebound effects could
16 be managed through policy interventions. For example, in 2021 the French government regulated that
17 all airlines operating in France suspend domestic airline flights on routes if a direct rail alternative with
18 a travel time of less than 2.5 hours is available. Other air travel demand reduction measures that have
19 been proposed include regulations to ban frequent flyer reward schemes, mandates that all marketing
20 of air travel declare flight emissions information to the prospective consumer (i.e., the carbon footprint
21 of the nominated flight), the introduction of a progressive ‘Air Miles Levy’ as well as the inclusion of
22 all taxes and duties that are presently exempt from air ticketing (Carmichael 2019). Moreover, China
23 has the highest use of HSR in the world in part due to its network and competitive speeds and in part
24 due to heavy regulation of the airline industry, in particular restrictions imposed on low-cost air carrier
25 entry and subsidisation of HSR (Li et al. 2019). These air travel demand reduction strategies in addition
26 to stimulating HSR ridership may induce shifts to other alternative modes.
27 Despite the risk of a rebound effect, and due to the probable reality of an incremental adoption of
28 sustainable aviation fuel technology in the coming decades, the commencement of appropriate HSR
29 services has the potential to provide, particularly in the short to medium-term, additional means of
30 aviation emissions mitigation.
31 10.5.4 Assessment of aviation-specific projections and scenarios
32 The most recent projection from ICAO (prior to the COVID-19 pandemic) for international traffic (mid-
33 range growth) is shown in Figure 10.11 (Fleming and de Lépinay 2019). This projection shows the
34 different contributions of mitigation measures from two levels of improved technology, as well as
35 improvements in air traffic management (ATM) and infrastructure use. The projections indicate an
36 increase of CO2 emissions by a factor of 2.2 in 2050 over 2020 levels for the most optimistic set of
37 mitigation assumptions. The high/low traffic growth assumptions would indicate increases by factors
38 of 2.8 and 1.1, respectively in 2050, over 2020 levels (again, for the most optimistic mitigation
39 assumptions).
40 The International Energy Agency has published several long-term aviation scenarios since the AR5
41 within a broader scope of energy projections. Their first set of aviation scenarios include a ‘reference
42 technology scenario’ (RTS), a ‘2° Scenario’ (2DS) and a ‘Beyond 2° Scenario’ (B2DS). The scenarios
43 are simplified in assuming a range of growth rates and technological/operational improvements (IEA
44 2017b) Mitigation measures brought about by policy and regulation are treated in a broad-brush manner,
45 noting possible uses of taxes, carbon pricing, price and regulatory signals to promote innovation.
46 The IEA has more recently presented aviation scenarios to 2070 in their ‘Sustainable Development
47 Scenario’ that assume some limited reduced post-COVID-19 pandemic demand, and potential
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1 technology improvements in addition to direct reductions in fossil kerosene usage from substitution of
2 biofuels and synthetic fuels (IEA 2021b). There is much uncertainty in how aviation will recover from
3 the COVID-19 pandemic but, in this scenario, air travel returns to 2019 levels in three years, and then
4 continues to expand, driven by income. Government policies could dampen demand (12% lower by
5 2040 than the IEA ‘Stated Policies Scenario,’ which envisages growth at 3.4% per year, which in turn
6 is lower than ICAO at 4.3%). Mitigation takes place largely by fuel substitution – lower-carbon biofuels
7 and synthetic fuels, with a smaller contribution from technology. Approximately 85% of the actual
8 cumulative CO2 emissions (to 2070) are attributed to use of fuel at their lowest Technology Readiness
9 Level of ‘Prototype,’ which is largely made up of biofuels and synthetic fuels, as shown in Figure 10.12.
10 Details of the technological scenarios and the fuel availability/uptake assumptions are given in (IEA
11 2021b), which also makes clear that the relevant policies are not currently in place to make any such
12 scenario happen.
13
14
15 Figure 10.11 Projections of international aviation emissions of CO 2. Data in Mt yr-1, to 2050, showing
16 contributions of improved technology, and air traffic management and infrastructure to emissions
17 reductions to 2050.
18 Data from Fleming and de Lépinay (2019); projections made pre-COVID-19 global pandemic
19
20
21
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1
2 Figure 10.12 The International Energy Agency’s scenario of future aviation fuel consumption for the
3 States Policies Scenario (‘STEPS’) and composition of the Sustainable Development Scenario
4 (from (IEA 2021b))
5
6 Within the Coupled Model Intercomparison Project Phase 6 (CMIP6) emissions database, a range of
7 aviation emission scenarios for a range of SSP scenarios are available (see Figure 10.13). This figure
8 suggests that by 2050, direct emissions from aviation could be 1.5 to 6.5 (5-95th percentile) times higher
9 than in the 2020 model year under the scenarios without firm commitments to meet a long-term
10 temperature target (i.e., C7-8 scenarios with temperature change above 2.5°C by 2100). In the C1-2
11 scenario group, which limit temperature change below 1.5°C, aviation emissions could still be up to 2.5
12 times higher in 2050 than emissions in the 2020 model year (95th percentile) but may need to decrease
13 by 10% by 2050 (5th percentile).
14
15
16 Figure 10.13 CO2 emission from AR6 aviation scenarios indexed to 2020 modelled year. Data from the
17 AR6 scenario database.
18
19 The COVID-19 pandemic of 2020 has changed many activities and consequentially, associated
20 emissions quite dramatically (Le Quéré et al. 2018; Friedlingstein et al. 2020; Liu et al. 2020c; UNEP
21 2020). Aviation was particularly affected, with a reduction in commercial flights in April 2020 of ~74%
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1 over 2019 levels, with some recovery over the following months, remaining at 42% lower as of October
2 2020 (Petchenik 2021). The industry is considering a range of potential recovery scenarios, with the
3 International Air Transport Association (IATA) speculating that recovery to 2019 levels may take up
4 until 2024 (see Box on COVID-19 and (Early and Newman 2021). Others suggest, however, that the
5 COVID-19 pandemic and increased costs as a result of feed-in quotas or carbon taxes, could slow down
6 the rate of growth of air travel demand, though global demand in 2050 would still grow 57%–187%
7 between 2018 and 2050 (instead of 250% in a baseline recovery scenario) (Gössling et al. 2021a).
8
9 10.5.5 Accountability and governance options
10 Under Article 2.2 of the Kyoto Protocol, Annex I countries were called to “…pursue limitation or
11 reduction of emissions of GHGs not controlled by the Montreal Protocol from aviation and marine
12 bunker fuels, working through the International Civil Aviation Organization and the International
13 Maritime Organization, respectively.” The Paris Agreement is different, in that ICAO (and the IMO)
14 are not named. As a result, the Paris Agreement, through the NDCs, seemingly covers CO2 emissions
15 from domestic aviation (currently 35% of the global total) but does not cover international emissions.
16 A number of states and regions, including the UK, France, Sweden, and Norway, have declared their
17 intentions to include international aviation in their net zero commitments, while the EU, New Zealand,
18 California, and Denmark are considering doing the same (Committee on Climate Change 2019). The
19 Paris Agreement describes temperature-based goals, such that it is unclear how emissions of GHGs
20 from international aviation would be accounted for. Clearly, this is a less than ideal situation for clarity
21 of governance of international GHG emissions from both aviation and shipping. At its 40th General
22 Assembly (October 2019) the ICAO requested its Council to “…continue to explore the feasibility of a
23 long-term global aspirational goal for international aviation, through conducting detailed studies
24 assessing the attainability and impacts of any goals proposed, including the impact on growth as well
25 as costs in all countries, especially developing countries, for the progress of the work to be presented
26 to the 41st Session of the ICAO Assembly”. What form this goal will take is unclear until work is
27 presented to the 41st Assembly (Autumn, 2022). It is likely, however, that new accountability and
28 governance structures will be needed to support decarbonisation of the aviation sector.
29
30 10.6 Decarbonisation of Shipping
31 Maritime transport is considered one of the key cornerstones enabling globalisation (Kumar and
32 Hoffmann 2002). But as for aviation, shipping has its challenges in decarbonisation, with a strong
33 dependency on fossil fuels without major changes since AR5. At the same time, the sector has a range
34 of opportunities that could help reduce emissions through not only changing fuels, but also by increasing
35 the energy efficiency, optimising operations and ship design, reducing demand, improving regulations,
36 as well as other options that will be reviewed in this section.
37
38 10.6.1 Historical and current emissions from shipping
39 Maritime transport volume has increased by 250% over the past 40 years, reaching an all-time high of
40 11 billion tons of transported goods in 2018 (UNCTAD 2019). This growth in transport volumes has
41 resulted in continued growth in GHG emissions from the shipping sector, despite an improvement in
42 the carbon intensity of ship operations, especially since 2014. The estimated total emissions from
43 maritime transport can vary depending on data set and calculation method, but range over 600 – 1,100
44 Mt CO2 per year over the past decade (Figure 10. 14), corresponding to 2 - 3% of total anthropogenic
45 emissions. The legend in Figure 10.14 refers to the following data sources: (Endresen et al. 2003),
46 (Eyring et al. 2005), (Dalsøren et al. 2009), DNV-GL (DNV GL 2019), CAMS-GLOB-SHIP (Jalkanen
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1 et al. 2014; Granier et al. 2019), EDGAR (Crippa et al. 2019), (Hoesly et al. 2018), (Johansson et al.
2 2017), ICCT (Olmer et al. 2017), the IMO GHG Studies; IMO 2nd (Buhaug et al. 2009), IMO 3rd
3 (Smith et al. 2014), IMO 4th-vessel and IMO 4th-voyage (Faber et al. 2020), and (Kramel et al. 2021).
4
5
6
7 Figure 10.14 CO2 emissions (Mt year-1) from shipping 2000 – 2018. Data from various inventories as
8 shown in the label.
9
10 10.6.2 Short lived climate forcers and shipping
11 Like aviation, shipping is also a source of emissions of the SLCFs described in Section 10.5, including
12 nitrogen oxides (NOx), sulphur oxides (SO2 and SO4), carbon monoxide (CO), black carbon (BC), and
13 non-methane volatile organic carbons (NMVOCs) (Naik et al. 2021). Though SLCF have a shorter
14 lifetime than the associated CO2 emissions, these short-lived forcers can have both a cooling effect (e.g.,
15 SOx) or a warming effect (e.g., ozone from NOx). The cooling from the SLCF from a pulse emission
16 will decay rapidly and diminish after a couple of decades, whilst the warming from the long-lived
17 substances lasts for centuries (Naik et al. 2021).
18 Emissions of SLCF from shipping not only affects the climate, but also the environment, air quality,
19 and human health. Maritime transport has been shown to be a major contributor to coastal air quality
20 degradation (Viana et al. 2014; Zhao et al. 2013; Jalkanen et al. 2014; Goldsworthy and Goldsworthy
21 2015; Goldsworthy 2017). Sulphur emissions may contribute towards acidification of the ocean
22 (Hassellöv et al. 2013). Furthermore, increases in sulphur deposition on the oceans has also been shown
23 to increase the flux of CO2 from the oceans to the atmosphere (Hassellöv et al. 2013). To address the
24 risks of SOx emissions from shipping, there is now a cap on the on the amount of sulphur content
25 permissible in marine fuels (IMO 2013). There is also significant uncertainty about the impacts of
26 pollutants emitted from ships on the marine environment (Blasco et al. 2014).
27 Pollution control is implemented to varying degrees in the modelling of the SSP scenarios (Rao et al.
28 2017); for example, SSPs 1 and 5 assume that increasing concern for health and the environment result
29 in more stringent air pollution policies than today (Naik et al. 2021). There is a downward trend in SOx
30 and NOx emissions from shipping in all the SSPs, in compliance with regulations. The SLCF emission
31 reduction efforts, within the maritime sector, are also contributing towards achieving the UN SDGs. In
32 essence, while long lived GHGs are important for long term mitigation targets, accounting for short
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1 lived climate forcers is important both for current and near-term forcing levels as well as broader air
2 pollution and SDG implications.
3
4 10.6.3 Shipping in the Arctic
5 Shipping in the Arctic is a topic of increasing interest. The reduction of Arctic summer sea ice increases
6 the access to the northern sea routes (Melia et al. 2016; Smith and Stephenson 2013; Aksenov et al.
7 2017; Fox-Kemper et al. 2021). Literature and public discourse on the increased access sometimes has
8 portrayed this trend as positive (Zhang et al. 2016b), as it allows for shorter shipping routes, e.g.
9 between Asia and Europe with estimated travel time savings of 25 – 40% (Aksenov et al. 2017).
10 However, the acceleration of Arctic cryosphere melt and reduced sea ice that enable Arcitc shipping
11 reduce surface albedo and amplify climate warming (Eyring et al. 2021). Furthermore, local air
12 pollutants can play different roles in the Arctic. For example, Black Carbon (BC) emissions reduce
13 albedo and absorb heat in air, on snow and ice (Messner 2020; Browse et al. 2013; Kang et al. 2020;
14 Eyring et al. 2021). Finally, changing routing from Suez to the north-eastern sea route may reduce total
15 emissions for a voyage, but also shift emissions from low to high latitudes. Changing the location of
16 the emissions adds complexity to the assessment of the climatic impacts of Arctic shipping, as the local
17 conditions are different and the SLCF may have a different impact on clouds, precipitation, albedo and
18 local environment (Marelle et al. 2016; Fuglestvedt et al. 2014; Dalsøren et al. 2013). Observations
19 have shown that 5-25% of air pollution in the Arctic stem from shipping activity within the Arctic itself
20 (Aliabadi et al. 2015). Emissions outside of the Arctic can affect Arctic climate, and changes within the
21 Arctic may have global climate impacts. Both modelling and observations have shown that aerosol
22 emissions from shipping can have a significant effect on air pollution, and shortwave radiative forcing
23 (Peters et al. 2012; Roiger et al. 2014; Marelle et al. 2016; Dalsøren et al. 2013; Ødemark et al. 2012;
24 Righi et al. 2015).
25 Increased Arctic shipping activity may also impose increased risks to local marine ecosystems and
26 coastal communities from invasive species, underwater noise, and pollution (Halliday et al. 2017; IPCC
27 2019). Greater levels of Arctic maritime transport and tourism have political, as well as socio-economic
28 implications for trade, and nations and economies reliant on the traditional shipping corridors. There
29 has been an increase in activity from cargo, tankers, supply, and fishing vessels in particular (Zhao et
30 al. 2015; Winther et al. 2014). Projections indicate more navigable Arctic waters in the coming decades
31 (Smith and Stephenson 2013; Melia et al. 2016) and continued increases in transport volumes through
32 the northern sea routes (Winther et al. 2014; Corbett et al. 2010; Lasserre and Pelletier 2011). Emission
33 patterns and quantities, however, are also likely to change with future regulations from IMO, and
34 depend on technology developments, and activity levels which may depend upon geopolitics,
35 commodity pricing, trade, natural resource extractions, insurance costs, taxes, and tourism demand
36 (Johnston et al. 2017). The need to include indigenous peoples’ voices when shaping policies and
37 governance of shipping activities in the high north is increasing (Dawson et al. 2020).
