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

=== 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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==== 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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==== 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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