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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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; both teacher heads agree on what is shown here.
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".