Emission Tax or Standard: The Roles of Productivity Dispersion and Abatement
Bibliographic record
Abstract
We compare the welfare effects of different emission-reduction policies in a general equilibrium model with heterogeneous plants. We found that an emission standard could outperform an emission tax or a tradable permit. We characterize the equilibrium conditions for this result to hold. We understand that an emission tax (or Pigouvian tax, Pigou 1954) can maximize social welfare under two conditions: (1) complete information and (2) we consider only the pollution market. The welfare effects of different policies with incomplete information are thoroughly analyzed in the literature (Weitzman 1974, among others). This literature uses a partial-equilibrium analysis. We analyze the welfare effects of different policies when the plants’ responses to policies affect the efficiency of two markets simultaneously: the goods market and the pollution market. A tax policy changes the market behavior of plants in the goods market when the purpose is only to interfere with the pollution market, increasing the gap between the plant-preferred level of output and the society-preferred level of output in the goods market if plants have some market power in the goods market. A standard-policy directly reduces the emissions and causes less goods-market distortion. We show that when some advanced abatement technology is available, the standard policy could achieve higher welfare than the tax policy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".