Editing IPCC:AR6/WGIII/Chapter-5 (section)

===== Shared mobility =====

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Shared mobility is characterised by the sharing of an asset (e.g., a bicycle, e-scooter, vehicle), and the use of technology (i.e., apps and the Internet) to connect users and providers. It succeeded by identifying market inefficiencies and transferring control over transactions to consumers. Even though most shared mobility providers operate privately, their services can be considered as part of a public transport system in so far as it is accessible to most transport users and does not require private asset ownership. Shared mobility reduces GHG emissions if it substitutes for more GHG-intensive travel (usually private car travel) ( [[#Martin--2011|Martin and Shaheen 2011]] ; [[#Shaheen--2016|Shaheen and Chan 2016]] ; Santos 2018; [[#Axsen--2019|Axsen and Sovacool 2019]] ; [[#Shaheen--2019|Shaheen and Cohen 2019]] ), and especially if it changes consumer behaviour in the long run ‘by shifting personal transportation choices from ownership to demand-fulfilment’ ( [[#Mi--2019|Mi and Coffman 2019]] ).

Demand is an important driver for energy use and emissions because decreased cost of travel time by sharing an asset (e.g., a vehicle) could lead to an increase in emissions, but a high level of vehicle sharing could reduce negative impacts associated with this ( [[#Brown--2019|Brown and Dodder 2019]] ). One example is the megacity Kolkata, India, which has as many as twelve different modes of public transportation that co-exist and offer means of mobility to its 14 million citizens (Box 5.8). Most public transport modes are shared mobility options ranging from sharing between two people in a rickshaw or between a few hundred in metro or suburban trains. Sharing also happens informally as daily commuters avail shared taxis and neighbours borrow each other’s car or bicycle for urgent or day trips.

Shared mobility using private vehicle assets is categorised into four models (Santos 2018): peer-to-peer platforms where individuals can rent the vehicle when not in use ( [[#Ballús-Armet--2014|Ballús-Armet et al. 2014]] ); short-term rental managed and owned by a provider ( [[#Enoch--2006|Enoch and Taylor 2006]] ; [[#Schaefers--2016|Schaefers et al. 2016]] ; [[#Bardhi--2012|Bardhi and Eckhardt 2012]] ); Uber-like ridehailing services ( [[#Wallsten--2015|Wallsten 2015]] ; [[#Angrist--2017|Angrist et al. 2017]] ); and ride pooling using private vehicles shared by passengers to a common destination ( [[#Liyanage--2019|Liyanage et al. 2019]] ; [[#Shaheen--2019|Shaheen and Cohen 2019]] ). The latest model – ride pooling – is promising in terms of congestion and per capita CO 2 emissions reductions and is a common practice in developing countries, however it is challenging in terms of waiting and travel time, comfort, and convenience, relative to private cars (Santos 2018; [[#Shaheen--2019|Shaheen and Cohen 2019]] ). The other three models often yield profits to private parties, but remain mostly unrelated to reduction in CO 2 emissions (Santos 2018). Shared travel models, especially Uber-like models, are criticised because of the flexibilisation of labour, especially in developing countries, in which unemployment rates and unregulated labour markets lay a foundation of precarity that lead many workers to seek out wide-ranging means towards patching together a living ( [[#Ettlinger--2017|Ettlinger 2017]] ; [[#Wells--2020|Wells et al. 2020]] ). Despite the advantages of shared mobility, such as convenience and affordability, consumers may also perceive risk formed by possible physical injury from strangers or unexpected poor service quality ( [[#Hong--2019|Hong et al. 2019]] ).

From a mitigation perspective, the current state of shared mobility looks at best questionable ( [[#Fishman--2014|Fishman et al. 2014]] ; [[#Ricci--2015|Ricci 2015]] ; [[#Martin--2016|Martin 2016]] ; [[#Zhang--2018|Zhang and Mi 2018]] ; [[#Creutzig--2019b|Creutzig et al. 2019b]] ; [[#Mi--2019|Mi and Coffman 2019]] ; [[#Zhang--2019|Zhang et al. 2019]] ). Transport entrepreneurs and government officials often conflate ‘smart’ and ‘shared’ vehicles with ‘sustainable’ mobility, a conflation not withstanding scrutiny (Noy and Givoni 2018). Surveys demonstrate that many users take free-floating car sharing instead of public transit, rather than to replace their private car ( [[#Herrmann--2014|Herrmann et al. 2014]] ); while in the United States, ride-hailing and sharing data indicate that these services have increased road congestion and lowered transit ridership, with an insignificant change in vehicle ownership, and may further lead to net increases in energy use and CO 2 emissions due to deadheading ( [[#Diao--2021|Diao et al. 2021]] ; [[#Ward--2021|Ward et al. 2021]] ). If substitution effects and deadheading, which is the practice of allowing employees of a common carrier to use a vehicle as a non-revenue passenger, are accounted for, flexible motor-cycle sharing in Djakarta, Indonesia, is at best neutral to overall GHG emissions ( [[#Suatmadi--2019|Suatmadi et al. 2019]] ). Passenger surveys conducted in Denver, Colorado, US, indicated that around 22% of all trips travelled with Uber and Lyft would have been travelled by transit, 12% would have walked or biked, and another 12% of passengers would not have travelled at all ( [[#Henao--2019|Henao and Marshall 2019]] ).

