Testing different approaches to estimate the potential supply of carbon offsets from beef grazing systems
Bibliographic record
Abstract
Agricultural lands have the biological potential to sequester many millions of tonnes of carbon dioxide equivalents thus have been recognised by many scientists and policymakers as a key component of efforts to mitigate climate change. However, the operational changes required to create these offsets are neither insignificant nor uniform across agricultural enterprises and the incentives needed and the likelihood and rate of participation as well as the impacts at farm level have not been examined in detail. Acknowledging this complexity, this research examines the potential for pastoral land in Queensland, Australia to supply carbon offsets using three approaches; desktop bioeconomic modelling, experimental field auctions with landholders and the use of behavioural economic theories to understand stated preferences in a choice model. The bioeconomic model is additionally tested using a comparative case study from Canada. The comparative case study structure allows for identification of the extent to which political and broader economic drivers are likely to affect supply of carbon offsets from agricultural land. The three approaches provide difference results as the bioeconomic model estimates only the opportunity costs while the experimental auctions and choice modelling estimate more comprehensively landholders preferences for supplying carbon offsets. The difference in results shows that the use of choice modelling can make a valuable contribution to understanding preferences for provision of ecosystem services, particularly when the preferences being investigated involve new products or enterprises for which there is little historical information. The impact of bounded rationality and a lack of heuristics was demonstrated by the difference in preferences between a single unit of the service and supplying multiple units. In the context of carbon trading, agricultural offsets are found to be a potentially efficient source of carbon offsets at relatively low carbon prices but the results also highlight the need for case specific modelling when major policy initiatives are being evaluated.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".