Balancing act: Corporate governance and biodiversity conservation in extractive sector
Bibliographic record
Abstract
Extractive industries such as mining and oil extraction drive habitat loss, soil erosion, water pollution, and biodiversity decline. While prior research has examined corporate environmental performance in these sectors, little is known about how internal governance mechanisms, particularly equitable shareholder treatment, relate to biodiversity outcomes. Drawing on legitimacy theory, which holds that fair treatment signals genuine commitment to societal expectations, strengthens a firm's social license, and motivates environmental stewardship, this study examines whether equitable shareholder practices promote biodiversity conservation. Using US and Canadian extractive firms from 2006 to 2020, we find that fair shareholder treatment is positively associated with better biodiversity outcomes. We also integrate stakeholder theory, which suggests that board meetings serve as forums for directors to engage community and environmental concerns, and resource dependence theory, which emphasizes how strategic investors supply critical capital and expertise for long-term stewardship, and find that more frequent board meetings and the presence of strategic investors strengthen this relationship. Findings remain robust under propensity score matching, two-stage least squares, and system generalised method of moments. These findings enrich corporate governance research, showing how fair shareholder treatment, active board deliberations, and strategic shareholders' presence can embed biodiversity conservation into strategy and offer actionable guidance for regulators and industry leaders to empower investors and strengthen board oversight. We also provide valuable insights for future academic research and practical policy formulation aimed at reconciling economic pursuits with ecological stewardship.
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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 | 0.002 | 0.008 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".