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

== 10.2 Systemic Changes in the Transport Sector ==

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Systemic change is the emergence of new organisational patterns that affect the structure of a system. While much attention has been given to engine and fuel technologies to mitigate GHG emissions from the transport sector, population dynamics, finance and economic systems, urban form, culture, and policy also drive emissions from the sector. Thus, systemic change requires innovations in these components. These systemic changes offer the opportunity to decouple transport emissions from economic growth. In turn, such decoupling allows environmental improvements like reduced GHG emissions without loss of economic activity ( [[#UNEP--2011|UNEP 2011]] ; [[#UNEP--2013|UNEP 2013]] ; [[#Newman--2017|Newman et al. 2017]] ; [[#IPCC--2018|IPCC 2018]] ).

There is evidence that suggests decoupling of transport emissions and economic growth is already happening in developed and developing countries. Europe and China have shown the most dramatic changes ( [[#Huizenga--2015|Huizenga et al. 2015]] ; [[#Gao--2018|Gao and Newman 2018]] ; [[#SLoCaT--2018b|SLoCaT 2018b]] ) and many cities are demonstrating decoupling of transport-related emissions through new net zero urban economic activity ( [[#Loo--2016|Loo and Banister 2016]] ; [[#SLoCaT--2018a|SLoCaT 2018a]] ). A continued and accelerated decoupling of the growth of transport-related GHG emissions from economic growth is crucial for meeting the SDGs, as outlined in [[#10.1|Section 10.1]] . This section focuses on several overlapping components of systemic change in the transport sector that affect the drivers of GHG emissions: urban form, physical geography, and infrastructure; behaviour and mode choice; and new demand concepts. Table 10.3 at the end of the section provides a high-level summary of the effect of these systemic changes on emissions from the transport sector.

'''Table 10.3 | Components of systemic change and their impacts on the transport sector.'''

{| class="wikitable"
|-
| '''Systemic change'''

| '''Mechanisms through which it affects emissions in transport sector and is likely to affect 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 to 25%, depending on the setting ( [[#Creutzig--2015a|Creutzig et al. 2015a]] ; Creutzig et al.2015b; [[#Pan--2020|Pan et al. 2020]] ).

|-
| Investments in transit and active transport infrastructure

| Improving public transit systems and building infrastructure to support active transport modes (walking and biking) could reduce car travel. Case studies suggest that active mobility could reduce emissions from urban transport by 2% to 10% depending on the setting ( [[#Creutzig--2016|Creutzig et al. 2016]] ; [[#Zahabi--2016|Zahabi et al. 2016]] ; [[#Keall--2018|Keall et al. 2018]] ; [[#Gilby--2019|Gilby et al. 2019]] ; [[#Neves--2019|Neves and Brand 2019]] ; [[#Bagheri--2020|Bagheri et al. 2020]] ; [[#Ivanova--2020|Ivanova et al. 2020]] ; [[#Brand--2021|Brand et al. 2021]] ). A shift to public transit modes can likely offer significant emissions reductions, but estimates are uncertain.

|-
| Changes in economic structures

| Higher demand as a result of higher incomes could increase emissions, particularly from aviation and shipping. Higher prices could have the opposite effect and reduce emissions. 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 and access information remotely could reduce travel demand. Case studies suggest that teleworking could reduce transport emissions by 20% in some instances, but likely by 1%, at most, across the entire transport system ( [[#Roth--2008|Roth et al. 2008]] ; [[#O’Keefe--2016|O’Keefe et al. 2016]] ; [[#Shabanpour--2018|Shabanpour et al. 2018]] ; O’Brien and Aliabadi 2020).

|-
| Dematerialisation of the economy

| A reduction in goods needed due to combining multiple functions into one 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 utilisation 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--2020|Jaller and Pahwa 2020]] ; [[#Le--2021|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|Creutzig 2021]] ).

|-
| 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|Schaller 2018]] ; [[#Ward--2021|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 emissions ( [[#ITF--2020a|ITF 2020a]] ; [[#ITF--2020b|ITF 2020b]] ). 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 ( [[#Nasri--2018|Nasri et al. 2018]] ; [[#Vahidi--2018|Vahidi and Sciarretta 2018]] ; [[#Massar--2021|Massar et al. 2021]] ; [[#Paddeu--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 vehicle-kilometre travelled ( [[#Harper--2016|Harper et al. 2016]] ; [[#Auld--2017|Auld et al. 2017]] ; [[#Sonnleitner--2021|Sonnleitner et al. 2021]] ). Drones could reduce energy use and GHG emissions from freight transport ( [[#Stolaroff--2018|Stolaroff et al. 2018]] ).

|}

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<span id="urban-form-physical-geography-and-transport-infrastructure"></span>
=== 10.2.1 Urban Form, Physical Geography, and Transport Infrastructure ===

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The physical characteristics that make up built areas define the urban form. These physical characteristics include the shape, size, density, and configuration of the human settlements. Urban form is intrinsically coupled with the infrastructure that allows human settlements to operate. In the context of the transport sector, urban form and urban infrastructure influence the time and cost of travel, which, in turn, drive travel demand and modal choice ( [[#Marchetti--2001|Marchetti and Ausubel 2001]] ; [[#Newman--2015|Newman and Kenworthy 2015]] ).

