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Record W2964983675 · doi:10.1029/2019gl083169

Changes in HCFC Emissions in China During 2011–2017

2019· article· en· W2964983675 on OpenAlexaboutno aff
Xuekun Fang, Bo Yao, Martin K. Vollmer, Stefan Reimann, Li Liu, L. Chen, Ronald G. Prinn, Jianxin Hu

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaUniversity of BristolNational Aeronautics and Space Administration
KeywordsEnvironmental scienceAtmospheric sciencesChinaClimatologyEmission inventoryOzoneMeteorologyGeographyGeologyAir quality index

Abstract

fetched live from OpenAlex

Abstract Hydrochlorofluorocarbons (HCFCs), the main substitutes of chlorofluorocarbons, are regulated by the Montreal Protocol. Chinese HCFC emissions increased fast from the beginning of this century. However, limit reports based on atmospheric measurement are available for years after 2011, an important period when significant changes are expected. Combining atmospheric observations at seven sites across China with a FLEXible PARTicle dispersion model‐based Bayesian inversion technique, we estimate emission magnitudes and changes of four major HCFCs in China during 2011–2017. The emissions of all four HCFCs reached peaks before 2015. Our results agreed well with the reported bottom‐up inventories. The Chinese ozone depletion potential (ODP)‐weighted emission of the three most abundant HCFCs accounted for 37% of global totals from 2011 to 2016. The total emission of HCFC‐22 from China, the European Union, and the United States accounted approximately a half of the global totals, suggesting large HCFC emission emitted from the rest of the world.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.004

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.

Opus teacher head0.026
GPT teacher head0.286
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations47
Published2019
Admission routes1
Has abstractyes

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