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The mitigation scenarios database of the Intergovernmental Panel on Climate Change’s Sixth Assessment Report is an important resource for informing policymaking on energy transitions. However, there is a large variety of models, scenario designs, and resulting outputs. Here we analyse the scenarios consistent with limiting warming to 2 °C or below regarding the speed, trajectory, and feasibility of different fossil fuel reduction pathways. In scenarios limiting warming to 1.5 °C with no or limited overshoot, global coal, oil, and natural gas supply (intended for all uses) decline on average by 95%, 62%, and 42%, respectively, from 2020 to 2050, but the long-term role of gas is highly variable. Higher-gas pathways are enabled by higher carbon capture and storage (CCS) and carbon dioxide removal (CDR), but are likely associated with inadequate model representation of regional CO2 storage capacity and technology adoption, diffusion, and path-dependencies. If CDR is constrained by limits derived from expert consensus, the respective modelled coal, oil, and gas reductions become 99%, 70%, and 84%. Our findings suggest the need to adopt unambiguous near- and long-term reduction benchmarks in coal, oil, and gas production and use alongside other climate mitigation targets.

Chapter 3 assesses the emissions pathways literature in order to identify their key characteristics (both in commonalities and differences) and to understand how societal choices may steer the system into a particular direction (high confidence). More than 2000 quantitative emissions pathways were submitted to the IPCC’s Sixth Assessment Report AR6 scenarios database, out of which 1202 scenarios included sufficient information for assessing the associated warming consistent with WGI.

The use of scenarios and modelling methods are pillars in IPCC Working Group III (WGIII) Assessment Reports. Past WGIII assessment report cycles identified knowledge gaps about the integration of modelling across scales and disciplines, mainly between global integrated assessment modelling methods and bottom-up modelling insights of mitigation responses. The need to improve the transparency of model assumptions and enhance the communication of scenario results was also recognised. This annex on Scenarios and Modelling Methods aims to address some of these gaps by detailing the modelling frameworks applied in the WGIII Sixth Assessment Report (AR6) chapters and disclose scenario assumptions and its key parameters. It has been explicitly included in the Scoping Meeting Report of the WGIII contribution to the AR6 and approved by the IPCC Panel at the 46th Session of the Panel.

As part of the IPCC's 6th Assessment Report (AR6), authors from Working Group III on Mitigation of Climate Change undertook a comprehensive exercise to collect and assess quantitative, model-based scenarios related to the mitigation of climate change. Building on previous assessments, such as those undertaken for the 5th Assessment Report (AR5) and the Special Report on Global Warming of 1.5°C (SR15), the calls for AR6 for scenarios have been expanded and includes economy-wide GHG emissions, energy, and sectoral scenarios from global to national scales, thus more broadly supporting the assessment across multiple chapters (see Annex III, Part 2 of the WGIII report for more details). The compilation and assessment of the scenario ensemble was conducted by authors of the IPCC AR6 report, and the resource is hosted by the International Institute for Applied Systems Analysis (IIASA) as part of a cooperation agreement with Working Group III of the IPCC. The scenario ensemble contains 3,131 quantitative scenarios with data on socio-economic development, greenhouse gas emissions, and sectoral transformations across energy, land use, transportation, buildings and industry. These scenarios derive from 191 unique modelling frameworks, 95+ model families that are either globally comprehensive, national, multi-regional or sectoral. The criteria for submission included that the scenario is presented in a peer-reviewed journal accepted for publication no later than October 11th, 2021, or published in a report determined by the IPCC WG III Bureau to be eligible grey literature by the same date. The AR6 scenario database is documented in Annex III.2 of the Sixth Assessment Report of Working Group III. For the purpose of the assessment, scenarios have been grouped in various categories relating to, among other things, climate outcomes, overshoot, technology availability and policy assumptions.

As they gain new users, climate change mitigation scenarios are playing an increasing role in transitions to net zero. One promising practice is the analysis of scenario ensembles. Here we argue that this practice has the potential to bring new and more robust insights compared with the use of single scenarios. However, several important aspects have to be addressed. We identify key methodological challenges and the existing methods and applications that have been or can be used to address these challenges within a three-step approach: (1) pre-processing the ensemble; (2) selecting a few scenarios or analysing the full ensemble; and (3) providing users with efficient access to the information.

