Descriptions
The descriptions contain contextual information about why a criterion is necessary/relevant and how its threshold values were chosen.
Historical Vetting
| NAME | JUSTIFICATION OF THE CRITERION (Why this criterion?) |
JUSTIFICATION OF THE THRESHOLD (Why this threshold?) |
NOTE |
|---|---|---|---|
Historical Vetting|Emissions|CO2|Energy and Industrial Processes |
This criterion flags scenarios that report CO₂ emissions in the energy supply, energy demand, and industry sectors that are inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on the remaining GHG emissions budget and the mitigation effort required to reach net-zero emissions. |
Historical values are provided by the Community Emissions Data System (Hoesly, 2025). Based on this historical data, we assume the following global threshold for years 2010, 2015, 2020 and 2025: • Lower and upper threshold, failed vetting: ±20% deviation. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. The threshold for 2025 is derived using the 2023 value and the 2022–2023 growth rate. To account for the COVID-19 shock, the threshold for 2020 is derived using both the reported value in 2020 and the value averaged over the period Jan 2018 until Dec 2022, taking whichever is more permissive for the scenario in question. |
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Historical Vetting|Emissions|CO2|AFOLU |
This scenario reports CO2 emissions from agriculture, forestry, and land-use that are inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on the remaining GHG emissions budget and the mitigation effort required to reach net-zero emissions. | Historical values for CO2 emissions from agriculture, forestry, and land-use are provided by EDGAR Community GHG Database (2025), Food and Agriculture Organization of the United Nations (2026), Friedlingstein (2026), Hansis (2015), Houghton (2023), Gasser (2023), Gütschow (2025), and Qin (2024). The thresholds are derived from these sources as follows: • 2010, 2015, and 2020: ±20% deviation will lead to exclusion. • 2025: ±30% deviation will lead to exclusion. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. To further account for uncertainty in the historical values, the most permissible source of all sources for each variable, region, and period is used to set the threshold. The threshold for 2025 is set to the value reported for 2020. |
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Historical Vetting|Emissions|CO2|AFOLU|Land |
This scenario reports CO2 emissions from forestry and other land use and land use change that are inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on the remaining GHG emissions budget and the mitigation effort required to reach net-zero emissions. | Historical values for CO2 emissions from forestry and other land use and land use change are provided by Gasser (2020), Friedlingstein (2026), and Gasser (2023). The thresholds are derived from these sources as follows: • 2010, 2015, and 2020: ±20% deviation will lead to exclusion. • 2025: ±30% deviation will lead to exclusion. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. To further account for uncertainty in the historical values, the most permissible source of all sources for each variable, region, and period is used to set the threshold. The threshold for 2025 is set to the value reported for 2020. |
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Historical Vetting|Emissions|CH4|AFOLU|Agriculture |
This scenario reports CH4 emissions from agriculture that are inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on the remaining GHG emissions budget and the mitigation effort required to reach net-zero emissions. | Historical values for CH4 emissions from agriculture are provided by EDGAR Community GHG Database (2025), Food and Agriculture Organization of the United Nations (2026), Gütschow (2025). The thresholds are derived from these sources as follows: • 2010, 2015, and 2020: ±20% deviation will lead to exclusion. • 2025: ±30% deviation will lead to exclusion. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. To further account for uncertainty in the historical values, the most permissible source of all sources for each variable, region, and period is used to set the threshold. The threshold for 2025 is set to the value reported for 2020. |
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Historical Vetting|Emissions|N2O|AFOLU|Agriculture |
This scenario reports N2O emissions from agriculture that are inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on the remaining GHG emissions budget and the mitigation effort required to reach net-zero emissions. | Historical values for N2O emissions from agriculture are provided by EDGAR Community GHG Database (2025), Food and Agriculture Organization of the United Nations (2026), Gütschow (2025). The thresholds are derived from these sources as follows: • 2010, 2015, and 2020: ±20% deviation will lead to exclusion. • 2025: ±30% deviation will lead to exclusion. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. To further account for uncertainty in the historical values, the most permissible source of all sources for each variable, region, and period is used to set the threshold. The threshold for 2025 is set to the value reported for 2020. |
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Historical Vetting|Primary Energy|Coal |
This criterion flags scenarios that report coal primary energy production that is inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on the mitigation effort required to reach net-zero emissions. |
