Spatially explicit benefit–cost analysis of fire management for greenhouse gas abatement
Bibliographic record
Abstract
Abstract This paper examines the economic potential for fire management to provide offsets to carbon markets in the savannas of northern Australia. Long‐term field trials in Australia's savannas have quantified greenhouse gas (GHG) emissions abatement resulting from improved fire management. However, little is known about the economic potential of fire management projects or the locations where projects might be economically viable for providing GHG offsets. A benefit–cost analysis of fire management for GHG offsets is presented here, which includes spatially explicit estimates for GHG abatement under three assumptions of management efficacy (conservative, empirically based, upper potential). The total supply of GHG abatement is estimated under different prices and management efficacy assumptions, and areas that pass the benefit–cost analysis are identified. At the Australian Government's carbon price of A$23 per metric tonne of carbon dioxide equivalents (CO2‐e), fire management would be economically viable across 51 million hectares, all within the higher monsoonal rainfall regions of northern Australia, abating 1.6 million tonnes of CO2‐e per year. These estimates suggest that fire management projects can contribute to GHG abatement targets and be financially viable across large areas of northern Australia. Additional benefits are anticipated from these projects for biodiversity conservation, livelihoods for indigenous Australians and economic development in remote regions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".