Downstream carbon leakage from upstream carbon tariffs: Evidence from trade tariffs
Bibliographic record
Abstract
Pricing the carbon content of imports, or carbon tariffs, is being considered as a solution to policyinduced carbon leakage. However, the unilateral implementation of carbon tariffs could have unintended consequences, such as further emission reshuffling or costly trade retaliation. This paper estimates how carbon tariffs will affect carbon leakage by exploiting variation in export tariffs. Using a two-country model, I show that trade tariffs can be used to identify the effect of carbon tariffs on carbon leakage. Empirically, I estimate the foreign emission effect of export tariffs using plausibly exogenous increases in export tariffs during the 2018-2019 trade war for US facilities, while controlling for other tariff changes. While I find evidence that US greenhouse gas emitting facilities respond to export tariffs on their outputs by reducing their emissions, I also find evidence of increased emissions from downstream facilities through input-output linkages. In the case of the US industries that faced export tariff increases during the trade war, emission increases from input users offset the emission reductions from facilities in the upstream targeted industries. This result highlights the importance of input-output linkages for the net emission effect of incomplete carbon tariffs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".