Assessing European HFC Emissions Using Inverse Modelling Systems
Bibliographic record
Abstract
Hydrofluorocarbons (HFCs) are potent greenhouse gases that contribute substantially to climate change. Their emissions are rapidly evolving due to changes in production and use that are driven by the Kigali Amendment to the Montreal Protocol and regional regulations. Atmospheric data and inverse modelling systems can be valuable for evaluating the effectiveness of these controls and the emissions reported to the United Nations Framework Convention on Climate Change (UNFCCC). Currently in Europe, the United Kingdom and Switzerland include atmospheric top-down emission estimates as part of their National Inventory Reports to the UNFCCC, and now the Horizon Europe project Process Attribution of Regional emISsions (PARIS) aims to expand similar inventory evaluation to several additional European countries. In this PARIS study, we derived HFC emissions for north-western Europe from 2012 to 2023 using the NAME transport model and three Bayesian inversion systems (InTEM, ELRIS, RHIME), focusing on HFC-134a, HFC-143a, HFC-32, HFC-125, HFC-23, HFC-152a, HFC-227ea, HFC-236fa, HFC-245fa, HFC-365mfc, and HFC-4310mee. Our results indicate an overall decline in HFC emissions in north-western Europe, broadly consistent with European F-gas regulations. Derived emissions trends are compared with National Inventory Reports, highlighting discrepancies. Moreover, we explore the driving factors behind these trends. These findings contribute to understanding emissions trends and improving inventory evaluations in Europe.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".