Editing IPCC:AR6/WGIII/Chapter-10 (section)

== 10.7 Scenarios from Integrated, Sectoral, and Regional Models ==

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=== 10.7.1 Transport Scenario Modelling ===

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This section reviews the results of three types of models that systemically combine options to assess different approaches to generating decarbonisation pathways for the transport system: (i) integrated assessment models (IAM); (ii) global transport energy models (GTEM); and (iii) national transport-energy models (NTEM) ( [[#Edelenbosch--2017|Edelenbosch et al. 2017]] ; [[#Yeh--2017|Yeh et al. 2017]] ). Common assumptions across the three model types include trajectories of socioeconomic development, technological development, resource availability, policy, and behavioural change. The key differences underlying these models are their depth of technological and behavioural detail versus scope in terms of sectoral and regional coverage. In very general terms, the narrower the scope in terms of sectors and regions, the more depth on spatial, technological, and behavioural detail. A large set of scenarios from these models were collected in a joint effort led by [[IPCC:Wg3:Chapter:Chapter-3|Chapter 3]] and supported by [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-10 Chapter 10] and others. The outcomes from over 100 models have been analysed for this chapter with the methodologies set out in Annex III for the whole report.

GHG emissions from transport are a function of travel demand, travel mode, transport technology, GHG intensity of fuels, and energy efficiency. These drivers can be organised around a group of levers that can advance the decarbonisation of the transport system. The levers thus include reducing travel activity, increasing use of lower-carbon modes, and reducing modal energy intensity and fuel carbon content. This section explores each lever’s contributions to the decarbonisation of the transport sector by reviewing the results from the three model types IAM, GTEM, and NTEM.

IAMs integrate factors from other sectors that interact with the transport system endogenously, such as fuel availability and costs. IAMs minimise mitigation costs to achieve a temperature goal ''across all sectors of the economy'' over a long time horizon (typically to 2100). IAMs typically capture mitigation options for energy and carbon intensity changes with greater technology/fuel details and endogeneity linked to the other sectors. In the scenarios with very large-scale electrification of the transport sector, the coupling with the other sectors in fuel production, storage, and utilisation becomes more important. G-/NTEMs and related regional transport sectoral models have more details on transport demand, technology, behaviours, and policies than IAMs, but treat the interactions with the other sectors exogenously, potentially missing some critical interactions, such as the fuel prices and carbon intensity of electricity. National models have detailed representation of national policies related to transport and energy, sometimes with greater spatial resolution. Compared with IAMs, G-/NTEMs typically have greater detailed representation to explore mitigation options along the activity and mode dimensions where spatial, cultural, and behavioural details can be more explicitly represented. Section 5 in Annex III provides more details about these types of models. Scenarios for shipping and aviation are handled in more detail in sections 10.5 and 10.6, respectively.

This section applies the following categorisation of scenarios (see Table 3.1 for more details):

• C1 (scenarios that limit warming to 1.5°C (&gt;50%) during the 21st century with no or limited overshoot)

'''•''' C2 (scenarios that return warming to 1.5°C (&gt;50%) during the 21st century after a high overshoot)

'''•''' C3 (scenarios that limit warming to 2°C (&gt;67%) throughout the 21st century)

'''•''' C4 (scenarios that limit warming to 2°C (&gt;50%) throughout the 21st century)

'''•''' C5 (scenarios that limit warming to 2.5°C (&gt;50%) throughout the 21st century)

'''•''' C6 (scenarios that limit warming to 3°C (&gt;50%) throughout the 21st century)

'''•''' C7 (scenarios that limit warming to 4°C (&gt;50%) throughout the 21st century)

• C8 (scenarios that exceed warming of 4°C (≥50%) during the 21st century)

A large share of the scenarios was developed prior to 2020. Results from such scenarios are indexed to a modelled (non-COVID) year 2020, referred to as 2020Mod.

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=== 10.7.2 Global Emissions Trajectories ===

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In 2018, transport emitted 8.5 GtCO 2 -eq, reaching a near doubling from 1990 levels after two decades of 2% per year emissions growth ( [[#10.1|Section 10.1]] ). Assessing future trajectories, Figure 10.17 provides an overview of direct CO 2 emissions estimates from the transport sector across IAMs (colour bars) and selected global transport models (grey bars). The results from the IAMs are grouped in bins by temperature goal. Global transport energy models are grouped into reference and policy bins, since the transport sector cannot by itself achieve fixed global temperature goals. The policy scenarios in GTEMs and NTEMs cover a wide range of ‘non-reference’ scenarios, which include, for example, assumptions based on the ‘fair share action’ principles. In these scenarios, transport emissions reach reductions consistent with the overall emissions trajectories aligning with warming levels of 2°C. These scenarios may also consider strengthening existing transport policies, such as increasing fuel economy standards or large-scale deployments of electric vehicles. In most cases, these Policy scenarios are not necessarily in line with the temperature goals explored by the IAMs.

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[[File:7e68c42701cd3beee0e92de7bac2caea IPCC_AR6_WGIII_Figure_10_17.png]]

'''Figure 10.17 | Direct CO''' 2 '''emissions from transport in 2030, 2050, and 2100 indexed to 2020 modelled year across R6 Regions and World.''' IAM results are grouped by temperature targets. Sectoral studies are grouped by referenceand policy categories. Plots show 5–95th percentile, 25–75th percentile, and median. Numbers above the bars indicate the number of scenarios. Data from the AR6 scenario database.

