The Use of Blockchains to Enhance Sustainability Reporting and Assurance*
Bibliographic record
Abstract
ABSTRACT The changing dynamics of the accounting profession have been strongly influenced by emerging technologies and the demand for nontraditional metrics and information by stakeholders and regulators. In this article, we perform an exploratory content analysis to examine the role that blockchain technology can play in enhancing sustainability reporting and assurance. The benefits to companies and assurance professionals in using the distributed ledger technology of blockchain are increased trust, transparency, and traceability, which matches stakeholders' demands as it relates to sustainability reporting. This article identifies and analyzes potential and current use cases of blockchain in the United States and Canada to assist accountants and auditors in preparing and reviewing sustainability information. We highlight how augmenting traditional reporting systems with blockchain can overcome problems with sustainability reporting. We discuss implications for practice in detail—finding that blockchain is well‐positioned to provide reliable tracking and custodial support as it relates to sustainability information currently being self‐reported by many firms, such as greenhouse gas emissions, conflict mineral disclosure, or product provenance, among others. Expanded adoption of blockchains by companies will lead to higher‐quality information being included in sustainability reports and allow assurance professionals to verify a wider range of information, potentially leading to uniform standards in the evaluation of sustainability reports.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".