Decision-making about project design changes during impact assessment review: theories of corporate voluntary environmental behaviours
Bibliographic record
Abstract
Impact assessment (IA) can play an important role in informing and improving project design, particularly when viewed and used by proponents as a planning tool rather than a means of obtaining regulatory approval for a predetermined project concept. There has, however, been limited consideration of proponent decision-making about voluntary project design modifications during IA. This paper reviews various theories of corporate voluntary environmental behaviours to evaluate their applicability and utility in this context. It finds that these do address some known or potential rationales for IA-related design changes and show general alignment with previous research on this topic, as well as highlighting some further possible motivations and approaches. Future research should focus on understanding how such decisions are actually made by proponents, including the development of new decision models that address how multiple factors may be considered, balanced and ultimately influential, and which reflect the IA context in general.
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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 | 0.155 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".