Environmental Policy Instruments and Uncertainties Under Free Trade and Capital Mobility
Bibliographic record
Abstract
We analyze the properties of environmental policy instruments in the face of uncertainty for an economy that is open to international trade and capital mobility (open economy). We incorporate three static environmental policy instruments which could be inefficient: cap-and-trade, pollution tax and emission intensity standard in our model and evaluate their properties under an exogenous temporary productivity shocks to simulate business cycles and an exogenous temporary abatement cost shock to represent reduced costs of clean inputs (for example cheap natural gas due to fracking). We then compare impacts on welfare, pollution levels, outputs, consumption, investment, supply of labor and trade flows in the economy. To date this literature has either focused on either economies under autarky or in a static modeling framework with a focus on strategic interaction among agents and thus ignore an additional channel of international trade and capital mobility that may smooth the intensity of business cycle shock or abatement cost breakthrough. We develop a small open economy (SOE) dynamic stochastic general equilibrium (DSGE) model where we incorporate international trade and capital mobility. We evaluate long term properties and use DYNARE to evaluate short term (dynamic) properties. Our results suggest that the preferred environmental policy instrument varies with the source of uncertainty. The cap-and-trade policies are best suited to smooth the business cycle while pollution taxes and intensity targets are most effective in the face of abatement cost shocks. We find that the magnitude of the productivity shock's impact on the economy swamps the impact of an abatement cost shock. This suggests that a cap-and-trade policy, which performs best in the face of productivity shocks, should be the preferred policy instrument in most cases. In our model, calibrated to Canadian data, a one standard deviation productivity shock has nearly an order of magnitude larger impact than a one standard deviation abatement cost shock.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".