38 The Arctic climate and environment pose unique hazards and challenges with regards to safe and
39 efficient shipping operations: low temperature challenges, implications for vessel design, evacuation
40 and rescue systems, communications, oil spills, variable sea ice, and meteorological conditions
41 (Buixadé Farré et al. 2014). To understand the total implications of shipping in the Arctic, including its
42 climate impacts, a holistic view of synergies, trade-offs, and co-benefits is needed, with assessments of
43 impacts on not only the physical climate, but also the local environment and ecosystems. To furthermore
44 ensure safe operations in the Arctic waters, close monitoring of activities may be valuable.
45
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1 10.6.4 Mitigation potential of fuels, operations and energy efficiency
2 A range of vessel mitigation options for the international fleet exist and are presented in this section. A
3 variety of feedstocks and energy carriers can be considered for shipping. As feedstocks, fuels from
4 biomass (advanced biofuels), fuels produced from renewable electricity and CO2 capture from flue gas
5 or the air (electro-, e-, or power-fuels), and fuels produced via thermochemical processes (solar fuels)
6 can be considered. As energy carriers, synthetic fuels and the direct use of electricity (stored in batteries)
7 are of relevance. The most prominent synthetic fuels discussed in literature are Hydrogen, Ammonia,
8 Methane, Methanol, and synthetic hydrocarbon diesel. Figure 10.15 shows the emissions reductions
9 potential for alternative energy carriers that have been identified as having the highest potential to
10 mitigate operational emissions from the sector (Psaraftis 2015; DNV GL 2017; Hansson et al. 2019;
11 Gilbert et al. 2018; Balcombe et al. 2019; Brynolf et al. 2014; Winebrake et al. 2019; Perčić et al. 2020;
12 Bongartz et al. 2018; Biernacki et al. 2018; Faber et al. 2020; Sharafian et al. 2019; Seddiek 2015; ITF
13 2018b; Seithe et al. 2020; Xing et al. 2020; Czermański et al. 2020; Hua et al. 2018; Bicer and Dincer
14 2018a; Kim et al. 2020; Liu et al. 2020a; Hansson et al. 2020; Singh et al. 2018; Valente et al. 2021;
15 Sadeghi et al. 2020; Nguyen et al. 2020; Stolz et al. 2021; Winkel et al. 2016; Chatzinikolaou and
16 Ventikos 2013; Lindstad et al. 2015; Tillig et al. 2015; Traut et al. 2014; Teeter and Cleary 2014).
17 Low-carbon Hydrogen and Ammonia are seen to have a positive potential as a decarbonised shipping
18 fuel. Hydrogen and Ammonia when produced from renewables or coupled to CCS, as opposed to mainly
19 by fossil fuels with high life-cycle emissions (Bhandari et al. 2014), may contribute to significant CO2-
20 eq reductions of up to 70 - 80% compared to low-sulphur heavy fuel oil (Bicer and Dincer 2018b;
21 Gilbert et al. 2018). These fuels have their own unique transport and storage challenges as Ammonia
22 requires a pilot fuel due to difficulty in combustion, and Ammonia combustion could lead to elevated
23 levels of NOx, N2O, or NH3 emissions depending on engine technology used (DNV GL 2020). There is
24 a need for the further development of technology and procedures for safe storage and handling of fuels
25 such as Hydrogen and Ammonia both onboard and onshore for a faster rate of uptake of such shipping
26 fuels (Hoegh-Guldberg et al. 2019), but they remain an encouraging decarbonisation option for shipping
27 in the next decade.
28 While Methanol produced from fossil sources induces an emission increase of +7.5% (+44%), e-
29 Methanol (via Hydrogen from electrolysis based on renewable energy and carbon from direct air
30 capture) reduces emission by 80% (82%). In general, several synthetic fuels, such as synthetic diesel,
31 methane, Methanol, ethanol, and dimethyl ether (DME) could in principle be used for shipping (Horvath
32 et al. 2018). The mitigation potential of these is though fully dependent on the sourcing of the Hydrogen
33 and carbon required for their synthesis.
34 As noted in Section 10.3, LNG has been found to have a relatively limited mitigation potential and may
35 not be viewed as a low-carbon alternative, but has a higher availability than other fuel options (Gilbert
36 et al. 2018). Emission reductions across the full fuel life cycle are found in the order of 10%, with ranges
37 reported from -30% (reduction) to +8% (increase), if switching from heavy fuel oil to LNG, as indicated
38 in Figure 10.15 (Bengtsson et al. 2011). Regardless of the production pathway, the literature points to
39 the risk of methane slip (emissions of unburnt methane especially at low engine loads and from transport
40 to ports) from LNG fuelled vessels, with no current regulation on emission caps (Ushakov et al. 2019;
41 Anderson et al. 2015; Peng et al. 2020). Leakage rates are a critical point for the total climate impact of
42 LNG as a fuel, where high pressure engines remedy this more than low pressure ones. As discussed in
43 10.3, some consider LNG as a transition fuel, whilst some literature point to the risk of stranded assets
44 due to the increasing decarbonisation regulation from IMO and the challenge of meeting IMO’s 2030
45 emissions reductions targets using this fuel.
46 In addition to fossil and e-fuels, advanced biofuels might play a role to provide the energy demand for
47 future shipping. Biomass is presently used to produce alcohol fuels (such as ethanol and Methanol),
48 liquid biogas, or biodiesel that can be used for shipping and could reduce CO2 emissions from this
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1 segment. As explained in Box 10.2 and Chapter 7, the GHG footprint associated with biofuels is
2 strongly dependent on the incurred land us and land use change emissions. Advanced biofuels from
3 processing cellulose rather than sugar are likely to be more attractive in terms of the quantities required
4 but are not commercially available Section 10.3. The estimates of emissions reductions from biofuels
5 shown in Figure 10.15 rely on data from the Integrated Assessment Models –Energy Modelling Forum
6 33 (IAM EMF33), partial models assuming constant land cover (CLC), and partial models using natural
7 growth (NRG). Box 10.2 and Section 10.4 include a more detailed description of the assumptions
8 underlying these models and their estimates. The results based on IAM EMF33 and CLC suggests
9 median mitigation potential of around 73% for advanced biofuels in shipping, while the NRG based
10 results suggest increased emissions from biofuels. The EMF33 and CLC results rely on modelling
11 approaches compatible with the scenarios in the AR6 database (see Chapters 6 and Box 7.7 for a
12 discussion about emissions from bioenergy systems).
13
14
15 Figure 10.15 Boxplot of emission reductions potential compared to conventional fuels in the shipping
16 sector. The x-axis is reported in %. Each individual marker represents a data point from the literature,
17 where the blue square indicates a full LCA CO2-eq value; light orange triangles tank – to – wake CO2-eq.;
18 light blue triangles well – to – wake CO2-eq; dark orange triangles well – to – wake CO2; and dark blue
19 circle tank – to – wake CO2 emission reduction potentials. The values in the figure rely on the 100-year
20 GWP value embedded in the source data, which may differ slightly with the updated 100-year GWP
21 values from WGI. ‘n’ indicates the number of data points per sub-panel. Grey shaded boxes represent
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1 data where the energy comes from fossil resources, and turquoise from low carbon renewable energy
2 sources. Advanced Biofuels EMF33 refers to emissions factors derived from simulation results from the
3 integrated assessment models EMF33 scenarios (darkest coloured box in top left panel). Biofuels partial
4 models CLC refers to partial models with constant land cover. Biofuels partial models NRG refers to
5 partial models with natural regrowth. For ammonia and Hydrogen, low-carbon electricity is produced
6 via electrolysis using low-carbon electricity, and ‘fossil’ refers to fuels produced via steam methane
7 reforming of natural gas.
8
9 In addition to the fuels, there are other measures that may aid the low-carbon transition shipping. The
10 amounts and speed of uptake of alternative low- or zero-carbon fuels in ports depend upon investments
11 in infrastructure – including bunkering infrastructure, refinery readiness, reliable supply of the fuels, as
12 well as sustainable production. The ship lifetime and age also play a role, whereupon retrofitting ships
13 to accommodate engines and fuel systems for new fuel types may not be an option for older vessels. As
14 such, operational efficiency becomes more important (Bullock et al. 2020). There is some potential to
15 continue to improve the energy efficiency of vessels through operational changes (e.g., Traut et al.
16 2018), reducing the speed or ‘slow steaming’ (Bullock et al. 2020), and improved efficiency in port
17 operations (Viktorelius and Lundh 2019; Poulsen and Sampson 2020). There is also a growing interest
18 in onboard technologies for capturing carbon, with prototype ships underway showing 65-90% potential
19 reduction in CO2 emissions (Japan Ship Technology Reserach Association et al. 2020; Luo and Wang
20 2017; Awoyomi et al. 2020). Challenges identified include CO2 capture efficiency (Zhou and Wang
21 2014), increased operating costs, and limited onboard power supply (Fang et al. 2019). Furthermore,
22 designing CO2 storage tanks for transport to shore may pose a challenge, as the volume and weight of
23 captured CO2 could be up to four times more than standard oil (Decarre et al. 2010).
24 Changes in design and engineering provide potential for reducing emissions from shipping through a
25 range of measures, e.g., by optimizing hull design and vessel shape, power and propulsion systems that
26 include wind or solar assisted propulsion, and through improved operations of vessels and ports. Figure
27 10.15 shows that such measures may decrease emissions by 5 - 40%, though with a broad range in
28 potential (Bouman et al. 2017). Nuclear propulsion could decrease emissions from individual vessels
29 by 98%. Battery- or hybrid-electric ships have been identified as a means to reduce emissions in short-
30 sea shipping such as ferries and inland waterways (Gagatsi et al., 2016), which may also importantly
31 reduce near-shore SLCF pollution (Nguyen et al. 2020). Figure 10.15 shows that the median emission
32 from electric ships can be ~40% lower than equivalent fossil-based vessels but can vary widely. The
33 wide reduction potential of battery-electric propulsion is due to different assumptions about the CO2
34 intensity of the electricity used and the levels of CO2 footprints associated with battery production.
35 Although projections indicate continued increase in freight demand in the future, demand-side
36 reductions could contribute to mitigation. The development of autonomous systems may play a role
37 (Colling and Hekkenberg 2020; Liu et al. 2021) while 3-D printing can reduce all forms of freight as
38 parts and products can be printed instead of shipped (UNCTAD 2018). As more than 40% of transported
39 freight is fossil fuels, a lessened demand for such products in low emission scenarios should contribute
40 to reduce the overall maritime transport needs and hence emissions in the future (Sharmina et al. 2017).
41 An increase in alternative fuels on the other hand, may increase freight demand (Mander et al. 2012).
42 Potentials for demand-side reduction in shipping emissions may arise from improving processes around
43 logistics and packaging, and further taxes and charges could serve as leverage for reducing demand and
44 emissions.
45 The coming decade is projected to be costly for the shipping sector, as it is preparing to meet the 2030
46 and 2050 emission reduction targets set by the IMO (UNCTAD 2018). With enough investments,
47 incentives, and regulation, substantial reductions of CO2 emissions from shipping could be achieved
48 through alternative energy carriers. The literature suggests that their cost could be manyfold higher than
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1 for conventional fuels, which in itself could reduce demand for shipping, and hence its emissions, but
2 make the transition difficult. Hence R&D may help reduce these costs. The literature points to the need
3 for developing technology roadmaps for enabling the maritime transport sector to get on to pathways
4 for decarbonisation early enough to reach global goals (Kuramochi et al. 2018). Accounting for the full
5 life cycle of emissions of the vessels and the fuels is required to meet the overall long-term objectives
6 of cutting GHG and SLCF emissions. The urgency of implementing measures for reducing emissions
7 is considered to be high, considering the lifetime of vessels is typically 20 years, if not more.
8
9 10.6.5 Accountability and governance options
10 Regulatory frameworks for the shipping sector have been developed over time and will continue to do
11 so through bodies such as the IMO, which was established by the UN to manage international shipping.
12 The IMO strategy involves a 50% reduction in GHG emissions from international shipping by 2050
13 compared to 2008 (IMO 2018). The strategy includes a reduction in carbon intensity of international
14 shipping by at least 40% by 2030, and 70% by 2050, compared to 2008. IMO furthermore aims for the
15 sectoral phase out of GHG emissions as soon as possible this century.
16 In 2020, the IMO approved the short-term goal-based measure to reduce the carbon intensity of existing
17 international vessels. This measure addresses both technical and operational strategies. The operational
18 element is represented by a Carbon Intensity Indicator (CII), and the technical element is represented
19 by the Energy Efficiency Existing Ship Index (EEXI), which will apply to ships from 2023. The EEXI
20 builds upon the Energy Efficiency Design Index (EEDI), which is a legally binding mitigation
21 regulation for newbuild ships, established as a series of baselines for the amount of fuel ships may burn
22 for a particular cargo-carrying capacity. The EEDI differs per ship segment. E.g., ships built in 2022
23 and beyond should be 50% more energy efficient than in 2013. This legislation aims to reduce GHG
24 emissions in particular. Energy efficiency may be improved by several of the mitigation options
25 outlined above. The ship energy efficiency management plan (SEEMP) is seen as the international
26 governance instrument to improve energy efficiency and hence emissions from ships. SEEMP is a
27 measure to enable changes to operational measures and retrofits (see Johnson et al., 2013). The
28 combination of EEXI, EEDI, and SEEMP may reduce emissions by 23% by 2030 compared to a ‘no
29 policy’ scenario (Sims et al. 2014). With regards to accountability, it is mandatory for ships of ≥5,000
30 gross tonnage to collect fuel consumption data, as well as specified data for e.g. transport work.
31 Similarly, the EU MRV (Monitoring, Reporting, Verification) requires mandatory reporting of a
32 vessel’s fuel consumption when operating in European waters.
33 Policy choices may enable or hinder changes, and gaps in governance structures may, to some degree,
34 hinder the objectives of mechanisms like SEEMP to improve energy efficiency and emissions. Policies
35 may be developed to incentivize investments in necessary changes to the global fleet and related
36 infrastructures. The literature argues that regulations and incentives that motivates mitigation through
37 speed optimisation, ship efficiency improvements, and retrofits with lower-carbon technologies at a
38 sub-global scale may contribute to immediate reductions in CO2 emissions from the sector (Bows-
39 Larkin 2015). The role of the financial sector through initiatives such as the Poseidon Principle,
40 whereupon financial institutions limit lending to companies that fail to uphold environmental standards,
41 could also become increasingly important (Sumaila et al. 2021).
