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

== 2.3 Overview of 1.5°C Mitigation Pathways ==

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

Limiting global mean temperature increase at any level requires global CO <sub>2</sub> emissions to become net zero at some point in the future (Zickfeld et al., 2009; Collins et al., 2013) <sup>[[#fn:r128|128]]</sup> . At the same time, limiting the residual warming of short-lived non-CO <sub>2</sub> emissions can be achieved by reducing their annual emissions as much as possible (Section 2.2, Cross-Chapter Box 2 in Chapter 1). This would require large-scale transformations of the global energy–agriculture–land-economy system, affecting the way in which energy is produced, agricultural systems are organized, and food, energy and materials are consumed (Clarke et al., 2014) <sup>[[#fn:r129|129]]</sup> . This section assesses key properties of pathways consistent with limiting global mean temperature to 1.5°C relative to pre-industrial levels, including their underlying assumptions and variations.

Since the AR5, an extensive body of literature has appeared on integrated pathways consistent with 1.5°C (Section 2.1) (Rogelj et al., 2015b, 2018; Akimoto et al., 2017; Löffler et al., 2017; Marcucci et al., 2017; Su et al., 2017; Bauer et al., 2018; Bertram et al., 2018; Grubler et al., 2018; Kriegler et al., 2018a; Liu et al., 2018; Luderer et al., 2018; Strefler et al., 2018a; van Vuuren et al., 2018; Vrontisi et al., 2018; Zhang et al., 2018) <sup>[[#fn:r130|130]]</sup> . These pathways have global coverage and represent all GHG-emitting sectors and their interactions. Such integrated pathways allow the exploration of the whole-system transformation, and hence provide the context in which the detailed sectoral transformations assessed in Section 2.4 of this chapter are taking place.

The overwhelming majority of published integrated pathways have been developed by global IAMs that represent key societal systems and their interactions, like the energy system, agriculture and land use, and the economy (see Section 6.2 in Clarke et al., 2014) <sup>[[#fn:r131|131]]</sup> . Very often these models also include interactions with a representation of the geophysical system, for example, by including spatially explicit land models or carbon cycle and climate models. The complex features of these subsystems are approximated and simplified in these models. IAMs are briefly introduced in Section 2.1 and important knowledge gaps identified in Section 2.6. An overview to the use, scope and limitations of IAMs is provided in Supplementary Material 2.SM.1.2.

The pathway literature is assessed in two ways in this section. First, various insights on specific questions reported by studies can be assessed to identify robust or divergent findings. Second, the combined body of scenarios can be assessed to identify salient features of pathways in line with a specific climate goal across a wide range of models. The latter can be achieved by assessing pathways available in the database to this assessment (Section 2.1, Supplementary Material 2.SM.1.2–4). The ensemble of scenarios available to this assessment is an ensemble of opportunity: it is a collection of scenarios from a diverse set of studies that was not developed with a common set of questions and a statistical analysis of outcomes in mind. This means that ranges can be useful to identify robust and sensitive features across available scenarios and contributing modelling frameworks, but do not lend themselves to a statistical interpretation. To understand the reasons underlying the ranges, an assessment of the underlying scenarios and studies is required. To this end, this section highlights illustrative pathway archetypes that help to clarify the variation in assessed ranges for 1.5°C-consistent pathways.

<span id="range-of-assumptions-underlying-1.5c-pathways"></span>
=== 2.3.1 Range of Assumptions Underlying 1.5°C Pathways ===

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

Earlier assessments have highlighted that there is no single pathway to achieve a specific climate objective (e.g., Clarke et al., 2014) <sup>[[#fn:r132|132]]</sup> . Pathways depend on the underlying development processes, and societal choices, which affect the drivers of projected future baseline emissions. Furthermore, societal choices also affect climate change solutions in pathways, like the technologies that are deployed, the scale at which they are deployed, or whether solutions are globally coordinated. A key finding is that 1.5°C-consistent pathways could be identified under a considerable range of assumptions in model studies despite the tightness of the 1.5°C emissions budget (Figures 2.4, 2.5) (Rogelj et al., 2018) <sup>[[#fn:r133|133]]</sup> .

The AR5 provided an overview of how differences in model structure and assumptions can influence the outcome of transformation pathways (Section 6.2 in Clarke et al., 2014 <sup>[[#fn:r134|134]]</sup> , as well as Table A.II.14 in Krey et al., 2014b) <sup>[[#fn:r135|135]]</sup> and this was further explored by the modelling community in recent years with regard to, e.g., socio-economic drivers (Kriegler et al., 2016; Marangoni et al., 2017; Riahi et al., 2017) <sup>[[#fn:r136|136]]</sup> , technology assumptions (Bosetti et al., 2015; Creutzig et al., 2017; Pietzcker et al., 2017) <sup>[[#fn:r137|137]]</sup> , and behavioural factors (van Sluisveld et al., 2016; McCollum et al., 2017) <sup>[[#fn:r138|138]]</sup> .

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

<span id="socio-economic-drivers-and-the-demand-for-energy-and-land-in-1.5c-pathways"></span>
==== 2.3.1.1 Socio-economic drivers and the demand for energy and land in 1.5°C pathways ====

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

There is deep uncertainty about the ways humankind will use energy and land in the 21st century. These ways are intricately linked to future population levels, secular trends in economic growth and income convergence, behavioural change and technological progress. These dimensions have been recently explored in the context of the SSPs (Kriegler et al., 2012; O’Neill et al., 2014) <sup>[[#fn:r139|139]]</sup> , which provide narratives (O’Neill et al., 2017) <sup>[[#fn:r140|140]]</sup> and quantifications (Crespo Cuaresma, 2017; Dellink et al., 2017; KC and Lutz, 2017; Leimbach et al., 2017; Riahi et al., 2017) <sup>[[#fn:r141|141]]</sup> of different world futures across which scenario dimensions are varied to explore differential challenges to adaptation and mitigation (Cross-Chapter Box 1 in Chapter 1). This framework is increasingly adopted by IAMs to systematically explore the impact of socio-economic assumptions on mitigation pathways (Riahi et al., 2017) <sup>[[#fn:r142|142]]</sup> , including 1.5°C-consistent pathways (Rogelj et al., 2018) <sup>[[#fn:r143|143]]</sup> . The narratives describe five worlds (SSP1–5) with different socio-economic predispositions to mitigate and adapt to climate change (Table 2.3). As a result, population and economic growth projections can vary strongly across integrated scenarios, including available 1.5°C-consistent pathways (Figure. 2.4). For example, based on alternative future fertility, mortality, migration and educational assumptions, population projections vary between 8.5 and 10.0 billion people by 2050 and between 6.9 and 12.6 billion people by 2100 across the SSPs. An important factor for these differences is future female educational attainment, with higher attainment leading to lower fertility rates and therefore decreased population growth up to a level of 1 billion people by 2050 (Lutz and KC, 2011; Snopkowski et al., 2016; KC and Lutz, 2017) <sup>[[#fn:r144|144]]</sup> . Consistent with population development, GDP per capita also varies strongly in SSP baselines, ranging from about 20 to more than 50 thousand USD2010 per capita in 2050 (in purchasing power parity values, PPP), in part driven by assumptions on human development, technological progress and development convergence between and within regions (Crespo Cuaresma, 2017; Dellink et al., 2017; Leimbach et al., 2017) <sup>[[#fn:r145|145]]</sup> . Importantly, none of the GDP projections in the mitigation pathway literature assessed in this chapter included the feedback of climate damages on economic growth (Hsiang et al., 2017) <sup>[[#fn:r146|146]]</sup> .

Baseline projections for energy-related GHG emissions are sensitive to economic growth assumptions, while baseline projections for land-use emissions are more directly affected by population growth (assuming unchanged land productivity and per capita demand for agricultural products) (Kriegler et al., 2016) <sup>[[#fn:r147|147]]</sup> . SSP-based modelling studies of mitigation pathways have identified high challenges to mitigation for worlds with a focus on domestic issues and regional security combined with high population growth (SSP3), and for worlds with rapidly growing resource and fossil-fuel intensive consumption (SSP5) (Riahi et al., 2017) <sup>[[#fn:r148|148]]</sup> . No model could identify a 2°C-consistent pathway for SSP3, and high mitigation costs were found for SSP5. This picture translates to 1.5°C-consistent pathways that have to remain within even tighter emissions constraints (Rogelj et al., 2018) <sup>[[#fn:r149|149]]</sup> . No model found a 1.5°C-consistent pathway for SSP3 and some models could not identify 1.5°C-consistent pathways for SSP5 (2 of 4 models, compared to 1 of 4 models for 2°C-consistent pathways). The modelling analysis also found that the effective control of land-use emissions becomes even more critical in 1.5°C-consistent pathways. Due to high inequality levels in SSP4, land use can be less well managed. This caused 2 of 3 models to no longer find an SSP4-based 1.5°C-consistent pathway even though they identified SSP4-based 2°C-consistent pathways at relatively moderate mitigation costs (Riahi et al., 2017) <sup>[[#fn:r150|150]]</sup> . Rogelj et al. (2018) <sup>[[#fn:r151|151]]</sup> further reported that all six participating models identified 1.5°C-consistent pathways in a sustainability oriented world (SSP1) and four of six models found 1.5°C-consistent pathways for middle-of-the-road developments (SSP2). These results show that 1.5°C-consistent pathways can be identified under a broad range of assumptions, but that lack of global cooperation (SSP3), high inequality (SSP4) and/or high population growth (SSP3) that limit the ability to control land use emissions, and rapidly growing resource-intensive consumption (SSP5) are key impediments.

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

<span id="table-2.3"></span>
<!-- START TABLE -->
'''Table 2.3'''

<span id="key-characteristics-of-the-five-shared-socio-economic-pathways-ssps-oneill-et-al.-2017"></span>
'''Key Characteristics of the Five Shared Socio-Economic Pathways (SSPs) (O’Neill et al., 2017)'''
<!-- TABLE -->
{| class="wikitable"
|-
! rowspan="2"| Socio-Economic Challenges to Mitigation
! colspan="3"| Socio-Economic Challenges to Adaptation
|-
! Low
! Medium
! High
|-
! High
| SSP5: Fossil-fuelled development
* low population
* very high economic growth per capita
* high human development
* high technological progress
* ample fossil fuel resources
* very resource intensive lifestyles
* high energy and food demand per capita
* economic convergence and global cooperation

|
| SSP3: Regional rivalry
* high population
* low economic growth per capita
* low human development
* low technological progress
* resource-intensive lifestyles
* resource-constrained energy and food demand per capita
* focus on regional food and energy security
* regionalization and lack of global cooperation

|-
! Medium
|
| SSP2: Middle of the road
* medium population
* medium and uneven economic growth
* medium and uneven human development
* medium and uneven technological progress
* resource-intensive lifestyles
* medium and uneven energy and food demand per capita
* limited global cooperation and economic convergence

|
|-
! Low
| SSP1: Sustainable development
* low population
* high economic growth per capita
* high human development
* high technological progress
* environmentally oriented technological and behavioural change
* resource-efficient lifestyles
* low energy and food demand per capita
* economic convergence and global cooperation

|
| SSP4: Inequality
* Medium to high population
* Unequal low to medium economic growth per capita
* low to medium human development
* unequal technological progress: high in globalized
* high-tech sectors, slow in domestic sectors
* unequal lifestyles and energy /food consumption: resource intensity depending on income
* Globally connected elite, disconnected domestic work forces

|}
<!-- END TABLE -->

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

<span id="figure-2.4"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.4'''

<span id="range-of-assumptions-about-socio-economic-drivers-and-projections-for-energy-and-food-demand-in-the-pathways-available-to-this-assessment.-1.5c-consistent-pathways-are-blue-other-pathways-grey."></span>
<!-- IMG CAPTION -->
'''Range of assumptions about socio-economic drivers and projections for energy and food demand in the pathways available to this assessment. 1.5°C-consistent pathways are blue, other pathways grey.'''
<!-- IMG FILE -->
[[File:ddd5af9bf56e5ef2f3f7a8bd1375919c Figure-2.4-1024x768.jpg]]

Trajectories for the illustrative 1.5°C-consistent archetypes used in this Chapter (LED, S1, S2, S5; referred to as P1, P2, P3, and P4 in the Summary for Policymakers.) are highlighted. S1 is a sustainability oriented scenario, S2 is a middle-of-the-road scenario, and S5 is a fossil-fuel intensive and high energy demand scenario. LED is a scenario with particularly low energy demand. Population assumptions in S2 and LED are identical. Panels show (a) world population, (b) gross world product in purchasing power parity values, (c) final energy demand, and (d) food demand.

