Stakeholder Orientation, Environmental Performance and Financial Benefits
Bibliographic record
Abstract
In this paper, we examine the impact of stakeholder orientation on environmental performance and on financial benefits from environmental performance. We use firm-level data from Canada and the United States spanning the years 2002 to 2020 and classify all Canadian firms and those U.S. firms located in states that have passed constituency statutes as stakeholder-oriented. We first show that Canadian firms and stakeholder-oriented U.S. firms have better environmental performance than shareholder-oriented U.S. firms. We then find that good environmental performance increases profits and valuations for all firms in the U.S., but especially for shareholder-oriented firms. For Canadian firms overall there is no consistent financial impact. Moreover, the financial impact of environmental performance becomes negative for Canadian firms after the Supreme Court decision in 2008 on BCE Inc. vs. 1976 Debentureholders, stating that the duty of the board of directors is to act in the best interest of the corporation, not its shareholders. The U.S. results for valuations are robust after taking into account potential endogeneity issues using instrumental variables and dynamic panel regressions. Thus, our results suggest a trade-off between firm environmental and financial performance under different governance schemes. On the one hand, stakeholder orientation decreases financial benefits from firms’ environmental performance. On the other hand, shareholder orientation may be detrimental to the environment. This has important policy implications for the current debate on climate change mitigation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".