Does Accounting Scope 3 Emissions Improve Sustainable Business Outcomes? Evidence From the S&P 500 Technology Companies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".