Editing IPCC:AR6/WGIII/Chapter-3 (section)

== 3.4 Integrating Sectoral Analysis Into Systems Transformations ==

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This section describes the role of sectors in long-term emissions pathways (Table 3.3). We discuss both sectoral aspects of IAM pathways and some insights from sectoral studies. Sectoral studies typically include more detail and additional mitigation options compared to IAMs. However, sectoral studies miss potential feedbacks and cross-sectoral linkages that are captured by IAMs. Additionally, since IAMs include all emissions sources, these models can be used to identify pathways to particular climate goals. In such pathways, emissions are balanced across sectors typically based on relative marginal abatement costs; as a result, some sectors are sources and some are sinks at the time of net zero CO 2 emissions. For these reasons, the mitigation observed in each sector in an IAM may differ from the potential in sectoral studies. Given the strengths and limitations of each type of model, IAMs and sectoral models are complementary, providing different perspectives.

'''Table 3.3| Section 3.''' '''4 structure, definitions, and relevant chapters.'''

{| class="wikitable"
|-
! Section

! Sector

! What is included

! Relevant chapter(s)

|-
| 3.4.1

| Cross-sector

| Supply and demand, bioenergy, timing of net zero CO 2 , other interactions among sectors

| Chapters 5, 12

|-
| 3.4.2

| Energy supply

| Energy resources, transformation (e.g., electricity generation, refineries, etc.)

| Chapter 6

|-
| 3.4.3

| Buildings a

| Residential and commercial buildings, other non-specified b

| Chapter 9

|-
| 3.4.4

| Transportation a

| Road, rail, aviation, and shipping

| Chapter 10

|-
| 3.4.5

| Industry a

| Industrial energy use and industrial processes

| Chapter 11

|-
| 3.4.6

| AFOLU

| Agriculture, forestry, and other land use

| Chapter 7

|-
| 3.4.7

| Other CDR

| CDR options not included in individual sectors (e.g., direct air carbon capture and sequestration, enhanced weathering)

| Chapter 12

|}

a Direct energy use and direct emissions only; emissions do not include those associated with energy production.

b Other non-specified fuel use, including military. Some models report this category in the buildings sector, while others report it in the ‘Other’ sector.

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<span id="cross-sector-linkages"></span>
=== 3.4.1 Cross-sector Linkages ===

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<span id="demand-and-supply-strategies"></span>
==== 3.4.1.1 Demand and Supply Strategies ====

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Most IAM pathways rely heavily on supply-side mitigation strategies, including fuel switching, decarbonisation of fuels, and CDR ( [[#Creutzig--2016|Creutzig et al. 2016]] ; [[#Bertram--2018|Bertram et al. 2018]] ; [[#Rogelj--2018|Rogelj et al. 2018]] b; [[#Mundaca--2019|Mundaca et al. 2019]] ). For demand-side mitigation, IAMs incorporate changes in energy efficiency, but many other demand-side options (e.g., behaviour and lifestyle changes) are often excluded from models ( [[#van%20Sluisveld--2015|van Sluisveld et al. 2015]] ; [[#Creutzig--2016|Creutzig et al. 2016]] ; [[#van%20den%20Berg--2019|van den Berg et al. 2019]] ; [[#Wilson--2019|Wilson et al. 2019]] ). In addition, this mitigation is typically price-driven and limited in magnitude ( [[#Yeh--2017|Yeh et al. 2017]] ; [[#Luderer--2018|Luderer et al. 2018]] ; [[#Wachsmuth--2019|Wachsmuth and Duscha 2019]] ; [[#Sharmina--2020|Sharmina et al. 2020]] ). In contrast, bottom-up modelling studies show considerable potential for demand-side mitigation ( [[#Creutzig--2016|Creutzig et al. 2016]] ; [[#Yeh--2017|Yeh et al. 2017]] ; [[#Mundaca--2019|Mundaca et al. 2019]] ; [[#Wachsmuth--2019|Wachsmuth and Duscha 2019]] ) (Chapter 5), which can slow emissions growth and/or reduce emissions ( [[#Creutzig--2016|Creutzig et al. 2016]] ; [[#Samadi--2017|Samadi et al. 2017]] ).

A small number of mitigation pathways include stringent demand-side mitigation, including changes in thermostat set points ( [[#van%20Sluisveld--2016|van Sluisveld et al. 2016]] ; [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ), more efficient or smarter appliances ( [[#van%20Sluisveld--2016|van Sluisveld et al. 2016]] ; [[#Grubler--2018|Grubler et al. 2018]] ; [[#Napp--2019|Napp et al. 2019]] ), increased recycling or reduced industrial goods ( [[#Liu--2018|Liu et al. 2018]] ; [[#van%20Sluisveld--2016|van Sluisveld et al. 2016]] ; [[#Grubler--2018|Grubler et al. 2018]] ; [[#van%20de%20Ven--2018|van de Ven et al. 2018]] ; [[#Napp--2019|Napp et al. 2019]] ), telework and travel avoidance ( [[#Grubler--2018|Grubler et al. 2018]] ; [[#van%20de%20Ven--2018|van de Ven et al. 2018]] ), shifts to public transit ( [[#van%20Sluisveld--2016|van Sluisveld et al. 2016]] ; [[#Grubler--2018|Grubler et al. 2018]] ; [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ), reductions in food waste ( [[#van%20de%20Ven--2018|van de Ven et al. 2018]] ) and less meat-intensive diets ( [[#Liu--2018|Liu et al. 2018]] ; [[#van%20de%20Ven--2018|van de Ven et al. 2018]] ; [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ). These pathways show reduced dependence on CDR and reduced pressure on land ( [[#Grubler--2018|Grubler et al. 2018]] ; [[#Rogelj--2018|Rogelj et al. 2018]] a; [[#van%20de%20Ven--2018|van de Ven et al. 2018]] ; [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ) ( [[IPCC:Wg3:Chapter:Chapter-5#5.3.3|Section 5.3.3]] ). However, the representation of these demand-side mitigation options in IAMs is limited, with most models excluding the costs of such changes ( [[#van%20Sluisveld--2016|van Sluisveld et al. 2016]] ), using stylised assumptions to represent them ( [[#van%20den%20Berg--2019|van den Berg et al. 2019]] ), and excluding rebound effects ( [[#Krey--2019|Krey et al. 2019]] ; [[#Brockway--2021|Brockway et al. 2021]] ). Furthermore, there are questions about the achievability of such pathways, including whether the behavioural changes included are feasible ( [[#Azevedo--2021|Azevedo et al. 2021]] ) and the extent to which development and demand can be decoupled ( [[#Steckel--2013|Steckel et al. 2013]] ; [[#Brockway--2021|Brockway et al. 2021]] ; [[#Keyßer--2021|Keyßer and Lenzen 2021]] ; [[#Semieniuk--2021|Semieniuk et al. 2021]] ).

Figure 3.18 shows indicators of supply- and demand-side mitigation in the IMPs, as well as the range across the database. Two of these IMPs ( ''IMP-SP'' , ''IMP-LD'' ) show strong reductions in energy demand, resulting in less reliance on bioenergy and limited CDR from energy supply. In contrast, ''IMP-Neg'' has higher energy demand, depending more on bioenergy and net negative CO 2 emissions from energy supply.

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[[File:31b52d10bd8df0667b8030b9cad2c666 IPCC_AR6_WGIII_Figure_3_18.png]]

'''Figure 3.18 | Indicators of demand and supply-side mitigation in the Illustrative Pathways (lines) and the 5–95% range of Reference, 1.''' '''5°C and 2°C scenarios (shaded areas).'''

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<span id="sectoral-emissions-strategies-and-the-timing-of-net-zero"></span>
==== 3.4.1.2 Sectoral Emissions Strategies and the Timing of Net Zero ====

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Mitigation pathways show differences in the timing of decarbonisation (Figure 3.20) and the timing of net zero (Figure 3.19) across sectors and regions ( ''high confidence'' ); the timing in a given sector depends on the cost of abatement in it, the availability of CDR options, the scenario design, near-term emissions levels, and the amount of non-CO 2 abatement ( [[#Yeh--2017|Yeh et al. 2017]] ; [[#Emmerling--2019|Emmerling et al. 2019]] ; [[#Rogelj--2019a|Rogelj et al. 2019a]] ,b; [[#Johansson--2020|Johansson et al. 2020]] ; [[#Azevedo--2021|Azevedo et al. 2021]] ; [[#Ou--2021|Ou et al. 2021]] ; [[#van%20Soest--2021b|van Soest et al. 2021b]] ) (Cross-Chapter Box 3 in this chapter). However, delaying emissions reductions, or more limited emissions reductions in one sector or region, involves compensating reductions in other sectors or regions if warming is to be limited ( ''high confidence'' ) ( [[#Price--2017|Price and Keppo 2017]] ; [[#Grubler--2018|Grubler et al. 2018]] ; [[#Rochedo--2018|Rochedo et al. 2018]] ; [[#van%20Soest--2021b|van Soest et al. 2021b]] ).

