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

=== 3.4.1 Cross-sector Linkages ===

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==== 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.

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==== 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.

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