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

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

Figure 2.4 compares the range of underlying socio-economic developments as well as energy and food demand in available 1.5°C-consistent pathways with the full set of published scenarios that were submitted to this assessment. While 1.5°C-consistent pathways broadly cover the full range of population and economic growth developments (except for the high population development in SSP3-based scenarios), they tend to cluster on the lower end for energy and food demand. They still encompass, however, a wide range of developments from decreasing to increasing demand levels relative to today. For the purpose of this assessment, a set of four illustrative 1.5°C-consistent pathway archetypes were selected to show the variety of underlying assumptions and characteristics (Figure 2.4). They comprise three 1.5°C-consistent pathways based on the SSPs (Rogelj et al., 2018) <sup>[[#fn:r153|153]]</sup> : a sustainability oriented scenario (S1 based on SSP1) developed with the AIM model (Fujimori, 2017) <sup>[[#fn:r154|154]]</sup> , a fossil-fuel intensive and high energy demand scenario (S5, based on SSP5) developed with the REMIND-MAgPIE model (Kriegler et al., 2017) <sup>[[#fn:r155|155]]</sup> , and a middle-of-the-road scenario (S2, based on SSP2) developed with the MESSAGE-GLOBIOM model (Fricko et al., 2017) <sup>[[#fn:r156|156]]</sup> . In addition, we include a scenario with low energy demand (LED) (Grubler et al., 2018) <sup>[[#fn:r157|157]]</sup> , which reflects recent literature with a stronger focus on demand-side measures (Bertram et al., 2018; Grubler et al., 2018; Liu et al., 2018; van Vuuren et al., 2018) <sup>[[#fn:r158|158]]</sup> . Pathways LED, S1, S2, and S5 are referred to as P1, P2, P3, and P4 in the Summary for Policymakers.

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

<span id="mitigation-options-in-1.5c-pathways"></span>
==== 2.3.1.2 Mitigation options in 1.5°C pathways ====

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

In the context of 1.5°C pathways, the portfolio of mitigation options available to the model becomes an increasingly important factor. IAMs include a wide variety of mitigation options, as well as measures that achieve CDR from the atmosphere (Krey et al., 2014a, b) <sup>[[#fn:r159|159]]</sup> (see Chapter 4, Section 4.3 for a broad assessment of available mitigation measures). For the purpose of this assessment, we elicited technology availability in models that submitted scenarios to the database as summarized in Supplementary Material 2.SM.1.2, where a detailed picture of the technology variety underlying available 1.5°C-consistent pathways is provided. Modelling choices on whether a particular mitigation measure is included are influenced by an assessment of its global mitigation potential, the availability of data and literature describing its techno-economic characteristics and future prospects, and the computational challenge of representing the measure, e.g., in terms of required spatio-temporal and process detail.

This elicitation (Supplementary Material 2.SM.1.2) confirms that IAMs cover most supply-side mitigation options on the process level, while many demand-side options are treated as part of underlying assumptions, which can be varied (Clarke et al., 2014) <sup>[[#fn:r160|160]]</sup> . In recent years, there has been increasing attention on improving the modelling of integrating variable renewable energy into the power system (Creutzig et al., 2017; Luderer et al., 2017; Pietzcker et al., 2017) <sup>[[#fn:r161|161]]</sup> and of behavioural change and other factors influencing future demand for energy and food (van Sluisveld et al., 2016; McCollum et al., 2017; Weindl et al., 2017) <sup>[[#fn:r162|162]]</sup> , including in the context of 1.5°C-consistent pathways (Grubler et al., 2018; van Vuuren et al., 2018) <sup>[[#fn:r163|163]]</sup> . The literature on the many diverse CDR options only recently started to develop strongly (Minx et al., 2017) <sup>[[#fn:r164|164]]</sup> (see Chapter 4, Section 4.3.7 for a detailed assessment), and hence these options are only partially included in IAM analyses. IAMs mostly incorporate afforestation and bioenergy with carbon capture and storage (BECCS) and only in few cases also include direct air capture with CCS (DACCS) (Chen and Tavoni, 2013; Marcucci et al., 2017; Strefler et al., 2018b) <sup>[[#fn:r165|165]]</sup> .

Several studies have either directly or indirectly explored the dependence of 1.5°C-consistent pathways on specific (sets of) mitigation and CDR technologies (Bauer et al., 2018; Grubler et al., 2018; Holz et al., 2018b; Kriegler et al., 2018a; Liu et al., 2018; Rogelj et al., 2018; Strefler et al., 2018b; van Vuuren et al., 2018) <sup>[[#fn:r166|166]]</sup> . However, there are a few potentially disruptive technologies that are typically not yet well covered in IAMs and that have the potential to alter the shape of mitigation pathways beyond the ranges in the IAM-based literature. Those are also included in Supplementary Material 2.SM.1.2. The configuration of carbon-neutral energy systems projected in mitigation pathways can vary widely, but they all share a substantial reliance on bioenergy under the assumption of effective land-use emissions control. There are other configurations with less reliance on bioenergy that are not yet comprehensively covered by global mitigation pathway modelling. One approach is to dramatically reduce and electrify energy demand for transportation and manufacturing to levels that make residual non-electric fuel use negligible or replaceable by limited amounts of electrolytic hydrogen. Such an approach is presented in a first-of-its kind low-energy-demand scenario (Grubler et al., 2018) <sup>[[#fn:r167|167]]</sup> which is part of this assessment. Other approaches rely less on energy demand reductions, but employ cheap renewable electricity to push the boundaries of electrification in the industry and transport sectors (Breyer et al., 2017; Jacobson, 2017) <sup>[[#fn:r168|168]]</sup> . In addition, these approaches deploy renewable-based Power-2-X (read: Power to “x”) technologies to substitute residual fossil-fuel use (Brynolf et al., 2018) <sup>[[#fn:r169|169]]</sup> . An important element of carbon-neutral Power-2-X applications is the combination of hydrogen generated from renewable electricity and CO <sub>2</sub> captured from the atmosphere (Zeman and Keith, 2008) <sup>[[#fn:r170|170]]</sup> . Alternatively, algae are considered as a bioenergy source with more limited implications for land use and agricultural systems than energy crops (Williams and Laurens, 2010; Walsh et al., 2016; Greene et al., 2017) <sup>[[#fn:r171|171]]</sup> .

Furthermore, a range of measures could radically reduce agricultural and land-use emissions and are not yet well-covered in IAM modelling. This includes plant-based proteins (Joshi and Kumar, 2015) <sup>[[#fn:r172|172]]</sup> and cultured meat (Post, 2012) <sup>[[#fn:r173|173]]</sup> with the potential to substitute for livestock products at much lower GHG footprints (Tuomisto and Teixeira de Mattos, 2011) <sup>[[#fn:r174|174]]</sup> . Large-scale use of synthetic or algae-based proteins for animal feed could free pasture land for other uses (Madeira et al., 2017; Pikaar et al., 2018) <sup>[[#fn:r175|175]]</sup> . Novel technologies such as methanogen inhibitors and vaccines (Wedlock et al., 2013; Hristov et al., 2015; Herrero et al., 2016; Subharat et al., 2016) <sup>[[#fn:r176|176]]</sup> as well as synthetic and biological nitrification inhibitors (Subbarao et al., 2013; Di and Cameron, 2016) <sup>[[#fn:r177|177]]</sup> could substantially reduce future non-CO <sub>2</sub> emissions from agriculture if commercialized successfully. Enhancing carbon sequestration in soils (Paustian et al., 2016; Frank et al., 2017; Zomer et al., 2017) <sup>[[#fn:r178|178]]</sup> can provide the dual benefit of CDR and improved soil quality. A range of conservation, restoration and land management options can also increase terrestrial carbon uptake (Griscom et al., 2017) <sup>[[#fn:r179|179]]</sup> . In addition, the literature discusses CDR measures to permanently sequester atmospheric carbon in rocks (mineralization and enhanced weathering, see Chapter 4, Section 4.3.7) as well as carbon capture and usage in long-lived products like plastics and carbon fibres (Mazzotti et al., 2005; Hartmann et al., 2013) <sup>[[#fn:r180|180]]</sup> . Progress in the understanding of the technical viability, economics and sustainability of these ways to achieve and maintain carbon neutral energy and land use can affect the characteristics, costs and feasibility of 1.5°C-consistent pathways significantly.

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

<span id="policy-assumptions-in-1.5c-pathways"></span>
==== 2.3.1.3 Policy assumptions in 1.5°C pathways ====

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

Besides assumptions related to socio-economic drivers and mitigation technology, scenarios are also subject to assumptions about the mitigation policies that can be put in place. Mitigation policies can either be applied immediately in scenarios or follow staged or delayed approaches. Policies can span many sectors (e.g., economy-wide carbon pricing), or policies can be applicable to specific sectors only (like the energy sector) with other sectors (e.g., the agricultural or the land-use sector) treated differently. These variations can have an important impact on the ability of models to generate scenarios compatible with stringent climate targets like 1.5°C (Luderer et al., 2013; Rogelj et al., 2013b; Bertram et al., 2015b; Kriegler et al., 2018a; Michaelowa et al., 2018) <sup>[[#fn:r181|181]]</sup> . In the scenario ensemble available to this assessment, several variations of near-term mitigation policy implementation can be found: immediate and cross-sectoral global cooperation from 2020 onward towards a global climate objective, a phase-in of globally coordinated mitigation policy from 2020 to 2040, and a more short-term oriented and regionally diverse global mitigation policy, following NDCs until 2030 (Kriegler et al., 2018a; Luderer et al., 2018; McCollum et al., 2018; Rogelj et al., 2018; Strefler et al., 2018b) <sup>[[#fn:r182|182]]</sup> . For example, the above-mentioned SSP quantifications assume regionally scattered mitigation policies until 2020, and vary in global convergence thereafter (Kriegler et al., 2014a; Riahi et al., 2017) <sup>[[#fn:r183|183]]</sup> . The impact of near-term policy choices on 1.5°C-consistent pathways is discussed in Section 2.3.5. The literature has also explored 1.5°C-consistent pathways that build on a portfolio of policy approaches until 2030, including the combination of regulatory policies and carbon pricing (Kriegler et al., 2018a) <sup>[[#fn:r184|184]]</sup> , and a variety of ancillary policies to safeguard other sustainable development goals (Bertram et al., 2018; van Vuuren et al., 2018) <sup>[[#fn:r185|185]]</sup> . A further discussion of policy implications of 1.5°C-consistent pathways is provided in Section 2.5.1, while a general discussion of policies and options to strengthen action are subject of Chapter 4, Section 4.4.

<span id="key-characteristics-of-1.5c-pathways"></span>
=== 2.3.2 Key Characteristics of 1.5°C Pathways ===

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

1.5°C-consistent pathways are characterized by a rapid phase out of CO <sub>2</sub> emissions and deep emissions reductions in other GHGs and climate forcers (Section 2.2.2 and 2.3.3). This is achieved by broad transformations in the energy; industry; transport; buildings; and agriculture, forestry and other land-use (AFOLU) sectors (Section 2.4) (Bauer et al., 2018; Grubler et al., 2018; Holz et al., 2018b; Kriegler et al., 2018b; Liu et al., 2018; Luderer et al., 2018; Rogelj et al., 2018; van Vuuren et al., 2018; Zhang et al., 2018) <sup>[[#fn:r186|186]]</sup> . Here we assess 1.5°C-consistent pathways with and without overshoot during the 21st century. One study also explores pathways overshooting 1.5°C for longer than the 21st century (Akimoto et al., 2017) <sup>[[#fn:r187|187]]</sup> , but these are not considered 1.5°C-consistent pathways in this report (Chapter 1, Section 1.1.3). This subsection summarizes robust and varying properties of 1.5°C-consistent pathways regarding system transformations, emission reductions and overshoot. It aims to provide an introduction to the detailed assessment of the emissions evolution (Section 2.3.3), CDR deployment (Section 2.3.4), energy (Section 2.4.1, 2.4.2), industry (2.4.3.1), buildings (2.4.3.2), transport (2.4.3.3) and land-use transformations (Section 2.4.4) in 1.5°C-consistent pathways. Throughout Sections 2.3 and 2.4, pathway properties are highlighted with four 1.5°C-consistent pathway archetypes (LED, S1, S2, S5; referred to as P1, P2, P3, and P4 in the Summary for Policymakers) covering a wide range of different socio-economic and technology assumptions (Figure 2.5, Section 2.3.1).