42 It has been proposed to make shipping corporations accountable for their emissions by making it
43 mandatory to disclose their vessel’s emissions reductions (Rahim et al. 2016). Market based
44 mechanisms may increasingly encourage ship operators to comply with IMO GHG regulations.
45 Development of policies such as carbon pricing / taxing to enable a business case for adopting low
46 carbon fuels could be a near term priority for acceleration of transformation of the sector (Hoegh-
47 Guldberg et al. 2019). The EU is considering including shipping in its carbon trading system, with the
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1 details still to be agreed upon but expected to come into force in 2023, along with the CII. The
2 proposition is that shipowners who conduct voyages within Europe, or start or end at an EU port, will
3 have to pay for carbon permits to cover the CO2 emitted by their vessel.
4 Regulations exist also to limit emissions of air pollution from shipping with the aim to improve
5 environment and health impacts from shipping in ports and coastal communities. In sulphur emission
6 control areas (SECAS), the maximum permissible sulphur content in marine fuels is 0.10% m/m
7 (mass/mass). These are further tightened by the IMO legislation on reducing marine fuel sulphur content
8 to a maximum of 0.5% in 2020 outside of SECAS, compared to 3.5% permissible since 2012 (MARPOL
9 Convention). The MARPOL Annex VI also limits the emissions of ozone depleting substances and
10 ozone precursors; NOx, and VOCs from tankers (Mertens et al. 2018). The implementation of the
11 emission control areas have been shown to reduce the impacts on health and the environment (Viana et
12 al. 2015).
13 While there are many governance and regulatory initiatives that help reduce emissions from the
14 shipping sector, few are transformative on their own, unless zero carbon fuels can become available at
15 a reasonable cost as suggested in 10.3 and in scenarios outlined next.
16
17 10.6.6 Transformation trajectories for the maritime sector
18 Figure 10.16 shows CO2 emissions from shipping in scenarios from the AR6 database and the 4th GHG
19 study by the IMO (Faber et al., 2020). Panel (a) shows that CO2 emissions from shipping go down by
20 33-70% (5-95% percentile) by 2050 in the scenarios limiting warming to 1.5°C (C1-C2). By 2080,
21 median values for the same set of scenarios reach net zero CO2 emissions. IAMs often do not report
22 emission pathways for shipping transport and the sector is underrepresented in most IAMs (Esmeijer et
23 al. 2020). Hence pathways established outside of IAMs can be different for the sector. Indeed, the IMO
24 projections for growth in transport demand (Faber et al. 2020) indicate increases by 40 - 100 % by 2050
25 for the global fleet. Faber and et al. (2020), at the same time predict, reductions in trade for fossil fuels
26 dependent on decarbonisation trajectories. The energy efficiency improvements of the vessels in these
27 scenarios are typically of 20 - 30%. This offsets some of the increases from higher demand in the future
28 scenarios. Fuels assessed by the 4th IMO GHG study were limited to HFO, MGO, LNG, and Methanol,
29 with a fuels mix ranging from 91 - 98% conventional fuel use and a small remainder of alternative fuels
30 (primarily LNG, and some Methanol). Panel (b) in Figure 10.16 shows average fleetwide emissions of
31 CO2 emissions based on these aggregate growth and emission trajectories from the IMO scenarios. In
32 these scenarios, CO2 emissions from shipping remain stable or grow compared to 2020 modelled levels.
33 These results contrast with the low emission trajectories in the C1-C2 bin in panel (a) of Figure 10.16.
34 It seems evident that the scenarios in the AR6 database explore a broader solutions space for the sector,
35 than the 4th GHG study by IMO. However, the 1.5°C - 2°C warming goal has led to an IMO 2050 target
36 of 40% reductions in carbon intensity by 2030, which would require emission reduction efforts to begin
37 immediately. Results from global models, suggest the solutions space for deep emission reductions in
38 shipping is available.
39
40
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1
2 Figure 10.16 CO2 emission from shipping scenarios indexed to 2020 Modelled year. Panel a) Scenarios
3 from the AR6 database. Panel b) Scenarios from the 4 th IMO GHG Study (Faber et al., 2020). Figures
4 show median, 5th and 95th percentile (shaded area) for each scenario group.
5
6 Combinations of measures are likely needed for transformative transitioning of the shipping sector to a
7 low-carbon future, particularly if an expected increase in demand for shipping services is realised
8 (Smith et al. 2014; Faber et al. 2020). Both GHG and SLCF emissions decrease significantly in SSP1-
9 1.9, where mitigation is achieved in the most sustainable way (Rao et al. 2017). Conversely, there are
10 no emissions reductions in the scenarios presented by the IMO 4th GHG study, even though these
11 scenarios incorporate some efficiency improvements and a slight increase in the use of LNG.
12 Options outlined in this chapter suggest a combination of policies to reduce demand, increase
13 investments by private actors and governments, and develop the TRL of alternative fuels and related
14 infrastructure (especially synthetic fuels). Some literature suggests that battery electric-powered short
15 distance sea shipping could yield emission reductions given access to low carbon electricity. For deep
16 sea shipping, advanced biofuels, Hydrogen, Ammonia, and synthetic fuels hold potential for significant
17 emission reductions, depending on GHG characteristics of the fuel chain and resource base. Other
18 options, such optimisation of speed and hull design and wind-assisted ships could also combine to
19 make significant contributions in 2050 to further bring emissions down. In total a suite of mitigation
20 options exists or is on the horizon for the maritime sector.
21
22
23 10.7 Scenarios from integrated, sectoral, and regional models
24 10.7.1 Transport scenario modelling
25 This section reviews the results of three types of models that systemically combine options to assess
26 different approaches to generate decarbonisation pathways for the transport system: (1) integrated
27 assessment models (IAMs); (2) global transport energy sectoral models (GTEM); and (3) national
28 transport/energy models (NTEMs) (Yeh et al. 2017; Edelenbosch et al. 2017). Common assumptions
29 across the three model types include trajectories of socioeconomic development, technological
30 development, resource availability, policy, and behavioural change. The key differences underlying
31 these models are their depth of technological and behavioural detail versus scope in terms of sectoral
32 and regional coverage. In very general terms, the narrower the scope in terms of sectors and regions,
33 the more depth on spatial, technological, and behavioural detail. A large set of scenarios from these
34 models were collected in a joint effort led by Chapter 3 and supported by Chapter 10 and others. The
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1 outcomes from over 100 models have been analysed for this chapter with the methodologies set out in
2 Annex III for the whole report.
3 GHG emissions from transport are a function of travel demand, travel mode, transport technology, GHG
4 intensity of fuels, and energy efficiency. These drivers can be organized around a group of levers that
5 can advance the decarbonisation of the transport system. The levers thus include reducing travel
6 activity, increasing use of lower-carbon modes, and reducing modal energy intensity and fuel carbon
7 content. This section explores each lever's contributions to the decarbonisation of the transport sector
8 by reviewing the results from the three model types IAM and G-/NTEMs.
9 IAMs integrate factors from other sectors that interact with the transport system endogenously, such as
10 fuel availability and costs. IAMs minimize mitigation costs to achieve a temperature goal across all
11 sectors of the economy over a long-time horizon (typically to 2100). IAMs typically capture mitigation
12 options for energy and carbon intensity changes with greater technology/fuel details and endogeneity
13 linked to the other sectors. In the scenarios with very large-scale electrification of the transport sector,
14 the coupling with the other sectors in fuel production, storage, and utilization becomes more important.
15 G-/NTEMs and related regional transport sectoral models have more details in transport demand,
16 technology, behaviours, and policies than IAMs, but treat the interactions with the other sectors
17 exogenously, potentially missing some critical interactions, such as the fuel prices and carbon intensity
18 of electricity. National models have detailed representation of national policies related to transport and
19 energy, sometimes with greater spatial resolution. Compared with IAMs, G-/NTEMs typically have
20 greater detailed representation to explore mitigation options along the activity and mode dimensions
21 where spatial, cultural, and behavioural details can be more explicitly represented. The appendix in
22 Annex III provides more details about these types of models. Scenarios for shipping and aviation are
23 handled in more detail in sections 10.5 and 10.6, respectively.
24 This section applies the following categorization of scenarios (see table 3.1 in Chapter 3 for more
25 details): C1 (1.5°C with no or limited Overshoot (OS)), C2 (1.5°C with high OS), C3 (>67% below
26 2°C), C4 (>50% below 2°C), C5 (below 2.5°C), C6 (below 3°C), C7 (above 3°C). A large share of the
27 scenarios was developed prior to 2020. Results from such the scenario are indexed to a modelled (non-
28 covid) year 2020, referred to as 2020Mod.
29
30 10.7.2 Global emission trajectories
31 In 2018, transport emitted 8.5 Gt CO2eq, reaching a near doubling from 1990 levels after two decades
32 of 2% per year emissions growth (see Section 10.1). Assessing future trajectories, Figure 10.17 provides
33 an overview of direct CO2 emissions estimates from the transport sector across IAMs (colour bars) and
34 selected global transport models (grey bars). The results from the IAMs are grouped in bins by different
35 temperature goal. Global energy transport models (GTEMs) are grouped into reference and policy bins,
36 since the transport sector cannot by itself achieve fixed global temperature goals. The policy scenarios
37 in G-/NTEMs cover a wide range of "non-reference" scenarios, which include, for example,
38 assumptions based on the "fair share action" principles. In these scenarios, transport emissions reach
39 emissions reductions consistent with the overall emission trajectories aligning with warming levels of
40 2°C. These scenarios may also consider strengthening existing transport policies such as increasing fuel
41 economy standards or large-scale deployments of electric vehicles. In most cases, these Policy scenarios
42 are not necessarily in line with the temperature goals explored by the IAMs.
43
44
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1
2 Figure 10.17 Direct CO2 emissions in 2030, 2050, and 2100 indexed to 2020 modelled year across R6
3 Regions and World. IAM results are grouped by temperature targets. Sectoral studies are grouped by
4 reference and policy categories. Plots show 5th/95th percentile, 25th/75th percentile, and median.
5 Numbers above the bars indicate the number of scenarios. Data from the AR6 scenario database.
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1
2 According to the collection of simulations from the IAM and GTEM models shown in Figure 10.17,
3 global transport emissions could grow up to 2–47% (5–95th percentile) by 2030 and -6–130% by 2050
4 under the scenarios without firm commitments to meet a long-term temperature goal (i.e., C7-8
5 scenarios with temperature change above 3.0°C by 2100). Population and GDP growth and the
6 secondary effects, including higher travel service demand per capita and increased freight activities per
7 GDP, drive the growth in emissions in these scenarios (see Section 10.7.3). Though transport
8 efficiencies (energy use per pkm travelled and per ton-km of delivery) are expected to continue to
9 improve in line with the historical trends (see Section 10.7.4), total transport emissions would grow due
10 to roughly constant carbon intensity (Section 10.7.5) under the C7-8 (>3.0°C) scenarios. Significant
11 increases in emissions (> 150% for the medium values by 2050) would come from Asia and developing
12 Pacific (APC), the Middle East and (ME), and Africa (AF), whereas Developed Countries (DEV) would
13 have lower transport emissions (medium value -25% for C7 and 15% for C8) than the estimated 2020
14 level in 2050.
15 To meet temperature goals, global transport emissions would need to decrease by 17% (67–+23% for
16 the 5–95th percentile) below 2020Mod levels in the C3-5 scenario group (1.5 - 2.5°C, orange bars), and
17 47% (14–80% for the 5–95th percentile) in the C1-2 scenario group (below 1.5°C, green bars) by 2050.
18 However, transport-related emission reductions may not happen uniformly across regions. For example,
19 transport emissions from the Developed Countries (DEV), and Eastern Europe and West-Central Asia
20 (EEA) would decrease from 2020 levels by 2050 across all C1-2 scenarios, but could increase in Africa
21 (AF), Asia and developing Pacific (APC), Latin America and Caribbean (LAM) and the Middle East
22 (ME), in some of these scenarios. In particular, the median transport emissions in India and Africa could
23 increase by 2050 in C1-2 scenarios, while the 95th percentile emissions in Asia and developing Pacific
24 (APC), Latin America and Caribbean (LAM), and the Middle East (ME), could be higher in 2050 than
25 in 2020.
26 The Reference scenario emission pathways from GTEMs described in Figure 10.17 have similar ranges
27 as C7-8 scenario groups in 2050. The Policy scenarios are roughly in line with C6-7 scenarios for the
28 world region. The results suggest that the majority of the Policy scenarios examined by the GTEMS
29 reviewed here are in the range of the 2-3°C temperature goal scenarios examined by the IAMs (Gota et
30 al. 2016; Yeh et al. 2017; IEA 2017b; Fisch-Romito and Guivarch 2019). The NDCs in the transport
31 sector include a mix of measures targeting efficiency improvements of vehicles and trucks; improving
32 public transit services; decarbonising fuels with alternative fuels and technologies including biofuels,
33 fossil- or bio-based natural gas, and electrification; intelligent transport systems; and vehicle restrictions
34 (Gota et al. 2016). Because of the long lag-time for technology turnover, these measures are not
35 expected to change 2030 emissions significantly. However, they could have greater impacts on 2050
36 emissions.
37 Several GTEMs not included in AR6 scenario database have examined ambitious CO2 mitigation
38 scenarios. For example, a meta-analysis of scenarios suggests that global transport emissions consistent
39 with warming levels of 2°C, would peak in 2020 at around 7-8 GtCO2 and decrease to 2.5-9.2 Gt for
40 2°C with an average of 5.4 Gt by 2050 (Gota et al. 2019). For comparison, the IEA’s Sustainable
41 Development Scenario (SDS) suggests global transport emissions decrease to 3.3 Gt (or 55% reduction
42 from 2020 level) by 2050 (IEA 2021f). In the latest IEA Net Zero by 2050 report proposes transport
43 emissions to be close to zero by 2050 (IEA 2021e). The latter is lower than the interquartile ranges of
44 the C1 group of scenarios from the AR6 database analysed here.