Historical values are provided by the International Energy Agency (IEA, 2025), where we assume that Primary Energy variables are given by the "total energy supply" reported by the IEA. Based on this historical data, we assume the following global threshold for years 2010, 2015, 2020 and 2025: • Lower and upper threshold, failed vetting: ±20% deviation. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. The threshold for 2025 is derived using the 2023 value and the 2021–2023 growth rate. To account for the COVID-19 shock, the threshold for 2020 is derived using both the reported value in 2020 and the value averaged over the period Jan 2018 until Dec 2022, taking whichever is more permissive for the scenario in question. |
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Historical Vetting|Primary Energy|Gas |
This criterion flags scenarios that report gas primary energy production that is inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on the mitigation effort required to reach net-zero emissions. |
Historical values are provided by the International Energy Agency (IEA, 2025), where we assume that Primary Energy variables are given by the "total energy supply" reported by the IEA. Based on this historical data, we assume the following global threshold for years 2010, 2015, 2020 and 2025: • Lower and upper threshold, failed vetting: ±20% deviation. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. The threshold for 2025 is derived using the 2023 value and the 2021–2023 growth rate. To account for the COVID-19 shock, the threshold for 2020 is derived using both the reported value in 2020 and the value averaged over the period Jan 2018 until Dec 2022, taking whichever is more permissive for the scenario in question. |
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Historical Vetting|Primary Energy|Oil |
This criterion flags scenarios that report oil primary energy production that is inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on the mitigation effort required to reach net-zero emissions. |
Historical values are provided by the International Energy Agency (IEA, 2025), where we assume that Primary Energy variables are given by the "total energy supply" reported by the IEA. Based on this historical data, we assume the following global threshold for years 2010, 2015, 2020 and 2025: • Lower and upper threshold, failed vetting: ±20% deviation. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. The threshold for 2025 is derived using the 2023 value and the 2021–2023 growth rate. To account for the COVID-19 shock, the threshold for 2020 is derived using both the reported value in 2020 and the value averaged over the period Jan 2018 until Dec 2022, taking whichever is more permissive for the scenario in question. |
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Historical Vetting|Primary Energy|Nuclear |
This criterion flags scenarios that report nuclear primary energy production that is inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on the mitigation effort required to reach net-zero emissions. |
Historical values are provided by the International Energy Agency (IEA, 2025), where we assume that Primary Energy variables are given by the "total energy supply" reported by the IEA. Based on this historical data, we assume the following global threshold for years 2010, 2015, 2020 and 2025: • Lower and upper threshold, failed vetting: ±20% deviation. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. The threshold for 2025 is derived using the 2023 value and the 2021–2023 growth rate. To account for the COVID-19 shock, the threshold for 2020 is derived using both the reported value in 2020 and the value averaged over the period Jan 2018 until Dec 2022, taking whichever is more permissive for the scenario in question. |
The value for Primary Energy|Nuclear is taken from the statistics of "Electricity generation by source, World, 1990-2023" of the IEA Energy Statistics Data Browser (here) and then converted from GWh to EJ (assuming 1 GWh = 3.6e-06 EJ). |
Historical Vetting|Final Energy |
This criterion flags scenarios that report final energy demand that is inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on the mitigation effort required to reach net-zero emissions. |
Historical values are provided by the International Energy Agency (IEA, 2025), where we assume that the Final Energy variable is given by the "total final consumption" reported by the IEA. Note that final energy includes non-energy use. Based on this historical data, we assume the following global threshold for years 2010, 2015, 2020 and 2025: • Lower and upper threshold, failed vetting: ±20% deviation. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. The threshold for 2025 is derived using the 2023 value and the 2021–2023 growth rate. To account for the COVID-19 shock, the threshold for 2020 is derived using both the reported value in 2020 and the value averaged over the period Jan 2018 until Dec 2022, taking whichever is more permissive for the scenario in question. |
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Historical Vetting|Cropland Yields |
This scenario reports crop-land yields that are inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on the crop-land yields and the effort required to reach net-zero emissions while feeding the population of the world. | Historical values are provided by the Food and Agriculture Organization of the United Nations (Food and Agriculture Organization of the United Nations, 2026). The thresholds are derived from these sources as follows: • 2010, 2015, and 2020: ±20% deviation will lead to exclusion. • 2025: ±30% deviation will lead to exclusion. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. To further account for uncertainty in the historical values, the most permissible source of all sources for each variable, region, and period is used to set the threshold. The threshold for 2025 is set to the value reported for 2020. |