Estimates of economic implications of climate policy are important inputs into policy-making. Despite care to contextualize quantitative assessments of mitigation costs, one strong view outside academic climate economics is that achieving Paris Agreement goals implies sizable macroeconomic losses. Here, we argue that this notion results from unwarranted simplification or omission of the complexities of quantifying mitigation costs, which generates ambiguity in communication and interpretation. We synthesize key factors influencing mitigation cost estimates to guide interpretation of estimates, for example from the Intergovernmental Panel on Climate Change, and suggest ways to improve the underlying models. We propose alternatives for the scenario design framework, the framing of mitigation costs and the methods used to derive them, to better inform public debate and policy.

Process-based integrated assessment models (IAMs) project long-term transformation pathways in energy and land-use systems under what-if assumptions. IAM evaluation is necessary to improve the models’ usefulness as scientific tools applicable in the complex and contested domain of climate change mitigation. We contribute the first comprehensive synthesis of process-based IAM evaluation research, drawing on a wide range of examples across six different evaluation methods including historical simulations, stylised facts, and model diagnostics. For each evaluation method, we identify progress and milestones to date, and draw out lessons learnt as well as challenges remaining. We find that each evaluation method has distinctive strengths, as well as constraints on its application. We use these insights to propose a systematic evaluation framework combining multiple methods to establish the appropriateness, interpretability, credibility, and relevance of process-based IAMs as useful scientific tools for informing climate policy. We also set out a programme of evaluation research to be mainstreamed both within and outside the IAM community.

Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change 90 45–61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend.

Integrated assessment models (IAMs) have emerged as key tools for building and assessing long term climate mitigation scenarios. Due to their central role in the recent IPCC assessments, and international climate policy analyses more generally, and the high uncertainties related to future projections, IAMs have been critically assessed by scholars from different fields receiving various critiques ranging from adequacy of their methods to how their results are used and communicated. Although IAMs are conceptually diverse and evolved in very different directions, they tend to be criticised under the umbrella of 'IAMs'. Here we first briefly summarise the IAM landscape and how models differ from each other. We then proceed to discuss six prominent critiques emerging from the recent literature, reflect and respond to them in the light of IAM diversity and ongoing work and suggest ways forward. The six critiques relate to (a) representation of heterogeneous actors in the models, (b) modelling of technology diffusion and dynamics, (c) representation of capital markets, (d) energy-economy feedbacks, (e) policy scenarios, and (f) interpretation and use of model results.

With country-specific development objectives and constraints, multiple market failures and limited international transfers, carbon prices do not need to be uniform across countries, but must be part of broader policy packages.

Burgeoning demands for mobility and private vehicle ownership undermine global efforts to reduce energy-related greenhouse gas emissions. Advanced vehicles powered by low-carbon sources of electricity or hydrogen offer an alternative to conventional fossil-fuelled technologies. Yet, despite ambitious pledges and investments by governments and automakers, it is by no means clear that these vehicles will ultimately reach mass-market consumers. Here, we develop state-of-the-art representations of consumer preferences in multiple global energy-economy models, specifically focusing on the non-financial preferences of individuals. We employ these enhanced model formulations to analyse the potential for a low-carbon vehicle revolution up to 2050. Our analysis shows that a diverse set of measures targeting vehicle buyers is necessary to drive widespread adoption of clean technologies. Carbon pricing alone is insufficient to bring low-carbon vehicles to the mass market, though it may have a supporting role in ensuring a decarbonized energy supply.

