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Record W4401357264 · doi:10.1038/s41467-024-51113-2

Rethinking time-lagged emissions and abatement potential of fluorocarbons in the post-Kigali Amendment era

2024· article· en· W4401357264 on OpenAlexaboutno aff
Heping Liu, Huabo Duan, Ning Zhang, Yin Ma, Gang Liu, T. Reed Miller, Ruichang Mao, Ming Xu, Jinhui Li, Jiakuan Yang

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaGuangdong University of Petrochemical Technology
KeywordsAmendmentEnvironmental scienceLawPolitical science

Abstract

fetched live from OpenAlex

The Montreal Protocol has been successful in safeguarding the ozone layer and curbing climate change. However, accurately estimating and reducing the time-lagged emissions of ozone-depleting substances or their substitutes, such as produced but not-yet-emitted fluorocarbon banks, remains a significant challenge. Here, we use a dynamic material flow analysis model to characterize the global stocks and flows of two fluorocarbon categories, hydrochlorofluorocarbons (HCFCs) and hydrofluorocarbons (HFCs), from 1986 to 2060. We assess emission pathways, time-lagged emission sizes, and potential abatement measures throughout different life cycle stages while focusing on the role of banked fluorocarbons in global and regional decarbonization efforts in the post-Kigali Amendment era. Although fluorocarbon releases are expected to decline, the cumulative global warming potential (GWP)-weighted emissions of HCFCs and HFCs are significant; these will be 6.4 (±1.2) and 14.8 (±2.5) gigatons CO2-equivalent, respectively, in 2022–2060 in our business-as-usual (BAU) scenario. Scenario analysis demonstrates that implementing currently available best environmental practices in developed economies can reduce cumulative GWP-weighted emissions by up to 45% compared with the BAU scenario. The cumulative global warming potential-weighted emissions of hydrochlorofluorocarbons and hydrofluorocarbons are significant, which will be 21.2 (±3.7) gigatons CO2-equivalent in 2022–2060 under a business-as-usual scenario.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.445

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)0.0000.000

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 head0.011
GPT teacher head0.265
Teacher spread0.254 · 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 designSimulation or modeling
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

Citations16
Published2024
Admission routes1
Has abstractyes

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