CLIMATE CHANGE, TRADE AND COMPETITIVENESS IS A COLLISION INEVITABLE? ADDRESSING THE LEAKAGE/COMPETITIVENESS ISSUE IN CLIMATE CHANGE POLICY PROPOSALS
Bibliographic record
Abstract
We will likely see increasing efforts to minimize leakage of carbon to non-participating countries and to address concerns on behalf of the competitiveness of carbon-intensive industry. Environmentalists on one side and free traders on the other side fear that border measures such as tariffs or permit-requirements against imports of carbon-intensive products will collide with the WTO. There need not necessarily be a conflict, if the measures are designed sensibly. There are precedents-- the shrimp turtle case and the Montreal Protocol-- that could justify border measures to avoid undermining the Kyoto Protocol or its successors. But import penalties should follow principles such as the following: Measures should follow guidelines multilaterally-agreed by countries participating in the emission targets of the Kyoto Protocol and/or its successors, against countries that are not doing so, rather than being applied unilaterally or by non-participants. Measures to address leakage to non-members can take the form of either tariffs or permit-requirements on carbon-intensive imports; they should not take the form of subsidies to domestic sectors that are considered to have been put at a competitive disadvantage. Independent panels of experts, not politicians, should be responsible for judgments as to findings of fact-- what countries are complying or not, what industries are
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| 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".