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

== 3.2 Which Mitigation Pathways are Compatible With Long-term Goals? ==

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<span id="scenario-and-emission-pathways"></span>
=== 3.2.1 Scenario and Emission Pathways ===

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Scenario and emission pathways are used to explore possible long-term trajectories, the effectiveness of possible mitigation strategies, and to help understand key uncertainties about the future. A '''scenario''' is an integrated description of a possible future of the human–environment system (Clarke et al. 2014), and could be a qualitative narrative, quantitative projection, or both. Scenarios typically capture interactions and processes that change key driving forces such as population, GDP, technology, lifestyles, and policy, and the consequences on energy use, land use, and emissions. Scenarios are not predictions or forecasts. An emission pathway is a modelled trajectory of anthropogenic emissions ( [[#Rogelj--2018|Rogelj et al. 2018]] a) and, therefore, a part of a scenario.

There is no unique or preferred method to develop scenarios, and future pathways can be developed from diverse methods, depending on user needs and research questions ( [[#Turnheim--2015|Turnheim et al. 2015]] ; [[#Trutnevyte--2019a|Trutnevyte et al. 2019a]] ; [[#Hirt--2020|Hirt et al. 2020]] ). The most comprehensive scenarios in the literature are qualitative narratives that are translated into quantitative pathways using models (Clarke et al. 2014; [[#Rogelj--2018|Rogelj et al. 2018]] a). Schematic or illustrative pathways can also be used to communicate specific features of more complex scenarios ( [[#Allen--2018|Allen et al. 2018]] ). Simplified models can be used to explain the mechanisms operating in more complex models (e.g., [[#Emmerling--2019|Emmerling et al. 2019]] ). Ultimately, a diversity of scenario and modelling approaches can lead to more robust findings ( [[#Schinko--2017|Schinko et al. 2017]] ; [[#Gambhir--2019|Gambhir et al. 2019]] ).

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<span id="reference-scenarios"></span>
==== 3.2.1.1 Reference Scenarios ====

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It is common to define a reference scenario (also called a baseline scenario). Depending on the research question, a reference scenario could be defined in different ways ( [[#Grant--2020|Grant et al. 2020]] ): (i) a hypothetical world with no climate policies or climate impacts ( [[#Kriegler--2014b|Kriegler et al. 2014b]] ), (ii) assuming current policies or pledged policies are implemented ( [[#Roelfsema--2020|Roelfsema et al. 2020]] ), or (iii) a mitigation scenario to compare sensitivity with other mitigation scenarios ( [[#Kriegler--2014a|Kriegler et al. 2014a]] ; [[#Sognnaes--2021|Sognnaes et al. 2021]] ).

No-climate-policy reference scenarios have often been compared with mitigation scenarios (Clarke et al. 2014). A no-climate-policy scenario assumes that no future climate policies are implemented, beyond what is in the model calibration, effectively implying that the carbon price is zero. No-climate-policy reference scenarios have a broad range depending on socio-economic assumptions and model characteristics, and consequently are important when assessing mitigation costs ( [[#Riahi--2017|Riahi et al. 2017]] ; [[#Rogelj--2018|Rogelj et al. 2018]] b). As countries move forward with climate policies of varying stringency, no-climate-policy baselines are becoming increasingly hypothetical ( [[#Hausfather--2020|Hausfather and Peters 2020]] ). Studies clearly show current policies are having an effect, particularly when combined with the declining costs of low-carbon technologies ( [[#IEA--2020a|IEA 2020a]] ; [[#Roelfsema--2020|Roelfsema et al. 2020]] ; [[#Sognnaes--2021|Sognnaes et al. 2021]] ; [[#UNEP--2020|UNEP 2020]] ), and, consequently, realised trajectories begin to differ from earlier no-climate-policy scenarios ( [[#Burgess--2020|Burgess et al. 2020]] ). High-end emission scenarios, such as RCP8.5 and SSP5-8.5, are becoming less likely with climate policy and technology change (Box 3.3), but high-end concentration and warming levels may still be reached with the inclusion of strong carbon or climate feedbacks ( [[#Hausfather--2020|Hausfather and Peters 2020]] ; [[#Pedersen--2020|Pedersen et al. 2020]] ).

