Delineating the embodied CO <sub>2</sub> emissions in Canada's exports: Routes, drivers, and paths
Bibliographic record
Abstract
Abstract This study combined the World Input–Output Database and Asian Development Bank's Multiregional Input–Output database to investigate Canada's embodied CO 2 emissions in exports (EEE) for the period of 2000–2018. We examined the key drivers and paths through structural decomposition analysis and structural path analysis. First, the results showed that embodied emissions in the intermediate exports were the major contributor to Canada's EEE, and emission paths involving more than three countries were on the rise, indicating that the expansion of the global industrial supply chains has complicated the paths of Canada's EEE. Second, the factors such as emission intensity of sectors, export structure, and export scale, had varying influences on Canada's EEE over time. For several sectors, the benefit from reduced emission intensity was largely offset by the additional emissions from the increased export scale. Hence, the design of emission regulations should consider the heterogeneity of industrial sectors in order to mitigate emissions for the diverse industries in Canada. Third, energy and resource industries (e.g., electricity, petroleum, wood, metals, and so on) played an essential role in Canada's exports. A significant amount of embodied emissions was transferred from these sectors to the downstream sectors along the supply chain, indicating that abatement measures should be adopted from the whole life cycle perspective of a product/service through an integrated governance of the supply chain. This article met the requirements for a Gold–Silver JIE data openness badge described at http://jie.click/badges .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".