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

=== 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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