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Historical Vetting|Food Availability |
This scenario reports levels of food availability that are inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on the current state of food availability and the effort required to reach net-zero emissions while growing sufficient food for the world population. | Historical values are provided by the Food and Agriculture Organization of the United Nations (Food and Agriculture Organization of the United Nations, 2026). The thresholds are derived from these sources as follows: • 2010, 2015, and 2020: ±20% deviation will lead to exclusion. • 2025: ±30% deviation will lead to exclusion. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. To further account for uncertainty in the historical values, the most permissible source of all sources for each variable, region, and period is used to set the threshold. The threshold for 2025 is set to the value reported for 2020. |
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Historical Vetting|Land Cover |
This scenario reports land cover that is inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on land cover today and the efforts needed to reduce emissions while feeding the planet. | Historical values for land cover are provided by Hurtt (2020), Chini (2025), Chini (2026), and Food and Agriculture Organization of the United Nations (2026). The thresholds are derived from these sources as follows: • 2010, 2015, and 2020: ±10% deviation for land cover and ±20% for crop-land cover. • 2025: ±20% deviation for land cover and ±30% for crop-land cover. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. To further account for uncertainty in the historical values, the most permissible source of all sources for each variable, region, and period is used to set the threshold. The threshold for 2025 is set to the value reported for 2020. |
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Historical Vetting|Land Cover|Cropland |
This scenario reports crop-land land cover that is inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on land cover today and the efforts needed to reduce emissions while feeding the planet. | Historical values for land cover are provided by Hurtt (2020), Chini (2025), Chini (2026), and Food and Agriculture Organization of the United Nations (2026). The thresholds are derived from these sources as follows: • 2010, 2015, and 2020: ±10% deviation for land cover and ±20% for crop-land cover. • 2025: ±20% deviation for land cover and ±30% for crop-land cover. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. To further account for uncertainty in the historical values, the most permissible source of all sources for each variable, region, and period is used to set the threshold. The threshold for 2025 is set to the value reported for 2020. |
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Historical Vetting|Land Cover|Forest |
This scenario reports forest land cover that is inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on land cover today and the efforts needed to reduce emissions while feeding the planet. | Historical values for land cover are provided by Hurtt (2020), Chini (2025), Chini (2026), and Food and Agriculture Organization of the United Nations (2026). The thresholds are derived from these sources as follows: • 2010, 2015, and 2020: ±10% deviation for land cover and ±20% for crop-land cover. • 2025: ±20% deviation for land cover and ±30% for crop-land cover. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. To further account for uncertainty in the historical values, the most permissible source of all sources for each variable, region, and period is used to set the threshold. The threshold for 2025 is set to the value reported for 2020. |
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Historical Vetting|Land Cover|Pasture |
This scenario reports pasture land cover that is inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on land cover today and the efforts needed to reduce emissions while feeding the planet. | Historical values for land cover are provided by Hurtt (2020), Chini (2025), Chini (2026), and Food and Agriculture Organization of the United Nations (2026). The thresholds are derived from these sources as follows: • 2010, 2015, and 2020: ±10% deviation for land cover and ±20% for crop-land cover. • 2025: ±20% deviation for land cover and ±30% for crop-land cover. Note that these ranges not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. To further account for uncertainty in the historical values, the most permissible source of all sources for each variable, region, and period is used to set the threshold. The threshold for 2025 is set to the value reported for 2020. |
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Historical Vetting|Solar PV Capacity |
This criterion flags scenarios that reports deployment of solar photovoltaics capacity that is inconsistent historical values. This is a concern because the underlying modelling likely makes false assumptions on the mitigation effort required to reach net-zero emissions. |
Existing capacities in 2024 are reported by IRENA (2025). Yearly additions for 2025–2030 are estimated in a market outlook by BNEF (2024). The market outlook data comprises 8 regional categories and a "buffer/unknown" category. Based on the existing capacity data and the market outlook, we assume the following global thresholds for 2025: • Lower threshold, failed vetting: 10% less than capacities operational in 2024, plus 33.75% of 2025 additions from the market outlook (without buffer/unknown). • Upper threshold, failed vetting: 10% more than capacities operational in 2024, plus 220.0% of 2025 additions from the market outlook (with buffer/unknown). |
