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Record W1975844927 · doi:10.1029/1999jd900761

An evaluation of historical methane emissions from the Soviet gas industry

2000· article· en· W1975844927 on OpenAlexaff
A. I. Reshetnikov, N. N. Paramonova, A. A. Shashkov

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsEnvironment and Climate Change Canada
FundersRoyal Holloway, University of London
KeywordsSoviet unionMethaneNatural gasExtrapolationGreenhouse gasEnvironmental scienceRange (aeronautics)Investment (military)Methane emissionsGas pipelineAtmospheric sciencesPhysicsChemistryGeologyMathematicsMaterials scienceStatisticsPolitical scienceEngineeringWaste management

Abstract

fetched live from OpenAlex

An inventory of natural gas losses from the former Soviet Union's gas industry has been constructed from published Russian‐language sources. The results imply that in the late 1980s/early 1990s annual losses from Russia were in the range 35–59×10 9 cubic meters (24–40 Tg of CH 4 ): estimates based on what are thought to be the more reliable sources place annual losses in the range 37–52×10 9 cubic meters (25–35 Tg of CH 4 ). Of this amount, one half to two thirds of the emissions may have been from the extremely long and ageing gas pipeline system. Extrapolation of the estimates for Russian losses to the whole territory of the former Soviet Union suggests a probable total annual emission level from the whole ex‐Soviet gas industry in the range 47–67×10 9 cubic meters of natural gas or 31–45 Tg of CH 4 in these years. The envelope of minimum and maximum estimates for emissions from the former Soviet Union ranges from 29 to 50 Tg of methane. The limited availability of systematic and accurate published information on the emissions introduces significant uncertainty into the estimate. In an attempt to constrain emissions better, estimates of losses from specific causes were made using two or more independent approaches, where possible. A reasonable agreement between estimates was achieved in those cases. Our results imply that substantial reductions in emissions could be achieved by investment to reduce losses. Because of the high global warming potential and short lifetime of methane compared to carbon dioxide, reducing the large losses from the FSU may be among the most cost‐effective short‐term approaches available to reduce global anthropogenic greenhouse warming.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0230.000

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 head0.047
GPT teacher head0.337
Teacher spread0.290 · 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.

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

Citations52
Published2000
Admission routes1
Has abstractyes

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