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Record W2057830866 · doi:10.1080/09638180.2013.837400

The Tracking of Environmental Costs: Motivations and Impacts

2013· article· en· W2057830866 on OpenAlex
Jean‐François Henri, Olivier Boiral, Marie‐Josée Roy

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

VenueEuropean Accounting Review · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSample (material)SustainabilityBusinessEmpirical evidenceTECVariable costOverhead (engineering)Tracking (education)AccountingEnvironmental economicsIndustrial organizationEconomicsComputer scienceEcology

Abstract

fetched live from OpenAlex

Most accounting systems separately capture and accumulate one portion of the overall environmental costs of firms, while the remainder is embedded in other cost pools, such as general overhead costs or administrative costs. Little empirical evidence has been provided to explain the impacts of cost accounting systems that make a larger portion of firms' total environmental costs visible. The aim of this study is to conceptually and empirically examine the relationships among the tracking of environmental costs (TEC) by firms, their environmental motivations, and the impacts in terms of environmental and economic performance. Using survey data from a large sample of manufacturing firms, the results suggest two main conclusions. First, the TEC has an indirect influence on economic performance through environmental performance. Second, this indirect effect is influenced by the environmental motivations of the firm. More specifically, this indirect effect is greater (lesser) for firms whose motivations are predominately business-oriented (sustainability-oriented).

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.206
Teacher spread0.197 · 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