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

== 5.3 Mapping the Opportunity Space ==

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Reducing global energy demand and resource inputs while improving well-being for all requires an identification of options, services and pathways that do not compromise essentials of a decent living. To identify such a solution space, this section summarises socio-cultural, technological and infrastructural interventions through the Avoid-Shift-Improve concept. ASI ( [[#5.1|Section 5.1]] ) provides a categorisation of options aimed at continuously eliminating waste in the current systems of service provision ( [[#5.3.1.1|Section 5.3.1.1]] ). It also concisely presents demand-side options to reduce GHG emissions by individual choices which can be leveraged by supporting policies, technologies and infrastructure. Two key concepts for evaluating the efficiency of service provision systems are: resource cascades and exergy. These concepts provide powerful analytical lenses through which to identify and substantially reduce energy and resource waste in service provision systems, both for decent living standards ( [[#5.3.2|Section 5.3.2]] ) and higher well-being levels. They typically focus on end-use conversion and service delivery improvements as the most influential opportunities for system-wide waste reductions. Review of the state of modelling low energy and resource demand pathways in long-term climate mitigation scenarios (recognising the importance of such scenarios for illuminating technology and policy pathways for more efficient service provision) and summary of the mitigation potentials estimated from relevant scenarios to date are in [[#5.3.3|Section 5.3.3]] . Finally, it reviews the role of three megatrends that are transforming delivery of services in innovative ways – digitalisation, the sharing economy, and the circular economy ( [[#5.3.4|Section 5.3.4]] ). The review of megatrends makes an assessment highlighting the potential risks of rebound effects, and even accelerated consumption; it also scopes for proactive and vigilant policies to harness their potential for future energy and resource demand reductions, and, conversely, avoiding undesirable outcomes.

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=== 5.3.1 Efficient Service Provision ===

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Thissection organises demand reductions under the ASI framework. It presents service-oriented demand-side solutions consistent with decent living standards ( [[#Creutzig--2018|Creutzig et al. 2018]] ) (Table 5.1). The sharing economy, digitalisation, and the circular economy can all contribute to ASI strategies, with the circular economy tentatively more on the supply side, and the sharing economy and digitalisation tentatively more on the demand side ( [[#5.3.4|Section 5.3.4]] ). These new service delivery models go beyond sectoral boundaries (IPCC sector chapter boundaries are explained in Chapter 12) and take advantage of technological innovations, design concepts, and innovative forms of cooperation, cutting across sectors to contribute to systemic changes worldwide. Some of these changes can be realised in the short term, such as energy access, while others may take a longer period, such as radical and systemic eco-innovations like shared electric autonomous vehicles. It is important to understand benefits and distributional impacts of these systemic changes.

'''Table 5.1 | Avoid-Shift-Improve options in selected sectors and services.''' Many options, such as urban form and infrastructures, are systemic, and influence several sectors simultaneously. Linkages to concepts presented in sectoral chapters are indicated in parentheses in the first column. Source: adapted from Creutzig at al. (2018).

{| class="wikitable"
|-
| '''Service'''

| '''Emission decomposition factors'''

| '''Avoid'''

| '''Shift'''

| '''Improve'''

|-
| '''Mobility'''

[passenger-km]

''(Chapters 8, 10, 11, 16)''

| kgCO 2 = (passenger km)*(MJ pkm –1 )*(kgCO 2 MJ –1 )

| '''Innovative mobility to reduce passenger-km:'''

Integrate transport and land-use planning

Smart logistics

Teleworking

Compact cities

Fewer long-haul flights

Local holidays

| '''Increased options for mobility MJ pkm''' –1 ''':'''

Modal shifts, from car to cycling, walking, or public transit

Modal shift from air travel to high-speed rail

| '''Innovation in equipment design MJ pkm''' –1 '''and CO''' 2 '''-eq MJ''' –1 ''':'''

Lightweight vehicles

Hydrogen vehicles

Electric vehicles

Eco-driving

|-
| '''Shelter'''

[square metres]

''(Chapters 8, 9, 11)''

| kgCO 2 = (square metres)*(tonnes material m –2 )*(kg CO 2 tonne material –1 )

| '''Innovative dwellings to reduce square metres:'''

Smaller decent dwellings

Shared common spaces

Multigenerational housing

| '''Materials-efficient housing tonnes material m''' –2 ''':'''

Less material-intensive dwelling designs

Shift from single-family to multi-family dwellings

| '''Low emission dwelling design kgCO''' 2 '''tonne''' –1 '''material:'''

Use wood as material

Use low-carbon production processes for building materials (e.g., cement and steel)

|-
| '''Thermal comfort'''

[indoor temperature]

''(Chapters 9, 16)''

| kgCO 2 = (Δ°C m 3 to warm or cool) (MJ m –3 )*(kgCO 2 MJ –1 )

| '''Choice of healthy indoor temperature Δ°C m''' 3 ''':'''

Reduce m 2 as above

Change temperature set-points

Change dress code

Change working times

| '''Design options to reduce MJ Δ°C''' –1 '''m''' –3 ''':'''

Architectural design (shading, natural ventilation, etc.)

| '''New technologies to reduce M''' '''J Δ°C''' –1 '''m''' – 3 '''and kgCO''' 2 '''MJ''' –1 ''':'''

Solar thermal devices

Improved insulation

Heat pumps

District heating

|-
| '''Goods'''

[units]

''(Chapters 11, 12)''

| kgCO 2 = (product units)*(kg material product –1 )*(kgCO 2 kg material –1 )

| '''More service per product:'''

Reduce consumption quantities

Long lasting fabric, appliances

Sharing economy

| '''Innovative product design kg material product''' –1 ''':'''

Materials-efficient product designs

| '''Choice of new materials kgCO''' 2 '''kg material''' –1 ''':'''

Use of low-carbon materials

New manufacturing processes and equipment use

|-
| '''Nutrition'''

[calories consumed]

''(Chapters 6, 12)''

| kgCO 2 -eq = (calories consumed)*(calories produced calories consumed –1 )*(kgCO 2 -eq calorie produced –1 )

| '''Reduce calories produced/calories consumed and optimise calories consumed:'''

Keep calories in line with daily needs and health guidelines

Reduce waste in supply chain and after purchase

| '''Add more variety in food plate to reduce kgCO''' 2 '''-eq''' '''cal''' –1 '''produced:'''

Dietary shifts from ruminant meat and dairy to other protein sources while maintaining nutritional quality

| '''Reduce kgCO''' 2 '''-eq''' '''cal''' –1 '''produced:'''

Improved agricultural practices

Energy efficient food processing

|-
| '''Lighting'''

[lumens]

''(Chapters 9, 16)''

| kgCO 2 = lumens*(kWh lumen –1 )*(kgCO 2 kWh –1 )

| '''Minimise artificial lumen demand:'''

Occupancy sensors

Lighting controls

| '''Design options to increase natural lumen supply:'''

Architectural designs with maximal daylighting

| '''Demand innovation lighting technologies kWh lumens''' –1 '''and power supply kgCO''' 2 '''kWh''' –1 ''':'''

LED lamps

|}

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==== 5.3.1.1 Integration of Service Provision Solutions with Avoid-Shift-Improve Framework ====

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Assessment of service-related mitigation options within the ASI framework is aided by decomposition of emissions intensities into explanatory contributing factors, which depend on the type of service delivered. Table 5.1 shows ASI options in selected sectors and services. It summarises resource, energy, and emissions intensities commonly used by type of service ( [[#Cuenot--2010|Cuenot et al. 2010]] ; [[#Lucon--2014|Lucon et al. 2014]] ; [[#Fischedick--2014|Fischedick et al. 2014]] ). Also relevant are the concepts of service provision adequacy ( [[#Arrow--2004|Arrow et al. 2004]] ; [[#Samadi--2017|Samadi et al. 2017]] ), establishing the extents to which consumption levels exceed (e.g., high-calorie diets contributing to health issues ( [[#Roy--2012|Roy et al. 2012]] ); excessive food waste) or fall short (e.g., malnourishment) of service level sufficiency (e.g., recommended calories) ( [[#Millward-Hopkins--2020|Millward-Hopkins et al. 2020]] ); and service level efficiency (e.g., effect of occupancy on the energy intensity of public transit passenger-km travelled ( [[#Schäfer--2020|Schäfer and Yeh 2020]] ). Service-oriented solutions are discussed in Table 5.1. Implementation of these solutions requires combinations of institutional, infrastructural, behavioural, socio-cultural, and business changes which are mentioned in [[#5.2|Section 5.2]] and discussed in [[#5.4|Section 5.4]] .