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<span id="mitigation-scenarios"></span>
==== 3.2.1.2 Mitigation Scenarios ====

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Mitigation scenarios explore different strategies to meet climate goals and are typically derived from reference scenarios by adding climate or other policies. Mitigation pathways are often developed to meet a predefined level of climate change, often referred to as a backcast. There are relatively few IAMs that include an endogenous climate model or emulator due to the added computational complexity, though exceptions do exist. In practice, models implement climate constraints by either iterating carbon-price assumptions ( [[#Strefler--2021b|Strefler et al. 2021b]] ) or by adopting an associated carbon budget ( [[#Riahi--2021|Riahi et al. 2021]] ). In both cases, other GHGs are typically controlled by CO 2 -equivalent pricing. A large part of the AR5 literature has focused on forcing pathways towards a target at the end of the century ( [[#van%20Vuuren--2007|van Vuuren et al. 2007]] , 2011; [[#Clarke--2009|Clarke et al. 2009]] ; [[#Blanford--2014|Blanford et al. 2014]] ; [[#Riahi--2017|Riahi et al. 2017]] ), featuring a temporary overshoot of the warming and forcing levels ( [[#Geden--2017|Geden and Löschel 2017]] ). In comparison, many recent studies explore mitigation strategies that limit overshoot ( [[#Johansson--2020|Johansson et al. 2020]] ; [[#Riahi--2021|Riahi et al. 2021]] ). An increasing number of IAM studies also explore climate pathways that limit adverse side effects with respect to other societal objectives, such as food security ( [[#van%20Vuuren--2019|van Vuuren et al. 2019]] ; [[#Riahi--2021|Riahi et al. 2021]] ) or larger sets of sustainability objectives ( [[#Soergel--2021a|Soergel et al. 2021a]] ).

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<span id="the-utility-of-integrated-assessment-models"></span>
=== 3.2.2 The Utility of Integrated Assessment Models ===

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Integrated Assessment Models (IAMs) are critical for understanding the implications of long-term climate objectives for the required near-term transition. For doing so, an integrated systems perspective including the representation of all sectors and GHGs is necessary. IAMs are used to explore the response of complex systems in a formal and consistent framework. They cover a broad range of modelling frameworks ( [[#Keppo--2021|Keppo et al. 2021]] ). Given the complexity of the systems under investigation, IAMs necessarily make simplifying assumptions and therefore results need to be interpreted in the context of these assumptions. IAMs can range from economic models that consider only carbon dioxide emissions through to detailed process-based representations of the global energy system, covering separate regions and sectors (such as energy, transport, and land use), all GHG emissions and air pollutants, interactions with land and water, and a reduced representation of the climate system. IAMs are generally driven by economics and can have a variety of characteristics such as partial-, general- or non-equilibrium; myopic or perfect foresight; be based on optimisation or simulation; have exogenous or endogenous technological change amongst many other characteristics. IAMs take as input socio-economic and technical variables and parameters to represent various systems. There is no unique way to integrate this knowledge into a model, and due to their complexity, various simplifications and omissions are made for tractability. IAMs therefore have various advantages and disadvantages which need to be weighed up when interpreting IAM outcomes. Annex III.I contains an overview of the different types of models and their key characteristics.

Most IAMs are necessarily broad as they capture long-term dynamics. IAMs are strong in showing the key characteristics of emission pathways and are most suited to questions related to short- versus long-term trade-offs, key interactions with non-climate objectives, long-term energy and land-use characteristics, and implications of different overarching technological and policy choices (Clarke et al. 2014; [[#Rogelj--2018|Rogelj et al. 2018]] a). While some IAMs have a high level of regional and sectoral detail, for questions that require higher levels of granularity (e.g., local policy implementation) specific region and sector models may be better suited. Utility of the IAM pathways increases when the quantitative results are contextualized through qualitative narratives or other additional types of knowledge to provide deeper insights ( [[#Geels--2016a|Geels et al. 2016a]] ; [[#Weyant--2017|Weyant 2017]] ; [[#Gambhir--2019|Gambhir et al. 2019]] ).

