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

==== 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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