Provincial nitrogen footprints highlight variability in drivers of reactive nitrogen emissions in Canada
Bibliographic record
Abstract
Abstract Nitrogen (N) footprints are one method to quantify consumer driven reactive nitrogen (Nr) emissions. Canada is a highly urbanized yet economically natural resource-dependent country, providing an illustrative case study to examine attribution of Nr emissions to per capita consumption, either domestically or abroad. Yet, considered only at the national scale, N footprints may obscure absolute and relative contributions of local drivers to Nr emissions. We apply a top-down N footprint approach drawing from national N budgets, emissions inventories, and agricultural statistics to estimate sub-national (provincial) drivers of Nr emissions across Canada. We calculate per capita provincial Nr footprints from four primary sectors in 2018: (a) crop production, (b) animal production, (c) wastewater treatment, and (d) fossil fuel burning. We estimate that Canada’s total N footprint is 995.7 Gg Nr yr −1 , which equates to an average per capita footprint nationally of 27.1 kg Nr capita −1 yr −1 . The largest national contributions come from a few key (sub)sectors, including transport, beef consumption, and wastewater treatment. Provincial per capita N footprints vary widely, with the largest (Saskatchewan 50.3 kg Nr cap −1 yr −1 ) more than double the smallest (Ontario 22.0 kg Nr cap −1 yr −1 ). Most variation across provinces is due to the fossil fuels sector, including emissions from energy generation and the oil and gas industry. We therefore compare our top-down approach for the fossil fuels sector with bottom-up N footprints and territorial emissions methodologies. Per capita N emissions vary considerably across these approaches. For example, Alberta’s per-capita fossil fuel Nr emissions are 45.9, 23.0, and 6.3 kg Nr cap −1 yr −1 using territorial, top-down and bottom-up footprint approaches, respectively. This analysis demonstrates the challenges of attributing Nr emission for export-oriented economies. Our study provides novel insights on sub-national drivers of Nr emissions, emphasizing the need to consider how heterogeneous geographic contexts contribute to national N footprints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".