Hydrofluorocarbon (HFC) Emissions in China: An Inventory for 2005–2013 and Projections to 2050
Bibliographic record
Abstract
Many hydrofluorocarbons (HFCs) that are widely used as substitutes for ozone-depleting substances (now regulated under the Montreal Protocol) are very potent greenhouse gases (GHGs). China's past and future HFC emissions are of great interest because China has emerged as a major producer and consumer of HFCs. Here, we present for the first time a comprehensive inventory estimate of China's HFC emissions during 2005-2013. Results show a rapid increase in HFC production, consumption, and emissions in China during the period and that the emissions of HFC with a relatively high global warming potential (GWP) grew faster than those with a relatively low GWP. The proportions of China's historical HFC CO2-equivalent emissions to China's CO2 emissions or global HFC CO2-equivalent emissions increased rapidly during 2005-2013. Using the "business-as-usual" (BAU) scenario, in which HFCs are used to replace a significant fraction of hydrochlorofluorocarbons (HCFCs) in China (to date, there are no regulations on HFC uses in China), emissions of HFCs are projected to be significant components of China's and global future GHG emissions. However, potentials do exist for minimizing China's HFC emissions (for example, if regulations on HFC uses are established in China). Our findings on China's historical and projected HFC emission trajectories could also apply to other developing countries, with important implications for mitigating global GHG emissions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".