Editing IPCC:AR6/SR15/Chapter-2 (section)

== 2.4 Disentangling the Whole-System Transformation ==

<div id="article-2-4-block-1"></div>

Mitigation pathways map out prospective transformations of the energy, land and economic systems over this century (Clarke et al., 2014) <sup>[[#fn:r362|362]]</sup> . There is a diversity of potential pathways consistent with 1.5°C, yet they share some key characteristics summarized in Table 2.5. To explore characteristics of 1.5°C pathways in greater detail, this section focuses on changes in energy supply and demand, and changes in the AFOLU sector.

<div id="article-2-4-block-2"></div>

<span id="table-2.5"></span>
<!-- START TABLE -->
'''Table 2.5'''

<span id="overview-of-key-characteristics-of-1.5c-pathways"></span>
'''Overview of Key Characteristics of 1.5°C Pathways'''
<!-- TABLE -->
{| class="wikitable"
|-
! 1.5°C Pathway Characteristic
! Supporting Information
! Reference
|-
| Rapid and profound near-term decarbonisation of energy supply
| Strong upscaling of renewables and sustainable biomass and reduction of unabated (no CCS) fossil fuels, along with the rapid deployment of CCS, lead to a zero-emission energy supply system by mid-century.
| Section 2.4.1<br />
Section 2.4.2
|-
| Greater mitigation efforts on the demand side
| All end-use sectors show marked demand reductions beyond the reductions projected for 2°C pathways. Demand reductions from IAMs for 2030 and 2050 lie within the potential assessed by detailed sectoral bottom-up assessments.
| Section 2.4.3
|-
| Switching from fossil fuels to electricity in end-use sectors
| Both in the transport and the residential sector, electricity covers markedly larger shares of total demand by mid-century.
| Section 2.4.3.2<br />
Section 2.4.3.3
|-
| Comprehensive emission reductions are implemented in the coming decade
| Virtually all 1.5°C-consistent pathways decline net annual CO <sub>2</sub> emissions between 2020 and 2030, reaching carbon neutrality around mid-century. In 2030, below-1.5°C and 1.5°C-low-OS pathways show maximum net CO <sub>2</sub> emissions<br />
of 18 and 28 GtCO <sub>2</sub> yr <sup>−1</sup> , respectively. GHG emissions in these scenarios are not higher than 34 GtCO <sub>2</sub> e yr <sup>−1</sup> in 2030.
| Section 2.3.4
|-
| Additional reductions, on top of reductions from both CO <sub>2</sub> and<br />
non-CO <sub>2</sub> required for 2°C,<br />
are mainly from CO <sub>2</sub>
| Both CO <sub>2</sub> and the non-CO <sub>2</sub> GHGs and aerosols are strongly reduced by 2030 and until 2050 in 1.5°C pathways.<br />
The greatest difference to 2°C pathways, however, lies in additional reductions of CO <sub>2</sub> , as the non-CO <sub>2</sub> mitigation<br />
potential that is currently included in integrated pathways is mostly already fully deployed for reaching a 2°C pathway.
| Section 2.3.1.2
|-
| Considerable shifts in investment patterns
| Low-carbon investments in the energy supply side (energy production and refineries) are projected to average<br />
1.6–3.8 trillion 2010USD yr <sup>−1</sup> globally to 2050. Investments in fossil fuels decline, with investments in unabated coal halted by 2030 in most available 1.5°C-consistent projections, while the literature is less conclusive for investments in unabated gas and oil. Energy demand investments are a critical factor for which total estimates are uncertain.
| Section 2.5.2
|-
| Options are available to align 1.5°C pathways with sustainable development
| Synergies can be maximized, and risks of trade-offs limited or avoided through an informed choice of mitigation strategies. Particularly pathways that focus on a lowering of demand show many synergies and few trade-offs.
| Section 2.5.3
|-
| CDR at scale before mid-century
| By 2050, 1.5°C pathways project deployment of BECCS at a scale of 3–7 GtCO <sub>2</sub> yr <sup>−1</sup> (range of medians across 1.5°C pathway classes), depending on the level of energy demand reductions and mitigation in other sectors. Some 1.5°C pathways are available that do not use BECCS, but only focus terrestrial CDR in the AFOLU sector.
| Section 2.3.3, 2.3.4.1
|}
<!-- END TABLE -->

<span id="energy-system-transformation"></span>
=== 2.4.1 Energy System Transformation ===

<div id="section-2-4-1-block-1"></div>

The energy system links energy supply (Section 2.4.2) with energy demand (Section 2.4.3) through final energy carriers, including electricity and liquid, solid or gaseous fuels, that are tailored to their end-uses. To chart energy-system transformations in mitigation pathways, four macro-level decarbonization indicators associated with final energy are useful: limits on the increase of final energy demand, reductions in the carbon intensity of electricity, increases in the share of final energy provided by electricity, and reductions in the carbon intensity of final energy other than electricity (referred to in this section as the carbon intensity of the residual fuel mix). Figure 2.14 shows changes of these four indicators for the pathways in the scenario database (Section 2.1.3 and Supplementary Material 2.SM.1.3) for 1.5°C and 2°C pathways (Table 2.1).

Pathways in both the 1.5°C and 2°C classes (Figure 2.14) generally show rapid transitions until mid-century, with a sustained but slower evolution thereafter. Both show an increasing share of electricity accompanied by a rapid decline in the carbon intensity of electricity. Both also show a generally slower decline in the carbon intensity of the residual fuel mix, which arises from the decarbonization of liquids, gases and solids provided to industry, residential and commercial activities, and the transport sector.

The largest differences between 1.5°C and 2°C pathways are seen in the first half of the century (Figure 2.14), where 1.5°C pathways generally show lower energy demand, a faster electrification of energy end-use, and a faster decarbonization of the carbon intensity of electricity and the residual fuel mix. There are very few pathways in the Below-1.5°C class (Figure 2.14). Those scenarios that are available, however, show a faster decline in the carbon intensity of electricity generation and residual fuel mix by 2030 than most pathways that are projected to temporarily overshoot 1.5°C and return by 2100 (or 2°C pathways). The Below-1.5°C pathways also appear to differentiate themselves from the other pathways as early as 2030 through reductions in final energy demand and increases in electricity share (Figure 2.14).

<div id="section-2-4-1-block-2"></div>

<span id="figure-2.14"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.14'''

<span id="decomposition-of-transformation-pathways-into-a-energy-demand-b-carbon-intensity-of-electricity-c-the-electricity-share-in-final-energy-and-d-the-carbon-intensity-of-the-residual-non-electricity-fuel-mix"></span>
<!-- IMG CAPTION -->
'''Decomposition of transformation pathways into (a) energy demand, (b) carbon intensity of electricity, (c) the electricity share in final energy, and (d) the carbon intensity of the residual (non-electricity) fuel mix'''
<!-- IMG FILE -->
[[File:c8651642a2cd50993c711a7ef13ec6be Figure-2.14-1024x674.jpg]]

Box plots show median, interquartile range and full range of pathways. Pathway temperature classes (Table 2.1) and illustrative pathway archetypes are indicated in the legend. Values following the class labels give the number of available pathways in each class.

Original Creation for this Report using IAMC 1.5°C Scenario Data hosted by IIASA

<!-- END IMG -->
<span id="energy-supply"></span>
=== 2.4.2 Energy Supply ===

<div id="section-2-4-2-block-1"></div>

Several energy supply characteristics are evident in 1.5°C pathways assessed in this section: (i) growth in the share of energy derived from low-carbon-emitting sources (including renewables, nuclear and fossil fuel with CCS) and a decline in the overall share of fossil fuels without CCS (Section 2.4.2.1), (ii) rapid decline in the carbon intensity of electricity generation simultaneous with further electrification of energy end-use (Section 2.4.2.2), and (iii) the growth in the use of CCS applied to fossil and biomass carbon in most 1.5°C pathways (Section 2.4.2.3).

<div id="section-2-4-2-1"></div>

<span id="evolution-of-primary-energy-contributions-over-time"></span>
==== 2.4.2.1 Evolution of primary energy contributions over time ====

<div id="section-2-4-2-1-block-1"></div>

By mid-century, the majority of primary energy comes from non-fossil-fuels (i.e., renewables and nuclear energy) in most 1.5°C pathways (Table 2.6). Figure 2.15 shows the evolution of primary energy supply over this century across 1.5°C pathways, and in detail for the four illustrative pathway archetypes highlighted in this chapter. Note that this section reports primary energy using the direct equivalent method on the basis of lower heating values (Bruckner et al., 2014) <sup>[[#fn:r363|363]]</sup> .

The share of energy from renewable sources (including biomass, hydro, solar, wind and geothermal) increases in all 1.5°C pathways with no or limited overshoot, with the renewable energy share of primary energy reaching 38–88% in 2050 (Table 2.6), with an interquartile range of 52–67%. The magnitude and split between bioenergy, wind, solar, and hydro differ between pathways, as can be seen in the illustrative pathway archetypes in Figure 2.15. Bioenergy is a major supplier of primary energy, contributing to both electricity and other forms of final energy such as liquid fuels for transportation (Bauer et al., 2018) <sup>[[#fn:r364|364]]</sup> . In 1.5°C pathways, there is a significant growth in bioenergy used in combination with CCS for pathways where it is included (Figure 2.15).

Nuclear power increases its share in most 1.5°C pathways with no or limited overshoot by 2050, but in some pathways both the absolute capacity and share of power from nuclear generators decrease (Table 2.15). There are large differences in nuclear power between models and across pathways (Kim et al., 2014; Rogelj et al., 2018) <sup>[[#fn:r365|365]]</sup> . One of the reasons for this variation is that the future deployment of nuclear can be constrained by societal preferences assumed in narratives underlying the pathways (O’Neill et al., 2017; van Vuuren et al., 2017b) <sup>[[#fn:r366|366]]</sup> . Some 1.5°C pathways with no or limited overshoot no longer see a role for nuclear fission by the end of the century, while others project about 95 EJ yr <sup>−1</sup> of nuclear power in 2100 (Figure 2.15).

The share of primary energy provided by total fossil fuels decreases from 2020 to 2050 in all 1.5°C pathways, but trends for oil, gas and coal differ (Table 2.6). By 2050, the share of primary energy from coal decreases to 0–11% across 1.5°C pathways with no or limited overshoot, with an interquartile range of 1–7%. From 2020 to 2050 the primary energy supplied by oil changes by −93 to −9% (interquartile range −77 to −39%); natural gas changes by −88 to +85% (interquartile range −62 to −13%), with varying levels of CCS. Pathways with higher use of coal and gas tend to deploy CCS to control their carbon emissions (see Section 2.4.2.3). As the energy transition is accelerated by several decades in 1.5°C pathways compared to 2°C pathways, residual fossil-fuel use (i.e., fossil fuels not used for electricity generation) without CCS is generally lower in 2050 than in 2°C pathways, while combined hydro, solar, and wind power deployment is generally higher than in 2°C pathways (Figure 2.15).

In addition to the 1.5°C pathways included in the scenario database (Supplementary Material 2.SM.1.3), there are other analyses in the literature including, for example, sector-based analyses of energy demand and supply options. Even though they were not necessarily developed in the context of the 1.5°C target, they explore in greater detail some options for deep reductions in GHG emissions. For example, there are analyses of transitions to up to 100% renewable energy by 2050 (Creutzig et al., 2017; Jacobson et al., 2017) <sup>[[#fn:r367|367]]</sup> , which describe what is entailed for a renewable energy share largely from solar and wind (and electrification) that is above the range of 1.5°C pathways available in the database, although there have been challenges to the assumptions used in high-renewable analyses (e.g., Clack et al., 2017) <sup>[[#fn:r368|368]]</sup> . There are also analyses that result in a large role for nuclear energy in mitigation of GHGs (Hong et al., 2015; Berger et al., 2017a, b; Xiao and Jiang, 2018) <sup>[[#fn:r369|369]]</sup> . BECCS could also contribute a larger share, but faces challenges related to its land use and impact on food supply (Burns and Nicholson, 2017) <sup>[[#fn:r370|370]]</sup> (assessed in greater detail in Sections 2.3.4.2, 4.3.7 and 5.4). These analyses could, provided their assumptions prove plausible, expand the range of 1.5°C pathways.

In summary, the share of primary energy from renewables increases while that from coal decreases across 1.5°C pathways ( ''high confidence'' ). This statement is true for all 1.5°C pathways in the scenario database and associated literature (Supplementary Material 2.SM.1.3), and is consistent with the additional studies mentioned above, an increase in energy supply from lower-carbon-intensity energy supply, and a decrease in energy supply from higher-carbon-intensity energy supply.

