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Table of Contents
Annex III: Scenarios and Modelling Methods ..................................................................................... I-1
Preamble .............................................................................................................................................. I-4
Part I.        Modelling methods .............................................................................................................. I-5
   1. Overview of modelling tools........................................................................................................ I-5
   2. Economic frameworks and concepts used in sectoral models and integrated assessment models I-
   3. Energy system modelling ........................................................................................................... I-11
       3.1. Bottom-up models ............................................................................................................... I-11
       3.2. Modelling of energy systems in context of economy ......................................................... I-13
       3.3. Hybrid models ..................................................................................................................... I-13
   4. Building sector models .............................................................................................................. I-14
       4.1. Models purpose, scope and types ........................................................................................ I-14
       4.2. Representation of energy demand and GHG emissions ...................................................... I-14
       4.3. Representation of mitigation options .................................................................................. I-15
       4.4. Representation of climate change impacts .......................................................................... I-15
       4.5. Representation of sustainable development dimensions ..................................................... I-16
       4.6. Models underlying the assessment in Chapter 9 ................................................................. I-18
   5. Transport models ....................................................................................................................... I-22
       5.1. Purpose and scope of models .............................................................................................. I-22
       5.2. Inventory of transportation models included in AR6 .......................................................... I-24
   6. Industry sector models ............................................................................................................... I-24
       6.1. Types of industry sector models ......................................................................................... I-24
       6.2. Representation of demand for industrial products .............................................................. I-24
       6.3. Representation of mitigation options - mitigation options, how their uptake is represented,
       how potentials and costs are represented ................................................................................... I-25
       6.4. Limitations and critical analysis ......................................................................................... I-26
   7. Land use modelling .................................................................................................................... I-27
       7.1. Modelling of land use and land use change ........................................................................ I-27
       7.2. Demand for food, feed, fibre and agricultural trade ............................................................ I-28
       7.3. Treatment of land-based mitigation options ....................................................................... I-28
       7.4. Treatment of environmental and socio-economic impacts of land use ............................... I-29
   8. Reduced complexity climate modelling ..................................................................................... I-29
   9. Integrated assessment modelling ............................................................................................... I-30
       9.1. Types of Integrated Assessment Models............................................................................. I-31
       9.2. Components of integrated assessment models .................................................................... I-32

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       9.3. Representation of nexus issues and sustainable development impacts in IAMs ................. I-33
       9.4. Policy analysis with IAMs .................................................................................................. I-35
       9.5. Limitations of IAMs............................................................................................................ I-37
   10. Key characteristics of models that contributed mitigation scenarios to the assessment .......... I-39
   11. Comparison of mitigation and removal measures represented by models that contributed
   mitigation scenarios to the assessment........................................................................................... I-42
Part II.       Scenarios ........................................................................................................................... II-48
   1. Overview on climate change scenarios ..................................................................................... II-48
       1.1. Purposes of climate change scenarios ................................................................................ II-48
       1.2. Types of climate change mitigation scenarios ................................................................... II-49
       1.3. Scenario framework for climate change research .............................................................. II-52
       1.4. Key design choices and assumptions in mitigation scenarios ............................................ II-54
   2. Use of scenarios in the assessment ........................................................................................... II-57
       2.1. Use of scenario literature and database .............................................................................. II-57
       2.2. Treatment of scenario uncertainty...................................................................................... II-58
       2.3. Feasibility of mitigation scenarios ..................................................................................... II-58
       2.4. Illustrative mitigation pathways ......................................................................................... II-60
       2.5. Scenario approaches to connect WG III with the WG I and WG II assessments .............. II-63
   3. WG III AR6 scenario database ................................................................................................. II-68
       3.1. Process of scenario collection and vetting ......................................................................... II-68
       3.2. Global pathways................................................................................................................. II-70
       3.3. National and regional pathways ......................................................................................... II-81
       3.4. Sector transition pathways ................................................................................................. II-83
References ......................................................................................................................................... II-85

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1    Preamble
2    The use of scenarios and modelling methods are pillars in IPCC WG III Assessment Reports. Past WG
3    III assessment report cycles identified knowledge gaps about the integration of modelling across scales
4    and disciplines, mainly between global integrated assessment modelling methods and bottom-up
5    modelling insights of mitigation responses. The need to improve the transparency of model assumptions
6    and enhance the communication of scenario results was also recognised.
 7   This annex on Scenarios and modelling methods aims to address some of these gaps by detailing the
 8   modelling frameworks applied in the WG III AR6 chapters and disclose scenario assumptions and its
 9   key parameters. It has been explicitly included in the Scoping Meeting Report of the WG III
10   contribution to the AR6 and approved by the IPCC Panel in the 46th Session of the Panel.
11   The annex includes two parts: Part I. on modelling methods summarises methods and tools available to
12   evaluate sectorial, technological and behavioural mitigation responses as well as integrated assessment
13   models (IAMs) for the analysis of “whole system” transformation pathways; Part II on scenarios sets
14   out the portfolio of climate change scenarios and mitigation pathways assessed in the WG III AR6
15   chapters, its underneath principles and interactions with scenario assessments by WG I and WG II.

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1                            Part I.        Modelling methods

3    1. Overview of modelling tools
4    Modelling frameworks vary vastly amongst themselves, and several key characteristics can be used as
5    basis for model classification (Scrieciu et al. 2013; Hardt and O’Neill 2017; Capellán-Pérez et al. 2020;
6    Dodds et al. 2015). Broadly, literature characterises models along three dimensions: (i) level of detail
7    and heterogeneity, (ii) mathematical algorithm concepts and (iii) and temporal and spatial system
8    boundaries (Krey 2014).
 9   Commonly climate mitigation models are referred to as bottom-up and top-down depending upon their
10   degree of detail (van Vuuren et al. 2009). Generally, bottom-up approaches present more systematic
11   individual technological details about a reduced number of mitigation strategies of a specific sector or
12   sub-sector. These models tend to disregard relations between specific sectors/technologies and miss
13   evaluating interactions with the whole system. On the other hand, top-down approaches present a more
14   aggregated and global analysis, in detriment of less detailed technological heterogeneity. They tend to
15   focus on interactions within the whole system, such as market and policy instrument interactions within
16   the global economy systems. Studies using top-down models are more capable of representing
17   economic structural change than adopting technology-explicit decarbonisation strategies (Kriegler et al.
18   2015a; van Vuuren et al. 2009). Integrated Assessment Models (IAMs) typically use a top-down
19   approach to model sectorial mitigation strategies.
20   Although this dichotomic classification has been while mentioned in the literature, since AR5, climate
21   mitigation models have evolved towards a more hybrid approach incorporating attributes of both
22   bottom-up and top-down approaches. This is partly due to different modelling communities having
23   different understandings of these two approaches principles, which can be misleading.
24   One of the most basic aspects of a modelling tool is how it approaches the system modelled from a
25   solution perspective. A broad interpretation of mathematical algorithm concepts classifies models as
26   simulation and optimisation models. Simulation models are based on the evaluation of the dynamic
27   behaviour of a system (Lund et al. 2017). They can be used to determine the performance of a system
28   under alternative options of key parameters in a plausible manner. Most often, simulation models
29   require comprehensive knowledge of each parameter, in order to choose a specific path under several
30   alternatives. On the other hand, optimisation models seek to maximise or minimise a mathematical
31   objective function under a set of constraints (Iqbal et al. 2014; Baños et al. 2011). Most often, the
32   objective function represents the total cost or revenue of a given system or the total welfare of a given
33   society. One major aspect of optimisation models is that the solution in achieved by simultaneously
34   binding a set of constraints, which can be used to represent real life limitation on the system, such as:
35   constraints on flows, resource and technology availability, labour and financial limitations,
36   environmental aspects, and many other characteristics that the model may require (Fazlollahi et al.
37   2012; Cedillos Alvarado et al. 2016; Pfenninger et al. 2014). Specifically, when modelling climate
38   mitigation responses, limiting carbon budgets is often used to represent future temperature level
39   pathways (Gidden et al. 2019; Rogelj et al. 2016; Millar et al. 2017; Peters 2018).
40   Another major distinction amongst modelling tools is related to the solution methodology from a
41   temporal perspective. They can have a perfect foresight intertemporal assumption or a recursive-
42   dynamic assumption. Intertemporal optimisation with perfect foresight is an optimisation method for
43   achieving an overall optimal solution over time. It is based on perfect information on all future states
44   of a system and assumptions (such as technology availability and prices) and, as such, today’s and future
45   decisions are made simultaneously, resulting in a single path of optimal actions that lead to the overall
46   optimal solution (Keppo and Strubegger 2010; Gerbaulet et al. 2019). Such modelling approach can
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1    present an optimal trajectory of the set of actions and policies that would lead to the overall first-best
2    solution. However, real-life decisions are not always based on optimal solutions (Ellenbeck and
3    Lilliestam 2019) and, therefore, solutions from perfect foresight models can be challenging to be
4    implemented by policymakers (Pindyck 2013, 2017). For instance, perfect foresight implies perfect
5    knowledge of the future states of the system, such as future demand on goods and products and
6    availability of production factors and technology.
 7   Recursive-dynamic models, also known as myopic or limited foresight models, make decisions over
 8   sequential periods of time. For each time step, the solution is achieved without information of future
 9   time steps. Therefore, the solution path is a series of solutions in short trajectories that, ultimately, are
10   very unlikely to achieve the overall optimal solution over the whole time period considered (Fuso Nerini
11   et al. 2017). Nonetheless, the solution represents a set of possible and plausible policies and behavioural
12   choices of the agents that could be taken in short-term cycles, without perfect information (Hanna and
13   Gross 2020; Heuberger et al. 2018). In between, some models consider imperfect or adaptive
14   expectations, where economic decisions are based on past, current and imperfectly anticipated future
15   information (Keppo and Strubegger 2010; Löffler et al. 2019; Kriegler et al. 2015a). Modelling tools
16   can also be differentiated by their level of representation of economic agents and sectors: they can have
17   a full representation of all agents of the economy and their interactions with each other (general
18   equilibrium) or focus on a more detailed representation of a subset of economic sector and agents
19   (partial equilibrium) (Babatunde et al. 2017; Cheng et al. 2015; Hanes and Carpenter 2017; Sanchez
20   et al. 2018; Guedes et al. 2019; Pastor et al. 2019) (Annex III, I.2).
21   The most basic aspect to differentiate models is their main objective function, which include the detail
22   at which they represent key sectors, systems and agents. This affects the decision on methodology and
23   other coverage aspects. Several models have been developed for different sectorial representation, such
24   as the energy (Annex III.I.3) buildings (Annex III.I.4), transports (Annex III.I.5), industry (Annex
25   III.I.6) and land use (Annex III.I.7).
26   Modelling exercises vary considerable in terms of key characteristics, including geographical scales,
27   time coverage, environmental variables, technologies portfolio, and socioeconomic assumptions. A
28   detailed comparison of key characteristics of global and national models used in this report is presented
29   in Annex III.I.9.Geographical coverage ranges from sub-national (Cheng et al. 2015; Feijoo et al. 2018;
30   Rajão et al. 2020), national (Vishwanathan et al. 2019; Li et al. 2019; Sugiyama et al. 2019; Schaeffer
31   et al. 2020), regional (Vrontisi et al. 2016; Hanaoka and Masui 2020) and global models (McCollum et
32   al. 2018; Gidden et al. 2018; Kriegler et al. 2018a; Rogelj et al. 2019b; Drouet et al. 2021). Even models
33   with the same geographical coverage can still be significantly different from each other, for instance,
34   due to the number of regions within the model. Models can also have spatially implicit and explicit
35   formulations, which in turn can have different spatial resolution. This distinction is especially important
36   for land use models, which account for changes in land use and agricultural practices (Annex III.7. Land
37   use modelling). The time horizon, time steps and time resolution are major aspects that differ across
38   models. Model horizon can range from short- to long-term, typically reaching from a few years to up
39   until the end of the century (Fujimori et al. 2019b; Rogelj et al. 2019a; Ringkjøb et al. 2020; Gidden et
40   al. 2019). Time resolution is particularly relevant for specific applications, such as power sector models,
41   which have detailed representation of power technologies dispatch and operation (Soria et al. 2016;
42   Abujarad et al. 2017; Guan et al. 2020).
43   Life Cycle Assessment (LCA) is an integrated technique to evaluate the sustainability of a product
44   throughout its life cycle. It quantifies the environmental burdens associated with all stages from the
45   extraction of raw materials, through the production of the product itself, its utilisation, and end-life,
46   either via reuse, recycling or final disposal (Rebitzer et al. 2004; Finnveden et al. 2009; Guinée et al.
47   2011; Curran 2013; Hellweg and Milà i Canals 2014). The environmental impacts covered include all
48   types of loads on the environment through the extraction of natural resources and emission of hazardous

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 1   substances. For this reason, LCA has the flexibility to evaluate an entire product system hence avoiding
 2   sub-optimisation in a single process and identifying the products/processes that result in the least
 3   environmental impact. Thus, it allows for the quantification of possible trade-offs between different
 4   environmental impacts (e.g. eliminating air emissions by increasing non-renewable energy resources)
 5   (Gibon et al. 2017; Nordelöf et al. 2014; Hawkins et al. 2013) and/or from one stage to other (e.g. reuse
 6   or recycling a product to bring it back in at the raw material acquisition phase) (Hertwich and Hammitt
 7   2001a,b). It gives a holistic view of complex systems and reduces the number of parameters for which
 8   decisions have to be taken, while not glossing over technical and economical details. In recent years,
 9   LCA has been widely used in both retrospective and prospective analysis of product chains in various
10   climate mitigation fields, namely comparing existing energy technologies with planned alternatives
11   (Portugal-Pereira et al. 2015; Cetinkaya et al. 2012), product innovation and development (Wender et
12   al. 2014; Sharp and Miller 2016; Portugal-Pereira et al. 2015), certification schemes (Prussi et al. 2021),
13   or supply chain management (Hagelaar 2001; Blass and Corbett 2018).
15   Two different types of LCA approaches can be distinguished: the Attributional Life Cycle Assessment
16   (ALCA) and the Consequential Life Cycle Assessment (CLCA). The Attributional Life Cycle
17   Assessment (ALCA) aims at describing the direct environmental impacts of a product. It typically uses
18   average and historical data to quantify the environmental burden during a products’ life cycle, and it
19   tends to exclude market effects or other indirect effects of the production and consumption of products
20   (Baitz 2017). CLCA, on the other hand, focus on the effects of changes due to product life cycle,
21   including both consequences inside and outside the product life cycle (Earles and Halog 2011). Thus,
22   the system boundaries are generally expanded to represent direct and indirect effects of products’
23   outputs. CLCA tends to describe more complex systems than ALCA that are highly sensitive to data
24   assumptions (Plevin et al. 2014; Weidema et al. 2018; Bamber et al. 2020).
25   Integrated Assessment Model (IAM) are simplified representations of the complex physical and social
26   systems, focusing on the interaction between economy, society and the environment (Annex III.I.9).
27   They represent the coupled energy-economy-land-climate system to varying degrees. In a way, IAM
28   differ themselves in all the topics discussed in this section: significant variation in geographical,
29   sectorial, spatial and time resolution; rely greatly on socioeconomic assumptions; different
30   technological representation; partial or general equilibrium assumptions; differentiated between perfect
31   foresight or recursive-dynamic methodology. The difficulty in fully representing the extent of climate
32   damages in monetary terms may be the most important and challenging limitation of IAMs and it is
33   mostly directed to cost benefit IAMs. However, both categories of IAMs present important limitations
34   (Annex III.I.9).
35   Following this brief synopsis of modelling taxonomies, Section 2. Economic frameworks and concepts
36   used in sectoral models and integrated assessment models details key aspects of economic frameworks
37   and principles used to modelling climate mitigation responses and estimates its costs. Sections I.3, I.4,
38   I.5, I.6, I.7 present key aspects of sectorial modelling approaches in energy systems, buildings,
39   transports, industry, and land use, respectively. Interactions between WG I climate emulators and WG
40   III mitigation models are described in Section I.8 A review of integrated assessment model (IAM)
41   approaches, their components and limitations are present in Section I.9. Sections I.10 and I.11 present
42   comparative tables of key characteristics and measures of national and global models that contributed
43   to the WG III AR6 scenario database.

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1    2. Economic frameworks and concepts used in sectoral models and
2        integrated assessment models
 3   Several types of ‘full economy’ frameworks are used in integrated assessment models. The general
 4   equilibrium framework – often referred to as Computable General Equilibrium (CGE) – represents the
 5   economic interdependencies between multiple sectors and agents, and the interaction between supply
 6   and demand on multiple markets (Robinson et al. 1999). It captures the full circularity of economic
 7   flows through income and demand relationships and feedbacks including the overall balance of
 8   payments. Most CGE approaches used are neoclassical supply-led models with market clearing based
 9   on price adjustment. Representative agents usually minimize production costs or maximize utility under
10   given production and utility function, although optimal behaviours are no a precondition per se. Most
11   CGE models also include assumptions of perfect markets with full employment of factors although
12   market imperfections and underemployment of factors (e.g. unemployment) can be assumed (Babiker
13   and Eckaus 2007; Guivarch et al. 2011). CGE frameworks can either be static or dynamic and represent
14   pathways as a sequence of equilibria in the second case.
15   Macro-econometric frameworks represent similar sectoral interdependence with balance of payments
16   as general equilibrium, and are sometimes considered a subset of it. They differ from standard
17   neoclassical CGE models in the main aspect that economic behaviours are not micro-founded
18   optimizing behaviours but represented by macroeconomic and sectoral functions estimated through
19   econometric techniques (Barker and Scrieciu 2010). In addition, they usually adopt a demand-led post-
20   Keynesian approach where final demand and investment determine supply and not the other way
21   around. Prices also do not instantaneously clear markets and adjust with lag.
22   Macro-economic growth framework are also full economy approaches derived from aggregated
23   growth models. They are based on a single macroeconomic production function combining capital,
24   labour and sometimes energy to produce a generic good for consumption and investment. They are used
25   as the macroeconomic component of cost benefit IAMs (Nordhaus 1993) and some detailed-process
26   IAMs.
27   The disaggregation of economic actors and sectors and the representation of their interaction
28   differ across full economy frameworks. A main distinction is between models based on full Social
29   Accounting Matrix (SAM) and aggregated growth approaches. On the one hand, SAM-based
30   frameworks – CGE and macro-econometric – follow a multi-sectoral approach distinguishing from
31   several to a hundred of different economic sectors or production goods and represent sector specific
32   value-added, final consumption and interindustry intermediary consumption (Robinson 1989). They
33   also represent economic agents (firms, households, public administration, etc.) with specific behaviours
34   and budget constraints. On the other hand, macro-economic growth frameworks are reduced to a single
35   macro-economic agent producing, consuming and investing a single macroeconomic good without
36   considering interindustry relationships. In some detailed process IAMs, the aggregated growth approach
37   is combined with a detailed representation of energy supply and demand systems that surmises different
38   economic actors and subsectors. However, the energy system is driven by an aggregated growth engine
39   (Bauer et al. 2008).
40   Partial equilibrium frameworks do not cover the full economy but only represent a subset of economic
41   sectors and markets disconnected from the rest of the economy. They basically represent market balance
42   and adjustments for a subset of sectors under ceteris paribus assumptions about other markets (labour,
43   capital, etc.), income, etc. ignoring possible feedbacks. Partial equilibrium frameworks are used in
44   sectoral models, as well as to model several sectors and markets at the same time – e.g. energy and
45   agriculture markets – in energy system models and some detailed process IAMs but still without
46   covering the full economy.

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 1   In most models the treatment of economic growth follows Solow or Ramsey growth approach based
 2   on the evolution through time of production factors endowment and productivity. Classically, labour
 3   endowment and demography are exogenous, and capital accumulates through investment. Partial
 4   equilibrium frameworks do not model economic growth but use exogenous growth assumptions derived
 5   from growth models. Factors productivity evolution is assumed exogenous in most cases i.e. general
 6   technical progress is assumed to be an autonomous process. A few models feature endogenous growth
 7   aspects where factor productivity increases with cumulated macroeconomic investment. Models also
 8   differ about the content of technical progress and alternatively consider un-biased total factor
 9   productivity improvement or labour specific factor augmenting productivity. In multi-sectoral
10   macroeconomic models, economic growth comes with endogenous changes of the sectoral composition
11   of GDP known as structural change. Structural change results from the interplay between
12   differentiated changes of productivity between sectors and of the structure of final demand as income
13   grows (Herrendorf et al. 2014). If general technical progress is mostly assumed exogenous and
14   autonomous at an aggregated level, innovation in relation to energy demand and technical systems
15   follow more detailed specifications in models. Energy efficiency can be assumed an autonomous
16   process at different levels – macroeconomic, sector or technology – or energy technical change can be
17   endogenous and induced as a learning by doing process or as a result of R&D investments (learning-
18   by-searching) (Löschel 2002).
19   Multi-regional models consider interactions between regions through trade of energy goods, non-
20   energy goods and services – depending on model scope – and emission permits in the context of climate
21   policy. For each type of goods, trade is usually represented as a common pool where regions interact
22   with the pool through supply (exports) or demand (imports). A few models consider bilateral trade flows
23   between regions. Traded goods can be assumed as perfectly substitutable between regions of origin
24   (Heckscher-Ohlin assumption) such as is often the case for energy commodities or as imperfectly
25   substitutable (e.g. Armington goods) for non-energy goods. The representation of trade and capital
26   imbalances at the regional level and their evolution through time vary across model and imbalances are
27   either not considered (regional current accounts are balanced at each point in time), or a constraint for
28   intertemporal balance is included (an export surplus today will be balanced by an import surplus in the
29   future) or else trade imbalances follow other rules such as a convergence towards zero in the long run
30   (Foure et al. 2020).
31   Strategic interaction can also occur between regions especially in the presence of externalities such as
32   climate change, energy prices or technology spillovers. Intertemporal models can include several types
33   of strategic interaction: i) a cooperative Pareto optimal solution where all externalities are internalised
34   and based on the maximization of a global discounted welfare with weighted regional welfare (Negishi
35   weights), ii) a non-cooperative solution that is strategically optimal for each region (Nash equilibrium)
36   (Leimbach et al. 2017b), and iii) partially cooperative solutions (Eyckmans and Tulkens 2003; Yang
37   2008; Bréchet et al. 2011; Tulkens 2019), akin to climate clubs (Nordhaus 2015).
38   Models cover different investment flows depending on the economic framework used. Partial
39   equilibrium models compute energy system and/or sectoral (transport, building, industry, etc.)
40   technology specific investment flows associated with productive capacities and equipment. Full
41   economy models compute both energy system and macroeconomic investment, the second being used
42   to increase macroeconomic capital stock. Full economy multi-sectoral models compute sector specific
43   (energy and non-energy sectors) investment and capital flows with some details about the investments
44   goods involved.
45   Full economy models differ in the representation of macro-finance. In most CGE and macro-economic
46   growth frameworks financial mechanisms are only implicit and total financial capacity and investment
47   are constrained by savings. Consequently, investment in a given sector (e.g. low carbon energy) fully
48   crowds-out investment in other sectors. In macro-econometric frameworks, macro-finance is sometimes

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1    explicit, and investments can be financed by credit on top of savings, which implies more limited
2    crowding-out of investments (Mercure et al. 2019). Macro-financial constraints are usually not
3    accounted for in partial equilibrium models.
4    Models compare economic flows over time through discounting. Table I.5summarizes key
5    characteristics of different models assessed in AR6, including the uses of discounting. In cost-benefit
6    analysis (CBA), discounting enables to compare mitigation costs and climate change damage. In the
7    context of mitigation and in cost-effectiveness analysis (CEA), discounting allows comparing
8    mitigation costs over time.
 9   In optimization models a social discount rate is used to compare costs and benefits over time. In the
10   case of partial equilibrium optimization models, the objective is typically to minimize total discounted
11   system cost. The social discount rate is then an exogenous parameter, which can be assumed constant
12   or changing (generally decreasing) over time (e.g. Gambhir et al. 2017 where a 5% discount rate is
13   used). In the case of intertemporal welfare optimization models, a Ramsey intertemporal optimization
14   framework is generally used, considering a representative agent who decides how to allocate her
15   consumption, and hence saving, over time subject to a resource constraint. Ramsey (1928) shows that
16   the solution must always satisfy the Ramsey Equation, which provides the determinants of the social
17   discount rate. The Ramsey Equation is given as follows:
18                                                 𝜌 = 𝛿 + 𝜂𝑔𝑡
19   where 𝜌 is the consumption discount rate (aka social discount rate), 𝛿 is the utility discount rate (aka
20   pure time discount rate, or time preferences rate) which is a value judgement that determines the present
21   value of a change in the utility experienced in the future and hence it is an ethical parameter, gt is the
22   growth rate of consumption per capita overtime, and 𝜂 is the elasticity of marginal utility of
23   consumption, which is also a value judgement and hence an ethical parameter. The parameter 𝜂 is also
24   a measure of risk aversion and a measure of society’s aversion to inequality within and across
25   generations. The pure time preference rate is an exogenous parameter, but the social discount rate is
26   endogenously computed by the model itself and depends on the growth rate of consumption per capita
27   over time. Note that more complex frameworks disentangle inequality aversion from risk aversion, and
28   introduce uncertainty, leading to extensions of the social discount rate equation (see for instance Gollier
29   2013)
30   Discounting is also used for ex-post comparison of mitigation cost pathways across models and
31   scenarios. Values typically used for such ex-post comparison are 2%-5% (e.g. Admiraal et al. 2016).
32   Across this report, whenever discounting is used for ex-post comparisons, the discount rate applied is
33   stated explicitly.
34   The choice of the appropriate social discount rate (and the appropriate rate of pure time preference when
35   applicable) is highly debated (see e.g. Arrow et al. (2013), Gollier and Hammitt (2014), Polasky and
36   Dampha (2021)) and two general approaches are commonly used. Based on ethical principles, the
37   prescriptive approach states that the discount rate should reflect how costs and benefits supported by
38   different generations should be weighted. The descriptive approach identifies the social discount rate to
39   the risk free rate of return to capital as observed in the real economy, which generally yields higher
40   values.
41   In CBA the choice of discount rate is crucial for the balance of mitigation costs and avoided climate
42   damages in the long run and a lower discount rate yields more abatement effort and lower global
43   temperature increases (Stern 2006; Hänsel et al. 2020). In CEA, the choice of social discount rate
44   influences the timing of emission reductions to limit warming to a given temperature level. A lower
45   discount rate increases short-term emissions reductions, lowers temperature overshoot, favours
46   currently available mitigation options (energy efficiency, renewable energy, etc.) over future

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1    deployment of net negative emission and distributes mitigation effort more evenly between generations
2    (Emmerling et al. 2019; Strefler et al. 2021b).
 3   Outside social discounting for intertemporal optimization, discounting is used in simulation models to
 4   compute the lifecycle costs of investment decisions (e.g. energy efficiency choices, choices between
 5   different types of technologies based on their levelized costs – LCOE). In this case, the discount rate
 6   can be interpreted as the cost of capital faced by investors. The cost of capital influences the merit order
 7   of technologies and lower capital cost favours capital intensive technologies over technologies with
 8   higher variable costs. Models can reflect regional, sectoral or technology specific cost of capital -
 9   through heterogeneous discount rates for lifecycle cost estimates in simulation models (Iyer et al. 2015)
10   or as hurdle rates in energy optimization models (Ameli et al. 2021). In some cases, simulation models
11   may also produce mitigation pathways following the Hotelling principle and assuming that the carbon
12   price rises at the social discount rate (e.g. GCAM scenarios in the SSP study with carbon prices
13   increasing at 5% yearly (Guivarch and Rogelj 2017)).

15   3. Energy system modelling
16   In the literature, the energy system models are categorized based on different criteria, such as (a) energy
17   sectors covered, (b) geographical coverage, (c) time resolution, (d) methodology, and (e) programming
18   techniques. In the following sections, examples on different types of energy system models applied in
19   Chapter 6 are presented.
20   3.1. Bottom-up models
21   3.1.1. Modelling electricity system operation and planning with large scale penetration of renewables
22   A number of advanced grid modelling approaches have been developed (Sani Hassan et al. 2018), such
23   as robust optimization (Jiang et al. 2012), interval optimization (Dvorkin et al. 2015), or stochastic
24   optimization (Meibom et al. 2011; Monforti et al. 2014) to optimally schedule the operation of the future
25   low carbon systems with high penetration of variable renewable energies (VRE). Advanced stochastic
26   models demonstrated that this would not only lead to significantly higher cost of system management
27   but may eventually limit the ability of the system to accommodate renewable generation (Badesa et al.
28   2020; Hansen et al. 2019; Perez et al. 2019; Bistline and Young 2019). Modelling tools such as
29   European Model for Power system Investment with Renewable Energy (EMPIRE) (Skar et al. 2016),
30   Renewable Energy Mix for Sustainable Electricity Supply (REMix) (Scholz et al. 2017), European Unit
31   Commitment And Dispatch model (EUCAD) (Després 2015), SWITCH (Fripp 2012), GenX (TNO
32   2021), and Python for Power System Analysis (PyPSA) (Brown et al. 2018) investigated these issues.
33   SWITCH is a stochastic model, in which investments in renewable and conventional power plants is
34   optimized over a multi-year period (Fripp 2012). In GenX the operational flexibility as well as capacity
35   planning is optimized from a system-wide perspective (TNO 2021). PyPSA is an optimization model
36   for modern electricity systems, including unit commitment of generation plants, renewable sources,
37   storage, and interaction with other energy vectors (Brown et al. 2018).
38   Furthermore, advanced modelling tools have been developed for the purpose of providing estimations
39   of system wide inertial frequency response that would assist system operators in maintaining adequate
40   system inertia (Sharma et al. 2011; Teng and Strbac 2017). These innovative models also provided
41   fundamental evidence regarding the role and value of advanced technologies and control systems in
42   supporting cost effective operation of future electricity systems with very high penetration of renewable
43   generation. In particular, the importance of enhancing the control capabilities of renewable generation
44   and applying flexible technologies, such as energy storage (Hall and Bain 2008; Obi et al. 2017;
45   Arbabzadeh et al. 2019), demand side response (DSR), interconnection (Aghajani et al. 2017) and

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1    transmission grid extensions (Schaber et al. 2012) for provide system stability control, is demonstrated
2    through novel system integration models (Sinsel et al. 2020; Lund et al. 2015).
 3   A novel modelling framework is proposed to deliver inertia and support primary frequency control
 4   through variable-speed wind turbines (Morren et al. 2006) and PVs (Waffenschmidt and Hui 2016; Liu
 5   et al. 2017), including quantification of the value of this technology in future renewable generation
 6   dominated power grids (Chu et al. 2020). Advanced models for controlling distributed energy storage
 7   systems to provide an effective virtual inertia have been developed, demonstrating the provision of
 8   virtual-synchronous-machine capabilities for storage devices with power electronic converters, which
 9   can support system frequency management following disturbances (Hammad et al. 2019; Markovic et
10   al. 2019). Regarding the application of interconnection for exchange of balancing services between
11   neighbouring power grids, alternative control schemes for High Voltage Direct Current (HVDC)
12   converters have been proposed demonstrating that this would reduce the cost of balancing (Tosatto et
13   al. 2020).
14   3.1.2. Modelling the interaction between different energy sectors
15   Several integrated models have been developed in order to study the interaction between different
16   energy vectors and whole system approaches, such as Integrated Energy System Simulation model
17   (IESM) (NREL 2020), Integrated Whole-Energy System (IWES) (Strbac et al. 2018), UK TIMES (Daly
18   and Fais 2014), and Calliope (Pfenninger and Pickering 2018).
19   IESM is an approach in which the multi-system energy challenge is investigated holistically rather than
20   looking at each of the systems in isolation. IESM capabilities include co-optimization across multiple
21   energy systems, including electricity, natural gas, hydrogen, and water systems. These provide the
22   opportunity to perform hydro, thermal, and gas infrastructure investment and resource use coordination
23   for time horizons ranging from sub-hourly (markets and operations) to multi-years (planning) (NREL
24   2020).
25   IWES model incorporates detail modelling of electricity, gas, transport, hydrogen, and heat systems
26   and captures the complex interactions across those energy vectors. The IWES model also considers the
27   short-term operation and long-term investment timescales (from seconds to years) simultaneously,
28   while coordinating operation of and investment in local district and national/international level energy
29   infrastructures (Strbac et al. 2018).
30   The UK TIMES Model (‘The Integrated MARKAL-EFOM System’) uses linear-programming to
31   produce a least-cost energy system, optimized according to a number of user constraints, over medium
32   to long-term time horizons. It portrays the UK energy system, from fuel extraction and trading to fuel
33   processing and transport, electricity generation and all final energy demands (Taylor et al. 2014; Daly
34   and Fais 2014). The model generates scenarios for the evolution of the energy system based on different
35   assumptions around the evolution of demands, future technology costs, measuring energy system costs
36   and all greenhouse gases (GHGs) associated with the scenario. UKTM is built using the TIMES model
37   generator: as a partial equilibrium energy system and technologically detailed model, is well suited to
38   investigate the economic, social, and technological trade-offs between long-term divergent energy
39   scenarios.
40   Calliope is an open source Python-based toolchain for developing energy system models, focusing on
41   flexibility, high temporal and spatial granularities. This model has the ability to execute many runs on
42   the same base model, with clear separation of model (data) and framework (code) (Pfenninger and
43   Pickering 2018).

