Sufficient or insufficient: Assessment on the Intended Nationally Determined Contributions (INDCs) of world¡¯s major emitters
Bibliographic record
Abstract
The recent conference of the parties to the United Nations Framework Convention on Climate Change (COP21) resulted in the Intended National Determined Contributions (INDCs)by 190 countries. The aim of this article is to offer an analysis of the ambition and fairness of the mitigation components of the INDCs submitted by parties. We use a unified framework to assess the 23 INDCs covering 50 countries (EU 28 countries as a Party to the Convention), representing 87.45% of global greenhouse gas emissions in 2012. First, we transform initial INDC files into reported reduction target. Second, we create four schemes and six scenarios to find out required reduction effort, which takes nationi¯s reduction responsibility, capacity and potential into consideration, reflecting historical and current development status of each nation. At last, we put reported reduction target and required reduction effort together to assess INDCs. In the evaluation of the 23emitters, two emitters (EU and Brazil) were rated as sufficient. Seven emitters, such as China, the United States and Canada were rated as moderate. Fourteen emitters, such as India, Russian and Japan were rated as insufficient. Most pledges reveal a great distance from representing a fair contribution.
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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 | 0.030 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".