Taxes versus Standards (Again): Misallocation and Productivity Consequences of Energy and Emission Intensity Targets
Bibliographic record
Abstract
Various policies, from energy taxes to emission intensity limits, promote energy conservation and emission reduction, and have potentially different effects on aggregate productivity and GDP. Theory and evidence regarding the superiority of either policy is mixed, leading some jurisdictions to adopt energy taxes and others to adopt intensity standards. We contribute to this debate by developing a tractable quantitative model reflecting the industrial structure and energy use patterns of developed economies. In contrast with most existing literature, we explicitly incorporate productivity dispersion across firms in multiple industries. This is important as policies can (1) differentially burden firms, leading some to exit, and (2) create factor-market distortions, lowering aggregate productivity. In the model, calibrated to Canadian data, a flat energy tax is usually superior to intensity standards, as the tax does not cause factor-market distortions, though it does induce firm exit. The cost of a firm-specific standard is mainly driven by factor-market distortions while the cost of a sector-specific standard is mainly from firm exit. In special cases where standards are superior to a flat tax, an alternative tax scheme – where firm-specific rates increase with energy intensity – dominates the standard. The scope for market-based emission reduction policies is therefore larger than previously recognized. JEL Classification: Q4, Q5, H2, E6
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".