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Record W3040190848 · doi:10.5267/j.ac.2020.6.009

Green accounting, material flow cost accounting and environmental performance

2020· article· en· W3040190848 on OpenAlex
I Gusti Ketut Agung Ulupui, Yunika Murdayanti, Astari Cita Marini, Unggul Purwohedi, Mardia Mardia, Heri Yanto

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

VenueAccounting · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsnot available
Fundersnot available
KeywordsAccountingEnvironmental accountingCost accountingManagement accountingBusinessEnvironmental full-cost accountingEnvironmental scienceThroughput accountingAccounting information systemFinancial accounting

Abstract

fetched live from OpenAlex

The purpose of this study is to determine the effects of green accounting and Material Flow Cost Accounting (MFCA) on environmental performance as indicated by PROPER rating. This study is conducted on cement manufacturing companies in Indonesia by using a descriptive quantitative research model tested on three variables: green accounting, MFCA, and environmental performance. The green accounting aspect is taken from the extent of Global Reporting Initiative (GRI) disclosure and MFCA is focused on the effectiveness of costs. The MFCA dimensions are production costs, size of production area, and production value. Environmental performance aspect is measured by the PROPER rating issued by the Ministry of Environment and Forestry. The study is conducted in several stages. First, a literature review of previous research related to green accounting, MFCA, and environmental performance is performed. Next, the research problems are formulated. After that, the data from the companies are collected and analyzed by using SmartPLS. Finally, it is concluded that green accounting affects environmental performance, whereas MFCA has no effect on environmental performance.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient 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 score0.749
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.005
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.180
Teacher spread0.171 · 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