Global Rules Mask the Mitigation Challenge Facing Developing Countries
Bibliographic record
Abstract
Abstract Focusing on global mitigation pathways masks key aspects of technical, political, and social feasibility, which play out at the country level. We illustrate the dilemma between a “carbon law” (halving emissions every decade) at the global level and the nationally determined contributions submitted at the country level. Our results suggest that even if the United States, European Union, China, and India could strengthen their nationally determined contributions by 2050, the rest of the world is required to immediately change from their current course to a very rapid decrease in emissions reaching almost zero emissions by 2030, to achieve the Paris 2015 goal. The greatest mitigation challenges lie in the developing world. Real progress toward the Paris Agreement goal awaits an effective commitment by leading countries to undertake breakthrough research and development of low‐, zero‐, or even negative‐carbon‐emissions energy technologies that can be deployed at scale in the developing world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 teacher head, 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".