Positive effects can be realised directly in bike sharing due to its very low marginal transport emissions. For example, in 2016, bike sharing in Shanghai, China, reduced CO 2 emissions by 25 ktCO 2 , with additional benefits to air quality ( [[#Zhang--2018|Zhang and Mi 2018]] ). However, bike-sharing can also increase emissions from motor vehicle usage when inventory management is not optimised during maintenance, collection, and redistribution of dock-less bikes ( [[#Fishman--2014|Fishman et al. 2014]] ; [[#Zhang--2019|Zhang et al. 2019]] ; [[#Mi--2019|Mi and Coffman 2019]] ).

Shared mobility scenarios demonstrate that GHG emission reduction can be substantial when mobility systems and digitalisation are regulated. One study modelled that ride pooling with electric cars (6 to 16 seats), which shifts the service to a more efficient transport mode, improves its carbon intensity by cutting GHG emissions by one-third ( [[#International%20Transport%20Forum--2016|International Transport Forum 2016]] ). Another study found that shared autonomous taxis had the potential to reduce per-mile GHG emissions to 63–82% below those of projected hybrid vehicles in 2030, 87% to 94% lower than a privately owned, gasoline-powered vehicle in 2014 ( [[#Greenblatt--2015|Greenblatt and Saxena 2015]] ). This also realises 95% reduction in space required for public parking; and total vehicle kilometres travelled would be 37% lower than the present day, although each vehicle would travel ten times the total distance of current vehicles ( [[#International%20Transport%20Forum--2016|International Transport Forum 2016]] ). Studies of Berlin, Germany, and Lisbon, Portugal, demonstrate that sharing strategies could reduce the number of cars by more than 90%, also saving valuable street space for human-scale activity ( [[#Bischoff--2016|Bischoff and Maciejewski 2016]] ; [[#Martinez--2017|Martinez and Viegas 2017]] ; [[#Creutzig--2019b|Creutzig et al. 2019b]] ). The impacts will depend on sharing levels – concurrent or sequential – and the future modal split among public transit, automated electric vehicles fleets, and shared or pooled rides. Evidence from attributional lifecycle assessments (LCAs) of ride-hailing, whether Uber-like or by taxi, suggests that the key determinants of net emissions effects are average vehicle occupancy and vehicle powertrain, with high-occupancy and electric drivetrain cars delivering the greatest emissions benefits, even rivalling traditional metro/urban rail and bus options (Figure 5.13b). It is possible that shared automated electric vehicle fleets could become widely used without many shared rides, and single- or even zero-occupant vehicles will continue to be the majority of vehicle trips. It is also feasible that shared rides could become more common, if automation makes route deviation more efficient, more cost effective, and more convenient, increasing total travel substantially ( [[#Wadud--2016|Wadud et al. 2016]] ). Car sharing with automated vehicles could even worsen congestion and emissions by generating additional travel demand ( [[#Rubin--2016|Rubin et al. 2016]] ). Travel time in autonomous vehicles can be used for other activities but driving and travel costs are expected to decrease, which most likely will induce additional demand for auto travel ( [[#Moeckel--2017|Moeckel and Lewis 2017]] ) and could even create incentives for further urban sprawl. More generally, increased efficiency generated by big data and smart algorithms may generate rebound effects in demand and potentially compromise the public benefits of their efficiency promise ( [[#Gossart--2015|Gossart 2015]] ).