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1    3.2. Modelling of energy systems in context of economy
2    To study the impact of low carbon energy systems on the economy, numerous integrated assessment
3    modelling tools (Top-down models) are applied, such as: General Equilibrium Model for Economy-
4    Energy-Environment (GEM-E3) (Capros et al. 2013), ENV-Linkages (Burniaux and Chateau 2010), and
5    Emissions Prediction and Policy Analysis (EPPA) (Chen et al. 2016).
 6   GEM-E3 is a recursive dynamic computable general equilibrium model that covers the interactions
 7   between the economy, the energy system and the environment. It is especially designed to evaluate
 8   energy, climate, and environmental policies. GEM-E3 can evaluate consistently the distributional and
 9   macro-economic effects of policies for the various economic sectors and agents across the
10   countries/regions (Capros et al. 2013).
11   The modelling work based on ENV-Linkages (as a successor to the OECD GREEN) provides insights
12   to policy makers in identifying least-cost policies by taking into account environmental issues, such as
13   phasing out fossil fuel subsidies, and climate change mitigation (Burniaux and Chateau 2010).
14   In the EPPA model different processes (e.g., economic and technological), which have impacts on the
15   environment from regional to global at multiple scales is simulated. The outputs of this modeling (e.g.,
16   greenhouse gas emissions, air and water pollutants) are provided to the MIT Earth System (MESM),
17   which investigated the interaction between sub-models of physical, dynamical and chemical processes
18   in different systems (Chen et al. 2016).
20   3.3. Hybrid models
21   Hybrid models are a combination of macro-economic models (i.e., top-down) with at least one energy
22   sector model (i.e., bottom-up) that could benefit from the advantages of both mentioned approaches. In
23   this regard, linking these two models can be carried out either manually through transferring the data
24   from one model to the other (soft-linking), or automatically (hard-linking) (Prina et al. 2020). In this
25   section, some of these models are presented including World Energy Model (WEM) (IEA 2020a), the
26   National Energy Modelling System (NEMS) (Fattahi et al. 2020).
27   The WEM is a simulation model covering energy supply, energy transformation and energy demand.
28   The majority of the end-use sectors use stock models to characterize the energy infrastructure. In
29   addition, energy-related CO2 emissions and investments related to energy developments are specified.
30   The model is focused on determining the share of alternative technologies in satisfying energy service
31   demand. This includes investment costs, operating and maintenance costs, fuel costs and in some cases
32   costs for emitting CO2 (IEA 2020a).
33   The NEMS is an energy-economy modelling system applied for the U.S.A. through 2030. NEMS
34   projects considers the production, imports, conversion, consumption, and prices of energy, subject to
35   assumptions on macroeconomic and financial factors, world energy markets, resource availability and
36   costs, behavioural and technological choice criteria, cost and performance characteristics of energy
37   technologies, and demographics. NEMS was designed and implemented by the Energy Information
38   Administration (EIA) of the U.S. Department of Energy. NEMS is used by EIA to project the energy,
39   economic, environmental, and security impacts on the United States considering alternative energy
40   policies and assumptions related to energy markets (Fattahi et al. 2020).

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1    4. Building sector models
2    4.1. Models purpose, scope and types
3    GHG emissions and mitigation potentials in the building sector are modelled using either a top-down,
4    a bottom-up or a hybrid approach, which combines both bottom-up and top-down (Figure I.1.).
 5       1. The top-down models are used for assessing economic-wide responses of building policies.
 6          These models are either economic or technological and have low granularity (Figure I.1.).
 7       2. The bottom-up models are data intensive and based on microscopic data of individual end-uses
 8          and the characteristics of each component of buildings. Bottom-up models can be either
 9          physics-based, also known as engineering models; data-driven, also known as statistical
10          models; or a combination of both, also known as hybrid bottom-up models. Bottom-up models
11          are useful to assess the technico-economic potentials of the overall building stock by
12          extrapolating the estimated energy consumption of a representative set of invidual buildings
13          (Duerinck et al. 2008; Hall and Buckley 2016; Bourdeau et al. 2019) (Figure I.1.).
14       3. Hybrid models used for buildings can be either optimisation or simulation models (Duerinck et
15          al. 2008; Hall and Buckley 2016; Bourdeau et al. 2019) (Figure I.1.). The latter can also be
16          agent-based models and could be combined with building performance models to allow for an
17          assessment of occupants behaviour (Sachs et al. 2019a; Papadopoulos and Azar 2016; Niamir
18          et al. 2020). Hybrid models are used for exploring the impacts of resource constraints and for
19          investigating the role of specific technological choices as well as for analysing the impact of
20          specific building policies.

21   The use of Geographical Information Systems (GIS) layers (Reinhart and Cerezo Davila 2016)
22   combined to machine learning techniques (Bourdeau et al. 2019) allows creating detailed datasets of
23   building characteristics while optimising the computing time. Thus, leading to a better representation
24   of energy demand of buildings and a more accurate assessment of GHG mitigation potential.

                                                GHG emissions

                        Top-down                                                   Bottom-up
                                          Optimisation       Simulation
              Economic       Technological                                         bottom-up

                                                                          Statistical     Engineering
27                Figure I.1. Modelling approaches of GHG emissions used in the building sector
29   4.2. Representation of energy demand and GHG emissions
30   Comprehensive models represent energy demand per energy carrier and end-use for both residential
31   and non-residential buildings, for different countries or set of countries, further disaggregated across
32   urban/rural and income groups. Drivers of energy demand considered include population, the floor area

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1    per capita, appliances ownership and to some extent occupants’ behaviour in residential buildings. The
2    former being included in top-down, hybrid and bottom-up models while the latter is, usually included
3    in bottom-up and agent-based models (IEA 2021; Niamir et al. 2020). In non-residential buildings,
4    value-added is considered among the drivers.
 5   GHG emissions from buildings are usually modelled on the basis of the estimated energy demand per
 6   energy carrier and appropriate emissions factors. The purpose of most building models is to assess the
 7   impact of mitigation measures on energy demand in the use phase of buildings and for a given
 8   assumption on the per-capita floor area and technological improvement (Pauliuk et al. 2021b) and (IEA
 9   2021). After decades of ignoring material cycles and embodied emissions (Pauliuk et al. 2017), few
10   IAMs are now including material stocks and flows (Zhong et al. 2021; Deetman et al. 2020; IEA 2021).
11   However, the top-down nature of these models and the modelling methodology of embodied emissions,
12   which are added onto the emissions estimated in the use phase, questions the policy relevance of these
13   estimates. As of today, the resource efficiency and climate change (RECC) scenario (Pauliuk and
14   Heeren 2021; Pauliuk et al. 2021b; Fishman et al. 2021; Hertwich et al. 2020) is the only global scenario
15   identified which includes measures to limit, at the first place, embodied emissions from buildings. The
16   scenario is modelled using the bottom-up ODYM-RECC model.
18   4.3. Representation of mitigation options
19   The assessment conducted in Chapter 9 was based on the SER (Sufficiency, Efficiency, Renewable)
20   framework with sufficiency being all the measures and daily practices which avoid, at the first place,
21   the demand for energy, materials, water, land and other natural resources over the life cycle of buildings
22   and appliances/equipment, while providing decent living standard for all within the planetary
23   boundaries. By contrast to efficiency, sufficiency measures do not consume energy in the use phase.
24   Efficiency improvement of the building envelope and appliances/equipment are the main mitigation
25   options considered in the existing models/scenarios. They are, usually, combined with market-based
26   and information instruments and to some extent with behaviour change. As of today, Grubler et al.
27   (2018), (Pauliuk et al. 2021b), Kuhnhenn et al. (2020), Millward-Hopkins et al. (2020), Kikstra et al.
28   (2021), van Vuuren et al. (2021) are the only six global models/scenarios to include sufficiency
29   measures, out of which detailed data were available only for two scenarios (Pauliuk et al. 2021b; van
30   Vuuren et al. 2021).
32   4.4. Representation of climate change impacts
33   In total, 931 scenarios were submitted to AR6 scenario database out of which only two scenarios
34   provided detailed data allowing for an assessment of climate change impacts based on the SER
35   framework considered in the building chapter. Additional 78 bottom-up models/scenarios were gathered
36   (Table I.1.). Mitigation potentials from these scenarios are assessed using either a decomposition
37   analysis (Chapter 9, Section 9.3.) or an aggregation of bottom-up potential estimates for different
38   countries into regional and then global figures (Chapter 9, Section 9.6.).
39   Scenarios considered in the illustrative mitigation pathways included in Chapter 3 were assessed,
40   compared to current policy scenario. The assessment was possible for only the combined direct CO 2
41   emissions for both residential and non-residential buildings due to lack of data on other gases as well
42   as on indirect and embodied emissions. The assessment shows mitigation potentials, compared to
43   current policies scenarios, at a global level ranging from 9% to 13% by 2030 and from 58% to 89% in
44   2050 (Figure I.2-b).

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 1   There are great discrepancies in the projected potentials by the IAMs across regions and scenarios. In
 2   the deep electrification and high renewable scenario, emissions in Africa are projected to increase by
 3   88% by 2030, followed by a decrease of 97% by 2050 compared to current policies scenario. Similarly,
 4   in the sustainable development scenario, emissions in developing Asia are projected, compared to
 5   current policies scenario, to increase by 56% by 2030, followed by a decrease of 75% by 2050. Such
 6   variations in emissions over two decades in the developing world raise questions about the policy
 7   relevance of these scenarios. In developed countries, emissions are projected to go down in all regions
 8   across all scenarios, except in SSP2 scenario in Asia-Pacific, where emissions are projected to increase
 9   by 18% by 2030 followed by a decrease of 25% by 2050, compared to current policies scenario. It is
10   worth noting that, across all scenarios, Eastern Asia is the region with the lowest estimated mitigation
11   potential compared to the current policies (Figure I.2-b).
13   4.5. Representation of sustainable development dimensions
14   Link to sustainable development goals is not always explicit in buildings models/scenarios. However,
15   some models include requirements to ensure the access to decent living standard for all Kikstra et al.
16   (2021), Millward-Hopkins et al. (2020), Grubler et al. (2018) or to specifically meet the 2030 SDG 7
17   goal (IEA 2020a, 2021).

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      CO2 emisisons (Direct ) MtCO2

                                                                    -9%                     -8%                   -13%


                                      1000                                                           -82%

                                              2030       2050       2030      2050         2030      2050         2030     2050

                                       NGFS2_Current Policies SSP2_openres_lc_50          SusDev_SDP-PkBudg1000
                                               DeepElec_SSP2_ HighRE_Budg900
                                        *The percentages correspond to the increase or decrease in relation to the same year
                                        with the Current Policies Scenario as a baseline.
2                                                                            a) Global

5                                                                           b) Regional
6            Figure I.2. GHG mitigation potentials is scenarios considered in the illustrative mitigation pathways
7                                                 considered in Chapter 3.

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2    4.6. Models underlying the assessment in Chapter 9
 3   The AR6 scenario database received 101 models, with a building component, out of which 96 were
 4   IAM models and five building specific models. This is equivalent to 931 scenarios. After an initial
 5   screening, quality control and further vetted to assess if they sufficiently represented historical trends
 6   and climate goals, 43 models (42 IAMs and 1 building specific model) were kept for the assessment.
 7   Thus, reducing the number of scenarios to assess to 554. The unvetted scenarios are still available in
 8   the database. After a final screening based on the SER (Sufficiency, Efficiency, Renewable) framework,
 9   only two IAMs were kept. Given the top-down nature of IAMs and their weaknesses in assessing
10   mitigation measures, especially sufficiency measures, 78 bottom-up models with technological
11   representation have been included in the assessment (Table I.1.). These additional bottom-up models
12   were not submitted to AR6 scenario database. However, scenario owners supplied Chapter 9 with the
13   underlying assumptions and data.

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                                                             Table I.1. Models underlying the assessment in Chapter 9.

Model name/Institution                                                                                                                                  Example of publications
                           Model description        Geographic scope      Building type included             Energy demand
  using the model

World Energy Model       A simulation model        Global                 Residential   and   non-    The building module includes a      (IEA 2020a);(IEA 2021)
(WEM)/International      with detailed bottom-up                          residential                 stock model with detailed
Energy Agency (IEA)      building stock model                                                         technologies, end uses and
                                                                                                      energy carriers.        Activity
                                                                                                      variables such as floor area and
                                                                                                      appliance     ownership       are
                                                                                                      projected by end-use. A cost-
                                                                                                      based approach, influenced by
                                                                                                      policy and other constrains, is
                                                                                                      used to allocate between almost
                                                                                                      100 technologies.         Energy
                                                                                                      demand projections are based
                                                                                                      on country-level historical data
                                                                                                      for both residential and non-
                                                                                                      residential    buildings.    The
                                                                                                      buildings module is integrated
                                                                                                      within the wider World Energy
IMAGE 3.2 model/         A modular Integrated      Global                 Residential and non-        Energy demand is calculated as      (van Vuuren et al. 2021)
                         Assessment    Model                              residential buildings       a function of household
                         using a simulation                                                           expenditures and population
                         model   for   energy                                                         growth, disaggregating across
Assessment Agency
                         demand                                                                       urban/rural and income groups.
                                                                                                      The model includes a building
                                                                                                      stock model (residential) with a
                                                                                                      detailed description of end-uses,
                                                                                                      energy carrier use and building
                                                                                                      technologies for both residential

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                                                                                                            and non-residential buildings. A
                                                                                                            scenario analysis assessing
                                                                                                            assumptions     on      lifestyle
                                                                                                            changes     has    also     been
Resource Efficiency and       Bottom-up       building   Global                  Residential buildings      Energy demand is calculated by      (Pauliuk et al. 2021b); (Hertwich et al. 2020); (Pauliuk
Climate Change (RECC)         stock-flow        model                                                       the model BuildME, a physical       et al. 2021a); (Fishman et al. 2021); (Pauliuk and
model.           Research     estimating material and                                                       model using the EnergyPlus          Heeren 2021)
Institutions: Norwegian       energy flows associated                                                       simulation                engine,
University of Science &       with housing stock                                                            incorporating country/region-
Technology,            and    growth, driven by input                                                       specific projections of envelope
University of Freiburg.       parameters            of                                                      and equipment efficiency
Funding       Institutions:   population and floor
UNEP and International        area per capita
Resource Panel
A total of 77 bottom-up       Bottom-up technology-      Three global (all       Residential and/or non-    In most cases, energy demand        (Alaidroos and Krarti 2015; Bashmakov 2017; Brugger
models out of which 67        rich    models    with     sufficiency models),    residential buildings      was modelled by multiplying         et al. 2021; Bürger et al. 2019; Butler et al. 2020; Calise
were     technology-rich      detailed building and      six regional (regions                              unit of energy consumption of       et al. 2021; Chaichaloempreecha et al. 2017;
and    10    sufficiency-     other technology stock     here refer to regions                              technologies/product/buildings      Colenbrander et al. 2019; Csoknyai et al. 2016; de la
focussed                      models                     including     several                              with stocks of corresponding        Rue du Can et al. 2019, 2018; de Melo and de Martino
                                                         countries),       two                              technologies/products and/or        Jannuzzi 2015; Department of Environmental Affairs
                                                         subnational, and the                               buildings at national level. The    2014; Dioha et al. 2019; Duscha et al. 2019; Energetics
                                                         rest national                                      stocks of buildings and/or          2016; Gagnon, Peter, Margolis, Robert, Melius,
                                                                                                            technologies/products rely on       Jennifer, Phillips, Saleb, Elmore 2016; González-
                                                                                                            very detailed stock modelling in    Mahecha et al. 2019; Grande-Acosta and Islas-
                                                                                                            the future relying on such          Samperio 2020; Horváth et al. 2016; Iten R., Jakob M.,
                                                                                                            statistics in the past. The         Catenazzi G, Reiter U., Wunderlich A. 2017; Kamal et
                                                                                                            potential    is    demonstrated     al. 2019; Khan et al. 2017; Krarti 2019; Krarti et al.
                                                                                                            replacing the business-as-usual     2017; Kusumadewi and Limmeechokchai 2015, 2017;
                                                                                                            technologies and practices with     Kwag et al. 2019; Markewitz et al. 2015; Merini et al.
                                                                                                            demonstrated best available or      2020; Minami et al. 2019; Momonoki et al. 2017; Nadel
                                                                                                            commercially             feasible   2016; Novikova et al. 2018a,b; Filippi Oberegger et al.
                                                                                                            technologies and practices. The     2020; Oluleye et al. 2018, 2016; Onyenokporo and
                                                                                                            studies rely on all, the            Ochedi 2019; Ostermeyer, Y.; Camarasa, C.; Naegeli,

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                                                     combination, or either of the        C.; Saraf, S.; Jakob, M.; Hamilton, I; Catenazzi 2018;
                                                     following mitigation options:        Ostermeyer et al. 2019a; Ostermeyer, Y.; Camarasa, C.;
                                                     the construction of new high-        Saraf, S.; Naegeli, C.; Jakob, M.; Palacios, A,
                                                     performance buildings using          Catenazzi 2018; Ostermeyer et al. 2018, 2019b; Ploss
                                                     building design, forms, and          et al. 2017; Prada-hernández et al. 2015; Radpour et al.
                                                     passive construction methods;        2017; Rosas-Flores and Rosas-Flores 2020; Roscini et
                                                     the       thermal      efficiency    al. 2020; Sandberg et al. 2021; Streicher et al. 2017;
                                                     improvement       of     building    Subramanyam et al. 2017a,b; Sugiyama et al. 2020b;
                                                     envelopes of the existing stock;     Tan et al. 2018; Timilsina et al. 2016; Toleikyte et al.
                                                     the installation of advanced         2018; Trottier 2016; Wakiyama and Kuramochi 2017;
                                                     HVAC systems, equipment and          Wilson et al. 2017b; Xing et al. 2021; Yeh et al. 2016;
                                                     appliances; the exchange of          Yu et al. 2018; Zhou et al. 2018; ADB 2017; Zhang et
                                                     lights, appliances, and office       al. 2020; Mirasgedis et al. 2017)(Grubler et al.
                                                     equipment, including ICT,            2018)(Millward-Hopkins et al. 2020)(Levesque et al.
                                                     water heating, and cooking;          2019)(Bierwirth and Thomas 2019)(Roscini et al.
                                                     active and passive DSM               2020)(Cabrera Serrenho et al. 2019) (Roca-Puigròs et
                                                     measures; as well as onsite          al. 2020)(Negawatt 2017)(Virage-Energie Nord-Pas-
                                                     production      and     use    of    de-Calais. 2016)
                                                     renewable energy.           Many
                                                     bottom-up studies considered
                                                     the measures as an integrated
                                                     package       due     to     their
                                                     technological complementarity
                                                     and interdependence, rather
                                                     than     the    penetration of
                                                     individual technologies applied
                                                     in an incremental manner in or
                                                     to these buildings.

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1    5. Transport models
2    5.1. Purpose and scope of models
3    GHG emissions from transport are largely a function of travel demand, transport mode, and
4    transport technology and fuel. The purpose of transportation system models is to describe how future
5    demand for transport can be fulfilled through different modes and technologies under different climate
6    change mitigation targets or policies. Within a given transport mode, technologies differ by efficiency
7    and fuel use.
 8   Common components of transportation energy systems models mirror these main drivers of GHG
 9   emissions. Most models will also quantify how much movement occurs, or the travel demand
10   associated with each mode. Models commonly quantify demand through transportation mode (e.g.
11   active transit, passenger vehicles, trucks, boats, planes, etc.) or how movement occurs (e.g. passenger
12   travel distance and freight distance Higher fidelity models provide more nuanced
13   breakdowns of demand by trips of various lengths such as short-, medium-, and long-distance trips or
14   by region (e.g. kilometres or per region). The scope of the model often determines how much
15   information it provides on where and when movement occurs. While larger scale models typically
16   provide aggregate travel demand, higher resolution travel demand models can be integrated into
17   transportation system models and provide much more information on origin and destination of trips,
18   when and where trips occur, and the route of travel taken. This level of detail is not often characterised
19   in the output of system models but can be employed as a “base” model to determine how travel occurs
20   before aggregation (Edelenbosch et al. 2017a; Yeh et al. 2017).
21   A key distinguishing feature between different model types is how they control the above components.
22   Our review of the transport energy system models can be broadly divided into three main categories: i)
23   optimisation models, ii) simulation models, and iii) accounting and exploratory models.
24       i)      Optimization models: Identify least cost pathways to meet policy targets (such as CO2
25               emission targets of transport modes or economy-wide) given constraints (such as rate of
26               adoption of vehicle technologies or vehicle efficiency standards). For example MessageIX-
27               TransportV5 (Krey et al. 2016) and TIMES (Daly et al. 2014).
29       ii)     Simulation models: Simulate behaviour of consumers and producers given prices, policies,
30               and other factors by using parameters calibrated to historically observed behaviours such
31               as demand price elasticity and consumer preferences. For example models by Barter et al.
32               (2015), Brooker et al. (2015) and Schäfer (2017).
34       iii)    Accounting and exploratory models: Track the outcomes (such as resources use and
35               emissions) of key decisions (such as the adoption of advanced fuels or vehicle technologies)
36               that are based on what-if scenarios. The major difference between accounting models
37               versus optimisation and simulation models are that key decision variables such as new
38               technologies adoptions typically follow modeler’s assumptions as opposed to being
39               determined by mathematical formulations as in optimisation and simulation models. See
40               models in Fulton et al. (2009), IEA (2020a), Gota et al. (2019) and Khalili et al. (2019).
42       Due to the model types’ relative strengths and weaknesses, they are commonly applied to certain
43       problem types (Table I.2.). Models can do forecasting, which makes projections of how futures
44       may evolve, or backcasting, which makes projections of a future that meets a predefined goal such
45       as a policy target of 80% reduction in GHG emissions from a historical level by a certain year.
46       Models often are also used to explore what-if questions, to confirm the feasibility of certain

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1        assumptions/outcomes, and to quantify the impacts of a change such as a policy under different
2        conditions. Enhancing fuel efficiency standards, banning internal combustion engines, setting fuel
3        quality standards, and the impacts of new technologies are the typical examples of problem types
4        analysed in energy system models.
6      Table I.2. Taxonomy of transport models by method (modelling type) and application (problem type).

                                          Optimization          Simulation         Accounting        Heuristic
              Problem Type
                                             model                model              model            model
               Backcasting                     x                                                        x
               Forecasting                     x                    x                   x
        Exploring feasibility space                                 x                   x                x
             Impact analysis                    x                   x                   x
 8   While these four model types drive the component dynamics in different ways, they commonly include
 9   modules that include: learning and diffusion (via exogenous, e.g. autonomous learning, or endogenous
10   learning regarding costs and efficiency: i.e. cost decreases and/or efficiency increases as a function of
11   adoption, and increased diffusion due to lower costs) (Jochem et al. 2018), stock turnover (the
12   performance and characteristics of vehicle fleets including survival ages, mileages, fuel economies and
13   loads/occupancy rates are tracked for each new sales/vehicle stocks), consumer choice (theories of how
14   people invest in new technology and utilize different mode of transport based on their individual
15   preferences given the characteristics of mode or technology) (Daly et al. 2014; Schäfer 2017), or other
16   feedback loops (Linton et al. 2015).
17   IAMs (Krey et al. 2016; Edelenbosch et al. 2017a) are typically global in scope and seek to solve for
18   feasible pathways meeting a global temperature target (Annex III.I.9). This implies solving for
19   mitigation options within and across sectors. In contrast, global/national transport energy system
20   models (GTEM/NTEMs) typically only solve for feasible pathways within the transport sector (Yeh et
21   al. 2017). The range of feasible pathways can be determined through optimisation, simulation,
22   accounting and exploratory methods as we explained in Table I.2. Some GTEMs are linked to IAMs
23   model (Krey et al. 2016; Edelenbosch et al. 2017a; Roelfsema et al. 2020). The key difference between
24   IAMs and GTEM or NTEMs is whether the transportation systems is integrated with the rest of the
25   energy systems specifically regarding energy and fuel productions and use, fuel prices, economic
26   drivers such as GDP, and mitigation options given a policy goal. IAMs can endogenously determine
27   these factors because the transport sector is just one of many sectors captured by the IAM. While this
28   gives IAMs certain advantages, IAMs sacrifice resolution and complexity for this broader scope. For
29   example, most IAMs lack a sophisticated travel demand model that reflects the heterogeneity of
30   demands and consumer preferences, whereas GTEM/NTEMs can incorporate greater levels of details
31   regarding travel demands, consumer choices, and the details of transport policies. Consequently, what
32   GTEM/NTEMs lack in integration with other sectors they make up through more detailed analyses of
33   travel patterns, policies, and impacts (Yeh et al. 2017).
34   Several noteworthy recent active research areas in long-term transportation energy systems modelling
35   involves the consideration of infrastructure investment and consumer acceptance for non-fossil fuel
36   vehicles including charging for electric vehicles (Statharas et al. 2021; Jochem et al. 2019) and
37   refuelling stations for hydrogen vehicles (Rose and Neumann 2020); and the greater integration of the
38   electric, transport, residential, and the industrial sectors in fuel production, storage, and utilization
39   (Rottoli et al. 2021; Lester et al. 2020; Bellocchi et al. 2020; Olovsson et al. 2021). While
40   national/regional transport energy models have the advantage of exploring these relationships in greater
41   spatial, temporal, and policy details for specific country/regions (Jochem et al. 2019; Rottoli et al. 2021;
42   Statharas et al. 2021; Lester et al. 2020; Bellocchi et al. 2020), the IAMs have the advantage of
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1    examining these interactions across the entire economy at the global level (Brear et al. 2020; Rottoli et
2    al. 2021).
4    5.2. Inventory of transportation models included in AR6
5    The global/national transport energy system models included in the transportation chapter (Chapter 10)
6    are listed below in Table I.3.
8                           Table I.3. GTEM/NTEMs models evaluated in Chapter 10.

       Model name       Organisation       Scope       Resolution      Period                 Method
      Mobility       International       Global        Country        2050        Soft-link Accounting
      model          Energy                            groups                               model
      (MoMo)         Agency
      Global         International       Global        Country        2050        No           Accounting
      Transportation Council on                        groups                                  model
      Roadmap        Clean
      MESSAGE-       International       Global        Country        2100        Yes          Optimization
      Transport V.5 Institute for                      groups                                  model
      GCAM           Pacific             Global        Country        2100        Yes          Partial
                     Northwest                         groups                                  equilibrium
                     National                                                                  model

11   6. Industry sector models
12   6.1. Types of industry sector models
13   Industry sector modelling approaches can vary considerably from one another. As other types of
14   models, industry sector models a key characteristic related to their geographical scope. While IAMs are
15   often global in scope, many bottom-up sector models are limited to individual countries or regions. The
16   models’ system boundaries also differ, with some models fully considering the use of energy for
17   feedstock purposes and other models focussing only on the use of energy for energetic purposes.
18   Differences between models also exist in regard to the differentiation between the industry sector and
19   the energy transformation sector, concerning e.g. the refineries and industrial power plants.
21   6.2. Representation of demand for industrial products
22   Industry sector models vary in regard to their representation of demand for industrial goods or products.
23   A more detailed representation of demand in a model allows for a more explicit discussion of different
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1    types of drivers of industrial demand and therefore a more detailed representation of demand side
2    strategies such as material recycling, longer use of products or sharing of products.
3    Particularly, in bottom-up models of the industry sector, demand for industrial products is often
4    considered in more detail than in top-down models by taking more drivers into account. These drivers
5    can be inter alia population, gross value added, construction activity, transport activity, but also changes
6    in material efficiency, recycling rates and scrap rates as well as product use efficiency (e.g. through
7    longer use of products or sharing of products) (Fleiter et al. 2018; Material Economics 2019; IEA
8    2020b).
10   6.3. Representation of mitigation options - mitigation options, how their uptake is
11          represented, how potentials and costs are represented
12   In most top-down IAMs, some energy-intensive sectors such as iron and steel or cement are included
13   separately at least in a generalised manner, but typically few if any sector-specific technologies are
14   explicitly represented. Instead, energy efficiency improvements in the industry sector and its subsectors
15   are often either determined by exogenous assumptions or are a function of energy prices. Likewise, fuel
16   switching occurs primarily as a result of changes in relative fuel prices, which in turn are influenced by
17   CO2 price developments. In IAMs that include specific technologies, fuel switching can be constrained
18   based on the characteristics of those technologies, while in IAMs with no technological detail more
19   generic constraints on fuel switching in the industry sector are embedded (Edelenbosch et al. 2017b).
20   In bottom-up models, individual technological mitigation options are represented in detail, especially
21   for energy-intensive sectors such as iron and steel, cement and chemicals. Typically, for each considered
22   technology not only specific energy demand but also investment and operating costs are included in
23   these models. Investment costs can change over time, either based on an exogenous assumption or on
24   an endogenized process such as a learning rate. While bottom-up models often consider technology-
25   specific learning, IAMs cover technological progress in a more general way associated to industry
26   branches. The uptake of new technologies is typically restricted in bottom-up models, for example by
27   assuming a minimum lifetime for existing stock or by assuming S-shaped diffusion curves (Fleiter et
28   al. 2018). The industrial sector models included in the industry chapter (Chapter 11) are listed in Table
29   I.4.

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1                   Table I.4 Models underlying specific assessments in Industry Sector (Chapter 11).

      Model           Model description            Geo-       Industrial        Demand for industrial        Examples of
      name and                                     graphic    sectors           products                     publications
      institution                                  scope      included/
      using the                                               distinguished
      Industry        The bottom-up industry       Global     Aluminium,        Demand for industrial        (IEA 2020b,
      sector          sector model is one of                  iron and steel,   products is derived based    2021)
      model of        four soft-linked models                 chemical and      on           country-level
      the    ETP      making up the ETP                       petrochemical,    historical data on per
      model           model: The four models                  cement, pulp      capita consumption. This
                      are an energy supply                    and paper and     per capita consumption is
                      optimization model and                  other industry.   projected forward by
                      three end-use sector                                      using          population
                      models        (transport,                                 projections and industry
                      industry,     buildings).                                 value-added projections.
                      Technologies and fuels                                    Demand for materials is
                      in the industry sector                                    derived by also taking the
                      model are chosen based                                    build-up of material
                      on cost optimization.                                     stocks into account.
      World           Simulation model             Global     See ETP           See ETP model                (IEA 2020a,
      Energy          consisting inter alia of                model                                          2021)
      Model           technologically
      (IEA)           detailed bottom-up
                      representations of
                      several industry
      Material        Modelling             tool   European   Steel,            Demand for industrial        (Material
      Economics       consisting of several        Union      chemicals         products is derived based    Economics
      modelling       separate      bottom-up                 (plastics &       on scenarios of future       2019)
      framework       models.                                 ammonia),         activity levels in key
                                                              cement            segments such as
                                                                                construction, mobility
                                                                                and food production.
                                                                                Separate models
                                                                                additionally explore
                                                                                opportunities for
                                                                                improving materials
                                                                                efficiency and increasing
                                                                                materials circulation.

