Emissions of HFC-134a in China and Reconciliation of Discrepancies between Observation-Based and Inventory-Based Emission Estimates
Bibliographic record
Abstract
HFC-134a (1,1,1,2-tetrafluoroethane) is a potent greenhouse gas with global warming potential thousands of times larger than that of carbon dioxide (CO 2 ). HFC-134a is regulated under the Montreal Protocol. However, the emissions, consumption, and emission–consumption relationships of HFC-134a are unclear. Here, this study reveals that observation-based HFC-134a emissions increased from 19.5 ± 2.5 Gg yr –1 in 2011 to 33.1 ± 7.5 Gg yr –1 in 2020 in China, but with a lower increase (2%) compared to those (48%) reported in a previous inventory-based emission estimate from 2015 to 2020. Consequently, the emission discrepancy between observation-based and inventory-based emissions reached 24.9 ± 7.5 Gg yr –1 (equivalent to 36.6 ± 11.0 Tg of CO 2 -eq yr –1 ) in 2020. Therefore, this study built a novel approach (Observation-based Sectoral Activity and Emission Function Attribution Model, OSAM) to quantitatively attribute these emission discrepancies. We found that the emission discrepancies of HFC-134a were generally attributed to the consumption (35.6%) and emission functions (64.4%) used in previous emission inventories; thereby an emission inventory with new emission–consumption relationships, which matched the observation-based estimates, was established in this study. This study provides an emission discrepancy reconciliation approach applicable worldwide.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".