<div id="section-2-4-2-1-block-2"></div>

<span id="figure-2.15"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.15'''

<span id="primary-energy-supply-for-the-four-illustrative-pathway-archetypes-plus-the-ieas-faster-transition-scenario-oecdiea-and-irena-2017-371-panel-a-and-their-relative-location-in-the-ranges-for-pathways-limiting-warming-to-1.5c-with-no-or-limited-overshoot-panel-b."></span>
<!-- IMG CAPTION -->
'''Primary energy supply for the four illustrative pathway archetypes plus the IEA’s Faster Transition Scenario (OECD/IEA and IRENA, 2017) <sup>[[#fn:r371|371]]</sup> (panel a), and their relative location in the ranges for pathways limiting warming to 1.5°C with no or limited overshoot (panel b).'''
<!-- IMG FILE -->
[[File:5166e16ac63ca47f503bb1215474fab2 Figure-2.15-1024x608.jpg]]

The category ‘Other renewables’ includes primary energy sources not covered by the other categories, for example, hydro and geothermal energy. The number of pathways that have higher primary energy than the scale in the bottom panel are indicated by the numbers above the whiskers. Black horizontal dashed lines indicates the level of primary energy supply in 2015 (IEA, 2017e) <sup>[[#fn:r372|372]]</sup> . Box plots in the lower panel show the minimum–maximum range (whiskers), interquartile range (box), and median (vertical thin black line). Symbols in the lower panel show the four pathway archetypes S1 (white square), S2 (yellow square), S5 (black square), LED (white disc), as well as the IEA–(red disc). Pathways with no or limited overshoot included the Below-1.5°C and 1.5°C-low-OS classes.

Original Creation for this Report using IAMC 1.5°C Scenario Data hosted by IIASA

<!-- END IMG -->
<div id="section-2-4-2-1-block-3"></div>

<span id="table-2.6"></span>
<!-- START TABLE -->
'''Table 2.6'''

<span id="global-primary-energy-supply-of-1.5c-pathways-from-the-scenario-database-supplementary-material-2.sm.1.3.-values-given-for-the-median-maximum-minimum-across-the-full-range-of-85-available-1.5c-pathways.-growth-factor-primary-energy-supply-in-2050primary-energy-supply-in-2020-1"></span>
'''Global primary energy supply of 1.5°C pathways from the scenario database (Supplementary Material 2.SM.1.3). Values given for the median (maximum, minimum) across the full range of 85 available 1.5°C pathways. Growth Factor = [(primary energy supply in 2050)/(primary energy supply in 2020) − 1]'''

Values given for the median (maximum, minimum) across the full range of 85 available 1.5°C pathways. Growth Factor = [(primary energy supply in 2050)/(primary energy supply in 2020) − 1]
<!-- TABLE -->
{| class="wikitable"
|-
! rowspan="2"|
! rowspan="2"| Median (max, min)
! rowspan="2"| Count
! colspan="3"| Primary Energy Supply (EJ)
! colspan="3"| Share in Primary Energy (%)
! rowspan="2"| Growth (factor)<br />
2020-2050
|-
! 2020
! 2030
! 2050
! 2020
! 2030
! 2050
|-
| rowspan="10"| Below-1.5°C and 1.5°C-<br />
low-OS pathways
| total primary
| 50
| 565.33
(619.70, 483.22)

| 464.50
(619.87, 237.37)

| 553.23
(725.40, 289.02)

| NA
| –0.05
(0.48, –0.51)

|-
| renewables
| 50
| 87.14
(101.60, 60.16)

| 146.96
(203.90, 87.75)

| 291.33
(584.78, 176.77)

| 14.90
(20.39, 10.60)

| 29.08
(62.15, 18.24)

| 60.24
(87.89, 38.03)

| 2.37
(6.71, 0.91)

|-
| biomass
| 50
| 60.41
(70.03, 40.54)

| 77.07
(113.02, 44.42)

| 152.30
(311.72, 40.36)

| 10.17
(13.66, 7.14)

| 17.22
(35.61, 9.08)

| 27.29
(54.10, 10.29)

| 1.71
(5.56, –0.42)

|-
| non-biomass
| 50
| 26.35
(36.57, 17.78)

| 62.58
(114.41, 25.79)

| 146.23
(409.94, 53.79)

| 4.37
(7.19, 3.01)

| 13.67
(26.54, 5.78)

| 27.98
(61.61, 12.04)

| 4.28
(13.46, 1.45)

|-
| wind &amp; solar
| 44
| 10.93
(20.16, 2.61)

| 40.14
(82.66, 7.05)

| 121.82
(342.77, 27.95)

| 1.81
(3.66, 0.45)

| 9.73
(19.56, 1.54)

| 21.13
(51.52, 4.48)

| 10.00
(53.70, 3.71)

|-
| nuclear
| 50
| 10.91
(18.55, 8.52)

| 16.26
(36.80, 6.80)

| 24.51
(66.30, 3.09)

| 2.10
(3.37, 1.45)

| 3.52
(9.61, 1.32)

| 4.49
(12.84, 0.44)

| 1.24
(5.01, –0.64)

|-
| fossil
| 50
| 462.95
(520.41, 376.30)

| 310.36
(479.13, 70.14)

| 183.79
(394.71, 54.86)

| 82.53
(86.65, 77.73)

| 66.58
(77.30, 29.55)

| 32.79
(60.84, 8.58)

| –0.59
(–0.21, –0.89)

|-
| coal
| 50
| 136.89
(191.02, 83.23)

| 44.03
(127.98, 5.97)

| 24.15
(71.12, 0.92)

| 25.63
(30.82, 17.19)

| 9.62
(20.65, 1.31)

| 5.08
(11.43, 0.15)

| –0.83
(–0.57, –0.99)

|-
| gas
| 50
| 132.95
(152.80, 105.01)

| 112.51
(173.56, 17.30)

| 76.03
(199.18, 14.92)

| 23.10
(28.39, 18.09)

| 22.52
(35.05, 7.08)

| 13.23
(34.83, 3.68)

| –0.40
(0.85, –0.88)

|-
| oil
| 50
| 197.26
(245.15, 151.02)

| 156.16
(202.57, 38.94)

| 69.94
(167.52, 15.07)

| 34.81
(42.24, 29.00)

| 31.24
(39.84, 16.41)

| 12.89
(27.04, 2.89)

| –0.66
(–0.09, –0.93)

|-
| rowspan="10"| 1.5°C-<br />
high-OS
| total primary
| 35
| 594.96
(636.98, 510.55)

| 559.04
(749.05, 419.28)

| 651.46
(1012.50, 415.31)

| NA
| 0.13
(0.59, –0.27)

|-
| renewables
| 35
| 89.84
(98.60, 66.57)

| 135.12
(159.84, 87.93)

| 323.21
(522.82, 177.66)

| 15.08
(18.58, 11.04)

| 23.65
(29.32, 13.78)

| 62.16
(86.26, 28.47)

| 2.68
(4.81, 1.17)

|-
| biomass
| 35
| 62.59
(73.03, 48.42)

| 69.05
(98.27, 56.54)

| 160.16
(310.10, 71.17)

| 10.30
(14.23, 8.03)

| 13.64
(16.37, 9.03)

| 23.79
(45.79, 10.64)

| 1.71
(3.71, 0.19)

|-
| non-biomass
| 35
| 28.46
(36.58, 17.60)

| 59.81
(92.12, 27.39)

| 164.91
(329.69, 55.72)

| 4.78
(6.64, 2.84)

| 10.23
(16.59, 4.49)

| 31.17
(45.86, 9.87)

| 6.10
(10.63, 1.38)

|-
| wind &amp; solar
| 26
| 11.32
(20.17, 1.91)

| 40.31
(65.50, 8.14)

| 139.20
(275.47, 30.92)

| 1.95
(3.66, 0.32)

| 7.31
(11.61, 1.83)

| 26.01
(38.79, 6.33)

| 16.06
(63.34, 3.13)

|-
| nuclear
| 35
| 10.94
(14.27, 8.52)

| 16.12
(41.73, 6.80)

| 22.98
(115.80, 3.09)

| 1.86
(2.37, 1.45)

| 2.99
(5.57, 1.20)

| 4.17
(13.60, 0.43)

| 1.49
(7.22, –0.64)

|-
| fossil
| 35
| 497.30
(543.29, 407.49)

| 397.76
(568.91, 300.63)

| 209.80
(608.39, 43.87)

| 83.17
(86.59, 79.39)

| 73.87
(82.94, 68.00)

| 33.58
(60.09, 7.70)

| –0.56
(0.12, –0.91)

|-
| coal
| 35
| 155.65
(193.55, 118.40)

| 70.99
(176.99, 19.15)

| 18.95
(134.69, 0.36)

| 25.94
(30.82, 19.10)

| 14.53
(26.35, 3.64)

| 4.14
(13.30, 0.05)

| –0.87
(–0.30, –1.00)

|-
| gas
| 35
| 138.01
(169.50, 107.07)

| 147.43
(208.55, 76.45)

| 97.71
(265.66, 15.96)

| 23.61
(27.35, 19.26)

| 25.79
(32.73, 14.69)

| 15.67
(33.80, 2.80)

| –0.31
(0.99, –0.88)

|-
| oil
| 35
| 195.02
(236.40, 154.66)

| 198.50
(319.80, 102.10)

| 126.20
(208.04, 24.68)

| 32.21
(38.87, 28.07)

| 33.27
(50.12, 24.35)

| 18.61
(27.30, 4.51)

| –0.34
(0.06, –0.87)

|-
| rowspan="10"| Two above classes combined
| total primary
| 85
| 582.12
(636.98, 483.22)

| 502.81
(749.05, 237.37)

| 580.78
(1012.50, 289.02)

| –
| 0.03
(0.59, –0.51)

|-
| renewables
| 85
| 87.70
(101.60, 60.16)

| 139.48
(203.90, 87.75)

| 293.80
(584.78, 176.77)

| 15.03
(20.39, 10.60)

| 27.90
(62.15, 13.78)

| 60.80
(87.89, 28.47)

| 2.62
(6.71, 0.91)

|-
| biomass
| 85
| 61.35
(73.03, 40.54)

| 75.28
(113.02, 44.42)

| 154.13
(311.72, 40.36)

| 10.27
(14.23, 7.14)

| 14.38
(35.61, 9.03)

| 26.38
(54.10, 10.29)

| 1.71
(5.56, –0.42)

|-
| non-biomass
| 85
| 26.35
(36.58, 17.60)

| 61.60
(114.41, 25.79)

| 157.37
(409.94, 53.79)

| 4.40
(7.19, 2.84)

| 11.87
(26.54, 4.49)

| 28.60
(61.61, 9.87)

| 4.63
(13.46, 1.38)

|-
| wind &amp; solar
| 70
| 10.93
(20.17, 1.91)

| 40.17
(82.66, 7.05)

| 125.31
(342.77, 27.95)

| 1.81
(3.66, 0.32)

| 8.24
(19.56, 1.54)

| 22.10
(51.52, 4.48)

| 11.64
(63.34, 3.13)

|-
| nuclear
| 85
| 10.93
(18.55, 8.52)

| 16.22
(41.73, 6.80)

| 24.48
(115.80, 3.09)

| 1.97
(3.37, 1.45)

| 3.27
(9.61, 1.20)

| 4.22
(13.60, 0.43)

| 1.34
(7.22, –0.64)

|-
| fossil
| 85
| 489.52
(543.29, 376.30)

| 343.48
(568.91, 70.14)

| 198.58
(608.39, 43.87)

| 83.05
(86.65, 77.73)

| 69.19
(82.94, 29.55)

| 33.06
(60.84, 7.70)

| –0.58
(0.12, –0.91)

|-
| coal
| 85
| 147.09
(193.55, 83.23)

| 49.46
(176.99, 5.97)

| 23.84
(134.69, 0.36)

| 25.72
(30.82, 17.19)

| 10.76
(26.35, 1.31)

| 4.99
(13.30, 0.05)

| –0.85
(–0.30, –1.00)

|-
| gas
| 85
| 135.58
(169.50, 105.01)

| 127.99
(208.55, 17.30)

| 88.97
(265.66, 14.92)

| 23.28
(28.39, 18.09)

| 24.02
(35.05, 7.08)

| 13.46
(34.83, 2.80)

| –0.37
(0.99, –0.88)

|-
| oil
| 85
| 195.02
(245.15, 151.02)

| 175.69
(319.80, 38.94)

| 93.48
(208.04, 15.07)

| 33.79
(42.24, 28.07)

| 32.01
(50.12, 16.41)

| 16.22
(27.30, 2.89)

| –0.54
(0.06, –0.93)

|}
<!-- END TABLE -->

<div id="section-2-4-2-1-block-4"></div>

<span id="table-2.7"></span>
<!-- START TABLE -->
'''Table 2.7'''

<span id="global-electricity-generation-of-1.5c-pathways-from-the-scenarios-database"></span>
'''Global electricity generation of 1.5°C pathways from the scenarios database'''