Throughout history, three main urban fabrics have developed, each with different effects on transport patterns based on a fixed travel time budget of around one hour ( [[#Newman--2016|Newman et al. 2016]] ). The high-density urban fabric developed over the past several millennia favoured walking and active transport for only a few kilometres (km). In the mid-19th century, urban settlements developed a medium-density fabric that favoured trains and trams traveling over 10 to 30 km corridors. Finally, since the mid-20th century, urban form has favoured automobile travel, enabling mass movement between 50 and 60 km. Table 10.2 describes the effect of these urban fabrics on GHG emissions and other well-being indicators.

'''Table 10.2 | The systemic effect of city form and transport emissions.'''

{| class="wikitable"
|-
| '''Annual transport emissions and co-benefits'''

| '''Walking urban fabric'''

| '''Transit urban fabric'''

| '''Automobile urban fabric'''

|-
| Transport GHG

| 4 tonnes per person

| 6 tonnes per person

| 8 tonnes per person

|-
| Health benefits from walkability

| High

| Medium

| Low

|-
| Equity of locational accessibility

| High

| Medium

| Low

|-
| Construction and household waste

| 0.87 tonnes per person

| 1.13 tonnes per person

| 1.59 tonnes per person

|-
| Water consumption

| 35 kilolitre per person

| 42 kilolitre per person

| 70 kilolitre per person

|-
| Land

| 133 square metres per person

| 214 square metres per person

| 547 square metres per person

|-
| Economics of infrastructure and transport operations

| High

| Medium

| Low

|}

Source: [[#Newman--2016|Newman et al. (2016)]] ; [[#Thomson--2018|Thomson and Newman (2018)]] ; [[#Seto--2021|Seto et al. (2021)]] .

Since AR5, urban design has increasingly been seen as a major way to influence the GHG emissions from urban transport systems. Indeed, research suggests that implementing urban form changes could reduce GHG emissions from urban transport by 25% in 2050, compared with a business-as-usual scenario ( [[#Creutzig--2015b|Creutzig et al. 2015b]] ; [[#Creutzig--2016|Creutzig 2016]] ). Researchers have identified a variety of variables to study the relationship between urban form and transport-related GHG emissions. Three notable aspects summarise these relationships: urban space utilisation, urban spatial form, and urban transportation infrastructure ( [[#Tian--2020|Tian et al. 2020]] ). Urban density (population or employment density) and land-use mix define the urban space utilisation. Increases in urban density and mixed function can effectively reduce per capita car use by reducing the number of trips and shortening travel distances. Similarly, the continuity of urban space and the dispersion of centres reduces travel distances ( [[#Tian--2020|Tian et al. 2020]] ), though such changes are rarely achieved without shifting transport infrastructure investments away from road capacity increases ( [[#Newman--2015|Newman and Kenworthy 2015]] ; [[#McIntosh--2017|McIntosh et al. 2017]] ). For example, increased investment in public transport coverage, optimal transfer plans, shorter transit travel time, and improved transit travel efficiency make public transit more attractive ( [[#Heinen--2017|Heinen et al. 2017]] ; [[#Nugroho--2018a|Nugroho et al. 2018a]] ; [[#Nugroho--2018b|Nugroho et al. 2018b]] ) and hence increase density and land values ( [[#Sharma--2020|Sharma and Newman 2020]] ). Similarly, forgoing the development of major roads for the development of pedestrian and bike pathways enhances the attractiveness of active transport modes ( [[#Zahabi--2016|Zahabi et al. 2016]] ; [[#Keall--2018|Keall et al. 2018]] ; [[#Tian--2020|Tian et al. 2020]] ).

Ultimately, infrastructure investments influence the structural dependence on cars, which in turn influence the lock-in or path dependency of transport options with their greenhouse emissions ( [[#Newman--2015|Newman et al. 2015]] ; [[#Grieco--2016|Grieco and Urry 2016]] ). The 21st century saw a new trend to reach peak car use in some countries as a result of a revival in walking and transit use( [[#Grieco--2016|Grieco and Urry 2016]] ; [[#Newman--2017|Newman et al. 2017]] ; [[#Gota--2019|Gota et al. 2019]] ). While some cities continue on a trend towards reaching peak car use on a per-capita basis, for example Shanghai and Beijing ( [[#Gao--2020|Gao and Newman 2020]] ), there is a need for increased investments in urban form strategies that can continue to reduce car dependency around the world.

