Does the level of assurance statement on environmental disclosure affect investor assessment?
Bibliographic record
Abstract
Purpose The purpose of this study is to examine whether different levels of assurance statements of environmental disclosures affect investment choices in the French context where environmental assurance was voluntary until 2012 and became regulated and mandatory since then. Design/methodology/approach The authors conducted an experiment during the voluntary context – which represents the vast majority of countries – on a sample of 108 financial analysts. Findings Environmental disclosure has a positive impact on investment recommendations. More surprisingly, financial analysts are less likely to give recommendations in favor of a company that displays environmental disclosure with low-level assurance than for a company with no assurance statement at all. Research limitations/implications When assurance is voluntary and there are at least two levels, this study results suggest that firms should avoid selecting the lowest level of assurance because it negatively affects investor decisions. From this perspective, firms should devote sufficient effort and resources to obtain at least Level 2 environmental disclosure assurance. Practical implications Given the recommendations made by financial analysts, the authors could expect that firms may prefer to engage in a higher level of assurance or to provide no assurance rather than minimize their financial efforts and resources to select a lower level of voluntary assurance regarding environmental disclosure. Social implications This study has implications for the voluntary assurance practices of environmental disclosure and can provide support to regulators to promote higher standards in environmental assurance. It documents the relevance to increase the level of requested assurance for environmental disclosure. Originality/value To the best of the authors’ knowledge, very few studies have examined the additional effect of assurance on environmental disclosure in investors’ decisions. The experiment is conducted with financial analysts in the context of voluntary assurance.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".