Pathway and Cost-Benefit Analysis to Achieve China’s Zero Hydrofluorocarbon Emissions
Bibliographic record
Abstract
Global hydrofluorocarbon (HFC) cumulative emissions will be more than 20 Gt CO 2 -equiv during 2020–2060 and have a non-negligible impact on global warming even in full compliance with the Kigali Amendment (KA). Fluorochemical manufacturers (including multinationals) in China have accounted for about 70% of global HFC production since 2015, of which about 60% is emitted outside China. This study built an integrated model (i.e., DECAF) to estimate both territorial and exported emissions of China under three scenarios and assess the corresponding climate effects as well as abatement costs. Achieving near-zero territorial emissions by 2060 could avoid 23 ± 4 Gt CO 2 -equiv of cumulative territorial emissions (compared to the 2019 Baseline scenario) during 2020–2060 at an average abatement cost of 9 ± 6 USD/t CO 2 -equiv. Under the near-zero emission (including territorial and abroad) pathway, radiative forcing from HFCs will peak in 2037 (60 ± 6 mW/m 2 ) with a 33% peak reduction and 8 years in advance compared to the path regulated by the KA, and the radiative forcing by 2060 will be lower than that in 2019. Accelerated phase-out of HFC production in China could provide a possibility for rapid global HFC abatement and achieve greater climate benefits.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".