According to the collection of simulations from the IAM and GTEM models shown in Figure 10.17, global transport emissions could grow up to 2–47% (5–95th percentile) by 2030 and –6–130% by 2050 under the C7 scenarios that limit warming to 4°C (&gt;50%) throughout the 21st century and C8 scenarios that exceed 4°C (≥50%) during the 21st century. Population and GDP growth and the secondary effects, including higher travel service demand per capita and increased freight activities per GDP, drive the growth in emissions in these scenarios ( [[#10.7.3|Section 10.7.3]] ). Though transport efficiencies (energy use per pkm travelled and per tkm of goods delivered) are expected to continue to improve in line with the historical trends ( [[#10.7.4|Section 10.7.4]] ), total transport emissions would grow due to roughly constant carbon intensity ( [[#10.7.5|Section 10.7.5]] ) under the C7 and C8 scenarios that limit warming to 4°C (&gt;50%) throughout the 21st century or exceed 4°C (≥50%) during the 21st century. In these scenarios, Significant increases in emissions (&gt;150% for the medium values by 2050) would come from Asia and Pacific, the Middle East, and Africa. Compared to estimated 2020 levels, in 2050 Developed Countries would have median 25% decrease in transport emissions in the C7 scenarios that limit warming to 4°C (&gt;50%) throughout the 21st century or median 15% increase in transport emissions in the C8 scenarios that exceed warming of 4°C (≥50%) during the 21st century.

To meet temperature goals, by 2050 global transport emissions would need to decrease by 17% (+67% to –23% for the 5–95th percentile) below 2020Mod levels in the scenarios that limit warming to 2°C (&gt;67%), 2°C (&gt;50%) and 2.5 °C (&gt;50%) throughout the 21st century (C3-C5 scenarios – orange bars), and 47% (14–80% for the 5–95th percentile) in the scenarios that limit warming to 1.5°C (&gt;50%) during the 21st century with no or limited overshoot or return to 1.5°C (&gt;50%) during the 21st century after high overshoot during the 21st century (C1–C2 scenarios – green bars). However, transport-related emission reductions may not happen uniformly across regions. For example, transport emissions from the Developed Countries and Eastern Europe and West Central Asia would decrease from 2020 levels by 2050 across all C1–C2 scenarios, but could increase in Africa, Asia and Pacific, Latin America and Caribbean, and the Middle East, in some of these scenarios. In particular, the median transport emissions in India and Africa could increase by 2050 in C1–C2 scenarios, while the 95th percentile emissions in Asia and Pacific, Latin America and Caribbean, and the Middle East, could be higher in 2050 than in 2020.

The Reference scenario emission pathways from GTEMs described in Figure 10.17 have similar ranges to C7–C8 scenario groups in 2050. The Policy scenarios are roughly in line with C6–C7 scenarios for the world region. The results suggest that the majority of the Policy scenarios examined by the GTEMS reviewed here are in the range of the C3–C6 scenarios examined by the IAMs ( [[#Gota--2016|Gota et al. 2016]] ; [[#IEA--2017b|IEA 2017b]] ; [[#Yeh--2017|Yeh et al. 2017]] ; [[#Fisch-Romito--2019|Fisch-Romito and Guivarch 2019]] ). The NDCs in the transport sector include a mix of measures targeting efficiency improvements of vehicles and trucks; improving public transit services; decarbonising fuels with alternative fuels and technologies including biofuels, fossil- or bio-based natural gas, and electrification; intelligent transport systems; and vehicle restrictions ( [[#Gota--2016|Gota et al. 2016]] ). Because of the long lag-time for technology turnover, these measures are not expected to change 2030 emissions significantly. However, they could have greater impacts on 2050 emissions.

Several GTEMs not included in AR6 scenario database have examined ambitious CO 2 mitigation scenarios. For example, a meta-analysis of scenarios suggests that global transport emissions consistent with warming levels of 2°C, would peak in 2020 at around 7–8 GtCO 2 and decrease to 2.5–9.2 Gt for 2°C, with an average of 5.4 Gt by 2050 ( [[#Gota--2019|Gota et al. 2019]] ). For comparison, the IEA’s Sustainable Development Scenario suggests global transport emissions decrease to 3.3 Gt (or 55% reduction from 2020 level) by 2050 ( [[#IEA--2021f|IEA 2021f]] ). The latest IEA ''Net Zero by 2050'' report proposes transport emissions to be close to zero by 2050 ( [[#IEA--2021e|IEA 2021e]] ). The latter is lower than the interquartile ranges of the C1 group of scenarios from the AR6 database analysed here.

Low-carbon scenarios are also available from national models (Latin America, Brazil, Canada, China, France, Germany, Indonesia, India, Italy, Japan, Mexico, South Africa, UK, US) with a good representation of the transport sector. The low-carbon scenarios are either defined with respect to a global climate stabilisation level of, for example, 2°C/1.5°C Scenario ( [[#Dhar--2018|Dhar et al. 2018]] ), or a CO 2 target that is more stringent than what has been considered in the NDCs, such as the net-zero emissions pathways ( [[#Bataille--2020|Bataille et al. 2020]] ; [[#IEA--2021e|IEA 2021e]] ). These studies have generally used bottom-up models (see Annex III) for the analysis, but in some cases, they are run by national teams using global models (e.g., the Global Change Assessment Model (GCAM) for China and India). National studies show that transport CO 2 emissions could decline significantly in low-carbon scenarios in all the developed countries reviewed ( [[#Bataille--2015|Bataille et al. 2015]] ; [[#Kainuma--2015|Kainuma et al. 2015]] ; Hillebrandt et al. 2015; [[#Mathy--2015|Mathy et al. 2015]] ; [[#Pye--2015|Pye et al. 2015]] ; [[#Virdis--2015|Virdis et al. 2015]] ; [[#Williams--2015|Williams et al. 2015]] ; [[#Zhang--2016a|Zhang et al. 2016a]] ) in 2050 from the emissions in 2010 and reductions could vary from 65% to 95%. However, in developing countries reviewed ( [[#Di%20Sbroiavacca--2014|Di Sbroiavacca et al. 2014]] ; [[#Altieri--2015|Altieri et al. 2015]] ; [[#Buira--2015|Buira and Tovilla 2015]] ; [[#Rovere--2015|Rovere et al. 2015]] ; [[#Shukla--2015|Shukla et al. 2015]] ; [[#Siagian--2015|Siagian et al. 2015]] ; [[#Teng--2015|Teng et al. 2015]] ; [[#Dhar--2018|Dhar et al. 2018]] ), emissions could increase in 2050 in the range of 35% to 83% relative to 2010 levels. Transport CO 2 emissions per capita in the developing countries were much lower in 2010 (varying from 0.15 to 1.39 tCO 2 per capita) relative to developed countries (varying from 1.76 to 5.95 tCO 2 per capita). However, results from national modelling efforts suggest that, by 2050, the CO 2 emissions per capita in developed countries (varying from 0.19 to 1.04 tCO 2 per capita) could be much lower than in developing countries (varying from 0.21 to 1.7 tCO 2 per capita).

