Shifting Drivers and Trends in Territorial Reactive Nitrogen Emissions for Canadian Provinces Over Three Decades
Bibliographic record
Abstract
Connecting the sources of reactive nitrogen (Nr) emissions to downstream environmental impacts is challenging, since Nr “cascades” through sequential ecosystems. It is therefore important to examine a jurisdiction’s overall Nr emissions to gain perspective on whether total levels and specific forms of Nr emissions are being reduced. We study subnational (provincial) trends and variations in Nr emissions in Canada over 30 years (1990-2017) to examine the effects of key policy, socioeconomic, and technological changes on Nr emissions. We use data from national Nr emissions inventories, agricultural nutrient models, and wastewater treatment reports to estimate specific (N2O, NOx, NH3, and NO3), total (Gg N) and per capita (Kg N / capita) Nr emissions by province. We divide the initial sources of Nr emissions into 1) agricultural-related emissions, 2) fossil-fuel emissions, and 3) waste management emissions. Preliminary results show that annual total Nr emissions in Canada increased between 1990-2000 (1250 Gg to 1490 Gg), and have since decreased (to 1180 Gg). There was a redistribution of the main species of Nr, with decreases of NOx from fossil fuels (from 660 to 488 Gg Nr / year) and increases in agricultural Nr emissions (from 452 Gg to 537 Gg Nr / year). Provincial trends vary. Overall NOx emissions decreased across all provinces due to more stringent vehicle regulations, except in Alberta, where NOx emissions from export-oriented oil production increased. The increase in national agricultural emissions comes primarily from Saskatchewan whose combined agricultural emissions increased from 43 Gg to 97 Gg Nr year. Improving agricultural nitrogen-use efficiency, especially in these provinces, would be a key area for reducing Canada’s Nr emissions. However, given that diesel and gasoline vehicles still contribute 121 Gg Nr / year, electrifying vehicle fleets would also have considerable potential for reducing Nr emissions. Our study demonstrates the variation in and reconfiguration of drivers of Nr emissions at the sub-national scale in Canada, emphasizing the need to consider local contexts and relative contributions of different economic sectors when examining national Nr emissions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".