Environmental Taxes and the Choice of Green Technology
Bibliographic record
Abstract
We study several important aspects of using environmental taxes or pollution fines to motivate the choice of innovative and “green” emissions-reducing technologies. In our model, the environmental regulator (Stackelberg leader) sets the tax level, and in response to it a profit-maximizing monopolistic firm (Stackelberg follower), facing price-dependent demand, selects emissions control technology, production quantity and price. The available technologies vary in environmental efficiency, as well as in the fixed and variable costs. We find that a firm’s reaction to an increase in taxes is in general non-monotone: while an initial increase in taxes may motivate a switch to a greener technology, further tax increases may motivate a reverse switch. This reverse effect can be avoided by subsidizing the fixed costs of the green technology; otherwise it could lead to cases under which a given technology cannot be induced with taxes. We then analyze the socially optimal tax level and the technology choice it motivates. We find that when the regulator is moderately concerned with environmental impacts, the tax level that maximizes social welfare simultaneously motivates the choice of clean technology, resulting in a so-called double dividend. Both low and high levels of environmental concerns lead to the choice of dirty technology. The latter effect can be avoided by subsidizing the capital cost of green technology. Overall, providing a subsidy in conjunction with taxing emissions is generally beneficial: it improves technology choice and increases social welfare; however it may increase the optimal tax level.
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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 | 0.004 |
| 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".