Reduction in surface climate change achieved by the 1987 Montreal Protocol
Bibliographic record
Abstract
Abstract The benefits of the 1987 Montreal Protocol in reducing chlorofluorocarbon emissions, repairing the stratospheric ozone hole, shielding incoming UV radiation, reducing the incidence of skin cancer and mitigating negative ecosystem effects are all well documented. Projected future climate impacts have also been described, mainly focused on a reduced impact of the mid-latitude jet as the ozone hole gradually repairs. However, there is little appreciation of the surface warming that has been avoided as a result of the Montreal Protocol, despite CFCs being potent greenhouse gases. Instead, the issue of ozone depletion and climate change are often thought of as two distinct problems, even though both ozone and CFCs impact Earth’s radiation budget. Here we show that a substantial amount of warming has been avoided because of the Montreal Protocol, even after factoring in the surface cooling associated with stratospheric ozone depletion. As of today, as much as 1.1 °C warming has been avoided over parts of the Arctic. Future climate benefits are even stronger, with 3 °C–4 °C Arctic warming and ∼1 °C global average warming avoided by 2050; corresponding to a ∼25% mitigation of global warming. The Montreal Protocol has thus not only been a major success in repairing the stratospheric ozone hole, it has also achieved substantial mitigation of anthropogenic climate change both today and into the future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".