Opportunities for avoiding waste associated with the provision of services, or avoiding overprovision of or excess demand for services, exist across multiple service categories. ‘Avoid’ options are relevant in all end-use sectors, namely, teleworking and avoiding long-haul flights, adjusting dwelling size to household size, and avoiding short-lifespan products and food waste. Cities and built environments can play an additional role. For example, more compact designs and higher accessibility reduce travel demand and translate into lower average floor space and corresponding heating/cooling and lighting demand, and thus reductions of between 5% to 20% of GHG emissions of end-use sectors ( [[#Creutzig--2021b|Creutzig et al. 2021b]] ). Avoidance of food loss and wastage – which equalled 8–10% of total anthropogenic GHG emissions from 2010–2016 ( [[#Mbow--2019|Mbow et al. 2019]] ), while millions suffer from hunger and malnutrition – is a prime example (Chapter 12). A key challenge in meeting global nutrition services is therefore to avoid food loss and waste while simultaneously raising nutrition levels to equitable standards globally. Literature results indicate that in developed economies, consumers are the largest source of food waste, and that behavioural changes such as meal planning, use of leftovers, and avoidance of over-preparation can be important service-oriented solutions ( [[#Gunders--2017|Gunders et al. 2017]] ; [[#Schanes--2018|Schanes et al. 2018]] ), while improvements to expiration labels by regulators would reduce unnecessary disposal of unexpired items ( [[#Wilson--2017|Wilson et al. 2017]] ) and improved preservation in supply chains would reduce spoilage ( [[#Duncan--2019|Duncan and Gulbahar 2019]] ). Around 931 million tonnes of food waste was generated in 2019 globally, 61% of which came from households, 26% from food service and 13% from retail.

Demand-side mitigations are achieved through changing ''Socio-cultural factors'' , ''Infrastructure use'' and ''Technology adoption'' by various social actors in urban and other settlements, food choice and waste management ( ''high confidence'' ) (Figure 5.7). In all sectors, end-use strategies can help reduce the majority of emissions, ranging from 28.7% (4.4 GtCO 2 ) emission reductions in the industry sector, to 44.2% (8.0 GtCO 2 -eq) in the food sector, to 66.75% (4.6 GtCO 2 ) emission reductions in the land transport sector, and 66% (6.8 GtCO 2 ) in the buildings sector. These numbers are median estimates and represent benchmark accounting. Estimates are approximations, as they are simple products of individual assessments for each of the three options listed above. If interactions were taken into account, the full mitigation potentials may be higher or lower, independent of relevant barriers to realising the median potential estimates. See more in [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material II, Table 5.SM.2.

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[[File:58235ad297f4c910a028551cb144ebbb IPCC_AR6_WGIII_Figure_5_7.png]]

'''Figure 5.7 | Demand-side mitigation options and indicative potentials.''' Demand-side mitigation response options related to demand for services have been categorised into three broad domains: ‘socio-cultural factors’, associated with individual choices, behaviour and lifestyle change, social norms and culture; ‘infrastructure use’, related to the design and use of supporting hard and soft infrastructure that enables changes in individual choices and behaviour; and ‘end-use technology adoption’, which refers to the uptake of technologies by end users. Demand-side mitigation is a central element of the IMP-LD and IMP-SP scenarios ( [[IPCC:Wg3:Chapter:Chapter-3#3.3|Section 3.3]] ). Food (nutrition) demand-side potentials in 2050 assessment is based on bottom-up studies and estimated following the 2050 baseline for the food sector presented in peer-reviewed literature (more information in [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material II and Chapter 7, [[IPCC:Wg3:Chapter:Chapter-7#7.4.5|Section 7.4.5]] ). Industry (manufactured products), land transport, aviation and shipping (mobility), and buildings (shelter) assessment of potentials for total emissions in 2050 are estimated based on approximately 500 bottom-up studies representing all global regions (detailed list is in Table 5.SM.2). Baseline is provided by the sectoral mean GHG emissions in 2050 of the two scenarios consistent with policies announced by national governments until 2020. The heights of the coloured columns represent the potentials represented by the median value. These are based on a range of values available in the case studies from literature shown in [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material II. The range is shown by the dots connected by dotted lines representing the highest and the lowest potentials reported in the literature. The demand-side potential of socio-cultural factors in food has two parts.The median value of direct emissions (mostly non-CO 2 ) reduction through socio-cultural factors is 1.9 GtCO 2 -eq without considering land-use change through reforestation of freed up land. If changes in land-use patterns enabled by this change in food demand are considered, the indicative potential could reach 7 GtCO 2 -eq. The ‘electricity’ panel presents how sectoral demand-side mitigation options (industry, transport and buildings) can change demand on the electricity distribution system. Electricity accounts for an increasing proportion of final energy demand in 2050 (‘additional electrification’ bar) in line with multiple bottom-up studies (detailed list is in Table 5.SM.3) and [[IPCC:Wg3:Chapter:Chapter-6|Chapter 6]] ( [[IPCC:Wg3:Chapter:Chapter-6#6.6|Section 6.6]] ). These studies are used to compute the impact of end-use electrification which increases overall electricity demand. Some of the projected increase in electricity demand can be avoided through demand-side mitigation options in the domains of socio-cultural factors and infrastructure use strategies in end-use electricity use in buildings, industry and land transport found in literature based on bottom-up assessments ( [[#5.3|Section 5.3]] and [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material II).

The technical mitigation potential of food loss and waste reductions globally has been estimated at 0.1–5.8 GtCO 2 -eq ( ''high confidence'' ) ( [[#Poore--2018|Poore and Nemecek 2018]] ; Smith, et al. 2019) ( [[IPCC:Wg3:Chapter:Chapter-7#7.4.5|Section 7.4.5]] , Figure 5.7 and Table 12.3). Coupling food waste reductions with dietary shifts can further reduce energy, land, and resource demand in upstream food provision systems, leading to substantial GHG emissions benefits. The estimated technical potential for GHG emissions reductions associated with shifts to sustainable healthy diets is 0.5–8 GtCO 2 -eq ( ''high confidence'' ) ( [[#Smith--2013|Smith et al. 2013]] ; [[#Jarmul--2020|Jarmul et al. 2020]] ; [[#Creutzig--2021b|Creutzig et al. 2021b]] ) (Figure 5.7, Table 12.2). Current literature on health, diets, and emissions indicates that sustainable food systems providing healthy diets for all are within reach but require significant cross-sectoral action, including improved agricultural practices, dietary shifts among consumers, and food waste reductions in production, distribution, retail, and consumption ( [[#Erb--2016|Erb et al. 2016]] ; [[#Muller--2017|Muller et al. 2017]] ; [[#Graça--2019|Graça et al. 2019]] ; Willett and al. 2019) (Table 12.9).

