Are the G20 economies making enough progress to meet their NDC targets?
Bibliographic record
Abstract
Under the Paris Agreement, countries committed to a variety of climate actions, including post-2020 greenhouse gas (GHG) emissions reduction targets. This study compares projected GHG emissions in the G20 economies under current climate policies to those under the GHG targets outlined in the nationally determined contributions (NDCs). It is based on an assessment of official governmental estimates and independent national and global studies. The study concludes that six G20 members (China, India, Indonesia, Japan, Russia and Turkey) are projected to meet their unconditional NDC targets with current policies. Eight members (Argentina, Australia, Canada, the European Union, Republic of Korea, South Africa and the United States) require further action to achieve their targets. Insufficient information is available for Saudi Arabia, and emission projections for Brazil and Mexico are subject to considerable uncertainty. The study also presents high-level decarbonisation indicators to better understand the current progress towards meeting the NDCs – Saudi Arabia and South Africa were found to continue increasing both emission intensity per unit GDP and emissions per capita under current policies by 2030 from 2015 levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".