Firm commitments on climate change: Effects of science‐based targets on financial outcomes during the COVID‐19 crisis
Bibliographic record
Abstract
Abstract Corporate social responsibility (CSR) can offer a protective buffer helping firms avoid the worst economic effects in times of crisis. We extend the extant literature by considering whether firm substantive climate commitments are effective at protecting the firm from financial losses during the COVID‐19 pandemic. We assess firm financial outcomes through (1) crash and post‐crash stock performance and (2) the severity of loss in the COVID‐19 stock market crash period. We identify substantive climate commitments as those carbon emission targets aligned with the Science Based Targets initiative (SBTi), which links firm's carbon targets to commitments made under the Paris Agreement. Using a sample of 336 US‐based companies, our findings show that science‐based targets are positively related to crash‐period returns and negatively related to severity of loss. Among firms with science‐based targets, only those externally verified and approved by the SBTi are influential in buffering financial losses during a crisis.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".