Reduced food waste and dietary shifts have highly relevant repercussions in the land-use sector that underpin the high GHG emission reduction potential. Demand-side measures lead to changes in consumption of land-based resources and can save GHG emissions by reducing or improving management of residues or making land areas available for other uses such as afforestation or bioenergy production ( [[#Smith--2013|Smith et al. 2013]] ; [[#Hoegh-Guldberg--2019|Hoegh-Guldberg et al. 2019]] ). Deforestation is the second-largest source of anthropogenic greenhouse gas emissions, caused mainly by expanding forestry and agriculture, and in many cases this agricultural expansion is driven by trade demand for food. For example, across the tropics, cattle and oilseed products account for half the deforestation carbon emissions, embodied in international trade to China and Europe ( [[#Creutzig--2019a|Creutzig et al. 2019a]] ; [[#Pendrill--2019|Pendrill et al. 2019]] ). Benefits from shifts in diets and resulting lowered land pressure are also reflected in reductions of land degradation and emissions.

Increased demand for biomass can increase the pressure on forest and conservation areas ( [[#Cowie--2013|Cowie et al. 2013]] ) and poses a heightened risk for biodiversity, livelihoods, and intertemporal carbon balances ( [[#Lamb--2016|Lamb et al. 2016]] ; [[#Creutzig--2021c|Creutzig et al. 2021c]] ), requiring policy and regulations to ensure sustainable forest management, which depends on forest type, region, climate, and ownership. This suggests that demand-side actions hold sustainability advantages over the intensive use of bioenergy and BECCS, but also enable land use for bioenergy by saving agricultural land for food.

In the transport sector, ASI opportunities exist at multiple levels, comprehensively summarised in [[#Bongardt--2013|Bongardt et al. (2013)]] , [[#Sims--2014|Sims et al. (2014)]] , and [[#Roy--2021|Roy et al. (2021)]] (Chapter 10). Modelling based on a plethora of bottom-up insights and options reveals that a balanced portfolio of ASI policies brings global transport sector emissions in line with global warming of not more than 1.5°C ( [[#Gota--2019|Gota et al. 2019]] ). For example, telework may be a significant lever for avoiding road transport associated with daily commutes, achievable through digitalisation, but its savings depend heavily on the modes, distances, and types of office use avoided ( [[#Hook--2020|Hook et al. 2020]] ) and whether additional travel is induced due to greater available time ( [[#Mokhtarian--2002|Mokhtarian 2002]] ) or vehicle use by other household members ( [[#Kim--2015|Kim et al. 2015]] ; [[#de%20Abreu%20e%20Silva--2018|de Abreu e Silva and Melo 2018]] ). More robustly, avoiding kilometres travelled through improved urban planning and smart logistical systems can lead to fuel, and, hence, emissions savings ( [[#Creutzig--2015a|Creutzig et al. 2015a]] ; [[#IEA--2016|IEA 2016]] ; [[#IEA--2017a|IEA 2017a]] ; [[#Wiedenhofer--2018|Wiedenhofer et al. 2018]] ), or through avoiding long-haul flights ( [[#IEA--2021|IEA 2021]] ). For example, reallocating road and parking space to exclusive public transit lanes, protected bike lanes and pedestrian priority streets can reduce vehicle kilometres travelled in urban areas ( [[#ITF--2021|ITF 2021]] ). At the vehicle level, lightweighting strategies ( [[#Fischedick--2014|Fischedick et al. 2014]] ) and avoiding inputs of carbon-intensive materials into vehicle manufacturing can also lead to significant emissions savings through improved fuel economy ( [[#Das--2016|Das et al. 2016]] ; [[#Hertwich--2019|Hertwich et al. 2019]] ; [[#IEA--2019b|IEA 2019b]] ).

Figure 5.7 shows socio-cultural factors can contribute up to 15% to land transport GHG emissions reduction by 2050, with 5% as our central estimate. Active mobility, such as walking and cycling, has 2–10% potential in GHG emissions reduction. Well designed teleworking policies can reduce transport-related GHG emissions by at least 1%. A systematic review demonstrates that 26 of 39 studies identified suggest that teleworking reduces energy use, induced mainly by distance travelled, and only eight studies suggest that teleworking increases or has a neutral impact on energy use ( [[#Hook--2020|Hook et al. 2020]] ). Infrastructure use (specifically urban planning and shared pooled mobility) has about 20–50% (on average) potential in land transport GHG emissions reduction, especially via redirecting the ongoing design of existing infrastructures in developing countries, and with 30% as our central estimate ( [[#5.3.4.2|Section 5.3.4.2]] ). Technology adoption, particularly banning combustion and diesel engines and 100% EV targets (and other zero-carbon fuels, especially in freight) and efficient lightweight cars, can contribute to between 30% and 70% of GHG emissions reduction from land transport in 2050, with 50% as our central estimate (see [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material II, Table 5.SM.2 and Chapter 10, Sections 10.4 and 10.7), consistent with scenario modelling (Figure 10.27) and based on rapid reduction in the GHG emission footprint of vehicle production. These numbers are consistent with the end of fossil fuel-based new cars in 2035 in major economies and of 100% of vehicles being zero-emission vehicles in 2050. Other economies that display vehicles obtained on second hand markets may phase out fossil fuel cars only after 2050, hence limiting the overall mitigation potential of electric vehicles to well below 100% in 2050. Higher energy use and CO 2 -footprint in BEV production compared to ICE production are to be met with more rapid decarbonisation of the industry sector and by the reduced need for overall vehicle stock, due to socio-cultural and infrastructure measures. Ehrenberger et al. (2021) shows that the development of technologies, fleets, and their use are decisive factors in reducing the use of fossil energies, resulting in 26–65% CO 2 emissions reduction potential until 2040 for the case of Germany. Electric vehicles can be used to provide new shared services. In this case, reductions of CO 2 emissions of close to 20% can be obtained in a scenario where 20% of car trips and all bus feeder trips are replaced, but considerably higher reductions are possible when shared pooled mobility replaces private vehicle trips in urban areas ( [[#ITF--2017b|ITF 2017b]] , ITF 2017d). A study shows that ICE vehicles reduce CO 2 emissions to 60% or 80% of current emissions levels by 2050 (Hill et al. 2019). Similarly, the power grid decarbonisation is assumed to improve to either 50% or 80% over current rates, with 80% being the expected decarbonisation and 50% a more conservative estimate. Each possibility for EV adoption rate, ICE efficiency improvement, and power decarbonisation is combined (Hill et al. 2019). Beyond consuming less energy, EVs enable greater use of low-carbon and renewable energy sources than is possible for conventional petroleum-based fuels. These technical advantages lead to the potential for greatly reducing petroleum use, air pollution and carbon emissions. International collaboration could better leverage existing efforts to promote zero-emission vehicles. The establishment of a zero-emission vehicle deployment target and an electric mobility target for 2035 would help in establishing a common long-term global electric-drive vision (Lutsey 2015).

Socio-cultural factors such as avoiding long-haul flights and shifting to train wherever possible can contribute between 10% and 40% to aviation GHG emissions reduction by 2050 (Figure 5.7). Maritime transport (shipping) emits around 940 MtCO 2 annually and is responsible for about 2.5% of global GHG emissions ( [[#IMO--2020|IMO 2020]] ). Technology measures and management measures, such as slow steaming, weather routing, contra-rotating propellers, and propulsion efficiency devices can deliver more fuel savings between 1% and 40% than the investment required ( [[#Bouman--2017|Bouman et al. 2017]] ) (Chapter 5, Supplementary Material II, Table 5.SM.2).

