Estimates of HFC-134a Emissions over Europe informed by observations show a recent increase
Bibliographic record
Abstract
Hydrofluorocarbons (HFCs) are a class of greenhouse gases (GHGs) primarily used as substitutes for ozone-depleting substances like chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs), phased out under the Montreal Protocol. However, HFCs significantly impact global warming due to their high global warming potential. In light of the pressing need to tackle climate change and mitigate the effects of GHG emissions, the United Nations Framework Convention on Climate Change (UNFCCC) has established rigorous commitments on emission reduction. As a commitment to the UNFCCC, Annex-I countries need to report their national emission estimates for regulated GHGs, including HFCs, based on the methodologies reported in the IPCC guidelines (Intergovernmental Panel on Climate Change). According to the guidelines, the comparison of estimates with top-down (models based on atmospheric measurements) is indicated as an effective tool for verifying the accuracy of inventories and there is a growing need for independent verification of these estimates. This study reports the most recent update on emissions of 1,1,1,2-Tetrafluoroethane (CH2FCF3) from 2008 to 2023, employing inverse modelling within the European domain, with a specific focus on Italy. CH2FCF3, commercially known as HFC-134a, stands as the most prevalent HFC on a global scale. Its thermodynamic properties, akin to those of dichlorodifluoromethane (CFC-12), render it an effective refrigerant for the RAC (refrigeration and air conditioning) sector. This study reveals a notable decline in HFC-134a emissions over the past decade, followed by a recent resurgence. Specifically, Italian emissions in 2020 show a 48% reduction compared to the levels of 2011 and a subsequent increase, with emissions rebounding by 25% in 2022. The availability of near real-time validated observations combined with the most recent inversion frameworks -such as Flexpart/flexinvert+ used here, could be a valuable tool to support the Inventories used to track progress and the effectiveness of the mitigation policies adopted by each country for this class of compounds (that could be extended to others major GHGs) to maximise the effectiveness of their investments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".