MétaCan
Menu
Back to cohort
Record W2143259621 · doi:10.5539/jsd.v6n4p118

Are There Effective Accounting Ways to Determining Accurate Accounting Tools and Methods to Reporting Emissions Reduction?

2013· article· en· W2143259621 on OpenAlexvenueno aff
Ali Ahmed Ali Almihoub, Joseph M. Mula, Mafiz Rahman

Bibliographic record

VenueJournal of Sustainable Development · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental full-cost accountingSustainabilityEnvironmental accountingActivity-based costingAccountingManagement accountingCost accountingAccounting information systemAccounting methodBusinessGreenhouse gasEnvironmental economicsSustainability reportingThroughput accountingEnvironmental resource managementEconomicsAccounting management

Abstract

fetched live from OpenAlex

Over the last century, many studies have used accounting methods and tools in focusing on environmental issues. This paper introduces readers to developments within the appropriate accounting tools designed to support firms and sectors reduction energy use as well as reducing greenhouse gases (GHGs) emissions. Current practice of traditional accounting (to date) has not covered environmental costs. Using Activity Based Costing could help firms to increase their understanding of sustainability and how to develop way to incorporate opportunity costs of environmental activities which are becoming significant issues on stakeholders. Moreover, an environmental management accounting approach can enhance information available on emissions to be more accurate. The net present value and internal rate of return also are considered the biggest hurdles to enhancing sustainability in business. This paper concludes that there is considerable potential to use environmental management accounting approach which is based on actual data to obtain more accurate information.

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)0.005
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.005
Open science0.0000.001
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 head0.025
GPT teacher head0.293
Teacher spread0.268 · 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

Citations11
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sustainable DevelopmentSame topicEnvironmental Sustainability in BusinessFrench-language works237,207