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<span id="cross-chapter-box-7-urban-form-simultaneously-reducing-urban-transport-emissions-avoiding-infrastructure-lock-in-and-providing-accessible-services"></span>
=== Cross-Chapter Box 7 | Urban Form: Simultaneously Reducing Urban Transport Emissions, Avoiding Infrastructure Lock-in, and Providing Accessible Services ===

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'''Authors:''' Felix Creutzig (Germany), Karen C. Seto (the United States of America), Peter Newman (Australia)

Urban transport is responsible for about 8% of global CO 2 emissions or 3 GtCO 2 per year (Chapters 5 and 8). In contrast to energy supply technologies, urban transport directly interacts with mobility lifestyles ( [[IPCC:Wg3:Chapter:Chapter-5#5.4|Section 5.4]] ). Similarly, non-GHG emission externalities, such as congestion, air pollution, noise, and safety, directly affect urban quality of life, and result in considerable welfare losses. Low-carbon, highly accessible urban design is not only a major mitigation option, it also provides for more inclusive city services related to well-being (Sections 5.1 and 5.2). Urban planning and design of cities for people are central to realise emission reductions without relying simply on technologies, though the modes of transport favoured will influence the ability to overcome the lock-in around automobile use ( [[#Gehl--2010|Gehl 2010]] ; [[#Creutzig--2015b|Creutzig et al. 2015b]] ).

Where lock-in has occurred, other strategies may alleviate the GHG emissions burden. Urban planning still plays a key role in recreating local hubs. Available land can be used to build rail-based transit, made financially viable by profiting from land value captured around stations ( [[#Ratner--2013|Ratner and Goetz 2013]] ). Shared or pooled mobility can offer flexible on-demand mobility solutions that are efficient also in suburbs and for integrating with longer commuting trips ( [[#ITF--2017|ITF 2017]] ).

Global emissions trajectories of urban transport will be decided in rapidly urbanising Asia and Africa. Urban transport-related GHG emissions are driven by incomes and car ownership but there is considerable variation among cities with similar income and car ownership levels ( [[#Newman--2015|Newman and Kenworthy 2015]] ). While electrification is a key strategy to decarbonise urban transport, urban infrastructures can make a difference of up to a factor of 10 in energy use and induced GHG emissions ( [[#Erdogan--2020|Erdogan 2020]] ). Ongoing urbanisation patterns risk future lock-in of induced demand on GHG emissions, constraining lifestyles to energy-intensive and high CO 2 -related technologies ( [[#Erickson--2015|Erickson and Tempest 2015]] ; [[#Seto--2016|Seto et al. 2016]] ) (Sections 5.4, 8.2.3 and 10.2.1). Instead, climate solutions can be locked into urban policies and infrastructures ( [[#Ürge-Vorsatz--2018|Ürge-Vorsatz et al. 2018]] ) especially through the enhancement of the walking and transit urban fabric. Avoiding urban sprawl, associated with several externalities ( [[#Dieleman--2004|Dieleman and Wegener 2004]] ), is a necessary decarbonisation condition, and can be guided macro-economically by increasing fuel prices and marginal costs of motorised transport ( [[#Creutzig--2014|Creutzig 2014]] ). Resulting urban forms not only reduce GHG emission from transport but also from buildings, as greater compactness results in reduced thermal loss ( [[#Borck--2018|Borck and Brueckner 2018]] ). Health benefits from reduced car dependence are an increasing element driving this policy agenda ( [[#Speck--2018|Speck 2018]] ) ( [[#10.8|Section 10.8]] ).

Low-carbon highly accessible urban design is not only a major mitigation option, it also provides for more inclusive city services related to well-being (Sections 5.1 and 5.2). Solutions involve planning cities around walkable sub-centres, where multiple destinations, such as shopping, jobs, leisure activities, and others, can be accessed within a 10 minute walk or bicycle ride ( [[#Newman--2006|Newman and Kenworthy 2006]] ). Overall, the mitigation potential of urban planning is about 25% in 2050 compared with a business-as-usual scenario ( [[#Creutzig--2015a|Creutzig et al. 2015a]] ; [[#Creutzig--2015b|Creutzig et al. 2015b]] ). Much higher levels of decarbonisation can be achieved if cities take on a regenerative development approach and act as geo-engineering systems on the atmosphere ( [[#Thomson--2016|Thomson and Newman 2016]] ).

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<span id="behaviour-and-mode-choice"></span>
=== 10.2.2 Behaviour and Mode Choice ===

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Behaviour continues to be a major source of interest in the decarbonisation of transport as it directly addresses demand. Behaviour is about people’s actions based on their preferences. [[IPCC:Wg3:Chapter:Chapter-5|Chapter 5]] described an ‘Avoid, Shift, Improve’ process for demand-side changes that affect sectoral emissions. This section discusses some of the drivers of behaviour related to the transport sector and how they link to this ‘Avoid, Shift, Improve’ process.

