Economic Input−Output Life-Cycle Assessment of Trade Between Canada and the United States
Bibliographic record
Abstract
With increasing trade liberalization, attempts at accounting for environmental impacts and energy use across the manufacturing supply chain are complicated by the predominance of internationally supplied resources and products. This is particularly true for Canada and the United States, the world's largest trading partners. We use an economic input-output life-cycle assessment (EIO-LCA) technique to estimate the economy-wide energy intensity and greenhouse gas (GHG) emissions intensity for 45 manufacturing and resource sectors in Canada and the United States. Overall, we find that U.S. manufacturing and resource industries are about 1.15 times as energy-intensive and 1.3 times as GHG-intensive as Canadian industries, with significant sector-specific discrepancies in energy and GHG intensity. This trend is mainly due to a greater direct reliance on fossil fuels for many U.S. industries, in addition to a highly fossil-fuel based electricity mix in the U.S. To account for these differences, we develop a 76 sector binational EIO-LCA model that implicitly considers trade in goods between Canada and the U.S. Our findings show that accounting for trade can significantly alter the results of life-cycle assessment studies, particularly for many Canadian manufacturing sectors, and the production/consumption of goods in one country often exerts significant energy- and GHG-influences on the other.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".