Due to reporting inconsistencies in legacy projects, several models reported capacity-values that were far below actual power generation from solar PV, causing an overly restrictive application of this criterion. To remedy this inconsistency, we implemented an exception in the evaluation workflow for Release v1.0 and v1.1 of the SCI ensemble: any high concern flag was changed to medium if actual power generation from solar PV was above 10.5 EJ/yr in 2030, corresponding to the lowest generation value of scenarios that meet the lower bound of the capacity threshold. Visit https://github.com/IAMconsortium/scenariocompass/tree/main/scripts/sci_v1.0 for more information. |
Historical Vetting|Wind Capacity |
This criterion flags scenarios that assume a deployment of total wind capacity that is inconsistent with near-term market outlooks. This technology is available at scale and has short project lead times, meaning that there are no fundamental obstacles to fast deployment. Its near-term growth can be estimated based on today's installed capacity and near-term market outlooks. While these estimates have their limitations, they can be used to set broad ranges of near-term feasibility. |
Existing capacities in 2024 are reported by the International Renewable Energy Agency (IRENA, 2025). Yearly additions for 2025 are estimated in a market outlook by the GWEC (2024). The outlook is based on input from regional wind associations, government targets, tender results, announced auction plans, available project pipelines, and input from industry experts. Based on the existing capacity data and the market outlook, we assume the following global thresholds for 2025: • Lower threshold, failed vetting: 10% less than capacities operational in 2024, plus 33.75% of 2025 additions from the market outlook. • Upper threshold, failed vetting: 10% more than capacities operational in 2024, plus 220.0% of 2025 additions from the market outlook. |
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Historical Vetting|Onshore Wind Capacity |
This criterion flags scenarios that assume a deployment of onshore wind capacity that is inconsistent with near-term market outlooks. This technology is available at scale and has short project lead times, meaning that there are no fundamental obstacles to fast deployment. Its near-term growth can be estimated based on today's installed capacity and near-term market outlooks. While these estimates have their limitations, they can be used to set broad ranges of near-term feasibility. |
Existing capacities in 2024 are reported by the International Renewable Energy Agency (IRENA, 2025). Yearly additions for 2025 are estimated in a market outlook by the GWEC (2024). The outlook is based on input from regional wind associations, government targets, tender results, announced auction plans, available project pipelines, and input from industry experts. Based on the existing capacity data and the market outlook, we assume the following global thresholds for 2025: • Lower threshold, failed vetting: 10% less than capacities operational in 2024, plus 33.75% of 2025 additions from the market outlook. • Upper threshold, failed vetting: 10% more than capacities operational in 2024, plus 220.0% of 2025 additions from the market outlook. |
Feasibility Concern
| NAME | JUSTIFICATION OF THE CRITERION (Why this criterion?) |
JUSTIFICATION OF THE THRESHOLD (Why this threshold?) |
NOTE |
|---|---|---|---|
Feasibility Concern|Biodiversity |
This scenario reports a near-term decline of biodiversity intactness that is stronger than what has been reported historically. This is a concern because this near-term trend is inconsistent with historically change rates. The Global Biodiversity Framework (GBF) targets halting human-induced species extinction by 2030, which we interpret as halting reducing biodiversity intactness by 2030. |
Historical values for the change rate of biodiversity intactness are reported by (Pereira, 2024). The threshold is set as the lowest historical change rate. |
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Feasibility Concern|Deforestation |
This scenario reports a level of near-term deforestation that is above what has been reported historically. This is a concern because it would mean unprecedented deforestation, which would go beyond what has been experienced historically. |
The highest annual rate of deforestation in 1990–2020 was estimated at 16 million hectars per year (12 million hectars for primary deforestation) for the period of 1990-2000 (Food and Agriculture Organization of the United Nations (FAO), 2020). | |
Feasibility Concern|Forest Expansion |
This scenario reports a near-term forest expansion rate that is above what has been reported historically. This is a concern because it would mean unprecedented forest expansion, which would go beyond what has been experienced historically. |
The highest annual rate of forest expansion in the last four decades (1990–2020) was estimated at 10 million hectars per year (5 million hectars for planted forest) for the period of 2000-2010 (Food and Agriculture Organization of the United Nations (FAO), 2020). | |
Feasibility Concern|Land Cover |
This scenario reports land cover that is inconsistent with near-term trends. This is a concern because the underlying modelling likely makes false assumptions on land cover and the efforts needed to reduce emissions while growing sufficient food. |
