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

=== 5.3.4 Transformative Megatrends ===

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The sharing economy, the circular economy, and digitalisation have all received much attention from the research, advocacy, business models and policy communities as potentially transformative trends for climate change mitigation ( [[#IEA--2017a|IEA 2017a]] ; [[#Material%20Economics--2018|Material Economics 2018]] ; [[#TWI2050--2019|TWI2050 2019]] ). All are essentially emerging and contested concepts ( [[#Gallie--1955|Gallie 1955]] ) that have the common goal of increasing convenience for users and rendering economic systems more resource efficient, but which exhibit variability in the literature on their definitions and system boundaries. Historically, both sharing and circular economies have been commonplace in developing countries, where reuse, repair, and waste scavenging and recycling comprise the core of informal economies facilitated by human interventions ( [[#Wilson--2006|Wilson et al. 2006]] ; [[#Asim--2012|Asim et al. 2012]] ; [[#Pacheco--2012|Pacheco et al. 2012]] ). Digitalisation is now propelling sharing and circular economy concepts in developed and developing countries alike ( [[#Roy--2021|Roy et al. 2021]] ), and the three megatrends are highly interrelated, as seen in Figure 5.11. For example, many sharing economy concepts rely on corporate or, to lesser degree, non-profit digital platforms that enable efficient information and opportunity sharing, thus making it part of the digitalisation trend. Parts of the sharing economy are also included in some circular economy approaches, as shared resource use renders utilisation of material more efficient. Digital approaches to material management also support the circular economy, such as through waste exchanges and industrial symbiosis. Digitalisation aims more broadly to deliver services in more efficient, timely, intelligent, and less resource-intensive ways (i.e., by moving bits and not atoms), through the use of increasingly interconnected physical and digital systems in many facets of economies. With rising digitalisation also comes the risk of increased electricity use to power billions of devices and the internet infrastructure that connects them, as well as growing quantities of e-waste, presenting an important policy agenda for monitoring and balancing the carbon and resource costs and benefits of digitalisation ( [[#Malmodin--2018|Malmodin and Lundén 2018]] ; [[#TWI2050--2019|TWI2050 2019]] ). Rebound effects and instigated consumption of digitalisation are risking to lead to a net increase in GHG emissions ( [[#Belkhir--2018|Belkhir and Elmeligi 2018]] ). The determinants and possible scales of mitigation potentials associated with each megatrend are discussed below.

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[[File:43cbe6bcdcce3a0b60c2d492048f82a4 IPCC_AR6_WGIII_Figure_5_11.png]]

'''Figure 5.11 | The growing nexus between digitalisation, the sharing economy, and the circular economy in service delivery systems.''' While these trends started mostly independently, rapid digitalisation is creating new synergistic opportunities with systemic potential to improve the quality of jobs, particularly in developing economies. Widespread digitalisation may lead to net increases in electricity use, demand for electronics manufacturing resources, and e-waste, all of which must be monitored and managed via targeted policies.

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==== 5.3.4.1 Digitalisation ====

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In the context of service provision, there are numerous opportunities for consumers to buy, subscribe to, adopt, access, install or use digital goods and services ( [[#Wilson--2020b|Wilson et al. 2020b]] ). Digitalisation has opened up new possibilities across all domains of consumer activity, from travel and retail to domestic living and energy use. Digital platforms allow surplus resources to be identified, offered, shared, transacted and exchanged ( [[#Frenken--2017|Frenken 2017]] ). Real-time information flows on consumers’ preferences and needs mean service provision can be personalised, differentiated, automated, and optimised ( [[#TWI2050--2019|TWI2050 2019]] ). Rapid innovation cycles and software upgrades drive continual improvements in performance and responsiveness to consumer behaviour. These characteristics of digitalisation enable new business models and services that affect both service demand, from shared ride-hailing ( [[#ITF--2017a|ITF 2017a]] ) to smart heating ( [[#IEA--2017a|IEA 2017a]] ), and how services are provisioned, from online farmers’ markets ( [[#Richards--2018|Richards and Hamilton 2018]] ) to peer-to-peer electricity trading to enable distributed power systems ( [[#Morstyn--2018|Morstyn et al. 2018]] ).