At the time of net zero global CO 2 emissions, emissions in some sectors are positive and some negative. In cost-effective mitigation pathways, the energy supply sector typically reaches net zero CO 2 before the economy as a whole, while the demand sectors reach net zero CO 2 later, if at all ( [[#Pietzcker--2014|Pietzcker et al. 2014]] ; [[#Price--2017|Price and Keppo 2017]] ; [[#Luderer--2018|Luderer et al. 2018]] ; [[#Rogelj--2018|Rogelj et al. 2018]] a,b; [[#Méjean--2019|Méjean et al. 2019]] ; [[#Azevedo--2021|Azevedo et al. 2021]] ) ( [[IPCC:Wg3:Chapter:Chapter-6#6.7|Section 6.7]] ). CO 2 emissions from transport, industry, and buildings are positive, and non-CO 2 GHG emissions are also positive at the time of global net zero CO 2 emissions (Figure 3.20).

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[[File:927d1ee874869f6ec709f85ea62d4591 IPCC_AR6_WGIII_Figure_3_19.png]]

'''Figure 3.19''' | '''Decade in which sectoral CO''' 2 '''emissions first reach net negative values.''' Each panel is a different temperature level. The colours indicate the decade in which CO 2 emissions go negative; the y-axis indicates the share of scenarios achieving net zero in that decade. Only scenarios that pass the vetting criteria are included ( [[#3.2|Section 3.2]] ). Scenarios achieving net zero prior to 2020 are excluded.

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[[File:eed9917ff2503f981437ef52849baf62 IPCC_AR6_WGIII_Figure_3_20.png]]

'''Figure 3.20''' | '''Greenhouse gas (GHG) emissions, including CO''' 2 '''emissions by sector and total non-CO''' 2 '''GHGs in 2050 (top left), 2100 (top middle), year of global net zero CO''' 2 '''(top right), cumulative CO''' 2 '''emissions from 2020–2100 (bottom left), and cumulative CO''' 2 '''emissions from 2020 until the year of net zero CO''' 2 '''for scenarios that limit warming to below 2°C.''' Scenarios are grouped by their temperature category. ‘Industry’ includes CO 2 emissions associated with industrial energy use only; sectors shown in this figure do not necessarily sum to total CO 2 . In this, and other figures in [[#3.4|Section 3.4]] , unless stated otherwise, only scenarios that pass the vetting criteria are included ( [[#3.2|Section 3.2]] ). Boxes indicate the interquartile range, the median is shown with a horizontal black line, while vertical lines show the 5–95% interval.

So, while pathways indicate some flexibility in emissions reductions across sectors, all pathways involve substantial CO 2 emissions reductions in all sectors and regions ( ''high confidence'' ) ( [[#Luderer--2018|Luderer et al. 2018]] ; [[#Rogelj--2018|Rogelj et al. 2018]] a,b; [[#Méjean--2019|Méjean et al. 2019]] ; [[#Azevedo--2021|Azevedo et al. 2021]] ). Projected CO 2 emissions reductions between 2019 and 2050 in 1.5°C (&gt;50%) pathways with no or limited overshoot are around 77% for energy demand, with a 5–95% range of 31–96%, [[#footnote-008|12]] 115% for energy supply (90–167%), and 148% for AFOLU (94–387%). In pathways that limit warming to 2°C (&gt;67%), projected CO 2 emissions are reduced between 2019 and 2050 by around 49% for energy demand, 97% for energy supply, and 136% for AFOLU (Sections 3.4.2–3.4.6). Almost 75% of GHG reductions at the time of net zero GHG are from the energy system, 13% are from AFOLU CO 2 , and 13% from non-CO 2 (Figure 3.21). These reductions are achieved through a variety of sectoral strategies, illustrated in Figure 3.21 (Figure 3.21b), and described in Sections 3.4.2 to 3.4.7; the primary strategies include declines in fossil energy, increases in low-carbon energy use, and CDR to address residual emissions.

'''Table 3.4 | Energy and emissions characteristics of the pathways by climate category for 2030, 2050, 2100.''' Source: AR6 scenarios database.

{| class="wikitable"
|-
! '''p50'''

'''(p5–p95)''' ''a''

! colspan="2"| '''Global Mean Surface Air Temperature change'''

! colspan="3"| '''Low-carbon share of Primary Energy''' ''d, e''

'''[%]'''

'''2020 = 16 (12–18)'''

! colspan="3"| '''Energy &amp; Industrial Processes Index'''

'''2020 = 100'''

! colspan="3"| '''Final energy demand'''

'''[EJ/yr]'''

'''2020 = 419 (367–458)'''

! colspan="3"| '''Final energy intensity of GDP Index'''

'''2020 = 100'''

! colspan="3"| '''Electricity share in final energy'''

'''[%]'''

'''2020 = 20 (18–25)'''

! colspan="3"| '''CO2 intensity of electricity'''

'''[Mt CO''' ''2'' '''/TWh]'''

'''2020 = 469 (419–538)'''

! colspan="3"| '''Non-energy GHG emissions'''

'''[Gt CO''' ''2'' '''-eq]'''

'''2020 = 18 (15–21)'''

! colspan="4"| '''Fossil CCS (2100)'''

'''[Gt CO''' ''2'' ''']'''

'''2020 = 0 (0–0)'''

|-
! '''Category [# pathways]''' ''b, c''

! '''Category/ subset'''

! '''WG1 SSP &amp; IPs alignment'''

! '''2030'''

! '''2050'''

! '''2100'''

! '''2030'''

! '''2050'''

! '''2100'''

! '''2030'''

! '''2050'''

! '''2100'''

! '''2030'''

! '''2050'''

! '''2100'''

! '''2030'''

! '''2050'''

! '''2100'''

! '''2030'''

! '''2050'''

! '''2100'''

! '''2030'''

! '''2050'''

! '''2100'''

! '''2030'''

! '''2050'''

! '''2100'''

! '''2020–2100'''

|-
| rowspan="2"| '''C1 [97]'''

| rowspan="2"| '''limit warming to 1.5°C (&gt;50%) with no or limited overshoot'''

| rowspan="2"| IMP-SD, IMP-LD,IMP-Ren, SSP1-1.9

| 32

| 68

| 75

| 65

| 8

| –3

| 399

| 410

| 612

| 71

| 46

| 26

| 27

| 52

| 66

| 99

| –5

| –4

| 10

| 5

| 2

| 1

| 2

| 3

| 196

|-
| (17–48)

| (25–86)

| (19–98)

| (49–75)

| (–8–24)

| (–20–8)

| (293–447)

| (325–540)

| (321–818)

| (59–81)

| (34–60)

| (14–45)

| (23–35)

| (40–64)

| (50–78)

| (4–215)

| (–66–11)

| (–104–1)

| (5–13)

| (1–9)

| (–2–9)

| (0–5)

| (0–13)

| (0–16)

| (3–882)

|-
| rowspan="2"| '''C2 [133]'''

| rowspan="2"| '''return warming to 1.5°C (&gt;50%) after a high overshoot'''

| rowspan="2"| IMP-Neg

| 24

| 57

| 86

| 79

| 18

| –14

| 458

| 442

| 675

| 76

| 44

| 23

| 25

| 45

| 61

| 218

| 0

| –1

| 13

| 6

| 1

| 0

| 3

| 1

| 280

|-
| (11–35)

| (19–77)

| (25–97)

| (66–94)

| (2–37)

| (–25–0)

| (372–504)

| (345–561)

| (415–819)

| (64–88)

| (35–63)

| (15–45)

| (20–29)

| (34–56)

| (49–73)

| (99–353)

| (–75–16)

| (–118–3)

| (10–19)

| (2–9)

| (–7–7)

| (0–4)

| (0–13)

| (0–16)

| (7–831)

|-
| rowspan="2"| '''C3 [311]'''

| rowspan="2"| limit warming to 2°C (&gt;67%)

|
| 24

| 51

| 73

| 84

| 31

| –1

| 446

| 448

| 625

| 77

| 50

| 26

| 24

| 42

| 60

| 248

| 5

| –8

| 12

| 7

| 5

| 0

| 3

| 5

| 266

|-
|
| (16–32)

| (29–75)

| (34–94)

| (70–95)

| (9–47)

| (–19–8)

| (356–491)

| (344–540)

| (421–788)

| (65–88)

| (36–62)

| (18–41)

| (20–29)

| (30–54)

| (43–72)

| (93–375)

| (–72–51)

| (–105–5)

| (6–18)

| (3–12)

| (–1–8)

| (0–3)

| (0–12)

| (0–15)

| (7–773)