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

<span id="variation-in-system-transformations-underlying-1.5c-pathways"></span>
==== 2.3.2.1 Variation in system transformations underlying 1.5°C pathways ====

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

Be it for the energy, transport, buildings, industry, or AFOLU sector, the literature shows that multiple options and choices are available in each of these sectors to pursue stringent emissions reductions (Section 2.3.1.2, Supplementary Material 2.SM.1.2, Chapter 4, Section 4.3). Because the overall emissions total under a pathway is limited by a geophysical carbon budget (Section 2.2.2), choices in one sector affect the efforts that are required from others (Clarke et al., 2014) <sup>[[#fn:r188|188]]</sup> . A robust feature of 1.5°C-consistent pathways, as highlighted by the set of pathway archetypes in Figure 2.5, is a virtually full decarbonization of the power sector around mid-century, a feature shared with 2°C-consistent pathways. The additional emissions reductions in 1.5°C-consistent compared to 2°C-consistent pathways come predominantly from the transport and industry sectors (Luderer et al., 2018) <sup>[[#fn:r189|189]]</sup> . Emissions can be apportioned differently across sectors, for example, by focussing on reducing the overall amount of CO <sub>2</sub> produced in the energy end-use sectors, and using limited contributions of CDR by the AFOLU sector (afforestation and reforestation, S1 and LED pathways in Figure 2.5) (Grubler et al., 2018; Holz et al., 2018b; van Vuuren et al., 2018) <sup>[[#fn:r190|190]]</sup> , or by being more lenient about the amount of CO <sub>2</sub> that continues to be produced in the above-mentioned end-use sectors (both by 2030 and mid-century) and strongly relying on technological CDR options like BECCS (S2 and S5 pathways in Figure 2.5) (Luderer et al., 2018; Rogelj et al., 2018) <sup>[[#fn:r191|191]]</sup> . Major drivers of these differences are assumptions about energy and food demand and the stringency of near-term climate policy (see the difference between early action in the scenarios S1, LED and more moderate action until 2030 in the scenarios S2, S5). Furthermore, the carbon budget in each of these pathways depends also on the non-CO <sub>2</sub> mitigation measures implemented in each of them, particularly for agricultural emissions (Sections 2.2.2, 2.3.3) (Gernaat et al., 2015) <sup>[[#fn:r192|192]]</sup> . Those pathways differ not only in terms of their deployment of mitigation and CDR measures (Sections 2.3.4 and 2.4), but also in terms of the resulting temperature overshoot (Figure 2.1). Furthermore, they have very different implications for the achievement of sustainable development objectives, as further discussed in Section 2.5.3.

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

<span id="figure-2.5"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.5'''

<span id="section-3"></span>
<!-- IMG CAPTION -->
Evolution and break down of global anthropogenic CO <sub>2 </sub> emissions until 2100.
<!-- IMG FILE -->
[[File:dc806298e16950294c07c4ddda22564c Figure-2.5-1024x735.jpg]]

The top-left panel shows global net CO <sub>2</sub> emissions in Below-1.5°C, 1.5°C-low-overshoot (OS), and 1.5°C-high-OS pathways, with the four illustrative 1.5°C-consistent pathway archetypes of this chapter highlighted. Ranges at the bottom of the top-left panel show the 10th–90th percentile range (thin line) and interquartile range (thick line) of the time that global CO <sub>2</sub> emissions reach net zero per pathway class, and for all pathways classes combined. The top-right panel provides a schematic legend explaining all CO <sub>2</sub> emissions contributions to global CO <sub>2</sub> emissions. The bottom row shows how various CO <sub>2</sub> contributions are deployed and used in the four illustrative pathway archetypes (LED, S1, S2, S5, referred to as P1, P2, P3, and P4 in the Summary for Policymakers) used in this chapter (see Section 2.3.1.1). Note that the S5 scenario reports the building and industry sector emissions jointly. Green-blue areas hence show emissions from the transport sector and the joint building and industry demand sector, respectively.

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

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

<span id="pathways-keeping-warming-below-1.5c-or-temporarily-overshooting-it"></span>
==== 2.3.2.2 Pathways keeping warming below 1.5°C or temporarily overshooting it ====

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

This subsection explores the conditions that would need to be fulfilled to stay below 1.5°C warming without overshoot. As discussed in Section 2.2.2, to keep warming below 1.5°C with a two-in-three (one-in-two) chance, the cumulative amount of CO <sub>2</sub> emissions from 2018 onwards need to remain below a carbon budget of 420 (580) GtCO <sub>2</sub> ; accounting for the effects of additional Earth system feedbacks until 2100 reduces this estimate by 100 GtCO <sub>2</sub> . Based on the current state of knowledge, exceeding this remaining carbon budget at some point in time would give a one-in-three (one-in-two) chance that the 1.5°C limit is overshot (Table 2.2). For comparison, around 290 ± 20 (1 standard deviation range) GtCO <sub>2</sub> have been emitted in the years 2011–2017, with annual CO <sub>2</sub> emissions in 2017 around 42 ± 3 GtCO <sub>2</sub> yr <sup>−1</sup> (Jackson et al., 2017; Le Quéré et al., 2018) <sup>[[#fn:r193|193]]</sup> . Committed fossil-fuel emissions from existing fossil-fuel infrastructure as of 2010 have been estimated at around 500 ± 200 GtCO <sub>2</sub> (with about 200 GtCO <sub>2</sub> already emitted through 2017) (Davis and Caldeira, 2010) <sup>[[#fn:r194|194]]</sup> . Coal-fired power plants contribute the largest part. Committed emissions from existing coal-fired power plants built through the end of 2016 are estimated to add up to roughly 200 GtCO <sub>2</sub> , and a further 100–150 GtCO <sub>2</sub> from coal-fired power plants under construction or planned (González-Eguino et al., 2017; Edenhofer et al., 2018) <sup>[[#fn:r195|195]]</sup> . However, there has been a marked slowdown of planned coal-power projects in recent years, and some estimates indicate that the committed emissions from coal plants that are under construction or planned have halved since 2015 (Shearer et al., 2018) <sup>[[#fn:r196|196]]</sup> . Despite these uncertainties, the committed fossil-fuel emissions are assessed to already amount to more than two thirds (half) of the remaining carbon budget.

An important question is to what extent the nationally determined contributions (NDCs) under the Paris Agreement are aligned with the remaining carbon budget. It was estimated that the NDCs, if successfully implemented, imply a total of 400–560 GtCO <sub>2</sub> emissions over the 2018–2030 period (considering both conditional and unconditional NDCs) (Rogelj et al., 2016a) <sup>[[#fn:r197|197]]</sup> . Thus, following an NDC trajectory would already exhaust 95–130% (70–95%) of the remaining two-in-three (one-in-two) 1.5°C carbon budget (unadjusted for additional Earth system feedbacks) by 2030. This would leave no time ( 0–9 years) to bring down global emissions from NDC levels of around 40 GtCO <sub>2</sub> yr <sup>−1</sup> in 2030 (Fawcett et al., 2015; Rogelj et al., 2016a) <sup>[[#fn:r198|198]]</sup> to net zero (further discussion in Section 2.3.5).

Most 1.5°C-consistent pathways show more stringent emissions reductions by 2030 than implied by the NDCs (Section 2.3.5) The lower end of those pathways reach down to below 20 GtCO <sub>2</sub> yr <sup>−1</sup> in 2030 (Section 2.3.3, Table 2.4), less than half of what is implied by the NDCs. Whether such pathways will be able to limit warming to 1.5°C without overshoot will depend on whether cumulative net CO <sub>2</sub> emissions over the 21st century can be kept below the remaining carbon budget at any time. Net global CO <sub>2</sub> emissions are derived from the gross amount of CO <sub>2</sub> that humans annually emit into the atmosphere reduced by the amount of anthropogenic CDR in each year. New research has looked more closely at the amount and the drivers of gross CO <sub>2</sub> emissions from fossil-fuel combustion and industrial processes (FFI) in deep mitigation pathways (Luderer et al., 2018) <sup>[[#fn:r199|199]]</sup> , and found that the larger part of remaining CO <sub>2</sub> emissions come from direct fossil-fuel use in the transport and industry sectors, while residual energy supply sector emissions (mostly from the power sector) are limited by a rapid approach to net zero CO <sub>2</sub> emissions until mid-century. The 1.5°C pathways with no or limited (&lt;0.1°C) overshoot that were reported in the scenario database project remaining FFI CO <sub>2</sub> emissions of 610–1260 GtCO <sub>2</sub> over the period 2018–2100 (5th–95th percentile range; median: 880 GtCO <sub>2</sub> ). Kriegler et al. (2018b) <sup>[[#fn:r200|200]]</sup> conducted a sensitivity analysis that explores the four central options for reducing fossil-fuel emissions: lowering energy demand, electrifying energy services, decarbonizing the power sector and decarbonizing non-electric fuel use in energy end-use sectors. By exploring these options to their extremes, they found a lowest value of 500 GtCO <sub>2</sub> (2018–2100) gross fossil-fuel CO <sub>2</sub> emissions for the hypothetical case of aligning the strongest assumptions for all four mitigation options. The two lines of evidence and the fact that available 1.5°C pathways cover a wide range of assumptions (Section 2.3.1) give a robust indication of a lower limit of about 500 GtCO <sub>2</sub> remaining fossil-fuel and industry CO <sub>2</sub> emissions in the 21st century.

To compare these numbers with the remaining carbon budget, CO <sub>2</sub> emissions from agriculture, forestry and other land use (AFOLU) need to be taken into account. In many of the 1.5°C-consistent pathways, AFOLU CO <sub>2</sub> emissions reach zero at or before mid-century and then turn to negative values (Table 2.4). This means human changes to the land lead to atmospheric carbon being stored in plants and soils. This needs to be distinguished from the natural CO <sub>2</sub> uptake by land, which is not accounted for in the anthropogenic AFOLU CO <sub>2</sub> emissions reported in the pathways. Given the difference in estimating the ‘anthropogenic’ sink between countries and the global integrated assessment and carbon modelling community (Grassi et al., 2017) <sup>[[#fn:r201|201]]</sup> , the AFOLU CO <sub>2</sub> estimates included here are not necessarily directly comparable with countries’ estimates at global level. The cumulated amount of AFOLU CO <sub>2</sub> emissions until the time they reach zero combine with the fossil-fuel and industry CO <sub>2</sub> emissions to give a total amount of gross emissions of 650–1270 GtCO <sub>2</sub> for the period 2018–2100 (5th–95th percentile; median 950 GtCO <sub>2</sub> ) in 1.5°C pathways with no or limited overshoot. The lower end of the range is close to what emerges from a scenario of transformative change that halves CO <sub>2</sub> emissions every decade from 2020 to 2050 (Rockström et al., 2017) <sup>[[#fn:r202|202]]</sup> . All these estimates are above the remaining carbon budget for a one-in-two chance of limiting warming below 1.5°C without overshoot, including the low end of the hypothetical sensitivity analysis of Kriegler et al. (2018b) <sup>[[#fn:r203|203]]</sup> , who assumes 75 Gt AFOLU CO <sub>2</sub> emissions adding to a total of 575 GtCO <sub>2</sub> gross CO <sub>2</sub> emissions. As almost no cases have been identified that keep gross CO <sub>2</sub> emissions within the remaining carbon budget for a one-in-two chance of limiting warming to 1.5°C, and based on current understanding of the geophysical response and its uncertainties, the available evidence indicates that avoiding overshoot of 1.5°C will require some type of CDR in a broad sense, e.g., via net negative AFOLU CO <sub>2</sub> emissions ( ''medium confidence'' ). (Table 2.2).