IAMs have a long history in addressing environmental problems, particularly in the IPCC assessment process ( [[#van%20Beek--2020|van Beek et al. 2020]] ). Many policy discussions have been guided by IAM-based quantifications, such as the required emission reduction rates, net zero years, or technology deployment rates required to meet certain climate outcomes. This has led to the discussion about whether IAM scenarios have become performative, meaning that they act upon, transform or bring into being the scenarios they describe ( [[#Beck--2017|Beck and Mahony 2017]] , 2018). Transparency of underlying data and methods is critical for scenario users to understand what drives different scenario results ( [[#Robertson--2020|Robertson 2020]] ). A number of community activities have thus focused on the provision of transparent and publicly accessible databases of both input and output data ( [[#Riahi--2012|Riahi et al. 2012]] ; [[#Huppmann--2018|Huppmann et al. 2018]] ; [[#Krey--2019|Krey et al. 2019]] ; [[#Daioglou--2020|Daioglou et al. 2020]] ), as well as the provision of open-source code, and increased documentation (Annex III.I.9). Transparency is needed to reveal conditionality of results on specific choices in terms of assumptions (e.g., discount rates) and model architecture. More detailed explanations of underlying model dynamics would be critical to increase the understanding of what drives results ( [[#Bistline--2020|Bistline et al. 2020]] ; [[#Butnar--2020|Butnar et al. 2020]] ; [[#Robertson--2020|Robertson 2020]] ).

Mitigation scenarios developed for a long-term climate constraint typically focus on cost-effective mitigation action towards a long-term climate goal. Results from IAM as well as sectoral models depend on model structure ( [[#Mercure--2019|Mercure et al. 2019]] ), economic assumptions ( [[#Emmerling--2019|Emmerling et al. 2019]] ), technology assumptions ( [[#Pye--2018|Pye et al. 2018]] ), climate/emissions target formulation ( [[#Johansson--2020|Johansson et al. 2020]] ), and the extent to which pre-existing market distortions are considered ( [[#Guivarch--2011|Guivarch et al. 2011]] ). The vast majority of IAM pathways do not consider climate impacts ( [[#Schultes--2021|Schultes et al. 2021]] ). Equity hinges upon ethical and normative choices. As most IAM pathways follow the cost-effectiveness approach, they do not make any additional equity assumptions. Notable exceptions include [[#Tavoni--2015|Tavoni et al. (2015)]] , [[#Pan--2017|Pan et al. (2017)]] , [[#van%20den%20Berg--2020|van den Berg et al. (2020)]] , and [[#Bauer--2020|Bauer et al. (2020)]] . Regional IAM results therefore need to be assessed with care, considering that emissions reductions are happening where it is most cost-effective, which needs to be separated from who is ultimately paying for the mitigation costs. Cost-effective pathways can provide a useful benchmark, but may not reflect real-world developments ( [[#Calvin--2014a|Calvin et al. 2014a]] ; [[#Trutnevyte--2016|Trutnevyte 2016]] ). Different modelling frameworks may lead to different outcomes ( [[#Mercure--2019|Mercure et al. 2019]] ). Recent studies have shown that other desirable outcomes can evolve with only minor deviations from cost-effective pathways ( [[#Bauer--2020|Bauer et al. 2020]] ; [[#Neumann--2021|Neumann and Brown 2021]] ). IAM and sectoral models represent social, political, and institutional factors only in a rudimentary way. This assessment is thus relying on new methods for the ''ex'' ''post'' assessment of feasibility concerns ( [[#Jewell--2020|Jewell and Cherp 2020]] ; [[#Brutschin--2021|Brutschin et al. 2021]] ). A literature is emerging that recognises and reflects on the diversity and strengths/weaknesses of model-based scenario analysis ( [[#Keppo--2021|Keppo et al. 2021]] ).

The climate constraint implementation can have a meaningful impact on model results. The literature so far includes many temperature overshoot scenarios with heavy reliance on long-term CDR and net negative CO 2 emissions to bring back temperatures after the peak ( [[#Rogelj--2019b|Rogelj et al. 2019b]] ; [[#Johansson--2020|Johansson et al. 2020]] ). New approaches have been developed to avoid temperature overshoot. The new generation of scenarios show that CDR is important beyond its ability to reduce temperature, but is essential also for offsetting residual emissions to reach net zero CO 2 emissions ( [[#Rogelj--2019b|Rogelj et al. 2019b]] ; [[#Johansson--2020|Johansson et al. 2020]] ; [[#Riahi--2021|Riahi et al. 2021]] ; [[#Strefler--2021b|Strefler et al. 2021b]] ).

