Adverse Climate-Related Incidents and Audit Pricing: International Evidence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT We examine the impact of negative news coverage about firm-specific climate change-related incidents on audit fees. Using a dataset of publicly traded firms, both U.S. and non-U.S., from 2000 to 2018, we find that audit fees for companies experiencing this negative coverage increase significantly. Cross-sectional tests reveal that the severity, influence, and frequency of these adverse incidents further increase audit fees, whereas factors such as engaging a Big 4 auditor or long auditor tenure mitigate these effects. Additionally, we find that this relation is stronger in countries with well-defined positions regarding climate change. Finally, we find no discernible changes in going concern options, the likelihood of restatements, and bankruptcy probability following these incidents. Overall, our results highlight the substantial influence of climate-related reputation risk on auditors and their work globally. Data Availability: The data used in this study were sourced under license from KLD, Audit Analytics, World Bank, RavenPack, and RepRisk, so they are not publicly available. However, the data are available from the authors upon request and with permission of the proprietary owners. JEL Classifications: Q54; H83; G32.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it