Examining the Sensitivity of Global CO<sub>2</sub> Emissions to Trade Restrictions over Multiple Years
Bibliographic record
Abstract
Shocks to international trade conditions, such as imposing tariffs, not only affects the global economy but also has substantial implications for carbon emissions. However, it is unclear whether the impact of changes in trade on carbon emissions will be consistent or change over time, as both trade patterns and emission intensity are dynamic in nature. Here, we simulated the economy and carbon dioxide (CO 2 ) emissions in four representative years from 2004 to 2014 under a free trade scenario and a trade restriction scenario. Our simulations show that trade restrictions would have decreased global emissions by 6.0%, 5.7%, 5.2%, and 4.7% in 2004, 2007, 2011 and 2014; however, restrictions also drove a relative increase in emission intensity for all years. Although more pressure to emit was placed on developing regions with trade development over the study period, the impacts of trade restrictions on CO 2 emissions weakened due to an absolute decrease in emission intensity across regions over time, especially for developing regions. Enabling continued improvements in emission intensity in developing regions by enhancing financial assistance, knowledge sharing, and technology exchange with trade is therefore critical to ensure win-win situations for both economic development and global carbon mitigation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".