Comparative Analysis of the NDCs of Canada, the European Union, Kenya and South Africa from an Equity Perspective : a research report funded by the Swedish Energy Agency
Bibliographic record
Abstract
In the lead-up to COP21 in Paris, 2015, all Parties to the UNFCCC were invited to communicate their intended nationally determined contributions (INDCs), which could include information on how the Party considers its INDC is fair and ambitious (1/CP.20, para 14). The same information to accompany nationally determined contributions (NDCs) was included in the Paris decision adopted at COP21. While there is extensive literature on climate equity, comparatively little research exists on equity in NDCs. Analysis of equity in NDCs is important, firstly because NDCs represent a unique step in UN climate negotiations, in that they are universal and applicable to all Parties, and secondly because NDCs are formulated bottom-up. As countries determine their own priorities and ambitions they self-differentiate their responsibilities to address climate change. This research report examines equity considerations in the domestic processes for the preparation of NDCs. Four Parties are examined in this analysis, selected based on having widely varying domestic contexts and processes for NDC preparation: Canada, Kenya, the European Union and South Africa
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".