|-
| rowspan="2"| '''C3a [204]'''

| rowspan="2"| '''… with action starting in 2020'''

| rowspan="2"| SSP2-2.6

| 21

| 39

| 71

| 92

| 45

| –3

| 459

| 489

| 641

| 76

| 45

| 22

| 23

| 35

| 56

| 322

| 24

| –14

| 13

| 9

| 2

| 0

| 2

| 6

| 279

|-
| (14–24)

| (24–63)

| (34–91)

| (80–100)

| (26–64)

| (–21–9)

| (379–497)

| (362–601)

| (450–796)

| (71–87)

| (39–65)

| (19–41)

| (19–28)

| (23–44)

| (44–69)

| (227–381)

| (–48–112)

| (–117–7)

| (8–19)

| (3–12)

| (–1–9)

| (0–2)

| (0–9)

| (0–16)

| (7–684)

|-
| rowspan="2"| '''C3b [97]'''

| rowspan="2"| '''… NDCs until 2030'''

| rowspan="2"| IMP-GS

| 21

| 31

| 67

| 92

| 66

| 9

| 466

| 519

| 680

| 77

| 51

| 23

| 23

| 32

| 53

| 341

| 107

| –3

| 15

| 10

| 4

| 0

| 1

| 5

| 200

|-
| (12–24)

| (22–44)

| (42–84)

| (84–102)

| (50–84)

| (–13–32)

| (389–499)

| (435–585)

| (383–812)

| (74–88)

| (45–66)

| (18–40)

| (19–28)

| (19–41)

| (40–65)

| (257–418)

| (14–208)

| (–73–34)

| (10–19)

| (5–15)

| (–1–11)

| (0–1)

| (0–7)

| (0–15)

| (5–730)

|-
| rowspan="2"| '''C4 [159]'''

| rowspan="2"| '''limit warming to 2°C (&gt;50%)'''

|
| 20

| 25

| 47

| 94

| 82

| 47

| 467

| 551

| 701

| 79

| 55

| 26

| 23

| 29

| 48

| 354

| 216

| 28

| 17

| 13

| 8

| 0

| 0

| 4

| 47

|-
|
| (11–23)

| (14–36)

| (28–65)

| (87–101)

| (67–92)

| (21–78)

| (410–508)

| (471–632)

| (432–910)

| (75–89)

| (50–70)

| (20–42)

| (19–28)

| (19–38)

| (30–56)

| (257–469)

| (69–317)

| (–20–166)

| (11–20)

| (9–17)

| (2–12)

| (0–0)

| (0–4)

| (0–16)

| (0–536)

|-
| rowspan="2"| '''C5 [212]'''

| rowspan="2"| '''limit warming to 2.5°C (&gt;50%)'''

|
| 17

| 19

| 29

| 98

| 94

| 73

| 492

| 599

| 804

| 85

| 64

| 33

| 24

| 29

| 41

| 414

| 311

| 185

| 19

| 19

| 16

| 0

| 0

| 0

| 0

|-
|
| (11–21)

| (8–29)

| (8–51)

| (91–101)

| (80–101)

| (56–106)

| (434–540)

| (513–701)

| (557–983)

| (76–91)

| (54–76)

| (27–48)

| (20–28)

| (23–35)

| (29–50)

| (311–538)

| (130–499)

| (12–461)

| (13–24)

| (14–25)

| (9–26)

| (0–0)

| (0–2)

| (0–8)

| (0–221)

|-
| rowspan="2"| '''C6 [97]'''

| rowspan="2"| '''limit warming to 3°C (&gt;50%)'''

| SSP2-4.5

| 13

| 13

| 29

| 102

| 106

| 91

| 540

| 696

| 941

| 89

| 73

| 47

| 26

| 31

| 43

| 463

| 425

| 189

| 20

| 21

| 20

| 0

| 0

| 0

| 0

|-
| Mod-Act

| (11–17)

| (9–20)

| (14–45)

| (99–103)

| (104–109)

| (87–95)

| (413–574)

| (504–856)

| (692–1136)

| (88–92)

| (64–79)

| (25–51)

| (22–30)

| (28–35)

| (35–50)

| (372–514)

| (352–484)

| (142–441)

| (19–25)

| (20–29)

| (13–31)

| (0–0)

| (0–0)

| (0–2)

| (0–38)

|-
| rowspan="2"| '''C7 [164]'''

| rowspan="2"| '''limit warming to 4°C (&gt;50%)'''

| SSP3-7.0

| 32

| 68

| 75

| 65

| 8

| –3

| 399

| 410

| 612

| 71

| 46

| 26

| 27

| 52

| 66

| 99

| –5

| –4

| 10

| 5

| 2

| 1

| 2

| 3

| 196

|-
| Cur-Pol

| (17–48)

| (25–86)

| (19–98)

| (49–75)

| (–8–24)

| (–20–8)

| (293–447)

| (325–540)

| (321–818)

| (59–81)

| (34–60)

| (14–45)

| (23–35)

| (40–64)

| (50–78)

| (4–215)

| (–66–11)

| (–104–1)

| (5–13)

| (1–9)

| (–2–9)

| (0–5)

| (0–13)

| (0–16)

| (3–882)

|-
| rowspan="2"| '''C8 [29]'''

| rowspan="2"| '''exceed warming of 4°C (≥50%)'''

| SSP5-8.5

| 24

| 57

| 86

| 79

| 18

| –14

| 458

| 442

| 675

| 76

| 44

| 23

| 25

| 45

| 61

| 218

| 0

| –1

| 13

| 6

| 1

| 0

| 3

| 1

| 280

|-
|
| (11–35)

| (19–77)

| (25–97)

| (66–94)

| (2–37)

| (–25–0)

| (372–504)

| (345–561)

| (415–819)

| (64–88)

| (35–63)

| (15–45)

| (20–29)

| (34–56)

| (49–73)

| (99–353)

| (–75–16)

| (–118–3)

| (10–19)

| (2–9)

| (–7–7)

| (0–4)

| (0–13)

| (0–16)

| (7–831)

|}

a Values in the table refer to the 50th and (5–95th) percentile values.

b See category descriptions in Table 3.1.

c The warming profile of ''IMP-Neg'' peaks around 2060 and declines thereafter to below 1.5°C (50% likelihood) shortly after 2100. Whilst technically classified as a C3, it strongly exhibits the characteristics of C2 high-overshoot scenarios.

d Primary Energy as calculated in ‘Direct Equivalent’ terms according to IPCC reporting conventions.

e Low-carbon energy here defined to include: renewables (including biomass, solar, wind, hydro, geothermal, ocean); fossil fuels when used with CCS; and, nuclear power.

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[[File:dcb652b7ddcd28456a52dd4c77eba9b8 IPCC_AR6_WGIII_Figure_3_21.png]]
'''Figure 3.21 | Left panel: Greenhouse gas (GHG) emissions reductions from 2019 by sector at the year of net zero GHG for all scenarios that reach net zero GHG.''' Emissions reductions by sector for direct (demand) and indirect (upstream supply) are shown as the percent of total GHG reductions. '''Right panel:''' key indicators in 2050 for the IMPs. Definitions of significant and very significant are defined relative to 2019 and vary between indicators, as follows: fossil energy (significant &gt;10%, very significant &gt;50%), renewables (&gt;150 EJ yr –1 , &gt;200 EJ yr –1 ), bioenergy (&gt;100%, &gt;200%), BECCS (&gt;2.0 GtCO 2 yr –1 , &gt;3.5 GtCO 2 yr –1 ), AFOLU (&gt;100% decline, &gt;130% decline), energy crops (&gt;150 million ha, &gt;400 million ha), forest (&gt;5% increase, &gt;15% increase). Source: AR6 Scenarios Database.

