Las estimaciones erróneas de los daños del cambio climático
Bibliographic record
Abstract
Los economistas predicen que los daños del calentamiento global serán de un 2,1% de la producción mundial si la temperatura media de la superficie del planeta aumenta en 3°C, y de un 7,9% si aumenta 6°C. Esas estimaciones contrastan con la predicción de los científicos: la fuerte reducción de la habitabilidad humana debida al cambio climático. Pero los modelos que usan para hacer esas predicciones influyen en el debate internacional sobre el tema y en las prescripciones de política. Aquí revisamos ese trabajo empírico y mostramos que subestima gravemente los daños del cambio climático al cometer varios errores. Y más importante, que el modelo de evaluación integrada DICE no genera un colapso económico, independientemente del nivel de daños. Debido a tales defectos, esas estimaciones se deberían rechazar por no ser científicas, y los modelos calibrados con ellas no se deberían utilizar para evaluar los riesgos económicos del cambio climático ni para proponer políticas que atenúen los daños.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".