In many cases, digitalisation provides a ‘radical functionality’ that enables users to do or accomplish something that they could not do before ( [[#Nagy--2016|Nagy et al. 2016]] ). Indeed the consumer appeal of digital innovations varies widely, from choice, convenience, flexibility and control to relational and social benefits ( [[#Pettifor--2020|Pettifor and Wilson 2020]] ). Reviewing over 30 digital goods and services for mobility, food buying and domestic living, [[#Wilson--2020b|Wilson et al. (2020b)]] also found shared elements of appeal across multiple innovations including (i) making use of surplus, (ii) using not owning, (iii) being part of wider networks, and (iv) exerting greater control over service provisioning systems. Digitalisation thus creates a strong value proposition for certain consumer niches. Concurrent diffusion of many digital innovations amplifies their disruptive potential ( [[#Schuelke-Leech--2018|Schuelke-Leech 2018]] ; [[#Wilson--2019b|Wilson et al. 2019b]] ). Besides basic mobile telephone service for communication, digital innovations have been primarily geared to population groups with high purchasing power, and too little to the needs of poor and vulnerable people.

The long-term sustainability implications of digitalised services hinge on four factors: (i) the direct energy demands of connected devices and the digital infrastructures (i.e., data centres and communication networks) that provide necessary computing, storage, and communication services ( [[IPCC:Wg3:Chapter:Chapter-9#9.4|Section 9.4]] .6); (ii) the systems-level energy and resource efficiencies that may be gained through the provision of digital services ( [[#Wilson--2020b|Wilson et al. 2020b]] ); (iii) the resource, material, and waste management requirements of the billions of ICT devices that comprise the world’s digital systems ( [[#Belkhir--2018|Belkhir and Elmeligi 2018]] ; [[#Malmodin--2018|Malmodin and Lundén 2018]] ) and (iv) the magnitude of potential rebound effects or induced energy demands that might unleash unintended and unsustainable demand growth, such as autonomous vehicles inducing more frequent and longer journeys due to reduced travel costs ( [[#Wadud--2016|Wadud et al. 2016]] ). Estimating digitalisation’s direct energy demand has historically been hampered by lack of consistent global data on IT device stocks, their power consumption characteristics, and usage patterns, for both consumer devices and the data centres and communication networks behind them. As a result, quantitative estimates vary widely, with literature values suggesting that consumer devices, data centres, and data networks account for anywhere from 6% to 12% of global electricity use ( [[#Gelenbe--2015|Gelenbe and Caseau 2015]] ; [[#Cook--2017|Cook et al. 2017]] ; [[#Malmodin--2018|Malmodin and Lundén 2018]] ). For example, within the literature on data centres, top-down models that project energy use on the basis of increasing demand for internet services tend to predict rapid global energy use growth, ( [[#Andrae--2015|Andrae and Edler 2015]] ; [[#Belkhir--2018|Belkhir and Elmeligi 2018]] ; [[#Liu--2020a|Liu et al. 2020a]] ), whereas bottom-up models that consider data centre technology stocks and their energy efficiency trends tend to predict slower but still positive growth ( [[#Shehabi--2018|Shehabi et al. 2018]] ; [[#Hintemann--2019|Hintemann and Hinterholzer 2019]] ; [[#Malmodin--2020|Malmodin 2020]] ; [[#Masanet--2020|Masanet et al. 2020]] ). Yet there is growing concern that remaining energy efficiency improvements might be outpaced by rising demand for digital services, particularly as data-intensive technologies such as artificial intelligence, smart and connected energy systems, distributed manufacturing systems, and autonomous vehicles promise to increase demand for data services even further in the future ( [[#TWI2050--2019|TWI2050 2019]] ; [[#Masanet--2020|Masanet et al. 2020]] ; [[#Strubell--2020|Strubell et al. 2020]] ). Rapid digitalisation is also contributing to an expanding e-waste problem, estimated to be the fastest growing domestic waste stream globally (Forti et al. 2020).

As digitalisation proliferates, an important policy objective is therefore to invest in data collection and monitoring systems and energy demand models of digitalised systems to guide technology and policy investment decisions for addressing potential direct energy demand growth ( [[#IEA--2017a|IEA 2017a]] ) and potentially concomitant growth in e-waste.

