Development of ambitious and realistic targets to reduce short-lived climate pollutant emissions in nationally determined contributions: case study for Colombia
Bibliographic record
Abstract
Abstract Limiting global temperature increases to below 1.5 °C requires reductions in Short-Lived Climate Pollutants (SLCPs), like methane, black carbon, and hydrofluorocarbons (HFCs), which is rarely reflected in targets within Nationally Determined Contributions (NDCs). Colombia’s 2020 NDC is explored as a case study for how Governments can integrate SLCP mitigation targets into climate change commitments. Methane and HFC mitigation contribute approximately 9% of Colombia’s GHG reduction commitment, and a separate target is included to reduce black carbon emissions by 40% by 2030 compared to 2014 levels. These targets are shown to be ambitious , due to the inclusion of a new black carbon target, realistic due to the identification of mitigation measures to achieve them, and additional to CO 2 mitigation. Analysis of the planning process establishing these targets emphasises the importance of long-term planning to obtain agreement between coordinating institutions and implementing institutions on the utility of SLCP targets, and capacity-building within national institutions.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".