In the context of mitigation pathways, only a few studies have examined solar radiation modification (SRM), typically focusing on Stratospheric Aerosol Injection ( [[#Arinoa--2016|Arinoa et al. 2016]] ; [[#Emmerling--2018a|Emmerling and Tavoni 2018a]] ,b; [[#Heutel--2018|Heutel et al. 2018]] ; [[#Helwegen--2019|Helwegen et al. 2019]] ; [[#Rickels--2020|Rickels et al. 2020]] ; [[#Belaia--2021|Belaia et al. 2021]] ). These studies find that substantial mitigation is required to limit warming to a given level, even if SRM is available ( [[#Moreno-Cruz--2017|Moreno-Cruz and Smulders 2017]] ; [[#Emmerling--2018b|Emmerling and Tavoni 2018b]] ; [[#Belaia--2021|Belaia et al. 2021]] ). SRM may reduce some climate impacts, reduce peak temperatures, lower mitigation costs, and extend the time available to achieve mitigation; however, SRM does not address ocean acidification and may involve risks to crop yields, economies, human health, or ecosystems (AR6 WGII Chapter 16; AR6 WGI TS and Chapter 5; SR1.5 SPM; and Cross-Working Group Box 4 in [[IPCC:Wg3:Chapter:Chapter-14|Chapter 14]] of this report). There are also significant uncertainties surrounding SRM, including uncertainties on the costs and risks, which can substantially alter the amount of SRM used in modelled pathways ( [[#Tavoni--2017|Tavoni et al. 2017]] ; [[#Heutel--2018|Heutel et al. 2018]] ; [[#IPCC--2018|IPCC 2018]] ; [[#Helwegen--2019|Helwegen et al. 2019]] ; [[#NASEM--2021|NASEM 2021]] ). Furthermore, the degree of international cooperation can influence the amount of SRM deployed in scenarios, with uncoordinated action resulting in larger SRM deployment and consequently larger risks/impacts from SRM ( [[#Emmerling--2018a|Emmerling and Tavoni 2018a]] ). Bridging research and governance involves consideration of the full range of societal choices and ramifications ( [[#Sugiyama--2018|Sugiyama et al. 2018]] ). More information on SRM, including the caveats, risks, uncertainties, and governance issues is found in AR6 WGI Chapter 4; AR6 WGIII Chapter 14; and Cross-Working Group Box 4 in [[IPCC:Wg3:Chapter:Chapter-14|Chapter 14]] of this report.

<div id="3.4.1.3" class="h3-container"></div>

<span id="linkages-among-sectors"></span>
==== 3.4.1.3 Linkages Among Sectors ====

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Mitigation in one sector can be dependent upon mitigation in another sector, or may involve trade-offs between sectors. Mitigation in energy demand often includes electrification ( [[#Pietzcker--2014|Pietzcker et al. 2014]] ; [[#Luderer--2018|Luderer et al. 2018]] ; [[#Sharmina--2020|Sharmina et al. 2020]] ; [[#DeAngelo--2021|DeAngelo et al. 2021]] ), however such pathways only result in reduced emissions ''if'' the electricity sector is decarbonised ( [[#Zhang--2020|Zhang and Fujimori 2020]] ) (Chapter 12). Relatedly, the mitigation potential of some sectors (e.g., transportation) depends on the decarbonisation of liquid fuels, for example, through biofuels ( [[#Pietzcker--2014|Pietzcker et al. 2014]] ; [[#Wise--2017|Wise et al. 2017]] ; [[#Sharmina--2020|Sharmina et al. 2020]] ) (Chapter 12). In other cases, mitigation in one sector results in reduced emissions in another sector. For example, increased recycling can reduce primary resource extraction; planting trees or green roofs in urban areas can reduce the energy demand associated with space cooling (Chapter 12).

Mitigation in one sector can also result in additional emissions in another. One example is electrification of end use which can result in increased emissions from energy supply. However, one comparitively well-researched example of this linkage is bioenergy. An increase in demand for bioenergy within the energy system has the potential to influence emissions in the AFOLU sector through the intensification of land and forest management and/or via land-use change ( [[#Daioglou--2019|Daioglou et al. 2019]] ; [[#Smith--2019|Smith et al. 2019]] ; [[#Smith--2020a|Smith et al. 2020a]] ; [[#IPCC--2019a|IPCC 2019a]] ). The effect of bioenergy and BECCS on mitigation depends on a variety of factors in modelled pathways. In the energy system, the emissions mitigation depends on the scale of deployment, the conversion technology, and the fuel displaced ( [[#Calvin--2021|Calvin et al. 2021]] ). Limiting or excluding bioenergy and/or BECCS increases mitigation cost and may limit the ability of a model to reach a low warming level ( [[#Edmonds--2013|Edmonds et al. 2013]] ; [[#Calvin--2014b|Calvin et al. 2014b]] ; [[#Luderer--2018|Luderer et al. 2018]] ; [[#Muratori--2020|Muratori et al. 2020]] ). In AFOLU, bioenergy can increase or decrease terrestrial carbon stocks and carbon sequestration, depending on the scale, biomass feedstock, land management practices, and prior land use ( [[#Calvin--2014c|Calvin et al. 2014c]] ; [[#Wise--2015|Wise et al. 2015]] ; [[#IPCC--2019a|IPCC 2019a]] ; [[#Smith--2019|Smith et al. 2019]] , 2020a; [[#Calvin--2021|Calvin et al. 2021]] ).

Pathways with very high biomass production for energy use typically include very high carbon prices in the energy system ( [[#Popp--2017|Popp et al. 2017]] ; [[#Rogelj--2018|Rogelj et al. 2018]] b), little or no land policy ( [[#Calvin--2014b|Calvin et al. 2014b]] ), a high discount rate ( [[#Emmerling--2019|Emmerling et al. 2019]] ), and limited non-BECCS CDR options (e.g., afforestation, DACCS) ( [[#Chen--2013|Chen and Tavoni 2013]] ; [[#Calvin--2014b|Calvin et al. 2014b]] ; [[#Marcucci--2017|Marcucci et al. 2017]] ; [[#Realmonte--2019|Realmonte et al. 2019]] ; [[#Fuhrman--2020|Fuhrman et al. 2020]] ). Higher levels of bioenergy consumption are likely to involve trade-offs with mitigation in other sectors, notably in construction (i.e., wood for material and structural products) and AFOLU (carbon stocks and future carbon sequestration), as well as trade-offs with sustainability ( [[#3.7|Section 3.7]] ) and feasibility concerns ( [[#3.8|Section 3.8]] ). Not all of these trade-offs are fully represented in all IAMs. Based on sectoral studies, the technical potential for bioenergy, when constraints for food security and environmental considerations are included, are 5–50 EJ yr –1 and 50–250 EJ yr –1 in 2050 for residues and dedicated biomass production systems, respectively (Chapter 7). Bioenergy deployment in IAMs is within the range of these potentials, with between 75 and 248 EJ yr –1 in 2050 in pathways that limit warming to 1.5°C with no or limited overshoot. Finally, IAMs do not include all potential feedstock and management practices, and have limited representation of institutions, governance, and local context ( [[#Brown--2019|Brown et al. 2019]] ; [[#Butnar--2020|Butnar et al. 2020]] ; [[#Calvin--2021|Calvin et al. 2021]] ).

The inclusion of CDR options, like BECCS, can affect the timing of emissions mitigation in IAM scenarios, that is, delays in mitigations actions are compensated by net negative emissions in the second half of the century. However, studies with limited net negative emissions in the long term require very rapid declines in emissions in the near term ( [[#van%20Vuuren--2017|van Vuuren et al. 2017]] ). Especially in forest-based systems, increased harvesting of forests can perturb the carbon balance of forestry systems, increasing emissions for some period; the duration of this period of increased emissions, preceding net emissions reductions, can be very variable ( [[#Mitchell--2012|Mitchell et al. 2012]] ; [[#Lamers--2013|Lamers and Junginger 2013]] ; [[#Röder--2019|Röder et al. 2019]] ; [[#Hanssen--2020|Hanssen et al. 2020]] ; [[#Cowie--2021|Cowie et al. 2021]] ). However, the factors contributing to differences in recovery time are known ( [[#Mitchell--2012|Mitchell et al. 2012]] ; [[#Zanchi--2012|Zanchi et al. 2012]] ; [[#Lamers--2013|Lamers and Junginger 2013]] ; [[#Laganière--2017|Laganière et al. 2017]] ; [[#Röder--2019|Röder et al. 2019]] ). Some studies that consider market-mediated effects find that an increased demand for biomass from forests can provide incentives to maintain existing forests and potentially to expand forest areas, providing additional carbon sequestration as well as additional biomass ( [[#Dwivedi--2014|Dwivedi et al. 2014]] ; [[#Kim--2018|Kim et al. 2018]] ; [[#Baker--2019|Baker et al. 2019]] ; [[#Favero--2020|Favero et al. 2020]] ). However, these responses are uncertain and likely to vary geographically.

<div id="3.4.2" class="h2-container"></div>

<span id="energy-supply"></span>
=== 3.4.2 Energy Supply ===

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Without mitigation, energy consumption and supply emissions continue to rise ( ''high confidence'' ) ( [[#Kriegler--2016|Kriegler et al. 2016]] ; [[#Bauer--2017|Bauer et al. 2017]] ; [[#Riahi--2017|Riahi et al. 2017]] ; [[#Mcjeon--2021|Mcjeon et al. 2021]] ) ( [[IPCC:Wg3:Chapter:Chapter-6#6.7|Section 6.7]] ). While the share of renewable energy continues to grow in reference scenarios, fossil fuel accounts for the largest share of primary energy ( [[#Bauer--2017|Bauer et al. 2017]] ; [[#Price--2017|Price and Keppo 2017]] ; [[#Riahi--2017|Riahi et al. 2017]] ). In scenarios that limit warming to 2°C or lower, transition of the energy-supply sector to a low- or no-carbon system is rapid ( [[#Rogelj--2016|Rogelj et al. 2016]] , 2018b; [[#Grubler--2018|Grubler et al. 2018]] ; [[#Luderer--2018|Luderer et al. 2018]] ; [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ). CO 2 emissions from energy supply reach net zero around 2041 (2033–2057) in pathways limiting warming to 1.5°C (&gt;50%) with no or limited overshoot and around 2053 (2040–2066) in pathways that limit warming to 2°C (&gt;67%). Emissions reductions continue, with emissions reaching –7.1 GtCO 2 yr –1 (–15 to –2.3 GtCO 2 yr –1 ) in 2100 in all pathways that limit warming to 2°C (&gt;67%) or lower.