However, the net systems-level energy and resource efficiencies gained through the provision of digital services could play an important role in dealing with climate change and other environmental challenges ( [[#Masanet--2010|Masanet and Matthews 2010]] ; [[#Melville--2010|Melville 2010]] ; [[#Elliot--2011|Elliot 2011]] ; [[#Watson--2012|Watson et al. 2012]] ; [[#Gholami--2013|Gholami et al. 2013]] ; [[#Añón%20Higón--2017|Añón Higón et al. 2017]] ). As shown in Figure 5.12, assessments of numerous digital service opportunities for mobility, nutrition, shelter, and education and entertainment suggest that net emissions benefits can be delivered at the systems level, although these effects are highly context dependent. Importantly, evidence of potential negative outcomes due to rebound effects, induced demand, or life-cycle trade-offs can also be observed. For example, telework has been shown to reduce emissions where long and/or energy-intensive commutes are avoided, but can lead to net emissions increases in cases where greater non-work vehicle use occurs or only short, low-emissions commutes (e.g., via public transit) are avoided ( [[#Hook--2020|Hook et al. 2020]] ; [[#IEA--2020a|IEA 2020a]] ; [[#Viana%20Cerqueira--2020|Viana Cerqueira et al. 2020]] ). Similarly, substitution of physical media by digital alternatives may lead to emissions increases where greater consumption is fuelled, whereas a shift to 3D printed structures may require more emissions-intensive concrete formulations or result in reduced thermal energy efficiency, leading to life-cycle emissions increases ( [[#Mahadevan--2020|Mahadevan et al. 2020]] ; [[#Yao--2020|Yao et al. 2020]] ).

Furthermore, digitalisation, automation and artificial intelligence, as general-purpose technologies, may lead to a plethora of new products and applications that are likely to be efficient on their own but that may also lead to undesirable changes or absolute increases in demand for products (Figure 5.12). For example, last-mile delivery in logistics is both expensive and cumbersome. Battery-powered drones enable a delivery of goods at similar lifecycle emissions to delivery vans ( [[#Stolaroff--2018|Stolaroff et al. 2018]] ). At the same time, drone delivery is cheaper in terms of time (immediate delivery) and monetary costs (automation saves the highest-cost component: personnel) ( [[#Sudbury--2016|Sudbury and Hutchinson 2016]] ). As a result, demand for package delivery may increase rapidly. Similarly, automated vehicles reduce the costs of time, parking, and personnel, and therefore may dramatically increase vehicle mileage ( [[#Wadud--2016|Wadud et al. 2016]] ; [[#Cohen--2019|Cohen and Cavoli 2019]] ). On-demand electric scooters offer mobility access preferable to passenger cars, but can replace trips otherwise taken on public transit ( [[#de%20Bortoli--2020|de Bortoli and Christoforou 2020]] ) and can come with significant additional energy requirements for night-time system rebalancing ( [[#Hollingsworth--2019|Hollingsworth et al. 2019]] ; ITF 2020). The energy requirements of cryptocurrencies is also a growing concern, although considerable uncertainty exists surrounding the energy use of their underlying blockchain infrastructure ( [[#Vranken--2017|Vranken 2017]] ; [[#de%20Vries--2018|de Vries 2018]] ; [[#Stoll--2019|Stoll et al. 2019]] ). For example, while it is clear that the energy requirements of global Bitcoin mining have grown significantly since 2017, recent literature indicates a wide range of estimates for 2020 (47 TWh to 125 TWh) due to data gaps and differences in modelling approaches ( [[#Lei--2021|Lei et al. 2021]] ). Initial estimates of the computational intensity of artificial intelligence algorithms suggest that energy requirements may be enormous without concerted effort to improve efficiencies, especially on the computational side ( [[#Strubell--2020|Strubell et al. 2020]] ). Efficiency gains enabled by digitalisation, in terms of reduced GHG emissions or energy use per service unit, may be overcompensated by activity/scale effects.

[[File:f91c2c3949403e1b0e7bebcaae4116d6 IPCC_AR6_WGIII_Figure_5_12.png]]

