Edition 2026-06-01
This page gives a criterion-by-criterion overview of all vetting criteria, combining the scientific justifications with the computed threshold values.
Historical Vetting
Cropland Yields
Why this criterion? 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.
Why this threshold? 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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Emissions|CH4|AFOLU|Agriculture
Why this criterion? 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.
Why this threshold? 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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Emissions|CO2|AFOLU
Why this criterion? 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.
Why this threshold? 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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Emissions|CO2|AFOLU|Land
Why this criterion? 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.
Why this threshold? 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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Emissions|CO2|Energy and Industrial Processes
Why this criterion? This scenario reports 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.
Why this threshold? Historical values are provided by the Community Emissions Data System (Hoesly, 2025).
The thresholds are derived from these sources as follows:
• Single region: ±35% deviation will lead to exclusion.
• Whole world: ±25% 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 derived from data in 2022 and 2023 through linear extrapolation. Due to the COVID-19 shock in 2020, the threshold value can be either the exact value in 2020 or the value averaged over the period Jan 2018 till Dec 2022, whichever is more permissible for the scenario in question.
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Emissions|N2O|AFOLU|Agriculture
Why this criterion? 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.
Why this threshold? 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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Final Energy
Why this criterion? This scenario reports 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.
Note that final energy includes non-energy use.
Why this threshold? Historical values are provided by the International Energy Agency (IEA, 2025).
The thresholds are derived from these sources for years 2010, 2015, 2020, and 2025 as follows:
• Single region: ±35% deviation will lead to exclusion.
• Whole world: ±25% deviation will lead to exclusion, ±15% will lead to a red flag.
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 derived from data in 2022 and 2023 through linear extrapolation. Due to the COVID-19 shock in 2020, a wider tolerance of ±40% is applied for that year instead of ±25%. The threshold for 2020 is derived using both the exact value in 2020 and the value averaged over the period Jan 2018 till Dec 2022, taking whichever is more permissive for the scenario in question.
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Food Availability
Why this criterion? 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.
Why this threshold? 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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GDP|PPP
Why this criterion? This scenario reports gross domestic production values that are inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on the the mitigation effort required to reach net-zero emissions.
Why this threshold? Historical values are provided by the World Development Indicators (World Bank, 2025).
The thresholds are derived from these sources for years 2010, 2015, 2020, and 2025 as follows:
• Single region: ±35% deviation will lead to exclusion.
• Whole world: ±25% 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 derived from data in 2022 and 2023 through linear extrapolation.
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Land Cover
Why this criterion? 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.
Why this threshold? 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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Land Cover|Cropland
Why this criterion? 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.
Why this threshold? 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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Land Cover|Forest
Why this criterion? 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.
Why this threshold? 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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Land Cover|Pasture
Why this criterion? 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.
Why this threshold? 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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Population
Why this criterion? This scenario reports population values that are inconsistent with historical values. This is a concern because the underlying modelling likely makes false assumptions on the the mitigation effort required to reach net-zero emissions.
Why this threshold? Historical values are reported by the World Development Indicators (World Bank, 2025).
The thresholds are derived from these sources for years 2010, 2015, 2020, and 2025 as follows:
• Single region: ±35% deviation will lead to exclusion, ±25% will lead to a red flag.
• Whole world: ±25% deviation will lead to exclusion, ±15% will lead to a red flag.
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 derived from data in 2022 and 2023 through linear extrapolation.
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Primary Energy|Coal
Why this criterion? This scenario reports 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.
Why this threshold? Historical values are provided by the International Energy Agency (IEA, 2025).
The thresholds are derived from these sources for years 2010, 2015, 2020, and 2025 as follows:
• Single region: ±35% deviation will lead to exclusion, ±25% will lead to a red flag.
• Whole world: ±25% deviation will lead to exclusion, ±15% will lead to a red flag.
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 derived from data in 2022 and 2023 through linear extrapolation. Due to the COVID-19 shock in 2020, a wider tolerance of ±40% is applied for that year instead of ±25%. The threshold for 2020 is derived using both the exact value in 2020 and the value averaged over the period Jan 2018 till Dec 2022, taking whichever is more permissive for the scenario in question.
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Primary Energy|Gas
Why this criterion? This scenario reports 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.
Why this threshold? Historical values are provided by the International Energy Agency (IEA, 2025).
The thresholds are derived from these sources for years 2010, 2015, 2020, and 2025 as follows:
• Single region: ±35% deviation will lead to exclusion, ±25% will lead to a red flag.
• Whole world: ±25% deviation will lead to exclusion, ±15% will lead to a red flag.
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 derived from data in 2022 and 2023 through linear extrapolation. Due to the COVID-19 shock in 2020, a wider tolerance of ±40% is applied for that year instead of ±25%. The threshold for 2020 is derived using both the exact value in 2020 and the value averaged over the period Jan 2018 till Dec 2022, taking whichever is more permissive for the scenario in question.
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Primary Energy|Nuclear
Why this criterion? This scenario reports 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.