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'''Figure 5.13 | (a) Published estimates from 72 studies with 185 observations (indicated by vertical bars) of the relative mitigation potential of different shared and circular economy strategies, demonstrating limited observations for many emerging strategies, a wide variance in estimated benefits for most strategies, and within the sharing economy, risk of increased emissions due to inefficient substitutions, induced demand, and rebound effects.''' Mitigation potentials are conditional on corresponding public policy and/or regulation. '''(b)''' Attributional LCA comparisons of ridesharing mobility options, which highlight the large effects of vehicle occupancy and vehicle technology on total CO 2 emissions per passenger-km and the preferability of high-occupancy and non-ICE configurations for emissions reductions compared to private cars. Also indicated are possible emissions increases associated with shared car mobility when it substitutes for non-motorised and public transit options. BEV = battery electric vehicle; FCEV = fuel cell electric vehicle; HEV = hybrid electric vehicle; ICE = internal combustion engine; PHEV = plug-in hybrid electric vehicle. Sources: data from [[#Jacobson--2009|Jacobson and King (2009)]] ; [[#Firnkorn--2011|Firnkorn and Müller (2011)]] ; [[#Baptista--2014|Baptista et al. (2014)]] ; [[#Liu--2014|Liu et al. (2014)]] ; [[#Namazu--2015|Namazu and Dowlatabadi (2015)]] ; [[#Nijland--2015|Nijland et al. (2015)]] ; [[#IEA--2016|IEA (2016)]] ; [[#Koh--2016|Koh (2016)]] ; [[#Martin--2016|Martin and Shaheen (2016)]] ; [[#Ghosh--2016|Rabbitt and Ghosh (2016)]] ; [[#Bruck--2017|Bruck et al. (2017)]] ; [[#Bullock--2017|Bullock et al. (2017)]] ; [[#Clewlow--2017|Clewlow and Mishra (2017)]] ; [[#Fremstad--2017|Fremstad (2017)]] ; ITF (2017a,b,c); [[#Nasir--2017|Nasir et al. (2017)]] ; [[#Nijland--2017|Nijland and van Meerkerk (2017)]] ; [[#Rademaekers--2017|Rademaekers et al. (2017)]] ; [[#Skjelvik--2017|Skjelvik et al. (2017)]] ; [[#Yin--2017|Yin et al. (2017)]] ; [[#Campbell--2018|Campbell (2018)]] ; [[#Favier--2018|Favier et al. (2018)]] ; [[#Ghisellini--2018|Ghisellini et al. (2018)]] ; [[#Hopkinson--2018|Hopkinson et al. (2018)]] ; [[#IEA--2018|IEA (2018)]] ; [[#ITF--2018|ITF (2018)]] ; [[#Lokhandwala--2018|Lokhandwala and Cai (2018)]] ; [[#Makov--2018|Makov and Font Vivanco (2018)]] ; [[#Malmqvist--2018|Malmqvist et al. (2018)]] ; [[#Material%20Economics--2018|Material Economics (2018)]] ; [[#Nasr--2018|Nasr et al. (2018)]] ; [[#Yu--2018|Yu et al. (2018)]] ; [[#Zhang--2018|Zhang and Mi (2018)]] ; [[#Brambilla--2019|Brambilla et al. (2019)]] ; [[#Brütting--2019|Brütting et al. (2019)]] ; [[#Buyle--2019|Buyle et al. (2019)]] ; [[#Castro--2019|Castro and Pasanen (2019)]] ; [[#Coulombel--2019|Coulombel et al. (2019)]] ; [[#Eberhardt--2019|Eberhardt et al. (2019)]] ; [[#IEA--2019b|IEA (2019b)]] ; [[#ITF--2019|ITF (2019)]] ; [[#Jones--2019|Jones and Leibowicz (2019)]] ; [[#Ludmann--2019|Ludmann (2019)]] ; [[#Merlin--2019|Merlin (2019)]] ; [[#Nußholz--2019|Nußholz et al. (2019)]] ; [[#Bonilla-Alicea--2020|Bonilla-Alicea et al. (2020)]] ; [[#Cantzler--2020|Cantzler et al. (2020)]] ; [[#Churkina--2020|Churkina et al. (2020)]] ; [[#Gallego-Schmid--2020|Gallego-Schmid et al. (2020)]] ; [[#Hertwich--2020|Hertwich et al. (2020)]] ; ITF (2020a,b); [[#Liang--2020|Liang et al. (2020)]] ; [[#Miller--2020|Miller (2020)]] ; [[#Wilson--2020c|Wilson et al. (2020c)]] ; [[#Yan--2020|Yan et al. (2020)]] ; [[#Cordella--2021|Cordella et al. (2021)]] ; [[#Diao--2021|Diao et al. (2021)]] ; [[#Pauliuk--2021|Pauliuk et al. (2021)]] ; [[#Ward--2021|Ward et al. (2021)]] ; [[#Wolfram--2021|Wolfram et al. (2021)]] .

In many countries, shared mobility and ride pooling are often the norm. Here the challenge is to improve service quality to keep users in shared mobility and public transport (Box 5.8). A key barrier in cities like Nairobi, Kenya, is the lack of public involvement of users and sustainability experts in designing transport systems, leaving planning to transport engineers, and thus preventing inclusive shared mobility system design ( [[#Klopp--2012|Klopp 2012]] ).

Altogether, travel behaviour, business models, and especially public policy will be key components in determining how impacts of pooling and shared automated electric vehicles unfold ( [[#Shaheen--2019|Shaheen and Cohen 2019]] ). Urban-scale governance of smart mobility holds potential for prioritising public transit and the use of public spaces for human activities, managing the data as a digital sustainable commons (e.g., via the installation of a Central Information Officer, as in Tel Aviv, Israel), and managing the social and environmental risks of smart mobility to realise its benefits ( [[#Creutzig--2019b|Creutzig et al. 2019b]] ). Pricing of energy use and GHG emissions will be helpful to achieve these goals. The governance of shared mobility is complicated, as it involves many actors, and is key to realising wider benefits of shared mobility ( [[#Akyelken--2018|Akyelken et al. 2018]] ). New actors, networks and technologies enabling shared mobility are already fundamentally challenging how transport is governed worldwide. This is not a debate about state versus non-state actors but instead about the role the state takes within these new networks to steer, facilitate, and also reject different elements of the mobility system ( [[#Docherty--2018|Docherty et al. 2018]] ).

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