All pathways that limit warming to 2°C (&gt;67%) or lower show substantial reductions in fossil fuel consumption and a near elimination of the use of coal without CCS ( ''high confidence'' ) ( [[#Bauer--2017|Bauer et al. 2017]] ; [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ; [[#Grubler--2018|Grubler et al. 2018]] ; [[#Luderer--2018|Luderer et al. 2018]] ; [[#Rogelj--2018|Rogelj et al. 2018]] a,b; [[#Azevedo--2021|Azevedo et al. 2021]] ; [[#Mcjeon--2021|Mcjeon et al. 2021]] ; [[#Welsby--2021|Welsby et al. 2021]] ) (Figure 3.22). In these pathways, the use of coal, gas and oil is reduced by 90%, 25%, and 41%, respectively, between 2019 and 2050 and 91%, 39%, and 78% between 2019 and 2100; coal without CCS is further reduced to 99% below its 2019 levels in 2100. These pathways show an increase in low-carbon energy, with 88% (69–97%) of primary energy from low-carbon sources in 2100, with different combinations of low-carbon fuels (e.g., non-biomass renewables, biomass, nuclear, and CCS) ( [[#Rogelj--2018|Rogelj et al. 2018]] a,b; [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ) (Sections 3.4.1 and 6.7). Across all pathways that limit warming to 2°C and below, non-biomass renewables account for 52% (24–77%) of primary energy in 2100 ( [[#Creutzig--2017|Creutzig et al. 2017]] ; [[#Pietzcker--2017|Pietzcker et al. 2017]] ; [[#Rogelj--2018|Rogelj et al. 2018]] b) ( [[IPCC:Wg3:Chapter:Chapter-6|Chapter 6]] and Figure 3.22). There are some studies analysing the potential for 100% renewable energy systems ( [[#Hansen--2019|Hansen et al. 2019]] ); however, there are a range of issues around such systems (Box 6.6).

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[[File:97e33177bc24a702d855ec3eb5601c51 IPCC_AR6_WGIII_Figure_3_22.png]]

'''Figure 3.22 | Primary energy consumption across scenarios: total primary energy (a), fossil fuels (b), coal without CCS (c), non-biomass renewables (d), and biomass (e).''' Scenarios are grouped by their temperature category. Primary energy is reported in direct equivalent, where one unit of nuclear or non-biomass renewable energy output is reported as one unit of primary energy. Not all subcategories of primary energy are shown.

Stringent emissions reductions at the level required to limit warming to 2°C (&gt;67%) or 1.5°C are achieved through increased electrification of end use, resulting in increased electricity generation in all pathways ( ''high confidence'' ) ( [[#Rogelj--2018|Rogelj et al. 2018]] a; [[#Azevedo--2021|Azevedo et al. 2021]] ) (Figure 3.23). Nearly all electricity in pathways ''likely'' to limit warming to 2°C and below is from low- or no-carbon fuels ( [[#Rogelj--2018|Rogelj et al. 2018]] a; [[#Azevedo--2021|Azevedo et al. 2021]] ), with different shares of nuclear, biomass, non-biomass renewables, and fossil CCS across pathways. Low-emissions scenarios also show increases in hydrogen use (Figure 3.23).

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[[File:d63dc577f037f7f4f4d240d4783e8fe1 IPCC_AR6_WGIII_Figure_3_23.png]]

'''Figure 3.23 | Electricity (top left),share of low-carbon electricity (top right), and hydrogen (bottom left) production across all scenarios, grouped by the categories introduced in Section 3.''' '''2.''' Low carbon includes non-biomass renewables, biomass, nuclear, and CCS.

<div id="3.4.3" class="h2-container"></div>

<span id="buildings"></span>
=== 3.4.3 Buildings ===

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Global final energy use inthe building sector increases in all pathways as a result of population growth and increasing affluence (Figure 3.24). There is very little difference in final energy intensity for the buildings sector across scenarios. Direct CO 2 emissions from the buildings sector vary more widely across temperature stabilisation levels than energy consumption. In 2100, scenarios above 3°C [C7–C8] still show an increase of CO 2 emissions from buildings around 29% above 2019, while all scenarios ''likely'' to limit warming to 2°C and below have emission reductions of around 85% (8–100%). Carbon intensity declines in all scenarios, but much more sharply as the warming level is reduced.

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[[File:d195fda17f8e139626ad269cae6dd9d5 IPCC_AR6_WGIII_Figure_3_24.png]]

'''Figure 3.24 | Buildings final energy (a), CO''' 2 '''emissions (b), carbon intensity (c), energy intensity (d), share of final energy from electricity (e), and share of final energy from gases (f).''' Energy intensity is final energy per unit of GDP. Carbon intensity is CO 2 emissions per EJ of final energy. The first four indicators are indexed to 2019, 12 where values less than 1 indicate a reduction.

In all scenarios, the share of electricity in final energy use increases, a trend that is accelerated by 2050 for the scenarios ''likely'' to limit warming to 2°C and below (Figure 3.23). By 2100, the low-warming scenarios show large shares of electricity in final energy consumption for buildings. The opposite is observed for gases.

While several global IAM models have developed their buildings modules considerably over the past decade ( [[#Daioglou--2012|Daioglou et al. 2012]] ; [[#Knobloch--2017|Knobloch et al. 2017]] ; [[#Clarke--2018|Clarke et al. 2018]] ; [[#Edelenbosch--2021|Edelenbosch et al. 2021]] ; [[#Mastrucci--2021|Mastrucci et al. 2021]] ), the extremely limited availability of key sectoral variables in the AR6 scenarios database (such as floor space and energy use for individual services) prohibit a detailed analysis of sectoral dynamics. Individual studies in the literature often focus on single aspects of the buildings sector, though collectively providing a more comprehensive overview ( [[#Edelenbosch--2020|Edelenbosch et al. 2020]] ; [[#Ürge-Vorsatz--2020|Ürge-Vorsatz et al. 2020]] ). For example, energy demand is driven by economic development that fulfills basic needs ( [[#Mastrucci--2019|Mastrucci et al. 2019]] ; [[#Rao--2019a|Rao et al. 2019a]] ), but also drives up floor space in general ( [[#Daioglou--2012|Daioglou et al. 2012]] ; [[#Levesque--2018|Levesque et al. 2018]] ; [[#Mastrucci--2021|Mastrucci et al. 2021]] ) and ownership of energy-intensive appliances such as air conditioners ( [[#Isaac--2009|Isaac and van Vuuren 2009]] ; Colelli and Cian 2020; [[#Poblete-Cazenave--2021|Poblete-Cazenave et al. 2021]] ). These dynamics are heterogeneous and lead to differences in energy demand and emission mitigation potential across urban/rural buildings and income levels ( [[#Krey--2012|Krey et al. 2012]] ; [[#Poblete-Cazenave--2021|Poblete-Cazenave et al. 2021]] ). Mitigation scenarios rely on fuel switching and technology ( [[#Knobloch--2017|Knobloch et al. 2017]] ; [[#Dagnachew--2020|Dagnachew et al. 2020]] ), efficiency improvement in building envelopes ( [[#Levesque--2018|Levesque et al. 2018]] ; [[#Edelenbosch--2021|Edelenbosch et al. 2021]] ) and behavioural changes ( [[#van%20Sluisveld--2016|van Sluisveld et al. 2016]] ; [[#Niamir--2018|Niamir et al. 2018]] , 2020). The in-depth dynamics of mitigation in the building sector are explored in Chapter 9. [[#footnote-007|13]]

<div id="3.4.4" class="h2-container"></div>

<span id="transport"></span>
=== 3.4.4 Transport ===

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Reference scenarios show growth in transport demand, particularly in aviation and freight ( [[#Yeh--2017|Yeh et al. 2017]] ; [[#Sharmina--2020|Sharmina et al. 2020]] ; [[#Müller-Casseres--2021b|Müller-Casseres et al. 2021b]] ). Energy consumption continues to be dominated by fossil fuels in reference scenarios, with some increases in electrification ( [[#Yeh--2017|Yeh et al. 2017]] ; [[#Edelenbosch--2020|Edelenbosch et al. 2020]] ; [[#Yeh--2017|Yeh et al. 2017]] ). CO 2 emissions from transport increase for most models in reference scenarios ( [[#Yeh--2017|Yeh et al. 2017]] ; [[#Edelenbosch--2020|Edelenbosch et al. 2020]] ).