Many factors influence the deployment of technologies in the IAMs. Since AR5, there has been fervent debate on the large-scale deployment of bioenergy with carbon capture and storage (BECCS) in scenarios ( [[#Fuss--2014|Fuss et al. 2014]] ; [[#Geden--2015|Geden 2015]] ; [[#Anderson--2016|Anderson and Peters 2016]] ; [[#Smith--2016|Smith et al. 2016]] ; [[#van%20Vuuren--2017|van Vuuren et al. 2017]] ; [[#Galik--2020|Galik 2020]] ; [[#Köberle--2019|Köberle 2019]] ). Hence, many recent studies explore mitigation pathways with limited BECCS deployment ( [[#Grubler--2018|Grubler et al. 2018]] ; [[#van%20Vuuren--2019|van Vuuren et al. 2019]] ; [[#Riahi--2021|Riahi et al. 2021]] ; [[#Soergel--2021a|Soergel et al. 2021a]] ). While some have argued that technology diffusion in IAMs occurs too rapidly ( [[#Gambhir--2019|Gambhir et al. 2019]] ), others argued that most models prefer large-scale solutions resulting in a relatively slow phase-out of fossil fuels ( [[#Carton--2019|Carton 2019]] ). While IAMs are particularly strong on supply-side representation, demand-side measures still lag in detail of representation despite progress since AR5 ( [[#Grubler--2018|Grubler et al. 2018]] ; [[#Lovins--2019|Lovins et al. 2019]] ; [[#van%20den%20Berg--2019|van den Berg et al. 2019]] ; [[#O’Neill--2020b|O’Neill et al. 2020b]] ; [[#Hickel--2021|Hickel et al. 2021]] ; [[#Keyßer--2021|Keyßer and Lenzen 2021]] ). The discount rate has a significant impact on the balance between near-term and long-term mitigation. Lower discount rates &lt;4% (than used in IAMs) may lead to more near-term emissions reductions – depending on the stringency of the target ( [[#Emmerling--2019|Emmerling et al. 2019]] ; [[#Riahi--2021|Riahi et al. 2021]] ). Models often use simplified policy assumptions ( [[#O’Neill--2020b|O’Neill et al. 2020b]] ) which can affect the deployment of technologies ( [[#Sognnaes--2021|Sognnaes et al. 2021]] ). Uncertainty in technologies can lead to more or less short-term mitigation ( [[#Grant--2021|Grant et al. 2021]] ; [[#Bednar--2021|Bednar et al. 2021]] ). There is also a recognition to put more emphasis on what drives the results of different IAMs ( [[#Gambhir--2019|Gambhir et al. 2019]] ) and suggestions to focus more on what is driving differences in result across IAMs ( [[#Nikas--2021|Nikas et al. 2021]] ). As noted by Weyant (2017, p. 131), ‘IAms can provide very useful information, but this information needs to be carefully interpreted and integrated with other quantitative and qualitative inputs in the decision-making process.’

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<span id="the-scenario-literature-and-scenario-databases"></span>
=== 3.2.3 The Scenario Literature and Scenario Databases ===

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IPCC reports have often used voluntary submissions to a scenario database in its assessments. The database is an ensemble of opportunity, as there is not a well-designed statistical sampling of the hypothetical model or scenario space: the literature is unlikely to cover all possible models and scenarios, and not all scenarios in the literature are submitted to the database. Model intercomparisons are often the core of scenario databases assessed by the IPCC ( [[#Cointe--2019|Cointe et al. 2019]] ; [[#Nikas--2021|Nikas et al. 2021]] ). Single-model studies may allow more detailed sensitivity analyses or address specific research questions. The scenarios that are organised within the scientific community are more likely to enter the assessment process via the scenario database ( [[#Cointe--2019|Cointe et al. 2019]] ), while scenarios from different communities, in the emerging literature, or not structurally consistent with the database may be overlooked. Scenarios in the grey literature may not be assessed even though they may have greater weight in a policy context.