'''Figure 5.12''' | '''Studies assessing net changes in CO''' 2 '''emissions, energy use, and activity levels indicate mitigation potentials for numerous end-user-oriented digitalisation solutions, but also risk of increased emissions due to inefficient substitutions, induced demand, and rebound effects.''' 90 studies were assessed with 207 observations (indicated by vertical bars) including those based on empirical research, attributional and consequential lifecycle assessments, and techno-economic analyses and scenarios at different scales, which are not directly comparable but are useful for indicating the directionality and determinants of net emissions, energy, and activity effects. Sources: [[#Erdmann--2010|Erdmann and Hilty (2010)]] ; [[#Gebler--2014|Gebler et al. (2014)]] ; [[#Huang--2016|Huang et al. (2016)]] ; [[#Verhoef--2018|Verhoef et al. (2018)]] ; [[#Alhumayani--2020|Alhumayani et al. (2020)]] ; [[#Court--2020|Court and Sorrell (2020)]] ; [[#Hook--2020|Hook et al. (2020)]] ; [[#IEA--2020a|IEA (2020a)]] ; [[#Saade--2020|Saade et al. (2020)]] ; [[#Torres-Carrillo--2020|Torres-Carrillo et al. (2020)]] ; [[#Wilson--2020c|Wilson et al. (2020c)]] ; [[#Yao--2020|Yao et al. (2020)]] ; [[#Muñoz--2021|Muñoz et al. (2021)]] .

Maximising the mitigation potential of digitalisation trends involves diligent monitoring and proactive management of both direct and indirect demand effects, to ensure that a proper balance is maintained. Direct energy demand can be managed through continued investments in, and incentives for, energy-efficient data centres, networks, and end-use devices ( [[#Masanet--2011|Masanet et al. 2011]] ; [[#Avgerinou--2017|Avgerinou et al. 2017]] ; [[#IEA--2017a|IEA 2017a]] ; [[#Koronen--2020|Koronen et al. 2020]] ). Shifts to low-carbon power are a particularly important strategy being undertaken by data centre and network operators ( [[#Cook--2014|Cook et al. 2014]] ; [[#Huang--2020|Huang et al. 2020]] ), which might be adopted across the digital device spectrum as a proactive mitigation strategy where data demands outpace hardware efficiency gains, which may be approaching limits in the near future ( [[#Koomey--2011|Koomey et al. 2011]] ). Most recently, data centres are being investigated as a potential resource for demand response and load balancing in renewable power grids ( [[#Koronen--2020|Koronen et al. 2020]] ; [[#Zheng--2020|Zheng et al. 2020]] ), while a large bandwidth for improving software efficiency has been suggested for overcoming slowing hardware efficiency gains ( [[#Leiserson--2020|Leiserson et al. 2020]] ). Ensuring efficiency benefits of digital services while avoiding potential rebound effects and demand surges will require early and proactive public policies to avoid excess energy use ( [[#TWI2050--2019|TWI2050 2019]] ; [[#WBGU--2019|WBGU 2019]] ), which will also necessitate investments in data collection and monitoring systems to ensure that net mitigation benefits are realised and that unintended consequences can be identified early and properly managed ( [[#IEA--2017a|IEA 2017a]] ).

Within a small but growing body of literature on the net effects of digitalisation, there is ''medium evidence'' that digitalised consumer services can reduce overall emissions, energy use, and activity levels, with ''medium agreement'' on the scale of potential savings, with the important caveat that induced demand and rebound effects must be managed carefully to avoid negative outcomes.

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==== 5.3.4.2 The Sharing Economy ====

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Opportunities to increase service per product include peer-to-peer based sharing of goods and services such as housing, mobility, and tools. Hence, consumable products become durable goods delivering a ‘product service’, which potentially could provide the same level of service with fewer products ( [[#Fischedick--2014|Fischedick et al. 2014]] ).The sharing economy is an old practice of sharing assets between many without transferring ownership, which has been made new through focuses on sharing underutilised products and assets in ways that promote flexibility and convenience, often in a highly developed context via gig economy or online platforms. However, the sharing economy offers the potential to shift from ‘asset-heavy’ ownership to ‘asset-light’ access, especially in developing countries ( [[#Retamal--2019|Retamal 2019]] ). General conclusions on the sharing economy as a framework for climate change mitigation are challenging and are better broken down to specific subsystems ( [[#Mi--2019|Mi and Coffman 2019]] ) ( [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I, 5.SM.4.3).

