Evaluating the efficiency and effectiveness of environmental policies for global and local air pollutants
Bibliographic record
Abstract
Unregulated global and local air pollutants impose high costs on society. For more than half a century, economists have argued that the introduction of market instruments such as pollution taxes or cap-and-trade markets can cut aggregate emissions at the lowest cost. Market instruments have been implemented by some countries to reduce local pollutants, such as nitrogen oxides, and global pollutants, such as greenhouse gas (GHG) emissions. One problem with this context is the lack of evidence on the cost savings of market-based policies relative to other policies. Particularly for global pollutants, a second problem with the patchwork of policies is carbon leakage, where emission reductions from regulated countries are offset by emission increases in unregulated countries. This dissertation seeks to explore these two problems. The first chapter, Do environmental markets improve allocative efficiency? Evidence from U.S. air pollution, develops a framework to test the allocative efficiency changes of introducing cap-and-trade markets. The framework is applied to landmark U.S. air pollution markets using manufacturing data. The chapter finds evidence of allocative efficiency gains for some markets. The second chapter, Carbon pricing and competitiveness pressures: The case of cement trade, provides empirical evidence of decreased net exports of a carbon-intensive product, cement, in British Columbia, Canada following the introduction of their carbon tax. The third chapter, Do carbon tariffs reduce carbon leakage? Evidence from trade tariffs, combines theory and data to study the effects of proposed carbon tariffs that price the carbon content of imports on foreign GHG emission changes. The chapter finds evidence of reduced GHG emissions from targeted industries and an unintended emission offset effect from downstream industries. Together, these chapters provide evidence on the efficiency and effectiveness of policies promoted to mitigate harmful air pollutants.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".