Projections of National-Gridded Emissions of Hydrofluoroolefins (HFOs) in China
Bibliographic record
Abstract
Hydrofluoroolefins (HFOs) are being used as substitutes for potent greenhouse gas hydrofluorocarbons (HFCs). However, the use and environmental impacts of HFOs are of great concern due to the rapid degradation of HFOs to produce persistent and phytotoxic trifluoroacetic acid (TFA). Here, we provide a comprehensive projection of HFO emissions in China during 2024–2060 for the first time. Under the Kigali Amendment to the Montreal Protocol, China’s HFO emissions are estimated to increase from 1.7 (1.3–2.3) to 148.8 (111.4–185.4) kt in 2024–2060 with cumulative emissions of 2.8 (2.0–3.5) Gt, and cumulative reduced HFCs emissions are evaluated to be 5.4 Gt CO 2 -equivalent. High HFO emissions would be distributed mainly in the North China Plain and the eastern and coastal areas. HFO-1234yf (2,3,3,3-tetrafluoropropene) contributes most of HFO emissions with a cumulative emission of 1.7 Gt in 2024–2060, while the cumulative increment of TFA deposition from HFO-1234yf emissions would reach 0.4–1.0 Gt. The long-term national-gridded HFO emission inventories can provide scientific support for evaluating the environmental risks of HFOs and developing HFC phase-out pathways for addressing climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".