Embodied Environmental Emissions in U.S. International Trade, 1997−2004
Bibliographic record
Abstract
Significant recent attention has been given to quantifying the environmental impacts of international trade. However, the United States, despite being the world's largest emitter of greenhouse gases and having large recent growth in international trade, has seen little analysis. This work uses a multi-country input-output model of the U.S. and its seven largest trading partners (Canada, China, Mexico, Japan, Germany, the UK, and Korea) to analyze the environmental effects of changes to U.S. trade structure and volume from 1997 to 2004. It is shown that increased import volume and shifting trade patterns during this time period led to a large increase in the U.S.' embodied emissions in trade (EET) for CO2, SO2, and NO(x). Methodological uncertainties, especially related to uncertainties of international currency conversion, lead to large differences in estimation of the total EET, but we estimate that the overall embodied CO2 in U.S. imports has grown from between 0.5 and 0.8 Gt of CO2 in 1997 to between 0.8 and 1.8 Gt of CO2 in 2004, representing between 9-14% and 13-30% of U.S. (2-4% to 3-7% of global) CO2 emissions in 1997 and 2004, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".