Environmental Taxes and the Choice of Green Technology
Bibliographic record
Abstract
We study several important aspects of using environmental taxes to motivate the choice of innovative and “green" emissions‐reducing technologies as well as the role of fixed cost subsidies and consumer rebates in this process. In our model, a profit‐maximizing monopolistic firm facing price‐dependent demand selects emissions control technology, production quantity, and price in response to the tax, subsidy, and rebate levels set by the regulator. The available technologies vary in environmental efficiency as well as in the fixed and variable costs. Both the optimal policy for the firm and the social‐welfare maximizing policy for the regulator are analyzed. We find that the firm's reaction to an increase in taxes may be non‐monotone: while an initial increase in taxes may motivate a switch to a greener technology, further tax increases may motivate a reverse switch. For the regulator, we compare the social welfare achievable in the centralized system (which serves as an upper bound) to the highest level achievable under different classes of environmental policies. If the regulator is limited to a tax‐only policy, then when the regulator is moderately concerned with environmental impacts, the tax level that maximizes social welfare simultaneously motivates the choice of clean technology and closes the gap to the upper bound; however, both low and high levels of societal environmental concerns may lead to the choice of dirty technology and significant welfare losses as compared to the centralized case. Supplementing the environmental taxation with fixed cost subsidies and consumer rebates can eliminate this effect, expanding the range of parameters over which the green technology is chosen and often closing the welfare gap to the centralized solution.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".