The transport scenario literature’s mean outcomes suggest that the transport sector may take a less steep emissions reduction trajectory than the cross-sectoral average and still be consistent with the 2°C goal. For example, most of the scenarios that limit or return warming to 1.5°C (&gt;50%) during the 21st century (C1–C2) reach zero emissions by 2060, whereas transport sector emissions are estimated in the range of 20% of the 2020Mod level (4–65% for the 10th to 90th percentiles) by 2100. This finding is in line with perspectives in the literature suggesting that transport is one of the most difficult sectors to decarbonise ( [[#Davis--2018|Davis et al. 2018]] ). There is, however, quite a spread in the results for 2050. Since temperature warming levels relate to global emissions from all sectors, modelling results from IAMs tend to suggest that in the short and medium term, there might be lower cost mitigation options outside the transport sector. On the other hand, compared with GTEMs/NTEMs, some IAMs may have limited mitigation options available, including technology, behavioural changes, and policy tools especially for aviation and shipping. The models therefore rely on other sectors and/or negative emissions elsewhere to achieve the overall desired warming levels. This potential shortcoming should be kept in mind when interpreting the sectoral results from IAMs.

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=== 10.7.3 Transport Activity Trajectories ===

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Growth in passenger and freight travel demand is strongly dependent on population growth and GDP. In 2015, transport activities were estimated at around 35–50 trillion pkm, or 5,000–7,000 pkm per person per year, with significant variations among studies ( [[#IEA--2017b|IEA 2017b]] ; [[#ITF--2019|ITF 2019]] ). The number of passenger cars in use has grown 45% globally between 2005–2015, with the most significant growth occurring in the developing countries of Asia and the Middle East (119%), Africa (79%), and South and Central America (80%), while growth in Europe and North America is the slowest (21% and 4% respectively) ( [[#IOMVM--2021|IOMVM 2021]] ). On the other hand, car ownership levels in terms of vehicles per 1000 people in 2015 were low in developing countries of Asia and the Middle East (141), Africa (42), South and Central America (176), while in Europe and North America they are relatively high (581 and 670 respectively) ( [[#IOMVM--2021|IOMVM 2021]] ). The growth rate in commercial vehicles (freight and passenger) was 41% between 2005 and 2015, with a somewhat more even growth across developed and developing countries ( [[#IOMVM--2021|IOMVM 2021]] ).

Figure 10.18 shows activity trajectories for both freight and passenger transport based on the AR6 database for IAMs. According to demand projections from the IAMs, global passenger and freight transport demand could increase relative to a modelled year 2020 across temperature goals. The median transport demand from IAMs for all the scenarios in line with warming levels below 2.5°C (C1–C5) suggests that global passenger transport demand could grow by 1.14–1.3 times in 2030 and by 1.5–1.8 times in 2050 (1.27–2.33 for the 5–95th percentile across C1–C5 scenarios) relative to modelled 2020 level. Developed regions including North America and Europe exhibit lower growth in passenger demand in 2050 compared to developing countries across all the scenarios. In 2030, most of the global passenger demand growth happens in Africa (44% growth relative to 2020), and Asia and Pacific (57% growth in China and 59% growth in India relative to 2020) in the scenarios that limit warming below 2.5°C (&gt;50%) throughout the 21st century (C5). These regions start from a low level of per capita demand. For example, in India, demand may grow by 84%. However, the per capita demand in 2010 was under 7000 km per person per year ( [[#Dhar--2015|Dhar and Shukla 2015]] ). Similarly, in China, demand may grow by 52%, starting from per capita demand of 8000 km per person per year in 2010 ( [[#Pan--2018|Pan et al. 2018]] ). The per capita passenger demand in these regions was lower than in developed countries in 2010, but it converges towards the per capita passenger transport demand of advanced economies in less stringent climate scenarios (C6–C7). Demand for passenger travel would grow at a slower rate in the stricter temperature stabilisation scenarios (&lt;2.5°C and 1.5°C scenarios, C1–C5) compared to the scenarios with higher warming levels (C7–C8). The median global passenger demand in the scenarios that limit or return warming to 1.5°C during the 21st century (C1–C2) is 27% lower in 2050 relative to C8.

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[[File:affb394906fc1525550c233b677c191d IPCC_AR6_WGIII_Figure_10_18.png]]

'''Figure 10.18 | Transport activity trajectories for passenger (bottom panel) and freight (top panel) in 2030, 2050, and 2100 indexed to 2020 modelled year across R6 Regions and World.''' Plots show 5–95th percentile, 25–75th percentile, and median. Numbers above thebars indicate the number of scenarios. Data from the AR6 scenario database.

Due to limited data availability, globally consistent freight data is difficult to obtain. In 2015, global freight demand was estimated to be 108 trillion tkm, most of which was transported by sea ( [[#ITF--2019|ITF 2019]] ). The growth rates of freight service demand vary dramatically among different regions: over the 1975–2015 period, road freight activity in India increased more than 9-fold, 30-fold in China, and 2.5-fold in the US ( [[#Mulholland--2018|Mulholland et al. 2018]] ). Global freight demand continues to grow but at a slower rate compared to passenger demand across all the scenarios in 2050 compared to modelled 2020 values. Global median freight demand could increase by 1.17–1.28 times in 2030 and 1.18–1.7 times in 2050 in all the scenarios with warming below 2.5°C (C1–C5). Like passenger transport, the models suggest that a large share of growth occurs in Africa and Asian regions (59% growth in India and 50% growth in China in 2030 relative to a modelled year 2020) in the C5 scenarios that limit warming below 2.5°C (&gt;50%) throughout the 21st century. Global median freight demand grows more slowly in the stringent temperature stabilisation scenarios, and is 40% and 22% lower in 2050 in the scenarios that limit or return warming to 1.5°C (&gt;50%) during the 21st century (C1–C2) and below 2.5°C scenarios (C3–C4), respectively, compared to scenarios with warming levels of above 4°C (C8).

