National contributions to observed global warming
Bibliographic record
Abstract
There is considerable interest in identifying national contributions to global warming as a way of allocating historical responsibility for observed climate change. This task is made difficult by uncertainty associated with national estimates of historical emissions, as well as by difficulty in estimating the climate response to emissions of gases with widely varying atmospheric lifetimes. Here, we present a new estimate of national contributions to observed climate warming, including CO 2 emissions from fossil fuels and land-use change, as well as methane, nitrous oxide and sulfate aerosol emissions While some countries' warming contributions are reasonably well defined by fossil fuel CO 2 emissions, many countries have dominant contributions from land-use CO 2 and non-CO 2 greenhouse gas emissions, emphasizing the importance of both deforestation and agriculture as components of a country's contribution to climate warming. Furthermore, because of their short atmospheric lifetime, recent sulfate aerosol emissions have a large impact on a country's current climate contribution We show also that there are vast disparities in both total and per-capita climate contributions among countries, and that across most developed countries, per-capita contributions are not currently consistent with attempts to restrict global temperature change to less than 2 °C above pre-industrial temperatures.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".