(Supplementary Material 2.SM.1.3). Values given for the median (maximum, minimum) values across the full range across 89 available 1.5°C pathways. Growth Factor = [(primary energy supply in 2050)/(primary energy supply in 2020) – 1].
<!-- TABLE -->
{| class="wikitable"
|-
! rowspan="2"|
! rowspan="2"| Median (max, min)
! rowspan="2"| Count
! colspan="3"| Electricity Generation (EJ)
! colspan="3"| Share in Electricity Generation (%)
! rowspan="2"| Growth (factor)<br />
2020–2050
|-
! 2020
! 2030
! 2050
! 2020
! 2030
! 2050
|-
| rowspan="10"| TBelow<br />
-1.5°C and 1.5°C-<br />
low-OS pathways
| total generation
| 50
| 98.45
(113.98, 83.53)

| 115.82
(152.40, 81.28)

| 215.58
(354.48, 126.96)

| NA
| 1.15
(2.55, 0.28)

|-
| renewables
| 50
| 26.28
(41.80, 18.50)

| 63.30
(111.70, 32.41)

| 145.50
(324.26, 90.66)

| 26.32
(41.84, 18.99)

| 53.68
(79.67, 37.30)

| 77.12
(96.65, 58.89)

| 4.48
(10.88, 2.65)

|-
| biomass
| 50
| 2.02
(7.00, 0.76)

| 4.29
(11.96, 0.79)

| 20.35
(39.28, 0.24)

| 1.97
(6.87, 0.82)

| 3.69
(13.29, 0.73)

| 8.77
(30.28, 0.10)

| 6.42
(38.14, –0.93)

|-
| non-biomass
| 50
| 24.21
(35.72, 17.70)

| 57.12
(101.90, 25.79)

| 135.04
(323.91, 53.79)

| 24.38
(40.43, 17.75)

| 49.88
(78.27, 29.30)

| 64.68
(96.46, 41.78)

| 4.64
(10.64, 1.45)

|-
| wind &amp; solar
| 50
| 1.66
(6.60, 0.38)

| 8.91
(48.04, 0.60)

| 39.04
(208.97, 2.68)

| 1.62
(7.90, 0.38)

| 8.36
(41.72, 0.53)

| 19.10
(60.11, 1.65)

| 26.31
(169.66, 5.23)

|-
| nuclear
| 50
| 10.84
(18.55, 8.52)

| 15.46
(36.80, 6.80)

| 21.97
(64.72, 3.09)

| 12.09
(18.34, 8.62)

| 14.33
(31.63, 5.24)

| 8.10
(27.53, 1.02)

| 0.71
(4.97, –0.64)

|-
| fossil
| 50
| 59.43
(68.75, 39.48)

| 36.51
(66.07, 2.25)

| 14.81
(57.76, 0.00)

| 61.32
(67.40, 47.26)

| 30.04
(52.86, 1.95)

| 8.61
(25.18, 0.00)

| –0.74
(0.01, –1.00)

|-
| coal
| 50
| 31.02
(42.00, 14.40)

| 8.83
(34.11, 0.00)

| 1.38
(17.39, 0.00)

| 32.32
(40.38, 17.23)

| 7.28
(27.29, 0.00)

| 0.82
(7.53, 0.00)

| –0.96
(–0.56, –1.00)

|-
| gas
| 50
| 24.70
(32.46, 13.44)

| 22.59
(42.08, 2.01)

| 12.79
(53.17, 0.00)

| 24.39
(35.08, 11.80)

| 20.18
(37.23, 1.75)

| 6.93
(24.87, 0.00)

| –0.47
(1.27, –1.00)

|-
| oil
| 50
| 2.48
(13.36, 1.12)

| 1.89
(7.56, 0.24)

| 0.10
(8.78, 0.00)

| 2.82
(11.73, 1.01)

| 1.95
(5.67, 0.21)

| 0.05
(3.80, 0.00)

| –0.92
(0.36, –1.00)

|-
| rowspan="4"| 1.5°C-<br />
high-OS
| total generation
| 35
| 101.44
(113.96, 88.55)

| 125.26
(177.51, 89.60)

| 251.50
(363.10, 140.65)

| NA
| 1.38
(2.19, 0.39)

|-
| renewables
| 35
| 26.38
(31.83, 18.26)

| 53.32
(86.85, 30.06)

| 173.29
(273.92, 84.69)

| 28.37
(32.96, 17.38)

| 42.73
(65.73, 25.11)

| 82.39
(94.66, 35.58)

| 5.97
(8.68, 2.37)

|-
| biomass
| 35
| 1.23
(6.47, 0.66)

| 2.14
(7.23, 0.86)

| 10.49
(40.32, 0.21)

| 1.22
(7.30, 0.63)

| 1.59
(6.73, 0.72)

| 3.75
(28.09, 0.08)

| 7.93
(33.32, –0.81)

|-
| non-biomass
| 35
| 24.56
(30.70, 17.60)

| 47.96
(85.83, 27.39)

| 144.13
(271.17, 55.72)

| 26.77
(31.79, 16.75)

| 40.07
(64.96, 23.10)

| 69.72
(94.58, 27.51)

| 5.78
(8.70, 1.38)

|-
| rowspan="6"| 1.5°C-<br />
high-OS
| wind &amp; solar
| 35
| 2.24
(5.07, 0.42)

| 8.95
(36.52, 1.18)

| 65.08
(183.38, 13.79)

| 2.21
(5.25, 0.41)

| 7.48
(27.90, 0.99)

| 25.88
(61.24, 8.71)

| 30.70
(106.95, 4.87)

|-
| nuclear
| 35
| 10.84
(14.08, 8.52)

| 16.12
(41.73, 6.80)

| 22.91
(115.80, 3.09)

| 10.91
(13.67, 8.62)

| 14.65
(23.51, 5.14)

| 11.19
(39.61, 1.12)

| 1.49
(7.22, –0.64)

|-
| fossil
| 35
| 62.49
(76.76, 49.09)

| 48.08
(87.54, 30.99)

| 11.84
(118.12, 0.78)

| 61.58
(71.03, 54.01)

| 42.02
(59.48, 24.27)

| 6.33
(33.19, 0.27)

| –0.80
(0.54, –0.99)

|-
| coal
| 35
| 32.37
(46.20, 26.00)

| 16.22
(43.12, 1.32)

| 1.18
(46.72, 0.01)

| 32.39
(40.88, 24.41)

| 14.23
(29.93, 1.19)

| 0.55
(12.87, 0.00)

| –0.96
(0.01, –1.00)

|-
| gas
| 35
| 26.20
(41.20, 20.11)

| 26.45
(51.99, 16.45)

| 10.66
(67.94, 0.76)

| 26.97
(39.20, 19.58)

| 22.29
(43.43, 14.03)

| 5.29
(32.59, 0.26)

| –0.57
(1.63, –0.97)

|-
| oil
| 35
| 1.51
(6.28, 1.12)

| 0.61
(7.54, 0.36)

| 0.04
(7.47, 0.00)

| 1.51
(6.27, 1.01)

| 0.55
(6.20, 0.26)

| 0.02
(3.31, 0.00)

| –0.99
(0.98, –1.00)

|-
| rowspan="10"| Two above classes combined
| total generation
| 85
| 100.09
(113.98, 83.53)

| 120.01
(177.51, 81.28)

| 224.78
(363.10, 126.96)

| NA
| 1.31
(2.55, 0.28)

|-
| renewables
| 85
| 26.38
(41.80, 18.26)

| 59.50
(111.70, 30.06)

| 153.72
(324.26, 84.69)

| 27.95
(41.84, 17.38)

| 51.51
(79.67, 25.11)

| 77.52
(96.65, 35.58)

| 5.08
(10.88, 2.37)

|-
| biomass
| 85
| 1.52
(7.00, 0.66)

| 3.55
(11.96, 0.79)

| 16.32
(40.32, 0.21)

| 1.55
(7.30, 0.63)

| 2.77
(13.29, 0.72)

| 8.02
(30.28, 0.08)

| 6.53
(38.14, –0.93)

|-
| non-biomass
| 85
| 24.48
(35.72, 17.60)

| 55.68
(101.90, 25.79)

| 136.40
(323.91, 53.79)

| 25.00
(40.43, 16.75)

| 47.16
(78.27, 23.10)

| 66.75
(96.46, 27.51)

| 4.75
(10.64, 1.38)

|-
| wind &amp; solar
| 85
| 1.66
(6.60, 0.38)

| 8.95
(48.04, 0.60)

| 43.20
(208.97, 2.68)

| 1.67
(7.90, 0.38)

| 8.15
(41.72, 0.53)

| 19.70
(61.24, 1.65)

| 28.02
(169.66, 4.87)

|-
| nuclear
| 85
| 10.84
(18.55, 8.52)

| 15.49
(41.73, 6.80)

| 22.64
(115.80, 3.09)

| 10.91
(18.34, 8.62)

| 14.34
(31.63, 5.14)

| 8.87
(39.61, 1.02)

| 1.21
(7.22, –0.64)

|-
| fossil
| 85
| 61.35
(76.76, 39.48)

| 38.41
(87.54, 2.25)

| 14.10
(118.12, 0.00)

| 61.55
(71.03, 47.26)

| 33.96
(59.48, 1.95)

| 8.05
(33.19, 0.00)

| –0.76
(0.54, –1.00)

|-
| coal
| 85
| 32.37
(46.20, 14.40)

| 10.41
(43.12, 0.00)

| 1.29
(46.72, 0.00)

| 32.39
(40.88, 17.23)

| 8.95
(29.93, 0.00)

| 0.59
(12.87, 0.00)

| –0.96
(0.01, –1.00)

|-
| gas
| 85
| 24.70
(41.20, 13.44)

| 25.00
(51.99, 2.01)

| 11.92
(67.94, 0.00)

| 24.71
(39.20, 11.80)

| 21.03
(43.43, 1.75)

| 6.78
(32.59, 0.00)

| –0.52
(1.63, –1.00)

|-
| oil
| 85
| 1.82
(13.36, 1.12)

| 0.92
(7.56, 0.24)

| 0.08
(8.78, 0.00)

| 2.04
(11.73, 1.01)

| 0.71
(6.20, 0.21)

| 0.04
(3.80, 0.00)

| –0.97
(0.98, –1.00)

|}
<!-- END TABLE -->

<div id="section-2-4-2-2"></div>

<span id="evolution-of-electricity-supply-over-time"></span>
==== 2.4.2.2 Evolution of electricity supply over time ====

<div id="section-2-4-2-2-block-1"></div>

Electricity supplies an increasing share of final energy, reaching 34–71% in 2050, across 1.5°C pathways with no or limited overshoot (Figure 2.14), extending the historical increases in electricity share seen over the past decades (Bruckner et al., 2014) <sup>[[#fn:r373|373]]</sup> . From 2020 to 2050, the quantity of electricity supplied in most 1.5°C pathways with no or limited overshoot more than doubles (Table 2.7). By 2050, the carbon intensity of electricity has fallen rapidly to −92 to +11 gCO <sub>2</sub> MJ <sup>−1</sup> electricity across 1.5°C pathways with no or limited overshoot from a value of around 140 gCO <sub>2</sub> MJ <sup>−1</sup> (range: 88–181 gCO <sub>2</sub> MJ <sup>−1</sup> ) in 2020 (Figure 2.14). A negative contribution to carbon intensity is provided by BECCS in most pathways (Figure 2.16).

By 2050, the share of electricity supplied by renewables increases from 23% in 2015 (IEA, 2017b) <sup>[[#fn:r374|374]]</sup> to 59–97% across 1.5°C pathways with no or limited overshoot. Wind, solar, and biomass together make a major contribution in 2050, although the share for each spans a wide range across 1.5°C pathways (Figure 2.16). Fossil fuels on the other hand have a decreasing role in electricity supply, with their share falling to 0–25% by 2050 (Table 2.7).

In summary, 1.5°C pathways include a rapid decline in the carbon intensity of electricity and an increase in electrification of energy end-use ( ''high confidence'' ). This is the case across all 1.5°C pathways and their associated literature (Supplementary Material 2.SM.1.3), with pathway trends that extend those seen in past decades, and results that are consistent with additional analyses (see Section 2.4.2.2).

<div id="section-2-4-2-2-block-2"></div>

<span id="figure-2.16"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.16'''

<span id="electricity-generation-for-the-four-illustrative-pathway-archetypes-plus-the-ieas-faster-transition-scenario-oecdiea-and-irena-2017-375-panel-a-and-their-relative-location-in-the-ranges-for-pathways-limiting-warming-to-1.5c-with-no-or-limited-overshoot-panel-b."></span>
<!-- IMG CAPTION -->
'''Electricity generation for the four illustrative pathway archetypes plus the IEA’s Faster Transition Scenario (OECD/IEA and IRENA, 2017) <sup>[[#fn:r375|375]]</sup> (panel a), and their relative location in the ranges for pathways limiting warming to 1.5°C with no or limited overshoot (panel b).'''
<!-- IMG FILE -->
[[File:c8454f2802a6e2b045d8be6ea97976af Figure-2.16-1024x610.jpg]]

The category ‘Other renewables’ includes electricity generation not covered by the other categories, for example, hydro and geothermal. The number of pathways that have higher primary energy than the scale in the bottom panel are indicated by the numbers above the whiskers. Black horizontal dashed lines indicate the level of primary energy supply in 2015 (IEA, 2017e) <sup>[[#fn:r376|376]]</sup> . Box plots in the lower panel show the minimum–maximum range (whiskers), interquartile range (box), and median (vertical thin black line). Symbols in the lower panel show the four pathway archetypes – S1 (white square), S2 (yellow square), S5 (black square), LED (white disc) – as well as the IEA’s Faster Transition Scenario (red disc). Pathways with no or limited overshoot included the Below-1.5°C and 1.5°C-low-OS classes.

Original Creation for this Report using IAMC 1.5°C Scenario Data hosted by IIASA

<!-- END IMG -->
<div id="section-2-4-2-3"></div>

<span id="deployment-of-carbon-capture-and-storage"></span>
==== 2.4.2.3 Deployment of carbon capture and storage ====

<div id="section-2-4-2-3-block-1"></div>

Studies have shown the importance of CCS for deep mitigation pathways (Krey et al., 2014a; Kriegler et al., 2014b) <sup>[[#fn:r377|377]]</sup> , based on its multiple roles to limit fossil-fuel emissions in electricity generation, liquids production, and industry applications along with the projected ability to remove CO <sub>2</sub> from the atmosphere when combined with bioenergy. This remains a valid finding for those 1.5°C and 2°C pathways that do not radically reduce energy demand or do not offer carbon-neutral alternatives to liquids and gases that do not rely on bioenergy.