Net CO <sub>2</sub> emissions can fall below gross CO <sub>2</sub> emissions, if CDR is brought into the mix. Studies have looked at mitigation and CDR in combination to identify strategies for limiting warming to 1.5°C (Sanderson et al., 2016; Ricke et al., 2017) <sup>[[#fn:r204|204]]</sup> . CDR, which may include net negative AFOLU CO <sub>2</sub> emissions, is deployed by all 1.5°C-consistent pathways available to this assessment, but the scale of deployment and choice of CDR measures varies widely (Section 2.3.4). Furthermore, no CDR technology has been deployed at scale yet, and all come with concerns about their potential (Fuss et al., 2018) <sup>[[#fn:r205|205]]</sup> , feasibility (Nemet et al., 2018) <sup>[[#fn:r206|206]]</sup> and/or sustainability (Smith et al., 2015; Fuss et al., 2018) <sup>[[#fn:r207|207]]</sup> (see Sections 2.3.4, 4.3.2 and 4.3.7 and Cross-Chapter Box 7 in Chapter 3 for further discussion). CDR can have two very different functions in 1.5°C-consistent pathways. If deployed in the first half of the century, before net zero CO <sub>2</sub> emissions are reached, it neutralizes some of the remaining CO <sub>2</sub> emissions year by year and thus slows the accumulation of CO <sub>2</sub> in the atmosphere. In this first function it can be used to remain within the carbon budget and avoid overshoot. If CDR is deployed in the second half of the century after carbon neutrality has been established, it can still be used to neutralize some residual emissions from other sectors, but also to create net negative emissions that actively draw down the cumulative amount of CO <sub>2</sub> emissions to return below a 1.5°C warming level. In the second function, CDR enables temporary overshoot. The literature points to strong limitations to upscaling CDR (limiting its first abovementioned function) and to sustainability constraints (limiting both abovementioned functions) (Fuss et al., 2018; Minx et al., 2018; Nemet et al., 2018) <sup>[[#fn:r208|208]]</sup> . Large uncertainty hence exists about what amount of CDR could actually be available before mid-century. Kriegler et al. (2018b) <sup>[[#fn:r209|209]]</sup> explore a case limiting CDR to 100 GtCO <sub>2</sub> until 2050, and the 1.5°C pathways with no or limited overshoot available in the report’s database project 40–260 GtCO <sub>2</sub> CDR until the point of carbon neutrality (5th to 95th percentile; median 110 GtCO <sub>2</sub> ). Because gross CO <sub>2</sub> emissions in most cases exceed the remaining carbon budget by several hundred GtCO <sub>2</sub> and given the limits to CDR deployment until 2050, most of the 1.5°C-consistent pathways available to this assessment are overshoot pathways. However, the scenario database also contains nine non-overshoot pathways that remain below 1.5°C throughout the 21st century (Table 2.1).

<span id="emissions-evolution-in-1.5c-pathways"></span>
=== 2.3.3 Emissions Evolution in 1.5°C Pathways ===

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

This section assesses the salient temporal evolutions of climate forcers over the 21st century. It uses the classification of 1.5°C pathways presented in Section 2.1, which includes a Below-1.5°C class, as well as other classes with varying levels of projected overshoot (1.5°C-low-OS and 1.5°C-high-OS). First, aggregate-GHG benchmarks for 2030 are assessed. Subsequent sections assess long-lived climate forcers (LLCF) and short-lived climate forcers (SLCF) separately because they contribute in different ways to near-term, peak and long-term warming (Section 2.2, Cross-Chapter Box 2 in Chapter 1).

Estimates of aggregated GHG emissions in line with specific policy choices are often compared to near-term benchmark values from mitigation pathways to explore their consistency with long-term climate goals (Clarke et al., 2014; UNEP, 2016, 2017; UNFCCC, 2016) <sup>[[#fn:r210|210]]</sup> . Benchmark emissions or estimates of peak years derived from IAMs provide guidelines or milestones that are consistent with achieving a given temperature level. While they do not set mitigation requirements in a strict sense, exceeding these levels in a given year almost invariably increases the mitigation challenges afterwards by increasing the rates of change and increasing the reliance on speculative technologies, including the possibility that its implementation becomes unachievable (see Cross-Chapter Box 3 in Chapter 1 for a discussion of feasibility concepts) (Luderer et al., 2013; Rogelj et al., 2013b; Clarke et al., 2014; Fawcett et al., 2015; Riahi et al., 2015; Kriegler et al., 2018a) <sup>[[#fn:r211|211]]</sup> . These trade-offs are particularly pronounced in 1.5°C pathways and are discussed in Section 2.3.5. This section assesses Kyoto-GHG emissions in 2030 expressed in CO <sub>2</sub> equivalent (CO <sub>2</sub> e) emissions using 100-year global warming potentials. <sup>[[#fn:3|3]]</sup>

Appropriate benchmark values of aggregated GHG emissions depend on a variety of factors. First and foremost, they are determined by the desired likelihood to keep warming below 1.5°C and the extent to which projected temporary overshoot is to be avoided (Sections 2.2, 2.3.2, and 2.3.5). For instance, median aggregated 2030 GHG emissions are about 10 GtCO <sub>2</sub> e yr <sup>−1</sup> lower in 1.5°C-low-OS compared to 1.5°C-high-OS pathways, with respective interquartile ranges of 26–31 and 36–49 GtCO <sub>2</sub> e yr <sup>−1</sup> (Table 2.4). These ranges correspond to about 25–30 and 35–48 GtCO <sub>2</sub> e yr <sup>−1</sup> in 2030, respectively, when aggregated with 100-year Global Warming Potentials from the IPCC Second Assessment Report. The limited evidence available for pathways aiming to limit warming below 1.5°C without overshoot or with limited amounts of CDR (Grubler et al., 2018; Holz et al., 2018b; van Vuuren et al., 2018) <sup>[[#fn:r212|212]]</sup> indicates that under these conditions consistent emissions in 2030 would fall at the lower end and below the above mentioned ranges. Due to the small number of 1.5°C pathways with no overshoot in the report’s database (Table 2.4) and the potential for a downward bias in the selection of underlying scenario assumptions, the headline range for 1.5°C pathways with no or limited overshoot is also assessed to be of the order of 25–30 GtCO <sub>2</sub> e yr <sup>−1</sup> . Ranges for the 1.5°C-low-OS and Lower-2°C classes only overlap outside their interquartile ranges, highlighting the more accelerated reductions in 1.5°C-consistent compared to 2°C-consistent pathways.

Appropriate emissions benchmark values also depend on the acceptable or desired portfolio of mitigation measures, representing clearly identified trade-offs and choices (Sections 2.3.4, 2.4, and 2.5.3) (Luderer et al., 2013; Rogelj et al., 2013a; Clarke et al., 2014; Krey et al., 2014a; Strefler et al., 2018b) <sup>[[#fn:r213|213]]</sup> . For example, lower 2030 GHG emissions correlate with a lower dependence on the future availability and desirability of CDR (Strefler et al., 2018b) <sup>[[#fn:r214|214]]</sup> . On the other hand, pathways that assume or anticipate only limited deployment of CDR during the 21st century imply lower emissions benchmarks over the coming decades, which are achieved in models through further reducing CO <sub>2</sub> emissions in the coming decades. The pathway archetypes used in the chapter illustrate this further (Figure 2.6). Under middle-of-the-road assumptions of technological and socioeconomic development, pathway ''S2'' suggests emission benchmarks of 34, 12 and −8 GtCO <sub>2</sub> e yr <sup>−1</sup> in the years 2030, 2050, and 2100, respectively. In contrast, a pathway that further limits overshoot and aims at eliminating the reliance on negative emissions technologies like BECCS as well as CCS (here labelled as the ''LED'' pathway) shows deeper emissions reductions in 2030 to limit the cumulative amount of CO <sub>2</sub> until net zero global CO <sub>2</sub> emissions (carbon neutrality). The ''LED'' pathway here suggests emission benchmarks of 25, 9 and 2 GtCO <sub>2</sub> e yr <sup>−1</sup> in the years 2030, 2050, and 2100, respectively. However, a pathway that allows and plans for the successful large-scale deployment of BECCS by and beyond 2050 ( ''S5'' ) shows a shift in the opposite direction. The variation within and between the abovementioned ranges of 2030 GHG benchmarks hence depends strongly on societal choices and preferences related to the acceptability and availability of certain technologies.

Overall these variations do not strongly affect estimates of the 1.5°C-consistent timing of global peaking of GHG emissions. Both Below-1.5°C and 1.5°C-low-OS pathways show minimum–maximum ranges in 2030 that do not overlap with 2020 ranges, indicating the global GHG emissions peaked before 2030 in these pathways. Also, 2020 and 2030 GHG emissions in 1.5°C-high-OS pathways only overlap outside their interquartile ranges.

Kyoto-GHG emission reductions are achieved by reductions in CO <sub>2</sub> and non-CO <sub>2</sub> GHGs. The AR5 identified two primary factors that influence the depth and timing of reductions in non-CO <sub>2</sub> Kyoto-GHG emissions: (i) the abatement potential and costs of reducing the emissions of these gases and (ii) the strategies that allow making trade-offs between them (Clarke et al., 2014) <sup>[[#fn:r215|215]]</sup> . Many studies indicate low-cost, near-term mitigation options in some sectors for non-CO <sub>2</sub> gases compared to supply-side measures for CO <sub>2</sub> mitigation (Clarke et al., 2014) <sup>[[#fn:r216|216]]</sup> . A large share of this potential is hence already exploited in mitigation pathways in line with 2°C. At the same time, by mid-century and beyond, estimates of further reductions of non-CO <sub>2</sub> Kyoto-GHGs – in particular CH <sub>4</sub> and N <sub>2</sub> O – are hampered by the absence of mitigation options in the current generation of IAMs, which are hence not able to reduce residual emissions of sources linked to livestock production and fertilizer use (Clarke et al., 2014; Gernaat et al., 2015) <sup>[[#fn:r217|217]]</sup> (Sections 2.3.1.2, 2.4.4, Supplementary Material  2.SM.1.2). Therefore, while net CO <sub>2</sub> emissions are projected to be markedly lower in 1.5°C-consistent compared to 2°C-consistent pathways, this is much less the case for methane (CH <sub>4</sub> ) and nitrous-oxide (N <sub>2</sub> O) (Figures 2.6–2.7). This results in reductions of CO <sub>2</sub> being projected to take up the largest share of emissions reductions when moving between 1.5°C-consistent and 2°C-consistent pathways (Rogelj et al., 2015b, 2018; Luderer et al., 2018) <sup>[[#fn:r218|218]]</sup> . If additional non-CO <sub>2</sub> mitigation measures are identified and adequately included in IAMs, they are expected to further contribute to mitigation efforts by lowering the floor of residual non-CO <sub>2</sub> emissions. However, the magnitude of these potential contributions has not been assessed as part of this report.

As a result of the interplay between residual CO <sub>2</sub> and non-CO <sub>2</sub> emissions and CDR, global GHG emissions reach net zero levels at different times in different 1.5°C-consistent pathways. Interquartile ranges of the years in which 1.5°C-low-OS and 1.5°C-high-OS reach net zero GHG emissions range from 2060 to 2080 (Table 2.4). A seesaw characteristic can be found between near-term emissions reductions and the timing of net zero GHG emissions. This is because pathways with limited emissions reductions in the next one to two decades require net negative CO <sub>2</sub> emissions later on (see earlier). Most 1.5°C-high-OS pathways lead to net zero GHG emissions in approximately the third quarter of this century, because all of them rely on significant amounts of annual net negative CO <sub>2</sub> emissions in the second half of the century to decline temperatures after overshoot (Table 2.4). However, in pathways that aim at limiting overshoot as much as possible or more slowly decline temperatures after their peak, emissions reach the point of net zero GHG emissions slightly later or at times never. Early emissions reductions in this case reduce the requirement for net negative CO <sub>2</sub> emissions. Estimates of 2030 GHG emissions in line with the current NDCs overlap with the highest quartile of 1.5°C-high-OS pathways (Cross-Chapter Box 9 in Chapter 4).

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

<span id="emissions-of-long-lived-climate-forcers"></span>
==== 2.3.3.1 Emissions of long-lived climate forcers ====

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

Climate effects of long-lived climate forcers (LLCFs) are dominated by CO <sub>2</sub> , with smaller contributions of N <sub>2</sub> O and some fluorinated gases (Myhre et al., 2013; Blanco et al., 2014) <sup>[[#fn:r219|219]]</sup> . Overall net CO <sub>2</sub> emissions in pathways are the result of a combination of various anthropogenic contributions (Figure 2.5) (Clarke et al., 2014) <sup>[[#fn:r220|220]]</sup> : (i) CO <sub>2</sub> produced by fossil-fuel combustion and industrial processes, (ii) CO <sub>2</sub> emissions or removals from the agriculture, forestry and other land use (AFOLU) sector, (iii) CO <sub>2</sub> capture and sequestration (CCS) from fossil fuels or industrial activities before it is released to the atmosphere, (iv) CO <sub>2</sub> removal by technological means, which in current pathways is mainly achieved by BECCS and AFOLU-related CDR, although other options could be conceivable (see Chapter 4, Section 4.3.7). Pathways apply these four contributions in different configurations (Figure 2.5) depending on societal choices and preferences related to the acceptability and availability of certain technologies, the timing and stringency of near-term climate policy, and the ability to limit the demand that drives baseline emissions (Marangoni et al., 2017; Riahi et al., 2017; Grubler et al., 2018; Rogelj et al., 2018; van Vuuren et al., 2018) <sup>[[#fn:r221|221]]</sup> , and come with very different implication for sustainable development (Section 2.5.3).

