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Record W2982994616 · doi:10.1016/j.accre.2019.10.002

The estimated schedule and mitigation potential for hydrofluorocarbons phase-down in China

2019· article· en· W2982994616 on OpenAlexaboutno aff
Yixi Li, Zhaoyang Zhang, Minde An, Ding Gao, Li-Ying Yi, Jianxin Hu

Bibliographic record

VenueAdvances in Climate Change Research · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsChinaBaseline (sea)Montreal ProtocolConsumption (sociology)Environmental scienceGlobal-warming potentialScheduleGreenhouse gasAgricultural economicsEconomicsMeteorologyGeographyPolitical science

Abstract

fetched live from OpenAlex

As the major producer and consumer of hydrofluorocarbons (HFCs), China is obligated to phase-down HFCs to mitigate global warming if China ratifies the Kigali Amendment (KA) to the Montreal Protocol. Based on historical HFCs consumption in each sector, here we estimated historical HFCs emissions with a bottom-up method, and projected the consumption baseline and schedule for HFCs phase-down in China under the KA and the corresponding potential for emission reduction. Results showed that China's HFCs consumption and emissions in 2017 were 164,000 t (311 Mt CO2-eq) and 108 Mt CO2-eq, respectively. HFCs consumption baseline was projected to be (724 ± 18) Mt CO2-eq in 2024, and China should take measures to phase-down HFCs by 2029, at the latest, to meet the requirements of the KA. HFCs consumption in 2050 under KA would reach the level of 2012–2013. Cumulative reduced consumption was estimated at 10.8 (10.1–11.6) Gt CO2-eq, and cumulative reduced emissions were estimated at 5.38 (4.90–5.64) Gt CO2-eq by 2050.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.379
Teacher spread0.327 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations42
Published2019
Admission routes1
Has abstractyes

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