45 Low carbon scenarios are also available from national models (Latin America, Brazil, Canada, China,
46 France, Germany, Indonesia, India, Italy, Japan, Mexico, South Africa, UK, US) with a good
47 representation of the transport sector. The low carbon scenarios are either defined with respect to a
48 global climate stabilization level of e.g., 2°C /1.5°C Scenario (Dhar et al. 2018), or a CO2 target that is
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1 more stringent than what has been considered in the NDCs, such as the net zero emissions pathways
2 (Bataille et al. 2020; IEA 2021e). These studies have generally used bottom-up models (see Annex III)
3 for the analysis, but in some cases, they are run by national teams using global models (e.g., GCAM for
4 China and India). National studies show that transport CO2 emissions could decline significantly in low-
5 carbon scenarios in all the developed countries reviewed (Bataille et al. 2015; Kainuma et al. 205AD;
6 Virdis et al. 2015; Pye et al. 2015; Criqui et al. 2015; Kemfert et al.; Williams et al. 2015; Zhang et al.
7 2016a) in 2050 from the emissions in 2010 and reductions vary from 65% to 95%. However, in
8 developing countries reviewed (Altieri et al. 2015; Buira and Tovilla 2015; Teng et al. 2015; Rovere et
9 al. 2015; Siagian et al. 2015; Shukla et al. 2015; Di Sbroiavacca et al. 2014; Dhar et al. 2018), emissions
10 could increase in 2050 in the range of 35% - 83% relative to 2010 levels. Transport CO2 emissions per
11 capita in the developing countries were much lower in 2010 (vary from 0.15 to 1.39 tCO 2 per capita)
12 relative to developed countries (vary from 1.76 to 5.95 tCO2 per capita). However, results from national
13 modelling efforts suggest that, by 2050, the CO2 emissions per capita in developed countries (vary from
14 0.19 to 1.04 tCO2 per capita) could be much lower than in developing countries (vary from 0.21 to 1.7
15 tCO2 per capita).
16 The transport scenario literature's mean outcomes suggest that the transport sector may take a less steep
17 emission reduction trajectory than the cross-sectoral average and still be consistent with the 2°C goal.
18 For example, most of the 1.5°C pathway scenarios (C1-2) reach zero-emission by 2060, whereas
19 transport sector emissions are estimated in the range of 20% of the 2020Mod level (4-65% for the 10th
20 – 90th percentiles) by 2100. This finding is in line with perspectives in the literature suggesting that
21 transport is one of the most difficult sectors to decarbonise (Davis et al. 2018). There is, however,
22 quite a spread in the results for 2050. Since temperature warming levels relate to global emissions from
23 all sectors, modelling results from IAMs tend to suggest that in the short and medium-term, there might
24 be lower cost mitigation options outside the transport sector. On the other hand, compared with G-
25 /NTEMs, some IAMs may have limited mitigation options available including technology, behavioural
26 changes, and policy tools especially for aviation and shipping. The models therefore rely on other
27 sectors and/or negative emissions elsewhere to achieve the overall desired warming levels. This
28 potential shortcoming should be kept in mind when interpreting the sectoral results from IAMs.
29
30 10.7.3 Transport activity trajectories
31 Growth in passenger and freight travel demand is strongly dependent on population growth and GDP.
32 In 2015, transport activities were estimated at around 35-50 trillion pkm or 5,000-7,000 pkm per person
33 per year, with significant variations among studies (IEA 2017b; ITF 2019). The number of passenger
34 cars in use has grown 45% globally between 2005-2015, with the most significant growth occurring in
35 the developing countries of Asia and the Middle East (119%), Africa (79%) and, South and Central
36 America (80%), while the growth in Europe and North America is the slowest (21% and 4%
37 respectively) (IOMVM 2021). On the other hand, car ownership levels in terms of vehicles per 1,000
38 people in 2015 were low in developing countries of Asia and the Middle East (141), Africa (42), South
39 and Central America (176), while in Europe and North America they are relatively high (581 and 670
40 respectively) (IOMVM 2021). The growth rate in commercial vehicles (freight and passenger) was 41%
41 between 2005 and 2015, with a somewhat more even growth across developed and developing countries
42 (IOMVM 2021).
43 Figure 10.18 shows activity trajectories for both freight and passenger transport based on the AR6
44 database for IAMs. According to demand projections from the IAMs, global passenger and freight
45 transport demand could increase relative to a modelled year 2020 across temperature goals. The median
46 transport demand from IAMs for all the scenarios in line with warming levels below 2.5°C (C1-C5)
47 suggests the global passenger transport demand could grow by 1.14-1.3 times in 2030 and by 1.5-1.8
48 times in 2050 (1.27-2.33 for the 5th – 95th percentile across C1-C5 scenarios) relative to modelled 2020
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1 level. Developed regions including North America and Europe exhibit lower growth in passenger
2 demand in 2050 compared to developing countries across all the scenarios. In 2030, most of the global
3 passenger demand growth happens in Africa (AF) (44%% growth relative to 2020), and Asia and
4 developing Pacific (APC) (57%% growth in China and 59% growth in India relative to 2020) in the
5 below 2.5 scenario (C5). These regions start from a low level of per capita demand. For example, in
6 India, demand may grow by 84%. However, the per capita demand in 2010 was under 7,000 km per
7 person per year (Dhar and Shukla 2015). Similarly, in China, demand may grow by 52%, starting from
8 per capita demand of 8,000 km per person per year in 2010 (Pan et al. 2018). The per capita passenger
9 demand in these regions was lower than in developed countries in 2010, but it converges towards the
10 per capita passenger transport demand of advanced economies in less stringent climate scenarios (C6-
11 7). Demand for passenger travel would grow at a slower rate in the stricter temperature stabilization
12 scenarios (< 2.5 and 1.5 scenarios, C1-C5) compared to the scenarios with higher warming levels (C7-
13 C8). The median global passenger demand in the scenarios with warming levels below 1.5oC scenarios
14 (C1-C2) are 27% lower in 2050 relative to C8.
15 Due to limited data availability, globally consistent freight data is difficult to obtain. In 2015, global
16 freight demand was estimated to be 108 trillion tkm, most of which was transported by sea (ITF
17 2019). The growth rates of freight service demand vary dramatically among different regions: over the
18 1975–2015 period, road freight activity in India increased more than 9-fold, 30-fold in China, and 2.5-
19 fold in the US (Mulholland et al. 2018). Global freight demand continues to grow but at a slower rate
20 compared to passenger demand across all the scenarios in 2050 compared to modelled 2020 values.
21 Global median freight demand could increase by 1.17 -1.28 times in 2030 and 1.18-1.7 times in 2050
22 in all the scenarios with warming level below 2.5°C (C1-C5). Like passenger transport, the models
23 suggest that a large share of growth occurs in Africa (AF) and Asian regions (59% growth in India and
24 50% growth in China in 2030 relative to a modelled year 2020) in C5 scenario. Global median freight
25 demand grows slower in the stringent temperature stabilization scenarios, and is 40% and 22% lower
26 in 2050 in the below 1.5°C scenarios (C1-C2) and below 2.5°C scenarios (C3-C4), respectively,
27 compared to scenarios with warming levels of above 4°C (C8).
28
29
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1
2 Figure 10.18 Transport activity trajectories for passenger (bottom panel) and freight (top panel) in 2030,
3 2050, and 2100 indexed to 2020 modelled year across R6 Regions and World. Plots show 5-95%
4 percentile, 25th/75th percentile, and median. Numbers above the bars indicate the number of scenarios.
5 Data from the AR6 scenario database.
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1
2 GTEMs show broad ranges for future travel demand, particularly for the freight sector. These results
3 show more dependency on models than on baseline or policy scenarios. According to ITF Transport
4 Outlook (ITF 2019) , global passenger transport and freight demand could more than double by 2050
5 in a business-as-usual (BAU) scenario. Mulholland et al. (2018) suggest the freight sector could grow
6 by 2.4-fold over 2015–2050 in the reference scenario, with the majority of growth attributable to
7 developing countries. The IEA suggests a more modest increase in passenger transport, from 51 trillion
8 pkm in 2014 to 110 trillion pkm in 2060, in a reference scenario without climate policies and a climate
9 scenario that would limit emissions below 2oC. The demand for land-based freight transport in 2060 is,
10 however, slightly lower in the climate scenario (116 trillion tkm) compared to the reference scenario
11 (130 trillion tkm) (IEA 2017b)The ITF, however, suggests that ambitious decarbonisation policies could
12 reduce global demand for passenger transport by 13-20% in 2050, compared to the business-as-usual
13 scenario (ITF 2019, 2021). The reduction in vehicle travel through shared mobility could reduce
14 emissions from urban passenger transport by 30% compared to the BAU scenario. Others suggest
15 reductions larger than 25%, on average, for both passenger and freight in 2030 and 2050 may be needed
16 to achieve very low carbon emission pathways (Fisch-Romito and Guivarch 2019). In absence of large-
17 scale carbon dioxide removal, few global studies highlight the need for significant demand reduction in
18 critical sectors (aviation, shipping and road freight) in well below 2oC scenarios (van Vuuren et al.
19 2018; Grant et al. 2021; Sharmina et al. 2021).
20 Many models find small differences in passenger transport demand across temperature goals because
21 IAM models rely on historical relationships between population, GDP, and demand for services to
22 estimate future demand. This assumption poses a limitation to the modelling efforts, as mitigation
23 efforts would likely increase travel costs that could result in lower transport demand (Zhang et al. 2018).
24 In most models, demand is typically an exogenous input. These models often assume mode shifts of
25 activities from the most carbon-intensive modes (driving and flying for passenger travel and trucking
26 for freight) to less carbon-intensive modes (public transit and passenger rails, and freight rail) to reduce
27 emissions.
28 Traditionally there is a disconnection between IAM models and bottom-up sectoral or city-based
29 models due to the different scale (both spatial and temporal) and focus (climate mitigation vs. urban
30 pollutions, safety (Creutzig 2016)). The proliferation of shared and on-demand mobility solutions are
31 leading to rebound effects for travel demand (Chen and Kockelman 2016; Coulombel et al. 2019) and
32 this is a new challenge for modelling. Some IAM studies have recently begun to explore demand-side
33 solutions for reducing transport demand to achieve very low-carbon scenarios through a combination
34 of culture and low-carbon lifestyle (Creutzig et al. 2018; van Vuuren et al. 2018); urban development
35 (Creutzig et al. 2015a); increased vehicle occupancy (Grubler et al. 2018); improved logistics and
36 streamline supply chains for the freight sector (Mulholland et al. 2018); and disruptive low-carbon
37 innovation, described as technological and business model innovations offering "novel value
38 propositions to consumers and which can reduce GHG emissions if adopted at scale" (Wilson et al.
39 2019). In the literature from national models, demand has been differentiated between conventional and
40 sustainable development scenarios through narratives built around policies, projects, and programs
41 envisaged at the national level (Shukla et al. 2015; Dhar and Shukla 2015) and price elasticities of travel
42 demand (Dhar et al. 2018). However, a greater understanding of the mechanisms underlying energy-
43 relevant decisions and behaviours (Brosch et al. 2016), and the motivations for sustainable behaviour
44 (Steg et al. 2015) are critically needed to realize these solutions in reality.
45 Overall, passenger and freight activity are likely to continue to grow rapidly under the C7 (>3.0°C)
46 scenarios, but most growth would occur in developing countries. Most models treat travel demand
47 exogenously following the growth of population and GDP, but they have limited representation of
48 responses to price changes, policy incentives, behavioural shifts, nor innovative mobility solutions that
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1 can be expected to occur in more stringent mitigation scenarios Chapter 5 provides a more detailed
2 discussion of the opportunities for demand changes that may results from social and behavioural
3 interventions.
4
5 10.7.4 Transport modes trajectories
6 Globally over the last century, shares of faster transport modes have generally increased with increasing
7 passenger travel demand (Schafer and Victor 2000; Schäfer 2017). For short- to medium-distance
8 travel, private cars have displaced public transit, particularly in OECD countries, due to a variety of
9 factors, including faster travel times in many circumstances (Liao et al. 2020); increasing consumers'
10 value of time and convenience with GDP growth; and broader transport policies, e.g. provision of road
11 versus public transit infrastructure (Mattioli et al. 2020). For long-distance travel, travel via aviation for
12 leisure and business has increased (Lee et al. 2021). These trends do not hold in all countries and cities,
13 as many now have rail transit that is faster than driving (Newman et al. 2015a). For instance, public
14 transport demand rose from 1990 through 2016 in France, Denmark, and Finland (eurostat 2019). In
15 general, smaller and denser countries and cities with higher or increasing urbanization rates tend to have
16 greater success in increasing public transport share. However, other factors, like privatisation of public
17 transit (Bayliss and Mattioli 2018) and urban form (ITF 2021), also play a role. Different transport
18 modes can provide passenger and freight services, affecting the emissions trajectories for the sector.
19 Figure 10.19 shows activity trajectories for freight and passenger transport through 2100 relative to a
20 modelled year 2020 across different modes based on the AR6 database for IAMs and global transport
21 models. Globally, climate scenarios from IAMs, and policy and reference scenarios from global
22 transport models indicate increasing demand for freight and passenger transport via most modes through
23 2100 (Yeh et al. 2017; Zhang et al. 2018; Mulholland et al. 2018; Khalili et al. 2019). Road passenger
24 transport exhibits a similar increase (roughly tripling) through 2100 across scenarios. For road
25 passenger transport, scenarios that limit warming to 1.5 °C (C1-C2) have a smaller increase from
26 modelled 2020 levels (median increase of 2.4 times modelled 2020 levels) than do scenarios with higher
27 warming levels (C3-C8) (median increase of 2.7-2.8 times modelled 2020 levels). There are similar
28 patterns for passenger road transport via light-duty vehicle, for which median increases from modelled
29 2020 levels are smaller for C1-2 (3 times larger) than for C3-5 (3.1 times larger) or C6-7 (3.2 times
30 larger). Passenger transport via aviation exhibits a 2.2 times median increase relative to modelled 2020
31 levels under C1-2 and C3-5 scenarios but exhibits a 6.2 times increase under C6-C8. The only passenger
32 travel mode that exhibits a decline in its median value through 2100 according to IAMs is
33 walking/bicycling, in C3-5 and C6-8 scenarios. However, in C1-2 scenarios, walking/bicycling
34 increases by 1.4 times relative to modelled 2020 levels. At the 5th percentile of IAM solutions (lower
35 edge of bands in Figure 10.19), buses and walking/bicycling for passenger travel both exhibit significant
36 declines.
37
38
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1
2 Figure 10.19 Transport activity trajectories for passenger and freight across different modes. Global
3 passenger (billion pkm per year) and freight (billion tkm per year) demand projections relative to a
4 modelled year 2020 index. Results for IAM for selected stabilization temperatures by 2100. Also included
5 are global transport models Ref and Policy scenarios. Data from the AR6 scenario database. Trajectories
6 span the 5th/95th percentiles across models with a solid line indicating the median value across models.
