Can Crisis Periods Affect the ESG Reporting Scope? The Portuguese Euronext Entities Case
Bibliographic record
Abstract
Portuguese companies are increasingly responding to the demand of stakeholders for transparent information about companies’ environmental, social, and governance (ESG) performance by issuing non-financial reports (NFRs). While the number of NFRs published annually has been increasing over the last two decades, their quality and companies’ ESG performance have been questioned, especially in times of crisis. To address these concerns, several jurisdictions have introduced mandatory NFR rules, such as the European Directive 2014/95/EU. Employing an institutional theory lens, this paper’s research objective is to evaluate whether the last decade’s crises and whether the fact that NFRs became mandatory for certain entities positively affected companies’ activities covered in the ESG reporting scope. We used panel data regression models on 45 listed companies in Portugal during the period 2008–2021. Our results show that the ESG reporting scope is not positively influenced by the transition from NFRs to a mandatory and global financial crisis (GFC). However, the COVID-19 crisis positively affected NFR quality. These results have major implications for practitioners, reflecting the importance of promoting these tools in an organization to improve non-financial performance and companies’ sustainability.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".