How Malleable are the Greenhouse Gas Emission Intensities of the G7 Nations?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Why do countries’ greenhouse gas (GHG) intensities differ? How much of a country’s GHG intensity is set by inflexible national circumstances, and how much may be altered by policy? These questions are common in climate change policy discourse and may influence emission reduction allocations. Despite the policy relevance of the discussion, little quantitative analysis has been done. In this paper we address these questions in the context of the G7 by applying a pair of simple quantitative methodologies: decomposition analysis and allocation of fossil fuel production emissions to end-users instead of producers. According to our analysis and available data, climate and geographic size - both inflexible national characteristics - can have a significant effect on a country’s GHG intensity. A country’s methods for producing electricity and net trade in fossil fuels are also significant, while industrial structure has little effect at the available level of data disaggregation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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 it