GTEMs show broad ranges for future travel demand, particularly for the freight sector. These results show more dependency on models than on baseline or policy scenarios. According to ITF Transport Outlook ( [[#ITF--2019|ITF 2019]] ), global passenger transport and freight demand could more than double by 2050 in a business-as-usual scenario. [[#Mulholland--2018|Mulholland et al. (2018)]] suggest the freight sector could grow 2.4-fold over 2015–2050 in the reference scenario, with the majority of growth attributable to developing countries. The IEA suggests a more modest increase in passenger transport, from 51 trillion pkm in 2014 to 110 trillion pkm in 2060, in a reference scenario without climate policies and a climate scenario that would limit emissions below 2°C. The demand for land-based freight transport in 2060 is, however, slightly lower in the climate scenario (116 trillion tkm) compared to the reference scenario (130 trillion tkm) ( [[#IEA--2017b|IEA 2017b]] ). The ITF, however, suggests that ambitious decarbonisation policies could reduce global demand for passenger transport by 13–20% in 2050, compared to the business-as-usual scenario ( [[#ITF--2019|ITF 2019]] ; [[#ITF--2021|ITF 2021]] ). The reduction in vehicle travel through shared mobility could reduce emissions from urban passenger transport by 30% compared to the business-as-usual scenario. Others suggest that reductions larger than 25%, on average, for both passenger and freight in 2030 and 2050 may be needed to achieve very low carbon emissions pathways ( [[#Fisch-Romito--2019|Fisch-Romito and Guivarch 2019]] ). In the absence of large-scale carbon dioxide removal, few global studies highlight the need for significant demand reduction in critical sectors (aviation, shipping and road freight) in well below 2°C scenarios ( [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ; [[#Grant--2021|Grant et al. 2021]] ; [[#Sharmina--2021|Sharmina et al. 2021]] ).

Many models find small differences in passenger transport demand across temperature goals because IAM models rely on historical relationships between population, GDP, and demand for services to estimate future demand. This assumption poses a limitation to the modelling efforts, as mitigation efforts would likely increase travel costs that could result in lower transport demand ( [[#Zhang--2018|Zhang et al. 2018]] ). In most models, demand is typically an exogenous input. These models often assume mode shifts of activities from the most carbon-intensive modes (driving and flying for passenger travel and trucking for freight) to less carbon-intensive modes (public transit and passenger rail, and freight rail) to reduce emissions.

Traditionally there is a disconnection between IAM models and bottom-up sectoral or city-based models due to the different scale (both spatial and temporal) and focus (climate mitigation vs urban pollution, safety ( [[#Creutzig--2016|Creutzig 2016]] )). The proliferation of shared and on-demand mobility solutions is leading to rebound effects for travel demand ( [[#Chen--2016|Chen and Kockelman 2016]] ; [[#Coulombel--2019|Coulombel et al. 2019]] ) and this is a new challenge for modelling. Some IAM studies have recently begun to explore demand-side solutions for reducing transport demand to achieve very low-carbon scenarios through a combination of culture and low-carbon lifestyle ( [[#Creutzig--2018|Creutzig et al. 2018]] ; [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ); urban development ( [[#Creutzig--2015a|Creutzig et al. 2015a]] ); increased vehicle occupancy ( [[#Grubler--2018|Grubler et al. 2018]] ); improved logistics and streamlined supply chains for the freight sector ( [[#Mulholland--2018|Mulholland et al. 2018]] ); and disruptive low-carbon innovation, described as technological and business model innovations offering ‘novel value propositions to consumers and which can reduce GHG emissions if adopted at scale’ ( [[#Wilson--2019|Wilson et al. 2019]] ). In the literature from national models, demand has been differentiated between conventional and sustainable development scenarios through narratives built around policies, projects, and programmes envisaged at the national level ( [[#Dhar--2015|Dhar and Shukla 2015]] ; [[#Shukla--2015|Shukla et al. 2015]] ) and price elasticities of travel demand ( [[#Dhar--2018|Dhar et al. 2018]] ). However, a greater understanding of the mechanisms underlying energy-relevant decisions and behaviours ( [[#Brosch--2016|Brosch et al. 2016]] ), and the motivations for sustainable behaviour ( [[#Steg--2015|Steg et al. 2015]] ), are critically needed to realise these solutions.

Overall, passenger and freight activity are likely to continue to grow rapidly under the C7 (&gt;3.0°C) scenarios, but most growth would occur in developing countries. Most models treat travel demand exogenously following the growth of population and GDP, but they have limited representation of responses to price changes, policy incentives, behavioural shifts, nor innovative mobility solutions that can be expected to occur in more stringent mitigation scenarios. [[IPCC:Wg3:Chapter:Chapter-5|Chapter 5]] provides a more detailed discussion of the opportunities for demand changes that may result from social and behavioural interventions.

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=== 10.7.4 Transport Modes Trajectories ===

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Globally over the last century, shares of faster transport modes have generally increased with increasing passenger travel demand ( [[#Schäfer--2017|Schäfer 2017]] ; [[#Schafer--2000|Schafer and Victor 2000]] ). For short- to medium-distance travel, private cars have displaced public transit, particularly in OECD countries, due to a variety of factors, including faster travel times in many circumstances ( [[#Liao--2020|Liao et al. 2020]] ); consumers increasingly valuing time and convenience with GDP growth; and broader transport policies, such as provision of road versus public transit infrastructure ( [[#Mattioli--2020|Mattioli et al. 2020]] ). For long-distance travel, travel via aviation for leisure and business has increased ( [[#Lee--2021|Lee et al. 2021]] ). These trends do not hold in all countries and cities, as many now have rail transit that is faster than driving ( [[#Newman--2015|Newman et al. 2015]] ). For instance, public transport demand rose from 1990 through to 2016 in France, Denmark, and Finland ( [[#eurostat--2019|eurostat 2019]] ). In general, smaller and denser countries and cities with higher or increasing urbanisation rates tend to have greater success in increasing public transport share. However, other factors, like privatisation of public transit ( [[#Bayliss--2018|Bayliss and Mattioli 2018]] ) and urban form ( [[#ITF--2021|ITF 2021]] ), also play a role. Different transport modes can provide passenger and freight services, affecting the emissions trajectories for the sector.

