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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".