Carbon Motivated Regional Trade Arrangements: Analytics and Simulations
Bibliographic record
Abstract
This paper presents both analytics and numerical simulation results relevant to proposals for carbon motivated regional trade agreements summarized in Dong & Whalley(2008).Unlike traditional regional trade agreements, by lowing tariffs on participant's low carbon emission goods and setting penalties on outsiders to force them to join such agreements , carbon motivated regional trade agreements reflect an effective merging of trade and climate change regimes, and are rising in profile as part of the post 2012 Copenhagen UNFCC negotiation.By adding country energy extraction cost functions, we develop a multi-region general equilibrium structure with endogenously determined energy supply.We calibrate our model to business as usual scenarios for the period 2006-2036.Our results show that carbon motivated regional agreements can reduce global emissions, but the effect is very small and even with penalty mechanisms used, the effects are still small.This supports the basic idea in our previous policy paper that trade policy is likely to be a relatively minor consideration in climate change containment.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".