Sustained Increases in Hydrofluorocarbon Emissions from China and Implications for Global Emissions
Bibliographic record
Abstract
Hydrofluorocarbons (HFCs), which are potent greenhouse gases and widely used as replacements for ozone-depleting substances, are controlled under the Montreal Protocol. China is considered an emission hot region of HFCs, however, the observations and emission quantifications are still insufficient. In this study, we report new high-frequency in situ observations of HFC-125, HFC-134a, and HFC-143a at the Changdao (CHD) station, whose emission sensitivity to northern China is higher than those of previously used stations to better quantify emissions. Combining these observations at CHD with an inverse modeling approach, we present the most recent emission estimates for northern China and reveal the distinct spatial distributions of HFC emissions that have not been previously uncovered, facilitating different priorities of provinces in future controls for HFCs. Subsequently, we show that the combined CO 2 -equivalent emissions of these HFCs in China increased rapidly from 7.1 ± 2.5 Mt CO 2 -equivalent yr –1 (2.2 ± 0.8% of global totals) in 2005 to 206.4 ± 15.9 Mt CO 2 -equivalent yr –1 (20.3 ± 1.8% of global totals) in 2022. Finally, we reveal that in terms of per area, per capita, and per gross domestic product, CO 2 -equivalent emissions of HFCs in China were increasing fast and becoming larger than the global average level. Our new high-frequency in situ observations of HFCs and ongoing observations are crucially important to assess the historical and future emission evolution of HFCs under the Montreal Protocol.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".