Decomposing Three Decades of Nitrogen Emissions in Canada
Bibliographic record
Abstract
Abstract Reactive nitrogen (Nr) emissions arise from multiple economic sectors, each creating distinct Nr species and impacts on environmental quality. In Canada, the relative contribution of different Nr species to total Nr emissions varies considerably across provinces, yet these Nr species are often studied separately, making comparison difficult. Here, we synthesize data from national emission inventories, as well as agricultural and wastewater statistics, to estimate total and per capita Nr emissions trends across Canada's 10 provinces over three decades between 1990 and 2017. We classified emissions by 4 main species of Nr, 3 source sectors, and 13 subsectors. Nr emissions peaked around 2000 followed by reductions both nationally and across almost all provinces. Agriculture replaced fossil fuel combustion as the largest source of Nr emissions after 2008, coinciding with regulatory interventions that aimed to reduce NOx emissions from transportation, while NH3 from crop production increased in several provinces. Using an index decomposition analysis, we further assessed the socioeconomic drivers of Nr emissions changes, including the roles of emission intensity (Nr emissions per unit of economic output), affluence, population, and structural changes in the economy. Reduced emission intensity (an aggregate indicator of technology and policy changes) counteracted some of the effects of affluence and population as positive drivers of Nr emissions. Economic structural changes had both large negative and positive effects on Nr emissions. Our results underscore the importance of continued reductions in emissions intensity as well as shifting economies toward less Nr intensive sectors to further decouple affluence from Nr pollution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".