7
8 For freight, Figure 10.19 shows that the largest growth occurs in transport via road (Mulholland et al.
9 2018). By 2100, global transport models suggest a roughly 4 times increase in median- heavy-duty
10 trucking levels relative to modelled 2020 levels, while IAMs suggest a 2-4 times increase in freight
11 transport by road by 2100. Notably, the 95th percentile of IAM solutions see up to a 4.7 times increase
12 in road transport through 2100 relative to modelled 2020 levels, regardless of warming level. Other
13 freight transport modes – aviation, international shipping, navigation, and railways – exhibit less growth
14 than road transport. In scenarios that limit warming to 1.5 °C (C1-C2), navigation and railway remain
15 largely unchanged and international shipping roughly doubles by 2100. Scenarios with higher warming
16 (i.e., moving from C1-2 to C6-8) generally lead to more freight by rail and less freight by international
17 shipping.
18 Relative to global trajectories, upper-income regions – including North America, Europe, and the
19 Pacific OECD – generally see less growth in passenger road via light-duty vehicle and passenger
20 aviation, given more saturated demand for both. Other regions like China exhibit similar modal trends
21 as the global average, whereas regions such as the African continent and Indian subcontinent exhibit
22 significantly larger shifts, proportionally, in modal transport than the globe. In particular, the African
23 continent represents the starkest departure from global results. Freight and passenger transport modes
24 exhibit significantly greater growth across Africa than globally in all available scenarios. Across Africa,
25 median freight and passenger transport via road from IAMs increases by 5-16 times and 4-28 times,
26 respectively, across warming levels by 2100 relative to modelled 2020 levels. Even C1 has considerable
27 growth in Africa via both modes (3-16 times increase for freight and 4-29 times increase for passenger
28 travel at 5th and 95th percentiles of IAM solutions by 2100).
29 As noted in Section 10.2, commonly explored mitigation options related to mode change include a shift
30 to public transit, shared mobility, and demand reductions through various means, including improved
31 urban form, teleconferences that replace passenger travel (Creutzig et al. 2018; Grubler et al. 2018;
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1 Wilson et al. 2019), improved logistics efficiency, green logistics, and streamlined supply chains for
2 the freight sector (Mulholland et al. 2018). NDCs often prioritize options like bus improvements and
3 enhanced mobility that yield pollution, congestion, and urban development co-benefits, especially in
4 medium and lower-income countries (Fulton et al. 2017). Conversely, high-income countries, most of
5 which have saturated and entrenched private vehicle ownership, typically focus more on technology
6 options, e.g., electrification and fuel efficiency standards (Gota et al. 2016). Available IAM and regional
7 models are limited in their ability to represent modal shift strategies. As a result, mode shifts alone do
8 not differentiate climate scenarios. While this lack of representation is a limitation of the models, it is
9 unlikely that such interventions would completely negate the increases in demand the models suggest.
10 Therefore, transport via light-duty vehicle and aviation, freight transport via road, and other modes will
11 likely continue to increase through end-of-century. Consequently, fuel and carbon efficiency and fuel
12 energy and technology will probably play crucial roles in differentiating climate scenarios, as discussed
13 in the following sub-sections.
14
15 10.7.5 Energy and Carbon efficiency trajectories
16 This section explores what vehicle energy efficiencies and fuel carbon intensity trajectories, from the
17 data available in AR6 database from IAMs and GTEMs, could be compatible with different temperature
18 goals. Figure 10.20 shows passenger and freight energy intensity, and fuel carbon intensity indexed
19 relative to 2020Mod values. The top panel shows passenger energy intensity across all modes. LDVs
20 constitute a major share of this segment. (Yeh et al. 2017) report 2.5-2.75 MJ vkm-1 in 2020 across
21 models for the LDV segment, which is also very close to the IEA estimate of 2.5 MJ vkm-1 for the
22 global average fuel consumption for LDVs in 2017 (IEA 2020d). For reference, these numbers
23 correspond to 1.6-1.7 MJ pkm-1 for an occupancy rate of 1.5. The following results of the AR6 database
24 are conditional on the corresponding reductions in fuel carbon intensity. Figure 10.20 shows that the
25 scenarios suggest that passenger transport's energy intensity drops to between 10%-23% (interquartile
26 ranges across C1-C4) in 2030 for the scenarios in line with warming levels below 2°C. In 2050, the
27 medians across the group of 1.5°C scenarios (C1-C2) and 2°C scenarios (C2-C3) suggest energy
28 intensity reductions of 51% and 45-46% respectively. These values correspond to annual average
29 energy efficiency improvement rates of 2.3-2.4% and 2.0-2.1%, respectively, from 2020 to 2050. For
30 reference, the IEA reports an annual energy efficiency improvement rate of 1.85% per year in 2005-16
31 (IEA 2020d). In contrast, the results from GTEMs suggest lower energy efficiency improvement, with
32 median values for policy scenarios of 39% reduction in 2050, corresponding to annual energy efficiency
33 improvement rates close to 1.6%. The IAM scenarios suggest median energy intensity reductions of
34 passenger transport of 57-61% by the end of the century would align with warming levels of both 1.5°C
35 and 2°C (C1-C4) given the corresponding decarbonisation of the fuels.
36
37
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1
2 Figure 10.20 Energy efficiency and carbon intensity in 2030, 2050, and 2100 indexed to 2020 modelled
3 year across scenarios. Plots show 5th/95th percentile, 25th/75th percentile, and median. Numbers above the
4 bars indicate the number of scenarios. Data from the AR6 scenario database.
5
6 The scenarios in line with warming levels of 1.5°C or 2°C goals show different trends for freight's
7 energy intensity. The amount of overshoot and differences in demand for freight services and, to some
8 extent, fuel carbon intensities contribute to these differences. For the two scenarios aligning with the
9 warming levels of 1.5°C, the trajectories in 2030 and 2050 are quite different. The median scenario in
10 the high overshoot bin (C2) takes a trajectory with lower energy intensity improvements in the first half
11 of the century. In contrast, the limited overshoot scenario (C1) takes on a more steadily declining
12 trajectory across the means. The IAMs provide a less clear picture of required energy intensity
13 improvements for freight than for passenger associated with different temperature targets. As for the
14 carbon intensity of direct energy used across both passenger and freight, the modelling scenarios
15 suggest very moderate reductions by 2030. The interquartile ranges for the C1 scenarios suggest global
16 average reductions in carbon intensity of 5%-10%. Across the other scenarios compatible with warming
17 levels of 1.5°C or 2°C (C2-4), the interquartile ranges span from 1%-6% reductions in carbon intensity
18 of direct energy used for transport. For 2050 the scenarios suggest that dependence on fuel
19 decarbonisation increases with more stringent temperature targets. For the 1.5°C scenarios (C1), global
20 carbon intensity of energy used for transport decreases by 37%-60% (interquartile range) by 2050 with
21 a mean of 50% reduction. The IAM scenarios in the AR6 database do not suggest full decarbonisation
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1 of transport fuels by 2100. The interquartile ranges across the C1-C4 set of scenarios, compatible with
2 warming levels of 2°C and less, span from 61%-91% reduction from 2020Mod levels.
3 Increasing occupancy rate of passenger transport (Grubler et al. 2018) and reducing empty miles or
4 increasing payload in freight deliveries (Gucwa and Schäfer 2013; McKinnon 2018) via improved
5 logistics efficiency or streamlined supply chains (Mulholland et al. 2018), can present significant
6 opportunities to effectively improve energy efficiency and decrease GHG emissions in transport.
7 However, the recent trends of consumer behaviours have shown a declining occupancy rate of light-
8 duty vehicles in industrialized countries (Schäfer and Yeh 2020), and the accelerating growing
9 preference for SUVs challenges emissions reductions in the passenger car market (IEA 2019d). These
10 trends motivate a strong focus on demand-side options.
11 Based on the scenario literature, a 51% reduction in median energy intensity of passenger transport and
12 a corresponding reduction of 38%-50% reduction in median carbon intensity by 2050 would be aligned
13 with transition trajectories yielding warming levels below 1.5°C by the end of the century. For
14 comparison, the LCA literature suggests a switch from current ICEs to current BEVs would yield a
15 reduction in energy intensity well beyond 45% and up to 70%, for a mid-sized vehicle (see Sections
16 10.4). Correspondingly, a switch from diesel or gasoline to low-carbon electricity or low-carbon
17 Hydrogen would yield carbon intensity reduction beyond the median scenario value. Thus, the LCA
18 literature suggests technologies exist today that would already match and exceed the median energy and
19 carbon intensities values that might be needed by 2050 for low warming levels.
20
21 10.7.6 Fuel energy and technology trajectories
22 Two mechanisms for reducing carbon emissions from the transport sector are fuel switching for current
23 vehicle technologies and transitioning to low carbon vehicle technologies. Figure 10.21 combines data
24 from IAMs and GTEMs on shares of transport final energy by fuel. These shares account for fuels uses
25 across modes - road, aviation, rail, and shipping- and both passenger and freight transport. Since the
26 technologies have different conversion efficiencies, these shares of final energy by fuel are necessarily
27 different from the shares of service (passenger- or ton-km) by fuel and shares of vehicle stock by fuel.
28 For example, a current battery-electric LDV powertrain is roughly 3 times more energy-efficient than
29 a comparable ICE powertrain (see Section 10.3, and Table 10.9 in Appendix 10.1); thus, fuel shares of
30 0.25 for electricity and 0.75 for oil could correspond to vehicle stock shares of 0.5 and 0.5, respectively.
31 In general, while models may project that EVs constitute a greater share of road vehicle stock, and
32 provide a greater share of road passenger-kilometres, their share of transport final energy (shown in
33 Figure 10.21) can still remain lower than the final energy share of fuels used in less-efficient (e.g. ICE)
34 vehicles. Thus, the shares of transport final energy by fuel presented in Figure 10.21 should be
35 interpreted with care.
36
37
38
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1
2 Figure 10.21 Global shares of final fuel energy in the transport sector in 2030, 2050, and 2100 for freight
3 and passenger vehicles. Plots show 10th/90th percentile, 25th/75th percentile, and median. Data from the
4 AR6 scenario database.
5
6 IAM and GTEM scenarios indicate that fuel and technology shifts are crucial to reduce carbon
7 emissions to achieve lower levels of warming (Edelenbosch et al. 2017; IEA 2017b). Across the
8 transport sector, a technology shift towards advanced fuel vehicles is the dominant driver of
9 decarbonisation in model projections. This trend is consistent across climate scenarios, with larger
10 decreases in the final energy share of oil in scenarios that achieve progressively lower levels of
11 warming. Due to efficiency improvements; the higher efficiency of advanced fuel vehicles; and slower
12 progress in the freight sector, the final energy share of oil decreases more rapidly after 2030. By 2050,
13 the final energy shares of electricity, biofuels, and alternative gaseous fuels increase, with shares from
14 electricity generally about twice as high (median values from 10%–30% across warming levels) as the
15 shares from biofuels and gases (median values from 5%–10%). While IAMs suggest that the final
16 energy share of hydrogen will remain low in 2050, by 2100 the median projections include 5%–10%
17 hydrogen in transport final energy.
18 While only few IAMs report final energy shares by transport mode or passenger/freight , several
19 relevant studies provide insights into fuel share trends in passenger LDVs and freight. The IEA
20 suggests that full LDV electrification would be the most promising low-carbon pathway to meet a
21 1.75°C goal (IEA 2017b). The MIT Economic Projection and Policy Analysis (EPPA) model focuses
22 on the future deployment of gasoline versus EV technologies in the global LDV stock (Ghandi and
23 Paltsev 2019). These authors estimate that the global stock of vehicles could increase from 1.1 billion
24 vehicles in 2015 up to 1.8 billion by 2050, with a growth in EVs from about 1 million vehicles in 2015
25 up to 500 million in 2050. These changes are driven primarily by cost projections (mostly in battery
26 cost reductions). Similarly, the International Council on Clean Transport (ICCT) indicates that EV
27 technology adoption in the light-duty sector can lead to considerable climate benefits. Their scenarios
28 reach nearly 100% electrification of LDVs globally, leading to global GHG emissions ranging from 0%
29 to -50% of 2010 LDV levels in 2050 (Lutsey 2015). Khalili et al (2019) estimate transport stocks
30 through 2050 under aggressive climate mitigation scenarios that nearly eliminate road transport
31 emissions. They find the demand for passenger transport could triple through 2050, but emissions
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1 targets could be met through widespread adoption of BEVs (80% of LDVs) and, to a lesser extent, fuel
2 cell and plug-in hybrid electric vehicles. Contrary to these estimates, the US Energy Information
3 Administration (EIA) finds small adoption of electrification for LDVs and instead identifies diffusion
4 of natural gas-fuelled LDVs in OECD and, to a greater extent, non-OECD countries through 2040. This
5 trend occurs in a reference and a "low liquids" case, which lowers LDV ownership growth rates and
6 increases preferences for alternative fuel vehicles. A comprehensive overview of regional technology
7 adoption models across many methodological approaches can be found in (Jochem et al. 2018).
8 In freight transport, studies indicate a shift toward alternative fuels would need to be supplemented by
9 efficiency improvements. The IEA suggests efficiency improvements would be essential for
10 decarbonisation of trucks, aviation, and shipping in the short-to-medium term. At the same time, the
11 IEA suggests that fuel switching to advanced biofuels would be needed to decarbonise freight in the
12 long-term (IEA 2019d). Mulholland et al. (2018) investigated the impacts of decarbonising road freight
13 in two scenarios: countries complying with COP21 pledges and a second more ambitious reduction
14 scenario in line with limiting global temperature rise to 1.75oC. Despite the deployment of logistics
15 improvements, high-efficiency technologies, and low carbon fuels, activity growth leads to a 47%
16 increase in energy demand for road freight while overall GHG emissions from freight increase by 55%
17 (4.8 GtCO2eq) in 2050 (relative to 2015) in the COP21 scenario. In the 1.75oC scenario, decarbonisation
18 happens primarily through a switch to alternative fuels (hybrid electric and full battery-electric trucks),
19 which leads to a 60% reduction in GHG emissions from freight in 2050 relative to 2015. Khalili et al.
20 (2019) also find substantial shifts to alternative fuels in HDVs under aggressive climate mitigation
21 scenarios. Battery electricity, Hydrogen fuel cell, and plug-in hybrid electric vehicles constitute 50%,
22 30%, and 15% of heavy-duty vehicles, respectively, in 2050. They also find 90% of buses would be
23 electrified by 2050.
