Biodiversity Offsets Can Be a Valuable Tool in Achieving Sustainable Development Developing a Holistic Model for Biodiversity Offsets That Incorporates Environmental, Social and Economic Aspects of Sustainable Development
Bibliographic record
Abstract
The interpretation and use of biodiversity offsets in planning and development is a contentious issue because they rarely encompass each of the environmental, social and economic aspects of sustainable development. While currently agreed best practice for biodiversity offsets includes consideration of scope, scale, location, timing and duration, and monitoring, current literature on these components does not consider all aspects of sustainable development. Furthermore, much of the current agreed best practice focusses on the design of biodiversity offsets, without consideration of ongoing management or end-of-life. This manuscript reviews current best practice for biodiversity offsets, giving consideration to the environmental, social and economic aspects of sustainable development. In particular, we report that consideration of cost and risk is key and the use of planning frameworks, bonds and advanced offsets could mitigate these risks and allow for long-term success. Following this approach, a holistic model for design, implementation and ongoing management of direct biodiversity offsets that balances all aspects of sustainable development is presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".