An Analysis of Compatibility Between Popular Carbon Footprint Calculators and the Canadian National Inventory Report
Bibliographic record
Abstract
The recent removal of Canada’s national consumer carbon tax has eliminated a tool that could help guide meaningful reductions in national greenhouse gas (GHG) emissions. Personal lifestyle choices contribute up to 75% of national emissions and yet the GHG inventories included in the National Inventory Report (NIR) of Canada provide a limited window into these choices. Carbon footprint calculators, widely used to estimate individual emissions, vary in their input parameters, output data, and calculation methods. This study assessed five current calculators for compatibility with the NIR to determine which might best support it. A qualitative literature review identified criteria for evaluating each calculator’s ability to inform lifestyle changes and align with the NIR. The selected calculators were then scored quantitatively based on the type and depth of their output data. Results revealed significant disconnects between calculator outputs and the NIR. Most calculators used a consumption-based approach, while the NIR follows a territorial framework. Additionally, many calculators lacked critical data categories, such as the purchase of goods and services, needed to fully understand individual carbon footprints. Overall, the calculator with the strongest opportunity to work in tandem with the NIR was determined to be that offered by Carbon Footprint Ltd.
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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 | 0.052 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.035 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".