4    6.4. Limitations and critical analysis
 5   Aggregated, top-down models of the industry sector, as used in most IAMs, are typically calibrated
 6   based on long-term historical data, for example on the diffusion of new technologies or on new fuels.
 7   These models are therefore able to implicitly consider real-life restrictions of the whole sector that
 8   bottom-up models (with their focus on individual technologies) may not fully take into account. These
 9   restrictions may arise from inter alia delays in the construction of infrastructure or market actors
10   possessing incomplete information about new technologies. Furthermore, as IAMs also model the

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1    climate system, these models can principally take into account potential repercussions of climate change
2    impacts on the growth rate and structure of economies.
3    However, a downside of top-down models is that they are typically limited in their representation of
4    individual technologies and processes in the industry sector and particularly of technology-driven
5    structural change. This lack of technological detail limits the usefulness of these models to analyse
6    technology-specific and sector-specific mitigation measures and related policies. Top-down models
7    also tend to have a relatively aggregated representation of industrial energy demand, meaning demand-
8    side mitigations strategies such as recycling, product-service efficiency and demand reduction options
9    are difficult to assess with these models (Pauliuk et al. 2017).
10   In contrast, technology-rich bottom-up models allow detailed analysis of the potential of new
11   technologies, processes and fuels in individual industrial sectors to reduce GHG emission. Their often-
12   detailed analysis of the demand side allows demand-side mitigation strategies to be evaluated.
13   Furthermore, radical future changes in technology, climate policy or social norms can more easily be
14   reflected in bottom-up models than in top-down models which are calibrated on past observations. Both
15   types of models are typically not able to account for product substitution (e.g. steel vs. plastics) arising
16   from changing production cost differentials or changing product quality due to new production
17   processes. In principle, technology rich input-output models could fill this gap.

19   7. Land use modelling
20   Land use related IAM modelling results as presented in Chapter 7 are based on comprehensive land-
21   use models (LUMs) that are either integrated directly, or through emulators into the integrated
22   assessment framework. Given the increasing awareness of the importance of the land use sector to
23   achieve ambitious climate mitigation targets, LUMs and their integration into IAMs systems was one
24   of the key innovations to the integrated assessment over the past decade to allow for an economy wide
25   quantification of climate stabilization pathways.
26   LUMs allow to project developments in the land use sector over time and assess impacts of mitigation
27   policies on different economic (markets, trade, prices, demand, supply etc.) and environmental (land
28   use, emissions, fertiliser, irrigation water use, etc.) indicators. The following models submitted
29   scenarios to the AR6 database: AIM (Fujimori et al. 2014, 2017; Hasegawa et al. 2017), EPPA (Chen
30   et al. 2016), GCAM (Calvin et al. 2019), IMAGE (Stehfest et al. 2014), MERGE, MESSAGE-
31   GLOBIOM (Fricko et al. 2017; Havlík et al. 2014; Huppmann et al. 2019), POLES (Keramidas et al.
32   2017), REMIND-MAgPIE (Dietrich et al. 2019; Kriegler et al. 2017), WITCH (Emmerling et al. 2016).
34   7.1. Modelling of land use and land use change
35   LUMs represent different land use activities for managed land (agriculture including cropland and
36   pastures, managed forests, and dedicated energy crops) while natural lands (primary forests, natural
37   grasslands, shrubland, savannahs etc.) act as land reserve that can be converted to management
38   depending on other constraints (Popp et al. 2014a; Schmitz et al. 2014). Typically, the agricultural
39   sector has the greatest level of detail across land use sectors. LUMs include different crop- and livestock
40   production activities, some even at the spatially explicit level and differentiated by production system
41   (Havlík et al. 2014; Weindl et al. 2015). Forestry is covered with varying degree of complexity across
42   LUMs. While some models represent only afforestation/deforestation activities dynamically, others
43   have detailed representation of forest management activities and/or forest industries (Lauri et al. 2017).
44   The models endogenously determine the land allocation of different land use activities as well as land

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1    use changes according to different economic principles (land rent, substitution elasticities etc.) and/or
2    considering biophysical characteristics such as land suitability (Weindl et al. 2017; Schmitz et al. 2014).
4    7.2. Demand for food, feed, fibre and agricultural trade
 5   LUMs project demand for food, feed, other industrial or energy uses for different agriculture and
 6   forestry commodities over time. While partial equilibrium models typically use reduced-form demand
 7   functions with greater level of detail at the commodity level, however limited agriculture and forestry,
 8   CGE models represent demand starting from utility functions from which it is possible to derive demand
 9   functions, and functional forms for income and price elasticities however for a more limited set of
10   agricultural and forestry commodities but with full coverage of all economic sectors (Valin et al. 2014;
11   von Lampe et al. 2014). Over time, demand for food, feed, and other industrial uses is projected
12   conditional on population and income growth while bioenergy demand is typically informed in PE
13   models by linking with IAMs/energy systems models, and is usually endogenous in CGE/IAMs
14   (Hasegawa et al. 2020). Depending on the model, demand projections are sensitive to price changes
15   (Valin et al. 2014). International trade is often represented in LUMs using either Armington or spatial
16   equilibrium approaches (von Lampe et al. 2014).
18   7.3. Treatment of land-based mitigation options
19   Two broad categories of land-based mitigation options are represented in LUMs: i) reduction of GHG
20   (CO2, CH4 and N2O) emissions from land use, ii) carbon sink enhancement options including biomass
21   supply for bioenergy. Each of these categories is underpinned by a portfolio of mitigation options with
22   varying degree of complexity and parameterisation across LUMs. The representation of mitigation
23   measures is influenced on the one hand, by the availability of data for its techno-economic
24   characteristics and future prospects as well as the computational challenge, e.g. in terms of spatial and
25   process detail, to represent the measure, and on the other hand, by structural differences and general
26   focus of the different LUMs, and prioritization of different mitigation options by the modelling teams.
27   While GHG emission reduction and CO2 sequestration options such as afforestation, are typically
28   covered directly in LUMs (Hasegawa et al. 2021), carbon sequestration from biomass supplied for
29   bioenergy coupled with carbon sequestration (BECCs) is usually not accounted for in LUMs but in the
30   energy sector and hence is taken care of directly in the IAMs. Yet, LUMs provide estimates of available
31   biomass for energy production and the impacts of its production.
33   7.3.1. Treatment of GHG emissions reduction
34   Agricultural non-CO2 emissions covered in LUMs include CH4 from enteric fermentation, manure
35   management and cultivation of rice paddies, and N2O emissions from soils (fertilizer and manure
36   application, crop residues) and manure management and are based on IPCC accounting guidelines
37   (IPCC 2019a). For each of those sources, LUMs typically represent a (sub)set of technical, structural
38   and demand side mitigation options. Technical options refer to technologies such as anaerobic digesters,
39   feed supplements or nitrogen inhibitors that are either explicitly represented (Frank et al. 2018) or
40   implicitly via the use of MACCs (Beach et al. 2015; Harmsen et al. 2019; Lucas et al. 2007). Emission
41   savings from structural changes refer to more fundamental changes in the agricultural sector for
42   example through international trade, production system changes or reallocation and substitution effects
43   (Havlík et al. 2014). Demand side options include dietary changes and reduction of food waste (Mbow
44   et al. 2019; Rosenzweig et al. 2020; Springmann et al. 2016; Ivanova et al. 2020; Ritchie et al. 2018;
45   Creutzig et al. 2018; Clark et al. 2020; Popp et al. 2010; Frank et al. 2019). For the forest sector,
46   emission reduction options are mainly targeting CO2 from deforestation (Rochedo et al. 2018; Eriksson
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1    2020; Overmars et al. 2014; Bos et al. 2020; Hasegawa et al. 2017; Doelman et al. 2020).
2    Mitigation/restoration options for wetlands to reduce emissions from drained organic soils are typically
3    not represented in LUMs (Humpenöder et al. 2020).
 4   There are significant differences between UNFCCC nationally reported GHG inventories and analytical
 5   global land use models. According to Grassi et al. (2017), this discrepancy results in a 3GtCO2e
 6   difference in estimates between country reports and global models. The difference relies on different
 7   methods to classify and assess managed forests and its forest management fluxes (Houghton et al. 2012;
 8   Pongratz et al. 2014; Tubiello et al. 2015; Smith et al. 2014; Grassi et al. 2017, 2021). While global
 9   models account for GHG emissions from indirect human induced effects and natural effects in
10   unmanaged land, country only consider fluxes of land use and land use change in managed land. In
11   order to produce policy relevant land use model exercises, reconciling these differences is needed by
12   harmonising definitions and approaches of anthropogenic land and the treatment of indirect
13   environmental change (Grassi et al. 2017).
15   7.3.2. Treatment of terrestrial carbon dioxide removal options including biomass supply for
16            bioenergy
17   Terrestrial Carbon Dioxide Removal (tCDR) options are only partially included in LUMs and mostly
18   rely on afforestation and bioenergy with CCS (BECCS) (Smith et al. 2019; Fuss et al. 2014, 2018; Minx
19   et al. 2018; Butnar et al. 2020). Especially some nature-based solutions (Griscom et al. 2017) such as
20   soil carbon management (Paustian et al. 2016) which have the potential to alter the contribution of land-
21   based mitigation in terms of timing, potential and sustainability consequences are only recently
22   becoming implemented in LUMs (Frank et al. 2017; Humpenöder et al. 2020). The representation of
23   bioenergy feedstocks varies across models but typically LUMs have comprehensive representation of a
24   series of crops (starch, sugar, oil, wood/lignocellulosic feedstocks) or residues/byproducts that can be
25   used for liquid and solid bioenergy production (Hanssen et al. 2019).
27   7.4. Treatment of environmental and socio-economic impacts of land use
28   Aside reporting the implications on AFOLU GHG emissions, LUMs can provide a set of environmental
29   and socioeconomic impact indicators to assess the quantified climate stabilisation pathways in a broader
30   sustainable development agenda (Frank et al. 2021; Obersteiner et al. 2016; Soergel et al. 2021; van
31   Vuuren et al. 2019, 2015). These indicators typically span from land use area developments (Popp et
32   al. 2017; Stehfest et al. 2019), fertilizer use, irrigation water use and environmental flows (Bonsch et
33   al. 2015; Pastor et al. 2019; Chang et al. 2021; de Vos et al. 2021), and on biodiversity (Leclère et al.
34   2020; Marquardt et al. 2021), to market impacts on commodity prices and food consumption, or impact
35   on undernourishment (Fujimori et al. 2019a; Hasegawa et al. 2018; Doelman et al. 2019; Hasegawa et
36   al. 2020; Soergel et al. 2021).

38   8. Reduced complexity climate modelling
39   Climate model emulators (often referred to as reduced complexity or simple climate models) are used
40   to integrate the WG I knowledge of physical climate science in WG III assessment. Hence, emulators
41   are used to assess the climate implications of the GHG and other emissions trajectories that IAMs
42   produce (van Vuuren et al. 2008; Rogelj et al. 2018a; Clarke et al. 2014; Rogelj et al. 2011; Schaeffer
43   et al. 2015). The IAM literature typically uses one of two approaches: comprehensive emulators such
44   as MAGICC (Meinshausen et al. 2011) or Hector (Hartin et al. 2015) or minimal complexity
45   representations such as the representation used in DICE (Nordhaus 2018), PAGE (Yumashev et al.
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1    2019; Kikstra et al. 2021c) and Fund (Waldhoff et al. 2014). In physical science research, a wider range
2    of different emulators are used (Nicholls et al. 2020b, 2021a).
3    A key application of emulators within IPCC WG III is the classification of emission scenarios with
4    respect to their global mean temperature outcomes (Clarke et al. 2014; Rogelj et al. 2018a). WG III
5    relies on emulators to assess the full range of carbon-cycle, and climate response uncertainty of
6    thousands of scenarios, as assessed by AR6 WG I. An exercise of such amplitude is currently infeasible
7    with more computationally demanding state-of-the-art Earth system models. Cross-chapter Box 7.1 of
8    WG I documents how emulators used in AR6 WG3 are consistent with the physical science assessment
9    of WG I (Forster et al. 2021).

10   Previous IPCC Assessment Reports relied either on the climate output from each individual IAM (IPCC
11   2000) or a more streamlined approach, where one consistent emulator setup was used to assess all
12   scenarios. For instance, in AR5 and SR1.5, MAGICC was used for scenario classification (Clarke et al.
13   2014; Rogelj et al. 2018a). In recent years, numerous other emulators have been developed and
14   increased confidence and understanding can thus be gained by combining insights from more than one
15   emulator. For example, SR1.5 used MAGICC for its scenario classification, with additional insights
16   provided by the FaIR model (Smith et al. 2018) The SR1.5 experience highlighted that the veracity of
17   emulators “is a substantial knowledge gap in the overall assessment of pathways and their temperature
18   thresholds” (Rogelj et al. 2018a). Since SR1.5, international research efforts have demonstrated
19   tractable ways to compare emulator performance (Nicholls et al. 2020b) as well as their ability to
20   accurately represent a set of uncertainty ranges in physical parameters (Nicholls et al. 2021b), such as
21   those reported by the AR6 WG I assessment (Forster et al. 2021).

22   Finally, the recently developed OpenSCM-Runner package (Nicholls et al. 2020a) provides users with
23   the ability to run multiple emulators from a single interface. OpenSCM-Runner has been built in
24   collaboration with the WG III research community and forms part of the WG III climate assessment
25   (Annex III.II.2.4.1).

27   9. Integrated assessment modelling
28   Process-based Integrated assessment models (IAMs) describe the coupled energy-land-economy-
29   climate system (Weyant 2009, 2017; Krey 2014). They typically capture all greenhouse gas (GHG)
30   emissions induced by human activities and, in many cases, other emissions of climate forcers like
31   sulphate aerosols. Process-based IAMs represent most GHG and climate pollutant emissions by
32   modelling the underlying processes in energy and land use. Those models are able to endogenously
33   describe the change in emissions due to changes in energy and land use activities, particularly in
34   response to climate action. But IAMs differ in the extent to which all emissions and the corresponding
35   sources, processes and activities are represented endogenously and, thus, can be subjected to policy
36   analysis.1 IAMs also differ regarding the scope of representing carbon removal options and their
37   interlinkage with other vital systems such as the energy and the land-use sectors.
38   Typically, IAMs consider multi-level systems of global, regional, national and local constraints and
39   balance equations for different categories such as emissions, material and energy flows, financial flows,
40   land availability that are solved simultaneously. Intertemporal IAMs can fully incorporate not only flow
41   constraints that are satisfied in each period, but also stock constraints that are aggregated over time and
42   require to balance activities over time. Changes of activities, e.g. induced by policies to reduce
43   emissions are connected to a variety of balance equations and constraints and therefore such policies

     FOOTNOTE1 See the common IAM documentation at
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1    lead to system wide changes that can be analysed with IAMs. Many IAMs also contain gridded
2    components to capture, e.g., land use and climate change processes where the spatial distribution
3    matters greatly for the dynamics of the system. Processes that operate on smaller spatial and temporal
4    scales than resolved by IAMs, such as temporal variability of renewables, are included by
5    parameterisation and statistical modelling approaches that capture the impact of these subscale
6    processes on the system dynamics at the macro level (Pietzcker et al. 2017).
 7   Global IAMs are used to analyse global emissions scenarios extrapolating current trends under a variety
 8   of assumptions and climate change action pathways under a variety of global goals. In recent years, a
 9   class of national and regional IAMs have emerged that describe the coupled energy-land-economy
10   system in a given geography. They typically have higher sectorial, policy and technology resolution
11   than global models and make assumptions about boundary conditions set by global markets and
12   international policy regimes. These IAMs are used to study trends and transformation pathways for a
13   given region (Shukla and Chaturvedi 2011; Capros et al. 2014; Lucena et al. 2016).
15   9.1. Types of Integrated Assessment Models
16   IAMs include a variety of model types that can be distinguished into two broad classes (Weyant 2017).
17   The first class comprises cost-benefit IAMs that fully integrate a stylized socioeconomic model with a
18   reduced form climate model to simultaneously account for the costs of mitigation and the damages of
19   global warming using highly aggregate cost functions derived from more detailed models. In the model
20   context these functions do not explicitly represent the underlying processes, but map mitigation efforts
21   and temperature to costs. This closed-loop approach between climate and socioeconomic systems
22   enables cost-benefit analysis by balancing the cost of mitigation and the benefits of avoided climate
23   damages. This can be done in a globally cooperative setting to derive the globally optimal climate policy
24   where no region can further improve its welfare without reducing the welfare of another region (Pareto
25   optimum). Alternatively, it can be assumed that nations do not engage in emission mitigation at all or
26   mitigate in a non-cooperative way only considering the marginal benefit of their own action (Nash
27   equilibrium). Also, differing degrees of partial cooperation are possible.
28   The second class of IAMs, called process-based IAMs, focuses on the analysis of transformation
29   processes depending on a broad set of activities that induce emissions as side effects. They describe the
30   interlinkages between economic activity, energy use, land use, and emissions with emission reductions
31   and removals as well as broader sustainable development targets. GHGs and other climate pollutants
32   are caused by a broad range of activities that are driven by socioeconomic developments (Riahi et al.
33   2017) and also induce broader environmental consequences such as land-use change (Popp et al. 2017)
34   and air pollution (Rao et al. 2017b). With few exceptions, these models typically do not close the loop
35   with climate change and damages that affect the economy, but focus on emission scenarios and climate
36   change mitigation pathways. Due to the process based representations of emission sources and
37   alternatives it is not only possible to investigate the implications of policies on GHG emissions, but also
38   the trade-offs and synergies with social and environmental sustainability criteria (von Stechow et al.
39   2015) (Annex III.I.9.3). The analysis of different cross-sectorial synergies and trade-offs is frequently
40   termed a nexus analysis, such as the energy-water-land nexus. The analysis can also address
41   socioeconomic sustainability criteria such as energy access and human health. Process-based IAMs are
42   also used to explore the synergies and trade-offs of ‘common, but differentiated responsibilities’ by
43   analysing issues of burden sharing, equity, international cooperation, policy differentiation and transfer
44   measures (Tavoni et al. 2015; Leimbach and Giannousakis 2019; Bauer et al. 2020b; Fujimori et al.
45   2016).

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1    There exists a broad range of detailed process IAMs that differ regarding the economic modelling
2    approaches (Annex III.I.2) as well as the methodology and detail of sector representation (Annex III.I.3-
3    7) and how they are interlinked with each other.
 4   This leads to differences in model results regarding global aggregates as well as sectorial and regional
 5   outputs. Several approaches have been used to evaluate the performance of IAMs and understand
 6   differences in IAM behaviour (Wilson et al. 2017a; Schwanitz 2013), including sensitivity analysis
 7   (McJeon et al. 2011; Luderer et al. 2013; Rogelj et al. 2013a; Bosetti et al. 2015; Marangoni et al. 2017;
 8   Giannousakis et al. 2021), model comparisons (Kriegler et al. 2014a, 2016; Tavoni et al. 2015; Kriegler
 9   et al. 2015a; Riahi et al. 2015; Clarke et al. 2009; Riahi et al. 2017; Luderer et al. 2018; Roelfsema et
10   al. 2020; van Soest et al. 2021; Riahi et al. 2021), model diagnostics (Kriegler et al. 2015a; Wilkerson
11   et al. 2015; Harmsen et al. 2021), and comparison with historical patterns (Wilson et al. 2013; van
12   Sluisveld et al. 2015; Napp et al. 2017).
14   9.2. Components of integrated assessment models
15   9.2.1. Energy-economy component
16   Typically, IAMs comprise a model of energy flows, emissions and the associated costs (Krey 2014).
17   The demand for exploring the Paris Agreement climate goals led to model developments to make the
18   challenges and opportunities of the associated transformation pathways more transparent. Since AR5
19   much progress has been achieved to improve the representation of mitigation options in the energy
20   supply sector (e.g. renewable energy integration (Pietzcker et al. 2017), energy trade (Bauer et al. 2017,
21   2016; Jewell et al. 2018; McCollum et al. 2016), capacity inertia, carbon removals, decarbonisation
22   bottlenecks (Luderer et al. 2018) and technological and behavioural change measures in energy demand
23   sectors such as transport (Edelenbosch et al. 2017a; van Sluisveld et al. 2016; McCollum et al. 2017).
24   An energy sector model can be run as a partial equilibrium model using exogenous demand drivers for
25   final energy and energy services. These models derive mitigation policy costs in terms of additional
26   energy sector costs and area under the MAC curve.
27   Energy models can be also embedded into a broader, long-term macroeconomic context in a general
28   equilibrium model (Messner and Schrattenholzer 2000; Bauer et al. 2008). The demands for final energy
29   and energy services are endogenously driven by an economic growth model that also endogenizes the
30   economic allocation problem of macroeconomic resources for the energy sector that crowd out with
31   alternatives. This allows impact analysis of climate policies on economic growth and structural change,
32   investment financing and crowding-out as well as income distribution and tax revenue recycling
33   (Guivarch et al. 2011). Moreover, general equilibrium models also derive mitigation costs in terms of
34   GDP losses and Consumption losses, which comprise the full macroeconomic impacts rather than only
35   the narrow energy related costs (Paltsev and Capros 2013).
36   9.2.2. Land system component
37   In recent years substantial efforts have been devoted to improve and integrate land-use sector models
38   in IAMs (Popp et al. 2014b, 2017). This acknowledges the importance of land-use GHG emissions of
39   the agricultural and forestry sectors as well as the role of bioenergy, afforestation and other land-based
40   mitigation measures. The integration is particularly important in light of the long-term climate goals of
41   the Paris Agreement for four reasons (IPCC 2019b). First, the GHG emissions from the land use sector
42   accounts for LUC emissions account for more than 10% of global GHG emissions (Kuramochi et al.
43   2020) and some sources of CH4 and N2O constitute serious mitigation bottlenecks. Second, bioenergy
44   is identified as crucial primary energy source for low-emission energy supply and carbon removal
45   (Bauer et al. 2020a; Butnar et al. 2020; Calvin et al. 2021). Third, land use-based mitigation measures
46   such as afforestation and reduced deforestation have substantial mitigation potentials. Finally, land-

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 1   cover changes alter the earth surface albedo, which has implications for regional and global climate.
 2   Pursuing the Paris Agreement climate goals requires the inclusion of a broad set of options regarding
 3   GHG emissions and removals, which will intensify the interaction between the energy, the economy
 4   and the land use sector. Consequently, intersectoral policy coordination becomes more important and
 5   the land-related synergies and trade-offs with sustainable development targets will intensify (Calvin et
 6   al. 2014b; Humpenöder et al. 2018; Frank et al. 2017; Kreidenweis et al. 2016; van Vuuren et al. 2017a;
 7   Bauer et al. 2020d). IAMs used by the IPCC in the AR6 have continuously improved the integration of
 8   land-use models with energy models to explore climate mitigation scenarios under varying policy and
 9   technology conditions (Rogelj et al. 2018a; Smith et al. 2019). However, feedbacks from changes in
10   climate variables are not or only to a limited degree included in the land use sector models.
11   .9.2.3. Climate system component
12   Reduced complexity climate models (often called simple climate models or emulators) are used for
13   communicating WG I physical climate science knowledge to the research communities associated with
14   other IPCC working groups (Annex III.I.8). They are used by IAMs to model the climate outcome of
15   the multi-gas emissions trajectories that IAMs produce (van Vuuren et al. 2011a). A main application
16   of such models is related to scenario classifications in WG III of the IPCC (Clarke et al. 2014; Rogelj
17   et al. 2018a). Since WG III assesses a large number of scenarios, it must rely on the use of these simple
18   climate models; more computationally demanding models (as used by WG I) will not be feasible to
19   apply. For consistency across the AR6 reports, it is important that these reduced-complexity models are
20   up to date with the latest assessments from IPCC WG I. This relies on calibrating these models so that
21   they match, as closely as possible, the assessments made by WG I (Annex III.II.2.4). The calibrated
22   models can then be used by WG III in various parts of its assessment.
24   9.3. Representation of nexus issues and sustainable development impacts in IAMs
25   An energy-water-land nexus approach integrates the analysis of linked resources and infrastructure
26   systems to provide a consistent platform for multi-sector decision-making (Howells et al. 2013). Many
27   of the IAMs that contributed to the assessment incorporate a nexus approach that considers
28   simultaneous constraints on land, water and energy, as well as important mutual dependencies (Calvin
29   et al. 2019; Fricko et al. 2017; Dietrich et al. 2019; Fujimori et al. 2017; van Vuuren et al. 2019).
30   Recently IAMs have also been integrated with life cycle assessment tools in assessing climate
31   mitigation policies to better understand the relevance of life cycle GHG emissions in cost-optimal
32   mitigation scenarios (Tokimatsu et al. 2020; Portugal-Pereira et al. 2016; Pehl et al. 2017; Arvesen et
33   al. 2018). This holistic perspective ensures mitigation pathways do not exacerbate challenges for other
34   sectors or environmental indicators. At the same time, pathways are leveraging potential synergies
35   along the way towards achieving multiple goals.
36   IAMs rely on biophysical models with a relatively high-degree of spatial and temporal resolution to
37   inform coarser scale economic models of the potentials and costs for land, water and energy systems
38   (Johnson et al. 2019). IAMs leverage population, GDP and urbanization projections to generate
39   consistent water, energy and crop demand projections across multiple sectors (e.g., agriculture,
40   livestock, domestic, manufacturing and electricity generation) (Mouratiadou et al., 2016). The highly-
41   distributed nature of decisions and impacts across sectors, particularly for land and water, has been
42   addressed using multi-scale frameworks that embed regional and sub-regional models within global
43   IAMs (Mosnier et al. 2014; Hejazi et al. 2015; Bijl et al. 2018; Portugal-Pereira et al. 2018). These
44   analyses have demonstrated how local constraints and policies interact with national and international
45   strategies aimed at reducing emissions.
46   Sustainable development impacts extending beyond climate outcomes have been assessed by the IAMs
47   that contributed to the assessment, particularly in the context of the targets and indicators consistent
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 1   with the Sustainable Development Goals (SDGs). The representation of individual SDGs is diverse
 2   (Figure I.3.), and recent model development has focused mainly on improving capabilities to assess
 3   climate change mitigation policy combined with indicators for economic growth, resource access, air
 4   pollution and land use (van Soest et al. 2019). Synergies and trade-offs across sustainable development
 5   objectives can be quantified by analysing multi-sector impacts across ensembles of IAM scenarios
 6   generated from single or multiple models (McCollum et al. 2013; Mouratiadou et al. 2016). Modules
 7   have also been developed for IAMs with the specific purpose of incorporating policies that address non-
 8   climatic sustainability outcomes (Fujimori et al. 2018; Parkinson et al. 2019; Cameron et al. 2016).
 9   Similar features have been utilized to incorporate explicit adaptation measures and targeted policies that
10   balance mitigation goals with other sustainability criteria (Bertram et al. 2018; McCollum et al. 2018).

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2       Figure I.3. The representation of SDGs by IAMs. a) Individual target coverage from a multi-model
3    survey; and b) SDG interactions and coverage by IAM models according to a combination of expert and
4    model surveys. The strength dimension of SDG interactions is indicated by grey shading: darker shades
5    represent strong interactions while white represents no interactions. Orange cells indicate where there is
6       the highest agreement between the importance of interactions and model representation, while blue
7     coloured cells show the most important interactions without model representation. Source: van Soest et
8                                                   al. (2019).

9    9.4. Policy analysis with IAMs
10   A key purpose of IAMs is to provide orientation knowledge for the deliberation of future climate action
11   strategies by policy makers, civil society and the private sector. This is done by presenting different
12   courses of actions (climate change and climate action pathways) towards a variety of long-term climate
13   outcomes under a broad range of assumptions about future socio-economic, institutional and
14   technological developments. The resulting climate change and climate action pathways can be analysed
15   in terms of their outcomes towards a set of societal goals (such as the SDGs) and the resulting trade-
16   offs between different pathways. Key trade-offs that have been investigated in the IAM literature are
17   between (1) no, moderate, and ambitious mitigation pathways (Riahi et al. 2017), (2) early vs. delayed
18   mitigation action (Riahi et al. 2015; Luderer et al. 2018), (3) global action with a focus on economic
19   efficiency equalizing marginal abatement costs across countries and sectors vs. regionally and
20   sectorially fragmented action (Kriegler et al. 2015b; Bertram et al. 2015; Kriegler et al. 2018b;
21   Roelfsema et al. 2020; Bauer et al. 2020b; Blanford et al. 2014a), (4) pathways with different emphasis
22   on supply side vs. demand side mitigation measures (van Vuuren et al. 2018; Grubler et al. 2018) or
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1    more broadly different sustainable development strategies (Riahi et al. 2012; van Vuuren et al. 2015;
2    Soergel et al. 2021), and (5) pathways with different preferences about technology deployment, in
3    particular with regard to CCS and carbon dioxide removals (Kriegler et al. 2014a; Krey 2014; Riahi et
4    al. 2015; Strefler et al. 2018, 2021b; Rose et al. 2020; Luderer et al. 2021). Key uncertainties that were
5    explored in the IAM literature are between (1) different socio-economic futures as, e.g., represented by
6    the Shared Socioeconomic Pathways (SSPs) (Riahi et al. 2017; Bauer et al. 2017; Popp et al. 2017), (2)
7    different technological developments (Bosetti et al. 2015) and (3) different resource potentials (Kriegler
8    et al. 2016).
 9   Policy analysis with IAMs follows the approach that a baseline scenario is augmented by some kind of
10   policy intervention. To address the uncertainties in baseline projections, the scientific community has
11   developed the Shared Socioeconomic Pathways (SSPs) that provide a set of vastly different future
12   developments as reference cases (Annex III.II.1.2.2). Most scenarios used in AR6 are based on the
13   middle-of-the-road reference system (SSP2). Depending on the research interest the baseline can be
14   defined as a no-policy baseline or it can include policies that either address GHG emissions like the
15   NDCs or other pre-existing policies such as energy subsidies and taxes. There is no standard definition
16   for baseline scenarios regarding the inclusion of policies. The baseline scenario is augmented by
17   additional policies like a carbon tax aiming towards a long-term climate goal. Hence, the IAM based
18   policy analysis assumes a reference system like SSP2 within which policy scenarios are compared with
19   a baseline scenario.
20   Most policy analysis with process-based IAMs apply a mix of short-term policy evaluation and long-
21   term policy optimization. Policy evaluation applies an exogenous set of policies such as the stated NDCs
22   and evaluates the emission outcomes. Policy optimization is mostly implemented as a cost-effectiveness
23   analysis: a long-term climate stabilisation target is set to derive the optimal mitigation strategy that
24   equalizes marginal abatement cost across sectors, GHGs and countries. This optimal mitigation strategy
25   can be implemented by a broad set of well-coordinated sector specific policies or by comprehensive
26   carbon pricing policies.
27   Most commonly the baseline scenario is either a no-policy baseline or based on the NDCs applying an
28   extrapolation beyond 2030 (Roelfsema et al. 2020; Grant et al. 2020). The climate policy regimes most
29   commonly applied include a long-term target to be reached. The optimal climate strategy can be phased
30   in gradually or applied immediately after 2020. It can focus on a global carbon price equalizing marginal
31   abatement costs across countries or policy intensities can vary across countries and sectors in the near-
32   to medium-term. The climate policy regime can or cannot include effort sharing mechanisms and
33   transfers between regions. Also, it can be extended to include additional sector policies such as
34   improved forest protection or fossil fuel subsidy removal. If certain technologies or activities are related
35   to spill-overs such as technology learning carbon-pricing might be complemented by technology
36   support (Schultes et al. 2018). If carbon pricing policies are fragmented or delayed additional and early
37   sector policies can help reduce distortions and carbon leakage effects (Bauer et al. 2020b). All these
38   variations to the policy regime can lead to very different transformation pathways and policy costs,
39   which is a core result of the IAM analysis.
40   By applying sensitivity analysis IAMs can be used to assess the importance to strategically develop new
41   technologies and options for mitigation and identify sticking points in climate policy frameworks. The
42   sensitivity analysis evaluates differences in outcomes subject to changes in assumptions. For instance,
43   the assumption about the timing and costs of CCS and CDR availability can be varied (Bauer et al.
44   2020a). The differences in mitigation costs and the transformation pathway support the assessment of
45   policy prioritization by identifying and quantifying crucial levers for achieving long-term climate
46   mitigation targets such as R&D efforts and timing of policies.

