Grandfathering or benchmarking? The impact of carbon quota allocation rule on the joint emission reduction supply chain
Bibliographic record
Abstract
The choice of carbon quota allocation rule (CQA rule) is crucial for the efficient operation of cap-and-trade policy. This paper analyses the impact of grandfathering (GF) rule and benchmarking (BM) rule on the joint emission reduction (JER) supply chain by using a differential game approach. In the proposed model, the upstream manufacturer achieves its emission reduction target through green technology and recycling, and the downstream retailer promotes these products. Our results show that compared with the benchmark case, both CQA rules do not always encourage the manufacturer to increase emission reduction investments. Moreover, the environmental and economic performance of different CQA rules is time-varying. For low-emission products, both CQA rules may yield higher total carbon emissions. For medium and high-emission products, the BM rule has a better short-term environmental performance, while the GF rule always has the best long-term environmental performance. As such, the government should prioritise the inclusion of medium and high-emission manufacturers in the carbon trading market and adopt the BM rule at the initial stage, and then transition to the GF rule. We also provide practical insights on the timing of transition, setting free carbon quotas, balancing economic and environmental performance, and dealing with uncertain market changes.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".