Cap-and-trade under a dual-channel setting in the presence of information asymmetry
Bibliographic record
Abstract
Cap-and-trade, a widely used carbon regulation policy, encourages firms to adopt carbon abatement technologies to reduce emissions. Traditional supply-chain literature on this policy assumes symmetrical information, overlooking the fact that carbon abatement efforts and costs are often private and vary significantly across geographies, industries, and pollutants. In this paper we explore a dual-channel setting involving a manufacturer and a retailer, where the manufacturer, subject to cap-and-trade regulations, has undisclosed information about its carbon abatement costs. Our findings reveal that high abatement costs can paradoxically benefit the manufacturer, the environment, consumers, and overall social welfare. Our result also cautions that a higher carbon trading price (e.g., due to more ambitious emission reduction targets) can disincentivize the manufacturer from investing in carbon abatement. Moreover, a higher production cost, while resulting in lower market output, can increase pollution generation. We contribute the following to the practitioner debate about the impact of carbon policies: for an industry with a large market size, our findings lend support to governments to implement a cap-and-trade policy, because the manufacturer, customers and social welfare can be better off under a cap-and-trade policy than under a tax policy or no carbon policy. Additionally, we suggest that in such industries, governments need not enforce information transparency within the supply chain.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".