All 1.5°C pathways see global CO <sub>2</sub> emissions embark on a steady decline to reach (near) net zero levels around 2050, with 1.5°C-low-OS pathways reaching net zero CO <sub>2</sub> emissions around 2045–2055 (Table 2.4; Figure 2.5). Near-term differences between the various pathway classes are apparent, however. For instance, Below-1.5°C and 1.5°C-low-OS pathways show a clear shift towards lower CO <sub>2</sub> emissions in 2030 relative to other 1.5°C and 2°C pathway classes, although in all 1.5°C classes reductions are clear (Figure 2.6). These lower near-term emissions levels are a direct consequence of the former two pathway classes limiting cumulative CO <sub>2</sub> emissions until carbon neutrality in order to aim for a higher probability of limiting peak warming to 1.5°C (Section 2.2.2 and 2.3.2.2). In some cases, 1.5°C-low-OS pathways achieve net zero CO <sub>2</sub> emissions one or two decades later, contingent on 2030 CO <sub>2</sub> emissions in the lower quartile of the literature range, that is, below about 18 GtCO <sub>2</sub> yr <sup>−</sup> <sup>1</sup> . Median year-2030 global CO <sub>2</sub> emissions are of the order of 5–10 GtCO <sub>2</sub> yr <sup>−</sup> <sup>1</sup> lower in Below-1.5°C compared to 1.5°C-low-OS pathways, which are in turn lower than 1.5°C-high-OS pathways (Table 2.4). Below-1.5°C and 1.5°C-low-OS pathways combined show a decline in global net anthropogenic CO <sub>2</sub> emissions of about 45% from 2010 levels by 2030 (40–60% interquartile range). Lower-2°C pathways show CO <sub>2</sub> emissions declining by about 25% by 2030 in most pathways (10–30% interquartile range). The 1.5°C-high-OS pathways show emissions levels that are broadly similar to the 2°C-consistent pathways in 2030.

The development of CO <sub>2</sub> emissions in the second half of the century in 1.5°C pathways is characterized by the need to stay or return within a carbon budget. Figure 2.6 shows net CO <sub>2</sub> and N <sub>2</sub> O emissions from various sources in 2050 and 2100 in 1.5°C pathways in the literature. Virtually all 1.5°C pathways obtain net negative CO <sub>2</sub> emissions at some point during the 21st century, but the extent to which net negative emissions are relied upon varies substantially (Figure 2.6, Table 2.4). This net withdrawal of CO <sub>2</sub> from the atmosphere compensates for residual long-lived non-CO <sub>2</sub> GHG emissions that also accumulate in the atmosphere (like N <sub>2</sub> O) or cancels some of the build-up of CO <sub>2</sub> due to earlier emissions to achieve increasingly higher likelihoods that warming stays or returns below 1.5°C (see Section 2.3.4 for a discussion of various uses of CDR). Even non-overshoot pathways that aim at achieving temperature stabilization would hence deploy a certain amount of net negative CO <sub>2</sub> emissions to offset any accumulating long-lived non-CO <sub>2</sub> GHGs. The 1.5°C overshoot pathways display significantly larger amounts of annual net negative CO <sub>2</sub> emissions in the second half of the century. The larger the overshoot the more net negative CO <sub>2</sub> emissions are required to return temperatures to 1.5°C by the end of the century (Table 2.4, Figure 2.1).

N <sub>2</sub> O emissions decline to a much lesser extent than CO <sub>2</sub> in currently available 1.5°C pathways (Figure 2.6). Current IAMs have limited emissions-reduction potentials (Gernaat et al., 2015) <sup>[[#fn:r222|222]]</sup> (Sections 2.3.1.2, 2.4.4, Supplementary Material  2.SM.1.2), reflecting the difficulty of eliminating N <sub>2</sub> O emission from agriculture (Bodirsky et al., 2014) <sup>[[#fn:r223|223]]</sup> . Moreover, the reliance of some pathways on significant amounts of bioenergy after mid-century (Section 2.4.2) coupled to a substantial use of nitrogen fertilizer (Popp et al., 2017) <sup>[[#fn:r224|224]]</sup> also makes reducing N <sub>2</sub> O emissions harder (for example, see pathway ''S5'' in Figure 2.6). As a result, sizeable residual N <sub>2</sub> O emissions are currently projected to continue throughout the century, and measures to effectively mitigate them will be of continued relevance for 1.5°C societies. Finally, the reduction of nitrogen use and N <sub>2</sub> O emissions from agriculture is already a present-day concern due to unsustainable levels of nitrogen pollution (Bodirsky et al., 2012) <sup>[[#fn:r225|225]]</sup> . Section 2.4.4 provides a further assessment of the agricultural non-CO <sub>2</sub> emissions reduction potential.

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

<span id="figure-2.6"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.6'''

<span id="section-4"></span>
<!-- IMG CAPTION -->
Annual global emissions characteristics for 2020, 2030, 2050, 2100.
<!-- IMG FILE -->
[[File:9943509a36d49b42308928cfd6843289 Figure-2.6-766x1024.jpg]]

Data are shown for (a) Kyoto-GHG emissions, and (b) global total CO <sub>2</sub> emissions, (c) CO <sub>2</sub> emissions from the agriculture, forestry and other land use (AFOLU) sector, (d) global N2O emissions, and (e) CO <sub>2</sub> emissions from fossil fuel use and industrial processes. The latter is also split into (f) emissions from the energy supply sector (electricity sector and refineries) and (g) direct emissions from fossil-fuel use in energy demand sectors (industry, buildings, transport) (bottom row). Horizontal black lines show the median, boxes show the interquartile range, and whiskers the minimum–maximum range. Icons indicate the four pathway archetypes used in this chapter. In case less than seven data points are available in a class, the minimum–maximum range and single data points are shown. Kyoto-GHG, emissions in the top panel are aggregated with AR4 GWP-100 and contain CO <sub>2</sub> , CH4, N2O, HFCs, PFCs, and SF6. NF3 is typically not reported by IAMs. Scenarios with year-2010 Kyoto-GHG emissions outside the range assessed by IPCC AR5 WGIII assessed are excluded (IPCC, 2014b) <sup>[[#fn:r226|226]]</sup> .

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

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

<span id="emissions-of-short-lived-climate-forcers-and-fluorinated-gases"></span>
==== 2.3.3.2 Emissions of short-lived climate forcers and fluorinated gases ====

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

SLCFs include shorter-lived GHGs like CH <sub>4</sub> and some fluorinated gases as well as particles (aerosols), their precursors and ozone precursors. SLCFs are strongly mitigated in 1.5°C pathways, as is the case for 2°C pathways (Figure 2.7). SLCF emissions ranges of 1.5°C and 2°C pathway classes strongly overlap, indicating that the main incremental mitigation contribution between 1.5°C and 2°C pathways comes from CO <sub>2</sub> (Luderer et al., 2018; Rogelj et al., 2018) <sup>[[#fn:r227|227]]</sup> . CO <sub>2</sub> and SLCF emissions reductions are connected in situations where SLCF and CO <sub>2</sub> are co-emitted by the same process, for example, with coal-fired power plants (Shindell and Faluvegi, 2010) <sup>[[#fn:r228|228]]</sup> or within the transport sector (Fuglestvedt et al., 2010) <sup>[[#fn:r229|229]]</sup> . Many CO <sub>2</sub> -targeted mitigation measures in industry, transport and agriculture (Sections 2.4.3–4) hence also reduce non-CO <sub>2</sub> forcing (Rogelj et al., 2014b; Shindell et al., 2016) <sup>[[#fn:r230|230]]</sup> .

Despite the fact that methane has a strong warming effect (Myhre et al., 2013; Etminan et al., 2016) <sup>[[#fn:r231|231]]</sup> , current 1.5°C-consistent pathways still project significant emissions of CH <sub>4</sub> by 2050, indicating only a limited CH <sub>4</sub> mitigation potential in IAM analyses (Gernaat et al., 2015) <sup>[[#fn:r232|232]]</sup> (Sections 2.3.1.2, 2.4.4, Table 2.SM.2). The AFOLU sector contributes an important share of the residual CH <sub>4</sub> emissions until mid-century, with its relative share increasing from slightly below 50% in 2010 to around 55–70% in 2030, and 60–80% in 2050 in 1.5°C-consistent pathways (interquartile range across 1.5°C-consistent pathways for projections). Many of the proposed measures to target CH <sub>4</sub> (Shindell et al., 2012; Stohl et al., 2015) <sup>[[#fn:r233|233]]</sup> are included in 1.5°C-consistent pathways (Figure 2.7), though not all (Sections 2.3.1.2, 2.4.4, Table 2.SM.2). A detailed assessment of measures to further reduce AFOLU CH <sub>4</sub> emissions has not been conducted.

Overall reductions of SLCFs can have effects of either sign on temperature depending on the balance between cooling and warming agents. The reduction in SO <sub>2</sub> emissions is the dominant single effect as it weakens the negative total aerosol forcing. This means that reducing all SLCF emissions to zero would result in a short-term warming, although this warming is ''unlikely'' to be more than 0.5°C (Section 2.2 and Figure 1.5 (Samset et al., 2018) <sup>[[#fn:r234|234]]</sup> ). Because of this effect, suggestions have been proposed that target the warming agents only (referred to as short-lived climate pollutants or SLCPs instead of the more general short-lived climate forcers; e.g., Shindell et al., 2012) <sup>[[#fn:r235|235]]</sup> , though aerosols are often emitted in varying mixtures of warming and cooling species (Bond et al., 2013) <sup>[[#fn:r236|236]]</sup> . Black carbon (BC) emissions reach similar levels across 1.5°C-consistent and 2°C-consistent pathways available in the literature, with interquartile ranges of emissions reductions across pathways of 16–34% and 48–58% in 2030 and 2050, respectively, relative to 2010 (Figure 2.7). Recent studies have identified further reduction potentials for the near term, with global reductions of about 80% being suggested (Stohl et al., 2015; Klimont et al., 2017) <sup>[[#fn:r237|237]]</sup> . Because the dominant sources of certain aerosol mixtures are emitted during the combustion of fossil fuels, the rapid phase-out of unabated fossil fuels to avoid CO <sub>2</sub> emissions would also result in removal of these either warming or cooling SLCF air-pollutant species. Furthermore, SLCFs are also reduced by efforts to reduce particulate air pollution. For example, year-2050 SO <sub>2</sub> emissions (precursors of sulphate aerosol) in 1.5°C-consistent pathways are about 75–85% lower than their 2010 levels. Some caveats apply, for example, if residential biomass use would be encouraged in industrialised countries in stringent mitigation pathways without appropriate pollution control measures, aerosol concentrations could also increase (Sand et al., 2015; Stohl et al., 2015) <sup>[[#fn:r238|238]]</sup> .

