European sustainability reporting standards: An assessment of requirements and preparedness of EU companies
Bibliographic record
Abstract
The newly released European Sustainability Reporting Standards (ESRS) are a distinctively holistic legal instrument designed to enhance the disclosure of the sustainability performance of companies across the European Union (EU). However, there is currently a lack of evidence as what the standards are and how prepared companies are to comply with the ESRS. Through an analysis of secondary sources for 20 EU companies, this study therefore aimed to identify the preparedness of EU-based companies. Results indicate that there is substantial variation in preparedness; larger firms exhibit higher levels of alignment with the ESRS, whereas small and medium-sized enterprises (SMEs) struggle with resource limitations and insufficient external support. This timely and unique study contributes novel insights into the variable preparedness of companies transitioning to new, EU-wide compliance standards, and the factors involved in large-scale implementation. Such insights provide direct implications for regional-level policy implementation. • The European Sustainability Reporting Standards (ESRS) aim to standardise sustainability reporting across the EU. • Summary of the standards and requirements. • Highly uneven preparedness of EU companies to implement the ESRS. • Reputational aspects are variably a driver for organisational engagement. • Recommend targeted policy support and guidance for stronger compliance, and to embed longer-term, integrated planning.
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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 | 0.038 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".