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Record W7116706867 · doi:10.1002/bse.70370

Does Accounting Scope 3 Emissions Improve Sustainable Business Outcomes? Evidence From the S&P 500 Technology Companies

2025· article· en· W7116706867 on OpenAlexaff
Nuri C. Onat, Murat Küçükvar, Tadesse G. Wakjira, Amr Elalfy, Adeeb A. Kutty

Bibliographic record

VenueBusiness Strategy and the Environment · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsScope (computer science)Sustainability reportingSustainabilityCorporate governanceTransparency (behavior)ComparabilityCorporate sustainabilityEnvironmental accountingCorporate social responsibility

Abstract

fetched live from OpenAlex

ABSTRACT Corporate sustainability efforts increasingly emphasize Scope 3 emissions due to their substantial share of total corporate carbon footprints. However, reporting these emissions remains inconsistent, limiting transparency and comparability across firms. This study examines the role of carbon footprint accounting (especially Scope 3 emissions accounting) in shaping corporate sustainability outcomes among S&P 500 technology companies, focusing on how firms measure, disclose, and integrate these emissions into their environmental strategies. Using an empirical analysis of corporate sustainability reports and Environmental, Social, and Governance (ESG) performance data, this study investigates whether comprehensive Scope 3 accounting enhances corporate environmental performance. Findings indicate that firms adopting standardized Scope 3 reporting practices demonstrate improved sustainability integration and stronger ESG performance. However, methodological inconsistencies and voluntary disclosure limitations highlight the need for policy interventions and standardized adoption. This study contributes to the growing literature on carbon accounting by providing empirical insights into Scope 3 emissions disclosure and its implications for corporate sustainability. The findings inform regulatory discussions on mandatory emissions reporting and offer practical recommendations for enhancing transparency in corporate climate strategies.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.017
GPT teacher head0.247
Teacher spread0.230 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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