Real effects of media climate change concerns
Bibliographic record
Abstract
This study addresses the calls in prior research for evidence on the real effects of preferences for climate change risk by investigating whether companies with opposing externalities—brown versus green companies—adjust their climate-related activities in response to exogenous shocks in public concern for climate change, as reflected in news articles. We find that although brown companies reduce their direct and indirect greenhouse gas (GHG) emissions, they do not invest heavily in climate projects, suggesting a preference for cost-effective measures. Further textual analysis reveals that brown companies tend to use less complex stand-alone reports when communicating with external stakeholders. However, although green companies have greater access to low-cost external financial resources during unexpected changes in public concern, we find no evidence that they use these resources to reduce direct GHG emissions. Instead, our findings indicate only limited participation in indirect GHG reduction initiatives, with no significant allocation of these financial resources to dedicated climate projects. This reluctance of green companies to undertake direct GHG reductions is consistent with ongoing anecdotal discussions regarding the challenges of achieving net-zero targets, prompting a call for further research into potential barriers to greener transitions. • Concerns about climate change are often reflected in media coverage. • Media coverage of climate change concerns can prompt brown companies to reduce their footprint. • Brown companies adjust the readability of ESG reports in response to the concerns raised by the media. • Green companies do not set ambitious green targets when there is a high level of concern about climate change.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".