Fairness- and cost-effectiveness-based approaches to effort-sharing under the Paris agreement. Short study: On behalf of the German Environment Agency; Environmental Research of the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety. Project No. (FKZ) 3717 41 102 0 – short study within the project „Implikationen des Pariser Klimaschutzabkommens auf nationale Klimaschutzanstrengungen“ Report No. FB000249/ZW,KURZ,ENG
Bibliographic record
Abstract
Given that the Paris Agreement (PA) has strengthened the long-term temperature goal and that it calls for a balance of greenhouse gas (GHG) emissions and sinks within the 21st century, there is the urgent need to re-assess the climate targets worldwide. On top of that, the PA stresses that contributions from the states have to reflect “the highest possible ambition” and “respective capabilities”. This study has derived national GHG emissions reduction contributions for 2030 and 2050 that are consistent with the Paris Agreements’ long-term temperature goal, both based on fairness and cost-effectiveness approaches. The analysis focuses on countries that are particularly relevant because of their share in global GHG emissions and their role in international climate policy, namely Brazil, Canada, China, the EU, India, Japan the United States of America, and Germany respectively. The comparison of these approaches yields insights whether or not a country can or should in-crease the ambition of its NDC. The data can also be taken to show how large the efforts in the country domestically should be and to indicate the need for support to or from other countries. The analysis reveals for both approaches, that the more ambitious long-term temperature goal of the Paris Agreement results in substantially higher reduction requirements for all countries compared to the former Cancun targets.
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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 | 0.044 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".