In the buildings sector, avoidance strategies can occur at the end use or individual building operation level. End-use technologies and strategies such as the use of daylighting ( [[#Bodart--2002|Bodart and De Herde 2002]] ) and lighting sensors can avoid demand for lumens from artificial light, while passive houses, thermal mass, and smart controllers can avoid demand for space conditioning services. Eliminating standby power losses can avoid energy wasted for no useful service in many appliances and devices, which may reduce household electricity use by up to 10% ( [[#Roy--2012|Roy et al. 2012]] ). At the building level, smaller dwellings can reduce overall demand for lighting and space conditioning services, while smaller dwellings, shared housing, and building lifespan extension can all reduce the overall demand for carbon-intensive building materials such as concrete and steel ( [[#Material%20Economics--2018|Material Economics 2018]] ; [[#Hertwich--2019|Hertwich et al. 2019]] ; [[#IEA--2019b|IEA 2019b]] ; [[#Pauliuk--2021|Pauliuk et al. 2021]] ). Emerging strategies for materials efficiency, such as 3D printing to optimise the geometries and minimise the materials content of structural elements, may also play a key role if thermal performance and circularity can be improved ( [[#Mahadevan--2020|Mahadevan et al. 2020]] ; [[#Adaloudis--2021|Adaloudis and Bonnin Roca 2021]] ). Several scenarios estimate an ‘Avoid’ potential in the building sector, which includes reducing waste in superfluous floor space, heating and IT equipment, and energy use, of between 10% and 30%, in one case even by 50% ( [[#Nadel--2019|Nadel and Ungar 2019]] ) (Chapter 9).

Socio-cultural factors and behavioural and social practices in energy saving, like adaptive heating and cooling by changing temperature, can contribute about 15% to GHG emissions reduction in the buildings sector by 2050 (Figure 5.7). Infrastructure use such as compact city and urban planning interventions, living floor space rationalisation, and access to low-carbon architectural design has about 20% potential in building sector GHG emissions reduction. Technology adoption, particularly access to energy efficient technologies, and installation of renewable energy technologies can contribute between 30% and 70% to GHG emissions reduction in the buildings sector (Chapters 8 and 9 and [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material II, Table 5.SM.2).

Service efficiency strategies are emerging to avoid materials demand at the product level, including dematerialisation strategies for various forms of packaging ( [[#Worrell--2013|Worrell and Van Sluisveld 2013]] ) and the concept of ‘products as services’, in which product systems are designed and maintained for long lifespans to provide a marketable service ( [[#Oliva--2003|Oliva and Kallenberg 2003]] ), thereby reducing the number of products sold and tonnes of materials needed to provide the same service to consumers, consistent with circular economy and materials efficiency principles (Chapter 11). Successful examples of this approach have been documented for carpets ( [[#Stubbs--2008|Stubbs and Cocklin 2008]] ), copiers ( [[#Roy--2000|Roy 2000]] ), kitchens ( [[#Liedtke--1998|Liedtke et al. 1998]] ), vehicles ( [[#Williams--2006|Williams 2006]] ; [[#Ceschin--2010|Ceschin and Vezzoli 2010]] ) and more ( [[#Roy--2000|Roy 2000]] ).

‘Shift’ strategies unique to the service-oriented perspective generally involve meeting service demands at much lower lifecycle energy, emissions, and resource intensities ( [[#Roy--2009|Roy and Pal 2009]] ), through such strategies as shifting from single-family to multi-family dwellings (reducing the materials intensity per unit floor area ( [[#Ochsendorf--2011|Ochsendorf et al. 2011]] )), shifting from passenger cars to rail or bus (reducing fuel, vehicle manufacturing, and infrastructure requirements ( [[#Chester--2009|Chester and Horvath 2009]] )), shifting materials to reduce resource and emissions intensities (e.g., low-carbon concrete blends ( [[#Scrivener--2018|Scrivener and Gartner 2018]] )) and shifting from conventional to additive manufacturing processes to reduce materials requirements and improve end-use product performance ( [[#Huang--2016|Huang et al. 2016]] , 2017).

An important consideration in all ASI strategies is the potential for unintended rebound effects ( [[#Sorrell--2009|Sorrell et al. 2009]] ; [[#Brockway--2021|Brockway et al. 2021]] ) as indicated in Figures 5.8, 5.12, and 5.13a, which must be carefully avoided through various regulatory and behavioural measures ( [[#Santarius--2016|Santarius et al. 2016]] ). In many developing country contexts, rebound effects can help in accelerated provision of affordable access to modern energy and a minimum level of per capita energy consumption ( [[#Saunders--2021|Saunders et al. 2021]] ; [[#Chakravarty--2021|Chakravarty and Roy 2021]] ). Extending the lifespan of energy inefficient products may lead to net increases in emissions ( [[#Gutowski--2011|Gutowski et al. 2011]] ), whereas automated car sharing may reduce the number of cars manufactured at the expense of increased demand for passenger kilometres due to lower travel opportunity cost ( [[#Wadud--2016|Wadud et al. 2016]] ) ( [[#5.3.2|Section 5.3.2]] ).

Avoiding short lifespan products in favour of products with longer lifespan as a socio-cultural factor; and infrastructure use measures such as increasing the re-usability and recyclability of products’ components and materials, and adopting materials-efficient services and CO 2 -neutral materials, have about 29% indicative potential by 2050. ( [[IPCC:Wg3:Chapter:Chapter-11|Chapter 11]] and [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material II, Table 5.SM.2).

In summary, sector-specific demand-side mitigation options reflect the important role of socio-cultural, technological and infrastructural factors and the interdependence among them (Figure 5.7). The assessment in Figure 5.7 shows that by 2050 high emission reduction potential can be realised with demand-side actions alone, which can be complementary to supply-side interventions, with considerable impact by reducing the need for capacity addition on the electricity supply system. Integrated cross-sectoral actions shown through sector coupling is also important for investment decision-making and policy framing going beyond sector boundaries ( ''high evidence'' and ''high agreement'' ).

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==== 5.3.1.2 Options to Reduce GHG Emissions ====

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A systematic review of options to reduce the GHG emissions associated with household consumption activities identified 6,990 peer-reviewed journal papers, with 771 options that were aggregated into 61 consumption option categories ( [[#Ivanova--2020|Ivanova et al. 2020]] ) (Figure 5.8). Consistently with previous research ( [[#Herendeen--1976|Herendeen and Tanaka 1976]] ; [[#Pachauri--2002|Pachauri and Spreng 2002]] ; [[#Pachauri--2007|Pachauri 2007]] ; [[#Ivanova--2016|Ivanova et al. 2016]] ), a hierarchical list of mitigation options emerges. Choosing low-carbon options, such as car-free living, plant-based diets with no or very little animal products, low-carbon sources of electricity and heating at home, as well as local holiday plans, can reduce an individual’s carbon footprint by up to 9 tCO 2 -eq. Realising these options requires substantial policy support to overcome infrastructural, institutional and socio-cultural lock-in (Sections 5.4 and 5.6).

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[[File:5c20ac35c65049452cd628463f4946cf IPCC_AR6_WGIII_Figure_5_8.png]]

'''Figure 5.8 | Synthesis of 60 demand-side options ordered by the median GHG mitigation potential found across all estimates from the literature.''' The grey crosses are averages. The boxes represent the 25th percentile, median and 75th percentiles of study results. The whiskers or dots show the minimum and maximum mitigation potentials of each option. Negative values (in the red area) represent the potentials for backfire due to rebound, i.e., a net increase of GHG emissions due to adopting the option. Source: with permission from [[#Ivanova--2020|Ivanova et al. (2020)]] .