'''Avoid: the effect of prices and income on demand.''' Research has shown that household income and price have a strong influence on people’s preferences for transport services ( [[#Bakhat--2017|Bakhat et al. 2017]] ; [[#Palmer--2018|Palmer et al. 2018]] ). The relationship between income and demand is defined by the income elasticity of demand. For example, research suggests that in China, older and wealthier populations continued to show a preference for car travel ( [[#Yang--2019|Yang et al. 2019]] ) while younger and low-income travellers sought variety in transport modes ( [[#Song--2018|Song et al. 2018]] ). Similarly, [[#Bergantino--2018b|Bergantino et al. (2018b)]] evaluated the income elasticity of transport by mode in the UK. They found that the income elasticity for private cars is 0.714, while the income elasticities of rail and bus use are 3.253 (the greater elasticity, the more the demand will grow or decline, depending on income). Research has also shown a positive relationship between income and demand for air travel, with income elasticities of air travel demand being positive and as large as 2 ( [[#Gallet--2014|Gallet and Doucouliagos 2014]] ; [[#Valdes--2015|Valdes 2015]] ; [[#Hakim--2016|Hakim and Merkert 2016]] ; [[#Hakim--2019|Hakim and Merkert 2019]] ; [[#Hanson--2022|Hanson et al. 2022]] ). A survey in 98 Indian cities also showed income as the main factor influencing travel demand (Ahmad and de Oliveira 2016). Thus, as incomes and wealth across the globe rise, demand for travel is likely to increase as well.

The price elasticity of demand measures changes in demand as a result of changes in the prices of the services. In a meta-analysis of the price elasticity of energy demand, [[#Labandeira--2017|Labandeira et al. (2017)]] report the average long-term price elasticity of demand for gasoline and diesel to be –0.773 and –0.443, respectively. That is, demand will decline with increasing prices. A similar analysis of long-term data in the United States (US), the United Kingdom (UK), Sweden, Australia, and Germany reports the gasoline price elasticity of demand for car travel (as measured through vehicle-kilometre – vkm – per capita) ranges between –0.1 and –0.4 ( [[#Bastian--2016|Bastian et al. 2016]] ). For rail travel, the price elasticity of demand has been found to range between –1.05 and –1.1 ( [[#Zeng--2021|Zeng et al. 2021]] ). Similarly, price elasticities for air travel range from –0.53 to –1.91 depending on various factors such as purpose of travel (business or leisure), season, and month and day of departure ( [[#Morlotti--2017|Morlotti et al. 2017]] ). The price elasticities of demand suggest that car use is inelastic to prices, while train use is relatively inelastic to the cost of using rail. Conversely, consumers seem to be more responsive to the cost of flying, so that strategies that increase the cost of flying are likely to contribute to some avoidance of aviation-related GHG emissions.

While the literature continues to show that time, cost, and income dominate people’s travel choices (Ahmad and de Oliveira 2016; [[#Capurso--2019|Capurso et al. 2019]] ; [[#He--2020|He et al. 2020]] ), there is also evidence of a role for personal values, and environmental values in particular, shaping choices within these structural limitations ( [[#Bouman--2019|Bouman and Steg 2019]] ). For example, individuals are more likely to drive less when they care about the environment ( [[#De%20Groot--2008|De Groot et al. 2008]] ; [[#Abrahamse--2009|Abrahamse et al. 2009]] ; [[#Jakovcevic--2013|Jakovcevic and Steg 2013]] ; [[#Hiratsuka--2018|Hiratsuka et al. 2018]] ; [[#Ünal--2019|Ünal et al. 2019]] ). Moreover, emotional and symbolic factors affect the level of car use ( [[#Steg--2005|Steg 2005]] ). Differences in behaviour may also result due to differences in gender, age, norms, values, and social status. For example, women have been shown to be more sensitive to parking pricing than men ( [[#Simićević--2020|Simićević et al. 2020]] ).

Finally, structural shocks, such as a financial crisis, a pandemic, or the impacts of climate change could affect the price and income elasticities of demand for transport services ( [[#van%20Ruijven--2019|van Ruijven et al. 2019]] ). COVID-19 lockdowns reduced travel demand by 19% (aviation by 32%) and some of the patterns that have emerged from the lockdowns could permanently change the elasticity of demand for transport ( [[#Tirachini--2020|Tirachini and Cats 2020]] ; [[#Hendrickson--2020|Hendrickson and Rilett 2020]] ; [[#Newman--2020a|Newman 2020a]] ; [[#SLoCaT--2021|SLoCaT 2021]] ; [[#Hanson--2022|Hanson et al. 2022]] ). In particular, the COVID-19 lockdowns have spurred two major trends: electronic communications replacing many work and personal travel requirements; and revitalised local active transport and e-micromobility ( [[#Newman--2020a|Newman 2020a]] ; [[#SLoCaT--2021|SLoCaT 2021]] ). The permanence of these changes post-COVID-19 is uncertain but possible ( [[#Earley--2021|Earley and Newman 2021]] ) (Cross-Chapter Box 1 in Chapter 1). However, these changes will require growth of infrastructure for better ICT bandwidths in developing countries, and better provision for micromobility in all cities.

