Optimal environmental policy and distortionary fiscal policy interactions: A DSGE perspective
Bibliographic record
Abstract
This study examines the interactions between optimal environmental and distortionary fiscal policies within a dynamic stochastic general equilibrium (DSGE) framework using analytical and quantitative methods. We demonstrate that the marginal cost of public funds can exceed, be equal, or fall below one, based on utility specifications and the degree of relative risk aversion. This variation can lead to under-, over-, or optimally taxed environmental damages, with the latter two suggesting the potential for a strong double dividend. Furthermore, we challenge conventional labor tax smoothing theory, showing that a Ramsey-optimal policy allows labor tax volatility in the absence of carbon taxation. Our quantitative analysis reveals that an effective carbon policy reduces fluctuations and significantly mitigates contractions in major economic variables such as GDP, consumption, and welfare in response to environmental shocks. Increased pollution leads to higher emission costs, prompting the Ramsey planner to raise the carbon tax and increase abatement efforts. However, positive government spending or productivity shocks increase the cost of abatement, leading to lower carbon taxes. • Preexisting fiscal policies have complex effects on optimal carbon taxes. • MCPF defines the difference between first- and second-best carbon taxes. • Utility function’s form and degree of relative risk aversion determine MCPF. • Labor tax volatility is Ramsey-optimal in the absence of carbon tax policy. • Carbon taxes stabilize macroeconomic variables during environmental shocks.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".