Ratcheting ambition to limit warming to 1.5 °C – trade-offs between emission reductions and carbon dioxide removal
Bibliographic record
Abstract
Abstract Mitigation scenarios to limit global warming to 1.5 °C or less in 2100 often rely on large amounts of carbon dioxide removal (CDR), which carry significant potential social, environmental, political and economic risks. A precautionary approach to scenario creation is therefore indicated. This letter presents the results of such a precautionary modelling exercise in which the models C-ROADS and En-ROADS were used to generate a series of 1.5 °C mitigation scenarios that apply increasingly stringent constraints on the scale and type of CDR available. This allows us to explore the trade-offs between near-term stringency of emission reductions and assumptions about future availability of CDR. In particular, we find that regardless of CDR assumptions, near-term ambition increase (‘ratcheting’) is required for any 1.5 °C pathway, making this letter timely for the facilitative, or Talanoa, dialogue to be conducted by the UNFCCC in 2018. By highlighting the difference between net and gross reduction rates, often obscured in scenarios, we find that mid-term gross CO2 emission reduction rates in scenarios with CDR constraints increase to levels without historical precedence. This in turn highlights, in addition to the need to substantially increase CO2 reduction rates, the need to improve emission reductions for non-CO2 greenhouse gases. Further, scenarios in which all or part of the CDR is implemented as non-permanent storage exhibit storage loss emissions, which partly offset CDR, highlighting the importance of differentiating between net and gross CDR in scenarios. We find in some scenarios storage loss trending to similar values as gross CDR, indicating that gross CDR would have to be maintained simply to offset the storage losses of CO2 sequestered earlier, without any additional net climate benefit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".