One notable development since AR5 is the Shared Socio-economic Pathways (SSPs), conceptually outlined in [[#Moss--2010|Moss et al. (2010)]] and subsequently developed to support integrated climate research across the IPCC Working Groups ( [[#O’Neill--2014|O’Neill et al. 2014]] ). Initially, a set of SSP narratives were developed, describing worlds with different challenges to mitigation and adaptation ( [[#O’Neill--2017a|O’Neill et al. 2017a]] ): SSP1 (sustainability), SSP2 (middle of the road), SSP3 (regional rivalry), SSP4 (inequality) and SSP5 (rapid growth). The SSPs have now been quantified in terms of energy, land-use, and emission pathways ( [[#Riahi--2017|Riahi et al. 2017]] ), for both no-climate-policy reference scenarios and mitigation scenarios that follow similar radiative-forcing pathways as the Representative Concentration Pathways (RCPs) assessed in AR5 WGI. Since then the SSPs have been successfully applied in thousands of studies ( [[#O’Neill--2020b|O’Neill et al. 2020b]] ) including some critiques on the use and application of the SSP framework ( [[#Pielke--2021|Pielke and Ritchie 2021]] ; [[#Rosen--2021|Rosen 2021]] ). A selection of the quantified SSPs are used prominently in AR6 WGI as they were the basis for most climate modelling since AR5 ( [[#O’Neill--2016|O’Neill et al. 2016]] ). Since 2014, when the first set of SSP data was made available, there has been a divergence between scenario and historic trends ( [[#Burgess--2020|Burgess et al. 2020]] ). As a result, the SSPs require updating ( [[#O’Neill--2020b|O’Neill et al. 2020b]] ). Most of the scenarios in the AR6 database are SSP-based and consider various updates compared to the first release ( [[#Riahi--2017|Riahi et al. 2017]] ).

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<span id="the-ar6-scenario-database"></span>
=== 3.2.4 The AR6 Scenario Database ===

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To facilitate this assessment, a large ensemble of scenarios has been collected and made available through an interactive AR6 WGIII scenario database. The collection of the scenario outputs is coordinated by [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-3 Chapter 3] and expands upon the IPCC SR1.5 scenario explorer ( [[#Huppmann--2018|Huppmann et al. 2018]] ; [[#Rogelj--2018|Rogelj et al. 2018]] a). A complementary database for national pathways has been established by Chapter 4. Annex III.II.3 contains full details on how the scenario database was compiled.

The AR6 scenario database contains 3131 scenarios (Figure 3.5a). After an initial screening and quality control, scenarios were further vetted to assess if they sufficiently represented historical trends (Annex III.II.3.1). Of the initial 2266 scenarios with global scope, 1686 scenarios passed the vetting process and are assessed in this chapter. The scenarios that did not pass the vetting are still available in the database. The vetted scenarios were from over 50 different model families, or over 100 when considering all versions of the same family (Figure 3.1). The scenarios originated from over 15 different model intercomparison projects, with around one-fifth originating from individual studies (Figure 3.2). Because of the uneven distribution of scenarios from different models and projects, uncorrected statistics from the database can be misleading.

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[[File:faf24d48376532da5a55e9db29dc4c3a IPCC_AR6_WGIII_Figure_3_1.png]]

'''Figure 3.1 | Scenario counts from each model family defined as all versions under the same model’s name.'''

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[[File:194151b8847735652026edc899023068 IPCC_AR6_WGIII_Figure_3_2.png]]

'''Figure 3.2 | Scenario counts from each named project.'''

Each scenario with sufficient data is given a temperature classification using climate model emulators. Three emulators were used in the assessment: FAIR ( [[#Smith--2018|Smith et al. 2018]] ), CICERO-SCM ( [[#Skeie--2021|Skeie et al. 2021]] ), MAGICC ( [[#Meinshausen--2020|Meinshausen et al. 2020]] ). Only the

'''Table 3.1 | Classification of emissions scenarios into warming levels using MAGICC'''

{| class="wikitable"
|-
| '''Category'''

| '''Description'''

| '''WGI SSP'''

| '''WGIII IP/IMP'''

| '''Scenarios'''

|-
| '''C1: Limit warming to 1.5°C (&gt;50%) with no or limited overshoot'''

| Reach or exceed 1.5°C during the 21st century with a likelihood of ≤67%, and limit warming to 1.5°C in 2100 with a likelihood &gt;50%.

Limited overshoot refers to exceeding 1.5°C by up to about 0.1°C and for up to several decades.

| SSP1-1.9

| IMP-SP, IMP-LD, IMP-Ren

| '''97'''

|-
| '''C2: Return warming to 1.5°C (&gt;50%) after a high overshoot'''

| Exceed warming of 1.5°C during the 21st century with a likelihood of &gt;67%, and limit warming to 1.5°C in 2100 with a likelihood of &gt;50%.

