Assessing European HFC Emissions Using Inverse Modelling Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.006 |
| 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 it