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===== 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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===== Shared accommodation =====

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In developing countries and in many student accommodations globally, shared accommodation allows affordable housing for a large part of the population. For example, living arrangements are built expressly around the practice of sharing toilets, bathrooms and kitchens. While the sharing of such facilities does connote a lower level of service provision and quality of life, it provides access for a consumer base with very low and unreliable incomes. Thus, sharing key facilities can help guarantee the provision of affordable housing ( [[#Gulyani--2018|Gulyani et al. 2018]] ). In developed countries, large-scale developments are targeting students and ‘young professionals’ by offering shared accommodation and services. Historically shared accommodation has been part of the student life due to its flexible and affordable characteristics. However, the expansion of housing supply through densification can use shared facilities as an instrument to ‘commercialize small housing production, while housing affordability and accessibility are threatened’ ( [[#Uyttebrouck--2020|Uyttebrouck et al. 2020]] ).

With respect to travel accommodation, several models are emerging in which accommodation is offered to, or shared with, travellers by private individuals organised by business-driven or non-profit online platforms. Accommodation sharing includes peer-to-peer, ICT-enabled, short-term renting, swapping, borrowing or lending of existing privately-owned lodging facilities ( [[#Möhlmann--2015|Möhlmann 2015]] ; [[#Voytenko%20Palgan--2017|Voytenko Palgan et al. 2017]] ).

With shared accommodation services via the platform economy, there may be risks of negative sustainability effects, such as rebound effects caused by increased travel frequency ( [[#Tussyadiah--2016|Tussyadiah and Pesonen 2016]] ). This is particularly a problem if apartments are removed from long-term rental markets, thus indirectly inducing construction activities, with substantial GHG emissions of their own. However, if a host shares their accommodation with a guest, the use of some resources, such as heating and lighting, is shared, thereby leading to more efficient resource use per capita ( [[#Chenoweth--2009|Chenoweth 2009]] ; [[#Voytenko%20Palgan--2017|Voytenko Palgan et al. 2017]] ). Given the nascence of shared accommodation via the platform economy, quantifications of its systems-level energy and emissions impacts are lacking in the literature, representing an important area for future study.

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===== Mitigation potentials of sharing economy strategies =====

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Sharing economy initiatives play a central role in enabling individuals to share underutilised products. While the literature on the net effects of sharing economy strategies is still limited, available studies have presented different mitigation potentials to date, as shown in Figure 5.13. For many sharing economy strategies, there is a risk of negative rebound and induced demand effects, which may occur by changing consuming patterns, for example if savings from sharing housing are used to finance air travel. Thus, the mitigation potentials of sharing economy strategies will depend on stringent public policy and consumer awareness that reins in runaway consumption effects. Shared economy solutions generally relate to the ‘Avoid’ and ‘Shift’ strategies (Sections 5.1 and 5.3.2). On the one hand, they hold potential for providing similar or improved services for well-being (mobility, shelter) at reduced energy and resource input, with the proper policy signals and consumer responses. On the other hand, shared economy strategies may increase emissions, for example shared mobility may shift activity away from public transit and lead to lower vehicle occupancy, deadheading, and use of inefficient shared vehicles ( [[#Jones--2019|Jones and Leibowicz 2019]] ; [[#Merlin--2019|Merlin 2019]] ; [[#Bonilla-Alicea--2020|Bonilla-Alicea et al. 2020]] ; [[#Ward--2021|Ward et al. 2021]] ). Similarly to digitalisation, there is ''medium evidence'' that the sharing economy can reduce overall emissions, energy use, and activity levels, with ''medium agreement'' on the scale of potential savings if induced demand and rebound effects can be carefully managed to avoid negative outcomes.

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===== The circular economy =====

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While the demand for energy and materials will increase until 2060 following the traditional linear model of production and consumption, resulting in serious environmental consequences ( [[#OECD--2019b|OECD 2019b]] ), the circular economy (CE) provides strategies for reducing societal needs for energy and primary materials to deliver the same level of service with lower environmental impacts. The CE framework embodies multiple schools of thought with roots in a number of related concepts ( [[#Blomsma--2017|Blomsma and Brennan 2017]] ; [[#Murray--2017|Murray et al. 2017]] ), including cradle to cradle ( [[#McDonough--2002|McDonough and Braungart 2002]] ), performance economy ( [[#Stahel--2016|Stahel 2016]] ), biomimicry ( [[#Benyus--1997|Benyus 1997]] ), green economy ( [[#Loiseau--2016|Loiseau et al. 2016]] ) and industrial ecology ( [[#Saavedra--2018|Saavedra et al. 2018]] ). As a result, there are also many definitions of CE: a systematic literature review identified 114 different definitions ( [[#Kirchherr--2017|Kirchherr et al. 2017]] ). One of the most comprehensive models is suggested by the Netherlands Environmental Assessment Agency ( [[#Potting--2018|Potting et al. 2018]] ), which defines ten strategies for circularity: Refuse (R0), Rethink (R1), Reduce (R2), Reuse (R3), Repair (R4), Refurbish (R5), Remanufacture (R6), Repurpose (R7), Recycle (R8), and Recover energy (R9). Overall, the definition of CE is contested, with varying boundary conditions chosen. As illustrated in Figure 5.11, the CE overlaps with both the sharing economy and digitalisation megatrends.