The relative contribution of demand-side reduction, energy- efficiency improvements, fuel switching, and decarbonisation of fuels, varyies by model, level of mitigation, mitigation options available, and underlying socio-economic pathway ( [[#Longden--2014|Longden 2014]] ; [[#Wise--2017|Wise et al. 2017]] ; [[#Yeh--2017|Yeh et al. 2017]] ; [[#Luderer--2018|Luderer et al. 2018]] ; [[#Yeh--2017|Yeh et al. 2017]] ; [[#Edelenbosch--2020|Edelenbosch et al. 2020]] ; [[#Müller-Casseres--2021a|Müller-Casseres et al. 2021a]] ,b). IAMs typically rely on technology-focused measures like energy- efficiency improvements and fuel switching to reduce carbon emissions ( [[#Pietzcker--2014|Pietzcker et al. 2014]] ; [[#Edelenbosch--2017a|Edelenbosch et al. 2017a]] ; [[#Yeh--2017|Yeh et al. 2017]] ; [[#Zhang--2018a|Zhang et al. 2018a]] ,b; [[#Rogelj--2018|Rogelj et al. 2018]] b; [[#Zhang--2018a|Zhang et al. 2018a]] ,b; [[#Sharmina--2020|Sharmina et al. 2020]] ). Many mitigation pathways show electrification of the transport system ( [[#Luderer--2018|Luderer et al. 2018]] ; [[#Pietzcker--2014|Pietzcker et al. 2014]] ; [[#Longden--2014|Longden 2014]] ; [[#Luderer--2018|Luderer et al. 2018]] ; [[#Zhang--2018a|Zhang et al. 2018a]] ); however, without decarboniszation of the electricity system, transport electrification can increase total energy system emissions ( [[#Zhang--2020|Zhang and Fujimori 2020]] ). A small number of pathways include demand-side mitigation measures in the transport sector; these studies show reduced carbon prices and reduced dependence on CDR ( [[#Grubler--2018|Grubler et al. 2018]] ; [[#Méjean--2019|Méjean et al. 2019]] ; [[#van%20de%20Ven--2018|van de Ven et al. 2018]] ; Zhang et al. 2018c; [[#Méjean--2019|Méjean et al. 2019]] ) ( [[#3.4.1|Section 3.4.1]] ). [[#footnote-006|14]]

Across all IAM scenarios assessed, final energy demand for transport continues to grow, including in many stringent mitigation pathways (Figure 3.25). The carbon intensity of energy declines substantially by 2100 in ''likely'' 2°C (&gt;67%) and below scenarios, leading to substantial declines in transport sector CO 2 emissions with increased electrification of the transport system (Figure 3.23).

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[[File:87344fffd1ca49bcfd1633cdecaf99fc IPCC_AR6_WGIII_Figure_3_25.png]]

'''Figure 3.25 | Transport finalenergy (a),CO''' 2 '''emissions (b), carbon intensity (cand share of final energy from electricity (d), hydrogen (e), and biofuels (f).''' See [[IPCC:Wg3:Chapter:Chapter-10|Chapter 10]] for a discussion of energy intensity. Carbon intensity is CO 2 emissions per EJ of final energy. The first three indicators are indexed to 2019, 13 , where values less than 1 indicate a reduction.

The transport sector has more detail than other sectors in many IAMs ( [[#Edelenbosch--2020|Edelenbosch et al. 2020]] ); however, there is considerable variation across models. Some models (e.g., GCAM, IMAGE, MESSAGE-GLOBIOM) represent different transport modes with endogenous shifts across modes as a function of income, price, and modal speed ( [[#Edelenbosch--2020|Edelenbosch et al. 2020]] ). [[#footnote-005|15]] However, IAMs, including those with detailed transport, exclude several supply-side (e.g., synthetic fuels) and demand-side (e.g., behaviour change, reduced shipping, telework and automation) mitigation options ( [[#Pietzcker--2014|Pietzcker et al. 2014]] ; [[#Creutzig--2016|Creutzig et al. 2016]] ; [[#Mittal--2017|Mittal et al. 2017]] ; [[#Davis--2018|Davis et al. 2018]] ; [[#Köhler--2020|Köhler et al. 2020]] ; [[#Mittal--2017|Mittal et al. 2017]] ; [[#Gota--2019|Gota et al. 2019]] ; [[#Wilson--2019|Wilson et al. 2019]] ; [[#Creutzig--2016|Creutzig et al. 2016]] ; [[#Köhler--2020|Köhler et al. 2020]] ; [[#Sharmina--2020|Sharmina et al. 2020]] ; [[#Pietzcker--2014|Pietzcker et al. 2014]] ; [[#Lefèvre--2021|Lefèvre et al. 2021]] ; [[#Müller-Casseres--2021a|Müller-Casseres et al. 2021a]] ,b).

As a result of these missing options and differences in how mitigation is implemented, IAMs tend to show less mitigation than the potential from national transport/energy models ( [[#Wachsmuth--2019|Wachsmuth and Duscha 2019]] ; [[#Gota--2019|Gota et al. 2019]] ; [[#Yeh--2017|Yeh et al. 2017]] ; [[#Gota--2019|Gota et al. 2019]] ; [[#Wachsmuth--2019|Wachsmuth and Duscha 2019]] ; [[#Edelenbosch--2020|Edelenbosch et al. 2020]] ). For the transport sector as a whole, studies suggest a mitigation potential of 4–-5 GtCO 2 per year in 2030 ( [[#Edelenbosch--2020|Edelenbosch et al. 2020]] ) with complete decarbonization decarbonisation possible by 2050 ( [[#Gota--2019|Gota et al. 2019]] ; [[#Wachsmuth--2019|Wachsmuth and Duscha 2019]] ). However, in the scenarios assessed in this chapter that limit warming to below 1.5°C (&gt;50%) with no or limited overshoot, transport sector CO 2 emissions are reduced by only 59% (28–% to 81%) in 2050 compared to 2015. IAM pathways also show less electrification than the potential from other studies; pathways that limit warming to 1.5°C with no or limited overshoot show a median of 25% (7– to 43%) of final energy from electricity in 2050, while the IEA NZE scenario includes 45% ( [[#IEA--2021a|]] [[#IEA--2021|IEA 2021]] a ).

<div id="3.4.5" class="h2-container"></div>

<span id="industry-16"></span>
=== 3.4.5 Industry [[#footnote-004|16]] ===

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Reference scenarios show declines in energy intensity, but increases in final energy use in the industrial sector ( [[#Edelenbosch--2017b|Edelenbosch et al. 2017b]] ). These scenarios show increases in CO 2 emissions both for the total industrial sector ( [[#Edelenbosch--2017b|Edelenbosch et al. 2017b]] , 2020; [[#Luderer--2018|Luderer et al. 2018]] ) and individual subsectors such as cement and iron and steel ( [[#van%20Ruijven--2016|van Ruijven et al. 2016]] ; [[#van%20Sluisveld--2021|van Sluisveld et al. 2021]] ) or chemicals ( [[#Daioglou--2014|Daioglou et al. 2014]] ; [[#van%20Sluisveld--2021|van Sluisveld et al. 2021]] ).

In mitigation pathways, CO 2 emissions reductions are achieved through a combination of energy savings (via energy-efficiency improvements and energy conservation), structural change, fuel switching, and decarbonisation of fuels ( [[#Edelenbosch--2017b|Edelenbosch et al. 2017b]] , 2020; [[#Grubler--2018|Grubler et al. 2018]] ; [[#Luderer--2018|Luderer et al. 2018]] ). Mitigation pathways show reductions in final energy for industry compared to the baseline ( [[#Edelenbosch--2017b|Edelenbosch et al. 2017b]] ; [[#Luderer--2018|Luderer et al. 2018]] ; [[#Edelenbosch--2020|Edelenbosch et al. 2020]] ) and reductions in the carbon intensity of the industrial sector through both fuel switching and the use of CCS ( [[#van%20Ruijven--2016|van Ruijven et al. 2016]] ; [[#Edelenbosch--2017b|Edelenbosch et al. 2017b]] , 2020; [[#Luderer--2018|Luderer et al. 2018]] ; [[#Paltsev--2021|Paltsev et al. 2021]] ; [[#van%20Sluisveld--2021|van Sluisveld et al. 2021]] ). The mitigation potential differs depending on the industrial subsector and the availability of CCS, with larger potential reductions in the steel sector ( [[#van%20Ruijven--2016|van Ruijven et al. 2016]] ) and cement industry ( [[#Sanjuán--2020|Sanjuán et al. 2020]] ) than in the chemicals sector ( [[#Daioglou--2014|Daioglou et al. 2014]] ). Many scenarios, including stringent mitigation scenarios, show continued growth in final energy; however, the carbon intensity of energy declines in all mitigation scenarios (Figure 3.26).