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

<span id="table-2.4"></span>
<!-- START TABLE -->
'''Table 2.4'''

'''Emissions in 2030, 2050 and 2100 in 1.5°C and 2°C scenario classes and absolute annual rate''' '''s of change between 2010–2030, 2020–2030 and 2030–2050, respectively.''' Values show median and interquartile range across available scenarios (25th and 75th percentile given in brackets). If fewer than seven scenarios are available (*), the minimum–maximum range is given instead. Kyoto-GHG emissions are aggregated with GWP-100 values from IPCC AR4. Emissions in 2010 for total net CO <sub>2</sub> , CO <sub>2</sub> from fossil-fuel use and industry, and AFOLU CO <sub>2</sub> are estimated at 38.5, 33.4, and 5 GtCO <sub>2</sub> yr−1, respectively (Le Quéré et al., 2018) <sup>[[#fn:r239|239]]</sup> . Percentage reduction numbers included in headline statement C.1 in the Summary for Policymakers are computed relative to 2010 emissions in each individual pathway, and hence differ slightly from a case where reductions are computed relative to the historical 2010 emissions reported above. A difference is reported in estimating the ‘anthropogenic’ sink by countries or the global carbon modelling community (Grassi et al., 2017) <sup>[[#fn:r240|240]]</sup> , and AFOLU CO <sub>2</sub> estimates reported here are thus not necessarily comparable with countries’ estimates. Scenarios with year-2010 Kyoto-GHG emissions outside the range assessed by IPCC AR5 WGIII are excluded (IPCC, 2014b) <sup>[[#fn:r241|241]]</sup> , as are scenario duplicates that would bias ranges towards a single study.
<!-- TABLE -->
{| class="wikitable"
|-
|
| colspan="3"| Annual emissions/sequestration<br />
(GtCO <sub>2</sub> yr <sup>-1</sup> )
| colspan="3"| Absolute Annual Change<br />
(GtCO <sub>2</sub> /yr <sup>–1</sup> )
| Timing of Global Zero
|-
| Name
| Category
| #
| 2030
| 2050
| 2100
| 2010–2030
| 2020–2030
| 2030–2050
| Year
|-
| rowspan="6"| Total CO <sub>2</sub> (net)
| Below-1.5°C
| 5*
| 13.4
(15.4, 11.4)

| –3.0
(1.7, –10.6)

| –8.0
(–2.6, –14.2)

| –1.2
(–1.0, –1.3)

| –2.5
(–1.8, –2.8)

| –0.8
(–0.7, –1.2)

| 2044
(2037, 2054)

|-
| 1.5°C-low-OS
| 37
| 20.8
(22.2, 18.0)

| –0.4
(2.7, –2.0)

| –10.8
(–8.1, –14.3)

| –0.8
(–0.7, –1.0)

| –1.7
(–1.4, –2.3)

| –1.0
(–0.8, –1.2)

| 2050
(2047, 2055)

|-
| 1.5°C with no or limited OS
| 42
| 20.3
(22.0, 15.9)

| –0.5
(2.2, –2.8)

| –10.2
(–7.6, –14.2)

| –0.9
(–0.7, –1.1)

| –1.8
(–1.5, –2.3)

| –1.0
(–0.8, –1.2)

| 2050
(2046, 2055)

|-
| 1.5°C-high-OS
| 36
| 29.1
(36.4, 26.0)

| 1.0
(6.3, –1.2)

| –13.8
(–11.1, –16.4)

| –0.4
(0.0, –0.6)

| –1.1
(–0.5, –1.5)

| –1.3
(–1.1, –1.8)

| 2052
(2049, 2059)

|-
| Lower-2°C
| 54
| 28.9
(33.7, 24.5)

| 9.9
(13.1, 6.5)

| –5.1
(–2.6, –10.3)

| –0.4
(–0.2, –0.6)

| –1.1
(–0.8, –1.6)

| –0.9
(–0.8, –1.2)

| 2070
(2063, 2079)

|-
| Higher-2°C
| 54
| 33.5
(35.0, 31.0)

| 17.9
(19.1, 12.2)

| –3.3
(0.6, –11.5)

| –0.2
(–0.0, –0.4)

| –0.7
(–0.5, –0.9)

| –0.8
(–0.6, –1.0)

| 2085
(2070, post–2100)

|-
| rowspan="6"| CO <sub>2</sub> from fossil fuels and industry<br />
(gross)
| Below-1.5°C
| 5*
| 18.0
(21.4, 13.8)

| 10.5
(20.9, 0.3)

| 8.3
(11.6, 0.1)

| –0.7
(–0.6, –1)

| –1.5
(–0.9, –2.2)

| –0.4
(0, –0.7)

| –
|-
| 1.5°C-low-OS
| 37
| 22.1
(24.4, 18.7)

| 10.3
(14.1, 7.8)

| 5.6
(8.1, 2.6)

| –0.5
(–0.4, –0.6)

| –1.3
(–0.9, –1.7)

| –0.6
(–0.5, –0.7)

| –
|-
| 1.5°C with no or limited OS
| 42
| 21.6
(24.2, 18.0)

| 10.3
(13.8, 7.7)

| 6.1
(8.4, 2.6)

| –0.5
(–0.4, –0.7)

| –1.3
(–0.9, –1.8)

| –0.6
(–0.4, –0.7)

| –
|-
| 1.5°C-high-OS
| 36
| 27.8
(37.1, 25.6)

| 13.1
(17.0, 11.6)

| 6.6
(8.8, 2.8)

| –0.2
(0.2, –0.3)

| –0.8
(–0.2, –1.1)

| –0.7
(–0.6, –1.0)

| –
|-
| Lower-2°C
| 54
| 27.7
(31.5, 23.5)

| 15.4
(19.0, 11.1)

| 7.2
(10.4, 3.7)

| –0.2
(–0.0, –0.4)

| –0.8
(–0.5, –1.2)

| –0.6
(–0.5, –0.8)

| –
|-
| Higher-2°C
| 54
| 31.3
(33.4, 28.7)

| 19.2
(22.6, 17.1)

| 8.1
(10.9, 5.0)

| –0.1
(0.1, –0.2)

| –0.5
(–0.2, –0.7)

| –0.6
(–0.5, –0.7)

| –
|-
| rowspan="6"| CO <sub>2</sub> from fossil fuels and industry (net)
| Below-1.5°C
| 5*
| 16.4
(18.2, 13.5)

| 1.0
(7.0, 0)

| –2.7
(0, –9.8)

| –0.8
(–0.7, –1)

| –1.8
(–1.2, –2.2)

| –0.6
(–0.5, –0.9)

| –
|-
| 1.5°C-low-OS
| 37
| 20.6
(22.2, 17.5)

| 3.2
(5.6, –0.6)

| –8.5
(–4.1, –11.6)

| –0.6
(–0.5, –0.7)

| –1.4
(–1.1, –1.8)

| –0.8
(–0.7, –1.1)

| –
|-
| 1.5°C with no or limited OS
| 42
| 20.1
(22.1, 16.8)

| 3.0
(5.6, 0.0)

| –8.3
(–3.5, –10.8)

| –0.6
(–0.5, –0.8)

| –1.4
(–1.1, –1.9)

| –0.8
(–0.7, –1.1)

| –
|-
| 1.5°C-high-OS
| 36
| 26.9 (34.7, 25.3)
| 4.2 (10.0, 1.2)
| –10.7
(–6.9, –13.2)

| –0.3
(0.1, –0.3)

| –0.9
(–0.3, –1.2)

| –1.2
(–0.9, –1.5)

| –
|-
| Lower-2°C
| 54
| 28.2
(31.0, 23.1)

| 11.8
(14.1, 6.2)

| –3.1
(–0.7, –6.4)

| –0.2
(–0.1, –0.4)

| –0.8
(–0.5, –1.2)

| –0.8
(–0.7, –1.0)

| –
|-
| Higher-2°C
| 54
| 31.0
(33.0, 28.7)

| 17.0
(19.3, 13.1)

| –2.9
(3.3, –8.0)

| –0.1
(0.1, –0.2)

| –0.5
(–0.2, –0.7)

| –0.7
(–0.5, –1.0)

| –
|-
| rowspan="6"| CO <sub>2</sub> from AFOLU
| Below-1.5°C
| 5*
| –2.2
(–0.3, –4.8)

| –4.4
(–1.2, –11.1)

| –4.4
(–2.6, –5.3)

| –0.3
(–0.2, –0.4)

| –0.5
(–0.4, –0.8)

| –0.1
(0, –0.4)

| –
|-
| 1.5°C-low-OS
| 37
| –0.1
(0.8, –1.0)

| –2.3
(–0.6, –4.1)

| –2.4
(–1.2, –4.2)

| –0.2
(–0.2, –0.3)

| –0.4
(–0.3, –0.5)

| –0.1
(–0.1, –0.2)

| –
|-
| 1.5°C with no or limited OS
| 42
| –0.1
(0.7, –1.3)

| –2.6
(–0.6, –4.5)

| –2.6
(–1.3, –4.2)

| –0.2
(–0.2, –0.3)

| –0.4
(–0.3, –0.5)

| –0.1
(–0.1, –0.2)

| –
|-
| 1.5°C-high-OS
| 36
| 1.2
(2.7, 0.1)

| –2.1
(–0.3, –5.4)

| –2.4
(–1.5, –5.0)

| –0.1
(–0.1, –0.3)

| –0.2
(–0.1, –0.5)

| –0.2
(–0.0, –0.3)

| –
|-
| Lower-2°C
| 54
| 1.4
(2.8, 0.3)

| –1.4
(–0.5, –2.7)

| –2.4
(–1.3, –4.2)

| –0.2
(–0.1, –0.2)

| –0.3
(–0.2, –0.4)

| –0.1
(–0.1, –0.2)

| –
|-
| Higher-2°C
| 54
| 1.5
(2.7, 0.8)

| –0.0
(1.9, –1.6)

| –1.3
(0.1, –3.9)

| –0.2
(–0.1, –0.2)

| –0.2
(–0.1, –0.4)

| –0.1
(–0.0, –0.1)

| –
|-
| rowspan="6"| Bioenergy<br />
combined with carbon capture and storage (BECCS)
| Below-1.5°C
| 5*
| 0.4
(1.1, 0)

| 3.4
(8.3, 0)

| 5.7
(13.4, 0)

| 0
(0.1, 0)

| 0
(0.1, 0)

| 0.2
(0.4, 0)

| –
|-
| 1.5°C-low-OS
| 36
| 0.3
(1.1, 0.0)

| 4.6
(6.4, 3.8)

| 12.4
(15.6, 7.6)

| 0.0
(0.1, 0.0)

| 0.0
(0.1, 0.0)

| 0.2
(0.3, 0.2)

| –
|-
| 1.5°C with no or limited OS
| 41
| 0.4
(1.0, 0.0)

| 4.5
(6.3, 3.4)

| 12.4
(15.0, 6.4)

| 0.0
(0.1, 0.0)

| 0.0
(0.1, 0.0)

| 0.2
(0.3, 0.2)

| –
|-
| 1.5°C-high-OS
| 36
| 0.1
(0.4, 0.0)

| 6.8
(9.5, 3.7)

| 14.9
(16.3, 12.1)

| 0.0
(0.0, 0.0)

| 0.0
(0.0, 0.0)

| 0.3
(0.4, 0.2)

| –
|-
| Lower-2°C
| 54
| 0.1
(0.3, 0.0)

| 3.6
(4.6, 1.8)

| 9.5
(12.1, 6.9)

| 0.0
(0.0, 0.0)

| 0.0
(0.0, 0.0)

| 0.2
(0.2, 0.1)

| –
|-
| Higher-2°C
| 47
| 0.1
(0.2, 0.0)

| 3.0
(4.9, 1.6)

| 10.8
(15.3, 8.2) [46]

| 0.0
(0.0, 0.0)

| 0.0
(0.0, 0.0)

| 0.1
(0.2, 0.1)

| –
|-
| rowspan="6"| Kyoto GHG (AR4) [GtCO <sub>2</sub> e]
| Below-1.5°C
| 5*
| 22.1
(22.8, 20.7)

| 2.7
(8.1, –3.5)

| –2.6
(2.7, –10.7)

| –1.4
(–1.3, –1.5)

| –2.9
(–2.1, –3.3)

| –0.9
(–0.7, –1.3)

| 2066
(2044, post–2100)

|-
| 1.5°C-low-OS
| 31
| 27.9
(31.1, 26.0)

| 7.0
(9.9, 4.5)

| –3.8
(–2.1, –7.9)

| –1.1
(–0.9, –1.2)

| –2.3
(–1.8, –2.8)

| –1.1
(–0.9, –1.2)

| 2068
(2061, 2080)

|-
| 1.5°C with no or limited OS
| 36
| 27.4
(30.9, 24.7)

| 6.5
(9.6, 4.2)

| –3.7
(–1.8, –7.8)

| –1.1
(–1.0, –1.3)

| –2.4
(–1.9, –2.9)

| –1.1
(–0.9, –1.2)

| 2067
(2061, 2084)

|-
| 1.5°C-high-OS
| 32
| 40.4
(48.9, 36.3)

| 8.4
(12.3, 6.2)

| –8.5
(–5.7, –11.2)

| –0.5
(–0.0, –0.7)

| –1.3
(–0.6, –1.8)

| –1.5
(–1.3, –2.1)

| 2063
(2058, 2067)

|-
| Lower-2°C
| 46
| 39.6
(45.1, 35.7)