'''Shift: mode choice for urban and intercity transport.''' Shifting demand patterns (as opposed to avoiding demand) can be particularly important in decarbonising the transport sector. As a result, the cross-elasticity of demand across transport modes is of particular interest for understanding the opportunities for modal shift. The cross-elasticity represents the demand effect on mode i (e.g., bus) when an attribute of mode j (e.g., rail) changes marginally. Studies on the cross-elasticities of mode choice for urban travel suggest that the cross-elasticity for car demand is low, but the cross-elasticities of walking, bus, and rail with respect to cars are relatively large ( [[#Fearnley--2017|Fearnley et al. 2017]] ; [[#Wardman--2018|Wardman et al. 2018]] ). In practice, these cross-elasticities suggest that car drivers are not very responsive to increased prices for public transit, but transit users are responsive to reductions in the cost of driving. When looking at the cross-elasticities of public transit options (bus vs metro vs rail), research suggests that consumers are particularly sensitive to in-vehicle and waiting time when choosing public transit modes ( [[#Fearnley--2018|Fearnley et al. 2018]] ). These general results provide additional evidence that increasing the use of active and public transport requires interventions that make car use more expensive while making public transit more convenient (e.g., smart apps that tell the user the exact time for transit arrival (Box 10.1)).

The literature on mode competition for intercity travel reveals that while cost of travel is a significant factor ( [[#Zhang--2017|Zhang et al. 2017]] ), sensitivity decreases with increasing income as well as when the cost of the trip was paid by someone else ( [[#Capurso--2019|Capurso et al. 2019]] ). Some research suggests little competition between bus and air travel but the cross-elasticity between air and rail suggest strong interactions ( [[#Wardman--2018|Wardman et al. 2018]] ). Price reduction strategies such as discounted rail fares could enhance the switch from air travel to high-speed rail. Both air fares and flight frequency impact high-speed rail (HSR) usage ( [[#Zhang--2019b|Zhang et al. 2019b]] ). Airline companies reduce fares on routes that are directly competing with HSR (Bergantino et al. 2018a) and charge high fares on non-HSR routes ( [[#Xia--2016|Xia and Zhang 2016]] ). On the Rome to Milan route, better frequency and connections, and low costs of HSR resulting from competition between HSR companies have significantly reduced air travel and shares of buses and cars ( [[#Desmaris--2018|Desmaris and Croccolo 2018]] ).

Finally, and as noted in Chapter 5, recent research shows that individual, social, and infrastructure factors also affect people’s mode choices. For example, perceptions about common travel behaviour (what people perceive to be ‘normal’ behaviour) influence their travel mode choice. The research suggests that well-informed individuals whose personal norms match low-carbon objectives, and who believe they have control over their decisions, are most motivated to shift mode. Nonetheless, such individual and social norms can only marginally influence mode choice unless infrastructure factors can enable reasonable time and cost savings ( [[#Convery--2019|Convery and Williams 2019]] ; [[#Javaid--2020|Javaid et al. 2020]] ; [[#Feng--2020|Feng et al. 2020]] ; [[#Wang--2021|Wang et al. 2021]] ).

'''Improve: consumer preferences for improved and alternative vehicles.''' While reductions in demand for travel and changes in mode choice can contribute to reducing GHG emissions from the transport sector, cars are likely to continue to play a prominent role. As a result, improving the performance of cars will be crucial for the decarbonisation of the transport sector. Sections 10.3 and 10.4 describe the technological options available for reduced CO 2 emissions from vehicles. The effectiveness in deploying such technologies will partly depend on consumer preferences and their effect on adoption rates. Given the expanded availability of electric vehicles, there is also a growing body of work on the drivers of vehicle choice. A survey in Nanjing found women had more diverse travel purposes than men, resulting in a greater acceptance of electric bikes ( [[#Lin--2017|Lin et al. 2017]] ). Individuals are more likely to adopt an electric vehicle (EV) when they think this adoption benefits the environment or implies a positive personal attribute ( [[#Noppers--2014|Noppers et al. 2014]] ; [[#Noppers--2015|Noppers et al. 2015]] ; [[#Haustein--2018|Haustein and Jensen 2018]] ). Other work suggests that people’s preference for EVs depends upon vehicle attributes, infrastructure availability, and policies that promote EV adoption, specifically, purchasing and operating costs, driving range, charging duration, vehicle performance, and brand diversity ( [[#Liao--2016|Liao et al. 2016]] ). Behaviour change to enable transport transformations will need to make the most of these factors while also working on the more structural issues of time, space, and cost.

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<span id="new-demand-concepts"></span>
=== 10.2.3 New Demand Concepts ===

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Structural and behavioural choices that drive transport-related GHG emissions, such as time and cost based on geography of freight and urban fabric, are likely to continue to be major factors. But there is also a variation within each structural choice that is based around personal demand factors related to values that indirectly change choices in transport. [[IPCC:Wg3:Chapter:Chapter-5|Chapter 5]] identified three megatrends that affect demand for services, including circular economy, the shared economy, and digitalisation. These three megatrends can have specific effect on transport emissions, as described below.

