How do financial markets reward companies tackling climate change concerns? A natural experiment based on the Brexit referendum
Bibliographic record
Abstract
Abstract This paper explores whether environmental management activities and corporate commitment to tackle climate change concerns play a role in hedging a company's market valuation after a political or economic shock. Based on the literature on corporate social responsibility (CSR) and insurance‐like effects, this study advances knowledge on the impact of companies' engagement to tackle climate change on share price variation after the 2016 Brexit Referendum. The study also contributes to the limited number of studies using a difference‐in‐differences (DID) methodology in the CSR and climate change research area. Specifically, it uses DID, a natural experimental research design, and a multi‐variate regression analysis. The paper concludes that companies' concrete commitment to climate change has a buffer role in mitigating uncertainty related to Brexit. As the study found that financial markets reward companies that pay attention through the adoption of concrete actions and best practices on environmental issues during uncertain periods with respect to those that do not, the findings are in line with previous literature suggesting that corporate environmental commitment plays a buffering role during troubling periods. Results on the buffer role played by screenings, assessments, or disclosure activities to address environmental issues are unclear. We thus argue that during uncertain times, markets sustain companies taking proactive actions to tackle climate change.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".