Canadian Provincial Input-Output Tables of Embodied Emissions, 2010-2015
Bibliographic record
Abstract
This dataset was created using an environmentally extended multi-region input-output (EE-MRIO) model, which uses an input and output approach that tracks flows of monetary values of trade to allot environmental impacts between regions and sectors. The MRIO model is a predominant method for the estimation of consumption-based greenhouse gas (GHG) accounting. Our particular model was constructed using Python. The tabulated results show the flows of emissions embodied in imports, exports, and domestic consumption of a region. In our case, we calculated the consumption emissions of all Canadian provinces and territories from 2010-2015. The tables show intersectoral, interprovincial, and international flows of embodied emissions, presented in the North American Industry Classification System (NAICS)" Regarding our model’s inputs for estimating embodied emissions in international trade, we utilized the Environmental Accounts and financial input-output tables from the World Input-Output Database (WIOD). For estimating embodied emissions in Canada and Canada’s trade, we used Canada’s Physical Flow Accounts, Interprovincial Input-Output and Supply-Use Tables, and Canada’s Trade Online Data. These datasets were accessed in August 2019 from the following websites (see links): • WIOD Input-Output Tables: http://www.wiod.org/home • WIOD Environmental Accounts 2013: http://www.wiod.org/home • WIOD Environmental Accounts 2019: https://ec.europa.eu/jrc/en/research-topic/economic-environmental-and-social-effects-of-globalisation • Interprovincial Input-Output Tables: https://www150.statcan.gc.ca/n1/en/catalogue/15-211-X • Interprovincial Supply-Use Tables: https://www150.statcan.gc.ca/n1/en/catalogue/15-602-X • Physical Flow Accounts: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3810009701 • Canada’s Trade Online Data: https://www.ic.gc.ca/eic/site/tdo-dcd.nsf/eng/Home
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.024 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".