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1    9.5. Limitations of IAMs
 2   The application of IAMs and its results for providing orientation knowledge on climate change response
 3   strategies has been criticised based on four arguments (Keppo et al. 2021; Gambhir et al. 2019). First,
 4   there are concerns that IAMs are missing important dynamics, e.g. with regard to climate damages and
 5   economic co-benefits of mitigation (Stern 2016), demand side responses (Wilson et al. 2012),
 6   bioenergy, land degradation and management (Creutzig et al. 2014; IPCC 2019b), carbon dioxide
 7   removal (Smith et al. 2016), rapid technological progress in the renewable energy sector (Creutzig et
 8   al. 2017), actor heterogeneity, and distributional impacts of climate change and climate policy. This has
 9   given rise to criticism that IAMs lack credibility in set of crucial assumptions, among which stands out
10   the critique on the availability of carbon dioxide removal technologies (Bednar et al. 2019; Anderson
11   and Peters 2016).
12   These concerns spur continuous model development and improvements in scenario design (Keppo et
13   al. 2021), particularly with regard to improved representations of energy demand, renewable energy,
14   carbon dioxide removal technologies, and land management. IAMs are aiming to keep pace with the
15   development of sector-specific models, including latest advances in estimating and modelling climate
16   damages (Piontek et al. 2018). In places, where dynamic modelling approaches are lacking, scenarios
17   are being used to explore relevant futures (Grubler et al. 2018). Moreover, sector-specific model
18   comparison studies have brought together domain experts and modellers to improve model
19   representations in these areas (Pietzcker et al. 2017; Edelenbosch et al. 2017a; Harmsen et al. 2020;
20   Rose et al. 2020; Bauer et al. 2020a). Although most models are still relying on the concept of a single
21   representative household representing entire regions, efforts are under way to better represent agent
22   heterogeneity and distributional impacts of climate change and climate mitigation policies (Rao et al.
23   2017a; Peng et al. 2021).
24   Second, concerns have been raised that IAMs are non-transparent and thus make it difficult to grasp
25   context and meaning of their results (Skea et al. 2021). These concerns have facilitated a substantially
26   increase in model documentation (see the common IAM documentation at
27   as entry point) and open-source models. Nonetheless, more communication tools and co-production of
28   knowledge formats will be needed to contextualize IAM results for users (Auer et al. 2021). When
29   projecting over a century, uncertainties are large and cannot be ignored. Efforts have been undertaken
30   (Marangoni et al. 2017; Gillingham et al. 2018; Harmsen et al. 2021; Wilson et al. 2021) to diagnose
31   key similarities and differences between models and better gauge robust findings from these models
32   and how much they depend on key assumptions (as for example long term growth of the economy, the
33   monetary implication of climate damages or the diffusion and cost of key mitigation technologies).
34   Third, there are concerns that IAMs are describing transformative change on the level of energy and
35   land use, but are largely silent about the underlying socio-cultural transitions that could imply
36   restructuring of society and institutions. Weyant (2017) notes the inability of IAMs to mimic extreme
37   and discontinuous outcomes related to these underlying drivers as one of their major limitations. This
38   is relevant when modelling extreme climate damages as well as when modelling disruptive changes.
39   Dialogues and collaborative work between IAM researchers and social scientists have explored ways
40   to bridge insights from the various communities to provide a more complete picture of high impact
41   climate change scenarios and, on the other end, deep transformation pathways (Turnheim et al. 2015;
42   Geels et al. 2016; Trutnevyte et al. 2019). The extension of IAM research to sustainable development
43   pathways is giving rise to further inter-disciplinary research on underlying transformations towards the
44   Paris climate goals and other sustainable development goals (Kriegler et al. 2018c; Sachs et al. 2019b).
45   Finally, there are concerns that IAM analysis could focus on only a subset of relevant futures and thus
46   push society in certain direction without sufficient scrutiny (Beck and Mahony 2017). IAMs aim to
47   explore a wide range of socio-economic, technology and policy assumptions (Riahi et al. 2017), but it
48   remains a constant challenge to capture all relevant perspectives (O’Neill et al. 2020). These concerns
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1   can be addressed by adopting an iterative approach between researchers and societal actors in shaping
2   research questions and IAM applications (Edenhofer and Kowarsch 2015). IAM research is constantly
3   taking up concerns about research gaps and fills it with new pathway research, as e.g. occurred for low
4   energy demand and limited bioenergy with CCS scenarios (Grubler et al. 2018; van Vuuren et al. 2018).

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1   10. Key characteristics of models that contributed mitigation scenarios to the assessment2
2      Table I.5: Comparison of modelling characteristics as stated by contributing modelling teams to the AR6 database. Attributes include regional scope, sectoral
3   coverage, type of baseline or benchmark setup as a basis for mitigation policies comparison, technology diffusion, capital vintaging and "sunsetting" of technologies
4                                                                and variety of discount rates approaches.

                                                                                                                                                   Global integrated and energy models                                                                                                                                                                                                                                          National integrated models

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             STEM (Swiss TIMES Energy Systems Model)
                                                                                                                                                                                  GMM (Global MARKAL Model)

                                                                                                                                                                                                                                                                                                                                                          WEM (World Energy Model)
                                                                                                                                                                                                                                         MESSAGEix-GLOBIOM 1.1

                                                                                                                                                                                                                                                                                                                                REMIND 2.1 - MAgPIE 4.2

                                                                                                                                                                                                                                                                                                                                                                                                                                                                        E4SMA-EU-TIMES 1.0

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  TIMES-Sweden 2.0
                                                                                                                                                                                                                                                                                                                REmap GRO2020
                                                                                                                                 IMAGE 3.0 & 3.2

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      TIMES-China 2.0
                                                                                                                                                                                                                                                                                                                                                                                                                                                        CONTO-RUS 1.0


                                                                                                                                                                                                                                                                                                                                                                                                                                          China DREAM

                                                                                                                                                                                                                                                                                                 TIAM-ECN 1.1

                                                                                                                                                                                                              McKinsey 1.0

                                                                                                           COFFEE 1.1
                                                                                               C3IAM 2.0

                                                                                                                                                                                                                                                                                                                                                                                                                              BLUES 2.0

                                                                                                                                                                                                                                                                 MUSE 1.0
                                                                                                                        EPPA 6



                                      Non-global multi-region

                                      Full system (covering all GHGs from all sectors)
                  Sectoral coverage


5                                     Industry


    FOOTNOTE2 The tables are limited to the integrated models that have provided the information to a survey circulated in 2021, and therefore do not have a comprehensive
    coverage of all models that submitted scenarios to the AR6 scenario database.
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                                                                                                                                                                                                Global integrated and energy models                                                                                                                                                                                                                                          National integrated models

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          STEM (Swiss TIMES Energy Systems Model)
                                                                                                                                                                                                                               GMM (Global MARKAL Model)

                                                                                                                                                                                                                                                                                                                                                                                                       WEM (World Energy Model)
                                                                                                                                                                                                                                                                                      MESSAGEix-GLOBIOM 1.1

                                                                                                                                                                                                                                                                                                                                                                             REMIND 2.1 - MAgPIE 4.2

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     E4SMA-EU-TIMES 1.0

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               TIMES-Sweden 2.0
                                                                                                                                                                                                                                                                                                                                                             REmap GRO2020
                                                                                                                                                                              IMAGE 3.0 & 3.2

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   TIMES-China 2.0
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     CONTO-RUS 1.0


                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       China DREAM

                                                                                                                                                                                                                                                                                                                                              TIAM-ECN 1.1

                                                                                                                                                                                                                                                           McKinsey 1.0

                                                                                                                                                        COFFEE 1.1
                                                                                                                                            C3IAM 2.0

                                                                                                                                                                                                                                                                                                                                                                                                                                                                           BLUES 2.0

                                                                                                                                                                                                                                                                                                              MUSE 1.0
                                                                                                                                                                     EPPA 6


                  and "sunsetting" of Technology diffusion baseline/benchma

                                                                                 Well-functioning markets in equilibrium
                                                            Characteristics of

                                                                                 Regulatory and/or pricing policies
                                                                rk setup

                                                                                 Socioeconomic costs & benefits of climate
                                                                                 change impacts
                                                                                 Physical impacts of climate change on key
                                                                                 Logit substitution
                                                                                 Constant elasticity of substitution
                                                                                 Lowest marginal cost w/ expansion constraints
                                                                                 Technology choice depends on agents'
                                                                                 Technologies w/o constraints or marginal cost
                                                                                 w/ expansion constraints
                                                                                 Single capital stock with fixed lifetime and load
                   Capital vintaging

                                                                                 factor, early retirement via reduction in load

                                                                                 Capital vintaging with fixed lifetime and load
                                                                                 factors, early retirement of vintages or reduction
                                                                                 Single capital stock with fixed lifetime and load
                                                                                 factor, without early retirement
                                                                                 Mix of the above for different technologies
                                                                                 As a property of an intertemporal welfare
                                       Discount rates

                                                                                 function (social discount rate)
                                                                                 In an objective function of an intertemporal
                                                                                 optimization, to sum values at different times
                                                                                 To compute lifecycle costs of investment
                                                                                 decisions or return on investments, in functions
1                                                                                representing agents investment choices


    Do Not Cite, Quote or Distribute                                                                                   I-40                                                                                                                                                   Total pages: 119
    Final Government Distribution                                             Annex III                                                                                                                 IPCC AR6 WGIII

2    Table I.6: Overview of evaluated GHG emissions as stated by contributing modelling teams to the AR6 database: carbon dioxide (CO 2) from energy, industrial
3     processes and land use change, methane (CH4) from fossil fuel combustion, from fugitive and process activities, and agricultural biogenic fluxes, nitrous oxide
4     (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6), sulphur dioxide (SO2), black and organic carbon, and non-methane
5    volatile organic compounds (NMVOC). Levels of emission factor (EF) evaluation were classified in four categories: linked to explicit technology but for average
6                    fuel, linked to the evolution of other emissions, dependent on average technology classes, and based on an average activity sector.

            Type of GHG emissions evaluation                                                                                                 Global integrated and energy models                                                                                                                                                                                                                                              National integrated models

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     STEM (Swiss TIMES Energy Systems Model)
                   EF linked to explicit technology w/ or
                                 w/o fuel representation

                                                                                                                                                                          GMM (Global MARKAL Model)
                          EF linked to evolution of other

                                                                                                                                                                                                                                                                                                                                                  WEM (World Energy Model)

                                                                                                                                                                                                                                 MESSAGEix-GLOBIOM 1.1

                                                                                                                                                                                                                                                                                                                        REMIND 2.1 - MAgPIE 4.2
                        Average EF for technology class          c

                                                                                                                                                                                                                                                                                                                                                                                                                                                                E4SMA-EU-TIMES 1.0

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          TIMES-Sweden 2.0
                                           EF for sector         d

                                                                                                                                                                                                                                                                                                        REmap GRO2020
                                                                                                                         IMAGE 3.0 & 3.2

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              TIMES-China 2.0
                                                                                                                                                                                                                                                                                                                                                                                                                                                CONTO-RUS 1.0


                                                                                                                                                                                                                                                                                                                                                                                                                                  China DREAM

                                                                                                                                                                                                                                                                                         TIAM-ECN 1.1

                                                                                                                                                                                                      McKinsey 1.0
                                       Not represented           e

                                                                                                   COFFEE 1.1
                                                                                       C3IAM 2.0

                                                                                                                                                                                                                                                                                                                                                                                                                      BLUES 2.0

                                                                                                                                                                                                                                                         MUSE 1.0
                                                                                                                EPPA 6


                                                                      CO2 energy a      a           a            a         a                a        a       a                a                         a             a             a                     a         a        a             a              a                 a                         a                      a        a              a                 a           a              a                a                        a                                    a              a                 a             a           a
                                                     CO2 industrial processes a         d           a            a         a                b        a       e                d                         a             a             a                     a         a         c            a              a                 d                         a                      b        a              a                 a           a              a                a                        c                                    a              e                 e             a           a
                                                            CO2 land-use change a       d           a            a         a                b        a       e                e                         c             d             a                     e         d        e             a              d                 a                         e                       c       e              e                 a           e              d                e                        e                                    e              e                 e             e           e
                                                       CH4 fossil (combustion) a        a           a            a         a                b        a       e                e                         c             a             a                     a         a         c            a              e                 a                         a                      a        e              a                 a           a              a                e                        e                                    e              e                 e             a           e
                                              CH4 fossil (fugitive and process) a       d           a            a         a                b        a       e                e                         a             a             a                     e         a         c            a              e                  c                        e                      d        e              d                 a           a              c                e                        e                                    e              e                 e             a           e
                                                                     CH4 biogenic a     e           a            a         a                b        a       e                e                         a             d             a                     e         d        b             a              e                 d                         e                       c       e              a                 a           e              d                e                        e                                    e              e                 e             a           e
                                                                            N2 O a      d           a            a         a                b        a       e                e                         a             d             a                     a         d         c            a              e                 a                         e                       c       e              a                 a           a              a                e                        e                                    e              e                 e             a           e
                                                                            HFCs d      e           e            a         a                e        a       e                e                         e             d             d                     e          c       d             e              e                 e                         e                      d        e              c                 e           a              b                e                        e                                    e              e                 e             e           e
                                                                            PFCs d      e           e            a         a                e        a       e                e                         e             d             e                     e          c       d             e              e                 e                         e                      d        e              c                 e           a              b                e                        e                                    e              e                 e             e           e
                                                                             SF6 d      e           e            a         a                e        a       e                e                         e             d             d                     e          c       d             e              e                 e                         e                      d        e              c                 e           a              b                e                        e                                    e              e                 e             e           e
                                                                             SO2 a      a           e            d         a                e        a       e                e                         e             d             a                     e         a        e             e              a                 a                         e                      a        e              e                 a           e              b                e                        e                                    e              e                 e             e           a
                                                                     Black carbon a     d           e            d         a                e        a       e                e                         e             e             a                     e         a        e             e              e                 a                         e                      a        e              e                 e           a              e                e                        e                                    e              e                 e             e           a
                                                                Organic carbon a        d           e            d         a                e        a       e                e                         e             e             a                     e         a        e             e              e                 a                         e                      a        e              e                 e           e              e                e                        e                                    e              e                 e             e           a

7                        Non-methane volatile organic compounds (NMVOC) a               a           e            d         a                e        a       e                e                         e             d             a                     e         a        e             e              a                 a                         e                      a        e              e                 a           e              e                e                        e                                    e              e                 e             e           a

    Do Not Cite, Quote or Distribute                                      I-41                                                                                                                                                   Total pages: 119
    Final Government Distribution                                        Annex III                                                                                                              IPCC AR6 WGIII

1   11. Comparison of mitigation and removal measures represented by models that contributed mitigation scenarios to the
2        assessment3
3       Table I.7: Overview of demand- and supply-side mitigation and removal measures in the energy, transport, building, industry and AFOLU sectors, as stated by
4    contributing modelling teams to the AR6 database. Levels of inclusion were classified in two dimensions of explicit versus implicit and endogenous or exogenous. An
5      explicit level suggests that the measure is directly represented in the model, while an implicit level refers to measures that are estimated indirectly by a proxy. An
6    endogenous level reflects measures that are included in the dynamics of the model framework, whereas an exogenous level refers to measures that are not part of the
7                                                                                model dynamics.

    Level of inclusion                                                                                                           Global integrated and energy models                                                                                                                                                                                                                                           National integrated models

                                                                                                                                                                                                                                                                                                                                                                                                                                                                      STEM (Swiss TIMES Energy Systems Model)
                                                                                                                                                           GMM (Global MARKAL Model)

                                                                                                                                                                                                                                                                                                                                   WEM (World Energy Model)
                                                                                                                                                                                                                  MESSAGEix-GLOBIOM 1.1

                                                                                                                                                                                                                                                                                                         REMIND 2.1 - MAgPIE 4.2

                                                                                                                                                                                                                                                                                                                                                                                                                                                 E4SMA-EU-TIMES 1.0

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           TIMES-Sweden 2.0
                                                                                                                                                                                                                                                                                         REmap GRO2020
                                                                                                          IMAGE 3.0 & 3.2

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               TIMES-China 2.0
                                                                                                                                                                                                                                                                                                                                                                                                                                 CONTO-RUS 1.0


                                                                                                                                                                                                                                                                                                                                                                                                                   China DREAM
                                                                                                                                                                                                                                                                          TIAM-ECN 1.1

                                                                                                                                                                                       McKinsey 1.0

                                                                                    COFFEE 1.1
                                                                        C3IAM 2.0

                                                                                                                                                                                                                                                                                                                                                                                                       BLUES 2.0
                                                                                                                                                                                                                                          MUSE 1.0
                                                                                                 EPPA 6



                                      Demand side measures
          Energy efficiency improvements in energy end uses       A     B           A            C        A                 A         C      B             A                           A              C           C                       A          C       A            A              A               A                         A                          C       B             A                  A           B             A               A                    A                                         A              A                 A              A          A
                          Electrification of transport demand     A     C           A            A        A                 A         A      A             A                           A              A           A                       A          A       A            A              A               A                         A                          A       B             A                  A           B             B               A                    A                                         A              A                 A              A          A
                Electrification of energy demand for buildings    A     C           A            C        A                 C         A      A             A                           A              A           A                       A          A       A            A              A               A                         A                          C       B             A                  A           B             B               A                    A                                         A              A                 A              A          A
                   Electrification of industrial energy demand    A     C           A            C        A                 C         C      A             A                           A              A           A                       A          C       A            A              A               A                         A                          C       B             A                  A           B             B               A                    A                                         A              A                 A              A          A
                         CCS in industrial process applications   A     A           A            A        A                 E         A      E             A                           A              A           A                       A          A       A            A              A               A                         A                          C       B             A                  A           E             B               A                    A                                         A              A                 A              A          A

    FOOTNOTE3 The tables are limited to the integrated models that have provided the information to a survey circulated in 2021, and therefore do not have a comprehensive coverage
    of all models that submitted scenarios to the AR6 scenario database.
    Do Not Cite, Quote or Distribute                               I-42                                                                                                                                           Total pages: 119
Final Government Distribution                                        Annex III                                                                                                              IPCC AR6 WGIII

Level of inclusion                                                                                                           Global integrated and energy models                                                                                                                                                                                                                                           National integrated models

                                                                                                                                                                                                                                                                                                                                                                                                                                                                  STEM (Swiss TIMES Energy Systems Model)
                                                                                                                                                       GMM (Global MARKAL Model)

                                                                                                                                                                                                                                                                                                                               WEM (World Energy Model)
                                                                                                                                                                                                              MESSAGEix-GLOBIOM 1.1

                                                                                                                                                                                                                                                                                                     REMIND 2.1 - MAgPIE 4.2

                                                                                                                                                                                                                                                                                                                                                                                                                                             E4SMA-EU-TIMES 1.0

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       TIMES-Sweden 2.0
                                                                                                                                                                                                                                                                                     REmap GRO2020
                                                                                                      IMAGE 3.0 & 3.2

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           TIMES-China 2.0
                                                                                                                                                                                                                                                                                                                                                                                                                             CONTO-RUS 1.0


                                                                                                                                                                                                                                                                                                                                                                                                               China DREAM
                                                                                                                                                                                                                                                                      TIAM-ECN 1.1

                                                                                                                                                                                   McKinsey 1.0

                                                                                COFFEE 1.1
                                                                    C3IAM 2.0

                                                                                                                                                                                                                                                                                                                                                                                                   BLUES 2.0
                                                                                                                                                                                                                                      MUSE 1.0
                                                                                             EPPA 6


              Higher share of useful energy in final energy   C     B           A            C        A                 D         A      D             A                           A              C           C                       A          A       C            A              C               C                         A                          C       B             A                  A           B             B               A                    A                                         A              A                 A              A          A
          Reduced energy and service demand in industry       C     C           A            C        A                 C         C      D             C                           B              C           C                       D          C       C            C              B               C                         B                          C       B             B                  A           A             C               C                    A                                         C              C                 C              B          A
         Reduced energy and service demand in buildings       C     C           D            C        A                 D         C      D             C                           B              C           C                       D          A       A            C              B               C                         B                          C       B             B                  A           B             D               C                    A                                         C              C                 C              B          B
        Reduced energy and service demand in transport        C     C           A            C        A                 A         A      A             D                           B              D           C                       D          A       C            C              B               C                         B                          D       B             B                  A           B             D               C                    A                                         C              C                 C              B          A
     Reduced energy and service demand in international
                                                              C     E           A            C        C                 C         C      D             D                           B              D           C                       D          A       C            C              B               C                         B                          D       B             E                  A           B             E               C                    E                                         C              C                 C              E          B
                                Reduced material demand       C     B           B            C        C                 D         E      E             E                           A              E           E                       E          E       E            E              B               E                         B                          E       D             B                  B           B             D               E                    E                                         C              C                 B              B          B
                                               Urban form     E     E           B            E        C                 D         E      D             E                           E              E           E                       E          E       E            E              E               E                         C                          E       D             B                  B           B             E               E                    A                                         E              E                 E              E          D
        Switch from traditional biomass and modern fuels      B     A           A            B        A                 E         A      C             E                           B              A           D                       A          A       A            A              A               B                         A                          D       B             E                  A           B             B               A                    A                                         A              A                 E              A          E
       Dietary changes (e.g., reducing meat consumption)      B     E           B            A        B                 B         A      E             E                           A              E           A                       E          E       E            E              E               B                         E                          E       E             E                  B           E             E               E                    E                                         E              E                 E              E          E
                                          Food processing     A     E           A            C        B                 B         E      E             E                           E              A           E                       E          E       E            E              E               E                         E                          E       D             E                  A           E             E               E                    E                                         E              E                 E              E          E
                                   Reduction of food waste    B     E           E            E        B                 E         C      E             E                           B              E           B                       E          E       E            E              E               B                         E                          E       E             E                  D           E             E               E                    E                                         E              E                 E              E          E
 Substitution of livestock-based products with plant-based
                                                              A     E           B            A        B                 D         E      E             E                           B              E           E                       E          E       E            E              E               B                         E                          E       E             E                  B           E             E               E                    E                                         E              E                 E              E          E
                                    Supply side measures
                            Decarbonisation of electricity:
                                                  Solar PV    A     A           A            A        A                 A         A      A             A                           A              A           A                       A          A       A            A              A               A                         A                          A       B             A                  A           A             A               A                    A                                         A              A                 A              A          A
                                                 Solar CSP    E     E           A            E        A                 A         A      A             A                           A              A           A                       A          A       A            A              A               A                         A                          A       B             E                  A           A             E               A                    A                                         A              A                 A              A          E
                                              Hydropower      A     A           A            A        A                 A         B      A             A                           A              A           A                       A          A       A            A              A               A                         A                          D       B             A                  A           A             A               A                    A                                         A              A                 A              A          A
                                           Nuclear energy     A     A           A            A        A                 A         A      A             A                           A              A           A                       A          A       A            A              A               A                         A                          A       B             A                  A           A             A               A                    A                                         A              A                 A              A          A
  Advanced, small modular nuclear reactor designs (SMR)       E     E           E            C        C                 E         E      E             A                           E              A           E                       E          E       C            A              D               E                         C                          E       B             E                  E           E             E               A                    A                                         E              E                 E              E          E

Do Not Cite, Quote or Distribute                               I-43                                                                                                                                           Total pages: 119
Final Government Distribution                                         Annex III                                                                                                              IPCC AR6 WGIII

Level of inclusion                                                                                                            Global integrated and energy models                                                                                                                                                                                                                                           National integrated models

                                                                                                                                                                                                                                                                                                                                                                                                                                                                   STEM (Swiss TIMES Energy Systems Model)
                                                                                                                                                        GMM (Global MARKAL Model)

                                                                                                                                                                                                                                                                                                                                WEM (World Energy Model)
                                                                                                                                                                                                               MESSAGEix-GLOBIOM 1.1

                                                                                                                                                                                                                                                                                                      REMIND 2.1 - MAgPIE 4.2

                                                                                                                                                                                                                                                                                                                                                                                                                                              E4SMA-EU-TIMES 1.0

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        TIMES-Sweden 2.0
                                                                                                                                                                                                                                                                                      REmap GRO2020
                                                                                                       IMAGE 3.0 & 3.2

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            TIMES-China 2.0
                                                                                                                                                                                                                                                                                                                                                                                                                              CONTO-RUS 1.0


                                                                                                                                                                                                                                                                                                                                                                                                                China DREAM
                                                                                                                                                                                                                                                                       TIAM-ECN 1.1

                                                                                                                                                                                    McKinsey 1.0

                                                                                 COFFEE 1.1
                                                                     C3IAM 2.0

                                                                                                                                                                                                                                                                                                                                                                                                    BLUES 2.0
                                                                                                                                                                                                                                       MUSE 1.0
                                                                                              EPPA 6


                                      Fuel cells (hydrogen)    E     A           A            A        A                 E         A      A             A                           A              A           A                       A          A       A            A              B               A                         A                          A       B             A                  A           B             E               A                    A                                         A              A                 A              A          A
                     CCS at coal and gas-fired power plants    A     A           A            A        A                 A         A      A             A                           A              A           A                       A          A       A            A              A               A                         A                          A       B             A                  A           E             B               A                    A                                         A              A                 A              A          A
             Ocean energy (incl. tidal and current energy)     E     E           E            E        C                 E         E      A             A                           D              A           E                       E          A       E            A              A               E                         A                          E       B             A                  E           E             E               A                    A                                         A              A                 A              A          E
                       High-temperature geothermal heat        A     E           A            E        C                 E         A      A             A                           D              A           A                       A          A       C            A              A               A                         A                          E       B             A                  E           E             B               A                    A                                         A              A                 A              A          E
          Wind (on-shore and off-shore lumped together)        A     A           E            A        A                 A         E      A             E                           E              A           A                       E          E       A            A              A               A                         A                          A       B             E                  E           A             A               A                    A                                         E              E                 E              E          E
   Wind (on-shore and off-shore represented individually)      E     E           A            E        A                 A         A      A             A                           A              A           A                       A          A       A            A              A               E                         A                          A       B             A                  A           E             A               A                    E                                         A              A                 A              A          A
   Bio-electricity, including biomass co-firing, without CCS   A     A           A            A        A                 A         A      A             A                           A              A           A                       A          A       A            A              A               A                         A                          A       B             A                  A           A             B               A                    A                                         A              A                 A              A          A
      Bio-electricity, including biomass co-firing, with CCS   A     A           A            A        A                 A         A      A             A                           A              A           A                       A          A       A            A              A               A                         A                          A       B             A                  A           E             E               A                    A                                         A              A                 A              A          A
                     Decarbonisation of non-electric fuels:
                                   1st generation biofuels     A     E           A            A        A                 A         A      E             A                           C              A           B                       A          A       A            A              A               B                         A                          A       B             A                  A           B             E               A                    A                                         A              A                 A              A          A
          2nd generation biofuels (grassy/woody biomass
                                                               A     E           A            A        A                 A         A      A             A                           C              A           A                       A          A       C            A              A               A                         A                          A       B             A                  A           E             E               A                    A                                         A              A                 A              A          A
                                   to liquids) without CCS
          2nd generation biofuels (grassy/woody biomass
                                                               A     E           A            A        A                 A         A      A             A                           C              A           A                       A          A       C            A              E               A                         A                          A       B             A                  A           E             E               A                    A                                         A              A                 A              A          A
                                       to liquids) with CCS
                             Solar and geothermal heating      A     E           A            E        C                 E         E      A             A                           C              A           A                       E          A       C            A              A               A                         A                          E       B             A                  A           B             B               A                    A                                         A              A                 A              A          A
                                      Nuclear process heat     E     E           E            E        C                 E         E      E             A                           E              A           E                       E          E       C            E              E               E                         A                          E       B             E                  E           E             B               A                    A                                         A              E                 E              E          E
                       Hydrogen from fossil fuels with CCS     E     E           A            A        A                 E         C      A             A                           A              A           A                       A          A       A            A              A               A                         A                          A       B             A                  A           E             E               A                    A                                         A              A                 A              A          A
                                Hydrogen from electrolysis     E     E           A            A        A                 E         A      A             A                           A              A           A                       A          A       A            A              A               A                         A                          A       B             A                  A           B             E               A                    A                                         A              A                 A              A          A
                      Hydrogen from biomass without CCS        E     E           A            A        A                 A         A      E             A                           D              A           A                       A          A       A            A              A               A                         A                          E       B             A                  A           E             E               A                    A                                         A              E                 A              A          A
                         Hydrogen from biomass with CCS        E     E           A            A        A                 E         A      E             A                           D              A           A                       A          A       A            A              A               A                         A                          E       B             A                  A           E             E               A                    A                                         A              E                 A              A          A
                                Algae biofuels without CCS     E     E           E            E        E                 E         E      E             E                           E              E           E                       A          E       E            E              E               E                         C                          E       B             E                  E           E             E               E                    E                                         E              E                 E              E          E

Do Not Cite, Quote or Distribute                                I-44                                                                                                                                           Total pages: 119
Final Government Distribution                                           Annex III                                                                                                              IPCC AR6 WGIII

Level of inclusion                                                                                                              Global integrated and energy models                                                                                                                                                                                                                                           National integrated models

                                                                                                                                                                                                                                                                                                                                                                                                                                                                     STEM (Swiss TIMES Energy Systems Model)
                                                                                                                                                          GMM (Global MARKAL Model)

                                                                                                                                                                                                                                                                                                                                  WEM (World Energy Model)
                                                                                                                                                                                                                 MESSAGEix-GLOBIOM 1.1

                                                                                                                                                                                                                                                                                                        REMIND 2.1 - MAgPIE 4.2

                                                                                                                                                                                                                                                                                                                                                                                                                                                E4SMA-EU-TIMES 1.0

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          TIMES-Sweden 2.0
                                                                                                                                                                                                                                                                                        REmap GRO2020
                                                                                                         IMAGE 3.0 & 3.2

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              TIMES-China 2.0
                                                                                                                                                                                                                                                                                                                                                                                                                                CONTO-RUS 1.0


                                                                                                                                                                                                                                                                                                                                                                                                                  China DREAM
                                                                                                                                                                                                                                                                         TIAM-ECN 1.1

                                                                                                                                                                                      McKinsey 1.0

                                                                                   COFFEE 1.1
                                                                       C3IAM 2.0

                                                                                                                                                                                                                                                                                                                                                                                                      BLUES 2.0
                                                                                                                                                                                                                                         MUSE 1.0
                                                                                                EPPA 6