Near-term values for land cover are provided by Chini (2026). The threshold for 2030 is derived from this source as follows: • ±10% deviation will lead to exclusion. Note that this range not only give models some leeway to deviate, but also account for uncertainty in the underlying data sources. |
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Feasibility Concern|Hydropower Capacity |
This criterion flags scenarios that assume a deployment of hydropower capacity that is inconsistent with near-term projections. Near-term upper and lower capacity projections can be derived from existing capacities and from current project announcements and known project lead times. Robust upper projections can be made because projects take at least 5 years to plan and construct and therefore will not be operational by 2030 if not yet announced by today. Robust lower projections can be made based on exisiting capacities and because retirement rates can reasonably be assumed to be low. Specifically, hydropower plants take 4–7 years to construct (for medium-sized projects) and up to 15 years (for large projects) to plan and construct. Small projects that can be completed within less than 4 years make up less than 3% of all projects planned globally in 2021. For details on hydropower project lead times, see the Hydropower Special Market Report by the International Energy Agency (IEA, 2021b). |
Data on hydropower plant capacities was published by the IEA (2021a). The capacity data is published in three categories: • Operational: in operation in 2021 • Expected: expected to be operational in 2030 under normal conditions. • Accelerated: expected to be operational in 2030 under accelerated efforts. Based on this capacity data, we assume the following global thresholds for 2030: • Lower threshold, high concern: 10% less than hydropower capacity (without pumped storage) that was operational in 2021. • Lower threshold, medium concern: 5% less than hydropower capacity (without pumped storage) that is expected to be operational in 2030 under normal conditions. • Upper threshold, medium concern: 5% more than hydropower and pumped storage capacity that is expected to be operational in 2030 under accelerated efforts. • Upper threshold, high concern: 43% more than hydropower capacity (without pumped storage) that is expected to be operational in 2030 under accelerated efforts, plus 10% more than pumped storage capacity that is expected to be operational in 2030 under normal conditions. Moreover, we assume the following regional thresholds for 2030: • Lower threshold, medium concern: 40% less than hydropower capacity (without pumped storage) that is expected to be operational in 2030 under normal conditions. • Upper threshold, medium concern: 82% more than hydropower capacity (without pumped storage) that is expected to be operational in 2030 under accelerated efforts, plus 10% more than pumped storage capacity that is expected to be operational in 2030 under normal conditions. |
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Feasibility Concern|Nuclear Capacity |
This criterion flags scenarios that assume a deployment of nuclear power-plant capacity that is inconsistent with near-term projections. Near-term upper and lower capacity projections can be derived from existing capacities and from current project announcements and known project lead times. Robust upper projections can be made because projects take at least 5 years to plan and construct and therefore will not be operational by 2030 if not yet announced by today. Robust lower projections can be made based on exisiting capacities and because retirement rates can reasonably be assumed to be low. |
Data on nuclear power plants is published by the International Atomic Energy Agency (IAEA). This includes plant-level data of existing capacities (IAEA, 2026) and estimates until 2050 (IAEA, 2024). The capacity data is published in three categories: • Operational: in operation in 2024 • Construction: currently under construction • Retired: inactive capacity that has been retired Based on this capacity data, we assume the following global thresholds for 2030: • Lower threshold, high concern: 0.0 GW to account for the fact that nuclear energy could, in principle, be phased out quickly, as observed in a few countries (e.g. Germany). • Lower threshold, medium concern: 15% less than capacities operational in 2026, as reported by the IAEA (2026). • Upper, medium concern: 5% more than capacities operational plus 5% more than inactive capacities in Japan (which we assume can be brought back online) plus 79% of plants under construction in 2026, as reported by the IAEA (2026). • Upper threshold, high concern: 10% more than the highest estimate for 2030 (461 GW) published by the IAEA (2024). Moreover, we assume the following regional thresholds for 2030: • Lower, medium concern: 52% less than capacities operational in 2026, as reported by the IAEA (2026). • Upper, medium concern: 40% more than capacities operational plus 40% more than inactive capacities in Japan (which we assume can be brought back online) plus 40% more than plants under construction in 2026, as reported by the IAEA (2026). |
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Feasibility Concern|Carbon Capture and Storage |
