Decomposing Three Decades of Nitrogen Emissions in Canada
Bibliographic record
Abstract
Abstract Reactive nitrogen (N r ) emissions arise from multiple economic sectors, each creating distinct N r species and impacts on environmental quality. In Canada, the relative contribution of different N r species to total N r emissions varies considerably across provinces, yet these N r 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 N r emissions trends across Canada's 10 provinces over three decades between 1990 and 2017. We classified emissions by 4 main species of N r , 3 source sectors, and 13 subsectors. N r emissions peaked around 2000 followed by reductions both nationally and across almost all provinces. Agriculture replaced fossil fuel combustion as the largest source of N r emissions after 2008, coinciding with regulatory interventions that aimed to reduce NO x emissions from transportation, while NH 3 from crop production increased in several provinces. Using an index decomposition analysis, we further assessed the socioeconomic drivers of N r emissions changes, including the roles of emission intensity (N r 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 N r emissions. Economic structural changes had both large negative and positive effects on N r emissions. Our results underscore the importance of continued reductions in emissions intensity as well as shifting economies toward less N r intensive sectors to further decouple affluence from N r pollution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".