Climate justice and a fair allocation of national greenhouse gas emissions
Bibliographic record
Abstract
Rajamani et al. have presented estimates for a fair and equitable allocation of the remaining global greenhouse gas emissions that are compatible with meeting the temperature targets of the Paris Agreement. In this paper, we find that their approach yields a high emission allowance per capita to currently high-emitting countries such as Australia, South Africa, Saudi Arabia, Canada, and China. In fact, Rajamani et al. propose that these countries should get two to three times more allowances (emission space on a per capita basis) than for instance India and Ghana and they refer to this as a ‘fair’ allocation despite the fact that the latter countries have significantly lower per capita emissions, per capita income, and historical emissions. Furthermore, the allocation to several Western European countries, e.g. the UK and Sweden, is strongly negative. Hence, their approach tends to reward countries with high emissions and discriminate against countries with low emissions per capita despite the fact that Rajamani et al. argue that grandfathering cannot be seen as a fair principle for allocating emissions allowances. Our findings are not only of academic interest, but they carry important implications for the debates about climate litigation since several organizations have sued states based on essentially the same method as that used by Rajamani et al.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".