'''Circular economy.''' The problem of resources and their environmental impacts is driving the move to a circular economy ( [[#Bleischwitz--2017|Bleischwitz et al. 2017]] ). Circular economy principles include increased material efficiency, reusing or extending product lifetimes, recycling, and green logistics. Dematerialisation, the reduction in the quantity of the materials used in the production of one unit of output, is a circular economy principle that can affect the operations and emissions of the transport sector, as reductions in the quantities of materials used reduce transport needs, while reductions in the weight of products improve the efficiency of transporting them. Dematerialisation can occur through more efficient production processes but also when a new product is developed to provide the same functionality as multiple products. The best example of this trend is a smart phone, which provides the service of at least 22 other former devices ( [[#Rifkin--2019|Rifkin 2019]] ). A move to declutter lifestyles can also drive dematerialisation ( [[#Whitmarsh--2017|Whitmarsh et al. 2017]] ). Some potential for dematerialisation has been suggested due to 3-D printing, which would also reduce transport emissions through localised production of product components ( [[#d’Aveni--2015|d’Aveni 2015]] ; [[#UNCTAD--2018|UNCTAD 2018]] ). There is evidence to suggest, however, that reductions in material use resulting from more efficient product design or manufacturing are offset by increased consumer demand ( [[#Kasulaitis--2019|Kasulaitis et al. 2019]] ). Whether or not dematerialisation can lead to reduction of emissions from the transport sector is still an open question that requires evaluating the entire product ecosystem (Van Loon et al. 2014; [[#Coroama--2015|Coroama et al. 2015]] ; [[#Kasulaitis--2019|Kasulaitis et al. 2019]] ).

'''Shared economy.''' Shared mobility is arguably the most rapidly growing and evolving sector of the sharing economy and includes bike sharing, e-scooter sharing, car sharing, and on-demand mobility ( [[#Greenblatt--2015|Greenblatt and Shaheen 2015]] ). The values of creating a more shared economy are related to both reduced demand and greater efficiency, as well as the notion of community well-being associated with the act of sharing instead of simply owning for oneself ( [[#Maginn--2018|Maginn et al. 2018]] ; [[#Sharp--2018|Sharp 2018]] ). The literature on shared mobility is expanding, but there is much uncertainty about the effect shared mobility will have on transport demand and associated emissions (Nijland and Jordy 2017; [[#ITF--2018a|ITF 2018a]] ; [[#Tikoudis--2021|Tikoudis et al. 2021]] ).

Asia represents the largest car-sharing region with 58% of worldwide membership and 43% of global fleets deployed ( [[#Dhar--2020|Dhar et al. 2020]] ). Europe accounts for 29% of worldwide members and 37% of shared vehicle fleets ( [[#Shaheen--2018|Shaheen et al. 2018]] ). Ride-sourcing and carpooling systems are among the many new entrants in the short-term shared mobility options. On-demand transport options complemented with technology have enhanced the possibility of upscaling ( [[#Alonso-González--2018|Alonso-González et al. 2018]] ). Car sharing could provide the same level of service as taxis, but taxis could be three times more expensive ( [[#Cuevas--2016|Cuevas et al. 2016]] ). The sharing economy, as an emerging economic-technological phenomenon ( [[#Kaplan--2010|Kaplan and Haenlein 2010]] ), is likely to be a key driver of demand for transport of goods although data shows increasing container movement due to online shopping ( [[#Suel--2018|Suel and Polak 2018]] ).

There is growing evidence that this more structured form of behavioural change through shared economy practices, supported by a larger group than a single family, has a much greater potential to save transport emissions, especially when complemented with decarbonised grid electricity ( [[#Greenblatt--2015|Greenblatt and Shaheen 2015]] ; [[#Sharp--2018|Sharp 2018]] ). Carpooling, for example, could result in an 11% reduction in vehicle-kilometres and a 12% reduction in emissions, as carpooling requires less empty or non-productive passenger-kilometres (pkm) ( [[#ITF--2020a|ITF 2020a]] ; [[#ITF--2020b|ITF 2020b]] ). However, the use of local shared mobility systems such as on-demand transport may create more transport emissions if there is an overall modal shift out of transit ( [[#ITF--2018a|ITF 2018a]] ; [[#Schaller--2018|Schaller 2018]] ). Similarly, some work suggests that commercial shared vehicle services such as Uber and Lyft are leading to increased vehicle km travelled (and associated GHG emissions) in part due to deadheading ( [[#Schaller--2018|Schaller 2018]] ; [[#Tirachini--2020|Tirachini and Gomez-Lobo 2020]] ; [[#Ward--2021|Ward et al. 2021]] ). Successful providers compete by optimising personal comfort and convenience rather than enabling a sharing culture ( [[#Eckhardt--2015|Eckhardt and Bardhi 2015]] ), and concerns have been raised regarding the wider societal impacts of these systems and for specific user groups such as older people ( [[#Fitt--2018|Fitt 2018]] ; [[#Marsden--2018|Marsden 2018]] ). Concerns have also been expressed over the financial viability of demand-responsive transport systems ( [[#Ryley--2014|Ryley et al. 2014]] ; [[#Marsden--2018|Marsden 2018]] ), how the mainstreaming of shared mobility systems can be institutionalised equitably, and the operation and governance of existing systems that are only mode- and operator-focused ( [[#Akyelken--2018|Akyelken et al. 2018]] ; [[#Jittrapirom--2018|Jittrapirom et al. 2018]] ; [[#Pangbourne--2020|Pangbourne et al. 2020]] ; [[#Marsden--2018|Marsden 2018]] ).

