Global temperature goals should determine the time horizons for greenhouse gas emission metrics
Bibliographic record
Abstract
Abstract Emission metrics, a crucial tool in setting effective exchange rates between greenhouse gases, currently require an arbitrary choice of time horizon. Here, we propose a novel framework to calculate the time horizon that aligns with scenarios achieving a specific temperature goal. We analyze the Intergovernmental Panel on Climate Change Special Report on Global Warming of 1.5 °C Scenario Database to find that time horizons aligning with the 1.5 °C and 2 °C global warming goals of the Paris Agreement are 24 [90% prediction interval: 7, 41] and 58 [90% PI: 41, 74] years, respectively. We then use these time horizons to quantify time-dependent emission metrics for methane. We find that the Global Warming Potential (GWP) values that align with the 1.5 °C and 2 °C goals are GWP 1.5 °C = 75 [90% PI: 54, 107] and GWP 2 °C = 42 [90% PI: 35, 54]. For the Global Temperature change Potential (GTP) they are GTP 1.5 °C = 41 [90% PI: 16, 102] and GTP 2 °C = 9 [90% PI: 7, 16]. The most commonly used time horizon, 100 years, underestimates methane’s GWP and GTP by 34% and 38%, respectively, relative to the values we calculate that align with the 2 °C goal and by 63% and 87%, respectively, relative to the 1.5 °C goal. To best align emission metrics with the Paris Agreement 1.5 °C goal, we recommend a 24 year time horizon, using 2045 as the endpoint time, with its associated GWP 1.5 °C = 75 and GTP 1.5 °C = 41.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".