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=== 5.3.2 Technical Tools to Identify Avoid-Shift-Improve Options ===

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Service delivery systems to satisfy a variety of service needs (e.g., mobility, nutrition, thermal comfort, etc.) comprise a series of interlinked processes to convert primary resources (e.g., coal, minerals) into useable products (e.g., electricity, copper wires, lamps, light bulbs). It is useful to differentiate between conversion and processing steps ‘upstream’ of end users (mines, power plants, manufacturing facilities) and ‘downstream’, that is, those associated with end-users, including service levels, and direct well-being benefits for people ( [[#Kalt--2019|Kalt et al. 2019]] ). Illustrative examples of such resource processing systems and associated conversion losses drawn from the literature are shown in Figure 5.9, in the form of resource processing cascades for energy (direct energy conversion efficiencies ( [[#Nakićenović--1993|Nakićenović et al. 1993]] ; [[#De%20Stercke--2014|De Stercke 2014]] )), water use in food production systems (water use efficiency and embodied water losses in food delivery and consumption ( [[#Lundqvist--2008|Lundqvist et al. 2008]] ; [[#Sadras--2011|Sadras et al. 2011]] )), and materials ( [[#Ayres--1994|Ayres and Simonis 1994]] ; [[#Fischer-Kowalski--2011|Fischer-Kowalski et al. 2011]] ), using the example of steel manufacturing, use and recycling at the global level ( [[#Allwood--2012|Allwood and Cullen 2012]] ). Invariably, conversion losses along the entire service delivery systems are substantial, ranging from 83% (water) to 86% (energy) and 87% (steel) of primary resource inputs ( [[#TWI2050--2018|TWI2050 2018]] ). In other words, only between 14 to 17% of the harnessed primary resources remain at the level of ultimate service delivery.

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[[File:00e32b3160b84d83a47357b19e0ab4e3 IPCC_AR6_WGIII_Figure_5_9.png]]

'''Figure 5.9 | Resource processing steps and efficiency cascades (in percentage of primary resource inputs [vertical axis] remaining at respective steps until ultimate service delivery) for illustrative global service delivery systems for energy (panel (a), disaggregated into three sectoral service types and the aggregate total), food (panel (b), water use in agriculture and food processing, delivery and use), and materials (panel (c), example steel).''' The aggregate efficiencies of service delivery chains is with 13–17% low. Source: [[#TWI2050--2018|TWI2050 (2018)]] .

Examples of conversion losses on the supply side of resource processing systems include, for instance: for energy, electricity generation (global output/input conversion efficiency of electric plants of 45% as shown in energy balance statistics ( [[#IEA--2020b|IEA 2020b]] )); for water embodied in food, irrigation water use efficiency (some 40% ( [[#Sadras--2011|Sadras et al. 2011]] )) and calorific conversion efficiency (food calories in to food calories out) in meat production of 60% ( [[#Lundqvist--2008|Lundqvist et al. 2008]] ), or for materials, globally only 47% of primary iron ore extracted and recovered steel scrap end up as steel in purchased products, (i.e., a loss of 57%) ( [[#Allwood--2012|Allwood and Cullen 2012]] ).

A substantial part of losses happens at the end-use point and in final service delivery (where losses account for 47% to 60% of aggregate systems losses for steel and energy respectively, and 23% in the case of water embodied in food). The efficiency of service delivery ( [[#Brand-Correa--2017|Brand-Correa and Steinberger 2017]] ) has usually both a technological component (efficiency of end-use devices such as cars, light bulbs) and a behavioural component (i.e., how efficiently end-use devices are used, e.g., load factors) ( [[#Dietz--2009|Dietz et al. 2009]] ; [[#Laitner--2009|Laitner et al. 2009]] ; [[#Norton--2012|Norton 2012]] ; [[#Kane--2014|Kane and Srinivas 2014]] ; [[#Ehrhardt-Martinez--2015|Ehrhardt-Martinez 2015]] ; [[#Thaler--2015|Thaler 2015]] ; [[#Lopes--2017|Lopes et al. 2017]] ). Using the example of mobility, where service levels are usually expressed by passenger-km, service delivery efficiency is thus a function of the fuel efficiency of the vehicle and its drivetrain (typically only about 20%–25% for internal combustion engines, but close to 100% for electric motors) plus how many passengers the vehicle actually transports (load factor, typically as low as 20–25%, i.e. one passenger per vehicle that could seat four to five), that is, an aggregate end-use efficiency of between 4–6% only. Aggregated energy end-use efficiencies at the global level are estimated as low as 20% ( [[#De%20Stercke--2014|De Stercke 2014]] ), 13% for steel (recovered post-use scrap) ( [[#Allwood--2012|Allwood and Cullen 2012]] ), and some 70% for food (including distribution losses and food waste of some 30%) ( [[#Lundqvist--2008|Lundqvist et al. 2008]] ).

To harness additional gains in efficiency by shifting the focus in service delivery systems to the end user can translate into large upstream resource reductions. For each unit of improvement at the end-use point of the service delivery system (examples shown in Figure 5.9), primary resource inputs are reduced between a factor of 6 to 7 units (water, steel, energy) ( [[#TWI2050--2018|TWI2050 2018]] ). For example, reducing energy needs for final service delivery equivalent to 1 EJ, reduces primary energy needs by some 7 EJ. There is thus ''high evidence'' and ''high agreement'' in the literature that the leverage effect for improvements in end-use service delivery efficiency through behavioural, technological, and market organisational innovations is very large, ranging from a factor 6 to 7 (resource cascades) to up to a factor 10 to 20 (exergy analysis), with the highest improvement potentials at the end-user and service provisioning levels (for systemic reviews see [[#Nakićenović--1996a|Nakićenović et al. (1996a)]] , [[#Grubler--2012b|Grubler et al. (2012b)]] , and [[#Sousa--2017|Sousa et al. (2017)]] ). Also, the literature shows ''high agreement'' that current conversion efficiencies are invariably low, particularly for those components at the end-use and service-delivery back end of service provisioning systems. It also suggests that efficiencies might actually be even lower than those revealed by direct input-output resource accounting, as discussed above (Figure 5.9). Illustrative exergy efficiencies of entire national or global service delivery systems range from 2.5% (USA ( [[#Ayres--1989|Ayres 1989]] )) to 5% (OECD average ( [[#Grubler--2012b|Grubler et al. 2012b]] )) and 10% (global (Nakićenović et al., 1996)). Studies that adopt more restricted systems boundaries, either leaving out upstream resource processing/conversion or conversely end-use and service provision, show typical exergetic efficiencies between 15% (city of Geneva ( [[#Grubler--2012a|Grubler et al. 2012a]] )) to below 25% (Japan, Italy, and Brazil, albeit with incomplete systems coverage that miss important conversion losses ( [[#Nakićenović--1996b|Nakićenović et al. 1996b]] )). These findings are confirmed by more recent exergy efficiency studies that also include longitudinal time trend analysis ( [[#Cullen--2010|Cullen and Allwood 2010]] ; [[#Brockway--2014|Brockway et al. 2014]] ; [[#Serrenho--2014|Serrenho et al. 2014]] ; [[#Brockway--2015|Brockway et al. 2015]] ; [[#Guevara--2016|Guevara et al. 2016]] ). Figure 5.10 illustrates how energy demand reductions can be realised by improving the resource efficiency cascades shown in Figure 5.9.

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[[File:9c002465618c321cc3f147dea70646dd IPCC_AR6_WGIII_Figure_5_10.png]]

'''Figure 5.10 | Realisable energy efficiency improvements by region and by end-use type between 2020 and 2050 in an illustrative Low Energy Demand scenario (in EJ).''' Efficiency improvements are decomposed by respective steps in the conversion chain from primary energy to final, and useful, energy, and to service delivery, and disaggregated by region (developed and developing countries) and end-use type (buildings, transport, materials). Improvements are dominated by improved efficiency in service delivery (153 EJ) and by more efficient end-use energy conversion (134 EJ). Improvements in service efficiency in transport shown here are conservative in this scenario but could be substantially higher with the full adoption of integrated urban shared mobility schemes. Increases in energy use due to increases in service levels and system effects of transport electrification (grey bars on top of first pair in the bar charts) that counterbalance some of the efficiency improvements are also shown. Examples of options for efficiency improvements and decision involved (grey text in the chart), the relative weight of generic demand-side strategies (Avoid-Shift-Improve blue arrows), as well as prototype actors involved, are also illustrated. Data source: Figure 5.9 and [[#Grubler--2018|Grubler et al. (2018)]] .

