Biodiversity offsetting: implications for the oil and gas industry in Australia
Bibliographic record
Abstract
Energy and infrastructure developments often involve or traverse extensive tracts of land that are frequently covered with stands of native vegetation, providing habitat to a range of different plant and animal species. The biodiversity (biological diversity) contained in these stands is the subject of several pieces of legislation in Australia that place restrictions and conditions on those whose activities interfere with this biodiversity. Social licence to operate is another motivation for development organisations to tread softly when it comes to preparing for activities within these zones. With sound and early planning, much interference can be prevented, but sometimes it is unavoidable and measures will need to be developed to address the resulting impacts. Offsetting is one form of measure available to conserve biodiversity when all other options fail, although it can also be used in addition to other measures. Offsetting allows for actions to be taken by developers to compensate for adverse impacts of their developments. Several policies have been released outlining State and Commonwealth positions on biodiversity offsetting in the last few years. When seeking approvals, energy and infrastructure development organisations need to plan ahead according to these policies. To this purpose, this paper outlines the various policy frameworks that exist for biodiversity offsetting across Australia. Implications for the oil and gas industry are provided. The industry has several characteristics that allow it to take advantage of the new policies, which are discussed. Among these are the ready access to land for offsets and, in the case of the coal seam gas industry, the availability of water to kick-start the creation or restoration of biodiversity on land that was previously cleared.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".