High overshoot refers to temporarily exceeding 1.5°C global warming by 0.1°C–0.3°C for up to several decades.

|
| IMP-Neg a

| '''133'''

|-
| '''C3:''' '''Limit warming to 2°C (&gt;67%)'''

| Limit peak warming to 2°C throughout the 21st century with a likelihood of &gt;67%.

| SSP1-2.6

| IMP-GS

| '''311'''

|-
| '''C4: Limit warming to 2°C (&gt;50%)'''

| Limit peak warming to 2°C throughout the 21st century with a likelihood of &gt;50%.

|
| '''159'''

|-
| '''C5: Limit warming to 2.5°C (&gt;50%)'''

| Limit peak warming to 2.5°C throughout the 21st century with a likelihood of &gt;50%.

|
| '''212'''

|-
| '''C6: Limit warming to 3°C (&gt;50%)'''

| Limit peak warming to 3°C throughout the 21st century with a likelihood of &gt;50%.

| SSP2-4.5

| ModAct

| '''97'''

|-
| '''C7: Limit warming to 4°C (&gt;50%)'''

| Limit peak warming to 4°C throughout the 21st century with a likelihood of &gt;50%.

| SSP3-7.0

| CurPol

| '''164'''

|-
| '''C8: Exceed warming of 4°C (≥50%)'''

| Exceed warming of 4°C during the 21st century with a likelihood of ≥50%.

| SSP5-8.5

|
| '''29'''

|-
| '''C1, C2, C3: limit warming to 2°C (&gt;67%) or lower'''

| All scenarios in Categories C1, C2 and C3

|
| '''541'''

|}

a The Illustrative Mitigation Pathway ‘Neg’ has extensive use of carbon dioxide removal (CDR) in the AFOLU, energy and the industry sectors to achieve net negative emissions. Warming peaks around 2060 and declines to below 1.5°C (50% likelihood) shortly after 2100. Whilst technically classified as C3, it strongly exhibits the characteristics of C2 high-overshoot pathways, hence it has been placed in the C2 category. See Box SPM.1 for an introduction of the IPs and IMPs.

results of MAGICC are shown in this chapter as it adequately covers the range of outcomes. The emulators are calibrated against the behaviour of complex climate models and observation data, consistent with the outcomes of AR6 WGI (Cross-Chapter Box 7.1). The climate assessment is a three-step process of harmonisation, infilling and a probabilistic climate model emulator run (Annex III.II.2.5). Warming projections until the year 2100 were derived for 1574 scenarios, of which 1202 passed vetting, with the remaining scenarios having insufficient information (Figure 3.3 and Table 3.1). For scenarios that limit warming to 2°C or lower, the SR1.5 classification was adopted in AR6, with more disaggregation provided for higher warming levels (Table 3.1). These choices can be compared with the selection of common global warming levels (GWLs) of 1.5°C, 2°C, 3°C and 4°C to classify climate change impacts in the WGII assessment.

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[[File:a9a8821c0cd82cf390f97cb10557f394 IPCC_AR6_WGIII_Figure_3_3.png]]

'''Figure 3.3 | Of the 1686 scenarios that passed vetting, 1202 had sufficient data available to be classified according to temperature, with an uneven distribution across warming levels.'''

In addition to the temperature classification, each scenario is assigned to one of the following policy categories: (P0) diagnostic scenarios – 99 of 1686 vetted scenarios; (P1) scenarios with no globally coordinated policy (500) and (P1a) no climate mitigation efforts – 124, (P1b) current national mitigation efforts – 59, (P1c) Nationally Determined Contributions (NDCs) – 160, or (P1d) other non-standard assumptions – 153; (P2) globally coordinated climate policies with immediate action (634) and (P2a) without any transfer of emission permits – 435, (P2b) with transfers – 70; or (P2c) with additional policy assumptions – 55; (P3) globally coordinated climate policies with delayed (i.e., from 2030 onwards or after 2030) action (451), preceded by (P3a) no mitigation commitment or current national policies – 7, (P3b) NDCs – 426, (P3c) NDCs and additional policies – 18; (P4) cost-benefit analysis (CBA) – 2. The policy categories were identified using text pattern matching on the scenario metadata and calibrated on the best-known scenarios from model intercomparisons, with further validation against the related literature, reported emission and carbon price trajectories, and exchanges with modellers. If the information available is enough to qualify a policy category number but not sufficient for a subcategory, then only the number is retained (e.g., P2 instead of P2a/b/c). A suffix added after P0 further qualifies a diagnostic scenario as one of the other policy categories. To demonstrate the diversity of the scenarios, the vetted scenarios were classified into different categories along the dimensions of population, GDP, energy, and cumulative emissions (Figure 3.4). The number of scenarios in each category provides some insight into the current literature, but this does not indicate a higher probability of that category occurring in reality. For population, the majority of scenarios are consistent with the SSP2 ‘middle of the road’ category, with very few scenarios exploring the outer extremes. GDP has a slightly larger variation, but overall most scenarios are around the SSP2 socio-economic assumptions. The level of CCS and CDR is expected to change depending on the extent of mitigation, but there remains extensive use of both CDR and CCS in scenarios. CDR is dominated by bioenergy with CCS (BECCS) and sequestration on land, with relatively few scenarios using direct air capture with carbon storage (DACCS) and even less with enhanced weathering (EW) and other technologies (not shown). In terms of energy consumption, final energy has a much smaller range than primary energy as conversion losses are not included in final energy. Both mitigation and reference scenarios are shown, so there is a broad spread in different energy carriers represented in the database. Bioenergy has a number of scenarios at around 100 EJ, representing a constraint used in many model intercomparisons.

