The Costs and Benefits of Approved Methods for Sequestering Carbon in Soil Through the Australian Government’s Emissions Reduction Fund
Bibliographic record
Abstract
<p class="1Body">This paper investigates the net benefits of sequestering carbon in soil from a biophysical and economic perspective. This study is important because sequestering carbon (C) in soil is a key component of the Australian government’s Direct Action Policy to offset the nation’s greenhouse gas (GHG) emissions. The biophysical potential for sequestering C using one of four permitted project management activities (new irrigation, managing soil acidity, stubble retention and converting cropland to permanent pasture) was calculated according to the Methodology Determination - Carbon Credits (Carbon Farming Initiative) Estimating Sequestration of Carbon in Soil Using Default Values, 2015. The economic prospects of those activities that show a net C abatement were then evaluated to determine whether they were profitable for a farmer to implement. Finally, the costs and benefits from society’s perspective of those activities found to be profitable were calculated. Of these activities only stubble retention and liming provided net benefits to a farmer, although there were limitations as to how widely these activities could be implemented nationally. We estimated a cost to government of approximately $35 M annually to achieve a net abatement of 2.84 M t CO<sub>2</sub>-e. Because this represents only 0.52 percent of Australia’s annual GHG emissions of 549 M t CO<sub>2</sub>-e, the policy is both expensive and relatively ineffective as a C offset policy alone. However, if viewed as an investment in farmland sustainability, this payment to farmers could be good public policy.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".