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Record W2078367998 · doi:10.5539/ass.v8n12p28

The Efficiency for the Three Industries of Provinces in China, Considering the Effects of Carbon Emission: An Application of the Parallel DEA Model

2012· article· en· W2078367998 on OpenAlex
Feng Feng, Jinlai Wang

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economic and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsChinaProduction efficiencyProduction (economics)Carbon fibersPrimary sector of the economySecondary sector of the economyIndustry of ChinaAgricultural economicsBusinessTertiary sector of the economyEnvironmental scienceEnvironmental economicsNatural resource economicsEconomicsEconomyGeographyMathematicsEngineeringMicroeconomics

Abstract

fetched live from OpenAlex

In this paper, a parallel DEA model was established and applied to evaluate the production performance of 29 provinces from China in 2009, considering the effects of CO2 emission. Meanwhile, a CCR DEA model was also used to evaluate the efficiencies for each industry, respectively. Our calculated results reveal that: a) the production efficiency for each province is relevant to the economic development level; b) the efficiencies are of great differences between regions, i.e., the eastern coastal area is more highly efficiency than the western interior area significantly; c) the efficiency of the primary industry are higher than those of the secondary industry and tertiary industry in average, and the score for tertiary industry efficiency is of the least influences of regions.

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)0.001
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 score0.622
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.022
GPT teacher head0.236
Teacher spread0.214 · 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