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[[File:691abbffd152ca36af1e3a1082607b94 IPCC_AR6_WGIII_Figure_3_4.png]]

'''Figure 3.4 | Histograms for key categories in the AR6 scenario database.''' Only scenarios that passed vetting are shown. For population and GDP, the SSP input data are also shown. The grey shading represents the 0–100% range (light grey), 25–75% range (dark grey), and the median is a black line. The figures with white areas are outside of the scenario range, but the axis limits are retained to allow comparability with other categories. Each sub-figure potentially has different x- and y-axis limits. Each figure also potentially contains different numbers of scenarios, depending on what was submitted to the database. Source: AR6 scenarios database.

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<span id="illustrative-mitigation-pathways"></span>
=== 3.2.5 Illustrative Mitigation Pathways ===

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Successive IPCC Assessment Reports (ARs) have used scenarios to illustrate key characteristics of possible climate (policy) futures. In AR5 four RCPs made the basis of climate modelling in WGI and WGII, with WGIII assessing over 1000 scenarios spanning those RCPs (Clarke et al. 2014). Of the over 400 scenarios assessed in SR1.5, four scenarios were selected to highlight the trade-off between short-term emission reductions and long-term deployment of BECCS ( [[#Rogelj--2018|Rogelj et al. 2018]] a), referred to as ‘Illustrative Pathways’ (IPs). AR6 WGI and WGII rely on the scenarios selected for CMIP6, called ScenarioMIP ( [[#O’Neill--2016|O’Neill et al. 2016]] ), to assess warming levels. In addition to the full set of scenarios, AR6 WGIII also uses selected Illustrative Mitigation Pathways (IMPs).

In WGIII, IMPs were selected to denote the implications of different societal choices for the development of future emissions and associated transformations of main GHG-emitting sectors (Figure 3.5a and Box 3.1). The most important function of the IMPs is to illustrate key themes that form a common thread in the report, both with a storyline and a quantitative illustration. The storyline describes the key characteristics that define an IMP. The quantitative versions of the IMPs provide numerical values that are internally consistent and comparable across chapters of the report. The quantitative IMPs have been selected from the AR6 scenario database. No assessment of the likelihood of each IMP has been made.

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'''Figure 3.5 |''' '''(a) Process for creating the AR6 scenario database and selecting the illustrative (mitigation) pathways.''' The compiled scenarios in the AR6 scenarios database were vetted for consistency with historical statistics and subsequently a temperature classification was added using climate model emulators. The illustrative (mitigation) pathways were selected from the full set of pathways based on storylines of critical mitigation strategies that emerged from the assessment. '''(b)''' An overview of the Illustrative Pathways selected for use in IPCC AR6 WGIII, consisting of pathways illustrative of higher emissions, Current Policies ( ''CurPol'' ) and Moderate Action ( ''ModAct'' ), and Illustrative Mitigation Pathways (IMPs): gradual strengthening of current policies ( ''IMP-GS'' ), extensive use of net negative emissions ( ''IMP-Neg'' ), renewables ( ''IMP-Ren'' ), low demand ( ''IMP-LD'' ), and shifting pathways ( ''IMP-SP'' ). The Ren2.0 and Neg2.0 scenarios are alternative scenarios to the IMPs. These pathways are based on renewables and extensive use of negative emissions, respectively, but leading to temperature levels comparable to the C3 category and have sometimes been used for comparison.