                                   Algae biofuels with CCS       E     E           E            E        E                 E         E      E             E                           E              E           E                       E          E       E            E              E               E                         C                          E       B             E                  E           E             E               E                    E                                         E              E                 E              E          E
    Power-to-gas, methanisation, synthetic fuels, fed with
                                                                 E     E           A            A        C                 E         E      A             A                           E              A           E                       E          A       A            A              A               E                         A                          E       B             A                  A           E             E               A                    A                                         A              E                 A              A          A
                                                   fossil CO2
 Power-to-gas, methanisation, syn-fuels, fed with biogenic
                                                                 E     E           A            E        C                 E         E      A             A                           E              A           E                       E          A       A            A              A               E                         A                          E       B             A                  A           E             E               A                    A                                         A              E                 A              A          A
                                        or atmospheric CO2
   Fuel switching and replacing fossil fuels by electricity in
                                                                 C     A           A            A        A                 A         A      A             A                           A              A           A                       A          A       A            A              A               A                         A                          A       B             A                  A           B             B               A                    A                                         A              A                 A              A          A
                                            end-use sectors
                                            Other processes:
Substitution of halocarbons for refrigerants and insulation      E     E           E            E        A                 E         C      E             E                           E              E           D                       E          C       C            E              E               E                         E                          C       E             A                  E           B             E               E                    E                                         E              E                 E              E          E
    Reduced gas flaring and leakage in extractive industries     C     E           A            B        C                 E         C      E             E                           A              E           D                       E          A       E            B              E               C                         A                          C       E             E                  A           B             B               E                    E                                         E              E                 E              E          E
 Electrical transmission efficiency improvements, including
                                                                 E     E           A            C        C                 E         E      D             C                           E              D           E                       E          E       C            B              A               E                         A                          C       D             E                  A           B             B               B                    C                                         E              A                 E              B          E
                                                smart grids
               Grid integration of intermittent renewables       C     E           A            C        A                 C         A      C             C                           E              C           A                       A          A       C            A              A               A                         A                          A       D             A                  A           A             E               A                    A                                         C              E                 E              A          C
                                           Electricity storage   C     D           A            A        A                 E         A      A             C                           A              C           A                       A          A       A            A              A               A                         A                          A       B             A                  A           A             D               A                    A                                         C              A                 A              A          A
                                        AFOLU Measures:
  Reduced deforestation, forest protection, avoided forest
                                                                 A     D           A            A        A                 B         A      E             E                           A              E           A                       E          C       E            B              D               A                         E                          C       E             E                  A           E             B               E                    E                                         E              E                 E              E          E
                        Methane reductions in rice paddies       A     E           A            C        A                 C         C      E             E                           A              E           A                       E          C       E            B              E               C                         E                          C       E             A                  A           B             E               E                    E                                         E              E                 E              E          E
                        Livestock and grazing management         A     E           A            C        A                 A         C      E             E                           A              E           A                       E          C       E            B              E               C                         E                          C       E             A                  A           B             D               E                    E                                         E              E                 E              E          E
                         Increasing agricultural productivity    A     C           A            C        A                 A         A      E             E                           A              E           A                       A          C       E            D              D               C                         E                          E       E             E                  A           E             D               E                    E                                         E              E                 E              E          E
                              Nitrogen pollution reductions      A     E           B            C        A                 A         A      E             E                           A              E           A                       E          C       E            D              E               C                         E                          E       E             A                  B           B             B               E                    E                                         E              E                 E              E          E
     Changing agricultural practices enhancing soil carbon       E     E           E            C        A                 E         E      E             E                           A              E           E                       A          C       E            B              E               E                         E                          E       E             E                  A           E             D               E                    E                                         E              E                 E              E          E

Do Not Cite, Quote or Distribute                                  I-45                                                                                                                                           Total pages: 119
Final Government Distribution                                       Annex III                                                                                                              IPCC AR6 WGIII

Level of inclusion                                                                                                          Global integrated and energy models                                                                                                                                                                                                                                           National integrated models

                                                                                                                                                                                                                                                                                                                                                                                                                                                                 STEM (Swiss TIMES Energy Systems Model)
                                                                                                                                                      GMM (Global MARKAL Model)

                                                                                                                                                                                                                                                                                                                              WEM (World Energy Model)
                                                                                                                                                                                                             MESSAGEix-GLOBIOM 1.1

                                                                                                                                                                                                                                                                                                    REMIND 2.1 - MAgPIE 4.2

                                                                                                                                                                                                                                                                                                                                                                                                                                            E4SMA-EU-TIMES 1.0

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      TIMES-Sweden 2.0
                                                                                                                                                                                                                                                                                    REmap GRO2020
                                                                                                     IMAGE 3.0 & 3.2

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          TIMES-China 2.0
                                                                                                                                                                                                                                                                                                                                                                                                                            CONTO-RUS 1.0


                                                                                                                                                                                                                                                                                                                                                                                                              China DREAM
                                                                                                                                                                                                                                                                     TIAM-ECN 1.1

                                                                                                                                                                                  McKinsey 1.0

                                                                               COFFEE 1.1
                                                                   C3IAM 2.0

                                                                                                                                                                                                                                                                                                                                                                                                  BLUES 2.0
                                                                                                                                                                                                                                     MUSE 1.0
                                                                                            EPPA 6


                             Agroforestry and silviculture   E     C           A            E        D                 E         E      E             E                           B              E           E                       E          E       E            B              E               E                         E                          E       E             E                  A           E             D               E                    E                                         E              E                 E              E          E
                                       Land-use planning     E     D           A            E        B                 E         E      E             E                           E              E           E                       E          E       E            E              E               E                         E                          E       E             E                  A           E             B               E                    E                                         E              E                 E              E          E
            Urban and peri-urban agriculture and forestry    E     E           E            E        D                 E         E      E             E                           E              E           E                       E          E       E            E              E               E                         E                          E       E             E                  E           E             E               E                    E                                         E              E                 E              E          E
           Fire management and (ecological) pest control     C     E           E            E        D                 E         D      E             E                           E              E           E                       E          E       E            E              E               E                         E                          E       E             E                  E           E             D               E                    E                                         E              E                 E              E          E
                                 Conservation agriculture    E     E           A            E        D                 E         E      E             E                           E              E           A                       E          E       E            D              E               E                         E                          E       E             E                  A           E             E               E                    E                                         E              E                 E              E          E
              Influence on land albedo of land use change    E     E           E            E        A                 E         E      E             E                           E              E           E                       E          E       E            E              E               D                         E                          E       E             E                  E           E             E               E                    E                                         E              E                 E              E          E
                                   Manure management         A     E           E            E        A                 C         C      E             E                           A              E           A                       E          E       E            B              E               C                         E                          C       E             A                  A           B             E               E                    E                                         E              E                 E              E          B
                         Reduce food post-harvest losses     B     D           E            E        D                 E         D      E             E                           E              E           B                       E          E       E            E              E               E                         E                          E       E             E                  E           E             E               E                    E                                         E              E                 E              E          E
            Recovery of forestry and agricultural residues   E     E           A            E        A                 B         A      E             E                           E              E           A                       E          C       E            E              D               E                         E                          E       E             E                  A           E             B               E                    E                                         E              E                 E              E          E
      Forest Management – increasing forest productivity     C     E           E            C        C                 B         D      E             E                           E              E           A                       E          C       E            E              D               E                         E                          C       E             E                  E           E             D               E                    E                                         E              E                 E              E          A
        Forest Management – increasing timber/biomass
                                                             C     E           E            E        C                 B         D      E             E                           E              E           A                       E          C       E            E              D               E                         E                          C       E             E                  E           E             D               E                    E                                         E              E                 E              E          A
  Forest Management – remediating natural disturbances       E     E           E            E        B                 B         E      E             E                           E              E           E                       E          E       E            E              E               E                         E                          C       E             E                  E           E             D               E                    E                                         E              E                 E              E          E
          Forest Management – conservation for carbon
                                                             E     D           E            E        B                 B         D      E             E                           A              E           A                       E          E       E            E              D               E                         E                          C       E             E                  E           E             E               E                    E                                         E              E                 E              E          E
                               Carbon dioxide removal:
           Bioenergy production with carbon capture and
                                                             A     A           A            A        A                 A         A      A             A                           A              A           A                       A          A       A            A              A               A                         A                          A       B             A                  A           E             E               A                    A                                         A              A                 A              A          A
                                  sequestration (BECCS)
                    Direct air capture and storage (DACS)    E     E           A            A        A                 E         E      A             A                           A              A           E                       E          A       A            A              A               A                         A                          A       B             A                  A           E             E               E                    A                                         A              A                 E              A          E
    Mineralization of atmospheric CO2 through enhanced
                                                             E     E           E            E        E                 E         E      E             E                           C              E           E                       E          E       E            E              E               A                         E                          E       E             E                  E           E             E               E                    E                                         E              E                 E              E          E
                                      weathering of rocks
                            Afforestation / Reforestation    A     A           A            A        A                 B         A      E             E                           C              E           A                       E          C       C            B              C               A                         E                          A       E             E                  A           E             B               E                    E                                         E              E                 E              E          E

Do Not Cite, Quote or Distribute                              I-46                                                                                                                                           Total pages: 119
    Final Government Distribution                                         Annex III                                                                                                              IPCC AR6 WGIII

    Level of inclusion                                                                                                            Global integrated and energy models                                                                                                                                                                                                                                           National integrated models

                                                                                                                                                                                                                                                                                                                                                                                                                                                                       STEM (Swiss TIMES Energy Systems Model)
                                                                                                                                                            GMM (Global MARKAL Model)

                                                                                                                                                                                                                                                                                                                                    WEM (World Energy Model)
                                                                                                                                                                                                                   MESSAGEix-GLOBIOM 1.1

                                                                                                                                                                                                                                                                                                          REMIND 2.1 - MAgPIE 4.2

                                                                                                                                                                                                                                                                                                                                                                                                                                                  E4SMA-EU-TIMES 1.0

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            TIMES-Sweden 2.0
                                                                                                                                                                                                                                                                                          REmap GRO2020
                                                                                                           IMAGE 3.0 & 3.2

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                TIMES-China 2.0
                                                                                                                                                                                                                                                                                                                                                                                                                                  CONTO-RUS 1.0


                                                                                                                                                                                                                                                                                                                                                                                                                    China DREAM
                                                                                                                                                                                                                                                                           TIAM-ECN 1.1

                                                                                                                                                                                        McKinsey 1.0

                                                                                     COFFEE 1.1
                                                                         C3IAM 2.0

                                                                                                                                                                                                                                                                                                                                                                                                        BLUES 2.0
                                                                                                                                                                                                                                           MUSE 1.0
                                                                                                  EPPA 6


                                       Restoration of wetlands     E     E           E            E        C                 E         E      E             E                           E              E           E                       E          E       E            E              E               E                         E                          E       E             E                  E           E             E               E                    E                                         E              E                 E              E          E
                                                        Biochar    E     E           E            E        D                 E         E      E             E                           E              A           E                       E          E       E            E              E               E                         E                          E       E             E                  E           E             E               E                    A                                         E              E                 E              E          E
                 Soil carbon enhancement, enhancing carbon
                                                                   E     E           A            C        D                 D         E      E             E                           E              A           A                       E          C       E            E              E               C                         E                          E       E             E                  A           E             E               E                    E                                         E              E                 E              E          E
                               sequestration in biota and soils
             Material substitution of fossil CO2 with bio-CO2 in
                                                                   E     E           A            C        A                 E         E      E             E                           E              A           E                       E          E       E            D              E               E                         A                          E       D             E                  A           E             E               A                    E                                         E              E                 E              E          E
                                          industrial application
                                        Ocean iron fertilization   E     E           E            E        E                 E         E      E             E                           E              E           E                       E          E       E            E              E               E                         E                          E       E             E                  E           E             E               E                    E                                         E              E                 E              E          E
                                           Ocean alkalinisation    E     E           E            E        E                 E         E      E             E                           E              E           E                       E          E       E            E              E               E                         E                          E       E             E                  E           E             E               E                    E                                         E              E                 E              E          E
                             Carbon capture and usage (CCU):
     Bioplastics, carbon fibre and other construction materials    E     E           A            A        E                 E         C      E             A                           D              A           E                       E          E       A            E              A               E                         A                          E       E             E                  A           E             B               A                    E                                         A              E                 E              A          E

    Do Not Cite, Quote or Distribute                                I-47                                                                                                                                           Total pages: 119
     Final Government Distribution                          Annex III                      IPCC AR6 WGIII

1                                    Part II.        Scenarios

3    1. Overview on climate change scenarios
4    Scenarios are descriptions of alternative future developments. They are used to explore the potential
5    implications of possible future developments and how they might depend on alternative courses of
6    action. They are particularly useful in the context of deep uncertainty. Scenarios are conditional on the
7    realization of external assumptions and can be used to explore possible outcomes under a variety of
8    assumptions.
 9   Future climate change is a prime example for the application of scenarios. It is driven by human
10   activities across the world and thus can be altered by human agency. It affects all regions over many
11   centuries to come. Humankind’s response to climate change touches not only on the way we use energy
12   and land, but also on socio-economic and institutional layers of societal development. Climate change
13   scenarios provide a central tool to analyse this wicked problem.
14   1.1. Purposes of climate change scenarios
15   Climate change scenarios are developed for a number of purposes (O’Neill et al. 2020). First, they are
16   constructed to explore possible climate change futures covering the causal chain from (i) socio-
17   economic developments to (ii) energy and land use to (iii) greenhouse gas emissions to (iv) changes in
18   the atmospheric composition of greenhouse gases and short-lived climate forcers and the associated
19   radiative forcing to (v) changes in temperature and precipitation patterns to (vi) bio-physical impacts of
20   climate change and finally to (vii) impacts on socio-economic developments, thus closing the loop.
21   Quantitative scenarios exploring possible climate change futures are often called climate change
22   projections and climate change impact projections
23   Second, climate change scenarios are developed to explore pathways towards long-term climate goals.
24   Goal-oriented scenarios often carry the word pathway in their name, such as climate change mitigation
25   pathway, climate change adaptation pathway, or more generally climate change transition /
26   transformation pathway. They are sometimes called backcasting4 scenarios, or short backcasts, in the
27   literature, particularly when contrasted with forecasts (Robinson 1982). Goal-oriented / backcasting
28   scenarios are inherently normative and intricately linked to human intervention. They can be used to
29   compare and contrast different courses of actions. For example, they are applied in climate change
30   mitigation analysis by comparing reference scenarios without or with only moderate climate policy
31   intervention, sometimes called baseline scenarios, with mitigations pathways that achieve certain
32   climate goals (Grant et al. 2020). Transformation pathways to climate goals are examples of backcasting
33   scenarios. Among other things, they can be used to learn about the multi-dimensional trade-offs between
34   raising or lowering ambition (Clarke et al. 2014; Schleussner et al. 2016). In addition, different
35   transformation pathways to the same goal are often used to analyse trade-offs between different routes
36   towards this goal (Rogelj et al. 2018a). These scenarios need to be looked at as a set to understand
37   attainable outcomes and the trade-offs between them. With scenarios, context matters.
38   Third, climate change scenarios are used to integrate knowledge and analysis between the three different
39   climate change research communities working on the climate system and its response to human
40   interference (linked to WG I of the IPCC), climate change impacts, adaptation and vulnerability (linked

     FOOTNOTE4 Backcasting is different from Hindcasting. Hindcasting refers to testing the ability of a
     mathematical model to reproduce past events. In contrast Backcasting begins with a desired future outcome and
     calculates a pathway from the present to that outcome consistent with constraints.
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1    to WG II) and climate change mitigation (linked to WG III) (IPCC 2000; van Vuuren et al. 2011b;
2    O’Neill et al. 2016) (Annex III.II.1.3). This involves the adoption of common scenario frameworks that
3    allow the consistent use of, e.g., shared emissions scenarios, socio-economic development scenarios
4    and climate change projections (Moss et al. 2010; Kriegler et al. 2012; van Vuuren et al. 2012, 2014;
5    O’Neill et al. 2014). The integrative power of scenarios extends beyond the climate change research
6    community into neighbouring fields such as the social sciences and ecology (Rosa et al. 2020; Pereira
7    et al. 2020). To foster such integration, underlying scenario narratives have proven extremely useful as
8    they allow to develop and link quantitative scenario expressions in very different domains of knowledge
9    (O’Neill et al. 2020).
10   Fourth, climate change scenarios and their assessment aim to inform society (Kowarsch et al. 2017;
11   Weber et al. 2018; Auer et al. 2021). To achieve this, it is important to connect climate change scenarios
12   to broader societal development goals (Riahi et al. 2012; van Vuuren et al. 2015; Kriegler et al. 2018c;
13   Soergel et al. 2021) and relate them to social, sectoral and regional contexts (Absar and Preston 2015;
14   Frame et al. 2018; Kok et al. 2019; Aguiar et al. 2020). To this end, scenarios can be seen as tools for
15   societal discourse and decision making to coordinate perceptions about possible and desirable futures
16   between societal actors (Edenhofer and Kowarsch 2015; Beck and Mahony 2017).
18   1.2. Types of climate change mitigation scenarios
19   Different types of climate change scenarios are linked to different purposes and knowledge domains
20   and different models used to construct them (Annex III, Part I). Global reference and mitigation
21   scenarios and their associated emissions projections, which are often called emission scenarios, and
22   national, sector and service transition scenarios are key types of scenarios assessed in the Working
23   Group III report. They are briefly summarized below5.
24   A brief description of the common climate change scenario framework with relevance for all three IPCC
25   Working Groups is provided in Annex III.II.1.3, and a discussion how the WG I and WG II assessments
26   relate to the WG III scenario assessment is given in Annex III.II.2.4.
27   1.2.1. Global mitigation scenarios
28   Global mitigation scenarios are mostly derived from global integrated assessment models (Annex III.9.
29   Integrated assessment modelling) and have been developed in single model studies as well as multi-
30   model comparison studies. The research questions of these studies have evolved together with the
31   climate policy debate and the knowledge about climate change, drivers, and response measures. The
32   assessment of global mitigation pathways in the 5th Assessment Report (AR5) (Clarke et al. 2014) was
33   informed, inter alia, by a number of large-scale multi model studies comparing overshoot and not-to-
34   exceed scenarios for a range of concentration stabilization targets (Energy Modelling Forum (EMF)
35   study 22: EMF22) (Clarke et al. 2009), exploring the economics of different decarbonisation strategies
36   and robust characteristics of the energy transition in global mitigation pathways (EMF27, RECIPE)
37   (Luderer et al. 2012; Krey and Riahi 2013; Kriegler et al. 2014a), and analysing co-benefits and trade-
38   offs of mitigation strategies with energy security, energy access, and air quality objectives (Global
39   Energy Assessment: GEA) (Riahi et al. 2012; McCollum et al. 2011, 2013; Rogelj et al. 2013b; Rao et
40   al. 2013). They also investigated the importance of international cooperation for reaching ambitious
41   climate goals (EMF22, EMF27, AMPERE) (Clarke et al. 2009; Blanford et al. 2014b; Kriegler et al.
42   2015b), the implications of collective action towards the 2°C goal from 2020 onwards vs. delayed
43   mitigation action (AMPERE, LIMITS) (Riahi et al. 2015; Kriegler et al. 2014b), and the distribution of

     FOOTNOTE5 The terms mitigation / transition / transformation scenarios and mitigation / transition /
     transformation pathways are used interchangeably, as they refer to goal-oriented scenarios.
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1    mitigation costs and burden sharing schemes in global mitigation pathways (LIMITS) (Tavoni et al.
2    2014, 2015). Scenarios from these and other studies were collected in a scenario database supporting
3    the AR5 assessment (Krey et al. 2014). With a shelf life of 8 to 14 years, they are now outdated and no
4    longer part of this assessment.
 5   Since AR5, many new studies published global mitigation pathways and associated emissions
 6   projections. After the adoption of the Paris Agreement, several large-scale multi-model studies newly
 7   investigated pathway limiting warming to 1.5°C (ADVANCE: Luderer et al. (2018); CD-LINKS:
 8   McCollum et al. (2018a); ENGAGE: Riahi et al. (2021); SSPs: Rogelj et al. (2018b)), allowing this
 9   report to conduct a robust assessment of 1.5°C pathways. Most scenario studies took the hybrid climate
10   policy architecture of the Paris Agreement with global goals, nationally determined contributions
11   (NDCs) and an increasing number of implemented national climate policies as a starting point,
12   including hybrid studies with participation of global and national modelling teams to inform the global
13   stocktake (ENGAGE: Fujimori et al. (2021); COMMIT: van Soest et al. (2021); CD-LINKS: Schaeffer
14   et al. (2020), Roelfsema et al. (2020)). Multi-model studies covered a range of scenarios from
15   extrapolating current policy trends and the implementation of NDCs, respectively, to limiting warming
16   to 1.5°-2°C with immediate global action and after passing through the NDCs in 2030, respectively.
17   These scenarios are used to investigate, among others, the end of century warming implications of
18   extrapolating current policy trends and NDCs (Perdana et al. 2020); the ability of the NDCs to keep
19   limiting warming to 1.5-2°C in reach (Luderer et al. 2018; Vrontisi et al. 2018; Roelfsema et al. 2020),
20   the scope for global accelerated action to go beyond the NDCs in 2030 (van Soest et al. 2021), and the
21   benefits of early action vs. the risk of overshoot and the use of net negative CO2 emissions in the long-
22   term (Riahi et al. 2021; Bertram et al. 2021; Hasegawa et al. 2021). Other large-scale multi-model
23   studies looked into specific topics: the international economic implications of the NDCs in 2030
24   (EMF36) (Böhringer et al. 2021), the impact of mitigating short-lived climate forcers on warming and
25   health co-benefits in mitigation pathways (EMF30) (Harmsen et al. 2020; Smith et al. 2020b) and the
26   role and implications of large-scale bioenergy deployment in global mitigation pathways (EMF33)
27   (Rose et al. 2020; Bauer et al. 2020a).
28   A large variety of recent modelling studies, mostly based on individual models, deepened research on
29   a diverse set of questions (Annex III.3.2. Global pathways). Selected examples are the impact of peak
30   vs. end of century targets on the timing of action in mitigation pathways (Rogelj et al. 2019a; Strefler
31   et al. 2021a); demand-side driven deep mitigation pathways with sustainable development co-benefits
32   (van Vuuren et al. 2018; Grubler et al. 2018; Bertram et al. 2018); synergies and trade-offs between
33   mitigation and sustainable development goals (Fujimori et al. 2020; Soergel et al. 2021); and the
34   integration of climate impacts into mitigation pathways (Schultes et al. 2021). There have also been a
35   number of recent sectoral studies with global integrated assessment models and other global models
36   across all sectors, e.g. the energy sector (IEA 2021; IRENA 2020; Kober et al. 2020) and transport
37   sector (Rottoli et al. 2021; Fisch-Romito and Guivarch 2019; Edelenbosch et al. 2017a; Zhang et al.
38   2018; Paltsev et al. 2022; Mercure et al. 2018; Lam and Mercure 2021). Very recent work investigated
39   the impact of COVID on mitigation pathways (Kikstra et al. 2021a) and co-designed global scenarios
40   for users in the financial sector (NGFS 2021). In addition to these policy-, technology- and sector-
41   oriented studies, a few diagnostic studies developed mitigation scenarios to diagnose model behaviour
42   (Harmsen et al. 2021) and explore model harmonization (Giarola et al. 2021).
43   The scenarios from most of these and many other studies were collected in the AR6 scenario database
44   (Annex III.II.3.2) and are primarily assessed in Chapter 3 of the report. However sectoral chapters have
45   also used the scenarios, including their climate mitigation categorizations to ensure consistent cross-
46   chapter treatment. Only a small fraction of these scenarios were already available to the assessment of
47   global mitigation pathways in the Special Report on 1.5°C Warming (SR15) (Rogelj et al. 2018a) and
48   were included in the supporting SR15 database (Huppmann et al. 2018).

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1    1.2.2. National transition scenarios
2    A large number of transition scenarios is developed on a national/regional level by national integrated
3    assessment, energy-economy or computable general equilibrium models, among others. These aim to
4    analyse the implications of current climate plans of countries and regions, as well as long-term strategies
5    until 2050 investigating different degrees of low carbon development. National/regional transition
6    scenarios are assessed in Chapter 4 of the Report.
 7   Recent research has focused on several different types of national transition scenarios that focus on
 8   accelerated climate mitigation pathways in the near-term to 2050. These include scenarios considered
 9   by the authors as tied to meeting specific global climate goals6 and scenarios tied to specific policy
10   targets (e.g., carbon neutrality or 80-95% reduction from a certain baseline year). A majority of the
11   accelerated national transition modelling studies up to 2050 evaluate pathways that the authors consider
12   compatible with a 2˚C global warming limit, with fewer scenarios defined as compatible with 1.5˚C
13   global pathways. Regionally, national transition scenarios have centred on countries in Asia
14   (particularly in China, India, Japan), in the European Union, and in North America, with fewer and
15   more narrowly focused scenario studies in Latin America and Africa (Lepault and Lecocq 2021).
16   1.2.3. Sector transition scenarios
17   There are also a range of sector transition scenarios, both on the global and the country level. These
18   include scenarios for the transition of the electricity, buildings, industry, transport and AFOLU sectors
19   until 2050. Due to the accelerated electrification in mitigation pathways, sector coupling plays an
20   increasingly important role to overcome decarbonisation bottlenecks, complicating a separate sector-
21   by-sector scenario assessment. Likewise, the energy-water-land nexus limits the scope a separate
22   assessment of the energy and agricultural sectors. Nevertheless, sector transition scenarios play an
23   important role for this assessment as they can usually offer much more technology, policy and behaviour
24   detail than integrated assessment models. They are primarily assessed in the sector chapters of the
25   report. Their projections of emissions reductions in the sectors in the near- to medium-term is used to
26   check the sector dynamics of global models in Chapter 3 of the Report.
27   Recent transition scenarios considered overarching accelerated climate mitigation strategies across
28   multiple sectors, including demand reduction, energy efficiency improvement, electrification and
29   switching to low carbon fuels. The sectoral strategies considered are often specific to national resource
30   availability, political, economic, climate, and technological conditions. Many sectoral transition
31   strategies have focused on the energy supply sectors, particularly the power sector, and the role for
32   renewable and bio-based fuels in decarbonising energy supply and carbon capture and sequestration
33   (CCS). Some studies present comprehensive scenarios for both supply-side and demand-side sectors,
34   including sector-specific technologies, strategies, and policies. Nearly all demand sector scenarios have
35   emphasized the need for energy efficiency, conservation and reduction through technological changes,
36   with a limited number of models also exploring possible behavioural changes enabled by new
37   technological and societal innovations.
38   1.2.4. Service transition scenarios
39   A central feature of service transition pathways is a focus on the provision of adequate energy services
40   to provide decent standards of living for all as the main scenario objective. Energy services are proxies
41   for well-being, with common examples being provision of shelter (expressed as m 2/capita), mobility
42   (expressed as passenger-kilometres), nutrition (expressed as kCal/capita), and thermal comfort

     FOOTNOTE6 National emission pathways in the near- or mid-term cannot be linked to long-term mitigation goals
     without making additional assumptions about emissions by other countries up to the mid-term, and assumptions
     by all countries up to 2100 (see Chapter 4, Box 4.1).
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 1   (expressed as degree-days) (Creutzig et al. 2018). (Creutzig et al. 2018). Service transition pathways
 2   seek to meet adequate levels of such energy services with minimal carbon emissions, using
 3   combinations of demand- and supply-side options. Ideally this is done by improving the efficiency of
 4   service provision systems to minimize overall final energy and resource demand, thereby reducing
 5   pressure on supply-side and carbon dioxide removal technologies (Grubler et al. 2018). Specifically,
 6   this includes providing convenient access to end-use services (health care, education, communication,
 7   etc.), while minimizing both primary and end-use energy required. Service transition pathways provide
 8   a compelling scenario narrative focused on wellbeing, resulting in technology and policy pathways that
 9   give explicit priority to decent living standards. Furthermore, more efficient service provision often
10   involves combinations of behavioural, infrastructural and technological change, expanding the options
11   available to policymakers for achieving mitigation goals (van Sluisveld et al. 2016, 2018). These
12   dimensions are synergistic, in particular in that behavioural and lifestyle changes often require
13   infrastructures adequately matching lifestyles. Service transition scenarios are primarily assessed in
14   Chapter 5 of the report.
16   1.3. Scenario framework for climate change research
17   1.3.1. History of scenario frameworks used by the IPCC
18   For the first three assessment reports the IPCC directly commissioned emission scenarios, with social,
19   economic, energy and partially policy aspects as drivers of projected GHG emissions. The first set of
20   scenarios, the ‘SA90’ of the IPCC First Assessment Report (IPCC 1990), had four distinct scenarios,
21   ‘business-as-usual’ and three policy scenarios of increasing ambition. The set of ‘IS92’ scenarios used
22   in the Second Assessment Report (SAR) investigated variations of business-as-usual scenarios with
23   respect to uncertainties about the key drivers of economic growth, technology and population (Leggett
24   et al. 1992). The SRES scenarios from the IPCC Special Report on Emission Scenarios (SRES) (IPCC
25   2000) were produced by multiple modelling organizations and were used in the Third and Fourth
26   Assessment reports (TAR and AR4). Four distinct scenario families were characterized by narratives
27   and projections of key drivers like population development and economic growth (but no policy
28   measures) to examine their influence on a range of GHG and air pollutant emissions. Until the 4 th
29   Assessment Report, the IPCC organized the scenario development process centrally. Since then,
30   scenarios are developed by the research community and the IPCC limited its role to catalysing and
31   assessing scenarios. To shorten development times, a parallel approach was chosen (Moss et al. 2010)
32   and representative concentration pathways (RCPs) were developed (van Vuuren et al. 2011b) to inform
33   the next generation of climate modelling for the 5th Assessment Report (AR5). RCPs explored four
34   different emissions and atmospheric composition pathways structured to result in different levels of
35   radiative forcing in 2100: 2.6, 4.5, 6.0 and 8.5 W/m2. They were used as an input to the Climate Model
36   Intercomparison Project Phase 5 (CMIP5) (Taylor et al. 2011) and its results were assessed in AR5
37   (Collins et al. 2013).
38   1.3.2. Current scenario framework and SSP-based emission scenarios
39   The current scenario framework for climate change research (van Vuuren et al. 2014; O’Neill et al.
40   2014; Kriegler et al. 2014c) is based on the concept of Shared Socio-Economic Pathways (SSPs)
41   (Kriegler et al. 2012; O’Neill et al. 2014). Unlike their predecessor scenarios from the SRES (IPCC
42   2000), their underlying narratives are motivated by the purpose of using the framework for mitigation
43   and adaptation policy analysis. Hence the narratives are structured to cover the space of socio-economic
44   challenges to both adaptation and mitigation. They tell five stories of sustainability (SSP1), middle of
45   the road development (SSP2), regional rivalry (SSP3), inequality (SSP4) and fossil-fuelled
46   development (SSP5) (O’Neill et al. 2017). SSP1, SSP2, and SSP3 were structured to explore futures
47   with socio-economic challenges to adaptation and mitigation increasing from low to high with

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1    increasing number of SSP. SSP4 was structured to explore a world with high socio-economic challenges
2    to adaptation but low socio-economic challenges to mitigation, while SSP5 explored a world with low
3    challenges to adaptation but high challenges to mitigation. The five narratives have been translated into
4    population and education (Kc and Lutz 2017), economic growth (Dellink et al. 2017; Crespo Cuaresma
5    2017; Leimbach et al. 2017a), and urbanization projections (Jiang and O’Neill 2017) for each of the
6    SSPs.
 7   The SSP narratives and associated projections of socio-economic drivers provide the core components
 8   for building SSP-based scenario families. These basic SSPs are not scenarios or goal-oriented pathways
 9   themselves (despite carrying “pathway” in the name), but building blocks from which to develop full-
10   fledged scenarios. In particular, their basic elements do not make quantitative assumptions about energy
11   and land use, emissions, climate change, climate impacts and climate policy. Even though including
12   these aspects in the scenario building process may alter some of the basic elements, e.g. projections of
13   economic growth, the resulting scenario remains associated with its underlying SSP. To improve the
14   ability of SSPs to capture socio-economic environments, basic SSPs have been extended in various
15   ways, including the addition of quantitative projections on further key socio-economic dimensions like
16   inequality (Rao et al. 2019), governance (Andrijevic et al. 2019), and gender equality (Andrijevic et al.
17   2020a). Extensions also included spatially downscaled projections of, e.g., population developments
18   (Jones and O’Neill 2016). By now, the SSPs have been widely used in climate change research ranging
19   from projections of future climate change to mitigation, impact, adaptation and vulnerability analysis
20   (O’Neill et al. 2020).
21   The integrated assessment modelling community has used the SSPs to provide a set of global integrated
22   energy-land use-emissions scenarios (Riahi et al. 2017; Rogelj et al. 2018b; Bauer et al. 2017; Popp et
23   al. 2017; Rao et al. 2017b; van Vuuren et al. 2017b; Fricko et al. 2017; Fujimori et al. 2017; Calvin et
24   al. 2017; Kriegler et al. 2017) in line with the matrix architecture of the scenario framework (van Vuuren
25   et al. 2014) (Figure II.1.). It is structured along two dimensions: socio-economic assumptions varied
26   along the SSPs, and climate (forcing) outcomes varied along the Representative Concentration
27   Pathways (RCPs) (van Vuuren et al. 2011b). To distinguish resulting emission scenarios from the
28   original four RCPs (RCP2.6, RCP4.5, RCP6.0, and RCP8.5), they are typically named SSPx-y with x
29   = 1,…,5 the SSP label and y = {1.9, 2.6, 3.4, 4.5, 6.0, 7.0, 8.5} W/m2 the nominal forcing level in 2100.
30   The four forcing levels that were already covered by the original RCPs are bolded here.
31   The new SSP-based emissions and concentrations pathways provided the input for CMIP6 (Eyring et
32   al. 2015; O’Neill et al. 2016) and its climate change projections are assessed in AR6 (WG1 AR6 Cross-
33   chapter Box 1.2, WGI AR6 Chapter 4). From the original set of more than 100 SSP-based energy-land
34   use-emissions scenarios produced by six IAMs (Figure II.1.), five Tier-1 scenarios (SSP1-1.9, SSP1-
35   2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5), and four Tier-2 scenarios (SSP4-3.4, SSP4-6.0, variants of SSP7-
36   3.0, SSP5-3.4) were selected7 (O’Neill et al. 2016), further processed and harmonized with historic
37   emissions and land use change estimates (Gidden et al. 2019; Hurtt et al. 2020), and then taken up by
38   CMIP6 models. WGI focuses its assessment of CMIP6 climate change projections on the five Tier-1
39   scenarios (WGI Chapter 4), but also uses the Tier 2 scenarios where they allow assessment of specific
40   aspects like air pollution. All SSP-based IAM scenarios from the original studies are included in the
41   AR6 emissions scenario database and are part of the assessment of global mitigation pathways in
42   Chapter 3.

