Addressing uncertainties in top-down estimates of national-scale greenhouse gas emissions across different inversion systems
Bibliographic record
Abstract
As part of the current international effort to limit global warming, signatories to the Paris Agreement are required to quantify their greenhouse gas (GHG) emissions. Former Kyoto Annex I countries thus report their emissions annually to the United Nations Framework Convention on Climate Change (UNFCCC) . This assessment allows countries to evaluate their progress in reducing GHG emissions and their compliance with existing agreements.The general approach to quantifying GHG emissions at the national level is to use activity data and emission factors (bottom-up method). An independent quantification can be achieved with inverse modelling, which makes use of an a priori estimate, atmospheric transport models (ATM), and atmospheric measurements of GHG concentrations (top-down method). However, the accuracy and uncertainty of inverse estimates are highly dependent on several parameters and modelling choices. Consequently, inter-model variability can be significant, potentially limiting the use of this technique in policy-relevant discussions.A representative quantification of GHG emissions based on inverse modelling requires an in-depth understanding of different inverse model estimates, their uncertainties and model limitations. An intercomparison of three inverse methods and a suite of sensitivity tests were performed. This exercise considered two fluorinated gases (HFC-143a and PFC-218), which are potent GHGs with very different emission characteristics (diffuse versus point source). Both are covered under the European F-gas regulation. Additionally, HFC-143a is expected to be phased-down under the Kigali Amendment to the Montreal Protocol.We found that top-down estimates for Central and Western European countries are most sensitive to the ATM used. For gases with localised emission sources, such as PFC-218, the choice of a priori emissions and assigned model-data mismatch uncertainty are particularly relevant. For gases with widely distributed emission sources, such as HFC-143a, the emission estimates are more consistent and less sensitive to modelling choices. This detailed understanding of uncertainties in top-down estimates is then used to inform how inverse modelling can be used to support the reporting of halogenated GHG emissions at the national and European level.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".