Incentives and Emission Responsibility Allocation in Supply Chains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Because greenhouse-gas (GHG) emissions from the supply chains of just the 2,500 largest global corporations account for more than 20% of global emissions, rationalizing emissions in supply chains could make an important contribution toward meeting the global CO 2 emission-reduction targets agreed upon in the 2015 Paris Climate Agreement. Accordingly, in this paper, we consider supply chains with joint production of GHG emissions, operating under either a carbon-tax regime, wherein a regulator levies a penalty on the emissions generated by the firms in the supply chain, or an internal carbon-pricing scheme. Supply chain leaders, such as Walmart, are assumed to be environmentally motivated to induce their suppliers to abate their emissions. We adopt a cooperative game-theory methodology to derive a footprint-balanced scheme for reapportioning the total carbon emissions amongst the firms in the supply chain. This emission responsibility-allocation scheme, which is the Shapley value of an associated cooperative game, is shown to have several desirable characteristics. In particular, (i) it is transparent and easy to compute; (ii) when the abatement-cost functions of the firms are private information, it incentivizes suppliers to exert pollution-abatement efforts that, among all footprint-balanced allocation schemes, minimize the maximum deviation from the socially optimal pollution level; and (iii) the Shapley value is the unique allocation mechanism satisfying certain contextually desirable properties. This paper was accepted by Jayashankar Swaminathan, operations management.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 it