The selected scenarios (IPs) are divided into two sets (Figures 3.5 and 3.6): two reference pathways illustrative of high emissions and five Illustrative Mitigation Pathways (IMPs). The narratives are explained in full in Annex III.II.2.4. The two reference pathways explore the consequences of current policies and pledges: Current Policies ( ''CurPol'' ) and Moderate Action ( ''ModAct'' ). The ''CurPol'' pathway explores the consequences of continuing along the path of implemented climate policies in 2020 and only a gradual strengthening after that. The scenario illustrates the outcomes of many scenarios in the literature that project the trend from implemented policies until the end of 2020. The ''ModAct'' pathway explores the impact of implementing the Nationally Determined Contributions (NDCs) as formulated in 2020 and some further strengthening after that. In line with current literature, these two reference pathways lead to an increase in global mean temperature of more than 2°C ( [[#3.3|Section 3.3]] ).

The Illustrative Mitigation Pathways (IMPs) properly explore different pathways consistent with meeting the long-term temperature goals of the Paris Agreement. They represent five different pathways that emerge from the overall assessment. The IMPs differ in terms of their focus, for example, placing greater emphasis on renewables (IMP-Ren), deployment of carbon dioxide removal that results in net negative global GHG emissions (IMP-Neg), and efficient resource use and shifts in consumption patterns, leading to low demand for resources, while ensuring a high level of services (IMP-LD). Other IMPs illustrate the implications of a less rapid introduction of mitigation measures followed by a subsequent gradual strengthening (IMP-GS), and how shifting global pathways towards sustainable development, including by reducing inequality, can lead to mitigation (IMP-SP) In the IMP framework, ''IMP-GS'' is consistent with limiting warming to 2°C (&gt;67%) (C3), ''IMP-Neg'' shows a strategy that also limits warming to 2°C (&gt;67%) but returns to nearly 1.5°C (&gt;50%) by the end of the century (hence indicated as C2*). The other variants that can limit warming to 1.5°C (&gt;50%) (C1) were selected. In addition to these IMPs, sensitivity cases that explore alternative warming levels (C3) for ''IMP-Neg'' and ''IMP-Ren'' are assessed ( ''IMP-Neg-2.0'' and ''IMP-Ren-2.0'' ).

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'''Figure 3.6 | Overview of the net CO''' 2 '''emissions and Kyoto greenhouse gas (GHG) emissions for each Illustrative Mitigation Pathway (IMP).'''

The IMPs are selected to have different mitigation strategies, which can be illustrated looking at the energy system and emission pathways (Figure 3.7 and Figure 3.8). The mitigation strategies show the different options in emission reduction (Figure 3.7). Each panel shows the key characteristics leading to total GHG emissions, consisting of residual (gross) emissions (fossil CO 2 emissions, CO 2 emissions from industrial processes, and non-CO 2 emissions) and removals (net land-use change, bioenergy with carbon capture and storage – BECCS, and direct air carbon capture and storage – DACCS), in addition to avoided emissions through the use of carbon capture and storage on fossil fuels. The ''IMP-Neg'' and ''IMP-GS'' scenarios were shown to illustrate scenarios with a significant role of CDR. The energy supply (Figure 3.8) shows the phase-out of fossil fuels in the ''IMP-LD'' , ''IMP-Ren'' and ''IMP-SP'' cases, but a less substantial decrease in the ''IMP-Neg'' case. The ''IMP-GS'' case needs to make up its slow start by (i) rapid reductions mid-century and (ii) massive reliance on net negative emissions by the end of the century. The ''CurPol'' and ''ModAct'' cases both result in relatively high emissions, showing a slight increase and stabilisation compared to current emissions, respectively.

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'''Figure 3.7 | The residual fossil fuel and industry emissions, carbon dioxide removal (CDR) {LUC, DACCS, BECCS} , and non-CO''' 2 '''emissions (using AR6''' '''GWP-100''' ''') for each of the seven illustrative pathways (IPs).''' Fossil CCS is also shown, though this does not lead to emissions to the atmosphere ( [[#3.2.5|Section 3.2.5]] ).

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'''Figure 3.8 |The energy system in each of the illustrative pathways (IPs).'''

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