There is a wide range of CCS that is deployed across 1.5°C pathways (Figure 2.17). A few 1.5°C pathways with very low energy demand do not include CCS at all (Grubler et al., 2018) <sup>[[#fn:r378|378]]</sup> . For example, the ''LED'' pathway has no CCS, whereas other pathways, such as the S5 pathway, rely on a large amount of BECCS to get to net-zero carbon emissions. The cumulative fossil and biomass CO <sub>2</sub> stored through 2050 ranges from zero to 300 GtCO <sub>2</sub> across 1.5°C pathways with no or limited overshoot, with zero up to 140 GtCO <sub>2</sub> from biomass captured and stored. Some pathways have very low fossil-fuel use overall, and consequently little CCS applied to fossil fuels. In 1.5°C pathways where the 2050 coal use remains above 20 EJ yr <sup>−1</sup> in 2050, 33–100% is combined with CCS. While deployment of CCS for natural gas and coal vary widely across pathways, there is greater natural gas primary energy connected to CCS than coal primary energy connected to CCS in many pathways (Figure 2.17).

CCS combined with fossil-fuel use remains limited in some 1.5°C pathways (Rogelj et al., 2018) <sup>[[#fn:r379|379]]</sup> , as the limited 1.5°C carbon budget penalizes CCS if it is assumed to have incomplete capture rates or if fossil fuels are assumed to continue to have significant lifecycle GHG emissions (Pehl et al., 2017) <sup>[[#fn:r380|380]]</sup> . However, high capture rates are technically achievable now at higher cost, although efforts to date have focussed on reducing the costs of capture (IEAGHG, 2006; NETL, 2013) <sup>[[#fn:r381|381]]</sup> .

The quantity of CO <sub>2</sub> stored via CCS over this century in 1.5°C pathways with no or limited overshoot ranges from zero to more than 1,200 GtCO <sub>2</sub> , (Figure 2.17). The IPCC Special Report on Carbon Dioxide Capture and Storage (IPCC, 2005) <sup>[[#fn:r382|382]]</sup> found that that, worldwide, it is ''likely'' that there is a technical potential of at least about 2,000 GtCO <sub>2</sub> of storage capacity in geological formations. Furthermore, the IPCC (2005) <sup>[[#fn:r383|383]]</sup> recognized that there could be a much larger potential for geological storage in saline formations, but the upper limit estimates are uncertain due to lack of information and an agreed methodology. Since IPCC (2005) <sup>[[#fn:r384|384]]</sup> , understanding has improved and there have been detailed regional surveys of storage capacity (Vangkilde-Pedersen et al., 2009; Ogawa et al., 2011; Wei et al., 2013; Bentham et al., 2014; Riis and Halland, 2014; Warwick et al., 2014; NETL, 2015) <sup>[[#fn:r385|385]]</sup> and improvement and standardization of methodologies (e.g., Bachu et al. 2007a, b) <sup>[[#fn:r386|386]]</sup> . Dooley (2013) <sup>[[#fn:r387|387]]</sup> synthesized published literature on both the global geological storage resource as well as the potential demand for geologic storage in mitigation pathways, and found that the cumulative demand for CO <sub>2</sub> storage was small compared to a practical storage capacity estimate (as defined by Bachu et al., 2007a) <sup>[[#fn:r388|388]]</sup> of 3,900 GtCO <sub>2</sub> worldwide. Differences remain, however, in estimates of storage capacity due to, for example, the potential storage limitations of subsurface pressure build-up (Szulczewski et al., 2014) <sup>[[#fn:r389|389]]</sup> and assumptions on practices that could manage such issues (Bachu, 2015) <sup>[[#fn:r390|390]]</sup> . Kearns et al. (2017) <sup>[[#fn:r391|391]]</sup> constructed estimates of global storage capacity of 8,000 to 55,000 GtCO <sub>2</sub> (accounting for differences in detailed regional and local estimates), which is sufficient at a global level for this century, but found that at a regional level, robust demand for CO <sub>2</sub> storage exceeds their lower estimate of regional storage available for some regions. However, storage capacity is not solely determined by the geological setting, and Bachu (2015) <sup>[[#fn:r392|392]]</sup> describes storage engineering practices that could further extend storage capacity estimates. In summary, the storage capacity of all of these global estimates is larger than the cumulative CO <sub>2</sub> stored via CCS in 1.5°C pathways over this century.

There is uncertainty in the future deployment of CCS given the limited pace of current deployment, the evolution of CCS technology that would be associated with deployment, and the current lack of incentives for large-scale implementation of CCS (Bruckner et al., 2014; Clarke et al., 2014; Riahi et al., 2017) <sup>[[#fn:r393|393]]</sup> . Given the importance of CCS in most mitigation pathways and its current slow pace of improvement, the large-scale deployment of CCS as an option depends on the further development of the technology in the near term. Chapter 4 discusses how progress on CCS might be accelerated.

<div id="section-2-4-2-3-block-2"></div>

<span id="figure-2.17"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.17'''

<span id="section-10"></span>
<!-- IMG CAPTION -->
CCS deployment in 1.5°C and 2°C pathways for (a) biomass, (b) coal and (c) natural gas (EJ of primary energy) and (d) the cumulative quantity of fossil (including from, e.g., cement production) and biomass  CO <sub>2</sub>  stored via CCS (in GtCO <sub>2</sub>  stored).
<!-- IMG FILE -->
[[File:cb4d3c78abee4a1910e8f532033e509f Figure-2.17-1024x674.jpg]]

TBox plots show median, interquartile range and full range of pathways in each temperature class. Pathway temperature classes (Table 2.1), illustrative pathway archetypes, and the IEA’s Faster Transition Scenario (IEA WEM) (OECD/IEA and IRENA, 2017) are indicated in the legend.

Original Creation for this Report using IAMC 1.5°C Scenario Data hosted by IIASA

<!-- END IMG -->
<span id="energy-end-use-sectors"></span>
=== 2.4.3 Energy End-Use Sectors ===

<div id="section-2-4-3-block-1"></div>

Since the power sector is almost decarbonized by mid-century in both 1.5°C and 2°C pathways, major differences come from CO <sub>2</sub> emission reductions in end-use sectors. Energy-demand reductions are key and common features in 1.5˚C pathways, and they can be achieved by efficiency improvements and various specific demand-reduction measures. Another important feature is end-use decarbonization including by electrification, although the potential and challenges in each end-use sector vary significantly.

In the following sections, the potential and challenges of CO <sub>2</sub> emission reductions towards 1.5°C and 2°C- consistent pathways are discussed for each end-use energy sector (industry, buildings, and transport). For this purpose, two types of pathways are analysed and compared: IAM (integrated assessment modelling) studies and sectoral (detailed) studies. IAM data are extracted from the database that was compiled for this assessment (see Supplementary Material 2.SM.1.3), and the sectoral data are taken from a recent series of publications; ‘Energy Technology Perspectives’ (ETP) (IEA, 2014, 2015b, 2016a, 2017a) <sup>[[#fn:r395|395]]</sup> , the IEA/IRENA report (OECD/IEA and IRENA, 2017) <sup>[[#fn:r396|396]]</sup> , and the Shell Sky report (Shell International B.V., 2018) <sup>[[#fn:r397|397]]</sup> . The IAM pathways are categorized according to their temperature rise in 2100 and the overshoot of temperature during the century (see Table 2.1 in Section 2.1). Since the number of Below-1.5°C pathways is small, the following analyses focus only on the features of the 1.5°C-low-OS and 1.5°C-high-OS pathways (hereafter denoted together as 1.5°C overshoot pathways or IAM-1.5DS-OS) and 2°C-consistent pathways (IAM-2DS). In order to show the diversity of IAM pathways, we again show specific data from the four illustrative pathways archetypes used throughout this chapter (see Sections 2.1 and 2.3).

IEA ETP-B2DS (‘Beyond 2 Degrees’) and ETP-2DS are pathways with a 50% chance of limiting temperature rise below 1.75°C and 2°C by 2100, respectively (IEA, 2017a) <sup>[[#fn:r398|398]]</sup> . The IEA-66%2DS pathway keeps global mean temperature rise below 2°C, not just in 2100 but also over the course of the 21st century, with a 66% chance of being below 2°C by 2100 (OECD/IEA and IRENA, 2017) <sup>[[#fn:r399|399]]</sup> . The comparison of CO <sub>2</sub> emission trajectories between ETP-B2DS and IAM-1.5DS-OS show that these are consistent up to 2060 (Figure 2.18). IEA scenarios assume that only a very low level of BECCS is deployed to help offset emissions in difficult-to-decarbonize sectors, and that global energy-related CO <sub>2</sub> emissions do not turn net negative at any time but stay at zero from 2060 to 2100 (IEA, 2017a) <sup>[[#fn:r400|400]]</sup> . Therefore, although its temperature rise in 2100 is below 1.75°C rather than below 1.5°C, this scenario can give information related to a 1.5°C overshoot pathway up to 2050. The trajectory of IEA-66%2DS (also referred to in other publications as IEA’s ‘Faster Transition Scenario’) lies between IAM-1.5DS-OS and IAM-2DS pathway ranges, and IEA-2DS stays in the range of 2°C-consistent IAM pathways. The Shell-Sky scenario aims to hold the temperature rise to well below 2°C, but it is a delayed action pathway relative to others, as can be seen in Figure 2.18.

Energy-demand reduction measures are key to reducing CO <sub>2</sub> emissions from end-use sectors for low-carbon pathways. The upstream energy reductions can be from several times to an order of magnitude larger than the initial end-use demand reduction. There are interdependencies among the end-use sectors and between energy-supply and end-use sectors, which elevate the importance of a wide, systematic approach. As shown in Figure 2.19, global final energy consumption grows by 30% and 10% from 2010 to 2050 for 2°C-consistent and 1.5°C overshoot pathways from IAMs, respectively, while much higher growth of 75% is projected for reference scenarios. The ranges within a specific pathway class are due to a variety of factors as introduced in Section 2.3.1, as well as differences between modelling frameworks. The important energy efficiency and conservation improvements that facilitate many of the 1.5°C pathways raise the issue of potential rebound effects (Saunders, 2015) <sup>[[#fn:r401|401]]</sup> , which, while promoting development, can make the achievement of low-energy demand futures more difficult than modelling studies anticipate (see Sections 2.5 and 2.6).

<div id="section-2-4-3-block-2"></div>

<span id="figure-2.18"></span>
<!-- START IMG -->
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'''Figure 2.18'''

<span id="comparison-of-co-2-emission-trajectories-of-sectoral-pathways-iea-etp-b2ds-etp-2ds-iea-662ds-shell-sky-with-the-ranges-of-iam-pathway-2ds-are-2c-consistent-pathways-and-1.5ds-os-are1.5c-overshoot-pathways.-the-co-2-emissions-shown-here-are-the-energy-related-emissions-including-industrial-process-emissions."></span>
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'''Comparison of CO <sub>2</sub> emission trajectories of sectoral pathways (IEA ETP-B2DS, ETP-2DS, IEA-66%2DS, Shell-Sky) with the ranges of IAM pathway (2DS are 2°C-consistent pathways and 1.5DS-OS are1.5°C overshoot pathways). The CO <sub>2</sub> emissions shown here are the energy-related emissions, including industrial process emissions.'''
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[[File:e322b16bc219b14e094fab9d8fa9e92b Figure-2.18-1024x788.jpg]]

Original Creation for this Report using IAMC 1.5°C Scenario Data hosted by IIASA

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<div id="section-2-4-3-block-3"></div>

<span id="figure-2.19"></span>
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'''Figure 2.19'''

<span id="a-global-final-energy-b-direct-co-2-emissions-from-the-all-energy-demand-sectors-c-carbon-intensity-and-d-structure-of-final-energy-electricity-liquid-fuel-coal-and-biomass."></span>
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'''(a) Global final energy, (b) direct CO <sub>2</sub> emissions from the all energy demand sectors, (c) carbon intensity, and (d) structure of final energy (electricity, liquid fuel, coal, and biomass).'''
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[[File:7d01fdeeec2dad8a304824fa0bfaedae Figure-2.19-1024x1024.jpg]]

The squares and circles indicate the IAM archetype pathways and diamonds indicate the data of sectoral scenarios. The red dotted line indicates the 2010 level. H2DS = Higher-2°C, L2DS = Lower-2°C, 1.5DS-H = 1.5°C-high-OS, 1.5DS-L = 1.5°C-low-OS. The label 1.5DS combines both high and low overshoot 1.5°C-consistent pathway. See Section 2.1 for descriptions.

Original Creation for this Report using IAMC 1.5°C Scenario Data hosted by IIASA

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<div id="section-2-4-3-block-4"></div>

Final energy demand is driven by demand in energy services for mobility, residential and commercial activities (buildings), and manufacturing. Projections of final energy demand depend heavily on assumptions about socio-economic futures as represented by the SSPs (Bauer et al., 2017) <sup>[[#fn:r402|402]]</sup> (see Sections 2.1, 2.3 and 2.5). The structure of this demand drives the composition of final energy use in terms of energy carriers (electricity, liquids, gases, solids, hydrogen etc.).