<div id="5.3.3" class="h2-container"></div>

<span id="low-demand-scenarios"></span>
=== 5.3.3 Low Demand Scenarios ===

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Long-term mitigation scenarios play a crucial role in climate policy design in the near term, by illuminating transition pathways, interactions between supply-side and demand-side interventions, their timing, and the scales of required investments needed to achieve mitigation goals (Chapter 3). Historically, most long-term mitigation scenarios have taken technology-centric approaches with heavy reliance on supply-side solutions and the use of carbon dioxide removal, particularly in 1.5°C scenarios ( [[#Rogelj--2018|Rogelj et al. 2018]] ). Comparatively less attention has been paid to deep demand-side reductions incorporating socio-cultural change and the cascade effects ( [[#5.3.2|Section 5.3.2]] ) associated with ASI strategies, primarily due to limited past representation of such service-oriented interventions in long-term integrated assessment models (IAMs) and energy systems models (ESMs) ( [[#Grubler--2018|Grubler et al. 2018]] ; [[#van%20de%20Ven--2018|van de Ven et al. 2018]] ; [[#Napp--2019|Napp et al. 2019]] ). There is ample evidence of savings from sector- or issue-specific bottom-up studies ( [[#5.3.1.2|Section 5.3.1.2]] ). However, these savings typically get lost in the dominant narrative provided by IAMs and ESMs and in their aggregate-level evaluations of combinations of ASI and efficiency strategies. As a result, their interaction effects do not typically get equal focus alongside supply-side and carbon dioxide removal options ( [[#Samadi--2017|Samadi et al. 2017]] ; [[#Van%20Vuuren--2018|Van Vuuren et al. 2018]] ; [[#Van%20den%20Berg--2019|Van den Berg et al. 2019]] ).

In response to 1.5°C ambitions, and a growing desire to identify participatory pathways with less reliance on carbon dioxide removal which has high uncertainty, some recent IAM and ESM mitigation scenarios have explored the role of deep demand-side energy and resource use reduction potentials at global and regional levels. Table 5.2 summarises long-term scenarios that aimed to: minimise service-level energy and resource demand as a central mitigation tenet; specifically evaluate the role of behavioural change and ASI strategies; and/or achieve a carbon budget with limited or no carbon dioxide removal. From assessment of this emerging body of literature, several general observations arise and are presented below.

First, socio-cultural changes within transition pathways can offer gigatonne-scale CO 2 savings potential at the global level, and therefore represent a substantial overlooked strategy in traditional mitigation scenarios. Two lifestyle change scenarios conducted with the IMAGE IAM suggested that behaviour and cultural changes such as heating and cooling set-point adjustments, shorter showers, reduced appliance use, shifts to public transit, less meat-intensive diets, and improved recycling can deliver an additional 1.7 Gt and 3 GtCO 2 savings in 2050, beyond the savings achieved in traditional technology-centric mitigation scenarios for the 2°C and 1.5 ° C ambitions, respectively ( [[#van%20Sluisveld--2016|van Sluisveld et al. 2016]] ; [[#Van%20Vuuren--2018|Van Vuuren et al. 2018]] ). In its Sustainable Development Scenario, the IEA’s behavioural change and resource efficiency wedges deliver around 3 GtCO 2 -eq reduction in 2050, combined savings, roughly equivalent to those of solar PV that same year ( [[#IEA--2019a|IEA 2019a]] ). In Europe, a Global Change Assessment Model (GCAM) scenario evaluating combined lifestyle changes such as teleworking, travel avoidance, dietary shifts, food waste reductions, and recycling reduced cumulative EU 27 CO 2 emissions 2011–2050 by up to 16% compared to an SSP2 baseline ( [[#van%20de%20Ven--2018|van de Ven et al. 2018]] ). Also in Europe, a multi-regional input-output analysis suggested that adoption of low-carbon consumption practices could reduce carbon footprints by 25%, or 1.4 Gt ( [[#Moran--2020|Moran et al. 2020]] ). A global transport scenario suggests that transport sector emissions can decline from business-as-usual 18 GtCO 2 -eq to 2 GtCO 2 -eq if ASI strategies are deployed ( [[#Gota--2019|Gota et al. 2019]] ), a value considerably below the estimates provided in IAM scenarios that have limited or no resolution in ASI strategies (Chapter 10).

The IEA’s Net-Zero Emissions by 2050 (NZE) scenario, in which behavioural changes lead to 1.7 GtCO 2 savings in 2030, expresses the substantial mitigation opportunity in terms of low-carbon technology equivalencies: to achieve the same emissions reductions, the global share of EVs in the NZE would have to increase from 20% to 45% by 2030 or the number of installed heat pumps in homes would have to increase from 440 to 660 million by 2030 ( [[#IEA--2021|IEA 2021]] ).

In light of the limited number of mitigation scenarios that represent socio-behavioural changes explicitly, there is ''medium evidence'' in the literature that such changes can reduce emissions at regional and global levels, but ''high agreement'' within that literature that such changes hold up to gigatonne-scale CO 2 emissions reduction potentials.

Second, pursuant to the ASI principle, deep demand reductions require parallel pursuit of behavioural change and advanced energy-efficient technology deployment; neither is sufficient on its own. The LED scenario (Figure 5.10) combines behavioural and technological change consistent with numerous ASI strategies that leverage digitalisation, sharing, and circular economy megatrends to deliver decent living standards while reducing global final energy demand in 2050 to 245 EJ ( [[#Grubler--2018|Grubler et al. 2018]] ). This value is 40% lower than final energy demand in 2018 ( [[#IEA--2019a|IEA 2019a]] ), and a lower 2050 outcome than other IAM/ESM scenarios with primarily technology-centric mitigation approaches ( [[#Teske--2015|Teske et al. 2015]] ; [[#IEA--2017b|IEA 2017b]] ). In the IEA’s B2DS scenario, Avoid/Shift in the transport sector accounts for around 2 GtCO 2 -eq yr –1 in 2060, whereas parallel vehicle efficiency improvements increase the overall mitigation wedge to 5.5 GtCO 2 -eq yr –1 in 2060 ( [[#IEA--2017b|IEA 2017b]] ). Through a combination of behavioural change and energy-efficient technology adoption, the IEA’s NZE requires only 340 EJ of global final energy demand with universal energy access in 2050, which is among the lowest of IPCC net zero SR1.5 scenarios ( [[#IEA--2021|IEA 2021]] ).

Third, low demand scenarios can reduce both supply-side capacity additions and the need for carbon capture and removal technologies to reach emissions targets. Of the scenarios listed in Table 5.2, one (LED-MESSAGE) reaches 2050 emissions targets with no carbon capture or removal technologies ( [[#Grubler--2018|Grubler et al. 2018]] ), whereas others report significant reductions in reliance on bioenergy with carbon capture and storage (BECCS) compared to traditional technology-centric mitigation pathways ( [[#Liu--2018|Liu et al. 2018]] ; [[#Van%20Vuuren--2018|Van Vuuren et al. 2018]] ; [[#Napp--2019|Napp et al. 2019]] ), with the IEA’s NZE notably requiring the least carbon dioxide removal (1.8 Gt in 2050) and primary bioenergy (100 EJ in 2050) compared to IPCC net zero SR1.5 scenarios ( [[#IEA--2021|IEA 2021]] ).