24
25 START BOX HERE
26
27 Box 10.4 Three Illustrative Mitigation Pathways.
28 Section 10.7 presents the full set of scenarios in the AR6 database and highlight the broader trends of
29 how the transport sector may transform in order to be compliant with different warming levels. This
30 box elaborates on three illustrative mitigation pathways (IMPs) to exemplify a few different ways the
31 sector may transform. A total of 7 illustrative pathways are introduced in section 3.2.5 of chapter 3. In
32 this box we focus on three of the IMPs: (1) focus on deep renewable energy penetration and
33 electrification (IMP-Ren), (2) low demand (IMP-LD) and (3) pathways that align with both sustainable
34 development goals as well as climate policies (IMP-SP). In particular, the variants of these three
35 scenarios limit warming to 1.5°C with no or limited overshoot (C1). All of the three selected pathways
36 reach global net zero CO2 emissions across all sectors between 2060 and 2070, but not all reach net
37 zero GHG emissions (see Figure 3.4 Chapter 3). Panel (a) in Box 10.4, Figure 1 below shows the CO2
38 trajectories for the transport sector for the selected IMPs. Please note that the year 2020 is modelled in
39 these scenarios. Therefore, the scenarios do not reflect the effects of the COVID-19 pandemic. For the
40 low demand scenarios IMP-LD and renewables pathway IMP-Ren, CO2 emissions from the transport
41 sectors decrease to 10% and 20% of modelled 2020 levels by 2050, respectively. In contrast, the IMP-
42 SP has a steady decline of transport sector CO2 emissions over the century. By 2050, this scenario has
43 a 50% reduction in emissions compared to modelled 2020 levels. Panels (b), (c) and (d) show energy
44 by different fuels for the three selected IMPs. The IMPs-SP yields a drop in energy for transport of
45 about 40% by the end of the century. CO2 emission reductions are obtained through a phase-out of fossil
46 fuels with electricity and biofuels, complemented by a minor share of Hydrogen by the end of the
47 century. In IMP Ren, the fuel energy demand at the end of the century is in par with the 2020 levels,
48 but the fuel mix has shifted towards a larger share of electricity complimented by biofuels and a minor
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1 share of Hydrogen. For the IMP-LD scenario, the overall fuel demand decreases by 45% compared to
2 2020 levels by the end of the century. Oil is largely phased out by mid-century, with electricity and
3 Hydrogen becoming the major fuels in the second half of the century. Across the three IMPs, electricity
4 plays a major role, in combination with biofuels, Hydrogen, or both.
5
6
7
8 Box 10.4, Figure 1 Three Illustrative mitigation pathways for the Transport sector. Panel (a) shows CO2
9 emissions from the transport sector indexed to simulated non-COVID 2020 levels. Panels (b), (c), and (c)
10 show fuels mix for 1.5 (IMP-SP), 1.5 REN (IMP-Ren) and 1.5 LD (IMP-LD), respectively. All data from
11 IPCC AR6 Scenario database.
12
13 END BOX HERE
14
15 10.7.7 Insights from the modelling literature
16 This section provides an updated, detailed assessment of future transport scenarios from IAM and G-
17 /NTEMs given a wide range of assumptions and under a set of policy targets and conditions. The
18 scenario modelling tools are necessary to aggregate individual options and understand how they fit into
19 mitigation pathways from a systems perspective. The scenarios suggest that 43% (30-63% for the inter
20 quartile ranges) reductions in CO2 emissions from the transport emissions CO2 (below modelled 2020
21 levels) by 2050 would be compatible with warming levels of 1.5°C (C1-C2 group). While the global
22 scenarios suggest emissions reductions in energy supply sectors at large precede those in the demand
23 sectors (see section 3.4.1), a subset of the scenarios also demonstrate that more stringent emission
24 reductions in the transport sector are feasible. For example, the illustrative mitigation pathways IMP-
25 REN and IMP-LD suggest emission reductions of respectively 80% and 90% are feasible by 2050 en-
26 route to warming levels of 1.5°C (C1-C2) with low or no overshoot by the end of the century.
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1 The scenarios from the different models project continued growth in demand for freight and passenger
2 services, particularly in developing countries. The potential of demand reductions is evident, but the
3 specifics of demand reduction measures remain less explored by the scenario literature. This limitation
4 not-withstanding, the IAM and GTEMs suggest interventions that reduce the energy and fuel carbon
5 intensity are likely crucial to successful mitigation strategies.
6 The scenario literature suggests that serious attempts at carbon mitigation in the transport sector must
7 examine the uptake of alternative fuels. The scenarios described in the IAMs and GTEMs literature
8 decarbonise through a combination of fuels. Across the scenarios, electrification plays a key role,
9 complemented by biofuels and Hydrogen. In general terms, electrification tends to play the key role in
10 passenger transport while biofuels and Hydrogen are more prominent in the freight segment. The three
11 illustrative mitigation pathways in Box 10.4 exemplify different ways these technologies may be
12 combined and still be compatible with warming levels of 1.5°C with low or no overshoot. Shifts towards
13 alternative fuels must occur alongside shifts towards clean technologies in other sectors, as all
14 alternative fuels have upstream impacts. Without considering other sectors, fuel shifts would not yield
15 their full mitigation potentials. These collective efforts are particularly important for the electrification
16 of transport, as the transformative mitigation potential is strongly dependent on the decarbonisation of
17 the power sector. In this regard, the scenario literature is well aligned with the LCA literature reviewed
18 in Section 10.4.
19 The models reviewed in this section would all generally be considered to have a good representation of
20 fuels, technologies, and costs, but they often better represent land transport modes than shipping and
21 aviation. While these models have their strengths in some areas, they have some limitations in other
22 areas, like behavioural aspects. Analogously, these models are also limited in their ability to account
23 for unexpected technological innovation such as a breakthrough in heavy vehicle fuels, AI, autonomy
24 and big data, even the extent of digital communications replacing travel (see Section 10.2). As a result
25 of these type of limitations, the models cannot yet provide a fully exhaustive set of options for
26 decarbonising the transport sectors. These limitations not-withstanding, the models can find solutions
27 encompassing the transport sector and its interactions with other sectors that are compatible with
28 stringent emissions mitigation efforts. The solution space of transportation technology trajectories is
29 therefore wider than explored by the models, so there is still a need to better understand how all options
30 in combination may support the transformative mitigation targets.
31
32 10.8 Enabling conditions
33 10.8.1 Conclusions across the chapter
34 This final section draws some conclusions from the chapter, provides an overview-based feasibility
35 assessment of the major transport mitigation options, as well as a description of emerging issues. The
36 section ends by outlining an integrated framework for enabling the transformative changes that are
37 emerging and required to meet the potential transformative scenarios from Section 10.7.
38 Transport is becoming a major focus for mitigation as its GHG emissions are large and growing faster
39 than for other sectors, especially in aviation and shipping. The scenarios literature suggests that without
40 mitigation actions, transport emissions could grow by up to 65% by 2050. Alternatively, successful
41 deployment of mitigation strategies could reduce sectoral emissions by 68%, which would be consistent
42 with the goal of limiting temperature change to 1.5°C above pre-industrial levels. This chapter has
43 reviewed the literature on all aspects of transport and has featured three special points of focus: (1) A
44 survey of life cycle analysis from the academic and industry community that uses these tools; (2) A
45 similar exercise of surveying the modelling community for top-down and bottom-up approaches to
46 identify decarbonisation pathways for the transport sector, and (3) For the first time in the IPCC,
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1 separate sections on shipping and aviation. The analysis of the literature suggests three crucial
2 components for the decarbonisation of the transport sector: demand and efficiency strategies,
3 electromobility, and alternative fuels for shipping and aviation.
4 The challenge of decarbonisation requires a transition of the socio-technical system, which in turn
5 depends on the combination of technological innovation and societal change (Geels et al. 2017). A
6 socio-technical system includes technology, regulation, user practices and markets, cultural meaning,
7 infrastructure, maintenance networks, and supply networks (Geels 2005) (see Cross Chapter Box 12 in
8 Chapter 16). The multi-level perspective (MLP) is a framework that provides insights to assist
9 policymakers when devising transformative transition policies (Rip and Kemp 1998; Geels 2002).
10 Under the MLP framework, strategies are grouped into three different categories. The Micro level
11 (niche) category includes strategies where innovation differs radically to that of the incumbent socio-
12 technical system. The niche provides technological innovations a protected space during development
13 and usually requires considerable R&D and demonstrations. In the Meso level (regime) state,
14 demonstrations begin to emerge as options that can be adopted by leading groups who begin to
15 overcome lock-in barriers from previous technological dependence. Finally, in the Macro level
16 (Landscape) stage, main-streaming happens, and the socio-technical system enables innovations to
17 breakthrough. Figure 10.22 maps the MLP stage for the major mitigation strategies identified in this
18 chapter.
19
20
21 Figure 10.22 Mitigation Options and Enabling Conditions for Transport. Niche scale includes strategies
22 that still require innovation.
23
24 Demand and behaviour. While technology options receive substantial attention in this chapter, there
25 are many social and equity issues that cannot be neglected in any transformative change to mitigate
26 climate change. Transport systems are socio-economic systems that include systemic factors that are
27 developing into potentially transformative drivers of emissions from the sector. These systemic drivers
28 include, for example, changes in urban form that minimises automobile dependence and reduces
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1 stranded assets; behaviour change programs that emphasise shared values and economies; smart
2 technologies that enable better and more equitable options for transit and active transport as well as
3 integrated approaches to using autonomous vehicles; new ways of enabling electric charging systems
4 to fit into electricity grids creating synergistic benefits to grids, improving the value of electric transit,
5 and reducing range anxiety for EV users; and, new concepts for the future economy such as circular
6 economy, dematerialisation, shared economy that have the potential to affect the structure of the
7 transport sector. The efficacy of demand reductions and efficiency opportunities depends on the degree
8 of prioritisation and focus by government policy. Figure 10.22 suggests that innovative demand and
9 efficiency strategies are at the regime scales. While these strategies are moving beyond R&D, they are
10 not mainstreamed yet and have been shown to work much more effectively if combined with technology
11 changes as has been outlined in the transformative scenarios from Section 10.7 and in Chapter 5.
12 Electromobility in Land-based Transport. Since AR5, there has been a significant breakthrough in the
13 opportunities to reduce transport GHG emissions in an economically efficient way due to electrification
14 of land-based vehicle systems, which are now commercially available. EV technologies are particularly
15 well-established for light duty passenger vehicles, including micro mobility. Furthermore, there are
16 positive developments to enable EV technologies for buses, light and medium-duty trucks, and some
17 rail applications (though advanced biofuels and hydrogen may also contribute to the decarbonisation of
18 these vehicles in some contexts). In developing countries, where micro mobility and public transit
19 account for a large share of travel, EVs are ideal to support mitigation of emissions. Finally, demand
20 from critical materials needed for batteries has become a focus of attention, as described in Box 10.6.
21 Electromobility options are moving from regime to landscape levels. This transition is evident in the
22 trend of incumbent automobile manufacturers producing an increasing range of EVs in response to
23 demand, policy, and regulatory signals. EVs for light-duty passenger travel are largely commercial and
24 likely to become competitive with ICE vehicles in the early 2020’s (Dia 2019; Bond et al. 2020;
25 Koasidis et al. 2020). As these adopted technologies increase throughout cities and regions,
26 governments and energy suppliers will have to deploy new supporting infrastructure to support them,
27 including reliable low-carbon grids and charging stations (Sierzchula et al. 2014). In addition,
28 regulatory reviews will be necessary to ensure equitable transition and achievement of SDG’s,
29 addressing the multitude of possible barriers that may be present due to the incumbency of traditional
30 automotive manufacturers and associated supporting elements of the socio-technical system (Newman
31 2020b); and Chapter 6). Similarly, new partnership between government, industry, and communities
32 will be needed to support the transition to electromobility. These partnerships could be particularly
33 effective at supporting engagement and education programs ((Newman 2020b); and Chapter 8).
34 Deployment of electromobility is not limited to developed countries. The transportation sector in low-
35 and middle-income countries includes millions of gas-powered motorcycles within cities across Africa,
36 Southeast Asia, and South America (Ampersand 2020; Ehebrecht et al. 2018; Posada et al. 2011). Many
37 of these motorcycles function as taxis. In Kampala, Uganda, estimates place the number of motorcycle
38 taxis, known locally as boda-bodas, at around 40,000 (Ehebrecht et al. 2018). The popularity of the
39 motorcycle for personal and taxi use is due to many factors including lower upfront costs, lack of
40 regulation, and mobility in highly congested urban contexts (UNECE 2018; Posada et al. 2011). While
41 motorcycles are often seen as a more fuel-efficient alternative, emissions can be worse from 2-wheelers
42 than cars, particularly nitrogen oxides (NOx), carbon monoxide (CO), and hydrocarbon (HC) emissions
43 (Vasic and Weilenmann 2006; Ehebrecht et al. 2018). These 2-wheeler emissions contribute to
44 dangerous levels of air pollution across many cities in low- and middle-income countries. In Kampala,
45 for example, air pollution levels frequently exceed levels deemed safe for humans by the World Health
46 Organization (WHO) (Airqo 2020; World Health Organization 2018; Kampala Capital City Authority
47 2018). To mitigate local and environmental impacts, electric boda boda providers are emerging in many
48 cities, including Zembo in Kampala and Ampersand in Kigali, Rwanda.
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1 Bulawayo, the capital city of Zimbabwe, is also looking at opportunities for deploying electromobility
2 solutions. The city is now growing again after a difficult recent history, and there is a new emphasis on
3 achieving the Sustainable Development Goals (City of Bulawayo 2020a,b). With this goal in mind,
4 Bulawayo is seeking opportunities for investment that can enable leapfrogging private, fossil fuel
5 vehicle ownership. In particular, trackless trams, paired with solar energy, have emerged as a potential
6 pathway forward (Kazunga 2019). Trackless trams are a new battery-based mid-tier transit system that
7 could enable urban development around stations that use solar energy for powering both transit and the
8 surrounding buildings (Newman et al. 2019). The new trams are rail-like in their capacities and speed,
9 providing a vastly better mobility system that is decarbonised and enable low transport costs (Ndlovu
10 and Newman 2020). While this concept is only under consideration in Bulawayo, climate funding could
11 enable the wider deployment of such projects in developing countries.