'''Digitalisation.''' In the context of the transport sector, digitalisation has enabled teleworking, which in turn reduces travel demand. On the other hand, the prevalence of online shopping, enabled by the digital economy, could have mixed effects on transport emissions ( [[#Le--2021|Le et al. 2021]] ). For example, online shopping could reduce vehicle-kilometres travelled but the move to expedited or rush delivery could mitigate some benefits as it prevents consolidation of freight ( [[#Jaller--2020|Jaller and Pahwa 2020]] ).

Digitalisation could also lead to systemic changes by enabling smart mobility. The smart mobility paradigm refers to the process and practices of assimilation of ICTs and other sophisticated high-technology innovations into transport ( [[#Noy--2018|Noy and Givoni 2018]] ). Smart mobility can be used to influence transport demand and efficiency ( [[#Benevolo--2016|Benevolo et al. 2016]] ). The synergies of emerging technologies (ICT, internet of things, big data) and shared economy could overcome some of the challenges facing the adoption of emerging technologies ( [[#Marletto--2014|Marletto 2014]] ; [[#Chen--2016|Chen et al. 2016]] ; [[#Weiss--2018|Weiss et al. 2018]] ; [[#Taiebat--2019|Taiebat and Xu 2019]] ) and enable the expected large growth in emerging cities to be more sustainable ( [[#Docherty--2018|Docherty et al. 2018]] ). However, ICT, in particular the internet of things (IoT), could also cause more global energy demand ( [[#Hittinger--2019|Hittinger and Jaramillo 2019]] ). Box 10.1 summarises the main smart technologies being adopted rapidly by cities across the world and their use in transport. There is a growing body of literature about the effect of smart technology (including sensors guiding vehicles) on the demand for transport services. Smart technologies can improve competitiveness of transit and active transport over personal vehicle use by combining the introduction of new electro-mobility that improves time and cost along with behaviour change factors ( [[#Pålsson--2017|Pålsson et al. 2017]] ; [[#SLoCaT--2018a|SLoCaT 2018a]] ; [[#SLoCaT--2018b|SLoCaT 2018b]] ; SLoCaT2021). However, it is unclear what the net effect of smart technology on GHG emissions from the transport sector will be ( [[#Debnath--2014|Debnath et al. 2014]] ; [[#Lenz--2017|Lenz and Heinrichs 2017]] ).

Autonomous vehicles are the other emerging transport technology that have the potential to significantly improve ride quality and safety. Planes and high-speed trains are already largely autonomous as they are guided in all their movements, especially coming into stations and airports, although that does not necessarily mean they are driverless. Automation is also being used in new on-road transit systems like trackless trams ( [[#Ndlovu--2020|Ndlovu and Newman 2020]] )). Private vehicles are being fitted with more and more levels of autonomy and many are being trialled as ‘driverless’ in cities ( [[#Aria--2016|Aria et al. 2016]] ; [[#Skeete--2018|Skeete 2018]] ). If autonomous systems can be used to help on-road transit become more time- and cost-competitive with cars, then the kind of transformative and disruptive changes needed to assist decarbonisation of transport become more feasible ( [[#Bösch--2018|Bösch et al. 2018]] ; [[#Kassens-Noor--2020|Kassens-Noor et al. 2020]] ; [[#Abe--2021|Abe 2021]] ). Similarly, vehicle automation could improve vehicle efficiency and reduce congestion, which would in turn reduce emissions ( [[#Vahidi--2018|Vahidi and Sciarretta 2018]] ; [[#Massar--2021|Massar et al. 2021]] ). On the other hand, if autonomous cars make driving more convenient, they could reduce demand for transit ( [[#Auld--2017|Auld et al. 2017]] ; [[#Sonnleitner--2021|Sonnleitner et al. 2021]] ). Paradoxically, autonomous cars could provide access to marginal groups such as the elderly, people with disabilities, and those who cannot drive, which could in turn increase travel demand (as measured by pkm) ( [[#Harper--2016|Harper et al. 2016]] ).

Heavy haulage trucks in the mining industry are already autonomous ( [[#Gaber--2021|Gaber et al. 2021]] ) and automation of long-haul trucks may happen sooner than automation of LDVs ( [[#Hancock--2019|Hancock et al. 2019]] ). Autonomous trucks may facilitate route and speed optimisation, and reduce fuel use, which can in turn reduce emissions ( [[#Nasri--2018|Nasri et al. 2018]] ; [[#Paddeu--2021|Paddeu and Denby 2021]] ). There is growing interest in using drones for package delivery. Drones could have lower impacts than ground-based delivery and, if deployed carefully, drones could reduce energy use and GHG emissions from freight transport ( [[#Stolaroff--2018|Stolaroff et al. 2018]] ). Overall, some commentators are optimistic that smart and autonomous technologies can transform the GHG emissions from the transport sector ( [[#Seba--2014|Seba 2014]] ; [[#Rifkin--2019|Rifkin 2019]] ; [[#Sedlmeir--2020|Sedlmeir et al. 2020]] ). Others are more sanguine unless policy interventions can enable the technologies to be used for purposes that include zero carbon and the SDGs ( [[#Faisal--2019|Faisal et al. 2019]] ; [[#Hancock--2019|Hancock et al. 2019]] ).

