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Record W4392713246 · doi:10.54097/hbem.v19i.11792

The Environmental Impact on the Airline Industry and Financial Analysis of Air China, China Southern Airlines, and China Eastern Airlines

2023· article· en· W4392713246 on OpenAlexaff
Yubing Yao

Bibliographic record

VenueHighlights in Business Economics and Management · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsMcMaster University
Fundersnot available
KeywordsChinaBusinessFinanceAir transportAeronauticsGeographyEngineering

Abstract

fetched live from OpenAlex

This paper presents a comprehensive overview of the environmental impact on the airline industry, focusing on three of China's largest airlines - Air China, China Southern Airlines, and China Eastern Airlines. It provides a financial analysis of these airlines using the Capital Asset Pricing Model (CAPM). It includes a literature review to underline the importance of environmental factors on the industry and its financial performance. Scrutinizes the reciprocal relationship between the environment and the airline industry, focusing on how environmental factors influence the industry. The study involves reviewing the literature on physical environmental factors, policy-related factors, and the industry's response to these influences. The objective is to comprehensively understand the ecological impacts on the industry and the consequent strategic adjustments and technological innovations being undertaken.

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)0.001
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 score0.543
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.205
Teacher spread0.192 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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