Bibliographic record
Abstract
This article first reviews the nature of biodiversity offsets and their use in selected jurisdictions, including the UK, US, Canada and Australia. The unique approach to biodiversity offsets in New Zealand under the Resource Management Act 1991 (RMA) is then examined in detail, including judicial consideration and analysis of the concept in several recent decisions. The RMA is the primary legislation governing the protection of the environment and the use of land, air and water resources in New Zealand, guided by the principle of 'sustainable management'. The Crown Minerals Act 1991 (CMA) governs the allocation of mining rights and access to minerals over private and Crown land. Opportunities for offsets through the mineral permitting and resource consenting regime is discussed, and mining and energy development case studies are used to illustrate the use of biodiversity offsets in practice. The article also examines the value of national policy guidance in the design of biodiversity offsets, the use of conservation covenants to ensure durability of offset arrangements, and the idea of 'conservation banking' to facilitate and encourage industry 'buy-in'. Conclusions and recommendations are made, which hopefully may inform and advance the debate on the use of biodiversity offsets in other jurisdictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".