Improving the temporal and spatial distribution of CO <sub>2</sub> emissions from global fossil fuel emission data sets
Bibliographic record
Abstract
Through an analysis of multiple global fossil fuel CO 2 emission data sets, Vulcan emission data for the United States, Canada's National Inventory Report, and NO 2 variability based on satellite observations, we derive scale factors that can be applied to global emission data sets to represent weekly and diurnal CO 2 emission variability. This is important for inverse modeling and data assimilation of CO 2 , which use in situ or satellite measurements subject to variability on these time scales. Model simulations applying the weekly and diurnal scaling show that, although the impacts are minor far away from sources, surface atmospheric CO 2 is perturbed by up to 1.5−8 ppm and column‐averaged CO 2 is perturbed by 0.1−0.5 ppm over some major cities, suggesting the magnitude of model biases for urban areas when these modes of temporal variability are not represented. In addition, we also derive scale factors to account for the large per capita differences in CO 2 emissions between Canadian provinces that arise from differences in per capita energy use and the proportion of energy generated by methods that do not emit CO 2 , which are not accounted for in population‐based global emission data sets. The resulting products of these analyses are global 0.25° × 0.25° gridded scale factor maps that can be applied to global fossil fuel CO 2 emission data sets to represent weekly and diurnal variability and 1° × 1° scale factor maps to redistribute spatially emissions from two common global data sets to account for differences in per capita emissions within Canada.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".