This criterion flags scenarios that assume a deployment of CCS technologies that is inconsistent with near-term projections (2030) or long-term feasibility constraints (2035–2040). Near-term (2030): near-term upper and lower capacity projections can be derived from existing capacities and from current project announcements and known project lead times. Robust upper projections can be made because projects take at least 5 years to plan and construct and therefore will not be operational by 2030 if not yet announced by today. Robust lower projections can be made based on existing capacities and because retirement rates can reasonably be assumed to be low. Long-term (2035–2040): CCS involves capturing CO₂ from flue gases and storing it geologically. The required technologies for these activities are highly non-modular and application-specific. Additionally, CCS depends on infrastructure like CO₂ pipeline networks, which still have to be built. Therefore it will likely grow more slowly than modular technologies with some degree of existing infrastructure, such as solar, wind, and batteries. |
Near-term (2030): CCUS projects are tracked and published annually in a database by the IEA (2026). The projects in the CCUS database are assigned one of the following statuses: • Operational: in operation in 2024 • Construction: under construction in 2024 • Planned: announced but without final investment decision in 2024. Projects that were decommissioned or suspended are not counted. Based on the CCUS database, we assume the following global thresholds for 2030: • Lower threshold, high concern: 0.0 Mt CO₂/yr to account for the fact that CCUS is an immature technology. • Lower threshold, medium concern: 10% less than capacities operational in 2026, as reported by the CCUS database (IEA, 2026). • Upper threshold, medium concern: 5% more than capacities operational or under construction, plus 21% of planned projects (without final investment decision) in 2026, as reported by the CCUS database (IEA, 2026). • Upper threshold, high concern: 10% more than all projects operational, under construction, or announced, as reported by the CCUS database (IEA, 2026). Moreover, we assume the following regional thresholds for 2030: • Lower, medium concern: 40% less than capacities operational in 2026 plus 30% of the projects under construction, as reported by the CCUS database (IEA, 2026). • Upper, medium concern: 40% more than capacities operational or under construction, plus 28% of planned projects (without final investment decision) in 2026, as reported by the CCUS database (IEA, 2026). Long-term (2035–2040): Kazlou (2024) derive an upper bound for feasible CCS capacity deployment in 2030 of 370 Mt CO₂/yr and 2040 of 4,300 Mt CO₂/yr (see Table 2 in the work by Kazlou (2024)), which assumes a doubling of existing plans and a 45% failure rate. Based on this work, we assume the following global thresholds: • Upper, medium concern in 2035: 1,300 Mt CO₂/yr, which is the geometric-mean (constant-growth-rate) interpolation between the reported upper bounds for 2030 (370 Mt CO₂/yr) and 2040 (4,300 Mt CO₂/yr) derived by Kazlou (2024). • Upper, medium concern in 2040: 4,300 Mt CO₂/yr, which is the reported upper bound derived by Kazlou (2024). These threshold values all refer to CCS and are applied to the variable Carbon Capture|Geological Storage. However this variable is often underreported, especially in older scenarios. Therefore, the variable Carbon Capture is also assessed using the same threshold values. Note that this may lead to precautious flagging scenarios with more granular reporting of carbon capture. In these scenarios, additional categories such as CO₂ leakage or CO₂ utilization contribute to total carbon capture, which as a result may exceed the corresponding threshold even when geological storage remains below its threshold. |
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Feasibility Concern|DACCS Capacity |
This scenario assumes a high deployment of Direct Air Capture with Carbon Storage (DACCS). DACCS is a technology that is very immature today. Moreover, CO₂ is captured directly from the atmosphere, which has a much lower CO₂ concentration than flue gases from points sources (e.g. a cement or waste-incineration plant), the purification of CO₂ is much harder and cannot be achieved with established carbon capture technologies. In summary, this causes DACCS to be a technology that will likely take a long time to develop and scale up. |
"If DACCS growth mirrors high-growth analogs (e.g., solar photovoltaics), it can reach up to 4.9 GtCO2 removal by midcentury" (Edwards, 2024). Based on this work, we assume the following threshold: • Upper, medium concern in 2050: 4,900 Mt CO₂/yr. |
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Feasibility Concern|Solar PV Capacity |
This criterion flags scenarios that assume a deployment of solar photovoltaics capacity that is inconsistent with near-term market outlooks. This technology is available at scale and has short project lead times, meaning that there are no fundamental obstacles to fast deployment. Its near-term growth can be estimated based on today's installed capacity and near-term market outlooks. While these estimates have their limitations, they can be used to set broad ranges of near-term feasibility. |
Existing capacities in 2024 are reported by IRENA (2025). Yearly additions for 2025–2030 are estimated in a market outlook by BNEF (2024). The market outlook data comprises 8 regional categories and a "buffer/unknown" category. Based on the existing capacity data and the market outlook, we assume the following global thresholds for 2030: • Lower threshold, high concern: 10% less than capacities operational in 2024, plus 33.75% of 2025–2030 additions from the market outlook (without buffer/unknown). • Lower threshold, medium concern: 5% less than capacities operational in 2024, plus 71.25% of 2025–2030 additions from the market outlook (without buffer/unknown). • Upper threshold, medium concern: 5% more than capacities operational in 2024, plus 157.5% of 2025–2030 additions from the market outlook (without buffer/unknown). • Upper threshold, high concern: 10% more than capacities operational in 2024, plus 220.0% of 2025–2030 additions from the market outlook (with buffer/unknown). Moreover, we assume the following regional thresholds for 2030: • Lower threshold, medium concern: 40% less than capacities operational in 2024 plus 22.5% of 2025–2030 additions from the market outlook (without buffer/unknown). • Upper threshold, medium concern: 40% more than capacities operational in 2024 plus 280% of 2025–2030 additions from the market outlook (without buffer/unknown). |