Why this threshold? Historical values are provided by the International Energy Agency (IEA, 2025).
The thresholds are derived from these sources for years 2010, 2015, 2020, and 2025 as follows:
• Single region: ±35% deviation will lead to exclusion, ±25% will lead to a red flag.
• Whole world: ±25% deviation will lead to exclusion, ±15% will lead to a red flag.
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 derived from data in 2022 and 2023 through linear extrapolation. Due to the COVID-19 shock in 2020, a wider tolerance of ±40% is applied for that year instead of ±25%. The threshold for 2020 is derived using both the exact value in 2020 and the value averaged over the period Jan 2018 till Dec 2022, taking whichever is more permissive for the scenario in question.
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Primary Energy|Oil
Why this criterion? This scenario reports 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.
Why this threshold? Historical values are provided by the International Energy Agency (IEA, 2025).
The thresholds are derived from these sources for years 2010, 2015, 2020, and 2025 as follows:
• Single region: ±35% deviation will lead to exclusion, ±25% will lead to a red flag.
• Whole world: ±25% deviation will lead to exclusion, ±15% will lead to a red flag.
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 derived from data in 2022 and 2023 through linear extrapolation. Due to the COVID-19 shock in 2020, a wider tolerance of ±40% is applied for that year instead of ±25%. The threshold for 2020 is derived using both the exact value in 2020 and the value averaged over the period Jan 2018 till Dec 2022, taking whichever is more permissive for the scenario in question.
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Feasibility Concern
Biodiversity
Why this criterion? 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.
Why this threshold? 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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Cropland Yields
Why this criterion? This scenario assumes future long-term crop-land yields that go beyond what is technically feasible.
Why this threshold? TO BE INSERTED
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DACCS Capacity
Why this criterion? 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.
Why this threshold? An upper limit of 4.89 Gt CO2eq/yr of DACCS is derived by Edwards (2024).
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Deforestation
Why this criterion? 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.
Why this threshold? 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).
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Forest Expansion
Why this criterion? 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.
Why this threshold? 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).
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Hydropower Capacity
Why this criterion? This scenario assumes a deployment of hydropower plants 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-sized 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 projects, see the Hydropower Special Market Report (IEA, 2021).
Why this threshold? Data on hydro power plant projects was published by the IEA in 2021. Details can be found here: https://www.iea.org/data-and-statistics/data-tools/hydropower-data-explorer
The IEA publishes capacities in three categories:
• Operational: in operation in 2021
• Expected: expected to come online by 2030
• Accelerated: capacity that could come online by 2030 given an acceleration in efforts.
We assume the following thresholds for 2030 based on data provided by the IEA:
• Lower, red: 10% (globally) less than currently operational capacities.
• Lower, yellow: 5% (globally) or 40% (regionally) less than what is expected to come online according to the IEA by 2030.
• Upper, yellow: 5% (globally) or 80% (regionally) more than what is expected to come online assuming accelerated efforts according to the IEA by 2030.
• Upper, red: 45% (globally) more than what is expected to come online assuming accelerated efforts according to the IEA by 2030.
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Land Cover
Why this criterion? 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.
Why this threshold? 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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Long-term Carbon Capture
Why this criterion? This scenario assumes a high deployment of CCU/S in 2035--2040. CCU/S involves capturing CO₂ from flue gases and storing it geologically or in products. 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.
Why this threshold? This scenario assumes capacity deployment of CCS in 2035 and 2040 that would require unlikely growth rates (compare Kazlou (2024)).
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Long-term Nuclear Capacity
Why this criterion? This scenario assumes a high long-term deployment of nuclear power plants.
Nuclear power is a technology that is known to exhibit low growth rates. While governmental programmes to speed up development could results in a increased growth rates, such developments seem unlikely given recent trends and the required public funding. This is reflected by the fact that even the industry's own association (the International Atomic Energy Agency) estimates a maximum increase of today's capacity by 150% (IAEA, 2024).
Why this threshold? The International Atomic Energy Agency publishes its own long-term estimates for nuclear power-plant capacity (IAEA, 2024). The highest estimates are at 694 GWe in 2040 and 950 GWe in 2050.
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Near-term Carbon Capture
Why this criterion? This scenario assumes a deployment of CCU/S technologies 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.
Why this threshold? CCU/S projects are tracked by the IEA and published annually in its CCUS Database. Details can be found here: https://www.iea.org/data-and-statistics/data-product/ccus-projects-database
Projects in the CCUS database have one of the following statuses:
• Operational: in operation in 2024
• Construction: currently under construction
• Planned: announced but without final investment decision in 2024. Projects that were decommissioned or suspended are not
We assume the following thresholds for 2030 based on data provided by the IEA CCUS database:
• Lower, red: 10% (globally) less than currently operational capacities.
• Lower, yellow: 5% (globally) or 40% (regionally) less than currently operational capacities and 50% of projects under construction.
• Upper, yellow: 5% (globally) or 40% (regionally) more than plants operational today plus all projects under construction plus 20% of projects planned without FID.