Figure 10.19 shows activity trajectories for freight and passenger transport through 2100 relative to a modelled year 2020 across different modes, based on the AR6 database for IAMs and global transport models. Globally, climate scenarios from IAMs, and policy and reference scenarios from global transport models, indicate increasing demand for freight and passenger transport via most modes through 2100 ( [[#Yeh--2017|Yeh et al. 2017]] ; [[#Mulholland--2018|Mulholland et al. 2018]] ; [[#Zhang--2018|Zhang et al. 2018]] ; [[#Khalili--2019|Khalili et al. 2019]] ). Road passenger transport exhibits a similar increase (roughly tripling) through 2100 across scenarios. For road passenger transport, scenarios that limit or return warming to 1.5°C during the 21st century (C1–C2) have a smaller increase from modelled 2020 levels (median increase of 2.4 times modelled 2020 levels) than do scenarios with higher warming levels (C3–C8) (median increase of 2.7–2.8 times modelled 2020 levels). There are similar patterns for passenger road transport via light-duty vehicle, for which median increases from modelled 2020 levels are smaller for C1–C2 (3 times larger) than for C3–C5 (3.1 times larger) or C6–C7 (3.2 times larger). Passenger transport via aviation exhibits a 2.2 times median increase relative to modelled 2020 levels under C1–C2 and C3–C5 scenarios but exhibits a 6.2 times increase under C6–C8. The only passenger travel mode that exhibits a decline in its median value through 2100 according to IAMs is walking/bicycling, in C3–C5 and C6–C8 scenarios. However, in C1–C2 scenarios, walking/bicycling increases by 1.4 times relative to modelled 2020 levels. At the 5th percentile of IAM solutions (lower edge of bands in Figure 10.19), buses and walking/bicycling for passenger travel both exhibit significant declines.

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[[File:fc918ecc73b0a1b98684a4346fd2b207 IPCC_AR6_WGIII_Figure_10_14.png]]

'''Figure 10.19 | Transport activity trajectories for passenger and freight across different modes.''' Global passenger (billion pkm per year) and freight (billion tkm per year) demand projections relative to a modelled year 2020 index. Results for IAM are for selected stabilisation temperatures by 2100. Also included are global transport models Reference and Policy scenarios. Data from the AR6 scenario database. Trajectories span the 5th to 95th percentiles across models, with a solid line indicating the median value across models.

For freight, Figure 10.19 shows that the largest growth occurs in transport via road ( [[#Mulholland--2018|Mulholland et al. 2018]] ). By 2100, global transport models suggest a roughly four-fold increase in median-heavy-duty trucking levels relative to modelled 2020 levels, while IAMs suggest a two- to four-fold increase in freight transport by road by 2100. Notably, the 95th percentile of IAM solutions see road transport by up to 4.7 times through 2100 relative to modelled 2020 levels, regardless of warming level. Other freight transport modes – aviation, international shipping, navigation, and railways – exhibit less growth than road transport. In scenarios that limit or return warming to 1.5°C (&gt;50%) during the 21st century (C1–C2), navigation and rail transport remain largely unchanged and international shipping roughly doubles by 2100. Scenarios with higher warming (i.e., moving from C1–C2 to C6–C8) generally lead to more freight by rail and less freight by international shipping.

Relative to global trajectories, upper-income regions – including North America, Europe, and the Pacific OECD – generally see less growth in passenger road via light-duty vehicle and passenger aviation, given more saturated demand for both. Other regions like China exhibit similar modal trends as the global average, whereas regions such as the African continent and Indian subcontinent exhibit significantly larger shifts, proportionally, in modal transport than the globe. In particular, the African continent represents the starkest departure from global results. Freight and passenger transport modes exhibit significantly greater growth across Africa than globally in all available scenarios. Across Africa, median freight and passenger transport via road from IAMs increases by 5 to 16 times and 4 to 28 times, respectively, across warming levels by 2100 relative to modelled 2020 levels. Even C1 has considerable growth in Africa via both modes (3 to 16 times increase for freight and 4 to 29 times increase for passenger travel at 5th and 95th percentiles of IAM solutions by 2100).

As noted in [[#10.2|Section 10.2]] , commonly explored mitigation options related to mode change include a shift to public transit, shared mobility, and demand reductions through various means, including improved urban form, teleconferences that replace passenger travel ( [[#Creutzig--2018|Creutzig et al. 2018]] ; [[#Grubler--2018|Grubler et al. 2018]] ; [[#Wilson--2019|Wilson et al. 2019]] ), improved logistics efficiency, green logistics, and streamlined supply chains for the freight sector ( [[#Mulholland--2018|Mulholland et al. 2018]] ). NDCs often prioritise options like bus improvements and enhanced mobility that yield pollution, congestion, and urban development co-benefits, especially in medium- and lower-income countries ( [[#Fulton--2017|Fulton et al. 2017]] ). Conversely, high-income countries, most of which have saturated and entrenched private vehicle ownership, typically focus more on technology options, such as electrification and fuel efficiency standards ( [[#Gota--2016|Gota et al. 2016]] ). Available IAM and regional models are limited in their ability to represent modal shift strategies. As a result, mode shifts alone do not differentiate climate scenarios. While this lack of representation is a limitation of the models, it is unlikely that such interventions would completely negate the increases in demand the models suggest. Therefore, transport via light-duty vehicle and aviation, freight transport via road, and other modes will likely continue to increase through to the end of the century. Consequently, fuel and carbon efficiency and fuel energy and technology will probably play crucial roles in differentiating climate scenarios, as discussed in the following sub-sections.

