Incorporating social indicators for an equitable carbon budget allocation among different geographical regions
Bibliographic record
Abstract
Abstract As the global temperature continues to rise each year, limiting global warming to 1.5 °C is increasingly becoming a challenge due to the shrinking size of the remaining carbon budget. Under the Paris Agreement, countries committed to reducing their emissions based on national circumstances and priorities. Current emission reduction approaches account for past emissions, per capita emissions, ability to pay for mitigation, and other factors. Based on these factors, researchers have recommended various ways, such as the blended approach, for allocating the remaining carbon budget. However, it does not fully account for the social conditions that play a vital role in allocating the remaining carbon budget. In response, this research formulates a way to equitably allocate the remaining carbon budget among different geographical regions based on their social conditions. It incorporates critical social indicators based on the Doughnut framework to the Contraction and Convergence (C&C) principle, a commonly used allocation mechanism in climate policy, to determine equitable carbon budget shares at the national and regional levels. The share is obtained based on the proportion of current emissions associated with social performance and the proportion of the present population for 158 countries, which are grouped into 12 geographical regions. According to the equitable allocation mechanism, regions with poor social scores are assigned greater carbon budgets and vice versa. That is, countries and regions experiencing large shortfalls in their social foundation (e.g., frequent food famine) are allowed to burn more CO2 to enable food security and rapid industrialization. Thus, this study addresses the challenges of addressing equity issues in carbon emission reduction necessary to achieve the temperature targets outlined in the Paris Agreement by introducing a robust method to account for a country’s socio-economic circumstances for an equitable allocation of the remaining carbon budget. Moreover, it establishes a reasonable benchmark for regional decision-making on climate action for countries vulnerable to climate change.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".