The reallocation effect of emissions cap‐and‐trade: Evidence from China
Bibliographic record
Abstract
Abstract This paper investigates the reallocation effects of emissions cap‐and‐trade policy, leveraging China's phased implementation of chemical oxygen demand (COD) regulations as a quasi‐experiment. Our theoretical model posits that a pro rata emissions cap is more stringent for more productive firms, resulting in negative reallocation , whereas emissions trading restores efficiency through positive reallocation by reallocating emissions towards more productive firms. Utilizing the spatial and temporal variation in policy implementation, our empirical findings demonstrate that emission intensities of more productive firms, relative to less productive counterparts, declined after adopting the cap policy but subsequently increased with the introduction of cap‐and‐trade, aligning with our theoretical predictions. We also find that firms operating under a cap‐and‐trade regime, on average, experienced faster output growth compared to those operating under a cap‐only regime, highlighting the role of emissions trading in enhancing production efficiency, even within imperfect markets.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".