Analyzing Canada’s ecological footprint embodied in international trade : a unidirectional multi‐regional input‐output approach
Bibliographic record
Abstract
The ‘Ecological Footprint’ (EF) of a specified population is a comprehensive sustainability index that estimates the ‘bio‐capacity‘ (hectares of global average productivity) required to produce the resources consumed by that population and assimilate its carbon emissions. The greater the population’s material consumption and waste production, the larger its eco‐footprint (EF). The standardized method for Ecological Footprint Analysis (EFA) is maintained and regularly updated by the Global Footprint Network (GFN), a non‐profit organization in California. In recent years, various EF analysts have experimented with wedding Input‐Output (I‐O) analysis to the standard method. I‐O based models are potentially superior for estimating the trade portion of the footprint because: (1) they account for country‐specific technological efficiencies when estimating the trade component of eco-footprints (rather than world‐average techno‐efficiency); (2) they account for the service‐related consumption which is absent from the existing method; and (3) they provide more detail on the origins of the imports. This thesis contributes to I-O based ecological footprint estimates. I develop a unidirectional trade‐inclusive multi‐regional input-output (MRIO) model for Canada using 2005 data. The results show that Canada relies for about 25% of its consumption-related resource needs on bio-capacity imported from other countries, compared to 44% using the GFN approach. Over 60% of Canada’s import‐embodied footprint comes from the U.S. and China. Food‐related sectors including agriculture were the largest contributors to Canada’s footprint overseas. Overall, my MRIO model yields a larger EF for Canada (9.77 gha) than the GFN standard method (7.33 gha). This difference is explained by the fact that the GFN standard method overestimates the footprint of exports for Canada (which presumably has production efficiencies that are higher than world-average) and hence leading to an underestimate of the footprint of consumption. Therefore, I conclude that while the MRIO approach is possibly more accurate, the important finding is that the two methods mutually reaffirms the fact that Canadians on average use four to five times more bio-capacity compared to their “fair share”. I discuss several policy implications of my analysis from an environmental, economic and social perspective using an interregional analytic framework.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".