Common Institutional Ownership and Corporate Carbon Emissions
Bibliographic record
Abstract
Abstract There has been a growing interest in comparative work exploring when and why firms embark on green paths. It has been concluded that in national contexts where inter‐firm ties are stronger, progress has been stronger. In turn, this raises questions about the impact of inter‐firm ties within, rather than between, national contexts, and in settings where progress towards renewables has been uneven and contested. Accordingly, we explore how common institutional ownership may foster collaboration among firms within the same industry against climate change. Using a sample of US‐listed firms from 2006 to 2019, we obtain robust evidence that firms with industrial peers that are owned by the same institutional investors have lower carbon emissions. In addition, we find that a threshold exists for which the impact on carbon emissions holds only when firms are commonly connected with a substantial number of peers. The existence of this threshold suggests potential free‐riding issues and highlights the beneficial role of investors in promoting cross‐industry collaboration. Overall, our results highlight the role played by institutional investors in tackling climate issues, with important implications for both climate‐ and antitrust‐related regulations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".