     FOOTNOTE7 Each SSPx-y combination was calculated by multiple IAMs. The specific scenarios developed by
     the marker models for the associated SSPs (SSP1: IMAGE; SSP2: MESSAGE-GLOBIOM; SSP3: AIM; SSP4:
     GCAM; SSP5: REMIND-MAgPIE) were selected as Tier 1/Tier 2 scenario for use in CMIP6. Tier 2 variants
     include SSP7-3.0 with low emissions of short lived climate forcers and SSP5-3.4 with high overshoot from
     following SSP5-8.5 until 2040.
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 1   IAMs could not identify SSP-based emissions scenarios for all combinations of SSPs and RCPs (Figure
 2   II.1.) (Riahi et al. 2017; Rogelj et al. 2018b). The highest emission scenarios leading to forcing levels
 3   similar to RCP8.5 could only be obtained in a baseline without climate policy in SSP5 (SSP5-8.5).
 4   Since by now climate policies are implemented in many countries around the world, the likelihood of
 5   future emission levels as high as in SSP5-8.5 has become small (Ho et al. 2019). Baselines without
 6   climate policies for SSP1 and SSP4 reach up to 6.0-7.0 W/m2, with baselines for SSP2 and SSP3 coming
 7   in higher at around 7.0 W/m2. On the lower end, no 1.5°C (RCP1.9) and likely 2°C scenarios (RCP2.6)
 8   could be identified for SSP3 due to the lack of cooperative action in this world of regional rivalry. 1.5°C
 9   scenarios (RCP1.9) could only be reached by all models under SSP1 assumptions. Models struggled to
10   limit warming to 1.5°C under SSP4 assumptions due to limited ability to sustainably manage land, and
11   under SSP5 assumptions due to its high dependence on ample fossil fuel resources in the baseline
12   (Rogelj et al. 2018b).

15    Figure II.1. The SSP/RCP matrix showing the SSPs on the horizontal axis and the forcing levels on the
16     vertical axis [Adapted from Rogelj et al. (2018b) Figure 5; A = AIM, G = GCAM; I = IMAGE, M =
17     MESSAGE-GLOBIOM, R = REMIND-MAgPIE, W = WITCH]. Not all SSP/RCP combinations are
18    feasible (red triangles), and not all combinations were tried (grey triangles). Corresponding scenarios
19    were published in Riahi et al. (2017) and Rogelj et al. (2018b) and included the AR6 scenario database.

21   1.4. Key design choices and assumptions in mitigation scenarios
22   The development of a scenario involves design choices, in addition to the selection of the model. This
23   section will focus on key choices related to design of the scenario, and the respective socioeconomic,
24   technical, and policy assumptions. Model selection cannot be separated from these choices, but the

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1    various advantages and disadvantages of models are described in Annex III, Part I (Modelling
2    Methods).
 3   Target setting: Goal-oriented scenarios in the climate scenario literature initially focussed on
 4   concentration stabilisation but have now shifted towards temperature limits and associated carbon
 5   budgets. In early model intercomparisons, climate targets were often specified as a CO2 equivalent
 6   concentration level that could not be crossed, for example, 450ppm CO2-eq or 550ppm CO2-eq (Clarke
 7   et al. 2009). These targets were either applied as not-to-exceed or overshoot targets. In the latter case,
 8   concentration levels could be returned to the target level by 2100. Overshoot was particularly allowed
 9   for low concentration and temperature targets as many models could not find a solution otherwise
10   (Clarke et al. 2009; Kriegler et al. 2014a; Blanford et al. 2014b; Rogelj et al. 2018b). Bioenergy with
11   Carbon Capture and Storage (BECCS) was an important technology that facilitated aggressive targets
12   to be met in 2100. Due to its ability to remove CO2 from the atmosphere and produce net negative CO2
13   emissions, it enabled overshoot of the target leading to a distinctive peak-and-decline behaviour in
14   concentration, radiative forcing, and temperature (Clarke et al. 2014; Fuss et al. 2014). The mitigation
15   scenarios based on the SSP-RCP framework also applied radiative forcing levels in 2100 (Riahi et al.
16   2017). Temperature targets were often implemented by imposing end-of-century carbon budgets, i.e.
17   cumulative emissions up until 2100. In the case of 2°C pathways, those budgets were usually chosen
18   such that the 2°C limit was not overshoot with some pre-defined probability (Luderer et al. 2018).
19   Arguably, the availability of net negative CO2 emissions has led to high levels of carbon dioxide
20   removal (CDR) in the second half of the century, although CDR deployment is often already substantial
21   to compensate residual emissions (Rogelj et al. 2018a).
22   Recent literature has increasingly focused on alternative approaches such as peak warming or peak CO2
23   budget constraints to implement targets (Rogelj et al. 2019b; Johansson et al. 2020; Riahi et al. 2021).
24   Nevertheless, due to the availability of net negative CO2 emissions and the assumption of standard
25   (exponentially increasing) emissions pricing profiles from economic theory, peak and decline
26   temperature profiles still occurred in a large number of mitigation pathways in the literature even in the
27   presence of peak warming and carbon budget targets (Strefler et al. 2021b). This has led to proposals
28   to combine peak targets with additional assumptions affecting the timing of emissions reductions like a
29   constraint on net negative CO2 emissions (Obersteiner et al. 2018; Rogelj et al. 2019a; Riahi et al. 2021)
30   and different carbon pricing profiles (Strefler et al. 2021b). These proposals are aiming at a stabilization
31   rather than a peak and decline of warming under a given warming limit. However, arguments in support
32   of peak and decline warming profiles also exist: the goal of hedging against positive feedback loops in
33   the Earth system (Lenton et al. 2019) and the aim of increasing the likelihood of staying below a
34   temperature limit towards the end of the century (Schleussner et al. 2016). It is also noteworthy that
35   peak and decline temperature pathways are connected to achieving net zero GHG emissions (with CO2-
36   eq emissions calculated using GWP100) in the second half of the century (Rogelj et al. 2021).
37   Efficiency considerations: Process-based IAMs typically calculate cost-effective mitigation pathways
38   towards a given target as benchmark case (Clarke et al. 2014). In these pathways, global mitigation
39   costs are minimized by exploiting the abatement options with the least marginal costs across all sectors
40   and regions at any time, implicitly assuming a globally integrated and harmonized mitigation regime.
41   This idealized benchmark is typically compared across different climate targets or with reference
42   scenarios extrapolating current emissions trends (UNEP 2019). It naturally evolves over time as the
43   onset of cost-effective action is being set to the immediate future of respective studies. This onset was
44   pushed back from 2010-2015 in studies assessed by AR5 (Clarke et al. 2014) to the first modelling time
45   step after 2020 in studies assessed by AR6.
46   The notion of cost-effectiveness is sensitive to economic assumptions in the underlying models,
47   particularly concerning the assumptions on pre-existing market distortions (Guivarch et al. 2011; Clarke
48   et al. 2014; Krey et al. 2014) and the discount rate on future values. Those assumptions are often not

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1    clearly expressed. Most models have a discount rate of 3-5%, though the range of alternatives is larger.
2    Cost-benefit IAMs have had a tradition of exploring the importance of discount rates, but process-based
3    IAMs have generally not. A lower discount rate brings mitigation forward in time and uses less net
4    negative CO2 emissions in cases where target overshoot is allowed (Realmonte et al. 2019; Emmerling
5    et al. 2019). While most models report discount rates in documentation, there is arguably too little
6    sensitivity analysis of how the discount rate affects modelled outcomes.
 7   Cost-effective pathways typically do not account for climate impacts below the temperature limit,
 8   although recent updates to climate damage estimates suggest a strengthening of near-term action in
 9   cost-effective mitigation pathways (Schultes et al. 2021). Recently, the research community has begun
10   to combine mitigation pathway analysis with ex-post analysis of associated climate impacts and the
11   benefits of mitigation (Drouet et al. 2021). Cost-effective pathways that tap into least cost abatement
12   options globally without considering compensation schemes to equalize the mitigation burden between
13   countries are not compatible with equity considerations. There is a large body of literature exploring
14   international burden sharing regimes to accompany globally cost-effective mitigation pathways (Tavoni
15   et al. 2015; van den Berg et al. 2020; Pan et al. 2017).
16   Policy assumptions: Cost-effective mitigation scenarios assume that climate policies are globally
17   uniform. There is a substantial literature contrasting these benchmark cases with pathways derived
18   under the assumption of regionally fragmented and heterogeneous mitigation policy regimes(Blanford
19   et al. 2014b; Kriegler et al. 2015b, 2018b; Roelfsema et al. 2020; van Soest et al. 2021; Bauer et al.
20   2020b). For example, the Shared Policy Assumptions (Kriegler et al. 2014c) used in the SSP-RCP
21   framework allow for some fragmentation of policy implementation, and many scenarios follow current
22   policies or emission pledges until 2030 before implementing stringent policies (Vrontisi et al. 2018;
23   Roelfsema et al. 2020; Riahi et al. 2015). Other studies assume a gradual strengthening of emissions
24   pledges and regulatory measures converging to a globally harmonized mitigation regime slowly over
25   time (Kriegler et al. 2018b; van Soest et al. 2021). With increasing announcements of mid-century
26   strategies and the rise of net zero CO2 or GHG targets, global mitigation scenario analysis has begun to
27   build in nationally specific policy targets until mid-century (NGFS 2021).
28   Scenarios limiting warming to below 2°C phase in climate policies in all regions and sectors. Almost
29   all converge to a harmonized global mitigation regime before the end of century (with the exception of
30   Bauer et al. (2020b)). In practice, policies are often a mix of regulations, standards, or subsidies.
31   Implementing these real-world policies can give different outcomes to optimal uniform carbon pricing
32   (Mercure et al. 2019). Modelled carbon prices will generally be lower when other policies are
33   implemented (Calvin et al. 2014a; Bertram et al. 2015). As countries implement more and a diverse set
34   of policies, the need to further develop the policy assumptions in models is becoming apparent (O’Neill
35   et al. 2020; Grant et al. 2020; Keppo et al. 2021).
36   Socio-economic drivers: Key socio-economic drivers of emission scenarios are assumptions on
37   population and economic activity. There are other socio-economic assumptions, often included in
38   underlying narratives (O’Neill et al. 2017), that strongly affect energy demand per capita / unit of GDP
39   and dietary choices (Popp et al. 2017; Bauer et al. 2017; Grubler et al. 2018; van Vuuren et al. 2018).
40   The SSPs are often used to help harmonise socio-economic assumptions, and further explore the
41   scenario space. Many studies focus on the middle-of-the-road SSP2 as their default assumption, and
42   many use SSP variations to explore the sensitivity of their results to socio-economic drivers (Riahi et
43   al. 2017; Rogelj et al. 2017; Marangoni et al. 2017). While the SSPs help harmonisation, they are not
44   unique and do not fully explore the scenario space (O’Neill et al. 2020). A wider range of narratives
45   describing alternative worlds is also conceivable. The sustainability world (SSP1), for example, is a
46   world with strong economic growth, but sustainability worlds with low growth or even elements of
47   degrowth in developed countries could also be explored. Thus, standardisation of scenario narratives
48   and drivers has advantages, but can also risk narrowing the scenario space that is explored by the

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1    literature. Consequently, many studies in the literature have adopted other socio-economic assumptions,
2    for example with regard to population and GDP (Kriegler et al. 2016; Gillingham et al. 2018) and
3    sustainable development trends (Soergel et al. 2021).
 4   Technology availability and costs: Technology assumptions are a key component of IAMs, with some
 5   models representing hundreds or thousands of technologies. Despite the importance of technology costs
 6   (Creutzig et al. 2017), there has been limited comparison of technology assumptions across models
 7   (Krey et al. 2019; Kriegler et al. 2015b). There is, however, a substantial literature on the sensitivity of
 8   mitigation scenarios to technology assumptions, including model comparisons (Kriegler et al. 2014a;
 9   Riahi et al. 2015), single model sensitivity studies (McJeon et al. 2011; Krey and Riahi 2013;
10   Giannousakis et al. 2021) and multi-model sensitivity studies (Bosetti et al. 2015). Not only are the
11   initial technology costs important, but also how these costs evolve over time either exogenously or
12   endogenously. Since IAMs have so many interacting technologies, assumptions on one technology can
13   affect the deployment of another. For example, limits on solar energy expansion rates, or integration,
14   may lead to higher levels of deployment for alternative technologies. Because of these interactions, it
15   can be difficult to determine what factors affect deployment across a range of models.
16   Within these key scenario design choices, model choice cannot be ignored. Not all models can
17   implement aspects of a scenario or implement in the same way. Alternative target implementations are
18   difficult for some model frameworks, and implementation issues also arise around technological change
19   and policy implementation. Certain scenario designs may lock out certain modelling frameworks. These
20   issues indicate the need for a diversity of scenario designs (Johansson et al. 2020) to ensure that model
21   diversity can be fully exploited.
22   It is possible for many assumptions to be harmonised, depending on the research question. The SSPs
23   were one project aimed at increasing harmonisation and comparability. It is also possible to harmonise
24   emission data, technology assumptions, and policies (Giarola et al. 2021). While harmonisation
25   facilitates greater comparability between studies, it also limits scenario and model diversity. The
26   advantages and disadvantages of harmonisation need to be discussed for each model study.

28   2. Use of scenarios in the assessment
29   2.1. Use of scenario literature and database
30   The WGIII assessment draws on the full literature on mitigation scenarios. To support the assessment,
31   as many as possible mitigation scenarios in the literature were collected in a scenario database with
32   harmonized output reporting (Annex III.II.3). The collection of mitigation pathways in a common
33   database is motivated by a number of reasons: First, to establish comparability of quantitative scenario
34   information in the literature which is often only sporadically available from tables and figures in peer-
35   reviewed publications, reports and electronic supplementary information. Moreover, this information is
36   often reported using different output variables and definitions requiring harmonization. Second, to
37   increase latitude of the assessment by establishing direct access to quantitative information underlying
38   the scenario literature. Third, to improve transparency and reproducibility of the assessment by making
39   the quantitative information underlying the scenario figures and tables shown in the report available to
40   the readers of AR6. The use of such scenario databases in AR5 of WG III (Krey et al. 2014) and SR1.5
41   (Huppmann et al. 2018) proved its value for the assessment as well as for broad use of the scenario
42   information by researchers and stakeholders. This is now being continued for AR6.

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1    2.2. Treatment of scenario uncertainty
 2   The calls for scenarios issued in preparation of this assessment report allowed to collect a large
 3   ensemble of scenarios, coming from many modelling teams using various modelling frameworks in
 4   many different studies. Although large ensembles of scenarios were gathered, it should be
 5   acknowledged that only a portion of the full uncertainty space is investigated, and that scenarios
 6   ensemble distribution of results are an “artefact” of the context of the studies the scenarios were
 7   developed in. This introduces “biases” in the ensemble, e.g. (i) the topics of the scenario studies
 8   collected in the database determine coverage of the scenario space, with large model-comparison studies
 9   putting large weight on selected topics over lesser explored topics explored by individual models, (ii)
10   some models are more represented than others, (iii) only “optimistic” models (i.e. models finding lower
11   mitigation costs) reach the lowest mitigation targets (Tavoni and Tol 2010). Where appropriate,
12   sampling bias was recognized in the assessment, but formal methods to reduce bias were not employed
13   due to conceptual limitations.
14   Furthermore, although it has been attempted to elicit scenario likelihoods from expert knowledge
15   (Christensen et al. 2018), scenarios are difficult to associate with probabilities as they typically describe
16   a situation of deep uncertainty (Grübler and Nakicenovic 2001). This and the non-statistical nature of
17   the scenario ensemble collected in the database does not allow a probabilistic interpretation of the
18   distribution of output variables in the scenario database. Throughout the report, descriptive statistics are
19   used to describe the spread of scenario outcomes across the scenarios ensemble. The ranges of results
20   and the position of scenarios outcomes relative to some thresholds of interest are analysed. In some
21   figures, the median of the distribution of results is plotted together with the interquartile range and
22   possibly other percentiles (5th-10th-90th-95th) to facilitate the assessment of results, but these should not
23   be interpreted in terms of likelihood of outcomes.
25   2.3. Feasibility of mitigation scenarios
26   In order to develop feasibility metrics of mitigation scenarios (Chapter 3.8), the assessment relied on
27   the multidimensional feasibility framework developed in Brutschin et al. (2021), considering five
28   feasibility dimensions: (i) geophysical, (ii) technological, (iii) economic, (iv) institutional and (v) socio-
29   cultural. For each dimension, a set of indicators were developed, capturing not only the scale but also
30   the timing and the disruptiveness of transformative change (Kriegler et al. 2018b). All AR6 scenarios
31   (C1-C3 climate categories) were categorized through this framework to quantify feasibility challenges
32   by climate category, time, policy architecture and by feasibility dimension, summarized in Figure 3.43
33   (Chapter 3).
34   Scenarios were categorized into three levels of concerns: (i) low levels of concern where transformation
35   is similar to the past or identified in the literature as feasible/plausible, (ii) medium levels of concern
36   that might be challenging but within reach, given certain enablers, (iii) high levels of concern
37   representing unprecedented levels of transformation attainable only under consistent enabling
38   conditions. Indicators’ thresholds defining these three levels of concern were obtained from the
39   available literature and developed with additional empirical literature. Table II.1 summarizes the main
40   indicators used and the associated thresholds for medium and high levels of concern. Finally, we
41   aggregated feasibility concerns for each dimension and each decade employing the geometric mean, a
42   non-compensatory method which limits the degree of substitutability between indicators, and used for
43   example by the United Nations for the HDI. Alternative aggregation scores such as the counting of
44   scenarios exceeding the thresholds was also implemented.

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1   Table II.1. Feasibility dimensions, associated indicators and thresholds for the onset of medium and high
2                                    concerns about feasibility (Chapter 3.8).

                                           Indicators              Computation              Medium       High           Source
                                                                                                                      (Frank et al.
                                                                Total primary energy
                                        Biomass potential    generation from biomass in     100 EJ/y   245 EJ/y
                                                                                                                       Creutzig et
                                                                    a given year
                                                                                                                        al. 2014)

                                         Wind potential       Total secondary energy                                  (Deng et al.
                                                                                             830         2000
                                                             generation from wind in a                                2015; Eurek
                                                                                            EJ/year     EJ/year
                                                                     given year                                       et al. 2017)
                                                                                                                     (Rogner et al.
                                                               Total primary energy
                                                                                             1600       50 000           2012;
                                         Solar potential     generation from solar in a
                                                                                            EJ/year     EJ/year       Moomaw et
                                                                    given year
                                                                                                                       al. 2011)
                                                                                                                       Analogy to
                                                                Decadal percentage                                       current
                                                               difference in GDP in                                    COVID-19
                                            GDP loss                                          5%         10 %
                                                               mitigation vs baseline                                   spending
                                                                      scenario                                        (Andrijevic
                                                                                                                      et al. 2020b)
                                                                                                                     (Brutschin et
                                                             Carbon price levels (NPV)                 120$ and
                                          Carbon price                                        60$                      al. 2021;
                                                               and decadal increases                      5×

                                                                                                                     OECD 2021)
                                                             Ratio between investments
                                             Energy                                                                   (McCollum
                                                             in mitigation vs baseline in     1.2         1.5
                                           Investments                                                                et al. 2018)
                                                                   a given decade
                                                                                                                      (Brutschin et
                                                                Share of prematurely                                    al. 2021;
                                          Stranded coal          retired coal power                                      Global
                                                                                             20 %        50 %
                                              assets            generation in a given                                    Energy
                                                                       decade                                           Monitor
                                                             Decadal percentage point                                 (Brutschin et
                                        Wind/Solar scale-    increase in the wind/solar                                 al. 2021;
                                                                                             10 pp       20 pp
                                              up                 share in electricity                                 Wilson et al.
                                                                     generation                                           2020)

                                                                                                                      (Brutschin et
                                                             Decadal percentage point                                   al. 2021;
                                                              increase in the nuclear                                  Markard et

                                        Nuclear scale-up                                      5 pp       10 pp
                                                                share in electricity                                    al. 2020;
                                                                    generation                                        Wilson et al.
                                                              Amount of CO2 captured           3                      (Warszawski
                     New Technologies

                                        BECCS scale-up                                                 7 GtCO2/y
                                                                 in a given year            GtCO2/y                    et al. 2021)
                                        Fossil CCS scale-     Amount of CO2 captured          3.8        8.8          (Budinis et
                                                up               in a given year            GtCO2/y    GtCO2/y         al. 2018)
                                            Biofuels in      Decadal percentage point                                 (Nogueira et
                                                                                              5 pp       10 pp
                                        transport scale-up    increase in the share of                                  al. 2020)

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                                              biofuels in the final energy
                                               demand of the transport
                                              Decadal percentage point
                                               increase in the share of
                          Electricity in                                                              (Muratori et
                                                electricity in the final      10 pp      15 pp
                       transport scale-up                                                              al. 2021)
                                                energy demand of the
                                                   transport sector
                       industry/residential      Decadal percentage                                    (Grubler et
                                                                              10 %       20 %
                         energy demand           decrease in demand                                     al. 2018)

                                                                                                      (Grubler et
                       Decline of livestock      Decadal percentage
                                                                                                       al. 2018;
                         share in food         decrease in the livestock      0.5 pp     1 pp
                                                                                                      Bajželj et al.
                            demand            share in total food demand
                          Forest cover           Decadal percentage                                   (Brutschin et
                                                                               2%         5%
                            increase           increase in forest cover                                 al. 2021)
                          Pasture cover         Decadal percentage                                    (Brutschin et
                                                                               5%        10 %
                            decrease          decrease in pasture cover                                 al. 2021)

                        Governance level                                                              (Brutschin et
                                              Governance levels and per
                             and                                             >0.6 and   <0.6 and        al. 2021;
                                                 capita CO2 emission
                        decarbonization                                       <20%       >20%         Andrijevic et
                                               reductions over a decade
                             rate                                                                       al. 2019)

3    2.4. Illustrative mitigation pathways
4    In the IPCC Special Report on 1.5°C Warming (SR1.5), illustrative pathways (IPs) were used in
5    addition to descriptions of the key characteristics of the full set of scenarios in the database to assess
6    and communicate the results from the scenario literature. While the latter express the spread in scenario
7    outcomes highlighting uncertain vs. robust outcomes, IPs can be used to contrast different stories of
8    mitigating climate change (Rogelj et al. 2018a).
 9   Following the example of the SR1.5, IPs have also been selected for the AR6 of WGIII. In contrast to
10   SR1.5, the selection needed to cover a larger range of climate outcomes while keeping the number of
11   IPs limited. The selection focused on a range of critical themes that emerged from the AR6 assessment:
12   1) the level of ambitious of climate policy, 2) the different mitigation strategies, 3) timing of mitigation
13   actions and 4) the combination of climate policy with sustainable development policies. The IPs consist
14   of narratives (Table II.2) as well as possible quantifications. The IPs are illustrative and denote
15   implications of different societal choices for the development of future emissions and associated
16   transformations of main GHG emitting sectors. For Chapter 3, for each of the IPs a quantitative scenario
17   was selected from the AR6 scenario database to have particular characteristics and from diverse
18   modelling frameworks (Table II.3).
19   In total two reference pathways with warming above 2°C and five Illustrative Mitigation Pathways
20   (IMPs) limiting warming in the 1.5-2°C range were selected. The first reference pathway follows
21   current policies as formulated around 2018 (Current Policies, Cur-Pol) through to 2030 and then
22   continues to follow a similar mitigation effort to 2100. The associated quantitative scenario (NGFS

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1    2021) selected by Chapter 3 leads to about 3-4 degree C warming at the end of the century. The second
2    reference pathway follows emission pledges to 2030 (NDCs) and then continues with moderate climate
3    action over time (Moderate Action, Mod-Act).
 4   The five IMPs are deep mitigation pathways with warming in the 1.5-2°C range. The first IMP pursues
 5   gradual strengthening beyond NDC ambition levels until 2030 and then acts to likely limit warming to
 6   2°C warming (Climate Category C3) (IMP-GS) (van Soest et al. 2021) (Chapter 3.5.3). Three others
 7   follow different mitigation strategies focusing on low energy demand (IMP-LD) (Grubler et al. 2018),
 8   renewable electricity (IMP-Ren) (Luderer et al. 2021) and large-scale deployment of carbon dioxide
 9   removal measures resulting in net negative CO2 emissions in the second half of the century (IMP-Neg).
10   The fifth IMP explicitly pursues a broad sustainable development agenda and follows SSP1 socio-
11   economic assumptions (IMP-SP) (Soergel et al. 2021). IMP-LD, IMP-Ren and IMP-SP limit warming
12   to 1.5°C with no or low overshoot (C1), while IMP-Neg has a higher overshoot and only returns to
13   nearly 1.5°C (50% chance) by 2100 (close to C2). In addition, two sensitivity cases for IMP-Ren and
14   IMP-Neg are considered that likely limit warming to 2°C (C3) rather than pursuing to limit warming to
15   1.5°C.
16   The IMPs are used in different parts of the report. We just mention some examples here. In Chapter 3,
17   they are used to illustrate key differences between the mitigation strategies, for instance in terms of
18   timing and sectoral action. In Chapter 6, Box 6.9 discusses the consequences for energy systems.
19   Chapter 7 discusses some of the land-use consequences. In Chapter 8, the implications of the IMPs are
20   further explored for urban systems where the elements of energy, innovation, policy, land use and
21   lifestyle interact {8.3, 8.4}. In Chapter 10, the consequences of different mitigation strategies for
22   mobility are highlighted in different figures. The IMPs are discussed further in Chapter 1.3, Chapter 3.2
23   and the respective sector chapters.

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  1              Table II.2. Storylines for the two reference pathways and five Illustrative Mitigation Pathways (IMPs)
  2                                       limiting warming to 1.5-2°C considered in the Report.

                                                                                                                                  Land use, food
                         General char.                   Policy                   Innovation                 Energy                                              Lifestyle

                    Continuation of current     Implementation of           Business-as-usual;       Fossil fuels remain       Further expansion of       Demand will continue
                    policies and trends;        current climate policies    slow progress in low-    important; lock-in        western diets; further     to grow; no significant
  Cur-Pol                                       and neglect of stated       carbon technologies                                slow expansion of          changes in current
                                                goals and objectives;                                                          agriculture area           habits
                                                Grey Covid recovery)

                    NDCs in 2030; as            Strengthening of policies   Modest change            Mostly moving away        Afforestation/reforest     Modest change
                    announced in 2020,          to implement NDCs;          compared to CurPol       from coal; growth of      ation policies as in       compared to CurPol
                    fragmentated policy         some further >2030                                   renewables; some          NDcs
 Mod-Act            landscape; post-2030        strengthening and                                    lock-in in fossil
                    action consistent with      mixed Covid recovery                                 investments
                    modest action until

                    Mitigation in all sectors   Successful international    Further development      CDR, transport            Afforestation/reforest     Not critical – some
                    also includes a heavy       climate policy regime       of CDR options;          H2/Elec based on          ation, BECCS,              induced via price
                    reliance on net negative    with a focus on a long-                              negative emissions        increased                  increases
                    emissions (supply-side)     term temperature goal                                                          competition for land

                    Rapid deployment and        Successful international    Rapid further            Renewable energy,                                    Service provisioning
                    technology                  climate policy regime;      development of           electrification; sector                              and demand changes
                    development of              policies and financial      innovative electricity   coupling; storage or                                 to better adapt to
                    renewables;                 incentives favouring        technologies and         power-to-X                                           high RE supply
                    electrification;            renewable energy            policy regimes           technologies; better

                    Reduced demand leads                                    Social innovation;       Demand reduction;         Lower food and             Service provisioning
                    to early emission                                       efficiency; across all   modal shifts in           agricultural waste;        and demand changes;
            LD      reductions                                              sectors                  transport; rapid          less meat-intensive        behavioural changes
IMP                                                                                                  diffusion of BAT in       lifestyles
                                                                                                     buildings and industry

                    Mitigation action is        Until 2030, primarily                                Similar to Sup, but       Similar to Sup, but
                    gradually strengthened      current NDCs are                                     with some delay.          with some delay.
         GS         until 2030 compared to      implemented – but
                    NDCs,                       move towards strong,
                                                universal regime > 2030

                    Shifting pathways.          SDG policies in addition                             Demand reduction;         Lower food and             Service provisioning
                    Major transformations       to climate policy                                    renewable energy          agricultural waste;        and demand changes
                    shift development           (poverty reduction;                                                            less meat-intensive
            SP      towards sustainability      environmental                                                                  lifestyles;
                    and reduced inequality,     protection                                                                     afforestation.
                    including deep GHG
                    emissions reduction

  4          Table II.3. Quantitative scenario selection by Chapter 3 to represent the two reference pathways and five
  5          Illustrative Mitigation Pathways (IMPs) limiting warming to 1.5-2°C for the assessment in Chapter 3. These
  6          quantitative representations of the IMPs have also been taken up by a few other chapters where suitable.
  7          The warming profile of IMP-Neg peaks around 2060 and declines to below 1.5 (50% likelihood) shortly
  8          after 2100. Whilst technically classified as a C3, it exhibits the characteristics of C2 high overshoot
  9          pathways.

