Greenhouse gases embodied in international trade : an input-output analysis for Canada : 2002
Bibliographic record
Abstract
Both climate change and actions to fight it are occurring against rapidly expanding international trade flows, which increasingly lead to the separation of production and consumption patterns. Through the trade of goods in a globally interdependent world, the consumption in each country is linked to greenhouse gases (GHG) emissions in other countries because GHGs are emitted throughout the supply chain involved in producing those goods; a phenomenon referred to as ‘embodied GHG’. In this research, taking a consumption-based approach and using an environmental input-output analysis, I explore the amount of GHGs embodied in Canada’s imports and export for the year 2002 and determine that Canada has a negative balance of embodied emissions in trade (BEET). This implies that the GHGs emitted in connection with the production of exported goods surpass those emitted in connection with the production of imported goods. In light of Canada’s large trade surplus in 2002, my finding support the hypothesis that there may be an inherent conflict between a national GHG reduction target for domestic emissions and the aims of improving trade balances or maintaining trade surpluses. While my negative BEET result holds under different model specifications (i.e., single-country or multi-country model), I also show that it is highly dependent on the exchange rate used to convert the value of Canadian imports from the U.S. into the U.S. currency. Thus, my results demonstrate the weakness of using monetary flows of merchandise trade when trying to estimate physical quantities, in this case, the amount of GHGs embodied in the traded goods. Finally, I also discuss some of the key intersections between climate and trade policies, with a particular focus on climate policies that attempt to link the consumption of goods in a country with the amount of GHGs emitted during their production, whether in that country or elsewhere.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".