Dynamics of water-energy-food nexus interactions with climate change and policy options
Bibliographic record
Abstract
Abstract Understanding the dynamics of water-energy-food (WEF) nexus interactions with climate change and human intervention helps inform policymaking. This study demonstrates the WEF nexus behavior under ensembles of climate change, transboundary inflows, and policy options, and evaluates the overall nexus performance using a previously developed system dynamics-based WEF nexus model—WEF-Sask. The climate scenarios include a baseline (1986–2014) and near-future climate projections (2021–2050). The approach is demonstrated through the case study of Saskatchewan, Canada. Results show that rising temperature with increased rainfall likely maintains reliable food and feed production. The climate scenarios characterized by a combination of moderate temperature increase and slightly less rainfall or higher temperature increase with slightly higher rainfall are easier to adapt to by irrigation expansion. However, such expansion uses a large amount of water resulting in reduced hydropower production. In contrast, higher temperature, combined with less rainfall, such as SSP370 (+2.4 °C, −6 mm), is difficult to adapt to by irrigation expansion. Renewable energy expansion, the most effective climate change mitigation option in Saskatchewan, leads to the best nexus performance during 2021–2050, reducing total water demand, groundwater demand, greenhouse gas (GHG) emissions, and potentially increasing water available for food&feed production. In this study, we recommend and use food&feed and power production targets and provide an approach to assessing the impacts of hydroclimate and policy options on the WEF nexus, along with suggestions for adapting the agriculture and energy sectors to climate change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".