The Cost of Environmental Policy under Induced Technical Change
Bibliographic record
Abstract
Conventional wisdom argues that environmental policy is less costly if environmental policy induces the development of cleaner technologies. In contrast to this argument, we show that the cost of environmental policy (a reduction in emissions) may be larger with induced technical change than without. To explain this apparent paradox, we analyze three main issues. The first key issue is whether the new technology increases or reduces the marginal cost of abatement. While most analyses in environmental economics consider it natural that marginal abatement costs fall as new technology is developed, we argue that technological change may instead increase the productivity of polluting inputs, and thus marginal abatement costs. The second issue is whether environmental policy increases or decreases total investment and innovation. Even when stricter environmental policy induces some pollution-saving technological change, it may do so at the cost of a reduced overall rate of innovation, which crowds out production and consumption, and thus makes environmental policy more costly. Finally, the presence of additional distortions drive wedges between the social and private valuation of investment and pollution that may provide incentives for induced technological change with welfare-deteriorating effects.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".