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=== 10.7.5 Energy and Carbon Efficiency Trajectories ===

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This section explores what vehicle energy efficiencies and fuel carbon intensity trajectories, from the data available in the AR6 database from IAMs and GTEMs, could be compatible with different temperature goals. Figure 10.20 shows passenger and freight energy intensity, and fuel carbon intensity, indexed relative to 2020Mod values. The top panel shows passenger energy intensity across all modes. LDVs constitute a major share of this segment. [[#Yeh--2017|Yeh et al. (2017)]] report 2.5–2.75 MJ vkm –1 in 2020 across models for the LDV segment, which is very close to the IEA estimate of 2.5 MJ vkm –1 for the global average fuel consumption for LDVs in 2017 ( [[#IEA--2020d|IEA 2020d]] ). For reference, these numbers correspond to 1.6–1.7 MJ pkm –1 for an occupancy rate of 1.5. The following results of the AR6 database are conditional on the corresponding reductions in fuel carbon intensity. Figure 10.20 shows that the scenarios suggest that passenger transport’s energy intensity drops to between 10–23% (interquartile ranges across C1–C4) in 2030 for scenarios in line with warming levels below 2°C. In 2050, the medians across the group of scenarios that limit or return warming to 1.5°C (&gt;50%) during the 21st century (C1–C2), and scenarios that limit warming to 2°C (&gt;67% or &gt;50%) throughout the 2st century (C3–C4) suggest energy intensity reductions of 51% and 45–46% respectively. These values correspond to annual average energy efficiency improvement rates of 2.3–2.4% and 2.0–2.1%, respectively, from 2020 to 2050. For reference, the IEA reports an annual energy efficiency improvement rate of 1.85% per year in 2005–16 ( [[#IEA--2020d|IEA 2020d]] ). In contrast, the results from GTEMs suggest lower energy efficiency improvement, with median values for policy scenarios of 39% reduction in 2050, corresponding to annual energy efficiency improvement rates close to 1.6%. The IAM scenarios suggest median energy intensity reductions of passenger transport of 57–61% by the end of the century would align with warming levels of both 1.5°C and 2°C (C1–C4) given the corresponding decarbonisation of the fuels.

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[[File:7e88f275b9838e5ffcba305cb48ecd85 IPCC_AR6_WGIII_Figure_10_15.png]]

'''Figure 10.20 | Energy efficiency and carbon intensity of transport in 2030, 2050, and 2100 indexed to 2020 modelled year across scenarios.''' Plots show 5th/95th percentile, 25th/75th percentile, and median. Numbers above the bars indicate the number of scenarios. Data from the AR6 scenario database.

The scenarios in line with warming levels of 1.5°C or 2°C goals (C1 to C4) show different trends for freight’s energy intensity. The amount of overshoot and differences in demand for freight services and, to some extent, fuel carbon intensities contribute to these differences. For the two scenarios aligning with the warming levels of 1.5°C, the trajectories in 2030 and 2050 are quite different. The median C2 scenario that returns warming to 1.5°C (&gt;50%) during the 21st century after high overshoot takes a trajectory with lower energy intensity improvements in the first half of the century. In contrast, the C1 scenario that limits warming to 1.5°C (&gt;50%) during the 21st century with no or limited overshoot take on a more steadily declining trajectory across the means. The IAMs provide a less clear picture of required energy intensity improvements for freight than for passenger transport associated with different temperature targets. As for the carbon intensity of direct energy used across both passenger and freight, the modelling scenarios suggest very moderate reductions by 2030. The interquartile ranges for the C1 scenarios suggest global average reductions in carbon intensity of 5–10%. Across the other scenarios compatible with warming levels of 1.5°C or 2°C (C2–C4), the interquartile ranges span from 1–6% reductions in carbon intensity of direct energy used for transport. For 2050, the scenarios suggest that dependence on fuel decarbonisation increases with more stringent temperature targets. For the scenarios that limits warming to 1.5°C (&gt;50%) during the 21st century with no or limited overshoot (C1), global carbon intensity of energy used for transport decreases by 37–60% (interquartile range) by 2050 with a mean of 50% reduction. The IAM scenarios in the AR6 database do not suggest full decarbonisation of transport fuels by 2100. The interquartile ranges across the C1–C4 set of scenarios, compatible with warming levels of 2°C and less, span from 61–91% reduction from 2020Mod levels.

Increasing the occupancy rate of passenger transport ( [[#Grubler--2018|Grubler et al. 2018]] ) and reducing empty miles or increasing payload in freight deliveries ( [[#Gucwa--2013|Gucwa and Schäfer 2013]] ; [[#McKinnon--2018|McKinnon 2018]] ) via improved logistics efficiency or streamlined supply chains ( [[#Mulholland--2018|Mulholland et al. 2018]] ), can present significant opportunities to effectively improve energy efficiency and decrease GHG emissions in transport. However, the recent trends of consumer behaviours have shown a declining occupancy rate of light-duty vehicles in industrialised countries ( [[#Schäfer--2020|Schäfer and Yeh 2020]] ), and the accelerating growing preference for SUVs challenges emissions reductions in the passenger car market ( [[#IEA--2019d|IEA 2019d]] ). These trends motivate a strong focus on demand-side options.

Based on the scenario literature, a 51% reduction in median energy intensity of passenger transport and a corresponding 38–50% reduction in median carbon intensity by 2050 would be aligned with transition trajectories yielding warming levels below 1.5°C by the end of the century. For comparison, the LCA literature suggests a switch from current ICEs to current BEVs would yield a reduction in energy intensity well beyond 45% and up to 70%, for a mid-sized vehicle ( [[#10.4|Section 10.4]] ). Correspondingly, a switch from diesel or gasoline to low-carbon electricity or low-carbon hydrogen would yield carbon intensity reduction beyond the median scenario value. Thus, the LCA literature suggests technologies exist today that would already match and exceed the median energy and carbon intensities values that might be needed by 2050 for low warming levels.