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=== Box 10.1 | Smart City Technologies and Transport ===

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'''Information and communication technology (ICT).''' ICT is at the core of smart mobility and will provide the avenue for data to be collected and shared across the mobility system. The use of ICT can help cities by providing real-time information on mobility options that can inform those using private vehicles, along with transit users or those using bikes or walking. ICT can help with ticketing and payment for transit or for road user charges ( [[#Tafidis--2017|Tafidis et al. 2017]] ; [[#Gössling--2018|Gössling 2018]] ) when combined with other technologies such as Blockchain ( [[#Hargroves--2020|Hargroves et al. 2020]] ).

'''Internet of Things sensors.''' Sensors can be used to collect data to improve road safety, improve fuel efficiency of vehicles, and reduce CO 2 emissions ( [[#Kubba--2014|Kubba and Jiang 2014]] ; [[#Kavitha--2018|Kavitha et al. 2018]] ). Sensors can also provide data to digitally simulate transport planning options, inform the greater utilisation of existing infrastructure and modal interconnections, and significantly improve disaster and emergency responses ( [[#Hargroves--2017|Hargroves et al. 2017]] ). In particular, IoT sensors can be used to inform the operation of fast-moving trackless trams and their associated last-mile connectivity shuttles as part of a transit activated corridor ( [[#Newman--2019|Newman et al. 2019]] , 2021).

'''Mobility as a Service.''' New, app-based mobility platforms will allow for the integration of different transport modes (such as last-mile travel, shared transit, and even micro-transit such as scooters or bikes) into easy-to-use platforms. By integrating these modes, users will be able to navigate from A to B to C based on which modes are most efficient, with the necessary bookings and payments being made through one service. With smart city planning, these platforms can steer users towards shared and rapid transit (which should be the centrepiece of these systems), rather than encourage more people to opt for the perceived convenience of booking a single-passenger ride ( [[#Becker--2020|Becker et al. 2020]] ). In low-density car-dependent cities, however, MaaS services such as the use of electric scooters/bikes are less effective as the distances are too long and they do not enable the easy sharing that can happen in dense station precincts ( [[#Jittrapirom--2017|Jittrapirom et al. 2017]] ).

'''Artificial intelligence (AI) and big data analytics.''' The rapidly growing level of technology enablement of vehicles and urban infrastructure, combined with the growing ability to analyse larger and larger data sets, presents a significant opportunity for transport planning, design, and operation in the future. These technologies are used together to enable decisions about what kind of transport planning is used down particular corridors. Options such as predictive congestion management of roads and freeways, simulating planning options, and advanced shared transit scheduling can provide value to new and existing transit systems ( [[#Toole--2015|Toole et al. 2015]] ; [[#Anda--2017|Anda et al. 2017]] ; [[#Hargroves--2017|Hargroves et al. 2017]] ).

'''Blockchain or distributed ledger technology.''' Blockchain technology provides a non-hackable database that can be programmed to enable shared services like a local, solar microgrid where both solar and shared electric vehicles can be managed ( [[#Green--2017|Green and Newman 2017]] ). Blockchain can be used for many transport-related applications including being the basis of MaaS or any local shared mobility service as it facilitates shared activity without intermediary controls. Other applications include verified vehicle ownership documentation, establishing identification, real-time road user pricing, congestion zone charging, vehicle-generated collision information, collection of tolls and charges, enhanced freight tracking and authenticity, and automated car parking and payments ( [[#Hargroves--2020|Hargroves et al. 2020]] ). This type of functionality will be particularly valuable for urban regeneration along a transit activated corridor, where it can be used for managing shared solar in and around station precincts as well as managing shared vehicles linked to the whole transport system ( [[#Newman--2021|Newman et al. 2021]] ). This technology can also be used for road user charging along any corridor and by businesses accessing any services and in managing freight ( [[#Carter--2018|Carter and Koh 2018]] ; [[#Nguyen--2019|Nguyen et al. 2019]] ; [[#Hargroves--2020|Hargroves et al. 2020]] ; [[#Sedlmeir--2020|Sedlmeir et al. 2020]] ).

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=== 10.2.4 Overall Perspectives on Systemic Change ===

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The interactions between systemic factors set out here and technology factors discussed in much more detail in the next sections show that there is always going to be a need to integrate both approaches. Good technology that has the potential to transform transport will not be used unless it fulfils broad mobility and accessibility objectives related to time, cost, and well-being. [[IPCC:Wg3:Chapter:Chapter-5|Chapter 5]] has set out three transport transformations based on demand-side factors with highly transformative potential. Table 10.3 provides a summary of these systemic changes and their likely impact on GHG emissions. Note that the quantitative estimates provided in the table may not be additive and the combined effect of these strategies on GHG emissions from the transport sector require additional analysis.

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