The Paris Agreement is a milestone in international climate policy as it establishes a global mitigation framework towards 2030 and sets the ground for a potential 1.5 °C climate stabilization. To provide useful insights for the 2018 UNFCCC Talanoa facilitative dialogue, we use eight state-of-the-art climate-energy-economy models to assess the effectiveness of the Intended Nationally Determined Contributions (INDCs) in meeting high probability 1.5 and 2 °C stabilization goals. We estimate that the implementation of conditional INDCs in 2030 leaves an emissions gap from least cost 2 °C and 1.5 °C pathways for year 2030 equal to 15.6 (9.0–20.3) and 24.6 (18.5–29.0) GtCO2eq respectively. The immediate transition to a more efficient and low-carbon energy system is key to achieving the Paris goals. The decarbonization of the power supply sector delivers half of total CO2 emission reductions in all scenarios, primarily through high penetration of renewables and energy efficiency improvements. In combination with an increased electrification of final energy demand, low-carbon power supply is the main short-term abatement option. We find that the global macroeconomic cost of mitigation efforts does not reduce the 2020–2030 annual GDP growth rates in any model more than 0.1 percentage points in the INDC or 0.3 and 0.5 in the 2 °C and 1.5 °C scenarios respectively even without accounting for potential co-benefits and avoided climate damages. Accordingly, the median GDP reductions across all models in 2030 are 0.4%, 1.2% and 3.3% of reference GDP for each respective scenario. Costs go up with increasing mitigation efforts but a fragmented action, as implied by the INDCs, results in higher costs per unit of abated emissions. On a regional level, the cost distribution is different across scenarios while fossil fuel exporters see the highest GDP reductions in all INDC, 2 °C and 1.5 °C scenarios.

Scenarios showing future greenhouse gas emissions are needed to estimate climate impacts and the mitigation efforts required for climate stabilization. Recently, the Shared Socioeconomic Pathways (SSPs) have been introduced to describe alternative social, economic and technical narratives, spanning a wide range of plausible futures in terms of challenges to mitigation and adaptation. Thus far the key drivers of the uncertainty in emissions projections have not been robustly disentangled. Here we assess the sensitivities of future CO 2 emissions to key drivers characterizing the SSPs. We use six state-of-the-art integrated assessment models with different structural characteristics, and study the impact of five families of parameters, related to population, income, energy efficiency, fossil fuel availability, and low-carbon energy technology development. A recently developed sensitivity analysis algorithm allows us to parsimoniously compute both the direct and interaction effects of each of these drivers on cumulative emissions. The study reveals that the SSP assumptions about energy intensity and economic growth are the most important determinants of future CO 2 emissions from energy combustion, both with and without a climate policy. Interaction terms between parameters are shown to be important determinants of the total sensitivities.

Scenarios showing future greenhouse gas emissions are needed to estimate climate impacts and the mitigation efforts required for climate stabilization. Recently, the Shared Socioeconomic Pathways (SSPs) have been introduced to describe alternative social, economic and technical narratives, spanning a wide range of plausible futures in terms of challenges to mitigation and adaptation1. Thus far the key drivers of the uncertainty in emissions projections have not been robustly disentangled. Here we assess the sensitivities of future CO2 emissions to key drivers characterizing the SSPs. We use six state-of-the-art integrated assessment models with different structural characteristics, and study the impact of five families of parameters, related to population, income, energy efficiency, fossil fuel availability, and low-carbon energy technology development. A recently developed sensitivity analysis algorithm2 allows us to parsimoniously compute both the direct and interaction effects of each of these drivers on cumulative emissions. The study reveals that the SSP assumptions about energy intensity and economic growth are the most important determinants of future CO2 emissions from energy combustion, both with and without a climate policy. Interaction terms between parameters are shown to be important determinants of the total sensitivities.

AIM AND SCOPE Clarify the variations in carbon prices found in mitigation scenarios that limit global mean surface temperature increase to below 2°C relative to preindustrial levels. CONTEXT Integrated assessment models1 (IAM) are dominant tools for the development of long-term emissions scenarios in line with climate objectives. There is a large variety of IAMs and, together with variations in socioeconomic and technological assumptions, this variety results in important differences in model behavior. For the achievement of low emissions scenarios, models typically assume or produce an implicit shadow price for carbon or represent policy instruments, including carbon pricing. This briefing aims at exploring and understanding the variation in these carbon price estimates for stringent climate change mitigation scenarios. The focus of this exercise will be on scenarios that limit global mean temperature surface increase (henceforth, warming) to below 2°C relative to preindustrial levels with a greater than 66% probability. This choice is driven by data availability for this particular temperature objective, and does not represent an official or scientific interpretation of the Paris Agreement long-term temperature goal. The assumption is that the qualitative insights would also to a