Gestão de risco climático corporativo : ligação entre emissões de carbono e operações de fusão e aquisição
Bibliographic record
Abstract
This thesis studies the relationship between M&A activity and carbon risk, considering, at most, 183 deals between 2006 and 2023. First, it assesses the impact of carbon risk on merger likelihood. The findings suggest that (1) acquirors reporting on such a risk pre-merger are more likely to buy out firms that do so too, (2) firms subject to higher carbon risk are less likely to participate in deals and, thus, become acquirors or targets, and (3) acquirors subject to higher carbon risk pre-merger tend to buy out firms also subject to higher carbon risk pre-merger. Second, it investigates the relationship between targets’ pre-merger carbon risk and M&A deal-specific characteristics. The results prove that targets disclosing their carbon emissions pre-merger engage in higher-value and longer-duration deals (especially those from the U.S. or Canada) and that European targets subject to higher carbon risk pre-merger are more likely to engage in deals with larger values (the opposite is true for targets from the U.S. or Canada) and longer duration (the inverse is true for targets from the U.S.). Third, it explores the impact of acquirors’ pre-merger carbon risk on the short-term merger market response, concluding that the higher the acquirors’ carbon risk pre-merger, the lower the CARs around merger announcements. Finally, it analyses the impact of acquirors’ pre-merger carbon risk on the combined firms’ long-term post-merger sustainability performance. The findings point to acquirors with higher pre-merger carbon risk experiencing a higher increase in their ESG scores in the aftermath of the merger.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".