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[[File:6049af528c34f6efb0e6bd13f09312ff IPCC_AR6_WGIII_Figure_3_26.png]]

'''Figure 3.26 | Industrial final energy, including feedstocks (a), CO''' 2 '''emissions (b), carbon intensity (c), energy intensity (d), share of final energy from electricity (e), and share of final energy from gases (f).''' Energy intensity is final energy per unit of GDP. Carbon intensity is CO 2 emissions per EJ of final energy. The first four indicators are indexed to 2019, 15 where values less than 1 indicate a reduction. Industrial sector CO 2 emissions include fuel combustion emissions only.

The representation of the industry sector is very aggregated in most IAMs, with only a small subset of models disaggregating key sectors such as cement, fertiliser, chemicals, and iron and steel ( [[#Daioglou--2014|Daioglou et al. 2014]] ; [[#Edelenbosch--2017b|Edelenbosch et al. 2017b]] ; [[#Pauliuk--2017|Pauliuk et al. 2017]] ; [[#Napp--2019|Napp et al. 2019]] ; [[#van%20Sluisveld--2021|van Sluisveld et al. 2021]] ). IAMs often account for both energy combustion and feedstocks ( [[#Edelenbosch--2017b|Edelenbosch et al. 2017b]] ), but IAMs typically ignore material flows and miss linkages between sectors ( [[#Pauliuk--2017|Pauliuk et al. 2017]] ; [[#Kermeli--2019|Kermeli et al. 2019]] ). By excluding these processes, IAMs misrepresent the mitigation potential of the industry sector, for example by overlooking mitigation from material efficiency and circular economies ( [[#Sharmina--2020|Sharmina et al. 2020]] ), which can have substantial mitigation potential (Sections 5.3.4 and 11.3).

Sectoral studies indicate a large mitigation potential in the industrial sector by 2050, including the potential for net zero CO 2 emissions for steel, plastics, ammonia, and cement ( [[IPCC:Wg3:Chapter:Chapter-11#11.4.1|Section 11.4.1]] ). Detailed industry sector pathways show emissions reductions between 39% and 94% by mid-century compared to the present day [[#footnote-003|17]] ( [[IPCC:Wg3:Chapter:Chapter-11#11.4.2|Section 11.4.2]] ) and a substantial increase in direct electrification ( [[#IEA--2021a|]] [[#IEA--2021|IEA 2021]] a ). IAMs show comparable mitigation potential to sectoral studies with median reductions in CO 2 emissions between 2019 and 2050 of 70% in scenarios ''likely'' to limit warming to 2°C (&gt;67%) and below and a maximum reduction of 96% (Figure 3.26). Some differences between IAMs and sectoral models can be attributed to differences in technology availability, with IAMs sometimes including more technologies ( [[#van%20Ruijven--2016|van Ruijven et al. 2016]] ) and sometimes less ( [[#Sharmina--2020|Sharmina et al. 2020]] ).

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<span id="culture-forestry-and-other-land-use-afolu"></span>
=== 3.4.6 Culture, Forestry and Other Land Use (AFOLU) ===

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Mitigation pathways show substantial reductions in CO 2 emissions, but more modest reductions in AFOLU CH 4 and N 2 O emissions ( ''high confidence'' ) ( [[#Popp--2017|Popp et al. 2017]] ; [[#Roe--2019|Roe et al. 2019]] ; [[#Reisinger--2021|Reisinger et al. 2021]] ) (Figure 3.27). Pathways limiting warming to ''likely'' 2°C or lower are projected to reach net zero CO 2 emissions in the AFOLU sector around 2033 (2024–2060); however, AFOLU CH 4 and N 2 O emissions remain positive in all pathways (Figure 3.27). While IAMs include many land-based mitigation options, these models exclude several options with large mitigation potential, such as biochar, agroforestry, restoration/avoided conversion of coastal wetlands, and restoration/avoided conversion of peatland ( [[#IPCC--2019a|IPCC 2019a]] ; [[#Smith--2019|Smith et al. 2019]] ) ( [[IPCC:Wg3:Chapter:Chapter-7|Chapter 7]] and [[#3.4|Section 3.4]] ). Sectoral studies show higher mitigation potential than IAM pathways, as these studies include more mitigation options than IAMs ( ''medium confidence'' ) (Chapter 7).

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[[File:6ca1179605e4974fbfc970c271ee5845 IPCC_AR6_WGIII_Figure_3_27.png]]

'''Figure 3.27 | Reduction in AFOLU GHG emissions from 2019.''' The AFOLU CO 2 estimates in this figure are not necessarily comparable with country GHG inventories (see Chapter 7).

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[[File:b965bdd198cd34c14de6a4b756f2a66a IPCC_AR6_WGIII_Figure_3_28.png]]

'''Figure 3.28 | Change in''' '''land''' '''cover from 2019 in million hectares.''' Positive values indicate an increase in area.

Limiting warming to ''likely'' 2°C (&gt;67%) or lower can result in large-scale transformation of the land surface ( ''high confidence'' ) ( [[#Popp--2017|Popp et al. 2017]] ; [[#Rogelj--2018|Rogelj et al. 2018]] a,b; [[#Brown--2019|Brown et al. 2019]] ; [[#Roe--2019|Roe et al. 2019]] ). The scale of land transformation depends, ''inter alia'' , on the temperature goal and the mitigation options included ( [[#Popp--2017|Popp et al. 2017]] ; [[#Rogelj--2018|Rogelj et al. 2018]] a; [[#IPCC--2019a|IPCC 2019a]] ). Pathways with more demand-side mitigation options show less land transformation than those with more limited options ( [[#Grubler--2018|Grubler et al. 2018]] ; [[#van%20Vuuren--2018|van Vuuren et al. 2018]] ; [[#IPCC--2019a|IPCC 2019a]] ). Most of these pathways show increases in forest cover, with an increase of 322 million ha (–67 to 890 million ha) in 2050 in pathways that limit warming to 1.5°C (&gt;50%) with no or limited overshoot, whereas bottom-up models portray an economic potential of 300–500 million ha of additional forest (Chapter 7). Many IAM pathways also include large amounts of energy cropland area, to supply biomass for bioenergy and BECCS, with 199 (56–482) million ha in 2050 in pathways that limit warming to 1.5°C (&gt;50%) with no or limited overshoot. Large land transformations, such as afforestation/reforestation and widespread planting of energy crops, can have implications for biodiversity and sustainable development (Sections 3.7, 7.7.4 and 12.5).

Delayed mitigation has implications for land-use transitions ( [[#Hasegawa--2021a|Hasegawa et al. 2021a]] ). Delaying mitigation action can result in a temporary overshoot of temperature and large-scale deployment of CDR in the second half of the century to reduce temperatures from their peak to a given level ( [[#Smith--2019|Smith et al. 2019]] ; [[#Hasegawa--2021a|Hasegawa et al. 2021a]] ). IAM pathways rely on afforestation and BECCS as CDR measures, so delayed mitigation action results in substantial land-use change in the second half of the century with implications for sustainable development ( [[#Hasegawa--2021a|Hasegawa et al. 2021a]] ) ( [[#3.7|Section 3.7]] ). Shifting to earlier mitigation action reduces the amount of land required for this, though at the cost of larger land-use transitions earlier in the century ( [[#Hasegawa--2021a|Hasegawa et al. 2021a]] ). Earlier action could also reduce climate impacts on agriculture and land-based mitigation options ( [[#Smith--2019|Smith et al. 2019]] ).

