Nexus between Agricultural Land Use, Economic Growth and N2O Emissions in Canada: Is There an Environmental Kuznets Curve?
Bibliographic record
Abstract
The present study investigates the relationship between nitrous oxide emissions and economic growth using the ARDL bounds testing approach in Canada over the period of 1970–2020. The agricultural land use and exports are included in the estimated models as additional control variables. The empirical findings confirmed the environmental Kuznets curve hypothesis when total N2O emissions are used as a dependent variable in the case of Canada, and similar results are found when we used agricultural induced N2O emissions as a dependent variable. The results also indicate that Canada is already in the decreasing segment of the Kuznets curve, and the turning point of GDP per capita for the total N2O emissions is $41,718, while for agricultural induced N2O emissions, it is $38,825. Our empirical evidence confirms that agricultural land use had a positive and significant effect on total N2O emissions, while a negative but insignificant effect in the case of agricultural induced N2O emissions. However, Canadian exports are negatively associated with total N2O emissions as well as agricultural induced N2O emissions, but it requires more stringent laws to curb N2O emissions-oriented exports to keep the ecosystem in balance in the short-run and intends to meet its long-term target of reducing emissions as it progresses towards Canada’s 2050 net-zero ambition.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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 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".