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

=== 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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