Climate Change and Transregional Convergence: Carbon Markets in North Amer ica
Bibliographic record
Abstract
Ante la falta de compromisos importantesde los principales Estados emisores de gasesde efecto invernadero a escala global,y de la falta de políticas nacionales clarasal respecto, nuevas alternativas surgen enel ámbito regional en América del Norte.Estas alternativas producen nuevas formasde interacción entre diferentes actorespara enfrentar el problema del cambioclimático.El liderazgo de los gobiernos locales,especialmente de Columbia Británica,Ontario y Quebec, ha resultado en unaestrategia para enfrentar los problemasglobales desde un ámbito transregional. Ejemplo de estos mecanismos es la Western Climate Initiative, el Regional GreenhouseGas Initiative, o la Midwestern GreenhouseGas Reduction Accord. La estrategia es desarrollar mercados de carbón locales para hacer que los esfuerzos para mitigar y adaptarse al cambio climático sean lo menos costoso posible para las economías locales. A pesar de que son iniciativas relativamente nuevas, están creando estrategias para evitar problemas como el traslape entre ellos mismos, y la competencia con mercados nacionales y con mecanismos internacionales (en el marco del Protocolode Kioto).El objetivo de esta investigación es explorar cómo estos tres mercados de carbón norteamericanos han venido desarrollando políticas de convergencia dentro de sí mismos, a través del sistema de compensaciones y de la utilización de mercados secundarios.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".