Some AFOLU mitigation options can enhance vegetation and soil carbon stocks such as reforestation, restoration of degraded ecosystems, protection of ecosystems with high carbon stocks and changes to agricultural land management to increase soil carbon ( ''high confidence'' ) ( [[#Griscom--2017|Griscom et al. 2017]] ; [[#de%20Coninck--2018|de Coninck et al. 2018]] ; [[#Fuss--2018|Fuss et al. 2018]] ; [[#Smith--2019|Smith et al. 2019]] ) (AR6 WGIII Chapter 7). The time scales associated with these options indicate that carbon sinks in terrestrial vegetation and soil systems can be maintained or enhanced so as to contribute towards long-term mitigation ( ''high confidence'' ); however, many AFOLU mitigation options do not continue to sequester carbon indefinitely ( [[#Fuss--2018|Fuss et al. 2018]] ; [[#de%20Coninck--2018|de Coninck et al. 2018]] ; [[#IPCC--2019a|IPCC 2019a]] ) (AR6 WGIII Chapter 7). In the very long term (the latter part of the century and beyond), it will become more challenging to continue to enhance vegetation and soil carbon stocks, so that the associated carbon sinks could diminish or even become sources ( ''high confidence'' ) ( [[#de%20Coninck--2018|de Coninck et al. 2018]] ; [[#IPCC--2019a|IPCC 2019a]] ) (AR6 WGI Chapter 5). Sustainable forest management, including harvest and forest regeneration, can help to remediate and slow any decline in the forest carbon sink, for example by restoring degraded forest areas, and so go some way towards addressing the issue of sink saturation (IPCC 2019) (AR6 WGI Chapter 5; and [[IPCC:Wg3:Chapter:Chapter-7|Chapter 7]] in this report). The accumulated carbon resulting from mitigation options that enhance carbon sequestration (e.g., reforestation, soil carbon sequestration) is also at risk of future loss due to disturbances (e.g., fire, pests) ( [[#Boysen--2017|Boysen et al. 2017]] ; [[#de%20Coninck--2018|de Coninck et al. 2018]] ; [[#Fuss--2018|Fuss et al. 2018]] ; [[#Smith--2019|Smith et al. 2019]] ; [[#IPCC--2019a|IPCC 2019a]] ; [[#Anderegg--2020|Anderegg et al. 2020]] ) (AR6 WGI Chapter 5). Maintaining the resultant high vegetation and soil carbon stocks could limit future land-use options, as maintaining these carbon stocks would require retaining the land use and land-cover configuration implemented to achieve the increased stocks.

Anthropogenic land CO 2 emissions and removals in IAM pathways cannot be directly compared with those reported in national GHG inventories ( ''high confidence'' ) ( [[#Grassi--2018|Grassi et al. 2018]] , 2021) ( [[IPCC:Wg3:Chapter:Chapter-7#7.2|Section 7.2]] ). Due to differences in definitions for the area of managed forests and which emissions and removals are considered anthropogenic, the reported anthropogenic land CO 2 emissions and removals differ by about 5.5 GtCO 2 yr –1 between IAMs, which rely on bookkeeping approaches (e.g., [[#Houghton--2017|Houghton and Nassikas 2017]] ), and national GHG inventories ( [[#Grassi--2021|Grassi et al. 2021]] ). Such differences in definitions can alter the reported time at which anthropogenic net zero CO 2 emissions are reached for a given emission scenario. Using national inventories would lead to an earlier reported time of net zero ( [[#van%20Soest--2021b|van Soest et al. 2021b]] ) or to lower calculated cumulative emissions until the time of net zero ( [[#Grassi--2021|Grassi et al. 2021]] ) as compared to IAM pathways. The numerical differences are purely due to differences in the conventions applied for reporting the anthropogenic emissions and do not have any implications for the underlying land-use changes or mitigation measures in the pathways. Grassi et al. ( [[#Grassi--2021|Grassi et al. 2021]] ) offer a methodology for adjusting to reconcile these differences and enable a more accurate assessment of the collective progress achieved under the Paris Agreement ( [[IPCC:Wg3:Chapter:Chapter-7|Chapter 7]] and Cross-Chapter Box 6 in Chapter 7).

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<span id="other-carbon-dioxide-removal-options"></span>
=== 3.4.7 Other Carbon Dioxide Removal Options ===

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This subsection includes other CDR options not discussed in the previous subsections, including direct air carbon capture and storage (DACCS), enhanced weathering (EW), and ocean-based approaches, focusing on the role of these options in long-term mitigation pathways, using both IAMs ( [[#Chen--2013|Chen and Tavoni 2013]] ; [[#Marcucci--2017|Marcucci et al. 2017]] ; [[#Rickels--2018|Rickels et al. 2018]] ; [[#Fuhrman--2019|Fuhrman et al. 2019]] , 2020, 2021; [[#Realmonte--2019|Realmonte et al. 2019]] ; Akimoto et al. 2021; [[#Strefler--2021a|Strefler et al. 2021a]] ) and non-IAMs ( [[#Fuss--2013|Fuss et al. 2013]] ; [[#González--2016|González and Ilyina 2016]] ; [[#Bednar--2021|Bednar et al. 2021]] ; [[#Shayegh--2021|Shayegh et al. 2021]] ). There are other options discussed in the literature, such as methane capture ( [[#Jackson--2019|Jackson et al. 2019]] ), however, the role of these options in long-term mitigation pathways has not been quantified and is thus excluded here. [[IPCC:Wg3:Chapter:Chapter-12|Chapter 12]] includes a more detailed description of the individual technologies, including their costs, potentials, financing, risks, impacts, maturity and upscaling.

Very few studies and pathways include other CDR options (Table 3.5). Pathways with DACCS include potentially large removal from DACCS (up to 37 GtCO 2 yr –1 in 2100) in the second half of the century ( [[#Chen--2013|Chen and Tavoni 2013]] ; [[#Marcucci--2017|Marcucci et al. 2017]] ; [[#Realmonte--2019|Realmonte et al. 2019]] ; [[#Fuhrman--2020|Fuhrman et al. 2020]] , 2021; [[#Shayegh--2021|Shayegh et al. 2021]] ; Akimoto et al. 2021) and reduced cost of mitigation ( [[#Bistline--2021|Bistline and Blanford 2021]] ; [[#Strefler--2021a|Strefler et al. 2021a]] ). At large scales, the use of DACCS has substantial implications for energy use, emissions, land, and water; substituting DACCS for BECCS results in increased energy usage, but reduced land-use change and water withdrawals ( [[#Fuhrman--2020|Fuhrman et al., 2020]] , 2021) (Chapter 12.3.2; AR6 WGI Chapter 5). The level of deployment of DACCS is sensitive to the rate at which it can be scaled up, the climate goal or carbon budget, the underlying socio-economic scenario, the availability of other decarbonisation options, the cost of DACCS and other mitigation options, and the strength of carbon-cycle feedbacks ( [[#Chen--2013|Chen and Tavoni 2013]] ; [[#Fuss--2013|Fuss et al. 2013]] ; [[#Honegger--2018|Honegger and Reiner 2018]] ; [[#Realmonte--2019|Realmonte et al. 2019]] ; [[#Fuhrman--2020|Fuhrman et al. 2020]] ; [[#Bistline--2021|Bistline and Blanford 2021]] ; [[#Fuhrman--2021|Fuhrman et al. 2021]] ; [[#Strefler--2021a|Strefler et al. 2021a]] ) (AR6 WGI Chapter 5). Since DACCS consumes energy, its effectiveness depends on the type of energy used; the use of fossil fuels would reduce its sequestration efficiency ( [[#Creutzig--2019|Creutzig et al. 2019]] ; [[#NASEM--2019|NASEM 2019]] ; [[#Babacan--2020|Babacan et al. 2020]] ). Studies with additional CDR options in addition to DACCS (e.g., enhanced weathering, BECCS, afforestation, biochar, and soil carbon sequestration) find that CO 2 removal is spread across available options ( [[#Holz--2018|Holz et al. 2018]] ; [[#Strefler--2021a|Strefler et al. 2021a]] ). Similar to DACCS, the deployment of deep-ocean storage depends on cost and the strength of carbon-cycle feedbacks ( [[#Rickels--2018|Rickels et al. 2018]] ).

'''Table 3.5 |Carbon dioxide removal in assessed pathways.''' Scenarios are grouped by temperature categories, as defined in [[#3.2.4|Section 3.2.4]] . Quantity indicates the median and 5–95th percentile range of cumulative sequestration from 2020 to 2100 in GtCO 2 . Count indicates the number of scenarios with positive values for that option. Source: AR6 Scenarios Database.

{| class="wikitable"
|-
! rowspan="2"| CDR option

! colspan="2"| C1: Limit warming to 1.5°C (&gt;50%) with no or limited overshoot

! colspan="2"| C2: Return warming to 1.5°C (&gt;50%) after a high overshoot

! colspan="2"| C3: Limit warming to 2°C (&gt;67%)

|-
! Quantity

! Count

! Quantity

! Count

! Quantity

! Count

|-
| CO 2 removal on managed land including Afforestation/Reforestation 1

| 262 (17–397)

| 64

| 330 (28–439)

| 82

| 209 (20–415)

| 196

|-
| BECCS

| 334 (32–780)

| 91

| 464 (226–842)

| 122

| 291 (174–653)

| 294

|-
| Enhanced weathering

| 0 (0–47)

| 2

| 0 (0–0)

| 1

| 0 (0–0)

| 1

|-
| DACCS

| 30 (0–308)

| 31

| 109 (0 – 539)

| 24

| 19 (0–253)

| 91

|}

1 Cumulative CDR from AFOLU cannot be quantified precisely because models use different reporting methodologies that in some cases combine gross emissions and removals, and use different baselines.

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