Due to reporting inconsistencies in legacy projects, several models reported capacity-values that were far below actual power generation from solar PV, causing an overly restrictive application of this criterion. To remedy this inconsistency, we implemented an exception in the evaluation workflow for Release v1.0 and v1.1 of the SCI ensemble: any high concern flag was changed to medium if actual power generation from solar PV was above 10.5 EJ/yr in 2030, corresponding to the lowest generation value of scenarios that meet the lower bound of the capacity threshold. Visit https://github.com/IAMconsortium/scenariocompass/tree/main/scripts/sci_v1.0 for more information. |
Feasibility Concern|Wind Capacity |
This criterion flags scenarios that assume a deployment of total wind capacity that is inconsistent with near-term market outlooks. This technology is available at scale and has short project lead times, meaning that there are no fundamental obstacles to fast deployment. Its near-term growth can be estimated based on today's installed capacity and near-term market outlooks. While these estimates have their limitations, they can be used to set broad ranges of near-term feasibility. |
Existing capacities in 2024 are reported by the International Renewable Energy Agency (IRENA, 2025). Yearly additions for 2025–2030 are estimated in a market outlook by the GWEC (2024). The outlook is based on input from regional wind associations, government targets, tender results, announced auction plans, available project pipelines, and input from industry experts. Based on the existing capacity data and the market outlook, we assume the following global thresholds for 2030: • Lower threshold, high concern: 10% less than capacities operational in 2024, plus 33.75% of 2025–2030 additions from the market outlook. • Lower threshold, medium concern: 5% less than capacities operational in 2024, plus 71.25% of 2025–2030 additions from the market outlook. • Upper threshold, medium concern: 5% more than capacities operational in 2024, plus 157.5% of 2025–2030 additions from the market outlook. • Upper threshold, high concern: 10% more than capacities operational in 2024, plus 220.0% of 2025–2030 additions from the market outlook. Moreover, we assume the following regional thresholds for 2030: • Lower threshold, medium concern: 40% less than capacities operational in 2024 plus 22.5% of 2025–2030 additions from the market outlook (without buffer/unknown). • Upper threshold, medium concern: 40% more than capacities operational in 2024 plus 280% of 2025–2030 additions from the market outlook (without buffer/unknown). |
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Feasibility Concern|Onshore Wind Capacity |
This criterion flags scenarios that assume a deployment of onshore wind capacity that is inconsistent with near-term market outlooks. This technology is available at scale and has short project lead times, meaning that there are no fundamental obstacles to fast deployment. Its near-term growth can be estimated based on today's installed capacity and near-term market outlooks. While these estimates have their limitations, they can be used to set broad ranges of near-term feasibility. |
Existing capacities in 2024 are reported by the International Renewable Energy Agency (IRENA, 2025). Yearly additions for 2025–2030 are estimated in a market outlook by the GWEC (2024). The outlook is based on input from regional wind associations, government targets, tender results, announced auction plans, available project pipelines, and input from industry experts. Based on the existing capacity data and the market outlook, we assume the following global thresholds for 2030: • Lower threshold, high concern: 10% less than capacities operational in 2024, plus 33.75% of 2025–2030 additions from the market outlook. • Lower threshold, medium concern: 5% less than capacities operational in 2024, plus 71.25% of 2025–2030 additions from the market outlook. • Upper threshold, medium concern: 5% more than capacities operational in 2024, plus 157.5% of 2025–2030 additions from the market outlook. • Upper threshold, high concern: 10% more than capacities operational in 2024, plus 220.0% of 2025–2030 additions from the market outlook. Moreover, we assume the following regional thresholds for 2030: • Lower threshold, medium concern: 40% less than capacities operational in 2024 plus 22.5% of 2025–2030 additions from the market outlook (without buffer/unknown). • Upper threshold, medium concern: 40% more than capacities operational in 2024 plus 280% of 2025–2030 additions from the market outlook (without buffer/unknown). |
Sustainability Concern
| NAME | JUSTIFICATION OF THE CRITERION (Why this criterion?) |
JUSTIFICATION OF THE THRESHOLD (Why this threshold?) |
|---|---|---|
Sustainability Concern|Food Availability |
This criterion flags scenarios that report levels of food availability that are far beyond or below human dietary energy needs. This is a concern, because food availability below dietary energy needs would imply the presence of hunger and food availability far beyond above human needs would imply significant overconsumption and waste. |
