Comparing climate pledges and eco-taxation in a networked agricultural supply chain organisation
Bibliographic record
Abstract
Abstract This paper examines the effectiveness of climate pledges and eco-taxation as strategies for mitigating climate change within a networked agricultural supply chain organisation. We utilise variational inequality techniques within a multicriteria decision-making framework and validate our theoretical findings through numerical simulations using a machine learning augmented algorithm. By employing this approach, we position the Agricultural Sector Roadmap, aimed at capping global warming at 1.5°C, within the wider agricultural sector’s climate action framework. Our results demonstrate that environmental taxation emerges as the most effective approach for addressing climate change. Eco-taxation leads to a 57.87 per cent reduction in global emissions, whereas climate pledges only account for a 20.59 per cent reduction at the same level of production. Furthermore, eco-taxation results in a 45.68 per cent greater reduction in emission intensity compared to climate pledges. In contrast to climate commitments, an eco-fiscal policy is capable of achieving the objectives established by the European Union.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".