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=== 10.7.6 Fuel Energy and Technology Trajectories ===

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Two mechanisms for reducing carbon emissions from the transport sector are fuel switching for current vehicle technologies and transitioning to low-carbon vehicle technologies. Figure 10.21 combines data from IAMs and GTEMs on shares of transport final energy by fuel. These shares account for fuel uses across modes – road, aviation, rail, and shipping – and both passenger and freight transport. Since the technologies have different conversion efficiencies, these shares of final energy by fuel are necessarily different from the shares by service (passenger-km or tonne-km) by fuel and shares of vehicle stock by fuel. For example, a current battery electric LDV powertrain is roughly three times more energy-efficient than a comparable ICE powertrain ( [[#10.3|Section 10.3]] , Table 10.9 in Appendix 10.1); thus, fuel shares of 0.25 for electricity and 0.75 for oil could correspond to vehicle stock shares of 0.5 and 0.5, respectively. In general, while models may project that EVs constitute a greater share of road vehicle stock, and provide a greater share of road passenger-kilometres, their share of transport final energy (Figure 10.21) can still remain lower than the final energy share of fuels used in less-efficient (e.g., ICE) vehicles. Thus, the shares of transport final energy by fuel presented in Figure 10.21 should be interpreted with care.

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[[File:862a87fe74c2e1e94e15f5de13c89425 IPCC_AR6_WGIII_Figure_10_16.png]]

'''Figure 10.21 | Global shares of final fuel energy in the transport sector in 2030, 2050, and 2100 for freight and passenger vehicles.''' Plots show 10th/90th percentile, 25th–75th percentile, and median. Data from the AR6 scenario database.

IAM and GTEM scenarios indicate that fuel and technology shifts are crucial to reduce carbon emissions to achieve lower levels of warming ( [[#Edelenbosch--2017|Edelenbosch et al. 2017]] ; [[#IEA--2017b|IEA 2017b]] ). Across the transport sector, a technology shift towards advanced fuel vehicles is the dominant driver of decarbonisation in model projections. This trend is consistent across climate scenarios, with larger decreases in the final energy share of oil in scenarios that achieve progressively lower levels of warming. Due to efficiency improvements, the higher efficiency of advanced fuel vehicles, and slower progress in the freight sector, the final energy share of oil decreases more rapidly after 2030. By 2050, the final energy shares of electricity, biofuels, and alternative gaseous fuels increase, with shares from electricity generally about twice as high (median values from 10–30% across warming levels) as the shares from biofuels and gases (median values from 5–10%). While IAMs suggest that the final energy share of hydrogen will remain low in 2050, by 2100 the median projections include 5–10% hydrogen in transport final energy.

While few IAMs report final energy shares by transport mode or passenger/freight, several relevant studies provide insights into fuel share trends in passenger LDVs and freight vehicles. The IEA suggests that full LDV electrification would be the most promising low-carbon pathway to meet a 1.75°C goal ( [[#IEA--2017b|IEA 2017b]] ). The MIT Economic Projection and Policy Analysis model focuses on the future deployment of gasoline versus EV technologies in the global LDV stock ( [[#Ghandi--2019|Ghandi and Paltsev 2019]] ). These authors estimate that the global stock of vehicles could increase from 1.1 billion vehicles in 2015 up to 1.8 billion by 2050, with a growth in EVs from about 1 million vehicles in 2015 up to 500 million in 2050. These changes are driven primarily by cost projections (mostly battery cost reductions). Similarly, the International Council on Clean Transport (ICCT) indicates that EV technology adoption in the light-duty sector can lead to considerable climate benefits. Their scenarios reach nearly 100% electrification of LDVs globally, leading to global GHG emissions from LDVs ranging from 0% to 50% of 2010 levels in 2050 ( [[#Lutsey--2015|Lutsey 2015]] ). Khalili et al.(2019) estimate transport stocks through 2050 under aggressive climate mitigation scenarios that nearly eliminate road transport emissions. They find the demand for passenger transport could triple through 2050, but emissions targets could be met through widespread adoption of BEVs (80% of LDVs) and, to a lesser extent, fuel cell and plug-in hybrid electric vehicles. Contrary to these estimates, the US Energy Information Administration finds small adoption of electrification for LDVs and instead identifies diffusion of natural gas-fuelled LDVs in OECD and, to a greater extent, non-OECD countries through 2040. This trend occurs in a reference and a ‘low liquids’ case, which lowers LDV ownership growth rates and increases preferences for alternative fuel vehicles. A comprehensive overview of regional technology adoption models across many methodological approaches can be found in [[#Jochem--2018|Jochem et al. (2018)]] .

In freight transport, studies indicate a shift toward alternative fuels would need to be supplemented by efficiency improvements. The IEA suggests efficiency improvements would be essential for decarbonisation of trucks, aviation, and shipping in the short-to-medium term. At the same time, the IEA suggests that fuel switching to advanced biofuels would be needed to decarbonise freight in the long term ( [[#IEA--2019d|IEA 2019d]] ). [[#Mulholland--2018|Mulholland et al. (2018)]] investigated the impacts of decarbonising road freight in two scenarios: countries complying with COP21 pledges and a second more ambitious reduction scenario in line with limiting global temperature rise to 1.75°C. Despite the deployment of logistics improvements, high-efficiency technologies, and low-carbon fuels, activity growth leads to a 47% increase in energy demand for road freight while overall GHG emissions from freight increase by 55% (4.8 GtCO 2- eq) in 2050 (relative to 2015) in the COP21 scenario. In the 1.75°C scenario, decarbonisation happens primarily through a switch to alternative fuels (hybrid electric and full battery electric trucks), which leads to a 60% reduction in GHG emissions from freight in 2050 relative to 2015. [[#Khalili--2019|Khalili et al. (2019)]] also find substantial shifts to alternative fuels in HDVs under aggressive climate mitigation scenarios. Battery electric, hydrogen fuel cell, and plug-in hybrid electric vehicles constitute 50%, 30%, and 15% of heavy-duty vehicles respectively in 2050. They also find 90% of buses would be electrified by 2050.