| 18.3
(20.4, 15.2)

| 2.1
(4.2, –2.4)

| –0.5
(–0.1, –0.7)

| –1.5
(–0.9, –2.2)

| –1.1
(–0.9, –1.2)

| post–2100
(2090 post–2100)

|-
| Higher-2°C
| 42
| 45.3
(48.5, 39.3)

| 25.9
(27.9, 23.3)

| 5.2
(11.5, –4.8)

| –0.2
(–0.0, –0.6)

| –1.0
(–0.6, –1.2)

| –1.0
(–0.7, –1.2)

| post–2100
(2085 post–2100)

|}
<!-- END TABLE -->

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

Emissions of fluorinated gases (IPCC/TEAP, 2005; US EPA, 2013; Velders et al., 2015; Purohit and Höglund-Isaksson, 2017) <sup>[[#fn:r242|242]]</sup> in 1.5°C-consistent pathways are reduced by roughly 75–80% relative to 2010 levels (interquartile range across 1.5°C-consistent pathways) in 2050, with no clear differences between the classes. Although unabated hydrofluorocarbon (HFC) emissions have been projected to increase (Velders et al., 2015) <sup>[[#fn:r243|243]]</sup> , the Kigali Amendment recently added HFCs to the basket of gases controlled under the Montreal Protocol (Höglund-Isaksson et al., 2017) <sup>[[#fn:r244|244]]</sup> . As part of the larger group of fluorinated gases, HFCs are also assumed to decline in 1.5°C-consistent pathways. Projected reductions by 2050 of fluorinated gases under 1.5°C-consistent pathways are deeper than published estimates of what a full implementation of the Montreal Protocol including its Kigali Amendment would achieve (Höglund-Isaksson et al., 2017) <sup>[[#fn:r245|245]]</sup> , which project roughly a halving of fluorinated gas emissions in 2050 compared to 2010. Assuming the application of technologies that are currently commercially available and at least to a limited extent already tested and implemented, potential fluorinated gas emissions reductions of more than 90% have been estimated (Höglund-Isaksson et al., 2017) <sup>[[#fn:r246|246]]</sup> .

There is a general agreement across 1.5°C-consistent pathways that until 2030 forcing from the warming SLCFs is reduced less strongly than the net cooling forcing from aerosol effects, compared to 2010. As a result, the net forcing contributions from all SLCFs combined are projected to increase slightly by about 0.2–0.3 W m <sup>−2</sup> , compared to 2010. Also, by the end of the century, about 0.1–0.3 W m <sup>−2</sup> of SLCF forcing is generally currently projected to remain in 1.5°C-consistent scenarios (Figure 2.8). This is similar to developments in 2°C-consistent pathways (Rose et al., 2014b; Riahi et al., 2017) <sup>[[#fn:r247|247]]</sup> , which show median forcing contributions from these forcing agents that are generally no more than 0.1 W m <sup>−2</sup> higher. Nevertheless, there can be additional gains from targeted deeper reductions of CH <sub>4</sub> emissions and tropospheric ozone precursors, with some scenarios projecting less than 0.1 W m <sup>−2</sup> forcing from SLCFs by 2100.

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

<span id="figure-2.7"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.7'''

<span id="section-5"></span>
<!-- IMG CAPTION -->
Global characteristics of a selection of short-lived non-CO <sub>2 </sub> emissions until mid-century for five pathway classes used in this chapter.
<!-- IMG FILE -->
[[File:50f492894a82ac749d8e7191b365c452 Figure-2.7-1024x670.jpg]]

Data are shown for (a) methane (CH4), (b) fluorinated gases (F-gas), (c) black carbon (BC), and (d) sulphur dioxide (SO2) emissions. Boxes with different colours refer to different scenario classes. Icons on top the ranges show four illustrative pathway archetypes that apply different mitigation strategies for limiting warming to 1.5°C. Boxes show the interquartile range, horizontal black lines the median, and whiskers the minimum–maximum range. F-gases are expressed in units of CO <sub>2</sub> -equivalence computed with 100-year Global Warming Potentials reported in IPCC AR4.

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

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

<span id="figure-2.8"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.8'''

<span id="estimated-aggregated-effective-radiative-forcing-of-slcfs-for-1.5c-and-2c-pathway-classes-in-2010-2020-2030-2050-and-2100-as-estimated-by-the-fair-model-smith-et-al.-2018-248-."></span>
<!-- IMG CAPTION -->
'''Estimated aggregated effective radiative forcing of SLCFs for 1.5°C and 2°C pathway classes in 2010, 2020, 2030, 2050, and 2100, as estimated by the FAIR model (Smith et al., 2018) <sup>[[#fn:r248|248]]</sup> .'''
<!-- IMG FILE -->
[[File:1c68da6b4ecab0e7d0a8880a88f12464 Figure-2.8-1024x572.jpg]]

Aggregated short-lived climate forcer (SLCF) radiative forcing is estimated as the difference between total anthropogenic radiative forcing and the sum of CO <sub>0</sub> and N2 <sub>0</sub> radiative forcing over time, and is expressed relative to 1750. Symbols indicate the four pathways archetypes used in this chapter. Horizontal black lines indicate the median, boxes the interquartile range, and whiskers the minimum–maximum range per pathway class. Because very few pathways fall into the Below-1.5°C class, only the minimum–maximum is provided here.

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

<!-- END IMG -->
<span id="cdr-in-1.5c-pathways"></span>
=== 2.3.4 CDR in 1.5°C Pathways ===

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

Deep mitigation pathways assessed in AR5 showed significant deployment of CDR, in particular through BECCS (Clarke et al., 2014) <sup>[[#fn:r249|249]]</sup> . This has led to increased debate about the necessity, feasibility and desirability of large-scale CDR deployment, sometimes also called ‘negative emissions technologies’ in the literature (Fuss et al., 2014; Anderson and Peters, 2016; Williamson, 2016; van Vuuren et al., 2017a; Obersteiner et al., 2018) <sup>[[#fn:r250|250]]</sup> . Most CDR technologies remain largely unproven to date and raise substantial concerns about adverse side-effects on environmental and social sustainability (Smith et al., 2015; Dooley and Kartha, 2018) <sup>[[#fn:r251|251]]</sup> . A set of key questions emerge: how strongly do 1.5°C-consistent pathways rely on CDR deployment and what types of CDR measures are deployed at which scale? How does this vary across available 1.5°C-consistent pathways and on which factors does it depend? How does CDR deployment compare between 1.5°C- and 2°C-consistent pathways and how does it compare with the findings at the time of the AR5? How does CDR deployment in 1.5°C-consistent pathways relate to questions about availability, policy implementation and sustainable development implications that have been raised about CDR technologies? The first three questions are assessed in this section with the goal to provide an overview and assessment of CDR deployment in the 1.5°C pathway literature. The fourth question is only touched upon here and is addressed in greater depth in Chapter 4, Section 4.3.7, which assesses the rapidly growing literature on costs, potentials, availability and sustainability implications of individual CDR measures (Minx et al., 2017, 2018; Fuss et al., 2018; Nemet et al., 2018) <sup>[[#fn:r252|252]]</sup> . In addition, Section 2.3.5 assesses the relationship between delayed mitigation action and increased CDR reliance. CDR deployment is intricately linked to the land-use transformation in 1.5°C-consistent pathways. This transformation is assessed in Section 2.4.4. Bioenergy and BECCS impacts on sustainable land management are further assessed in Chapter 3, Section 3.6.2 and Cross-Chapter Box 7 in Chapter 3. Ultimately, a comprehensive assessment of the land implication of land-based CDR measures will be provided in the IPCC AR6 Special Report on Climate Change and Land (SRCCL).

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

<span id="cdr-technologies-and-deployment-levels-in-1.5c-pathways"></span>
==== 2.3.4.1 CDR technologies and deployment levels in 1.5°C pathways ====

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

A number of approaches to actively remove carbon-dioxide from the atmosphere are increasingly discussed in the literature (Minx et al., 2018) <sup>[[#fn:r253|253]]</sup> (see also Chapter 4, Section 4.3.7). Approaches under consideration include the enhancement of terrestrial and coastal carbon storage in plants and soils such as afforestation and reforestation (Canadell and Raupach, 2008) <sup>[[#fn:r254|254]]</sup> , soil carbon enhancement (Paustian et al., 2016; Frank et al., 2017; Zomer et al., 2017) <sup>[[#fn:r255|255]]</sup> , and other conservation, restoration, and management options for natural and managed land (Griscom et al., 2017) <sup>[[#fn:r256|256]]</sup> and coastal ecosystems (McLeod et al., 2011) <sup>[[#fn:r257|257]]</sup> . Biochar sequestration (Woolf et al., 2010; Smith, 2016; Werner et al., 2018) <sup>[[#fn:r258|258]]</sup> provides an additional route for terrestrial carbon storage. Other approaches are concerned with storing atmospheric carbon dioxide in geological formations. They include the combination of biomass use for energy production with carbon capture and storage (BECCS) (Obersteiner et al., 2001; Keith and Rhodes, 2002; Gough and Upham, 2011) <sup>[[#fn:r259|259]]</sup> and direct air capture with storage (DACCS) using chemical solvents and sorbents (Zeman and Lackner, 2004; Keith et al., 2006; Socolow et al., 2011) <sup>[[#fn:r260|260]]</sup> . Further approaches investigate the mineralization of atmospheric carbon dioxide (Mazzotti et al., 2005; Matter et al., 2016) <sup>[[#fn:r261|261]]</sup> , including enhanced weathering of rocks (Schuiling and Krijgsman, 2006; Hartmann et al., 2013; Strefler et al., 2018a) <sup>[[#fn:r262|262]]</sup> . A fourth group of approaches is concerned with the sequestration of carbon dioxide in the oceans, for example by means of ocean alkalinization (Kheshgi, 1995; Rau, 2011; Ilyina et al., 2013; Lenton et al., 2018) <sup>[[#fn:r263|263]]</sup> . The costs, CDR potential and environmental side effects of several of these measures are increasingly investigated and compared in the literature, but large uncertainties remain, in particular concerning the feasibility and impact of large-scale deployment of CDR measures (The Royal Society, 2009; Smith et al., 2015; Psarras et al., 2017; Fuss et al., 2018) <sup>[[#fn:r264|264]]</sup> (see Chapter 4.3.7). There are also proposals to remove methane, nitrous oxide and halocarbons via photocatalysis from the atmosphere (Boucher and Folberth, 2010; de Richter et al., 2017) <sup>[[#fn:r265|265]]</sup> , but a broader assessment of their effectiveness, cost and sustainability impacts is lacking to date.

Only some of these approaches have so far been considered in IAMs (see Section 2.3.1.2). The mitigation scenario literature up to AR5 mostly included BECCS and, to a more limited extent, afforestation and reforestation (Clarke et al., 2014) <sup>[[#fn:r266|266]]</sup> . Since then, some 2°C- and 1.5°C-consistent pathways including additional CDR measures such as DACCS (Chen and Tavoni, 2013; Marcucci et al., 2017; Lehtilä and Koljonen, 2018; Strefler et al., 2018b) <sup>[[#fn:r267|267]]</sup> and soil carbon sequestration (Frank et al., 2017) <sup>[[#fn:r268|268]]</sup> have become available. Other, more speculative approaches, in particular ocean-based CDR and removal of non-CO <sub>2</sub> gases, have not yet been taken up by the literature on mitigation pathways. See Supplementary Material 2.SM.1.2 for an overview on the coverage of CDR measures in models which contributed pathways to this assessment. Chapter 4.3.7 assesses the potential, costs, and sustainability implications of the full range of CDR measures.

