The Determinants of Company Response to Environmental Regulation
Bibliographic record
Abstract
The impact of governmental regulatory action on the economic performance of mining companies has been the focus of a great deal of attention by industry commentators and academic scholarship alike. The influence of environmental regulation on the strategic objectives of mining firms is less well understood, however. This article investigates what influences the way mining companies react to environmental approvals regulation. It presents the results of a recent cross‐national survey of Australian and Canadian mining companies into the effect of a range of possible determinants on company response to environmental approvals regulation. Possible influences included both external pressures (stakeholder pressure, jurisdictional culture and market characteristics) and internal pressures (organizational culture, organizational learning, the influence of individuals within firms and company size). While cross‐national comparison revealed some differences with respect to the influence of particular pressures, on balance the results suggest that for mining companies in both countries, internal pressures exert the greatest influence on company response. These results contradict a prevailing view in the literature, which suggests that external factors, particularly stakeholder groups, exert the most influence on the environmental responses of firms. The article concludes that the existing emphasis on external pressures to explain corporate environmental behaviour should be supplemented by a focus on the internal dynamics of firms.
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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 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".