Figure 2.19 shows the structure of global final energy demand in 2030 and 2050, indicating the trend toward electrification and fossil fuel usage reduction. This trend is more significant in 1.5°C pathways than 2°C pathways. Electrification continues throughout the second half of the century, leading to a 3.5- to 6-fold increase in electricity demand (interquartile range; median 4.5) by the end of the century relative to today (Grubler et al., 2018; Luderer et al., 2018) <sup>[[#fn:r403|403]]</sup> . Since the electricity sector is completely decarbonized by mid-century in 1.5°C pathways (see Figure 2.20), electrification is the primary means to decarbonize energy end-use sectors.

The CO <sub>2</sub> emissions <sup>[[#fn:6|6]]</sup> of end-use sectors and carbon intensity are shown in Figure 2.20. The projections of IAMs and IEA studies show rather different trends, especially in the carbon intensity. These differences come from various factors, including the deployment of CCS, the level of fuel switching and efficiency improvements, and the effect of structural and behavioural changes. IAM projections are generally optimistic for the industry sectors, but not for buildings and transport sectors. Although GDP increases by a factor of 3.4 from 2010 to 2050, the total energy consumption of end-use sectors grows by only about 30% and 20% in 1.5°C overshoot and 2°C-consistent pathways, respectively. However, CO <sub>2</sub> emissions would need to be reduced further to achieve the stringent temperature limits. Figure 2.20 shows that the reduction in CO <sub>2</sub> emissions of end-use sectors is larger and more rapid in 1.5°C overshoot than 2°C-consistent pathways, while emissions from the power sector are already almost zero in 2050 in both sets of pathways, indicating that supply-side emissions reductions are almost fully exploited already in 2°C-consistent pathways (see Figure 2.20) (Rogelj et al., 2015b, 2018; Luderer et al., 2016b) <sup>[[#fn:r404|404]]</sup> . The emission reductions in end-use sectors are largely made possible by efficiency improvements, demand reduction measures and electrification, but the level of emissions reductions varies across end-use sectors. While the carbon intensity of the industry and buildings sectors decreases to a very low level of around 10 gCO <sub>2</sub> MJ <sup>-1</sup> , the carbon intensity of transport becomes the highest of any sector by 2040 due to its higher reliance on oil-based fuels. In the following subsections, the potential and challenges of CO <sub>2</sub> emission reduction in each end-use sector are discussed in detail.

<div id="section-2-4-3-block-5"></div>

<span id="figure-2.20"></span>
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'''Figure 2.20'''

<span id="comparison-of-a-direct-co-2-emissions-and-b-carbon-intensity-of-the-power-and-energy-end-use-sectors-industry-buildings-and-transport-sectors-between-iams-and-sectoral-studies-iea-etp-and-ieairena."></span>
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'''Comparison of (a) direct CO <sub>2</sub> emissions and (b) carbon intensity of the power and energy end-use sectors (industry, buildings, and transport sectors) between IAMs and sectoral studies (IEA-ETP and IEA/IRENA).'''
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[[File:332935fa58dd4bbe9b8418e4636e94e1 Figure-2.20-1024x719.jpg]]

Diamond markers in panel (b) show data for IEA-ETP scenarios (2DS and B2DS), and IEA/IRENA scenario (66%2DS). Note: for the data from IAM studies, there is rather large variation of projections for each indicator. Please see the details in the following figures in each end-use sector section.

Original Creation for this Report using IAMC 1.5°C Scenario Data hosted by IIASA

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<div id="section-2-4-3-1"></div>

<span id="industry"></span>
==== 2.4.3.1 Industry ====

<div id="section-2-4-3-1-block-1"></div>

The industry sector is the largest end-use sector, both in terms of final energy demand and GHG emissions. Its direct CO <sub>2</sub> emissions currently account for about 25% of total energy-related and process CO <sub>2</sub> emissions, and emissions have increased at an average annual rate of 3.4% between 2000 and 2014, significantly faster than total CO <sub>2</sub> emissions (Hoesly et al., 2018) <sup>[[#fn:r405|405]]</sup> . In addition to emissions from the combustion of fossil fuels, non-energy uses of fossil fuels in the petrochemical industry and metal smelting, as well as non-fossil fuel process emissions (e.g., from cement production) contribute a small amount (~5%) to the sector’s CO <sub>2</sub> emissions inventory. Material industries are particularly energy and emissions intensive: together, the steel, non-ferrous metals, chemicals, non-metallic minerals, and pulp and paper industries accounted for close to 66% of final energy demand and 72% of direct industry-sector emissions in 2014 (IEA, 2017a) <sup>[[#fn:r406|406]]</sup> . In terms of end-uses, the bulk of energy in manufacturing industries is required for process heating and steam generation, while most electricity (but smaller shares of total final energy) is used for mechanical work (Banerjee et al., 2012; IEA, 2017a) <sup>[[#fn:r407|407]]</sup> .

As shown in Figure 2.21, a major share of the additional emission reductions required for 1.5°C-overshoot pathways compared to those in 2°C-consistent pathways comes from industry. Final energy, CO <sub>2</sub> emissions, and carbon intensity are consistent in IAM and sectoral studies, but in IAM-1.5°C-overshoot pathways the share of electricity is higher than IEA-B2DS (40% vs. 25%) and hydrogen is also considered to have a share of about 5% versus 0%. In 2050, final energy is increased by 30% and 5% compared with the 2010 level (red dotted line) for 1.5°C-overshoot and 2°C-consistent pathways, respectively, but CO <sub>2</sub> emissions are decreased by 80% and 50% and carbon intensity by 80% and 60%, respectively. This additional decarbonization is brought by switching to low-carbon fuels and CCS deployment.

<div id="section-2-4-3-1-block-2"></div>

<span id="figure-2.21"></span>
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'''Figure 2.21'''

<span id="section-11"></span>
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Comparison of (a) final energy, (b) direct CO <sub>2 </sub> emissions, (c) carbon intensity, (d) electricity and biomass consumption in the industry sector between IAM and sectoral studies.
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[[File:cbcf8b9105f0dd74c21060f4c715586f Figure-2.21-1024x953.jpg]]

The label 1.5DS combines both high and low overshoot 1.5°C-consistent pathways. Section 2.1 for descriptions. The squares and circles indicate the IAM archetype pathways and diamonds the data of sectoral scenarios. The red dotted line indicates the 2010 level. H2DS = Higher-2°C, L2DS = Lower-2°C, 1.5DS-H = 1.5°C-high-OS, 1.5DS-L = 1.5°C-low-OS. The label 1.5DS combines both high and low overshoot 1.5°C-consistent pathways. Section 2.1 for descriptions.

Original Creation for this Report using IAMC 1.5°C Scenario Data hosted by IIASA

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<div id="section-2-4-3-1-block-3"></div>

Broadly speaking, the industry sector’s mitigation measures can be categorized in terms of the following five strategies: (i) reducing demand, (ii) energy efficiency, (iii) increasing electrification of energy demand, (iv) reducing the carbon content of non-electric fuels, and (v) deploying innovative processes and application of CCS. IEA ETP estimates the relative contribution of different measures for CO <sub>2</sub> emission reduction in their B2DS scenario compared with their reference scenario in 2050 as follows: energy efficiency 42%, innovative process and CCS 37%, switching to low-carbon fuels and feedstocks 13% and material efficiency (include efficient production and use to contribute to demand reduction) 8%. The remainder of this section delves more deeply into the potential mitigation contributions of these strategies as well as their limitations.

Reduction in the use of industrial materials, while delivering similar services, or improving the quality of products could help to reduce energy demand and overall system-level CO <sub>2</sub> emissions. Strategies include using materials more intensively, extending product lifetimes, increasing recycling, and increasing inter-industry material synergies, such as clinker substitution in cement production (Allwood et al., 2013; IEA, 2017a) <sup>[[#fn:r408|408]]</sup> . Related to material efficiency, use of fossil-fuel feedstocks could shift to lower-carbon feedstocks, such as from oil to natural gas and biomass, and end-uses could shift to more sustainable materials, such as biomass-based materials, reducing the demand for energy-intensive materials (IEA, 2017a) <sup>[[#fn:r409|409]]</sup> .

Reaping energy efficiency potentials hinges critically on advanced management practices, such as energy management systems, in industrial facilities as well as targeted policies to accelerate adoption of the best available technology (see Section 2.5). Although excess energy, usually as waste heat, is inevitable, recovering and reusing this waste heat under economically and technically viable conditions benefits the overall energy system. Furthermore, demand-side management strategies could modulate the level of industrial activity in line with the availability of resources in the power system. This could imply a shift away from peak demand and as power supply decarbonizes, this demand-shaping potential could shift some load to times with high portions of low-carbon electricity generation (IEA, 2017a) <sup>[[#fn:r410|410]]</sup> .

In the industry sector, energy demand increases more than 40% between 2010 and 2050 in baseline scenarios. However, in the 1.5°C-overshoot and 2°C-consistent pathways from IAMs, the increase is only 30% and 5%, respectively (Figure 2.21). These energy-demand reductions encompass both efficiency improvements in production and reductions in material demand, as most IAMs do not discern these two factors.

CO <sub>2</sub> emissions from industry increase by 30% in 2050 compared to 2010 in baseline scenarios. By contrast, these emissions are reduced by 80% and 50% relative to 2010 levels in 1.5°C-overshoot and 2°C-consistent pathways from IAMs, respectively (Figure 2.21). By mid-century, CO <sub>2</sub> emissions per unit of electricity are projected to decrease to near zero in both sets of pathways (see Figure 2.20). An accelerated electrification of the industry sector thus becomes an increasingly powerful mitigation option. In the IAM pathways, the share of electricity increases up to 30% by 2050 in 1.5°C-overshoot pathways (Figure 2.21) from 20% in 2010. Some industrial fuel uses are substantially more difficult to electrify than others, and electrification would have other effects on the process, including impacts on plant design, cost and available process integration options (IEA, 2017a) <sup>[[#fn:r411|411]]</sup> . <sup>[[#fn:7|7]]</sup>

In 1.5°C-overshoot pathways, the carbon intensity of non-electric fuels consumed by industry decreases to 16 gCO <sub>2</sub> MJ <sup>−1</sup> by 2050, compared to 25 gCO <sub>2</sub> MJ <sup>−1</sup> in 2°C-consistent pathways. Considerable carbon intensity reductions are already achieved by 2030, largely via a rapid phase-out of coal. Biomass becomes an increasingly important energy carrier in the industry sector in deep-decarbonization pathways, but primarily in the longer term (in 2050, biomass accounts for only 10% of final energy consumption even in 1.5°C-overshoot pathways). In addition, hydrogen plays a considerable role as a substitute for fossil-based non-electric energy demands in some pathways.

Without major deployment of new sustainability-oriented low-carbon industrial processes, the 1.5°C-overshoot target is difficult to achieve. Bringing such technologies and processes to commercial deployment requires significant investment in research and development. Some examples of innovative low-carbon process routes include: new steelmaking processes such as upgraded smelt reduction and upgraded direct reduced iron, inert anodes for aluminium smelting, and full oxy-fuelling kilns for clinker production in cement manufacturing (IEA, 2017a) <sup>[[#fn:r412|412]]</sup> .

CCS plays a major role in decarbonizing the industry sector in the context of 1.5°C and 2°C pathways, especially in industries with higher process emissions, such as cement, iron and steel industries. In 1.5°C-overshoot pathways, CCS in industry reaches 3 GtCO <sub>2</sub> yr <sup>−1</sup> by 2050, albeit with strong variations across pathways. Given the projected long-lead times and need for technological innovation, early scale-up of industry-sector CCS is essential to achieving the stringent temperature target. Development and demonstration of such projects has been slow, however. Currently, only two large-scale industrial CCS projects outside of oil and gas processing are in operation (Global CCS Institute, 2016) <sup>[[#fn:r413|413]]</sup> . The estimated current cost <sup>[[#fn:8|8]]</sup> of CO <sub>2</sub> avoided (in USD2015) ranges from $20–27 tCO <sub>2</sub> <sup>−1</sup> for gas processing and bio-ethanol production, and $60–138 tCO <sub>2</sub> <sup>−1</sup> for fossil fuel-fired power generation up to $104–188 tCO <sub>2</sub> <sup>−1</sup> for cement production (Irlam, 2017) <sup>[[#fn:r414|414]]</sup> .

<div id="section-2-4-3-2"></div>

<span id="buildings"></span>
==== 2.4.3.2 Buildings ====

<div id="section-2-4-3-2-block-1"></div>

In 2014, the buildings sector accounted for 31% of total global final energy use, 54% of final electricity demand, and 8% of energy-related CO <sub>2</sub> emissions (excluding indirect emissions due to electricity). When upstream electricity generation is taken into account, buildings were responsible for 23% of global energy-related CO <sub>2</sub> emissions, with one-third of those from direct fossil fuel consumption (IEA, 2017a) <sup>[[#fn:r415|415]]</sup> .

Past growth of energy consumption has been mainly driven by population and economic growth, with improved access to electricity, and higher use of electrical appliances and space cooling resulting from increasing living standards, especially in developing countries (Lucon et al., 2014) <sup>[[#fn:r416|416]]</sup> . These trends will continue in the future and in 2050, energy consumption is projected to increase by 20% and 50% compared to 2010 in the IAM-1.5°C-overshoot and 2°C-consistent pathways, respectively (Figure 2.22). However, sectoral studies (IEA-ETP scenarios) show different trends. Energy consumption in 2050 decreases compared to 2010 in ETP-B2DS, and the reduction rate of CO <sub>2</sub> emissions is higher than in IAM pathways (Figure 2.22). Mitigation options are often more widely covered in sectoral studies (Lucon et al., 2014) <sup>[[#fn:r417|417]]</sup> , leading to greater reductions in energy consumption and CO <sub>2</sub> emissions.