Fourth, the costs of reaching mitigation targets may be lower when incorporating ASI strategies for deep energy and resource demand reductions. The TIAM-Grantham low demand scenarios displayed reduction in mitigation costs (0.87–2.4% of GDP), while achieving even lower cumulative emissions to 2100 (228 to ~475 GtCO 2 ) than its central demand scenario (741 to 1066 GtCO 2 ), which had a cost range of (2.4–4.1% of GDP) ( [[#Napp--2019|Napp et al. 2019]] ). The GCAM behavioural change scenario concluded that domestic emission savings would contribute to reducing the costs of achieving the internationally agreed climate goal of the EU by 13.5% to 30% ( [[#van%20de%20Ven--2018|van de Ven et al. 2018]] ). The AIMS lifestyle case indicated that mitigation costs, expressed as global GDP loss, would be 14% lower than the SSP2 reference scenario in 2100, for both 2 ° C and 1.5 ° C mitigation targets ( [[#Liu--2018|Liu et al. 2018]] ). These findings mirror earlier AIM results, which indicated lower overall mitigation costs for scenarios focused on energy service demand reductions ( [[#Fujimori--2014|Fujimori et al. 2014]] ). In the IEA’s NZE, behavioural changes that avoid energy and resource demand save USD4 trillion (cumulatively 2021–2050) compared to if those emissions reductions were achieved through low‐carbon electricity and hydrogen deployment ( [[#IEA--2021|IEA 2021]] ).

Based on the limited number of long-term mitigation scenarios that explicitly represent demand reductions enabled by ASI strategies, there is ''medium evidence'' but with ''high agreement'' within the literature that such scenarios can reduce dependence on supply-side capacity additions and carbon capture and removal technologies, with opportunites for lower overall mitigation costs.

If the limitations within most IAMs and ESMs regarding non-inclusion of granular ASI strategy analysis can be addressed, it will expand and improve long-term mitigation scenarios ( [[#Van%20den%20Berg--2019|Van den Berg et al. 2019]] ). These include broader inclusion of mitigation costs for behavioural interventions ( [[#van%20Sluisveld--2016|van Sluisveld et al. 2016]] ), much greater incorporation of rebound effects ( [[#Krey--2019|Krey et al. 2019]] ), including from improved efficiencies ( [[#Brockway--2021|Brockway et al. 2021]] ) and avoided spending ( [[#van%20de%20Ven--2018|van de Ven et al. 2018]] ), improved representation of materials cycles to assess resource cascades ( [[#Pauliuk--2017|Pauliuk et al. 2017]] ), broader coverage of behavioural change ( [[#Samadi--2017|Samadi et al. 2017]] ; [[#Saujot--2020|Saujot et al. 2020]] ), improved consideration of how economic development affects service demand ( [[#Semieniuk--2021|Semieniuk et al. 2021]] ), explicit representation of intersectoral linkages related to digitalisation, sharing economy, and circular economy strategies ( [[#5.3.4|Section 5.3.4]] ), and institutional, political, social, entrepreneurial, and cultural factors ( [[#van%20Sluisveld--2018|van Sluisveld et al. 2018]] ). Addressing the current significant modelling limitations will require increased investments in data generation and collection, model development, and inter-model comparisons, with a particular focus on socio-behavioural research, which has been underrepresented in mitigation research funding to date ( [[#Overland--2020|Overland and Sovacool 2020]] ).

COVID-19 interacts with demand-side scenarios (Box 5.2). Energy demand will mostly likely be reduced between 2020 and 2030 compared to the default pathway, and if recovery is steered towards low energy demand, carbon prices for a 1.5°C-consistent pathway will be reduced by 19%, energy supply investments until 2030 will be reduced by USD1.8 trillion, and the pressure to rapidly upscale renewable energy technologies will be softened ( [[#Kikstra--2021a|Kikstra et al. 2021a]] ).

'''Table 5.2 | Summary of long-term scenarios with elements that aimed to minimise service-level energy and resource demand.'''

{| class="wikitable"
|-
! colspan="11"| '''Global scenarios'''

|-
! rowspan="2"| '''#'''

! rowspan="2"| '''Scenario'''

'''[Temp]'''

! rowspan="2"| '''IAM/'''

'''ESM'''

! rowspan="2"| '''Final energy'''

! colspan="3"| '''Focused demand reduction element(s)'''

! rowspan="2"| '''Baseline scenario'''

! colspan="3"| '''Mitigation potential''' c

|-
! '''Scope'''

! '''Sectors''' a

! '''Key demand reduction measures considered (A, S, I)''' b

! '''CO''' 2 '''(Gt)'''

! '''Final energy'''

! '''Primary energy'''

|-
| '''1'''

| Lifestyle change scenario [2°C]

| IMAGE

| –

| Whole scenario

| R, T, I

| A: set-points, smaller houses, reduced shower times, wash temperatures, standby loss, reduced car travel, reduced plastics

S: from cars to bikes, rail

I: improved plastic recycling

| 2°C technology-centric scenario in 2050

| 1.9

| –

| –

|-
| '''2'''

| Sustainable Development scenario [1.8°C]

| World Energy Model (WEM)

| 398 EJ in 2040

| Behavioural change wedge and resource efficiency wedge

| T, I

| S: shifts from cars to mass transit, building lifespan extension, materials-efficient construction, product reuse

I: improved recycling

| Stated policies in 2050

| 3

| –

| –

|-
| '''3'''

| Beyond 2 Degrees scenario [1.75°C]

| ETP-TIMES

| 377 EJ in 2050

| Transport Avoid/Shift wedge and material efficiency wedge

| T, I

| A: shorter car trips, optimised truck routing and utilisation

S: shifts from cars to mass transit

I: plastics and metal recycling, production yield improvements

| Stated policies in 2060

| 2.8

| –

| –

|-
| '''4'''

| Lifestyle change scenario [1.5°C]

| IMAGE

| 322 EJ in 2050

| Whole scenario

| R, C, T, I

| A: set-points, reduced appliance use

S: from cars to mass transit, less meat-intensive diets, cultured meat

I: best available technologies across sectors

| 1.5°C technology-centric scenario in 2050

| 3.1

| –

| –

|-
| '''5'''

| Low Energy Demand scenario [1.5°C]

| MESSAGE

| 245 EJ in 2050

| Whole scenario

| R, C, T, I, F

| A: device integration, telework, shared mobility, material efficiency, dematerialisation, reduced paper

S: multi-purpose dwellings, healthier diets

I: best available technologies across sectors

| Final energy in 2020

| –

| 179 EJ

| –

|-
| '''6'''

| Advanced Energy [R]evolution

| –

| 279 EJ in 2050

| Whole scenario

| R, C, T, I

| S: shifts from cars to mass transit

I: best available technologies across sectors

| Continuation of current trends and policies in 2050

| –

| 260 EJ

| –

|-
| '''7'''

| Limited BECCS – lifestyle change [1.5°C]

| IMAGE

| –

| Whole scenario

| R, C, T, F

| A: set-points, reduced appliance use

S: from cars to mass transit, less meat-intensive diets, cultured meat

I: best available technologies across sectors

| 1.5°C technology-centric scenario in 2050

| 2.2 Gt

| –

| 82 EJ

|-
| '''8'''

| Lifestyle scenario [1.5 ° C]

| AIM

| 374 EJ in 2050

| Whole scenario

| T, I, F

| A: reduced transport services demand, reduced demand for industrial goods

S: less meat-intensive diets

| 1.5°C supply technology-centric scenario in 2050

| –

| 42 EJ

| –

|-
| '''9'''

| Transport scenario [1.5°C]

| Bottom-up construction

| –

| Whole scenario

| T

| A: multiple options

S: multiple options

I: multiple options

|
| 89% vs BAU: 16GtCO 2

| –

| –

|-
| '''10'''

| Net Zero Emissions 2050 scenario

| World Energy Model (WEM)

| –

| Behaviour change wedge

| R, T

| A: set-points, line drying, reduced wash temperatures, telework, reduced air travel

S: shifts to walking, cycling

I: eco-driving

| Stated policies in 2030

| 2

| –

| –

|-
| '''11'''

| Decent living with minimum energy

| Bottom-up construction

| 149 EJ in 2050

| Whole scenario

| R, T, I, F

| A: activity levels for mobility, shelter, nutrition, etc., consistent with decent living standards

S: shifts away from animal-based foods, shifts to public transit, etc.

