Uncertainty Analysis on Global Greenhouse Gas Inventories from Anthropogenic Sources
Bibliographic record
Abstract
As the concerns on climate change increased, accurately quantifying the greenhouse gas (GHG) emissions from anthropogenic sources has been emphasized more and more.In this paper, uncertainty analysis is conducted for multiple global GHG inventories from anthropogenic sources to explore the sources and the magnitude of them.We first summarize the principal characteristic for 17 global GHG inventories by four indexes.And then to assess the sources and magnitude of uncertainty for these inventories, the discrepancies are quantified on energy statistics data and estimation results of carbon dioxide (CO2) emission on anthropogenic sources at the global total and national scale.Finally, we determine the nations with larger magnitude (extent and proportion) of uncertainty by two indicators which will be helpful for the policy-making on GHG emissions mitigation.As the analysis result, we find that uncertainty of oil consumption data is the largest among major fuels in 2013 as much as 44.6 exajoules (EJ) and the magnitude of uncertainty in CO2 emissions data is significant at global perspective as much as 4.0 petagrams (Pg) CO2 yr -1 .At national perspective, as the largest emitter nation in 2013 China, uncertainty from the coal consumption data of which is the largest in major fuels as much as 15.5 EJ and the magnitude of uncertainty for CO2 emissions of China in 2013 is as much as 1.5 Pg CO2 yr -1 .
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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 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".