Emissions reductions are driven by a clear tempering of energy demand and a strong electrification of the buildings sector. The share of electricity in 2050 is 60% in 1.5°C-overshoot pathways, compared with 50% in 2°C-consistent pathways (Figure 2.22). Electrification contributes to the reduction of direct CO <sub>2</sub> emissions by replacing carbon-intensive fuels, like oil and coal. Furthermore, when combined with a rapid decarbonization of the power system (see Section 2.4.1) it also enables further reduction of indirect CO <sub>2</sub> emissions from electricity. Sectoral bottom-up models generally estimate lower electrification potentials for the buildings sector in comparison to global IAMs (see Figure 2.22). Besides CO <sub>2</sub> emissions, increasing global demand for air conditioning in buildings may also lead to increased emissions of HFCs in this sector over the next few decades. Although these gases are currently a relatively small proportion of annual GHG emissions, their use in the air conditioning sector is expected to grow rapidly over the next few decades if alternatives are not adopted. However, their projected future impact can be significantly mitigated through better servicing and maintenance of equipment and switching of cooling gases (Shah et al., 2015; Purohit and Höglund-Isaksson, 2017) <sup>[[#fn:r418|418]]</sup> .

IEA-ETP (IEA, 2017a) <sup>[[#fn:r419|419]]</sup> analysed the relative importance of various technology measures toward the reduction of energy and CO <sub>2</sub> emissions in the buildings sector. The largest energy savings potential is in heating and cooling demand, largely due to building envelope improvements and high efficiency and renewable equipment. In the ETP-B2DS, energy demand for space heating and cooling is 33% lower in 2050 than in the reference scenario, and these reductions account for 54% of total reductions from the reference scenario. Energy savings from shifts to high-performance lighting, appliances, and water heating equipment account for a further 24% of the total reduction. The long-term, strategic shift away from fossil-fuel use in buildings, alongside the rapid uptake of energy efficient, integrated and renewable energy technologies (with clean power generation), leads to a drastic reduction of CO <sub>2</sub> emissions. In ETP-B2DS, the direct CO <sub>2</sub> emissions are 79% lower than the reference scenario in 2050, and the remaining emissions come mainly from the continued use of natural gas.

The buildings sector is characterized by very long-living infrastructure, and immediate steps are hence important to avoid lock-in of inefficient carbon and energy-intensive buildings. This applies both to new buildings in developing countries where substantial new construction is expected in the near future and to retrofits of existing building stock in developed regions. This represents both a significant risk and opportunity for mitigation. <sup>[[#fn:9|9]]</sup> A recent study highlights the benefits of deploying the most advanced renovation technologies, which would avoid lock-in into less efficient measures (Güneralp et al., 2017) <sup>[[#fn:r420|420]]</sup> . Aside from the effect of building envelope measures, adoption of energy-efficient technologies such as heat pumps and, more recently, light-emitting diodes is also important for the reduction of energy and CO <sub>2</sub> emissions (IEA, 2017a) <sup>[[#fn:r421|421]]</sup> . Consumer choices, behaviour and building operation can also significantly affect energy consumption (see Chapter 4, Section 4.3).

<div id="section-2-4-3-2-block-2"></div>

<span id="figure-2.22"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.22'''

<span id="section-12"></span>
<!-- IMG CAPTION -->
Comparison of (a) final energy, (b) direct  CO <sub>2</sub>  emissions, (c) carbon intensity, (d) electricity and biomass consumption in the buildings sector between IAM and sectoral studies.
<!-- IMG FILE -->
[[File:c3d2c55eae9c83d5be4c7e3ad1c1799a Figure-2.22-1024x968.jpg]]

The squares and circles indicate the IAM archetype pathways and diamonds the data of sectoral scenarios. The red dotted line indicates the 2010 level. H2DS = Higher-2°C, L2DS = Lower-2°C, 1.5DS-H = 1.5°C-high-OS, 1.5DS-L = 1.5°C-low-OS. The label 1.5DS combines both high and low overshoot 1.5°C-consistent pathways. Section 2.1 for descriptions.

Original Creation for this Report using IAMC 1.5°C Scenario Data hosted by IIASA

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<div id="section-2-4-3-3"></div>

<span id="transport"></span>
==== 2.4.3.3 Transport ====

<div id="section-2-4-3-3-block-1"></div>

Transport accounted for 28% of global final energy demand and 23% of global energy-related CO <sub>2</sub> emissions in 2014. Emissions increased by 2.5% annually between 2010 and 2015, and over the past half century the sector has witnessed faster emissions growth than any other. The transport sector is the least diversified energy end-use sector; the sector consumed 65% of global oil final energy demand, with 92% of transport final energy demand consisting of oil products (IEA, 2017a) <sup>[[#fn:r422|422]]</sup> , suggesting major challenges for deep decarbonization.

Final energy, CO <sub>2</sub> emissions, and carbon intensity for the transport sector are shown in Figure 2.23. The projections of IAMs are more pessimistic than IEA-ETP scenarios, though both clearly project deep cuts in energy consumption and CO <sub>2</sub> emissions by 2050. For example, 1.5°C-overshoot pathways from IAMs project a reduction of 15% in energy consumption between 2015 and 2050, while ETP-B2DS projects a reduction of 30% (Figure 2.23). Furthermore, IAM pathways are generally more pessimistic in the projections of CO <sub>2</sub> emissions and carbon intensity reductions. In AR5 (Clarke et al., 2014; Sims et al., 2014) <sup>[[#fn:r423|423]]</sup> , similar comparisons between IAMs and sectoral studies were performed and these were in good agreement with each other. Since the AR5, two important changes can be identified: rapid growth of electric vehicle sales in passenger cars, and more attention towards structural changes in this sector. The former contributes to reduction of CO <sub>2</sub> emissions and the latter to reduction of energy consumption.

Deep emissions reductions in the transport sector would be achieved by several means. Technology-focused measures such as energy efficiency and fuel-switching are two of these. Structural changes that avoid or shift transport activity are also important. While the former solutions (technologies) always tend to figure into deep decarbonization pathways in a major way, this is not always the case with the latter, especially in IAM pathways. Comparing different types of global transport models, Yeh et al. (2016) <sup>[[#fn:r424|424]]</sup> find that sectoral (intensive) studies generally envision greater mitigation potential from structural changes in transport activity and modal choice. Though, even there, it is primarily the switching of passengers and freight from less- to more-efficient travel modes (e.g., cars, trucks and airplanes to buses and trains) that is the main strategy; other actions, such as increasing vehicle load factors (occupancy rates) and outright reductions in travel demand (e.g., as a result of integrated transport, land-use and urban planning), figure much less prominently. Whether these dynamics accurately reflect the actual mitigation potential of structural changes in transport activity and modal choice is a point of investigation. According to the recent IEA-ETP scenarios, the share of avoid (reduction of mobility demand) and shift (shifting to more efficient modes) measures in the reduction of CO <sub>2</sub> emissions from the reference to B2DS scenarios in 2050 amounts to 20% (IEA, 2017a) <sup>[[#fn:r425|425]]</sup> .

The potential and strategies to reduce energy consumption and CO <sub>2</sub> emissions differ significantly among transport modes. In ETP-B2DS, the shares of energy consumption and CO <sub>2</sub> emissions in 2050 for each mode are rather different (see Table 2.8), indicating the challenge of decarbonizing heavy-duty vehicles (HDV, trucks), aviation, and shipping. The reduction of CO <sub>2</sub> emissions in the whole sector from the reference scenario to ETP-B2DS is 60% in 2050, with varying contributions per mode (Table 2.8). Since there is no silver bullet for this deep decarbonization, every possible measure would be required to achieve this stringent emissions outcome. The contribution of various measures for the CO <sub>2</sub> emission reduction from the reference scenario to the IEA-B2DS in 2050 can be decomposed to efficiency improvement (29%), biofuels (36%), electrification (15%), and avoid/shift (20%) (IEA, 2017a) <sup>[[#fn:r426|426]]</sup> . It is noted that the share of electrification becomes larger compared with older studies, reflected by the recent growth of electric vehicle sales worldwide. Another new trend is the allocation of biofuels to each mode of transport. In IEA-B2DS, the total amount of biofuels consumed in the transport sector is 24EJ <sup>[[#fn:10|10]]</sup> in 2060, and allocated to LDV (light-duty vehicles, 17%), HDV (35%), aviation (28%), and shipping (21%), that is, more biofuels is allocated to the difficult-to-decarbonize modes (see Table 2.8).

<div id="section-2-4-3-3-block-2"></div>

<span id="table-2.8"></span>
<!-- START TABLE -->
'''Table 2.8'''

<span id="transport-sector-indicators-by-mode-in-2050-iea-2017a."></span>
'''Transport sector indicators by mode in 2050 (IEA, 2017a).'''

Share of energy consumption, biofuel consumption, CO <sub>2</sub> emissions, and reduction of energy consumption and CO <sub>2</sub> emissions from 2014. (CO <sub>2</sub> emissions are well-to-wheel emissions, including the emission during the fuel production.), LDV: light duty vehicle, HDV: heavy duty vehicle.
<!-- TABLE -->
{| class="wikitable"
|-
! rowspan="2"|
! colspan="3"| Share of Each Mode (%)
! colspan="2"| Reduction from 2014 (%)
|-
! Energy
! Biofuel
! CO <sub>2</sub>
! Energy
! CO <sub>2</sub>
|-
| LDV
| 36
| 17
| 30
| 51
| 81
|-
| HDV
| 33
| 35
| 36
| 8
| 56
|-
| Rail
| 6
| –
| –1
| –136
| 107
|-
| Aviation
| 12
| 28
| 14
| 56
|-
| Shipping
| 17
| 21
| 26
| 29
|}
<!-- END TABLE -->

<div id="section-2-4-3-3-block-3"></div>

In road transport, incremental vehicle improvements (including engines) are relevant, especially in the short to medium term. Hybrid electric vehicles are also instrumental to enabling the transition from internal combustion engine vehicles to electric vehicles, especially plug-in hybrid electric vehicles. Electrification is a powerful measure to decarbonize short-distance vehicles (passenger cars and two and three wheelers) and the rail sector. In road freight transport (trucks), systemic improvements (e.g., in supply chains, logistics, and routing) would be effective measures in conjunction with efficiency improvement of vehicles. Shipping and aviation are more challenging to decarbonize, while their demand growth is projected to be higher than other transport modes. Both modes would need to pursue highly ambitious efficiency improvements and use of low-carbon fuels. In the near and medium term, this would be advanced biofuels while in the long term it could be hydrogen as direct use for shipping or an intermediate product for synthetic fuels for both modes (IEA, 2017a) <sup>[[#fn:r428|428]]</sup> .

The share of low-carbon fuels in the total transport fuel mix increases to 10% and 16% by 2030 and to 40% and 58% by 2050 in 1.5°C-overshoot pathways from IAMs and the IEA-B2DS pathway, respectively. The IEA-B2DS scenario is on the more ambitious side, especially in the share of electricity. Hence, there is wide variation among scenarios, including the IAM pathways, regarding changes in the transport fuel mix over the first half of the century. As seen in Figure 2.23, the projections of energy consumption, CO <sub>2</sub> emissions and carbon intensity are quite different between IAM and ETP scenarios. These differences can be explained by more weight on efficiency improvements and avoid/shift decreasing energy consumption, and the higher share of biofuels and electricity accelerating the speed of decarbonization in ETP scenarios. Although biofuel consumption and electric vehicle sales have increased significantly in recent years, the growth rates projected in these pathways would be unprecedented and far higher than has been experienced to date.

<div id="section-2-4-3-3-block-4"></div>

<span id="figure-2.23"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.23'''

<span id="section-13"></span>
<!-- IMG CAPTION -->
Comparison of (a) final energy, (b) direct  CO <sub>2 </sub> emissions, (c) carbon intensity, (d) electricity and biofuel consumption in the transport sector between IAM and sectoral studies.
<!-- IMG FILE -->
[[File:5ee0b9397c5c07aa4ab8858cfa9aebca Figure-2.23-1024x982.jpg]]

The squares and circles indicate the IAM archetype pathways and diamonds the data of sectoral scenarios. The red dotted line indicates the 2010 level. H2DS = Higher-2°C, L2DS = Lower-2°C, 1.5DS-H = 1.5°C-high-OS, 1.5DS-L = 1.5°C-low-OS. The label 1.5DS combines both high and low overshoot 1.5°C-consistent pathways. Section 2.1 for descriptions.

Original Creation for this Report using IAMC 1.5°C Scenario Data hosted by IIASA

<!-- END IMG -->
<div id="section-2-4-3-3-block-5"></div>

The 1.5°C pathways require an acceleration of the mitigation solutions already featured in 2°C-consistent pathways (e.g., more efficient vehicle technologies operating on lower-carbon fuels), as well as those having received lesser attention in most global transport decarbonization pathways up to now (e.g., mode-shifting and travel demand management). Current-generation, global pathways generally do not include these newer transport sector developments, whereby technological solutions are related to shifts in traveller’s behaviour.