I: energy efficiency consistent with best available technologies

| IEA Stated Policies Scenario in 2050

| –

| 75%

| –

|-
| '''12'''

| Net‐Zero Emissions by 2050 Scenario (NZE)

| Hybrid model based on WEM and ETP-TIMES

| 340 EJ in 2050

| Behavioural change reductions

| R, C, T, I

| A: heating, air conditioning, and hot water set-points, reduce international flights, line drying, vehicle light-weighting, materials-efficient construction, building lifespan extension

S: shifts from regional flights to high-speed rail, cars to walking, cycling or public transport,

I: eco-driving, plastics recycling

| Stated policies in 2050

| 2.6

| 37 EJ

|
|-
| colspan="11"| '''Regional scenarios'''

|-
| '''13'''

| Urban mitigation wedge

| –

| 540 EJ in global cities in 2050

| Whole scenario

| R, C, T

| A: reduced transport demand

S: mixed-use developments

I: vehicle efficiency, building codes and retrofits

| Current trends to 2050

| –

| 180 EJ

| –

|-
| '''14'''

| France 2072 collective society

| TIMES-Fr

| 4.2 EJ in France in 2072

| Whole scenario

| R, T

| A: less travel by car and plane, longer building and device lifespans, less spending

S: shared housing, shifts from cars to walking, biking, mass transit

| Final energy in 2014

| –

| 1.7 EJ

| –

|-
| '''15'''

| EU 27 lifestyle change – enthusiastic profile

| GCAM

| –

| Whole scenario

| R, T, F

| A: telework, avoid short flights, closer holidays, food waste reduction, car sharing, set-points

S: vegan diet, shifts to cycling and public transit

I: eco-driving, composting, paper, metal, plastic, and glass recycling

| SSP2, cumulative emissions 2011–2050

| 16%

| –

| –

|-
| '''16'''

| Europe broader regime change scenario

| IMAGE

| 35 EJ in EU in 2050

| Whole scenario

| R, T

| A: reduced passenger and air travel, smaller dwellings, fewer appliances, reduced shower times, set points, avoid standby losses

S: car sharing, shifts to public transit

I: best available technologies

| SSP2 in 2050

| –

| 10 EJ

| –

|-
| '''17'''

| EU Carbon-CAP

| EXIOBASE 3 MRIO

| –

| Whole scenario

| R, T, F

| 90 demand-side behaviour change opportunities spanning A-S-I including changes to consumption patterns, reducing consumption, and switching to using goods with lower-carbon production and low-carbon use phases.

| Present day consumption footprint

| 1.4

| –

| –

|-
| '''18'''

| France ‘négawatt’ scenario

| Bottom-up construction

|
| Sufficiency wedge

| R, C, T, I, F

| A: increase building capacity utilisation, reduced appliance use, car sharing, telework, reduced goods consumption, less packaging

S: shifts to attached buildings; shifts from cars and air to public transit and active mobility, car sharing, freight shifts to rail and water, shifts away from animal proteins

I: reduced speed limits, vehicle efficiency, increased recycling

| Business as usual in 2050 (~2,300 TWh primary energy)

| –

| –

| ~500 TWh

|-
| '''19'''

| The Netherlands household energy behavioural changes

| BENCH-NLD agent-based model

| –

| Individual energy behavioural changes and social dynamics; considering carbon pricing

| R

| A: reduce energy consumption through changing lifestyle, habits and consumption patterns

S: to green energy provider; investment in solar PVs (prosumers)

I: investment in insulation and energy-efficient appliances

| SSP2 in 2030

| 50%

| –

| –

|-
| '''20'''

| The Netherlands household energy behavioural changes

| BENCH-NLD agent-based model

| –

| Individual energy behavioural changes and social dynamics

| R

| A: reduce energy consumption

S: investment in solar PVs (prosumers)

I: investment in insulation and energy-efficient appliances

| SSP2 in 2050

| 56%

| 51–71%

|
|-
| '''21'''

| Spain household energy behavioural changes

| BENCH-ESP agent-based model

| –

| Individual energy behavioural changes and social dynamics

| R

| A: reduce energy consumption

S: investment in solar PVs (prosumers)

I: investment in insulation and energy-efficient appliances

| SSP2 in 2050

| 44%

| 16–64%

|
|-
| '''22'''

| A Societal Transformation Scenario for Staying Below 1.5°C

| Global calculator

| 187 EJ in 2050

| Whole scenario

| R,C,I,F

| A: reduce energy, material and land use consumption

| n/a

| Down to 9.1 GtCO 2 in 2050

|
|}

Sources: a [[#van%20Sluisveld--2016|van Sluisveld et al. (2016)]] ; b [[#IEA--2019a|IEA (2019a)]] ; c [[#IEA--2017b|IEA (2017b)]] ; d [[#Van%20Vuuren--2018|Van Vuuren et al. (2018)]] ; e [[#Grubler--2018|Grubler et al. (2018)]] ; f [[#Teske--2015|Teske et al. (2015)]] ; g Esmeijer et al. (2018): h [[#Liu--2018|Liu et al. (2018)]] ; i [[#Gota--2019|Gota et al. (2019)]] ; j [[#IEA--2020a|IEA (2020a)]] ; k [[#Millward-Hopkins--2020|Millward-Hopkins et al. (2020)]] ; l [[#IEA--2021|IEA (2021)]] ; m [[#Creutzig--2015b|Creutzig et al. (2015b)]] ; n [[#Millot--2018|Millot et al. (2018)]] ; o [[#van%20de%20Ven--2018|van de Ven et al. (2018)]] ; p [[#van%20Sluisveld--2018|van Sluisveld et al. (2018)]] ; q [[#Moran--2020|Moran et al. (2020)]] ; r [[#négawatt%20Association--2018|négawatt Association (2018)]] ; s [[#Niamir--2020c|Niamir et al. (2020c)]] ; t, u [[#Niamir--2020a|Niamir et al. (2020a)]] ; v [[#Kuhnhenn--2020|Kuhnhenn et al. (2020)]] .

a R = residential (Chapters 8, 9); C = commercial (Chapters 8, 9), T = transport (Chapters 8, 10), I = industry (Chapter 11), F = food (Chapters 6, 12).

b A= Avoid; S = Shift, I = Improve, BAU = business as usual.

c Relative to indicated baseline scenario value in stated year.

<div id="5.3.4" class="h2-container"></div>

<span id="transformative-megatrends"></span>
=== 5.3.4 Transformative Megatrends ===

<div id="h2-14-siblings" class="h2-siblings"></div>

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.

<div id="5.3.4.1" class="h3-container"></div>

<span id="digitalisation"></span>
==== 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.

<div id="5.3.4.2" class="h3-container"></div>

<span id="the-sharing-economy"></span>
==== 5.3.4.2 The Sharing Economy ====

<div id="h3-8-siblings" class="h3-siblings"></div>

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).

<div id="Shared mobility" class="h4-container"></div>

<span id="shared-mobility"></span>
===== Shared mobility =====

<div id="h4-1-siblings" class="h4-siblings"></div>

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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[[File:d376787168737136f7f2848b50dbc693 IPCC_AR6_WGIII_Figure_5_13.png]]

'''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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