In line with the principles of SDG 12 (responsible consumption and production), the essence of building a CE is to retain as much value as possible from products and components when they reach the end of their useful life in a given application ( [[#Lewandowski--2016|Lewandowski 2016]] ; [[#Lieder--2016|Lieder and Rashid 2016]] ; [[#Stahel--2016|Stahel 2016]] ; [[#Linder--2017|Linder and Williander 2017]] ). This requires an integrated approach during the design phase that, for example, extends product usage and ensures recyclability after use (de Coninck et al. 2018). While traditional ‘Improve’ strategies tend to focus on direct energy and carbon efficiency, service-oriented strategies focus on reducing lifecycle emissions through harnessing the leverage effect ( [[#Creutzig--2018|Creutzig et al. 2018]] ). The development of closed-loop models in service-oriented businesses can increase resource and energy efficiency, reducing emissions and contributing to climate change mitigation goals at national, regional, and global levels ( [[#Johannsdottir--2014|Johannsdottir 2014]] ; [[#Korhonen--2018|Korhonen et al. 2018]] ). Key examples include remanufacturing of consumer products to extend lifespans while maintaining adequate service levels ( [[#Klausner--1998|Klausner et al. 1998]] ), reuse of building components to reduce demand for primary materials and construction processes ( [[#Shanks--2019|Shanks et al. 2019]] ), and improved recycling to reduce upstream resource pressures ( [[#IEA--2019b|IEA 2019b]] ; [[#IEA--2017b|IEA 2017b]] ).

Among the many schools of thought on the CE and climate change mitigation, two different trends can be distinguished from the literature to date. First, there are publications, many of them not peer-reviewed, that eulogise the perceived benefits of the CE, but in many cases stop short of providing a quantitative assessment. Promotion of CE from this perspective has been criticised as a greenwashing attempt by industry to avoid serious regulation ( [[#Isenhour--2019|Isenhour 2019]] ). Second, there are more methodologically rigorous publications, mostly originating in the industrial ecology field, but sometimes investigating only limited aspects of the CE ( [[#Bocken--2017|Bocken et al. 2017]] ; [[#Cullen--2017|Cullen 2017]] ; [[#Goldberg--2017|Goldberg 2017]] ). Conclusions on CE’s mitigation potential also differ, with diverging definitions of the CE. A systematic review identified 3,244 peer-reviewed articles addressing CE and climate change, but only 10% of those provide insights on how the CE can support mitigation, and most of them found only small potentials to reduce GHG emissions ( [[#Cantzler--2020|Cantzler et al. 2020]] ). Recycling is the CE category most investigated, while reuse and reduce strategies have seen comparatively less attention ( [[#Cantzler--2020|Cantzler et al. 2020]] ). However, mitigation potentials were also context- and material-specific, as illustrated by the ranges shown in Figure 5.13a.

