Sustainable development: The stock market's view of environmental policy
Bibliographic record
Abstract
Abstract This study applies panel data regression models to investigate how the stock market values the environmental policy of the firm. The empirical analysis relies on a cross‐country sample of public firms for the period between 2014 and 2017 and uses the ratings of environmental performance (EP) released by Eikon Thomson Reuters. For the first time in the related literature, the price‐to‐sales multiple is used to capture the assessment of the stock market towards EP. The study reveals that firms with the highest (lowest) scores of EP are quoted at significantly lower (higher) price‐to‐sales multiples than other firms, indicating a negative perception of the stock market towards EP. This finding is mostly driven by firms from the American region (United States and Canada). However, even in the Scandinavian region, considered as the most advanced area with regard to the concern towards environmental issues, stock market participants do not seem to have a positive view of EP. These results are robust to various checks and, particularly, to the use of the Tobin Q ratio as an alternative indicator of the stock market perceptions. The inference of this analysis suggests that the negative assessment of the stock market towards EP may constitute a deterrent for achieving more environmentally committed firms, making it difficult to accomplish the United Nations' Sustainable Development agenda.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".