Setting cumulative emissions targets to reduce the risk of dangerous climate change
Bibliographic record
Abstract
Avoiding "dangerous anthropogenic interference with the climate system" requires stabilization of atmospheric greenhouse gas concentrations and substantial reductions in anthropogenic emissions. Here, we present an inverse approach to coupled climate-carbon cycle modeling, which allows us to estimate the probability that any given level of carbon dioxide (CO2) emissions will exceed specified long-term global mean temperature targets for "dangerous anthropogenic interference," taking into consideration uncertainties in climate sensitivity and the carbon cycle response to climate change. We show that to stabilize global mean temperature increase at 2 degrees C above preindustrial levels with a probability of at least 0.66, cumulative CO2 emissions from 2000 to 2500 must not exceed a median estimate of 590 petagrams of carbon (PgC) (range, 200 to 950 PgC). If the 2 degrees C temperature stabilization target is to be met with a probability of at least 0.9, median total allowable CO2 emissions are 170 PgC (range, -220 to 700 PgC). Furthermore, these estimates of cumulative CO2 emissions, compatible with a specified temperature stabilization target, are independent of the path taken to stabilization. Our analysis therefore supports an international policy framework aimed at avoiding dangerous anthropogenic interference formulated on the basis of total allowable greenhouse gas emissions.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".