Integrated assessment modelling has not yet explored land conservation, restoration and management options to remove carbon dioxide from the atmosphere in sufficient depth, despite land management having a potentially considerable impact on the terrestrial carbon stock (Erb et al., 2018) <sup>[[#fn:r269|269]]</sup> . Moreover, associated CDR measures have low technological requirements, and come with potential environmental and social co-benefits (Griscom et al., 2017) <sup>[[#fn:r270|270]]</sup> . Despite the evolving capabilities of IAMs in accounting for a wider range of CDR measures, 1.5°C-consistent pathways assessed here continue to predominantly rely on BECCS and afforestation/reforestation (see Supplementary Material 2.SM.1.2). However, IAMs with spatially explicit land-use modelling include a full accounting of land-use change emissions comprising carbon stored in the terrestrial biosphere and soils. Net CDR in the AFOLU sector, including but not restricted to afforestation and reforestation, can thus in principle be inferred by comparing AFOLU CO <sub>2</sub> emissions between a baseline scenario and a 1.5°C-consistent pathway from the same model and study. However, baseline AFOLU CO <sub>2</sub> emissions can not only be reduced by CDR in the AFOLU sector but also by measures to reduce deforestation and preserve land carbon stocks. The pathway literature and pathway data available to this assessment do not yet allow separating the two contributions. As a conservative approximation, the additional net negative AFOLU CO <sub>2</sub> emissions below the baseline are taken as a proxy for AFOLU CDR in this assessment. Because this does not include CDR that was deployed before reaching net zero AFOLU CO <sub>2</sub> emissions, this approximation is a lower-bound for terrestrial CDR in the AFOLU sector (including all mitigation-policy-related factors that lead to net negative AFOLU CO <sub>2</sub> emissions).

The scale and type of CDR deployment in 1.5°C-consistent pathways varies widely (Figure 2.9 and 2.10). Overall CDR deployment over the 21st century is substantial in most of the pathways, and deployment levels cover a wide range, on the order of 100–1000 Gt CO <sub>2</sub> in 1.5°C pathways with no or limited overshoot (730 [260–1030] GtCO <sub>2</sub> , for median and 5th–95th percentile range). Both BECCS (480 [0–1000] GtCO <sub>2</sub> in 1.5°C pathways with no or limited overshoot) and AFOLU CDR measures including afforestation and reforestation (210 [10-540] GtCO <sub>2</sub> in1.5°C pathways with no or limited overshoot) can play a major role, <sup>[[#fn:4|4]]</sup> but for both cases pathways exist where they play no role at all. This shows the flexibility in substituting between individual CDR measures, once a portfolio of options becomes available. The high end of the CDR deployment range is populated by high overshoot pathways, as illustrated by pathway archetype ''S5'' based on SSP5 (fossil-fuelled development, see Section 2.3.1.1) and characterized by very large BECCS deployment to return warming to 1.5°C by 2100 (Kriegler et al., 2017) <sup>[[#fn:r271|271]]</sup> . In contrast, the low end is populated by a few pathways with no or limited overshoot that limit CDR to on the order of 100–200 GtCO <sub>2</sub> over the 21st century, coming entirely from terrestrial CDR measures with no or small use of BECCS. These are pathways with very low energy demand facilitating the rapid phase-out of fossil fuels and process emissions that exclude BECCS and CCS use (Grubler et al., 2018) <sup>[[#fn:r272|272]]</sup> and/or pathways with rapid shifts to sustainable food consumption freeing up sufficient land areas for afforestation and reforestation (Haberl et al., 2011; van Vuuren et al., 2018) <sup>[[#fn:r273|273]]</sup> . Some pathways use neither BECCS nor afforestation but still rely on CDR through considerable net negative CO <sub>2</sub> emissions in the AFOLU sector around mid-century (Holz et al., 2018b) <sup>[[#fn:r274|274]]</sup> . We conclude that the role of BECCS as a dominant CDR measure in deep mitigation pathways has been reduced since the time of the AR5. This is related to three factors: a larger variation of underlying assumptions about socio-economic drivers (Riahi et al., 2017; Rogelj et al., 2018) <sup>[[#fn:r275|275]]</sup> and associated energy (Grubler et al., 2018) <sup>[[#fn:r276|276]]</sup> and food demand (van Vuuren et al., 2018) <sup>[[#fn:r277|277]]</sup> ; the incorporation of a larger portfolio of mitigation and CDR options (Marcucci et al., 2017; Grubler et al., 2018; Lehtilä and Koljonen, 2018; Liu et al., 2018; van Vuuren et al., 2018) <sup>[[#fn:r278|278]]</sup> ; and targeted analysis of deployment limits for (specific) CDR measures (Holz et al., 2018b; Kriegler et al., 2018a; Strefler et al., 2018b) <sup>[[#fn:r279|279]]</sup> , including the availability of bioenergy (Bauer et al., 2018) <sup>[[#fn:r280|280]]</sup> , CCS (Krey et al., 2014a; Grubler et al., 2018) <sup>[[#fn:r281|281]]</sup> and afforestation (Popp et al., 2014b, 2017) <sup>[[#fn:r282|282]]</sup> . As additional CDR measures are being built into IAMs, the prevalence of BECCS is expected to be further reduced.

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

<span id="figure-2.9"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.9'''

<span id="section-6"></span>
<!-- IMG CAPTION -->
Cumulative CDR deployment in 1.5°C-consistent pathways in the literature as reported in the database collected for this assessment until 2050 (panel a) and until 2100 (panel b).
<!-- IMG FILE -->
[[File:8cc70c499928e076bd802a762902bc5a Figure-2.9-1024x729.jpg]]

Total CDR comprises all forms of CDR, including AFOLU CDR and BECCS, and, in a few pathways, other CDR measures like DACCS. It does not include CCS combined with fossil fuels (which is not a CDR technology as it does not result in active removal of CO <sub>2</sub> from the atmosphere). AFOLU CDR has not been reported directly and is hence represented by means of a proxy: the additional amount of net negative CO <sub>2</sub> emissions in the AFOLU sector compared to a baseline scenario (see text for a discussion). ‘Compensatory CO <sub>2</sub> ’ depicts the cumulative amount of CDR that is used to neutralize concurrent residual CO <sub>2</sub> emissions. ‘Net negative CO <sub>2</sub> ’ describes the additional amount of CDR that is used to produce net negative CO <sub>2</sub> emissions, once residual CO <sub>2</sub> emissions are neutralized. The two quantities add up to total CDR for individual pathways (not for percentiles and medians, see Footnote 4).

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

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

As discussed in Section 2.3.2, CDR can be used in two ways in mitigation pathways: (i) to move more rapidly towards the point of carbon neutrality and maintain it afterwards in order to stabilize global mean temperature rise, and (ii) to produce net negative CO <sub>2</sub> emissions, drawing down anthropogenic CO <sub>2</sub> in the atmosphere in order to decline global mean temperature after an overshoot peak (Kriegler et al., 2018b; Obersteiner et al., 2018) <sup>[[#fn:r283|283]]</sup> . Both uses are important in 1.5°C-consistent pathways (Figure 2.9 and 2.10). Because of the tighter remaining 1.5°C carbon budget, and because many pathways in the literature do not restrict exceeding this budget prior to 2100, the relative weight of the net negative emissions component of CDR increases compared to 2°C-consistent pathways. The amount of compensatory CDR remains roughly the same over the century. This is the net effect of stronger deployment of compensatory CDR until mid-century to accelerate the approach to carbon neutrality and less compensatory CDR in the second half of the century due to deeper mitigation of end-use sectors in 1.5°C-consistent pathways (Luderer et al., 2018) <sup>[[#fn:r284|284]]</sup> . Comparing median levels, end-of-century net cumulative CO <sub>2</sub> emissions are roughly 600 GtCO <sub>2</sub> smaller in 1.5°C compared to 2°C-consistent pathways, with approximately two thirds coming from further reductions of gross CO <sub>2</sub> emissions and the remaining third from increased CDR deployment. As a result, median levels of total CDR deployment in 1.5°C-consistent pathways are larger than in 2°C-consistent pathways (Figure 2.9), but with marked variations in each pathway class.

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

<span id="figure-2.10"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 2.10'''

<span id="section-7"></span>
<!-- IMG CAPTION -->
Accounting of cumulative CO <sub>2</sub>  emissions for the four 1.5°C-consistent pathway archetypes.
<!-- IMG FILE -->
[[File:23aa679f187a7ec52e7db9c53ef48d4d figure-2.10-1024x993.jpg]]

See top panel for explanation of the bar plots. Total CDR is the difference between gross (red horizontal bar) and net (purple horizontal bar) cumulative CO <sub>2</sub> emissions over the period 2018–2100, and it is equal to the sum of the BECCS (grey) and AFOLU CDR (green) contributions. Cumulative net negative emissions are the difference between peak (orange horizontal bar) and net (purple) cumulative CO <sub>2</sub> emissions. The blue shaded area depicts the estimated range of the remaining carbon budget for a two-in-three to one-in-two chance of staying below1.5°C. The grey shaded area depicts the range when accounting for additional Earth system feedbacks.

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

<!-- END IMG -->
<div id="section-2-3-4-1-block-5"></div>

Ramp-up rates of individual CDR measures in 1.5°C-consistent pathways are provided in Table 2.4. BECCS deployment is still limited in 2030, but ramps up to median levels of 3 (Below-1.5°C), 5 (1.5°C-low-OS) and 7 GtCO <sub>2</sub> yr <sup>−1</sup> (1.5°C-high-OS) in 2050, and to 6 (Below-1.5°C), 12 (1.5°C-low-OS) and 15 GtCO <sub>2</sub> yr <sup>−1</sup> (1.5°C-high-OS) in 2100, respectively. In 1.5°C pathways with no or limited overshoot, this amounts to 0–1, 0–8, and 0–16 GtCO <sub>2</sub> yr <sup>−</sup> <sup>1</sup> in 2030, 2050, and 2100, respectively (ranges refer to the union of the min-max range of the Below-1.5°C and the interquartile range of the 1.5°C-low-OS class; see Table 2.4). Net CDR in the AFOLU sector reaches slightly lower levels in 2050, and stays more constant until 2100. In 1.5°C pathways with no or limited overshoot, AFOLU CDR amounts to 0–5, 1–11, and 1–5 GtCO2 yr <sup>−</sup> <sup>1</sup> (see above for the definition of the ranges) in 2030, 2050, and 2100, respectively. In contrast to BECCS, AFOLU CDR is more strongly deployed in non-overshoot than overshoot pathways. This indicates differences in the timing of the two CDR approaches. Afforestation is scaled up until around mid-century, when the time of carbon neutrality is reached in 1.5°C-consistent pathways, while BECCS is projected to be used predominantly in the 2nd half of the century (Figure 2.5). This reflects the fact that afforestation is a readily available CDR technology, while BECCS is more costly and much less mature a technology. As a result, the two options contribute differently to compensating concurrent CO <sub>2</sub> emissions (until 2050) and to producing net negative CO <sub>2</sub> emissions (post-2050). BECCS deployment is particularly strong in pathways with high overshoots but can also feature in pathways with low overshoot (see Figure 2.5 and 2.10). Annual deployment levels until mid-century are not found to be significantly different between 2°C-consistent pathways and 1.5°C-consistent pathways with no or low overshoot. This suggests similar implementation challenges for ramping up BECCS deployment at the rates projected in the pathways (Honegger and Reiner, 2018; Nemet et al., 2018) <sup>[[#fn:r285|285]]</sup> . The feasibility and sustainability of upscaling CDR at these rates is assessed in Chapter 4.3.7.

Concerns have been raised that building expectations about large-scale CDR deployment in the future can lead to an actual reduction of near-term mitigation efforts (Geden, 2015; Anderson and Peters, 2016; Dooley and Kartha, 2018) <sup>[[#fn:r286|286]]</sup> . The pathway literature confirms that CDR availability influences the shape of mitigation pathways critically (Krey et al., 2014a; Holz et al., 2018b; Kriegler et al., 2018a; Strefler et al., 2018b) <sup>[[#fn:r287|287]]</sup> . Deeper near-term emissions reductions are required to reach the 1.5°C–2°C target range if CDR availability is constrained. As a result, the least-cost benchmark pathways to derive GHG emissions gap estimates (UNEP, 2017) <sup>[[#fn:r288|288]]</sup> are dependent on assumptions about CDR availability. Using GHG benchmarks in climate policy makes implicit assumptions about CDR availability (Fuss et al., 2014; van Vuuren et al., 2017a) <sup>[[#fn:r289|289]]</sup> . At the same time, the literature also shows that rapid and stringent mitigation as well as large-scale CDR deployment occur simultaneously in 1.5°C pathways due to the tight remaining carbon budget (Luderer et al., 2018) <sup>[[#fn:r290|290]]</sup> . Thus, an emissions gap is identified even for high CDR availability (Strefler et al., 2018b) <sup>[[#fn:r291|291]]</sup> , contradicting a wait-and-see approach. There are significant trade-offs between near-term action, overshoot and reliance on CDR deployment in the long-term which are assessed in Section 2.3.5.

<div id="section-2-3-4-1-block-6" class="box"></div>

<span id="box-2.1-bioenergy-and-beccs-deployment-in-integrated-assessment-modelling"></span>