There are three key concerns relating to the effectiveness of the CE concept. First, many proposals on the CE insufficiently reflect on thermodynamic constraints that limit the potential of recycling from both mass conservation and material quality perspectives or ignore the considerable amount of energy needed to reuse materials ( [[#Cullen--2017|Cullen 2017]] ). Second, demand for materials and resources will likely outpace efficiency gains in supply chains, becoming a key driver of GHG emissions and other environmental problems, rendering the CE alone an insufficient strategy to reduce emissions ( [[#Bengtsson--2018|Bengtsson et al. 2018]] ). In fact, the empirical literature points out that only 6.5% of all processed materials (4 Gt yr –1 ) globally originate from recycled sources ( [[#Haas--2015|Haas et al. 2015]] ). The low degree of circularity is explained by the high proportion of processed materials (44%) used to provide energy, thus not available for recycling; and the high rate of net additions to stocks of 17 Gt yr –1 . As long as long-lived material stocks (e.g., in buildings and infrastructure) continue to grow, strategies targeting end-of-pipe materials cannot keep pace with primary materials demand ( [[#Krausmann--2017|Krausmann et al. 2017]] ; [[#Haas--2020|Haas et al. 2020]] ). Instead, a significant reduction of societal stock growth, and decisive eco-design, are suggested to advance the CE ( [[#Haas--2015|Haas et al. 2015]] ). Third, cost-effectiveness underlying CE activities may concurrently also increase energy intensity and reduce labour intensity, causing systematically undesirable effects. To a large extent, the distribution of costs and beneﬁts of material and energy use depend on institutions in order to include demand-side solutions. Thus, institutional conditions have an essential role to play in setting rules differentiating profitable from nonprofitable activities in CE ( [[#Moreau--2017|Moreau et al. 2017]] ). Moreover, the prevalence of CE practices such as reuse, refurbishment, and recycling can differ substantially between developed and developing economies, leading to highly context-specific mitigation potentials and policy approaches ( [[#McDowall--2017|McDowall et al. 2017]] ).

One report estimates that the CE can contribute to more than 6 GtCO 2 emission reductions in 2030, including strategies such as material substitution in buildings ( [[#Blok--2016|Blok et al. 2016]] ). Reform of the tax system towards GHG emissions and the extraction of raw materials substituting taxes on labour is a key precondition to achieve such a potential. Otherwise, rebound effects tend to take back a high share of marginal CE efforts. A 50% reduction of GHG emissions in industrial processes, including the production of goods in steel, cement, plastic, paper, and aluminium, from 2010 until 2050, is impossible to attain only with reuse and radical product innovation strategies, but will need to also rely on the reduction of primary input ( [[#Allwood--2010|Allwood et al. 2010]] ).

CE strategies generally correspond to the ‘Avoid’ strategy for primary materials (Sections 5.1 and 5.3.2). CE strategies in industrial settings improve well-being mostly indirectly, via the reduction of environmental harm and climate impact. They can also save monetary resources of consumers by reducing the need for consumption. It may seem counterintuitive, but reducing consumers’ need to consume a particular product or service (e.g., reducing energy consumption) may increase consumption of another product or service (e.g., travel) associated with some type of energy use, or lead to greater consumption if additional secondary markets are created. Hence, carbon emissions could rise if the rebound effect is not considered ( [[#Chitnis--2013|Chitnis et al. 2013]] ; [[#Zink--2017|Zink and Geyer 2017]] ).

Looking at ‘Shift’ strategies (Sections 5.1 and 5.3.2), the role of individuals as consumers and users has received less attention than other aspects of the CE (e.g., technological interventions as ‘Improve’ strategies and waste minimisation as ‘Avoid’ strategies) within mainstream debates to date. One explanation is that CE has roots in the field of industrial ecology, which has historically emphasised materials systems more than the end user. By shifting this perspective from the supply side to the demand side in the CE, users are, for the most part, discussed as social entities that now must form new relations with businesses to meet their needs. That is, the demand-side approach largely replaces the concept of a consumer with that of a user, who must either accept or reject new business models for service provision, stimulated by the pushes and pulls of prices and performance ( [[#Hobson--2019|Hobson 2019]] ).

Relevant contributions to climate change mitigation at gigatonne scale by the CE will remain out of scope if decision-makers and industry fail to reduce primary inputs ( ''high confidence'' ). Systemic (consequential) analysis is required to avoid the risk that scaling effects negate efficiency gains; such analysis is however rarely applied to date. For example, material substitution or refurbishment of buildings brings risk of increasing emissions despite improving or avoiding current materials ( [[#Castro--2019|Castro and Pasanen 2019]] ; [[#Eberhardt--2019|Eberhardt et al. 2019]] ). Besides, CE concepts that extend the lifetime of products and increase the fraction of recycling are useful but are both thermodynamically limited and will remain relatively small in scale as long as demand for primary materials continues to grow, and scale effects dominate. In spite of presenting a large body of literature on CE in general, only a small but growing body of literature exists on the net effects of its strategies from a quantitative perspective, with key knowledge gaps remaining on specific CE strategies. There is ''medium evidence'' that the CE can reduce overall emissions, energy use, and activity levels, with ''medium evidence'' that the sharing economy can reduce overall emissions, energy use, and activity levels, with ''medium agreement'' on the scale of potential savings.

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