According to the FAO (2015), the Minimum Dietary Energy Requirement is 1,840 kilo calories food intake per person per day. Using a ÷0.83 conversion factor deducted from UNEP's food waste index report (UNEP, 2021), we define the lower threshold for a high concern as food availability below 2,220 kcal per person per day. The upper threshold is set to 4,000 kcal per person per day, which is the highest ever recorded regional food availability reported by FAO (2025). The lower medium concern threshold is based on recommendations provided by the EAT–Lancet Commission on healthy, sustainable, and just food systems (Rockström, 2025), which recommends an average estimate of 2090 kcal food intake across all ages and sex. Relying on the same conversion factor as before, this implies a food availability of 2,520 kcal per person per day. The upper medium concern threshold is set to match the FAO's Maximum Dietary Energy Requirement of 3,110 kcal food intake per day, resulting in a food availability of 3,750 kcal per person per day (FAO, 2016). This estimation of the Maximum Dietary Energy Requirement followed methodology proposed by Cafiero (2014). Based on these values, we assume the following global threshold across all years: • Upper threshold, high concern: 4,000 kcal/cap/day • Upper threshold, medium concern: 3,750 kcal/cap/day • Lower threshold, medium concern: 2,520 kcal/cap/day • Lower threshold, high concern: 2,220 kcal/cap/day |
Sustainability Concern|Biodiversity |
Halting biodiversity loss is considered a Sustainable Development Goal (SDG) by the United Nations for maritime (SDG14) and terrestrial (SDG15) life (United Nations, n.d.). According to the 2024 Planetary Boundary Health Check (Caesar, 2024), the loss of genetic diversity exceeded its safe levels in 2024. The GBF (2025) targets halting human-induced species extinction by 2030. | We interpret the target of halting human-induced species extinction by 2030 as a non-negative change rate of the biodiversity intactness index. Therefore, the lower threshold for the biodiversity intactness index is set to zero. |
Sustainability Concern|Unsustainable Hydropower Use |
Hydropower can negatively impact nature by altering river ecosystems, disrupting fish migration, and affecting water quality. Dams can change natural water flow, leading to habitat loss for aquatic and terrestrial species. They can also trap sediment, which is essential for maintaining downstream ecosystems. Thieme (2021) found that if all planned hydropower dams would complete construction, this would result in the loss of 260,000 kilometers of free-flowing rivers globally. | Opperman (2019) show that a low level of hydropower expansion of no more than 1500 GW hydropower globally combined with strategic planning of the siting of new hydropower could reduce impacts on free-flowing rivers by 90%. |
Sustainability Concern|Unsustainable Bioenergy Use |
This criterion flags scenarios that assume a high usage of bioenergy. Specifically, we here refer to 2nd generation bioenergy crops, crop and forestry residues, municipal solid waste bioenergy and traditional biomass. Bioenergy can be a drop-in replacement to currently used fossil fuels. When grown in a sustainable fashion, their life-cycle of production and use is GHG neutral. Bio-based energy crops can be grown at low cost and with readily available technologies. Some decarbonisation scenarios therefore tend to assume a high use of bioenergy. Meanwhile, such a high use of bioenergy can result in (a) growth practices that are no longer GHG neutral in their life cycle (e.g. through deforestation to create more arable land for energy crops) or result in (b) a loss of natural habitats, which creates other sustainability concerns, for instance linked to a loss of biodiversity. |
Creutzig (2014) have derived an upper limit for sustainable biomass use of 100–300 EJ/yr. In the 6th Assessment Report of Working Group 3 of the Intergovernmental Panel on Climate Change (IPCC), a value of 100 EJ/yr is defined as the threshold for the onset of medium concern and 245 EJ/yr a threshold for the onset of high concern (IPCC, 2022). Another study by Deprez (2024) suggests that medium sustainability risks arise at 50 EJ/yr and high risks at 120 EJ/yr. Based on these studies, we assume the following global thresholds across all years: • Upper threshold, medium concern: 100 EJ/yr • Upper threshold, high concern: 245 EJ/yr |
Sustainability Concern|Deforestation |
Managing forests sustainably is considered a Sustainable Development Goal (SDG) by the United Nations (SDG15) (United Nations, n.d.). The Target 1 put forward by the GBF (2025) implies close to zero deforestation by 2030. | We interpret the target of near-zero deforestation as 0 Mha/year of deforestation in 2030. Therefore, the lower threshold for deforestation is set to zero. |
Sustainability Concern|Exceeding Prudent Limit For Geological Carbon Storage |
This criterion flags scenarios that rely on an amount of geological carbon storage that exceeds estimated storage potentials. This is a concern, because relying on an exceedance of physical storage potentials is unsustainable. |
We use geological carbon storage volumes that exceed prudent technical and sustainability limits as upper bounds on cumulative CCS as quantified by Gidden (2025). Based on this study, we assume the following global thresholds for cumulative geological carbon storage in the period 2020–2100: • Upper threshold, high concern: 1,460 Gt CO₂, which was identified as the median of the range by Gidden (2025) when combining spatial risk layers. • Upper threshold, medium concern: 1,290 Gt CO₂, which was identified as the lower bound of the range by Gidden (2025) when combining spatial risk layers. |