12 Fuels for Aviation and Shipping. Despite technology improvements for land-based transport, equivalent
13 technologies for long distance aviation and shipping remain elusive. Alternative fuels for use in long
14 range aviation and shipping are restricted to the niche level. The aviation sector is increasingly looking
15 towards synthetic fuels using low-carbon combined with CO2 from direct air capture, while shipping is
16 moving towards Ammonia produced using low-carbon Hydrogen. Biofuels are also of interest for these
17 segments. To move out of the niche level, there is a need to set deployment targets to support
18 breakthroughs in these fuels. Similarly, there is a need for regulatory changes to remove barriers in new
19 procurement systems that accommodate uncertainty and risks inherent in the early adoption new
20 technologies and infrastructure (Borén 2019; Sclar et al. 2019; Marinaro et al. 2020). R&D programs
21 and demonstration trials are the best focus for achieving fuels for such systems. Finally, there is a need
22 for regulatory changes. Such regulatory changes need to be coordinated through ICAO and IMO as well
23 as with national implementation tools related to the Paris Agreement (see Box 10.5). Long-term visions,
24 including creative exercises for cities and regions will be required providing a protected space for the
25 purpose of trialling new technologies (Borén 2019; Geels 2019).
26
27 10.8.2 Feasibility Assessment
28 Figure 10.23 sets out the feasibility of the core mitigation options using the six criteria created for the
29 cross-sectoral analysis. This feasibility assessment outlines how the conclusions outlined in Section
30 10.8.1 fit into the broader criteria created for feasibility in the whole AR6 report and that emphasise the
31 SDGs. Figure 10.23 highlights that there is high confidence that demand reductions and mode shift can
32 be feasible as the basis of a GHG emissions mitigation strategy for the transport sector. However,
33 demand-side interventions work best when integrated with technology changes. The technologies that
34 can support such changes have a range of potential limitations as well as opportunities. EV have a
35 reliance on renewable resources (wind, solar, and hydro) for power generation, which could pose
36 constraints on geophysical resources, land use, water use. Furthermore, expanding the deployment of
37 EVs requires a rapid deployment of new power generation capacity and charging infrastructure. The
38 overall feasibility of electric vehicles for land transport is likely high and their adoption is accelerating.
39 HFCVs for land transport would also have constraints related to land geophysical resource needs, land
40 use, and water use. These constraints are likely higher than for EVs, since producing Hydrogen with
41 electricity reduces the overall efficiency of meeting travel demand. Furthermore, the infrastructure to
42 produce, transport, and deliver Hydrogen is under-developed and would require significant R&D and a
43 rapid scale-up. Thus, the feasibility of HFCV is likely lower than for EVs. Biofuels could be used in all
44 segments of the transport sector, but there may be some concerns about their feasibility. Specifically,
45 there are concerns about land use, water use, impacts on water quality and eutrophication, and
46 biodiversity impacts. Advanced biofuels could mitigate some concerns and the feasibility of using these
47 fuels likely varies by world region. The feasibility assessment for alternative fuels for shipping and
48 aviation suggests that Hydrogen-based fuels like Ammonia and synthetic fuels have the lowest
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1 technology readiness of all mitigation options considered in this chapter. Reliance on electrolytic
2 Hydrogen for the production of these fuels poses concerns about land and water use. Using Ammonia
3 for shipping could pose risks for air quality and toxic discharges to the environment. The DAC/BECCS
4 infrastructure that would be needed to produce synthetic fuel does not yet exist. Thus, the feasibility
5 suggests that the technologies for producing and using these Hydrogen-based fuels for transport are in
6 their infancy.
7
8
9
10 Figure 10.23 Summary of the extent to which different factors would enable or inhibit the deployment of
11 mitigation options in Transport. Blue bars indicate the extent to which the indicator enables the
12 implementation of the option (E) and orange bars indicate the extent to which an indicator is a barrier
13 (B) to the deployment of the option, relative to the maximum possible barriers and enablers assessed. An
14 ‘X’ signifies the indicator is not applicable or does not affect the feasibility of the option, while a forward
15 slash indicates that there is no or limited evidence whether the indicator affects the feasibility of the
16 option. The shading indicates the level of confidence, with darker shading signifying higher levels of
17 confidence. Appendix 10.3 provides an overview of the extent to which the feasibility of options may differ
18 across context (e.g., region), time (e.g., 2030 versus 2050), and scale (e.g., small versus large), and includes
19 a line of sight on which the assessment is based. The assessment method is explained in Annex II.11.
20
21 10.8.3 Emerging Transport Issues
22 Planning for integration with the power sector: Decarbonising the transport sector will require
23 significant growth in low-carbon electricity to power EVs, and more so for producing energy-intensive
24 fuels, such as Hydrogen, Ammonia and synthetic fuels. Higher electricity demand will necessitate
25 greater expansion of the power sector and increase land use. The strategic use of energy-intensive fuels,
26 focussed on harder-to-decarbonise transport segments, can minimise the increase in electricity demand.
27 Additionally, integrated planning of transport and power infrastructure could enable sectoral synergies
28 and reduce the environmental, social, and economic impacts of decarbonising transport and energy. For
29 example, smart charging of EVs could support more efficient grid operations. Hydrogen production,
30 which is likely crucial for the decarbonisation of shipping and aviation, could also serve as storage for
31 electricity produced during low-demand periods. Integrated planning of transport and power
32 infrastructure would be particularly useful in developing countries where “greenfield” development
33 doesn’t suffer from constraints imposed by legacy systems.
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1 Shipping and aviation governance: Strategies to deliver fuels in sufficient quantity for aviation and
2 shipping to achieve transformative targets are growing in intensity and often feature the need to review
3 international and national governance. Some literature suggests that the governance of the international
4 transport systems could be included the Paris Agreement process (Gençsü and Hino 2015; Traut et al.
5 2018; Lee 2018). Box 10.6 sets out these issues.
6
7 START BOX HERE
8
9 Box 10.5 Governance Options for shipping and aviation
10 Whenever borders are crossed, the aviation and shipping sector creates international emissions that are
11 not assigned to states’ Nationally Declared Contributions in the Paris Agreement. Emissions from these
12 segments are rapidly growing (apart from COVID affecting aviation) and are projected to grow between
13 60% to 220% by 2050 (IPCC 2018; UNEP 2020). Currently, the International Civil Aviation
14 Organization (ICAO) and the International Marine Organization (IMO), specialised UN Agencies, are
15 responsible for accounting and suggesting options for managing these emissions.
16 Transformational goals?
17 ICAO has two global aspirational goals for the international aviation sector: 2% annual fuel efficiency
18 improvement through 2050; and carbon neutral growth from 2020 onwards. To achieve these goals,
19 ICAO has established CORSIA – Carbon Offsetting and Reduction Scheme for International Aviation,
20 a market-based program.
21 In 2018, IMO adopted an ‘Initial Strategy’ on the reduction of GHG emissions from ships. This strategy
22 calls for a reduction of the carbon intensity of new ships through implementation of further phases of
23 the energy efficiency design index (EEDI). Similarly, the IMO calls for a 40% reduction of the carbon
24 intensity of international shipping by 2030, and is striving for a 70% reduction by 2050. Such reductions
25 in carbon intensity would result in an overall decline in emissions of 50% in 2050 (relative to 2008).
26 These goals are likely insufficiently transformative for the decarbonisation of aviation or shipping,
27 though they are moving towards a start of decarbonisation at a period in history where the options are
28 still not clear, as set out in Sections 10.5 and 10.6.
29 Regulations?
30 The ICAO is not a regulatory agency, but rather produces standards and recommended practices that
31 are adopted in national/international legislation. IMO does publish ‘regulations’ but does not have
32 power of enforcement. Non-compliance can be regulated by nation states if they so desire, as a ship’s
33 ‘MARPOL’ certificate, issued by the flag state of the ship, means there is some responsibility for states
34 with global shipping fleets.
35 Paris?
36 Some commentators have suggested that emissions from international aviation and shipping should be
37 part of the Paris Agreement (Gençsü and Hino 2015; Traut et al. 2018; Lee 2018; Rayner 2021) argue
38 that the shipping and aviation industries would prefer emissions to be treated under an international
39 regime rather than a national-oriented regime. If international aviation and shipping emissions were a
40 part of the Paris Agreement, it may remove something of the present ambiguity of responsibilities.
41 However, inclusion in the Paris agreement is unlikely to fundamentally change emissions trends unless
42 targets and enforcement mechanisms are developed either by ICAO and IMO or by nation states through
43 global processes.
44 Individual nations?
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1 If international regulations do not occur, then the transformation of aviation and shipping will be left to
2 individual nations like Switzerland. In 2020, Switzerland approved a new CO2 tax on flights (The Swiss
3 Parliament 2020) with part of its revenues earmarked for the development of synthetic aviation fuels,
4 to cover up to 80% of their additional costs compared to fossil jet fuel (Energieradar 2020). Hence,
5 appropriate financing frameworks will be a key to the large-scale market adoption of these fuels. Egli
6 et al. (2019) suggest that the successful design of renewable energy investment policies for solar and
7 wind power over the past 20 years could serve as a model for future synthetic aviation fuels production
8 projects “attracting a broad spectrum of investors in order to create competition that drives down
9 financing cost”, and with state investment banks building “investor confidence in new technologies.”
10 These national investment policies would provide the key enablers for successful deployments.
11 END BOX HERE
12 Managing critical minerals: Critical minerals are required to manufacture LIB’s and other renewable
13 power technologies. There has been growing awareness that critical minerals may face challenges
14 related to resource availability, labour rights, and costs. Box 10.6, below, sets out the issues showing
15 how emerging national strategies on critical minerals, along with requirements from major vehicle
16 manufacturers, are addressing the need for rapid development of new mines with a more balanced
17 geography, less use of cobalt through continuing LIB innovations, and a focus on recycling batteries.
18 The standardisation of battery modules and packaging within and across vehicle platforms, as well as
19 increased focus on design for recyclability are important. Given the high degree of potential
20 recyclability of LIBs, a near closed-loop system in the future would be a feasible opportunity to
21 minimise critical mineral issues.
22
23 START BOX HERE
24
25 Box 10.6 Critical Minerals and The Future of Electro-Mobility and Renewables
26 The global transition towards renewable energy technologies and battery systems necessarily involves
27 materials, markets, and supply chains on a hitherto unknown scale and scope. This has raised concerns
28 regarding mineral requirements central to the feasibility of the energy transition. Constituent materials
29 required for the development of these low carbon technologies are regarded as “critical” materials (US
30 Geological Survey 2018; Commonwealth of Australia 2019; Lee et al. 2020; Marinaro et al. 2020;
31 Sovacool et al. 2020). ‘Critical materials’ are critical because of their economic or national security
32 importance, or high risk of supply disruption (UK Government 2019). describes many of these materials
33 and rare earth elements (REEs) as “technologically critical” not only due to their strategic or economic
34 importance but the risk of short supply or price volatility (Marinaro et al. 2020). In addition to these
35 indicators, production growth and market dynamics are also incorporated into screening tools to assess
36 emerging trends in material commodities that are deemed as fundamental to the well-being of the nation
37 (NSTC 2018).
38 The critical materials identified by most nations are: REEs Neodymium and Dysprosium for permanent
39 magnets in wind turbines and electric motors; Lithium and Cobalt, primarily for batteries though many
40 other metals are involved (see figure below); and, Cadmium, Telluriam, Selenium, Gallium and Indium
41 for solar PV manufacture (Valero et al. 2018; Giurco et al. 2019). Predictions are that the transition to
42 a clean energy world will be significantly energy intensive (World Bank Group 2017; Sovacool et al.
43 2020) putting pressure on the supply chain for many of the metals and materials required.
44 Governance of the sustainability of mining and processing of many of these materials, in areas generally
45 known for their variable environmental stewardship, remains inadequate and often a source for conflict.
46 (Sovacool et al. 2020) propose four holistic recommendations for improvement to make these industries
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1 more efficient and resilient: diversification of mining enterprises for local ownership and livelihood
2 benefit; improve the traceability of material sources and transparency of mining enterprise; exploration
3 of alternative resources; and the incorporation of minerals into climate and energy planning by
4 connecting to the NDCs under the Paris Accord.
5 Resource Constraints?
6 Valero et al. (2018) highlights that the demand for many of the REEs and other critical minerals will,
7 at the current rate of RE infrastructure growth, increase a multiple of 3,000 times or more by 2050.
8 Some believe this growth may reach constraints in supply (Giurco et al. 2019). Others suggest that the
9 minerals involved are not likely to physically run out (Sovacool et al. 2020) if well managed, especially
10 as markets are found in other parts of the world (for example the transition away from Lithium in brine
11 lakes to hard rock sources). Lithium hydroxide, more suitable for batteries, now competes well, in terms
12 of cost, when extracted from rock sources (Azevedo et al. 2018) due to the ability to more easily create
13 high quality Lithium Hydroxide from rock sources, even though brines provide a cheaper source of
14 Lithium per se (Kavanagh et al. 2018). Australia has proven resources for all the Li-ion battery minerals
15 and has a strategy for their ethical and transparent production (Commonwealth of Australia 2019).
16 Changes in the technology have also been used to create less need for certain critical minerals
17 (Månberger and Stenqvist 2018). Recycling of all the minerals is not yet well developed but is likely to
18 be increasingly important (Habib and Wenzel 2014; Golroudbary et al. 2019; World Bank Group 2017;
19 Giurco et al. 2019).
20 International Collaboration
21 There have been many instances since the 1950’s when the supply of essential minerals has been
22 restricted by nations in times of conflict and world tensions, but international trade has continued under
23 the framework of the World Trade Organization. Keeping access open to critical minerals needed for a
24 low-carbon transition will be an essential role of the international community as the need for local
25 manufacture of such renewable and electro-mobility technologies will be necessary for local economies.
26 shows that the trend over the past 30 years has been for the US to move from being self-sufficient in
27 REEs to being 100% reliant on imports, predominantly from China, Japan, and France. In terms of
28 heavy REEs, essential for permanent magnets for wind turbines, China has a near-monopoly on REE
29 processing though other mines and manufacturing facilities are now responding to these constrained
30 markets (Stegen 2015; Gulley et al. 2018, 2019; Yan et al. 2020). China, on the other hand, is reliant
31 on other nations for the supply of other critical metals, particularly cobalt and lithium for batteries.
32 A number of Critical Materials Strategies have now been developed by nations developing the
33 manufacturing-base of new power and transport technologies. Some of these strategies pay particular
34 attention to the supply of lithium (Martin et al. 2017; Hache et al. 2019). For example, Horizon 2020, a
35 substantial EU Research and Innovation program, couples research and innovation in science, industry,
36 and society to foster a circular economy in Europe thus reducing these bottlenecks in the EU nations.
37 Similarly CREEN (Canada Rare Earth Elements Network) is supporting the US-EU-Japan resource
38 partnership with Australia (Klossek et al. 2016).
39 As renewables and electromobility-based development leapfrogs into the developing world it will be
40 important to ensure the critical minerals issues are managed for local security of supply as well as
41 participation in the mining and processing of such minerals to develop their own employment around
42 renewables and electro-mobility (Sovacool et al. 2020).