                                                                                             Scenario name in the AR6 scenario
      Acronym            Category                           Model                                                                                       Reference
                                                                                                       database (II.3)
      Cur-Pol                C7           GCAM 5.3                                         NGFS2_Current Policies                            (NGFS 2021)

      Mod-Act                C6           IMAGE 3.0                                        EN_INDCi2030_3000f                                (Riahi et al. 2021)

 Illustrative Mitigation Pathways (IMPs)

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      Neg           C2*     COFFEE 1.1                          EN_NPi2020_400f_lowBECCS             (Riahi et al. 2021)

      Ren           C1      REMIND-MAgPIE 2.1-4.3               DeepElec_SSP2_ HighRE_Budg900        (Luderer et al. 2021)

      LD            C1      MESSAGEix-GLOBIOM 1.0               LowEnergyDemand_1.3_IPCC             (Grubler et al. 2018)

       GS           C3      WITCH 5.0                           CO_Bridge                            (van Soest et al. 2021)

       SP           C1      REMIND-MAgPIE 2.1-4.2               SusDev_SDP-PkBudg1000                (Soergel et al. 2021)

Sensitivity cases
     Neg-2.0        C3      AIM/CGE 2.2                         EN_NPi2020_900f                      (Riahi et al. 2021)

     Ren-2.0        C3      MESSAGEix-GLOBIOM_GEI 1.0           SSP2_openres_lc_50                   (Guo et al. 2021)
 2       2.5. Scenario approaches to connect WG III with the WG I and WG II assessments
 3       2.5.1. Assessment of WG III scenarios building on WG I physical climate knowledge
 4       A transparent assessment pipeline has been set up across WG I and WG III to ensure integration of the
 5       WG I assessment in the climate assessment of emission scenarios in WG III. This pipeline consists of
 6       a step where emissions scenarios are harmonised with historical emissions (harmonisation), a step in
 7       which species not reported by an IAM are filled in (infilling), and a step in which the emission
 8       evolutions are assessed with three climate model emulators (Annex III.I.8) calibrated to the WG I
 9       assessment. These three steps ensure a consistent and comparable assessment of the climate response
10       across emission scenarios from the literature.
11       Harmonisation: IAMs may use different historical datasets, and emission scenarios submitted to the
12       AR6 WG III scenario database (Annex III.II.3) are therefore harmonised against a common source of
13       historical emissions. To be consistent with WG I, we use the same historical emissions that were used
14       for CMIP6 and RCMIP (Gidden et al. 2018; Nicholls et al. 2020b). This dataset comprises many
15       different emission harmonisation sources (Hoesly et al. 2018; van Marle et al. 2017; Velders et al. 2015;
16       Quéré et al. 2016; Gütschow et al. 2016; Meinshausen et al. 2017) including CO2 from agriculture,
17       forestry, and land use change (mainly CEDS, (Hoesly et al. 2018)) that is on the lower end of historical
18       observation uncertainty as assessed in Chapter 2. The harmonisation is performed so that different
19       climate futures resulting from two different scenarios are a result of different future emission evolutions
20       within the scenarios, not due to different historical definitions and starting points. Sectoral CO 2
21       emissions from energy and industrial processes and CO2 from agriculture, forestry, and land use change
22       were harmonised separately. All other emissions species are harmonised based on the total reported
23       emissions per species. For CO2 from energy and industrial processes we use a ratio-based method with
24       convergence in 2080, in line with CMIP6 (Gidden et al. 2018, 2019). For CO2 from agriculture, forestry,
25       and land-use change and other emissions species with high historical interannual variability, we use an
26       offset method with convergence target 2150, to avoid strong harmonization effects resulting from
27       uncertainties in historical observations. For all remaining F-Gases, constant ratio harmonisation is used.
28       For all other emissions species, we use the default settings of Gidden et al. (2018, 2019a).
29       Infilling missing species: Infilling ensures that scenarios include all relevant anthropogenic emissions.
30       This reduces the risk of a biased climate assessment and is important because not all IAMs report all
31       climatically active emission species. Infilling was only performed for scenarios where models provided
32       native reporting of CO2 energy and industrial process, CO2 land use, CH4, and N2O emissions to avoid
33       gases that have large individual radiative forcing contributions and cannot be infilled with high
34       confidence. Models that did not meet this minimum reporting requirement were not included in the
35       climate assessment. Infilling is performed following the methods and guidelines in Lamboll et al.
36       (2020). Missing species are infilled based on the relationship with CO2 from energy and industrial
37       processes as found in the harmonised set of all scenarios reported to the WG III scenario database that
38       pass the vetting requirements. To ensure high stability to small changes, we apply a Quantile Rolling

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1    Window method (Lamboll et al. 2020) for aerosol precursor emissions, volatile organic compounds and
2    greenhouse gases other than F-Gases, based on the quantile of the reported CO2 from energy and
3    industrial processes in the database at each time point. F-Gases and other gases with small radiative
4    forcing are infilled based on a pathway with lowest root mean squared difference, allowing for
5    consistency in spite of limited independently modelled pathways in the database.
 6   WG I-calibrated emulators: Using expert judgement, emulators that reproduce the best estimates and
 7   uncertainties of the majority of WG I assessed metrics are recommended for scenario classification use
 8   by WG III (see WG I Cross-Chapter Box 7.1). MAGICC (v7) was used for the main scenario
 9   classification, with both FaIR (v1.6.2) and CICERO-SCM (v2019vCH4) being used to provide
10   additional uncertainty ranges on reported statistics to capture climate model uncertainty. The WG I
11   emulators’ probabilistic parameter ensembles are derived such that they match a range of key climate
12   metrics assessed by WG I and the extent to which agreement is achieved is evaluated (WG I Cross-
13   Chapter Box 7.1). Of particular importance to this evaluation is the verification against the WG I
14   temperature assessment of the five scenarios assessed in Chapter 4 of WG I (SSP1-1.9, SSP1-2.6, SSP2-
15   4.5, SSP3-7.0, and SSP5-8.5). The inclusion of the temperature assessment as a benchmark for the
16   emulators provides the strongest verification that WG III’s scenario classification reflects the WG I
17   assessment. The comprehensive nature of the evaluation is a clear improvement on previous reports and
18   ensures that multiple components of the emulators, from their climate response to effective radiative
19   forcing through to their carbon cycles, have been examined before they are deemed fit for use by WG
20   III.
21   Scenario climate assessment: For the WG III scenario climate assessment, emulators are run hundreds
22   to thousands of times per scenario, sampling from an emulator-specific probabilistic parameter set,
23   which incorporates carbon cycle and climate system uncertainty in line with the WG I assessment (WG
24   I Cross-Chapter Box 7.1). Percentiles for different output variables provide information about the
25   spread in individual variables for a given scenario, but the set of variables for a given percentile do not
26   form an internally consistent climate change projection. Instead, joint distributions of these parameter
27   sets are employed by the calibrated emulators. Consistent climate change projections are represented
28   by individual ensemble member runs and the whole ensemble of these individual member runs. To
29   facilitate analysis, multiple percentiles of these large (hundred to thousand member) ensemble
30   distributions of projected climate variables are provided in the AR6 scenario database. The emulators
31   provide an assessment of global surface air temperature (GSAT) response to emission scenarios and its
32   key characteristics like peak warming and year of peak warming, ocean heat uptake, atmospheric CO2,
33   CH4 and N2O concentrations and effective radiative forcing from a range of species including CO 2,
34   CH4, N2O and aerosols for each emissions scenario as well as an estimate of CO2 and non-CO2
35   contributions to the temperature increase. The climate emulator’s GSAT projections are normalized to
36   match the WG I Ch.2 assessed total warming between 1850-1900 and 1995-2014 of 0.85°C.
37    The GSAT projections from the emulator runs are used for classifying those emissions scenarios in the
38   AR6 database that passed the initial vetting and allowed a robust climate assessment. MAGICC (v7)
39   was selected as emulator for the climate classification of scenarios, as it happens to be slightly warmer
40   than the other two considered climate emulators, particularly for the higher and long-term warming
41   scenarios - reflecting long-term warming in line with ESMs (WG1 Cross-Chapter Box 7.1). This means
42   that scenarios identified to stay below a given warming limit with a given probability by MAGICC will
43   in general be identified to have this property by the other two emulators as well. There is the possibility
44   that the other two emulators would classify a scenario in a lower warming class based on their slightly
45   cooler emulation of the temperature response. Unlike during the assessment of the SR1.5 database in
46   the IPCC SR1.5 report, the updated versions of FaIR and MAGICC are however very close, providing
47   robustness to the climate assessment. The other two emulators (FaIR and CICERO-SCM) were still
48   used to assess the overall uncertainty in the warming response for a single scenario or a set of scenarios,

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1    including both parametric and model uncertainty. Specifically, the 5th to 95th percentile range across the
2    three emulators is calculated, characterizing the joint climate uncertainty range of the three models.
 3   Carbon budgets in WG1 and WG3: The remaining carbon budget corresponding to a certain level of
 4   future warming depends on non-CO2 emissions of modelled pathways. Cross-Working Group Box 1
 5   highlighted this key uncertainty in estimating carbon budgets. In this section (Figure II.2.), we put this
 6   into the context of the dependence of carbon budgets on two aspects of the non-CO2 warming
 7   contribution: (i) assumptions on historical non-CO2 emissions and how they can impact future non-CO2
 8   warming estimates relative to a recent reference period (2010-2019) (Panel A) and (ii) the scenario set
 9   underlying estimates of non-CO2 warming at the time of reaching net zero CO2 (Panel B). Both aspects
10   affect the estimated remaining carbon budget by changing the non-CO2 warming contribution from the
11   base year to the time of reaching net zero CO2. MAGICC7 is used in WGI in conjunction with different
12   input files for the historical warming. For the reported remaining carbon budget estimates (WG1 CB)
13   WGI is using the non-CO2 warming contributions from MAGICC7 in line with Meinshausen et al.
14   (2020) and in line with the CMIP6 GHG concentration projections, while the WGI emulator setup in
15   line with WG1 Cross Chapter box7.1 was used for the WG3 climate assessment. The WGIII assessment
16   uses thus MAGICC7 in line with Nicholls et al. (2021) in line with the emission harmonisation process
17   employed in WG3 (see above). The difference in historical assumptions changes the estimated non-CO2
18   contribution by up to ~0.05°C for the lower temperature levels, or slightly more than 10% of the
19   warming until 1.5°C relative to 2010-2019. For peak warmings around 2°C relative to pre-industrial
20   levels (~0.97C warming relative to 2010-2019 in below plots), the difference is offset by the difference
21   arising from using either the SR1.5 or AR6 scenario databases (see panel B in below plot).
22   Estimates of the remaining carbon budget that take into account non-CO2 uncertainty are not only
23   dependent on historical assumptions, but also on future non-CO2 scenario characteristics, which are
24   different across the various scenarios in the AR6 database. In panel B of Figure II.2., we show how the
25   SR15 database of scenarios, which was used to inform the WG1 remaining carbon budget, differs from
26   the larger set considered in the WG3 report (both using MAGICC7 using input files in line with Nicholls
27   et al. (2021). Overall, there is limited difference in the covered range of non-CO2 warming at different
28   peak surface temperature levels, leading to no clear change in estimated carbon budgets compared to
29   SR1.5 based on the full scenario database. However, as discussed in Cross-Working Group Box 1 and
30   shown in panel C of Figure II.2., mitigation strategies expressed by both the IAM footprint and scenario
31   design (e.g. dietary change scenarios) can have strong effects on estimated carbon budgets for staying
32   below 1.5°C.

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2     Figure II.2. Comparison of non-CO2 warming relevant for the derivation of cumulative carbon budgets -
3        and its sensitivity to A) assumptions on historical emissions and B) the set of investigated scenarios
4    (right). Panel C) shows how the relationship across scenarios between peak surface temperature and non-
5      CO2 warming and peak cumulative CO2 is different for modelling frameworks. All dashed regression
6                  lines are at the 5th and 95th percentile, solid lines are a regression at the median.

 7   All panels depict non-CO2 warming in relation to 2010-2019 at the time of peak cumulative CO2, using
 8   MAGICC7. Coloured are those scenarios that reach net-zero CO2 this century, with dots in grey indicating
 9   scenarios that do not reach net-zero CO2 but still remain below 2°C median peak warming relative to 2010-2019
10   levels in this century. The scenario set “AR6 database” in B) includes only scenarios of those model frameworks
11   that are shown in panel C) which have a detailed land-use model and enough scenarios to imply a relationship.
12   Panel A) The WG1 remaining carbon budget takes into account the non-CO2 warming in dependence of peak
13   surface temperatures via a regression line approach (lighter blue-coloured solid line). For the same scenario set,
14   with historical emissions assumptions as used in CCB7.1 (darker blue-coloured solid line) a relationship is found
15   with a difference of approximately 0.05°C.
16   Panel B) The WG3 database of scenarios tends to imply very similar non-CO2 warming at peak cumulative CO2
17   as the SR15 scenario database, especially around 1.5°C above pre-industrial (0.43°C above 2010-2019 levels),
18   though with slightly lower non-CO2 warming for higher peak temperatures.
19   Panel C) Regressions at the 5th, 50th, and 95th percentile indicate a model framework footprint affecting the
20   relationship between peak warming and non-CO2 warming at peak cumulative CO2.
22   2.5.2. Relating the WG II and WG III assessments by use of warming levels
23   WG II sets out common climate dimensions to help contextualize and facilitate consistent
24   communication of impacts and synthesis across WGII, as well as to facilitate WG I and WG II
25   integration, with the dimensions adopted when helpful and possible across WGII (AR6 WGII Cross-
26   Chapter CLIMATE Box 1.1). “Common climate dimensions” are defined as common Global Warming
27   Levels (GWLs), time periods, and levels of other variables as needed by WGII authors (see below for
28   a list of variables associated with these dimensions). Projected ranges for associated climate variables
29   were derived from the AR6 WGI report and supporting resources and help contextualize and inform the
30   projection of potential future climate impacts and key risks. The information enables the mapping of

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 1   climate variable levels to climate projections by WGI (WGI SPM Table SPM.1) and vice versa, with
 2   ranges of results provided to characterize the physical uncertainties relevant to assessing climate
 3   impacts risk. Common socioeconomic dimensions are not adopted in WG II due to a desire to draw on
 4   the full literature, inform the broad ranges of relevant possibilities (climate, development, adaptation,
 5   mitigation), and be flexible. The impacts literature is wide-ranging and diverse, with a fraction based
 6   on global socioeconomic scenarios. WGII’s approach allows chapters and cross-chapter boxes to assess
 7   how impacts and ranges depend on socioeconomic factors affecting exposure, vulnerability, and
 8   adaptation independently as appropriate for their literature. For example, WG II Chapter 16 assesses
 9   how Representative Key Risks vary under low vs. high exposure/vulnerability conditions by drawing
10   on impact literature based on Shared Socio-economic Pathways (SSPs). In general, WGII chapters,
11   when possible and conducive with their literature, used GWLs or climate projections based on
12   Representative Concentration Pathways (RCPs) or SSPs to communicate information and facilitate
13   integration and synthesis, with impacts results characterized according to other drivers when possible
14   and relevant, such as socioeconomic condition.
15   In the context of common climate dimensions, WGII considers common projected GWL ranges by time
16   period, the timing for when GWLs might be reached, and projected continental level result ranges for
17   select temperature and precipitation variables by GWL (average and extremes), as well sea surface
18   temperature changes by GWL and ocean biome. Where available, WGII considers the assessed WGI
19   ranges as well as the raw CMIP5 and CMIP6 climate change projections (ranges and individual
20   projections) from Earth system models (Hauser et al. 2019). With WGII’s climate impacts literature
21   based primarily on climate projections available at the time of AR5 (CMIP5) and earlier, or assumed
22   temperature levels, it was important to be able to map climate variable levels to climate projections of
23   different vintages and vice versa. WG II’s common GWLs are based on AR6 WGI’s proposed “Tier 1”
24   dimensions of integration range—1.5, 2.0, 3.0, and 4.0˚C (relative to the 1850 to 1900 period), which
25   are simply proposed common GWLs to facilitate integration across and within WGs (WGI Chapter 1).
26   Within WG II, GWLs facilitate comparison of climate states across climate change projections,
27   assessment of the full impacts literature, and cross-chapter comparison. Across AR6, GWLs facilitate
28   integration across WGs of climate change projections, climate change risks, adaptation opportunities,
29   and mitigation.
30   For facilitating integration with WG III, GWLs need to be related to WG III’s classification of
31   mitigation efforts by temperature outcome. WG III’s Chapter 3 groups full century emissions
32   projections resulting from a large set of assessed mitigation scenarios into temperature classes (Annex
33   III.II.2.4, II.3.2.1, Chapter 3.2, 3.3). Scenarios are classified by median peak global mean temperature
34   increase since 1850-1900 in the bands <2°C, 2-2.5°C, 2.5-3°C, 3-4°C, and >4°C, with the range below
35   2°C broken out in greater detail using estimates of warming levels at peak and in 2100 for which the
36   warming response is projected to be likely higher (33th percentile), as likely as not higher or lower
37   (median), and likely lower (67th percentile) (Chapter 3.2, Annex III.II.3.1). WG II’s common GWLs
38   and WG III’s global warming scenario classes are relatable but differ in several important ways. While
39   GWLs represent temperature change that occurs at some point in time, emissions scenarios in a
40   temperature class result in an evolving warming response over time. The emissions scenario warming
41   also has a likelihood attached to the warming level at any point in time, i.e. actual warming outcomes
42   can be lower or higher than median warming projections within the range of the estimated uncertainty.
43   Thus, multiple WGII results across GWLs will be relevant to any particular WGIII emissions pathway,
44   including at the peak temperature level.
45   However, socioeconomic conditions are an important factor defining both impacts exposure,
46   vulnerability, and adaptation, as well as mitigation opportunity and costs, that needs special
47   considerations. The WG III scenario assessment is using additional classifications relating to, inter alia,
48   near term policy developments, technology availability, energy demand, population and economic

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 1   growth (Annex III.II.3.2.2, Chapter 3.3), and a set of illustrative mitigation pathways with varying
 2   socio-techno-economic assumptions (Annex III.II.2.4, Chapter 3.2). Synthesizing WG II assessments
 3   of climate change impacts and WG III assessments of climate change mitigation efforts for similar
 4   GWLs / global warming scenario classes would have to address how socio-techno-economic conditions
 5   affect impacts, adaptation, and mitigation outcomes. Furthermore, a synthesis of mitigation costs and
 6   mitigation benefits in terms of avoided climate change impacts would require a framework that ensures
 7   consistency in socioeconomic development assumptions and emissions and adaptation dynamics and
 8   allows for consideration of benefits and costs along the entire pathway (O’Neill et al. 2020) (Cross
 9   Working Group Box 1 “Economic benefits from avoided climate impacts along long-term mitigation
10   pathways”).

12   3. WG III AR6 scenario database
13   [Note: The scenario numbers documented in this section refer to all scenarios that were submitted and not
14   retracted by the literature acceptance deadline of October 11, 2021, and that fulfilled the requirement of being
15   supported by an eligible literature source. Not all those scenarios were used in the assessment, e.g. some did not
16   pass the vetting process as documented in II.3.1.
18   As for previous IPCC reports of Working Group III, including the Special Report on 1.5 degrees (SR15)
19   (Huppmann et al. 2018; Rogelj et al. 2018a) and the Fifth Assessment Report (AR5) (Clarke et al. 2014;
20   Krey et al. 2014), quantitative information on mitigation pathways is collected in a dedicated AR6
21   scenario database8 to underpin the assessment.
22   By the time of the AR6 Literature Acceptance deadline of IPCC WGIII (11th October 2021) the AR6
23   scenario database comprised 191 unique modelling frameworks (including different versions and
24   country setups) from 95+ model families –, of which 98 globally comprehensive, 71 national or multi-
25   regional, and 20 sectoral models – with in total 3,131 scenarios, summarized in Table II.4.-Table II.10.
26   (global mitigation pathways), Table II.11. (national and regional mitigation pathways) and Table II.12.
27   (sector transition pathways) below.
28   3.1. Process of scenario collection and vetting
29   To facilitate the AR6 assessment, modelling teams were invited to submit their available emissions
30   scenarios to a web-based database hosted by the International Institute for Applied Systems Analysis
31   (IIASA)9. The co-chairs of Working Group III as well as a range of scientific institutions, including the
32   Integrated Assessment Modelling Consortium (IAMC), University of Cape Town (UCT) and the Centre
33   International de Recherche sur l’Environnement (CIRED), support the open call for scenarios which is
34   subdivided into four dedicated calls,
35       1. a call for global long-term scenarios to underpin the assessment in Chapter 3 as well as
36          facilitating integration with sectoral chapters 6, 7, 8, 9, 10 and 11,
37       2. a call for short- to medium-term scenarios at the national and regional scale underpinning the
38          assessment in Chapter 4, and
39       3. a call for building-focused scenarios to inform the assessment in Chapter 9, and
40       4. a call for transport-focused scenarios to inform the assessment in Chapter 10.

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 1   A common data reporting template with a defined variable structure was used and all teams were
 2   required to register and submit detailed model and scenario metadata. Scenarios were required to come
 3   from a formal quantitative model and the scenarios must be published in accordance with IPCC
 4   literature requirements. The calls for scenarios were open for a period of 22 months (September 2019-
 5   July 2021), with updates possible until October 2021 in line with the literature acceptance deadline. The
 6   data submission process included various quality control procedures to increase accuracy and
 7   consistency in reporting. Additional categorization and processing of metadata over the full database
 8   provided a wide range of indicators and categories that were made centrally available to Lead Authors
 9   of the Report to enhance consistency of the assessment, such as: climate, policy and technology
10   categories; characteristics about emissions, energy, socioeconomics and carbon sequestration; metadata
11   such as literature references, model documentation and related projects.
12   For all scenarios reporting global data, a vetting process is undertaken to ensure that key indicators were
13   within reasonable ranges for the baseline period – primarily for indicators relating to emissions and the
14   energy sector (Table II.4). As part of the submission process, model teams were contacted individually
15   with information on the vetting outcome with regard to their submitted scenarios giving them the
16   opportunity to verify the reporting of their data. Checks on technology-specific variables for nuclear,
17   solar & wind and CCS screen not only for accuracy with respect to recent developments, but also
18   indicate reporting errors relating to different Primary Energy accounting methods. Whilst the criteria
19   ranges appear to be large, the focus of these scenarios is the medium-long term and there is also
20   uncertainty in the historical values. For vetting of the Illustrative Mitigation Pathways, the same criteria
21   were used, albeit with narrower ranges (Table II.4). Future values were also assessed and reported to
22   Lead Authors, but not used as exclusion criteria. Where possible the latest values available were used,
23   generally 2019, and if necessary extrapolated to 2020 as most models report only at 5-10 year intervals.
24   2020 as reported in most scenarios collected in the database does not include the impact of the COVID-
25   19 pandemic.
26   Almost three-quarters of submitted global scenarios passed the vetting. The remaining quarter
27   comprised a fraction of scenarios that were rolled over from the SR1.5 database, and were no longer
28   up-to-date with recent developments (excluding the COVID shock). This included scenarios that started
29   stringent mitigation action already in 2015. Other scenarios were expected to deviate from historical
30   trends due to their diagnostic design. All historical criteria for reported variables needed to be met in
31   order to pass the vetting.
32   2266 global scenarios were submitted to the scenario database that fulfilled a minimum requirement of
33   reporting at least one global emission or energy variable covering multiple sectors. 1686 global
34   scenarios passed the vetting criteria described in Table II.4. These scenarios were subsequently flagged
35   of meeting minimum quality standards for use in long term scenarios assessment. Additional criteria
36   for inclusion in the Chapter 3 climate assessment are described in Section 3.2.1. Climate classification
37   of global pathways

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1      Table II.4. Summary of the vetting criteria and ranges applied to the global scenarios for the climate
2    assessment and preliminary screening for Illustrative Mitigation Pathways. Rows do not sum to the same
3    total of scenarios as not all scenarios reported all variables. EIP stands for energy and industrial process
4                                                      emissions

                                                Reference value         Range (IP range)      Pass          Fail
      Historical Emissions (sources: EDGAR v6 IPCC and CEDS, 2019 values)
      CO2 total (EIP + AFOLU)
                                                44,251 MtCO2/yr          ±40% (±20%)          1848          23       395
      CO2 EIP emissions                         37,646 MtCO2/yr          ±20% (±10%)          2162          55       49
      CH4 emissions                              379 MtCH4/yr            ±20% (±20%)          1651          139      476
      CO2 emissions EIP 2010-2020 %
                                                        -                 +0 to +50%          1742          74       450
                                                                   0-250 (100) Mt
      CCS from Energy 2020                              -                             1624         77                565
      Historical energy production (sources: IEA 2019; IRENA; BP; EMBERS; trends extrapolated to 2020)

      Primary Energy (2020, IEA)                     578 EJ              ±20% (±10%)          1813          73       380
      Electricity Nuclear (2020, IEA)                9.77 EJ             ±30% (±20%)          1603          266      397
      Electricity Solar & Wind (2020.
                                                     8.51 EJ             ±50% (±25%)          1459          377      430
      Overall                                                                                1686           580       -
      Future criteria (not used for exclusion in climate assessment but flagged to authors as potentially
      No net negative CO2 emissions
                                               CO2 total in 2030 >0                           1867           4       395
      before 2030
      CCS from Energy in 2030                  < 2000 Mt CO2/yr                               1518          183      565
                                                   < 20 EJ/yr
      Electricity from Nuclear in 2030                                                        1595          274      397

      CH4 emissions in 2040                   100-1000 MtCH4/yr                               1775          15       476

6    3.2. Global pathways
 7   Scenarios were submitted by both individual studies and model inter-comparisons (see factsheets in the
 8   Supplementary Material to this Annex). The main model inter-comparisons submitting scenarios are
 9   shown in Table II.5. Model inter-comparisons have a shared experimental design and assess research
10   questions across different modelling platforms to enable more structured and systematic assessments.
11   The model comparison projects thus help to understand the robustness of the insights.
12   The number of submitted scenarios varies considerably by study, e.g. from 10 to almost 600 scenarios
13   for the model inter-comparison studies (Table II.5). The numbers of scenarios also vary substantially
14   by model (Table II.8.), highlighting the fact that the global scenario set collected in the AR6 scenario
15   database is not a statistical sample (Section II.2.2.).

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1   Table II.5. Model inter-comparison studies that submitted global scenarios to the AR6 scenario database
2   and for which at least one scenario passed the vetting. Scenario counts refer to all scenarios submitted by
3    a study (in brackets), those that passed vetting (centre) and those that passed the vetting and received a
4                                             climate assessment (left).

                                             Publication           Key                                 Number of
      Project           Description                                                 Website
                                                year            references                             scenarios

               The SSPs are part of a
               new framework that the
               climate change research
               community has adopted
                                                            (Riahi et al.
    SSP model- to facilitate the                                           https://tntcat.iias
                                                2018       2017; Rogelj et                            70 / 77 (126)
    comparison integrated analysis of                            
                                                             al. 2018b)
               future climate impacts,
               adaptation, and
               mitigation (II.1.3).

                  Developed a new
                  generation of advanced
                  IAMs and applied the
                                                            (Luderer et al.
                  improved models to                                            http://www.fp7-
                                                2018        2018; Vrontisi                             37 / 40 (72)
                  explore different                                   
                                                             et al. 2018)
                  climate mitigation
                  policy options in the
                  post-Paris framework.

                                                               (Edelenbosch     http://www.fp7-
                  Industry sector study         2017                                                    0 / 6 (6)
                                                                et al. 2017b)

                  Exploring the complex
                  interplay between
                  climate action and
                  development, while
                                                            (McCollum et
                  simultaneously taking
                                                              al. 2018;
    CD-LINKS      both global and national      2018                                                   41 / 52 (77)
                                                             Roelfsema et
                  perspectives and
                                                              al. 2020)
                  thereby informing the
                  design of
                  complementary climate-
                  development policies.

                  Exploring new climate
                  policy scenarios on the
                                                               (van Soest et    https://themasite
    COMMIT        global level and in           2021                                                   41 / 59 (68)
                                                                 al. 2021)
                  different parts of the

                  Exploring new climate
                  policy scenarios on the                      (Riahi et al.     http://www.enga
    ENGAGE                                      2021                                                 591 / 591 (603)
                  global level and in                             2021)
                  different parts of the

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                   Energy Modelling                           (Smith et al.
                   Forum study into the                         2020a;           emf-30-short-
    EMF30                                         2020                                                 61 / 69 (149)
                   role of non-CO2 climate                   Harmsen et al.      lived-climate-
                   forcers                                       2020)           forcers-air-

                   Energy Modelling                           (Rose et al.
    EMF33          Forum study into the           2020       2020; Bauer et      emf-33-bio-           67 / 68 (173)
                   role of bioenergy                           al. 2020a)        energy-and-land-

                   Energy Modelling                                              https://emf.stanf
                   Forum study into the                                
                                                                 (Böhringer et
    EMF36          role of carbon pricing         2021                           emf-36-carbon-        0 / 305 (320)
                                                                   al. 2021)
                   and economic                                                  pricing-after-
                   implications of NDCs                                          paris-carpri

                   Study for scenario-
                   based financial risk                                          https://www.ngfs
                                                                 (NGFS 2021)                            24 / 24 (24)
    NGFS           assessment with details        2021                           .net/ngfs-
                                                                 (NGFS 2020)                             2 / 2 (2)10
                   on impacts, and sectoral                                      scenarios-portal
                   and regional granularity

                   Study on the long-term
    PARIS                                                     (Perdana et al.    https://paris-
                   implications of current        2020                                                  3 / 25 (39)
    REINFORCE                                                     2020)
                   policies and NDCs

                   Study with a focus on
    PARIS          harmonizing socio-                         (Giarola et al.    https://paris-
    REINFORCE                                     2021                                                   0 / 8 (16)
                   economics and techno-                          2021)
                   economics in baselines

              Study on the role of
    CLIMACAP- climate change                                 (van der Zwaan
                                                  2016                      n.a.                        0 / 10 (22)
    LAMP      mitigation in Latin                              et al. 2016)

                                                                                                        937 / 1336



    FOOTNOTE10 The first NGFS scenario publication in 2020 comprised 15 scenarios from the literature and 2
    newly developed scenarios. The 15 scenarios are also contained in the database under their original study name.
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    Final Government Distribution                         Annex III                        IPCC AR6 WGIII

1    Table II.6. Single model studies that submitted global scenarios to the AR6 scenario database and for
2   which at least one scenario passed the vetting. Scenario counts refer to all scenarios submitted by a study
3   (in brackets), those that passed vetting (center) and those that passed the vetting and received a climate
4                                                assessment (left).

