Cross-country distribution dynamics of carbon emissions and intensity: Before and after the global financial crisis
Bibliographic record
Abstract
Our study employs the distribution dynamics approach to uncover transition probabilities and the long-term evolution of relative per capita carbon emissions (REPC) and relative carbon intensity (REPGDP) across 204 countries. We split the period of analysis into pre-crisis (2000-2007), and post-crisis (2007-2016) and divided countries into four income groups. The results indicate the emergence of new convergence clubs post-crisis in both REPC and REPGDP. On the one hand, the majority (many) of the low- (high) income countries congregate to extremely low (above the global average) REPC levels in the long run. On the other hand, most of the upper-medium- (high) income economies cluster around REPGDP levels far above (below) the global average. Finally, using mobility probability plots, we identify above-average carbon emitters with the highest probabilities of diverging further above the global average in the coming years. The study delivers nascent evidence supporting the usefulness of the MPP display tool in clarifying the positions and responsibilities of specific countries in future agreements on climate change. The results support the argument for using both measures of carbon emissions as a suite of future multilateral climate negotiations and policies.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".