<span id="land-use-transitions-and-changes-in-the-agricultural-sector"></span>
=== 2.4.4 Land-Use Transitions and Changes in the Agricultural Sector ===

<div id="section-2-4-4-block-1"></div>

The agricultural and land system described together under the umbrella of the AFOLU (agriculture, forestry, and other land use) sector plays an important role in 1.5°C pathways (Clarke et al., 2014; Smith and Bustamante, 2014; Popp et al., 2017) <sup>[[#fn:r429|429]]</sup> . On the one hand, its emissions need to be limited over the course of this century to be in line with pathways limiting warming to 1.5°C (see Sections 2.2-3). On the other hand, the AFOLU system is responsible for food and feed production; for wood production for pulp and construction; for the production of biomass that is used for energy, CDR or other uses; and for the supply of non-provisioning (ecosystem) services (Smith and Bustamante, 2014) <sup>[[#fn:r430|430]]</sup> . Meeting all demands together requires changes in land use, as well as in agricultural and forestry practices, for which a multitude of potential options have been identified (Smith and Bustamante, 2014; Popp et al., 2017) <sup>[[#fn:r431|431]]</sup> (see also Supplementary Material  2.SM.1.2 and Chapter 4, Section 4.3.1, 4.3.2 and 4.3.7).

This section assesses the transformation of the AFOLU system, mainly making use of pathways from IAMs (see Section 2.1) that are based on quantifications of the SSPs and that report distinct land-use evolutions in line with limiting warming to 1.5°C (Calvin et al., 2017; Fricko et al., 2017; Fujimori, 2017; Kriegler et al., 2017; Popp et al., 2017; Riahi et al., 2017; van Vuuren et al., 2017b; Doelman et al., 2018; Rogelj et al., 2018) <sup>[[#fn:r432|432]]</sup> . The SSPs were designed to vary mitigation challenges (O’Neill et al., 2014) <sup>[[#fn:r433|433]]</sup> (Cross-Chapter Box 1 in Chapter 1), including for the AFOLU sector (Popp et al., 2017; Riahi et al., 2017) <sup>[[#fn:r434|434]]</sup> . The SSP pathway ensemble hence allows for a structured exploration of AFOLU transitions in the context of climate change mitigation in line with 1.5°C, taking into account technological and socio-economic aspects. Other considerations, like food security, livelihoods and biodiversity, are also of importance when identifying AFOLU strategies. These are at present only tangentially explored by the SSPs. Further assessments of AFOLU mitigation options are provided in other parts of this report and in the IPCC Special Report on Climate Change and Land (SRCCL). Chapter 4 provides an assessment of bioenergy (including feedstocks, see Section 4.3.1), livestock management (Section 4.3.1), reducing rates of deforestation and other land-based mitigation options (as mitigation and adaptation option, see Section 4.3.2), and BECCS, afforestation and reforestation options (including the bottom-up literature of their sustainable potential, mitigation cost and side effects, Section 4.3.7). Chapter 3 discusses impacts land-based CDR (Cross-Chapter Box 7 in Chapter 3). Chapter 5 assesses the sustainable development implications of AFOLU mitigation, including impacts on biodiversity (Section 5.4). Finally, the SRCCL will undertake a more comprehensive assessment of land and climate change aspects. For the sake of complementarity, this section focusses on the magnitude and pace of land transitions in 1.5°C pathways, as well as on the implications of different AFOLU mitigation strategies for different land types. The interactions with other societal objectives and potential limitations of identified AFOLU measures link to these large-scale evolutions, but these are assessed elsewhere (see above).

Land-use changes until mid-century occur in the large majority of SSP pathways, both under stringent mitigation and in absence of mitigation (Figure 2.24). In the latter case, changes are mainly due to socio-economic drivers like growing demands for food, feed and wood products. General transition trends can be identified for many land types in 1.5°C pathways, which differ from those in baseline scenarios and depend on the interplay with mitigation in other sectors (Figure 2.24) (Popp et al., 2017; Riahi et al., 2017; Rogelj et al., 2018) <sup>[[#fn:r435|435]]</sup> . Mitigation that demands land mainly occurs at the expense of agricultural land for food and feed production. Additionally, some biomass is projected to be grown on marginal land or supplied from residues and waste, but at lower shares. Land for second-generation energy crops (such as ''Miscanthus'' or poplar) expands by 2030 and 2050 in all available pathways that assume a cost-effective achievement of a 1.5°C temperature goal in 2100 (Figure 2.24), but the scale depends strongly on underlying socio-economic assumptions (see later discussion of land pathway archetypes). Reducing rates of deforestation restricts agricultural expansion, and forest cover can expand strongly in 1.5°C and 2°C pathways alike compared to its extent in no-climate-policy baselines due to reduced deforestation and afforestation and reforestation measures. However, the extent to which forest cover expands varies highly across models in the literature, with some models projecting forest cover to stay virtually constant or decline slightly. This is due to whether afforestation and reforestation is included as a mitigation technology in these pathways and interactions with other sectors.

As a consequence of other land-use changes, pasture land is generally projected to be reduced compared to both baselines in which no climate change mitigation action is undertaken and 2°C-consistent pathways. Furthermore, cropland for food and feed production decreases in most 1.5°C pathways, both compared to a no-climate baseline and relative to 2010. These reductions in agricultural land for food and feed production are facilitated by intensification on agricultural land and in livestock production systems (Popp et al., 2017) <sup>[[#fn:r436|436]]</sup> , as well as changes in consumption patterns (Frank et al., 2017; Fujimori, 2017) <sup>[[#fn:r437|437]]</sup> (see also Chapter 4, Section 4.3.2 for an assessment of these mitigation options). For example, in a scenario based on rapid technological progress (Kriegler et al., 2017) <sup>[[#fn:r438|438]]</sup> , global average cereal crop yields in 2100 are assumed to be above 5 tDM ha <sup>−1</sup> yr <sup>−1</sup> in mitigation scenarios aiming at limiting end-of-century radiative forcing to 4.5 or 2.6 W m <sup>−2</sup> , compared to 4 tDM ha <sup>−1</sup> yr <sup>−1</sup> in the SSP5 baseline to ensure the same food production. Similar improvements are present in 1.5°C variants of such scenarios. Historically, cereal crop yields are estimated at 1 tDM ha <sup>−1</sup> yr <sup>−1</sup> and about 3 tDM ha <sup>−1</sup> yr <sup>−1</sup> in 1965 and 2010, respectively (calculations based on FAOSTAT, 2018) <sup>[[#fn:r439|439]]</sup> . For aggregate energy crops, models assume 4.2–8.9 tDM ha <sup>−1</sup> yr <sup>−1</sup> in 2010, increasing to about 6.9–17.4 tDM ha <sup>−1</sup> yr <sup>−1</sup> in 2050, which fall within the range found in the bottom-up literature yet depend on crop, climatic zone, land quality and plot size (Searle and Malins, 2014) <sup>[[#fn:r440|440]]</sup> .

<div id="section-2-4-4-block-2"></div>

<span id="figure-2.24"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.24'''

<span id="overview-of-land-use-change-transitions-in-2030-and-2050-relative-to-2010-based-on-pathways-based-on-the-shared-socio-economic-pathways-ssps-popp-et-al.-2017-riahi-et-al.-2017-rogelj-et-al.-2018-441-."></span>
<!-- IMG CAPTION -->
'''Overview of land-use change transitions in 2030 and 2050, relative to 2010 based on pathways based on the Shared Socio-Economic Pathways (SSPs) (Popp et al., 2017; Riahi et al., 2017; Rogelj et al., 2018) <sup>[[#fn:r441|441]]</sup> .'''
<!-- IMG FILE -->
[[File:62e3729dd7ca03f3ea341ef9467985c2 Figure-2.24-1024x554.jpg]]

Grey: no-climate-policy baseline; green: 2.6 W m−2 pathways; blue: 1.9 W m−2 pathways. Pink: 1.9 W m−2 pathways grouped per underlying socio-economic assumption (from left to right: SSP1 sustainability, SSP2 middle-of-the-road, SSP5 fossil-fuelled development). Ranges show the minimum–maximum range across the SSPs. Single pathways are shown with plus signs. Illustrative archetype pathways are highlighted with distinct icons. Each panel shows the changes for a different land type. The 1.9 and 2.6 W m−2 pathways are taken as proxies for 1.5°C and 2°C pathways, respectively. The 2.6 W m−2 pathways are mostly consistent with the Lower-2°C and Higher-2°C pathway classes. The 1.9 W m−2 pathways are consistent with the 1.5°C-low-OS (mostly SSP1 and SSP2) and 1.5°C-high-OS (SSP5) pathway classes. In 2010, pasture was estimated to cover about 3–3.5 103 Mha, food and feed crops about 1.5–1.6 103 Mha, energy crops about 0–14 Mha and forest about 3.7–4.2 103 Mha, across the models that reported SSP pathways (Popp et al., 2017) <sup>[[#fn:r442|442]]</sup> . When considering pathways limiting warming to 1.5°C with no or limited overshoot, the full set of scenarios shows a conversion of 50–1100 Mha of pasture into 0–600 Mha for energy crops, a 200 Mha reduction to 950 Mha increase forest, and a 400 Mha decrease to a 250 Mha increase in non-pasture agricultural land for food and feed crops by 2050 relative to 2010. The large range across the literature and the understanding of the variations across models and assumptions leads to medium confidence in the size of these ranges.

Original Creation for this Report using IAMC 1.5°C Scenario Data hosted by IIASA

<!-- END IMG -->
<div id="section-2-4-4-block-3"></div>

The pace of projected land transitions over the coming decades can differ strongly between 1.5°C and baseline scenarios without climate change mitigation and from historical trends (Table 2.9). However, there is uncertainty in the sign and magnitude of these future land-use changes (Prestele et al., 2016; Popp et al., 2017; Doelman et al., 2018) <sup>[[#fn:r443|443]]</sup> . The pace of projected cropland changes overlaps with historical trends over the past four decades, but in several cases also goes well beyond this range. By the 2030–2050 period, the projected reductions in pasture and potentially strong increases in forest cover imply a reversed dynamic compared to historical and baseline trends. This suggests that distinct policy and government measures would be needed to achieve forest increases, particularly in a context of projected increased bioenergy use.

<div id="section-2-4-4-block-4"></div>

<span id="table-2.9"></span>
<!-- START TABLE -->
'''Table 2.9'''

<span id="annual-pace-of-land-use-change-in-baseline-2c-and-1.5c-pathways."></span>
'''Annual pace of land-use change in baseline, 2°C and 1.5°C pathways.'''

All values in Mha yr−1. The 2.6 W m−2 pathways are mostly consistent with the Lower-2°C and Higher-2°C pathway classes. The 1.9 W m−2 pathways are broadly consistent with the 1.5°C-low-OS (mostly SSP1 and SSP2) and 1.5°C-high-OS (SSP5) pathway classes. Baseline projections reflect land-use developments projected by integrated assessment models under the assumptions of the Shared Socio-Economic Pathways (SSPs) in absence of climate policies (Popp et al., 2017; Riahi et al., 2017; Rogelj et al., 2018). Values give the full range across SSP scenarios. According to the Food and Agriculture Organization of the United Nations (FAOSTAT, 2018), 4.9 billion hectares (approximately 40% of the land surface) was under agricultural use in 2005, either as cropland (1.5 billion hectares) or pasture (3.4 billion hectares). FAO data in the table are equally from FAOSTAT (2018).
<!-- TABLE -->
{| class="wikitable"
|-
! colspan="6"| Annual Pace of Land-Use Change [Mha yr <sup>–1</sup> ]
|-
! Land Type
! Pathway
! colspan="2"| Time Window
! colspan="2"| Historical
|-
!
! 2010–2030
! 2030–2050
! 1970–1990
! 1990–2010
|-
! rowspan="3"| Pasture
| 1.9 W m <sup>–2</sup>
| [–14.6/3.0]
| [–28.7/–5.2]
| rowspan="3"| 8.7<br />
Permanent meadows and pastures (FAO)
| rowspan="3"| 0.9<br />
Permanent meadows and pastures (FAO)
|-
| 2.6 W m <sup>–2</sup>
| [–9.3/4.1]
| [–21.6/0.4]
|-
| Baseline
| [–5.1/14.1]
| [–9.6/9.0]
|-
! rowspan="3"| Cropland for food, feed and material
| 1.9 W m <sup>–2</sup>
| [–12.7/9.0]
| [–18.5/0.1]
| rowspan="3"|
|-
| 2.6 W m <sup>–2</sup>
| [–12.9/8.3]
| [–16.8/2.3]
|-
| Baseline
| [–5.3/9.9]
| [–2.7/6.7]
|-
! rowspan="3"| Cropland for energy
| 1.9 W m <sup>–2</sup>
| [0.7/10.5]
| [3.9/34.8]
| rowspan="3"|
|-
| 2.6 W m <sup>–2</sup>
| [0.2/8.8]
| [2.0/22.9]
|-
| Baseline
| [0.2/4.2]
| [–0.2/6.1]
|-
! rowspan="3"| Total cropland (Sum of cropland for food and feed &amp; energy)
| 1.9 W m <sup>–2</sup>
| [–6.8/12.8]
| [–5.8/26.7]
| rowspan="3"| 4.6<br />
Arable land and Permanent crops
| rowspan="3"| 0.9<br />
Arable land and<br />
Permanent crops
|-
| 2.6 W m <sup>–2</sup>
| [–8.4/9.3]
| [–7.1/17.8]
|-
| Baseline
| [–3.0/11.3]
| [0.6/11.0]
|-
! rowspan="3"| Forest
| 1.9 W m <sup>–2</sup>
| [–4.8/23.7]
| [0.0/34.3]
| rowspan="3"| N.A.<br />
Forest (FAO)
| rowspan="3"| –5.6<br />
Forest (FAO)
|-
| 2.6 W m <sup>–2</sup>
| [–4.7/22.2]
| [–2.4/31.7]
|-
| Baseline
| [–13.6/3.3]
| [–6.5/4.3]
|}
<!-- END TABLE -->