                                                                             Literature          Number of
                              Title of study
                                                                             reference11         scenarios

     Quantification of an efficiency–sovereignty trade-off in
                                                                        (Bauer et al. 2020b) 4 / 4 (4)
     climate policy.
     Transformation and innovation dynamics of the energy-              (Baumstark et al.
                                                                                                18 / 18 (18)
     economic system within climate and sustainability limits.          2021)
     Tracing international migration in projections of income and       (Benveniste et al.
                                                                                                0 / 10 (10)
     inequality across the Shared Socioeconomic Pathways.               2021)
     Targeted policies can compensate most of the increased             (Bertram et al.
                                                                                                3 / 3 (12)
     sustainability risks in 1.5 °C mitigation scenarios.               2018)
     Long term, cross country effects of buildings insulation           (Edelenbosch et al.
                                                                                                0 / 8 (8)
     policies                                                           2021)
     The role of the discount rate for emission pathways and            (Emmerling et al.
                                                                                                4 / 4 (28)
     negative emissions.                                                2019)
                                                                        (Reilly et al. 2018;
     Studies with the EPPA model on the costs of low-carbon
                                                                        Morris et al. 2019,
     power generation, the cost and deployment of CCS, the
                                                                        2021; Smith et al.
     economics of BECCS, the global electrification of light duty                               7 / 7 (10)
                                                                        2021; Fajardy et al.
     vehicles, the 2018 food, water, energy and climate outlook
                                                                        2021; Paltsev et al.
     and the 2021 global change outlook
                                                                        2021, 2022)
     Transportation infrastructures in a low carbon world: An           (Fisch-Romito and
                                                                                                0 / 24 (32)
     evaluation of investment needs and their determinants              Guivarch 2019)
     Measuring the sustainable development implications of              (Fujimori et al.
                                                                                                5 / 5 (5)
     climate change mitigation.                                         2020)
     How uncertainty in technology costs and carbon dioxide             (Giannousakis et al.
                                                                                                9 / 9 (9)
     removal availability affect climate mitigation pathways.           2021)
     A low energy demand scenario for meeting the 1.5 °C target
                                                                        (Grubler et al.
     and sustainable development goals without negative                                         1 / 1 (1)
     emission technologies.
     Global Energy Interconnection: A scenario analysis based on
                                                                        (Guo et al. 2021)       20 / 20 (20)
     the MESSAGEix-GLOBIOM Model.
                                                                        (Holden et al.
     Climate–carbon cycle uncertainties and the Paris Agreement.                                0 / 5 (5)

    FOOTNOTE11 Publication date of scenarios coincides with year of publication.
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 Ratcheting ambition to limit warming to 1.5 °C–trade-offs
                                                                   (Holz et al. 2018)       6 / 6 (6)
 between emission reductions and carbon dioxide removal.
 Peatland protection and restoration are key for climate           (Humpenöder et al.
                                                                                            0 / 3 (3)
 change mitigation                                                 2020)

 Energy Technology Perspectives 2020.                              (IEA 2020b)              0 / 1 (1)

 World Energy Outlook 2020 – Analysis - IEA                        (IEA 2020a)              0 / 1 (1)

 Net Zero by 2050 – A Roadmap for the Global Energy
                                                                   (IEA 2021)               0 / 1 (1)

 Global Renewables Outlook: Energy transformation 2050.            (IRENA 2020)             0 / 2 (2)

 Climate mitigation scenarios with persistent COVID-19-            (Kikstra et al.
                                                                                            19 / 19 (19)
 related energy demand changes.                                    2021a)
 Global anthropogenic emissions of particulate matter              (Klimont et al.
                                                                                            0 / 2 (2)
 including black carbon.                                           2017)
 Global energy perspectives to 2060 – WEC’s World Energy
                                                                   (Kober et al. 2020)      0 / 4 (4)
 Scenarios 2019.
 Prospects for fuel efficiency, electrification and fleet          (Kodjak and
                                                                                            0 / 4 (4)
 decarbonisation                                                   Meszler 2019)
 Short term policies to keep the door open for Paris climate       (Kriegler et al.
                                                                                            18 / 18 (18)
 goals.                                                            2018b)
 Deep decarbonisation of buildings energy services through         (Levesque et al.
                                                                                            4 / 4 (4)
 demand and supply transformations in a 1.5°C scenario.            2021)
 Designing a model for the global energy system-GENeSYS-
 MOD: An application of the Open-Source Energy Modeling            (Löffler et al. 2017)    0 / 1 (1)
 System (OSeMOSYS)
 Impact of declining renewable energy costs on electrification     (Luderer et al.
                                                                                            8 / 8 (8)
 in low emission scenarios.                                        2021)
 The road to achieving the long-term Paris targets: energy         (Marcucci et al.
                                                                                            1 / 1 (3)
 transition and the role of direct air capture.                    2017)
 The transition in energy demand sectors to limit global           (Méjean et al.
                                                                                            0 / 3 (27)
 warming to 1.5 °C.                                                2019)
 Deep mitigation of CO2 and non-CO2 greenhouse gases
                                                                   (Ou et al. 2021)         34 / 35 (36)
 toward 1.5 °C and 2 °C futures
 Alternative electrification pathways for light-duty vehicles in
                                                                   (Rottoli et al. 2021)    8 / 8 (8)
 the European transport sector.
 Economic damages from on-going climate change imply               (Schultes et al.
                                                                                            24 / 24 (24)
 deeper near-term emission cuts.                                   2021)

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      A sustainable development pathway for climate action within                 (Soergel et al.
                                                                                                                8 / 8 (8)
      the UN 2030 Agenda.                                                         2021)
      Delayed mitigation narrows the passage between large-scale                  (Strefler et al.
                                                                                                                7 / 7 (7)
      CDR and high costs                                                          2018)
      Alternative carbon price trajectories can avoid excessive                   (Strefler et al.
                                                                                                                9 / 9 (9)
      carbon removal.                                                             2021b)
                                                                                  (Strefler et al.
      Carbon dioxide removal technologies are not born equal.                                                   8 / 8 (8)
      The Impact of U.S. Re-engagement in Climate on the Paris                    (van de Ven et al.
                                                                                                                0 / 10 (10)
      Targets.                                                                    2021)
                                                                                  (Müller-Casseres et
                                                                                  al. 2021; van
      The 2021 SSP scenarios of the IMAGE 3.2 model.                                                            40 / 40 (40)
                                                                                  Vuuren et al. 2014,

      Pathway comparison of limiting global warming to 2°C.                       (Wei et al. 2021)             0 / 5 (5)

                                                                                                                265 / 350
2    3.2.1. Climate classification of global pathways
3    The global scenarios underpinning the assessment in Chapter 3 have been classified, to the degree
4    possible, by their warming outcome. The definition of the climate categories and the distribution of
5    scenarios in the database across these categories is shown in Table II.7. (Chapter 3.2). The first four of
6    these categories correspond to the ones used in the IPCC SR1.5 (Rogelj et al. 2018a) while the latter
7    four have been added as part of the AR6 to capture a broader range of warming outcomes.
 8   For inclusion in the climate assessment, in addition to passing the vetting (Section II.3.1.), scenarios
 9   needed to run until the end of century and report as a minimum CO2 (total and for energy & industrial
10   processes (EIP)), CH4 and N2O emissions to 2100. Where CO2 for AFOLU was not reported, the
11   difference between total and EIP in 2020 must be greater than 500 Mt CO2. Of the total 2425 global
12   scenarios submitted, 1594 could be assessed in terms of their associated climate response, and 1202 of
13   those passed the vetting process.
15        Table II.7. Classification of global pathways into warming levels using MAGICC (Chapter 3.2)

                      Description                               Definition                                Scenarios


            C1: Below 1.5°C with no or low   <1.5°C peak warming with ≥33% chance and < 1.5°C
                                                                                                     97           160
            overshoot                        end of century warming with >50% chance

            C2: Below 1.5°C with high        <1.5°C peak warming with <33% chance and < 1.5°C
                                                                                                     133          170
            overshoot                        end of century warming with >50% chance

            C3: Likely below 2°C             <2°C peak warming with >67% chance                      311          374

            C4: Below 2oC                    <2°C peak warming with >50% chance                      159          213

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                C5: Below 2.5°C                     <2.5°C peak warming with >50% chance                          212              258

                C6: Below 3°C                       <3°C peak warming with >50% chance                            97               129

                C7: Below 4°C                       <4°C peak warming with >50% chance                            164              230

                C8: Above 4°C                       >4°C peak warming with ≥50% chance                            29               40

                                                    Scenario time horizon <2100; insufficient emissions
                No climate assessment                                                                             484              692
                                                    species reported

                                                                                                    Total:        1686             2266


 2       Table II.8. Global scenarios by modelling framework and climate category. Table includes scenarios numbers
 3      that passed all vetting and checks and received categorization (in brackets total number of scenarios categorized
 4      but not passing vetting). Unique model versions have been grouped into modelling frameworks for presentation
 5               in this table12. For a full list of unique model versions, please see the AR6 Scenario Database.

                    C1:        C2:         C3:         C4:         C5:         C6:         C7:          C8:            No          Grand Total
                  Below       Below       Likely      Below       Below       Below       Below        Above        climate
                   1.5°C      1.5°C       below        2°C        2.5°C       3.0°C       4.0°C        4.0°C        assessm
 Model group      with no      with        2°C                                                                         ent
                  or low     high OS

AIM/ CGE+Hub       4 (18)        3 (7)    17 (37)     8 (23)     13 (23)       4 (7)      6 (32)          - (8)         7 (7)        55 (162)
  C-ROADS           3 (3)        2 (2)                                                                    1 (1)                           6 (6)
  COFFEE            1 (1)        4 (7)    14 (16)     15 (22)    21 (24)      9 (11)       1 (3)                                        65 (84)

  DNE21+            - (4)                  - (7)      - (10)       - (3)       - (4)       - (8)                       9 (10)             - (46)
     EPPA                                  1 (3)       3 (4)                   1 (1)       2 (2)                                         7 (10)

 En-ROADS           - (2)                                                                                 - (1)                           - (3)

     GCAM          6 (10)        6 (9)    13 (17)     9 (16)      6 (13)       - (1)       4 (6)          1 (1)     18 (63)          45 (136)
 GCAM-PR                                                           - (1)       1 (3)       2 (3)                    13 (14)              3 (21)

  GEM-E3            2 (2)       10 (10)   12 (12)      6 (6)       5 (5)       3 (3)       3 (3)                       4 (11)           41 (52)

 GRAPE-15                                              - (1)       - (7)       - (8)       - (2)                                          - (18)
     IMAGE         7 (16)        9 (9)    34 (34)     18 (18)    22 (22)      16 (16)     34 (34)         2 (2)         2 (2)        142 (153)

MERGE-ETL           - (1)                              1 (1)                                              - (1)                           1 (3)

 MESSAGE                         - (1)     - (4)       - (3)                               - (1)                        - (1)             - (10)

 GLOBIOM          20 (20)       43 (48)   59 (61)     39 (40)    57 (59)      20 (22)     28 (33)         - (1)                      266 (284)

     POLES         4 (14)       10 (15)   26 (26)     24 (26)    20 (21)      11 (12)     19 (23)                       1 (1)        114 (138)
  REMIND          13 (15)       12 (19)   34 (39)      1 (1)       7 (8)       6 (6)      22 (24)         9 (9)                      104 (121)

        FOOTNOTE12 Scenario numbers by modelling framework combine submissions from different model versions
        of the same model (indicated by version number or project name in the AR6 scenario database). For the AIM,
        MESSAGE and REMIND modelling frameworks, the grouping covers the following distinct models (including
        different versions):
        AIM/ CGE+Hub: AIM/CGE, AIM/Hub

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REMIND-MAgPIE         28 (36)     32 (33)   50 (50)   15 (15)      27 (27)      13 (13)     26 (26)     2 (2)                  193 (202)

  TIAM-ECN                                  20 (20)    6 (6)       10 (10)       4 (4)       5 (5)                - (13)        45 (58)

  TIAM-UCL                                   - (4)     - (1)                                 - (2)                               - (7)

 TIAM-WORLD                                                          - (3)       - (2)       - (4)                - (2)         - (11)

   WITCH              5 (13)       1 (9)    29 (35)   14 (16)      24 (24)       9 (9)       4 (4)      4 (4)                  90 (114)

  GLOBIOM              4 (5)       1 (1)     2 (9)     - (4)         - (8)       - (7)      8 (15)     10 (10)                  25 (59)
      Total                        133       311       159           212
                     97 (160)     (170)     (374)     (213)         (258)       97 (129)   164 (230)   29 (40)   54 (124)     1202 (1698)


  2           Table II.9. Global scenarios by modelling framework that were not included in the climate assessment
  3        due to a time horizon shorter than 2100 or a limited reporting of emissions species that did not include
  4       CO2 (total emissions or emissions from energy and industry), CH 4 and N2O. Unique model versions have
  5       been grouped into modelling frameworks for presentation in this table 13. For a full list of unique model
  6                                   versions, please see the AR6 Scenario Database.

          Model framework                                Time horizon                        Passed vetting                              Total
          BET                                                       2100                                 0                                  16
          C-GEM                                                     2030                                32                                  32
          C3IAM                                                     2100                                 5                                  14
          CGE-MOD                                                   2030                                32                                  32
          DART                                                      2030                                17                                  32
          E3ME                                                      2050                                10                                  10
          EC-MSMR                                                   2030                                32                                  32
          EDF-GEPA                                                  2030                                32                                  32
          EDGE-Buildings                                            2100                                 8                                   8
          ENV-Linkages                                              2060                                 7                                  15
          ENVISAGE                                                  2030                                32                                  32
          FARM                                                      2100                                 0                                  13
          GAINS                                                     2050                                 2                                   2
          GEMINI-E3                                                 2050                                 6                                   6
          GENeSYS-MOD                                               2050                                 1                                   1
          Global TIMES                                              2050                                 0                                  14
          GMM                                                       2060                                 4                                   4
          Global Transportation
                                                                    2050                                 4                                   4
          ICES                                                  2030/2050                               32                                  43
          IEA ETP                                                   2070                                 1                                   1
          IEA WEM                                                   2050                                 2                                   2
          IRENA REmap GRO2020                                       2050                                 2                                   2
          IMACLIM                                               2050/2080                               30                                  68
          IMACLIM-NLU                                               2100                                 1                                   3
          LUT-ESTM                                                  2050                                 0                                   1
          MAgPIE                                                    2100                                 3                                   3

         FOOTNOTE13 Scenario numbers by modelling framework combine submissions from different model versions
         of the same model (indicated by version number or project name in the AR6 scenario database).

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      MIGRATION                                     2100                        10                        10
      MUSE                                          2100                         5                        11
      McKinsey                                      2050                         0                         3
      PROMETHEUS                                    2050                         7                         7
      SNOW                                          2030                        32                        32
      TEA                                           2030                        32                        32
      TIAM-Grantham                                 2100                        17                        19
      WEGDYN                                        2030                        32                        32
      Total                                                                    430                       568
 2   Changes in climate classification of scenarios since SR1.5: Since the definition of warming classes
 3   was unchanged from SR1.5 for the lower range of scenarios limiting warming to 2°C and below,
 4   changes in overall emissions characteristics of scenarios in these classes from SR1.5 to AR6 would
 5   need to come from the substantially larger ensemble of deep mitigation scenarios collected in the AR6
 6   database compared to the SR1.5 database and from updates in the methodology of the climate
 7   assessment. Updates since SR1.5 include the methodology for infilling and harmonization and the use
 8   of an updated climate emulator (MAGICC v7) to provide consistency with WGI AR6 assessment
 9   (II.2.5.1). Out of the full set of SR1.5, 57% of the 411 scenarios that were represented with global
10   temperature assessments in SR1.5 also have been assessed in AR6. Some SR1.5 scenarios could not be
11   taken on board since they are outdated (too early emissions reductions) and failed the vetting or do not
12   provide sufficient information/data to be included in AR6.
13   Comparison between SR1.5 and AR6 scenarios and associated climate responses are shown in Figure
14   II.3, bottom panel. We show that changes in the climate assessment pipeline are minor compared to
15   climate model uncertainty ranges in WGI (in the order of 0.1°C), but show considerable variation due
16   to different scenario characteristics. The updated harmonization and infilling together have a small
17   cooling effect compared to raw modelled emissions for the subset of 95 scenarios in C1, C2, and C3
18   that also were assessed in SR15 (SR1.5 Chap. 2, Table 2.4). This is due to both applying more advanced
19   harmonization methods consistent with the CMIP6 harmonization used for WGI, and changing the
20   historical harmonization year from 2010 to 2015. Together with the update in the climate emulator, we
21   find that the total AR6 assessment is remarkably consistent with SR1.5, albeit slightly cooler (in the
22   order of 0.05°C at peak temperature, 0.1°C in 2100).
23   The lowest temperature category (C1, limiting warming to 1.5 with no or low overshoot) used for
24   classifying the most ambitious climate mitigation pathways in the literature, indicates that emissions
25   are on average higher in AR6 in the near term (e.g., 2030) and the time of net zero CO2 is later by about
26   5 years compared to SR1.5 (Figure II.3, middle panel). These differences can in part be ascribed to the
27   fact that historical emissions in scenarios, especially among those that passed the vetting, have risen
28   since SR1.5 in line with inventories. This increase has moved the attainable near-term emissions
29   reductions upwards. As a result, the scenarios in the lowest category have also a lower probability to
30   stay below 1.5°C peak warming. Using the WGI emulators, we find that the median probability to stay
31   below 1.5°C in the lowest category (C1) has dropped from about 46% in the SR1.5 scenarios to 38%
32   among the AR6 scenarios. Note that the likelihood of the SR1.5 scenarios limiting warming to 1.5C
33   with limited or no overshoot has changed from 41% in SR1.5 to 46% in AR6 due to the updated climate
34   assessment using the WGI AR6 climate emulator. Within C1, the vast majority of scenarios that are
35   submitted to AR6 but were not assessed in SR1.5 have a median peak temperatures close to 1.6°C. The
36   AR6 scenarios in the lowest category show higher emissions and have a lower chance to keep warming
37   below 1.5°C, as indicated by the panels showing the distribution of peak warming and exceedance
38   probability in AR6 vs SR1.5, with for instance C1 median peak temperature warming going from
39   1.55°C in SR1.5 (1.52°C if reassessed with AR6 assessment pipeline) to 1.58°C in AR6.
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3       Figure II.3. Comparing multiple characteristics of scenarios underlying SR1.5 Table 2.4 to the AR6
4                                                  assessment.

 5   Top row: The probability of exceeding 1.5C for scenarios using the AR6 climate assessment pipeline for
 6   C1, C2, and C3. AR6 shows all scenarios in AR6 that pass vetting requirements and get climate classification
 7   C1, C2, or C3, (‘AR6 (n=541)’). The scenarios that are both in the AR6 database (passing the vetting) and were
 8   used for SR1.5 Table 2.4, and are classified as C1, C2 and C3 using the AR6 assessment, are labelled as ‘AR6
 9   and SR1.5 overlap (n=95)’. ‘SR1.5 (n=127)’ shows all SR1.5 scenarios (except 5 that were not resubmitted for
10   the AR6 report), including those that fail AR6 vetting, that are classified C1, C2, C3 with the updated AR6
11   temperature assessment. Dashed lines indicate cut-off temperature exceedance probabilities that align with AR6
12   category definitions. The violin area is proportional to the number of scenarios. Coloured lines indicate the 25th
13   and 75th percentile, while the dashed black line indicates the median. The insets in each figure show how the
14   temperature category classification have changed from SR1.5 to AR6 for those scenarios that are in both
15   databases.
16   Middle row: Characteristics of CO2 emissions pathways and the distribution of median peak temperature
17   assessments for C1 and C3. From left to right: (i) change in CO2 emissions levels and reductions in 2030, 2040
18   and 2050 between the AR6 (n=408), AR6 and SR1.5 overlap (n=60) and SR15 sets (n=91). (ii) The distribution
19   of scenarios with different median peak temperature scenario outcomes for C1 and C3 for AR6 and SR1.5 (both
20   with AR6 temperature assessment as a solid line and with SR1.5 temperature assessment as a dashed line with
21   median in yellow). (iii) Year of net-zero CO2 for C1 and C3 for AR6 and SR1.5. Within C3, 27 AR6 scenarios
22   and 2 SR1.5 scenarios with no net-zero year before 2100 have not been visualised. The violin area is proportional
23   to the number of scenarios. Coloured lines indicate the 25th and 75th percentile, while the dashed black line
24   indicates the median.
25   Bottom-row: Change in median global-mean surface air temperature (GSAT) between the AR6 and SR1.5
26   climate assessments for both 2100 values and peak temperature values during the 21st century. Positive
27   values indicate that the temperature assessment is higher for the same scenario than the SR1.5 climate assessment.

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1    From left to right, the effect of using MAGICCv7 calibrated to the WGI assessment compared with MAGICC6
2    as used in SR1.5. The effect of more advanced emissions harmonization and infilling methods. The total is the
3    sum of the three components. Boxplots show the median and interquartile range, with the whiskers indicating the
4    95% range.
6    3.2.2. Policy classification of global scenarios
 7   Global scenarios were also classified based on their assumptions regarding climate policy. This
 8   information can be deduced from study protocols or the description of scenario designs in the published
 9   literature. It has also been elicited as meta-information for scenarios that were submitted to the AR6
10   database. There are multiple purposes for a policy classification, including controlling for the level of
11   near-term action (Chapter 3.5) and estimating costs and other differences between two policy classes
12   (Chapter 3.6). Policy classes can be combined with climate classes, e.g. to identify scenarios that follow
13   the NDC until 2030 and likely limit warming to 2°C.
14   Table II.8 presents the policy classification that was chosen for this assessment and the distribution of
15   scenarios across the policy classes. There is top level distinction between diagnostic scenarios, scenarios
16   from cost-benefit analyses, scenarios without globally coordinated action, scenarios with immediate
17   such action, and hybrid scenarios that move to globally coordinated action after a period of diverse and
18   uncoordinated nation. On the second hierarchy level, scenarios are classified along distinctive features
19   of scenarios in each class. Scenarios without globally coordinated action are often used as reference
20   scenarios and come as baselines without climate policy efforts, as an extrapolation of current policy
21   trends or as implementation and extrapolation of NDCs (Grant et al. 2020). Scenarios that act
22   immediately to limit warming to some level can be distinguished by whether or not they include
23   transfers to reflect equity considerations (Tavoni et al. 2015; van den Berg et al. 2020; Bauer et al.
24   2020c) or by whether or not they assume additional policies augmenting a global carbon price (Soergel
25   et al. 2021). Scenarios that delay globally coordinated action until 2030 can differ in their assumptions
26   about the level of near-term action (van Soest et al. 2021; Roelfsema et al. 2020).
27   To identify the policy classification of each global scenario in the AR6 database, classes are first
28   assigned via text pattern matching on all the metadata collected when submitting the scenarios to the
29   database. The algorithm first looks for keywords and text patterns to establish whether a scenario
30   represents a global, fragmented, diagnostic or CBA policy setup. Then it looks for evidence on the
31   presence of specific regional policies, delayed actions and transfers of permits. Eventually the different
32   pieces of evidence are harmonized into a single policy categorization decision. The process has been
33   calibrated on the best-known scenarios belonging to the larger model intercomparison projects, and
34   fine-tuned on the other scenarios via further validation against the related literature, consistency checks
35   on reported emission and carbon price trajectories, exchanges with modellers and supervision by the
36   involved IPCC authors. If the information available is enough to qualify a policy category number but
37   not sufficient for a subcategory, then only the number is retained (e.g., P2 instead of P2a/b/c). A suffix
38   added after P0 further qualifies a diagnostic scenario as one of the other policy categories.
40        Table II.10. Policy classification of global scenarios. If the total for a class exceeds the sum of the
41               subclasses, there are scenarios in the class that could not be assigned to a subclass.

           Class                                  Definition                                 Number of scenarios
        P0            Diagnostic scenario                                                       99           138
        P1            No globally coordinated climate policy and either                        500           632

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        P1a             • no climate mitigation efforts                               124           179
        P1b             • current national mitigation efforts                          59            72
        P1c             • NDCs                                                        160           184
        P1d             • other policy assumptions                                    153           189
        P2          Globally coordinated climate policies with immediate (i.e.
                                                                                      634           992
                    before 2030) action and
        P2a             • without any transfer of emission permits                    435           610
        P2b             • with transfers                                               70           143
        P2c             • with additional policy assumptions                           55            83
        P3          Globally coordinated climate policies with delayed (i.e. from
                                                                                      451           502
                    2030 onwards or after 2030) action, preceded by
        P3a             • no mitigation commitment or current national policies         7            9
        P3b             • NDCs                                                         426          464
        P3c             • NDCs and additional policies                                  18           29
        P4          Cost-benefits analysis                                              2            2
                                                                        Total         1686         2266
2    3.3. National and regional pathways
 3   National and regional pathways have been collected in the AR6 scenario database to support the Chapter
 4   4 assessment. In total more than 500 pathways for 24 countries/regions have been submitted to the AR6
 5   scenario database by integrated assessment, energy-economic and computable general equilibrium
 6   modelling research teams. This represents a limited sample of the overall literature on mitigation
 7   pathways at the national level. The majority of these pathways originate from a set of larger model
 8   intercomparison projects, JMIP/EMF35 (Sugiyama et al. 2020a) focusing on Japan, CD-LINKS
 9   (Schaeffer et al. 2020; Roelfsema et al. 2020), COMMIT (van Soest et al. 2021), ENGAGE (Fujimori
10   et al. 2021), Paris Reinforce (Perdana et al. 2020; Nikas et al. 2021) each covering several
11   countries/regions from the following set of countries: Australia, Brazil, China, EU, India, Indonesia,
12   Japan, Korea, Russia, Thailand, USA, Vietnam. The remaining pathways stem from individual
13   modelling studies that were submitted/collected (Table II.11.).
15     Table II.11. National and regional mitigation pathways by modelling framework, region and scenario
16                                                     type.

      Region     Model                              CP          NDC        Other        Total
      ARG        IMACLIM-ARG                                    1          2            3
      AUS        TIMES-Australia                    1                      7            8
      BRA        BLUES-Brazil                       2           2          15           19
      BRA        COPPE_MSB-Brazil                                          8            8
      BRA        IMACLIM-BRA                                               5            5
      CHE        STEM-Switzerland                   1                      11           12
      CHN        AIM/Hub-China                      1           1          7            9
      CHN        C3IAM                                          3          11           14
      CHN        DREAM-China                                               1            1
      CHN        GENeSYS-MOD-CHN                                           3            3

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 CHN       IPAC-AIM/technology-China   1           1      11     13
 CHN       PECE-China                                     2      2
 CHN       TIMES-Australia                         1             1
 CHN       TIMES-China                 1           2      8      11
 ECU       ELENA-Ecuador                                  2      2
 ETH       TIAM-ECN ETH                1                  1      2
 EU        E4SMA-EU-TIMES              1                         1
 EU        eTIMES-EU                                      23     23
 EU        JRC-EU-TIMES                                   8      8
 EU        PRIMES                      2           2      9      13
 EU        REMIND_EU                                      9      9
 FRA       TIMES-France                                   8      8
 GBR       7see                                           11     11
 IDN       AIM/Hub-Indonesia                              2      2
 IDN       DDPP Energy                                    4      4
 IND       AIM/Enduse India            1           1      5      7
 IND       AIM/Hub-India               1           1      7      9
 IND       MARKAL-INDIA                2           3      13     18
 JPN       AIM/CGE-Enduse-Japan                           6      6
 JPN       AIM/Enduse-Japan            3           3      69     75
 JPN       AIM/Hub-Japan               1           2      42     45
 JPN       DNE21-Japan                             1      30     31
 JPN       DNE21+ V.14 (national)      1           1      4      6
 JPN       IEEJ-Japan                              1      34     35
 KEN       TIAM-ECN KEN                1           1      2      4
 KOR       AIM/CGE-Korea               1           1      6      8
 KOR       AIM/Hub-Korea               1           1      7      9
 MDG       TIAM-ECN MDG                1           2             3
 MEX       GENeSYS-MOD-MEX                                4      4
 PRT       TIMES-Portugal                          1      3      4
 RUS       RU-TIMES                    1           1      4      6
 SWE       TIMES-Sweden                                   4      4
 THA       AIM/Hub-Thailand            1           2      19     22
 USA       GCAM-USA                    2           2      9      13
 USA       RIO-USA                                        12     12

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      VNM        AIM/Hub-Vietnam                     1               2             14             17
      ZAF        TIAM-ECN AFR                                                      4              4

                 Total                               29              39            466            534
1    Notes: The following scenario categories are distinguished in this table, CP = current policies, NDC =
2    implementation of Nationally Determined Contributions (NDCs) by 2025/30, Other = all other
3    scenarios.
5    3.4. Sector transition pathways
 6   Sectoral transition pathways based on the AR6 Scenario database are addressed in a number of
 7   Chapters, primarily Chapter 6 (Energy systems), 7 (AFOLU), 9 (Buildings), 10 (Transport) and 11
 8   (Industry). These analyses cover both contributions from global IAMs and from sector-specific models
 9   with regional or global coverage. The assessments cover a variety of perspectives, including long-term
10   global and macro-region trends for the sectors, sectoral analysis of the Illustrative Pathways, and
11   comparison of the scenarios between full-economy IAMs and sector-specific models on shorter time
12   horizons. These perspectives have a bi-directional utility – to understand how well IAMs are
13   representing sectoral trends from more granular models, and position sectoral models in the context of
14   full economy transitions to verify consistency with different climate outcomes.
16      Table II.12. Overview of how models and scenarios were used in sectoral chapters. All scenario and
17   model counts listed in the table are contained in the AR6 scenario database, with one exception: Chapter
18   9 (Buildings), which supplemented its dataset with a large number of scenarios separately pulled from the
19      sectoral literature. Scenario counts represents unique model-scenario combinations in the database.

      Sector        # models       # scenarios    Key            Key perspectives

      Energy             12             476          6.6         Regional and global energy system characteristics
      systems                                                    along mitigation pathways and at net-zero
      (Ch6)              18             536          6.7         emissions specifically: CO2 and GHG emissions;
                                                                 energy resource shares; electricity and hydrogen
                         13             776         6.7.1        shares of final energy; energy intensity; per-capita
                                                                 energy use; peak emissions; energy investments

      AFOLU              11             384         7.5.1        Regional and global GHG emissions and land use
      (Ch7)                                                      dynamics; economic mitigation potential for
                         14             572         7.5.2,       different GHGs; integrated mitigation pathways
                         13             559
                         3               4

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      Buildings            80                82         9.3, 9.6     A mixture of top-down and bottom-up models.
      (Ch9)                                                          The former were either national, regional or global
                       (of which 2      (of which 4                  while the latter were global only with a breakdown
                       are in AR6       are in AR6                   per end use, building type, technologies and
                         scenario         scenario                   energy carrier
                        database)        database)

      Transport            24              1210           10.7       Global and regional transport demand, activity,
      (Ch10)                                                         modes, vehicles, fuels, and mitigation options.

      Industry             14               508          11.4.2      Global final energy use, CO2 emissions, carbon
                                                                     sequestration, fuel shares

1    Note 1: The number of models and scenarios reported in the table cannot be summed across chapters, as there is
2    considerable overlap in selected model-scenario combinations across chapters, depending on the filtering
3    processes used for relevant analyses. Moreover, the numbers in the table - and certainly not their sum - are not
4    intended to match those reported by Chap. 3 in Section II.3.2.

 5   Note 2: Numbers shown in the model-count column are arrived at through the authors’ best judgement. This has
 6   to do with the overlapping nature of unique model versions (within a given model family) as models evolve over
 7   time. In this case, model versions with substantial overlap were considered the same model, whereas model
 8   versions that differ significantly were counted as unique. For example, ‘MESSAGEix-GLOBIOM 1.0’ and
 9   ‘MESSAGEix-GLOBIOM_1.1’ are counted as the same model, while ‘MESSAGEix-GLOBIOM 1.0’ and
10   ‘MESSAGE’ are counted as different. If instead counting all model versions uniquely, then the following counts
11   would apply to each chapter: Energy systems (30/38/29), AFOLU (18/27/25/4), Buildings (80), Transport (50),
12   Industry (32).

13   Note 3: The Transport chapter figures of Section 10.7 are produced from the final AR6 scenario database by the
14   code accompanying this report. The set of model and scenario names appearing in each plot or figure of 10.7
15   varies, depending on whether particular submissions to the database included the specific variables appearing in
16   that plot. Authors advise inspecting the data files accompanying each figure for the set of models/scenarios
17   specific to that figure, or running the code against the final database snapshot to reproduce the figures in question.


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