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=== Box 10.4 | Three Illustrative Mitigation Pathways ===

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[[#10.7|Section 10.7]] presents the full set of scenarios in the AR6 database and highlights the broader trends of how the transport sector may transform in order to be compliant with different warming levels. This box elaborates on three illustrative mitigation pathways (IMPs) to exemplify a few different ways the sector may transform. Seven illustrative pathways are introduced in [[IPCC:Wg3:Chapter:Chapter-3#3.2.5|Section 3.2.5]] . In this box we focus on three of the IMPs: (i) focus on deep renewable energy penetration and electrification (IMP-Ren), (ii) low demand (IMP-LD), and (iii) pathways that align with both Sustainable Development Goals and climate policies (IMP-SP). In particular, the variants of these three scenarios limit warming to 1.5°C with no or limited overshoot (C1).

All of the three selected pathways reach global net zero CO 2 emissions across all sectors between 2060 and 2070, but not all reach net zero GHG emissions (Figure 3.4). Panel (a) in Box 10.4, Figure 1 below shows the CO 2 trajectories for the transport sector for the selected IMPs. Please note that the year 2020 is modelled in these scenarios, therefore, the scenarios do not reflect the effects of

[[File:6a21951e36ff2810a2e1657235900895 IPCC_AR6_WGIII_Box_10_4_Figure_1.png]]

'''Box 10.4, Figure 1 | Three Illustrative mitigation pathways for the Transport sector.''' Panel '''(a)''' shows CO 2 emissions from the transport sector indexed to simulated non-COVID-2019 2020 levels. Panels '''(b)''' , '''(c)''' , and '''(d)''' show fuels mix to achieve 1.5°C warming through three illustrative mitigation pathways: IMP-SP, 1.5 IMP-Ren and IMP-LD, respectively. All data from IPCC AR6 scenario database.

the COVID-19 pandemic. For the low demand scenarios IMP-LD and renewables pathway IMP-Ren, CO 2 emissions from the transport sector decreases to 10% and 20% of modelled 2020 levels by 2050 respectively. In contrast, the IMP-SP has a steady decline of transport sector CO 2 emissions over the century. By 2050, this scenario has a 50% reduction in emissions compared to modelled 2020 levels. Panels (b), (c) and (d) show energy by different fuels for the three selected IMPs. The IMP-SP yields a drop in energy for transport of about 40% by the end of the century. CO 2 emissions reductions are obtained through a phase-out of fossil fuels with electricity and biofuels, complemented by a minor share of hydrogen, by the end of the century. In IMP-Ren, the fuel energy demand at the end of the century is on a par with the 2020 levels, but the fuel mix has shifted towards a larger share of electricity complimented by biofuels and a minor share of hydrogen. For the IMP-LD scenario, the overall fuel demand decreases by 45% compared to 2020 levels by the end of the century. Oil is largely phased out by mid century, with electricity and hydrogen becoming the major fuels in the second half of the century. Across the three IMPs, electricity plays a major role, in combination with biofuels, hydrogen, or both.

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=== 10.7.7 Insights from the Modelling Literature ===

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This section provides an updated, detailed assessment of future transport scenarios from IAM, GTEMs, and NTEMs given a wide range of assumptions and under a set of policy targets and conditions. The scenario modelling tools are necessary to aggregate individual options and understand how they fit into mitigation pathways from a systems perspective. The scenarios suggest that 43% (30–63% for the interquartile ranges) reductions in CO 2 emissions from transport (below modelled 2020 levels) by 2050 would be compatible with warming levels of 1.5°C (C1–C2 group). While the global scenarios suggest emissions reductions in energy supply sectors at large precede those in the demand sectors ( [[IPCC:Wg3:Chapter:Chapter-3#3.4.1|Section 3.4.1]] ), a subset of the scenarios also demonstrate that more stringent emission reductions in the transport sector are feasible. For example, the illustrative mitigation pathways IMP-REN and IMP-LD suggest emissions reductions of 80% and 90% respectively are feasible by 2050 ''en route'' to warming levels of 1.5°C with low or no overshoot by the end of the century.

The scenarios from the different models project continued growth in demand for freight and passenger services, particularly in developing countries. The potential for demand reductions is evident, but the specifics of demand-reduction measures remain less explored by the scenario literature. This limitation notwithstanding, the IAM and GTEMs suggest that interventions that reduce the energy and fuel carbon intensity of transport are likely crucial to successful mitigation strategies.

The scenario literature suggests that serious attempts at carbon mitigation in the transport sector must examine the uptake of alternative fuels. The scenarios described in the IAMs and GTEMs literature decarbonise through a combination of fuels. Across the scenarios, electrification plays a key role, complemented by biofuels and hydrogen. In general terms, electrification tends to play the key role in passenger transport while biofuels and hydrogen are more prominent in the freight segment. The three illustrative mitigation pathways described in Box 10.4 exemplify different ways these technologies may be combined and still be compatible with warming levels of 1.5°C with low or no overshoot. Shifts towards alternative fuels must occur alongside shifts towards clean technologies in other sectors, as all alternative fuels have upstream impacts. Without considering other sectors, fuel shifts would not yield their full mitigation potentials. These collective efforts are particularly important for the electrification of transport, as the transformative mitigation potential is strongly dependent on the decarbonisation of the power sector. In this regard, the scenario literature is well aligned with the LCA literature reviewed in [[#10.4|Section 10.4]] .

The models reviewed in this section would all generally be considered to have a good representation of fuels, technologies, and costs, but they often better represent land transport modes than shipping and aviation. While these models have their strengths in some areas, they have some limitations in other areas, like behavioural aspects. These models are also limited in their ability to account for unexpected technological innovation, such as a breakthrough in heavy vehicle fuels, artificial intelligence, autonomy and big data, even the extent of digital communications replacing travel ( [[#10.2|Section 10.2]] ). As a result of these limitations, the models cannot yet provide an exhaustive set of options for decarbonising the transport sectors. These limitations notwithstanding, the models can find solutions encompassing the transport sector and its interactions with other sectors that are compatible with stringent emissions mitigation efforts. The solutions space of transportation technology trajectories is therefore wider than explored by the models, so there is still a need to better understand how all options in combination may support the transformative mitigation targets.

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