Stock market returns and climate risk in the U.S.
Bibliographic record
Abstract
Using a data set for all companies forming the S&P 500 index, we investigate the stock price responses to acute physical risks, chronic physical risks, and transition risks. Our findings reveal that certain sectors are more vulnerable to climate risks, whereas others appear to be relatively unaffected. In addition, our results show that listed firms with poor environmental performance scores are more exposed to climate risk, as indicated by their stock returns being negatively affected, compared to firms with higher environmental performance scores. This suggests that improving environmental performance may help companies to better cope with climate risks and improve their financial performances. Our analysis provides evidence that the short-term systematic risk is more vulnerable to the climate risk events, whereas effects on long-term systematic risk do not appear to be statistically significant. These findings indicate that investors and firms should pay a particular attention to short-term systematic risk when considering the potential impact of climate risk on stock market performances. • We analyse the US stock market’s reaction to physical and transition climate risks. • An event study assesses how acute and transition risks affect stock returns. • Stock return of firms is negatively impacted by their lower environmental scores. • Environmental performance is linked to financial resilience against climate risks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".