Climate change concerns and the performance of green versus brown stocks
Bibliographic record
Abstract
We empirically test the prediction of Pastor, Stambaugh, and Taylor 2020 that green firms can outperform brown firms when climate change concerns strengthen unexpectedly for S&P 500 companies over the period January 2010 - June 2018. To capture unexpected increases in climate change concerns, we construct a Media Climate Change Concern index using climate change-related news published by major U.S. newspapers. We find a negative relationship between the firms' exposure to the Media Climate Change Concerns index and the level of the firm's greenhouse gas emission per unit of revenue. This result implies that when concerns about climate change rise unexpectedly, green firms' stock price increases, while brown firms' stock price decreases. Further, using topic modeling, we analyze which type of climate change news drives this relationship. We identify five themes that have an effect on green vs. brown stock returns. Some of those themes can be related to change in investors' expectations about the future cash-ow of green vs. brown firms, while others cannot. This result implies that the relationship between concern and green vs. brown stock returns arises from both investors updating their expectations about the future cash-ows of green and brown firms and changes in investors' sustainability taste.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".