Exploring water use pathways under deep decarbonization scenarios in Canada at subnational scales using GCAM-Canada
Bibliographic record
Abstract
Canada is a water rich country with annual per capita freshwater withdrawals that rank among the highest in the developed world. As global and national decarbonization efforts progress, the implications of these energy and land system transitions for Canadian water resources remain underexplored. This study employs the integrated assessment model, GCAM-Canada, to project Canadian water use to 2050 in six sectors - municipal, manufacturing, irrigation, livestock, primary energy mining and, thermal power generation - across provinces and river basins under six combinations of socio-economic and climate mitigation scenarios. The resulting water use projections elucidate the relative impacts of socio-economic development, technological change, carbon emission restrictions, and direct air capture (DAC) technologies on water use at subnational scales. Additionally, the study quantifies virtual water embodied in exported Canadian crops and electricity to assess the effects of global decarbonization on local water resources. Our findings project national withdrawals to decline by 9 %-26 % by 2050 in all scenarios, although patterns vary by province and river basin. Conversely, water consumption increases across all scales. Net-zero climate policies produce potential trade-offs and synergies with water use in different provinces, emphasizing the need for regional considerations in climate policy formulation. Green and blue virtual water exports are projected to increase in all scenarios, although to a lesser extent under decarbonization, while electricity sector virtual water exports are projected to increase under global net-zero scenarios with rising U.S. demand for Canadian hydropower. Our study emphasizes the need for tailored solutions within Canada's broader climate and water management frameworks for a more sustainable future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".