Cutting CO<sub>2</sub>emissions from the US energy sector: meeting a 50% target by 2030
Bibliographic record
Abstract
US energy-related CO2 emissions are of a globally significant scale and therefore the mitigation of these emissions may play an extremely important role in addressing anthropogenic climate change in the 21st century. In order to address this internationally relevant topic, this study set out to apply the concept of stabilization wedges to the US energy sector. The goal was to assess the cumulative emission abatement potential of a range of currently available strategies. We find that population, surface temperature, the type of industry present and the carbon intensity of the fuel mix are dominant drivers of energy-related emissions in the USA. In addition, we find that, if the right legislative and market mechanisms were in place, a cumulative CO2 reduction of 50% below 2005 levels by 2030 would be possible in the US energy sector. We demonstrate that this very large reduction in emissions can be achieved using 12 technologically deployable, economically feasible and politically acceptable mitigation strategies
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".