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Record W2324091296 · doi:10.1021/es3003684

Black Carbon Emissions in China from 1949 to 2050

2012· article· en· W2324091296 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironmental Science & Technology · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Development and Reform CommissionNational Natural Science Foundation of China
KeywordsCarbon blackEnvironmental scienceChinaGreenhouse gasCarbon fibersWaste managementEnvironmental chemistryEnvironmental protectionChemistryEngineeringGeographyComputer scienceGeologyOceanography

Abstract

fetched live from OpenAlex

Black carbon (BC) emissions from China are of global concern. A new BC emission inventory (PKU-BC(China)) has been developed with the following improvements: (1) The emission factor database was updated; (2) a 0.1° × 0.1° gridded map was produced for 2007 based on county-level proxies; (3) time trends were derived for 1949-2007 and predicted for 2008-2050; and (4) the uncertainties associated with the inventory were quantified. It was estimated that 1957 Gg of BC were emitted in China in 2007, which is greater than previously reported. Residential coal combustion was the largest source, followed by residential biofuel burning, coke production, diesel vehicles, and brick kilns. By using a county-level disaggregation method, spatial bias in province-level disaggregation, mainly due to uneven per capita emissions within provinces, was reduced by 42.5%. Emissions increased steadily since 1949 until leveling off in the mid-1990s, due to a series of technological advances and to socioeconomic progress. BC emissions in China in 2050 are predicted to be 920-2183 Gg/yr under various scenarios; and the industrial and transportation sectors stand to benefit the most from technological improvements.

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.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
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
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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