The equitable allocation of greenhouse gases
Bibliographic record
Abstract
National Emissions Inventories (NEI) use Production-Based Accounting (PBA), which include only emissions generated within a nation's territory. Thus, there are presently no considerations of indirect emissions linked to imported products in national accounts of greenhouse gas emissions. International trade has undermined climate policy to date — as much as 30% of global emissions are linked to production for export and are therefore not subject to mitigation policy. Furthermore, conventions are often mistakenly conflated with responsibility for climate change. This research proposes a weighting metric rooted in normative ethics to allocate Emissions Embodied in Trade (EET) between trading partners — Equity Weighted-Based Accounting (EWBA). Additionally, this study quantifies subsistence and luxury emissions. The metric is constructed to weigh EET inversely to how vital a given trade is for each country taking part. Need to engage in trade is quantified using use-value for money and products exchanged in a given trade, derived from the relation between human welfare and income and its mapping onto products purchased. New NEIs are then computed and compared with those by PBA and CBA. It is found that EWBA emissions suggest higher abatement responsibility than PBA emissions for most affluent countries, though usually less than if EET were divided equally between producers and consumers. EWBA provides a policy-ready alternative to PBA that is arguably fairer than CBA. If adopted as the convention in international climate policy, NEIs computed using EWBA would better reflect responsibility for climate change and emissions abatement, yielding more equitable and effective climate policy.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".