43 END BOX HERE
44
45 Enabling creative foresight: Human culture has always had a creative instinct that enables the future to
46 be better dealt with through imagination (Montgomery 2017). Science and engineering have often been
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1 preceded by artistic expressions such as Jules Verne, who first dreamed of the Hydrogen future in 1874
2 in his novel The Mysterious Island. Autonomous vehicles have regularly occupied the minds of science
3 fiction authors and filmmakers (Braun 2019). Such narratives, scenario building, and foresighting are
4 increasingly seen as a part of the climate change mitigation process (Lennon et al. 2015; Muiderman et
5 al. 2020) and can ‘liberate oppressed imaginaries’ (Luque-Ayala 2018). (Barber 2021) have emphasised
6 the important role of positive images about the future instead of dystopian visions and the impossibility
7 of business-as-usual futures.
8 Transport visions can be a part of this cultural change as well as the more frequently presented visions
9 of renewable energy (Wentland 2016; Breyer et al. 2017). There are some emerging technologies like
10 Maglev, Hyperloop, and Drones that are likely to continue the electrification of transport even further
11 (Daim 2021) and which are only recently at the imagination stage. Decarbonised visions for heavy
12 vehicle systems appear to be a core need from the assessment of technologies in this chapter. Such
13 visioning or foresighting requires deliberative processes and the literature contains a growing list of
14 transport success stories based on such processes (Weymouth and Hartz-Karp 2015). Ultimately,
15 reducing GHG emissions from the transport sector would benefit from creative visions that integrate a
16 broad set of ideas about technologies, urban and infrastructure planning (including transport, electricity,
17 and telecommunication infrastructure), and human behaviour and at the same time can create
18 opportunities to achieve the SDGs.
19 Enabling transport climate emergency plans, local pledges and net zero strategies: National, regional
20 and local governments are now producing transport plans with a climate emergency focus (e.g. (Jaeger
21 et al. 2015; Pollard 2019)). Such plans are often grounded in the goals of the Paris Agreement, based
22 around Local Low Carbon Transport Roadmaps that contain targets for and involve commitments or
23 pledges from local stakeholders, such as workplaces, local community groups, and civil society
24 organisations. Pledges often include phasing out fossil-fuel based cars, buses, and trucks (Plötz et al.
25 2020), strategies to meet the targets through infrastructure, urban regeneration and incentives, and
26 detailed programs to help citizens adopt change. These institution-led mechanisms could include bike-
27 to-work campaigns, free transport passes, parking charges, or eliminating car benefits. Community-
28 based solutions like solar sharing, community charging, and mobility as a service can generate new
29 opportunities to facilitate low-carbon transport futures. Cities in India and China have established these
30 transport roadmaps, which are also supported by the UNCRD’s Environmentally Sustainable Transport
31 program (Baeumler et al. 2012; Pathak and Shukla 2016; UNCRD 2020). There have been concerns
32 raised that these pledges may be used to delay climate action in some cases (Lamb et al. 2020) but but
33 such pledges can be calculated at a personal level and applied through every level of activity from
34 individual, household, neighbourhood, business, city, nation or groups of nations (Meyer and Newman
35 2020) and are increasingly being demonstrated through shared communities and local activism
36 (Bloomberg and Pope 2017; Sharp 2018; Figueres and Rivett-Carnac 2020). Finally, the world’s major
37 financing institutions are also engaging in decarbonisation efforts by requiring their recipients to
38 commit to Net Zero Strategies before they can receive their funding (COVID Box, Chapter 1; Chapter
39 15; (Robins 2018; Newman 2020a)). As a result, transparent methods are emerging for calculating what
40 these financing requirements mean for transport by companies, cities, regions, and infrastructure
41 projects (see Chapters 8, 15). The continued engagement of financial institutions may, like in other
42 sectors, become a major factor in enabling transformative futures for transport as long as governance
43 and communities continue to express the need for such change.
44
45 10.8.4 Tools and Strategies to Enable Decarbonisation of the Transport Sector
46 Using the right tools and strategies is crucial for the successful deployment of mitigation options. Table
47 10.7 summarises the tools and strategies to enable electromobility, new fuels for aviation and shipping,
48 and the more social aspects of demand efficiency.
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Final Government Distribution Chapter 10 IPCC AR6 WGIII
1
2 Table 10.7 Tools and Strategies for enabling mitigation options to achieve transformative scenarios
Travel Demand Reductions Light Vehicle Alternative Fuel Systems for
Tools and Strategies
and Fuel/Vehicle Efficiency Electromobility Systems Shipping and Aviation
Education and R&D TDR can be assisted with Behaviour change programs R&D is critical for new fuels
digitalisation, connected help EV’s become more and to test the full life cycle
autonomous vehicle, EVs and mainstream. R&D will help costs of various heavy vehicle
Mobility as a Service on the socio-economic options (Marinaro et al. 2020).
(Marsden et al. 2018; structures that impede
Shaheen et al. 2018). adoption of EV’s and the
urban structures that enable
Knowledge gaps on TDR
reduced car dependence and
exist for longer distance travel
how EV’s can assist grids
(intercity); non-mandatory
(Newman 2010; Taiebat and
trips (leisure; social trips),
Xu 2019; Seto et al. 2021).
and travel by elder people.
Travel demand foresighting
tools can be open source
(Marsden 2018).
Access and Equity TDR programs in cities can Significant equity issues Shipping is mostly freight and
be inequitable. To avoid such with EV’s in the transition is less of a problem but
inequities, there is a need for period can be overcome aviation has big equity issues
better links to spatial and with programs that enable (Bows-Larkin 2015)
economic development affordable electric mobility,
(Marsden et al, 2018), especially transit. (IRENA
mindful of diverse local 2016)
priorities, personal freedom
and personal data (See Box on
Smart Technologies in
Section 10.2)
Financing Economic Carbon budget implications Multiple opportunities for Taking R&D into
Incentives and of different demand futures financing, economic demonstration projects is the
Partnerships should be published and used incentives, and partnerships main stage for heavy vehicle
to help incentivize net zero with clear economic options and these are best done
projects (Marsden 2018). benefits can be assured as partnerships. Government
Business and community especially using the role of assistance will greatly assist in
pledges for net zero can be set value capture in enabling such projects as well as an
up in partnership agreements such benefits. The nexus R&D levy. Abolishing fossil
(see Section 10.8.3). between EV’s and the fuel subsidies and imposing
electricity grid needs carbon taxes are likely to help
opportunities to in the early stages of heavy
demonstrate positive vehicle transitions (Sclar et al.
partnership projects (Zhang 2019)
et al. 2014; Mahmud et al.
2018; Newman et al. 2018;
Sovacool et al. 2018;
Sharma and Newman 2020)
Co-benefits and A focus on people-centred The SDG benefits in zero Heavy vehicle systems can
Overcoming solutions for future mobility carbon light vehicle also demonstrate SDG co-
Fragmentation with more pluralistic and transport systems are being benefits if formulated with this
feasible sets of outcomes for demonstrated and can now in mind. Demonstrations of
all people can be achieved be quantified as nations how innovations can also help
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Final Government Distribution Chapter 10 IPCC AR6 WGIII
when they focus on more than mainstream this transition. SDGs will attract more
simple benefit cost ratios but Projects with transit and funding. Such projects need
include well-being and sustainable housing are cross-government
livelihoods, considering more able to show such consideration (Pradhan et al.
transport as a system, rather benefits. New Benefit Cost 2017).
than loosely connected modes Ratio methods that focus on
as well as behaviour change health benefits in
programs (Barter and Raad productivity are now
2000; Newman 2010; favouring transit and active
Martens 2020). transport (Buonocore et al.
2019; UK DoT 2019;
Hamilton et al. 2021).
Regulation and Implementing a flexible With zero carbon light Zero carbon heavy vehicle
Assessment regulatory framework is vehicle systems rapidly systems need to have
needed for most TDR (Li and growing the need for a regulatory barrier assessments
Pye 2018). Regulatory regulated target and as they are being evaluated in
assessment can help with assessment of regulatory R&D demonstrations (Sclar et
potential additional (cyber) barriers can assist each city al. 2019).
security risk due to and region to transition
digitalization, AVs, IoT, and more effectively.
big data (Shaheen and Cohen Regulating EV’s for
2019). Assessment tools and government fleets and
methods need to take account recharge infrastructure can
of greater diversity of establish incentives
population, regions, blurring (Bocken et al. 2016).
of modes, and distinct spatial
characteristics (Newman and
Kenworthy 2015).
Governance and TDR works better if adaptive Governance and Governance and institutional
Institutional Capacity decision-making approaches institutional capacity can capacity can help make
focus on more inclusive and now provide international significant progress if targets
whole of system benefit-cost exchanges and education with levies for not complying.
ratios (Yang et al. 2020; programs based on Carbon taxes would also affect
Marsden 2018) successful cities and nations these segments. A review of
enabling light vehicle international transport
decarbonisation to create governance is likely (Makan
more efficient and effective and Heyns 2018)
policy mechanisms towards
self-sustaining markets
(Greene et al. 2014;
Skjølsvold and Ryghaug
2019)..
Enabling infrastructure Ensuring space for active Large-scale electrification In addition to increasing the
transport and urban activities of LDVs requires expansion capabilities to produce low or
is taken from road space of low-carbon power zero-carbon fuels for shipping
where necessary (Gössling et systems, while charging or and aviation, there is a need to
al. 2021b). battery swapping invest in supporting
infrastructure is needed for infrastructure including low
Increasing the proportion
some segments (Gnann et al. carbon power generation. New
infrastructure that supports
2018; Ahmand et al. 2020) Hydrogen delivery and
walking in urban areas will
refuelling infrastructure may
structurally enable reductions
be needed (Maggio et al.
in car use (Section 10.2 and
2019). For zero-carbon
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Final Government Distribution Chapter 10 IPCC AR6 WGIII
(Newman and Kenworthy synthetic fuels, infrastructure
2015). is needed to support carbon
capture and CO2 transport to
Creating transit activated
fuel production facilities
corridors of TOD-based rail
(Edwards and Celia 2018).
or mid-tier transit using value
capture for financing will
create inherently less car
dependence (McIntosh et al.
2017; Newman et al. 2019)
1
2
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1 Frequently Asked Questions (FAQs)
2 FAQ 10.1 -How important is electro-mobility in decarbonising transport and are there major
3 constraints in battery minerals?
4 Electromobility is the biggest change in transport since AR5. When powered with low-carbon
5 electricity, electric vehicles (EVs) provide a mechanism for major GHG emissions reductions from the
6 largest sources in the transport sectors, including cars, motor-bikes, tuk tuks, buses and trucks. The
7 mitigation potential of EVs depends on the decarbonization of the power system. EVs can be charged
8 by home or business renewable power before or in parallel to the transition to grid-based low-carbon
9 power.
10 Electromobility is happening rapidly in micro-mobility (e-autorickshaws, e-scooters, e-bikes) and in
11 transit systems, especially buses. EV adoption is also accelerating for personal cars. EVs can be used
12 in grid stabilization through smart charging applications.
13 The state-of-the-art Lithium-Ion Batteries (LIBs) available in 2020 are superior to alternative cell
14 technologies in terms of battery life, energy density, specific energy, and cost. The expected further
15 improvements in LIBs suggest these chemistries will remain superior to alternative battery technologies
16 in the medium-term, and therefore LIBs will continue to dominate the electric vehicle market.
17 Dependence on LIB metals will remain, which may be a concern from the perspective of resource
18 availability and costs. However, the demand for such metals is much lower than the reserves available,
19 with many new mines starting up in response to the new market particularly in a diversity of places.
20 Recycling batteries will significantly reduce long-term resource requirements. The standardisation of
21 battery modules and packaging within and across vehicle platforms, as well as increased focus on design
22 for recyclability are important. Many mobility manufacturers and governments are considering battery
23 recycling issues to ensure the process is mainstreamed.
24 The most significant enabling condition in electro-mobility is to provide electric recharging
25 opportunities and a strategy to show they can be helping the grid.
26 FAQ 10.2 - How hard is it to decarbonise heavy vehicles in transport like long haul trucks, ships
27 and planes?
28 Unlike for land transport vehicles, there are few obvious solutions to decarbonizing heavy vehicles like
29 international ships and planes. The main focus has been increased efficiency, which so far has not
30 prevented these large vehicles from becoming the fastest growing source of GHG globally. These
31 vehicles likely need alternative fuels that can be fitted to the present propulsion systems. Emerging
32 demonstrations suggest that ammonia, advanced biofuels, or synthetic fuels could become commercial.
33 Electric propulsion using hydrogen fuel cells or Li-ion batteries could work with short-haul aviation
34 and shipping, but the large long-lived vessels and aircraft likely
35 need alternative liquid fuels for most major long-distance functions.
36 Advanced biofuels, if sourced from resources with low GHG footprints, offer decarbonisation
37 opportunities. As shown in Chapters 2, 6, and 12, there are multiple issues constraining traditional
38 biofuels. Sustainable land management and feedstocks, as well as R&D efforts to improve
39 lignocellulosic conversion routes are key to maximise the mitigation potential from advanced biofuels.
40 Synthetic fuels made using CO2 captured with DAC/BECCS and low-carbon hydrogen can
41 subsequently be refined to a net zero jet fuel or marine fuel. These fuels may also have less contrails-
42 based climate impacts and low emissions of local air pollution. However, these fuels still require
43 significant R&D and demonstration.
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1 The deployment of low-carbon aviation and shipping fuels that support decarbonisation of the transport
2 sector will likely require changes to national and international governance structures
3 FAQ 10.3 - How can governments, communities and individuals reduce demand and be more
4 efficient in consuming transport energy?
5 Cities can reduce their transport-related fuel consumption by around 25% through combinations of more
6 compact land use and less car dependent transport infrastructure.
7 More traditional programs for reducing unnecessary high-energy travel through behaviour change
8 programs (i.e., taxes on fuel, parking, and vehicles or subsidies for alternative low-carbon modes),
9 continue to be evaluated with mixed results due to the dominance of time savings in an individual’s
10 decision-making.
11 The circular economy, the shared economy, and digitalisation trends can support systemic changes that
12 lead to reductions in demand for transport services or expands the use of more efficient transport modes
13 COVID-19-based lockdowns have confirmed the transformative value of telecommuting replacing
14 significant numbers of work and personal journeys as well as promoting local active transport. These
15 changes may not last and impacts on productivity and health are still to be fully evaluated.
16 Solutions for individual households and businesses involving pledges and shared communities that set
17 new cultural means of reducing fossil fuel consumption, especially in transport, are setting out new
18 approaches for how climate change mitigation can be achieved.
19
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