<div id="section-2-4-4-block-5"></div>

Changes in the AFOLU sector are driven by three main factors: demand changes, efficiency of production, and policy assumptions (Smith et al., 2013; Popp et al., 2017) <sup>[[#fn:r447|447]]</sup> . Demand for agricultural products and other land-based commodities is influenced by consumption patterns (including dietary preferences and food waste affecting demand for food and feed) (Smith et al., 2013; van Vuuren et al., 2018) <sup>[[#fn:r448|448]]</sup> , demand for forest products for pulp and construction (including less wood waste), and demand for biomass for energy production (Lambin and Meyfroidt, 2011; Smith and Bustamante, 2014) <sup>[[#fn:r449|449]]</sup> . Efficiency of agricultural and forestry production relates to improvements in agricultural and forestry practices (including product cascades, by-products and more waste- and residue-based biomass for energy production), agricultural and forestry yield increases, and intensification of livestock production systems leading to higher feed efficiency and changes in feed composition (Havlík et al., 2014; Weindl et al., 2015) <sup>[[#fn:r450|450]]</sup> . Policy assumptions relate to the level of land protection, the treatment of food waste, policy choices about the timing of mitigation action (early vs late), the choice and preference of land-based mitigation options (for example, the inclusion of afforestation and reforestation as mitigation options), interactions with other sectors (Popp et al., 2017) <sup>[[#fn:r451|451]]</sup> , and trade (Schmitz et al., 2012; Wiebe et al., 2015) <sup>[[#fn:r452|452]]</sup> .

A global study (Stevanović et al., 2017) <sup>[[#fn:r453|453]]</sup> reported similar GHG reduction potentials for both production-side (agricultural production measures in combination with reduced deforestation) and consumption-side (diet change in combination with lower shares of food waste) measures on the order of 40% in 2100 <sup>[[#fn:11|11]]</sup> (compared to a baseline scenario without land-based mitigation). Lower consumption of livestock products by 2050 could also substantially reduce deforestation and cumulative carbon losses (Weindl et al., 2017) <sup>[[#fn:r454|454]]</sup> . On the supply side, minor productivity growth in extensive livestock production systems is projected to lead to substantial CO <sub>2</sub> emission abatement, but the emission-saving potential of productivity gains in intensive systems is limited, mainly due to trade-offs with soil carbon stocks (Weindl et al., 2017) <sup>[[#fn:r455|455]]</sup> . In addition, even within existing livestock production systems, a transition from extensive to more productive systems bears substantial GHG abatement potential, while improving food availability (Gerber et al., 2013; Havlík et al., 2014) <sup>[[#fn:r456|456]]</sup> . Many studies highlight the capability of agricultural intensification for reducing GHG emissions in the AFOLU sector or even enhancing terrestrial carbon stocks (Valin et al., 2013; Popp et al., 2014a; Wise et al., 2014) <sup>[[#fn:r457|457]]</sup> . Also the importance of immediate and global land-use regulations for a comprehensive reduction of land-related GHG emissions (especially related to deforestation) has been shown by several studies (Calvin et al., 2017; Fricko et al., 2017; Fujimori, 2017) <sup>[[#fn:r458|458]]</sup> . Ultimately, there are also interactions between these three factors and the wider society and economy, for example, if CDR technologies that are not land-based are deployed (like direct air capture – DACCS, see Chapter 4, Section 4.3.7) or if other sectors over- or underachieve their projected mitigation contributions (Clarke et al., 2014) <sup>[[#fn:r459|459]]</sup> . Variations in these drivers can lead to drastically different land-use implications (Popp et al., 2014b) <sup>[[#fn:r460|460]]</sup> (Figure 2.24).

Stringent mitigation pathways inform general GHG dynamics in the AFOLU sector. First, CO <sub>2</sub> emissions from deforestation can be abated at relatively low carbon prices if displacement effects in other regions (Calvin et al., 2017) <sup>[[#fn:r461|461]]</sup> or other land-use types with high carbon density (Calvin et al., 2014; Popp et al., 2014a; Kriegler et al., 2017) <sup>[[#fn:r462|462]]</sup> can be avoided. However, efficiency and costs of reducing rates of deforestation strongly depend on governance performance, institutions and macroeconomic factors (Wang et al., 2016) <sup>[[#fn:r463|463]]</sup> . Secondly, besides CO <sub>2</sub> reductions, the land system can play an important role for overall CDR efforts (Rogelj et al., 2018) <sup>[[#fn:r464|464]]</sup> via BECCS, afforestation and reforestation, or a combination of options. The AFOLU sector also provides further potential for active terrestrial carbon sequestration, for example, via land restoration, improved management of forest and agricultural land (Griscom et al., 2017) <sup>[[#fn:r465|465]]</sup> , or biochar applications (Smith, 2016) <sup>[[#fn:r466|466]]</sup> (see also Chapter 4, Section 4.3.7). These options have so far not been extensively integrated in the mitigation pathway literature (see Supplementary Material  2.SM.1.2), but in theory their availability would impact the deployment of other CDR technologies, like BECCS (Section 2.3.4) (Strefler et al., 2018a) <sup>[[#fn:r467|467]]</sup> . These interactions will be discussed further in the SRCCL.

Residual agricultural non-CO <sub>2</sub> emissions of CH <sub>4</sub> and N <sub>2</sub> O play an important role for temperature stabilization pathways, and their relative importance increases in stringent mitigation pathways in which CO <sub>2</sub> is reduced to net zero emissions globally (Gernaat et al., 2015; Popp et al., 2017; Stevanović et al., 2017; Rogelj et al., 2018) <sup>[[#fn:r468|468]]</sup> , for example, through their impact on the remaining carbon budget (Section 2.2). Although agricultural non-CO <sub>2</sub> emissions show marked reduction potentials in 2°C-consistent pathways, complete elimination of these emission sources does not occur in IAMs based on the evolution of agricultural practice assumed in integrated models (Figure 2.25) (Gernaat et al., 2015) <sup>[[#fn:r469|469]]</sup> . Methane emissions in 1.5°C pathways are reduced through improved agricultural management (e.g., improved management of water in rice production, manure and herds, and better livestock quality through breeding and improved feeding practices) as well as dietary shifts away from emissions-intensive livestock products. Similarly, N <sub>2</sub> O emissions decrease due to improved N-efficiency and manure management (Frank et al., 2018) <sup>[[#fn:r470|470]]</sup> . However, high levels of bioenergy production can also result in increased N <sub>2</sub> O emissions (Kriegler et al., 2017) <sup>[[#fn:r471|471]]</sup> , highlighting the importance of appropriate management approaches (Davis et al., 2013) <sup>[[#fn:r472|472]]</sup> . Residual agricultural emissions can be further reduced by limiting demand for GHG-intensive foods through shifts to healthier and more sustainable diets (Tilman and Clark, 2014; Erb et al., 2016b; Springmann et al., 2016) <sup>[[#fn:r473|473]]</sup> and reductions in food waste (Bajželj et al., 2014; Muller et al., 2017; Popp et al., 2017) <sup>[[#fn:r474|474]]</sup> (see also Chapter 4 and SRCCL). Finally, several mitigation measures that could affect these agricultural non-CO <sub>2</sub> emissions are not, or only to a limited degree, considered in the current integrated pathway literature (see Supplementary Material 2.SM.1.2). Such measures (like plant-based and synthetic proteins, methane inhibitors and vaccines in livestock, alternate wetting and drying in paddy rice, or nitrification inhibitors) are very diverse and differ in their development or deployment stages. Their potentials have not been explicitly assessed here.

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'''Agricultural emissions in transformation pathways.'''
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[[File:7d0b684ac93a18f3aae2e0a778936757 Figure-2.25-1024x358.jpg]]

Global agricultural (a) CH4 and (b) N2O emissions. Box plots show median, interquartile range and full range. Classes are defined in Section 2.1.

Original Creation for this Report using IPCC SR1.5 DB

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Pathways consistent with 1.5°C rely on one or more of the three strategies highlighted above (demand changes, efficiency gains, and policy assumptions), and can apply these in different configurations. For example, among the four illustrative archetypes used in this chapter (Section 2.1), the LED and S1 pathways focus on generally low resource and energy consumption (including healthy diets with low animal-calorie shares and low food waste) as well as significant agricultural intensification in combination with high levels of nature protection. Under such assumptions, comparably small amounts of land are needed for land-demanding mitigation activities such as BECCS and afforestation and reforestation, leaving the land footprint for energy crops in 2050 virtually the same compared to 2010 levels for the LED pathway. In contrast, future land-use developments can look very different under the resource- and energy-intensive S5 pathway that includes less healthy diets with high animal shares and high shares of food waste (Tilman and Clark, 2014; Springmann et al., 2016) <sup>[[#fn:r475|475]]</sup> combined with a strong orientation towards technology solutions to compensate for high reliance on fossil-fuel resources and associated high levels of GHG emissions in the baseline. In such pathways, climate change mitigation strategies strongly depend on the availability of CDR through BECCS (Humpenöder et al., 2014) <sup>[[#fn:r476|476]]</sup> . As a consequence, the S5 pathway sources significant amounts of biomass through bioenergy crop expansion in combination with agricultural intensification. Also, further policy assumptions can strongly affect land-use developments, highlighting the importance for land use of making appropriate policy choices. For example, within the SSP set, some pathways rely strongly on a policy to incentivize afforestation and reforestation for CDR together with BECCS, which results in an expansion of forest area and a corresponding increase in terrestrial carbon stock. Finally, the variety of pathways illustrates how policy choices in the AFOLU and other sectors strongly affect land-use developments and associated sustainable development interactions (Chapter 5, Section 5.4) in 1.5°C pathways.

The choice of strategy or mitigation portfolio impacts the GHG dynamics of the land system and other sectors (see Section 2.3), as well as the synergies and trade-offs with other environmental and societal objectives (see Section 2.5.3 and Chapter 5, Section 5.4). For example, AFOLU developments in 1.5°C pathways range from strategies that differ by almost an order of magnitude in their projected land requirements for bioenergy (Figure 2.24), and some strategies would allow an increase in forest cover over the 21st century compared to strategies under which forest cover remains approximately constant. High agricultural yields and application of intensified animal husbandry, implementation of best-available technologies for reducing non-CO <sub>2</sub> emissions, or lifestyle changes including a less-meat-intensive diet and less CO <sub>2</sub> -intensive transport modes, have been identified as allowing for such a forest expansion and reduced footprints from bioenergy without compromising food security (Frank et al., 2017; Doelman et al., 2018; van Vuuren et al., 2018) <sup>[[#fn:r477|477]]</sup> .

The IAMs used in the pathways underlying this assessment (Popp et al., 2017; Riahi et al., 2017; Rogelj et al., 2018) <sup>[[#fn:r478|478]]</sup> do not include all potential land-based mitigation options and side-effects, and their results are hence subject to uncertainty. For example, recent research has highlighted the potential impact of forest management practices on land carbon content (Erb et al., 2016a; Naudts et al., 2016) <sup>[[#fn:r479|479]]</sup> and the uncertainty surrounding future crop yields (Haberl et al., 2013; Searle and Malins, 2014) <sup>[[#fn:r480|480]]</sup> and water availability (Liu et al., 2014) <sup>[[#fn:r481|481]]</sup> . These aspects are included in IAMs in varying degrees but were not assessed in this report. Furthermore, land-use modules of some IAMs can depict spatially resolved climate damages to agriculture (Nelson et al., 2014) <sup>[[#fn:r482|482]]</sup> , but this option was not used in the SSP quantifications (Riahi et al., 2017) <sup>[[#fn:r483|483]]</sup> . Damages (e.g., due to ozone exposure or varying indirect fertilization due to atmospheric N and Fe deposition (e.g., Shindell et al., 2012; Mahowald et al., 2017) <sup>[[#fn:r484|484]]</sup> are also not included. Finally, this assessment did not look into the literature of agricultural sector models which could provide important additional detail and granularity to the discussion presented here. <sup>[[#fn:12|12]]</sup> This limits their ability to capture the full mitigation potentials and benefits between scenarios. An in-depth assessment of these aspects lies outside the scope of this Special Report. However, their existence affects the confidence assessment of the AFOLU transition in 1.5°C pathways.

Despite the limitations of current modelling approaches, there is ''high agreement'' and ''robust evidence'' across models and studies that the AFOLU sector plays an important role in stringent mitigation pathways. The findings from these multiple lines of evidence also result in ''high confidence'' that AFOLU mitigation strategies can vary significantly based on preferences and policy choices, facilitating the exploration of strategies that can achieve multiple societal objectives simultaneously (see also Section 2.5.3). At the same time, given the many uncertainties and limitations, only ''low to medium confidence'' can be attributed by this assessment to the more extreme AFOLU developments found in the pathway literature, and ''low to medium confidence'' to the level of residual non-CO <sub>2</sub> emissions.

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