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

=== 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.

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