• Upper, red: 10% (globally) more than all projects operational, under construction, and announced.
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Near-term Nuclear Capacity
Why this criterion? This scenario assumes a deployment of nuclear power plants 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.
Why this threshold? Data on nuclear power plant projects was published by the International Atomic Energy Agency (IAEA) in 2021. Details can be found here:
Country statistics today:
https://pris.iaea.org/PRIS/CountryStatistics/CountryStatisticsLandingPage.aspx
IAEA's own estimates:
https://www-pub.iaea.org/MTCD/Publications/PDF/RDS-1-44_web.pdf
The IAEA publishes capacities in three categories:
• Operational: in operation in 2024
• Construction: currently under construction
• Retired: inactive capacity that has been retired
We assume the following thresholds for 2030 based on data provided by the IAEA:
• Lower, red: 30% (globally) less than currently operational capacities.
• Lower, yellow: 15% (globally) or 50% (regionally) less than currently operational capacities.
• Upper, yellow: 5% (globally) or 40% (regionally) more than plants operational today plus bringing retired plants in Japan back online plus 75% of plants under construction.
• Upper, red: 10% (globally) more than the highest estimate published by the IAEA (461 GW).
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Onshore Wind Capacity
Why this criterion? This scenario assumes a deployment of on-shore wind 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. Yet, its near-term growth can be estimated based on today's market dynamics. While these estimates have their limitations, they can be used to set broad ranges of near-term feasibility.
Why this threshold? Existing capacities in 2022 are reported by Ember (2024). Yearly additions for 2023–2028 are estimated in a market outlook by the GWEC (2024).
This outlook is based on input from regional wind associations, government targets, tender results, announced auction plans, available project pipeline, and input from industry experts.
We assume the following thresholds for 2025 and 2030:
• Lower, red: 10% (globally) less than existing capacities plus 37.5% of market outlook.
• Lower, yellow: 5% (globally) or 40% (regionally) less than existing capacities plus 75% (globally) or 37.5% (regionally) of market outlook.
• Upper, yellow: 5% (globally) or 40% (regionally) more than existing capacities plus 150% (globally) or 200% (regionally) of market outlook.
• Upper, red: 10% (globally) more than existing capacities plus 200% of market outlook.
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Solar PV Capacity
Why this criterion? This scenario assumes a deployment of solar photovoltaics 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. Yet, its near-term growth can be estimated based on today's market dynamics. While these estimates have their limitations, they can be used to set broad ranges of near-term feasibility.
Why this threshold? Existing capacities in 2023 are reported by Ember (2024). Yearly additions for 2024–2030 are estimated in a market outlook by BNEF (2024).
We assume the following thresholds for 2025 and 2030:
• Lower, red: 10% (globally) less than existing capacities plus 37.5% of market outlook.
• Lower, yellow: 5% (globally) or 40% (regionally) less than existing capacities plus 75% (globally) or 37.5% (regionally) of market outlook.
• Upper, yellow: 5% (globally) or 40% (regionally) more than existing capacities plus 150% (globally) or 200% (regionally) of market outlook.
• Upper, red: 10% (globally) more than existing capacities plus 200% of market outlook.
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Sustainability Concern
Biodiversity
Why this criterion? 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.
Why this threshold? 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.
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Deforestation
Why this criterion? 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.
Why this threshold? 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.
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Exceeding Prudent Limit For Geological Carbon Storage
Why this criterion? Some scenarios rely strongly on the use of carbon capture and sequestration (CCS). We use geological carbon storage volumes that exceed prudent technical and sustainability limits as upper bounds as quantified by Gidden et al (2025).
Why this threshold? Estimates are provided by Gidden (2025).
• Upper threshold, major concern: exceeding cumulative CCS of 1,460 Gt CO2 until the end of the century, which was identified as the median of the range when combining spatial risk layers.
• Upper threshold, medium concern: exceeding cumulative CCS of 1,290 Gt CO2 until the end of the century, which was identified as the lower bound of the range when combining spatial risk layers.
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Food Availability
Why this criterion? This scenario reports levels of food availability that are above or below human nutritional needs.
This is a concern, because food availability below nutritional needs would mean hunger and because food availability above nutritional needs would mean overconsumption.
Why this threshold? According to the Food and Agriculture Organization of the United Nations (FAO) (2020), the Minimum Dietary Energy Requirement is 2,100 kilo calories per person per day, which is used as the lower threshold. Meanwhile, the highest historical level of food availability is 4,000 kilo calories per person per day, so that the threshold is set at 5,000 kilo calories per person per day.
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Unsustainable Bioenergy Use
Why this criterion? This scenario assumes 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.
Why this threshold? 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 sustainablity risks arise at 50 EJ/yr and high risks at 120 EJ/yr. Based on those studies the threshold for this sustainablity concern flag is set at 100 EJ/yr.
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Unsustainable Hydropower Use
Why this criterion? 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.
Why this threshold? 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%.
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Sources
| Identifier | Bibliographic information | Links |
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