Causality Analysis in the Water-Energy-Food Nexus in the Canadian Prairies 
Bibliographic record
Abstract
As global water, energy, and food (WEF) demands are continuously increasing because of population growth, climate change, and the modernization of the human lifestyle, sustainable resource management is of prime importance. Societies have been struggling with the planning and management of WEF resources under changing population, climate, and ecosystem. Integrated resource management is essential to achieve optimal and sustainable WEF management as sector-centric (e.g. water-centric) management can lead to poor outcome. To that end, WEF nexus as a multi-centric approach has been introduced to emphasize interlinkages among WEF sectors. Such interlinkages need to be identified, quantified, and analyzed to facilitate sustainable WEF resources management. This study aims to conduct a quantitative data analysis within the WEF nexus context to identify the interrelationships among WEF sectors and to understand how each sector interacts with other sectors in the Canadian Prairie provinces (Alberta, Saskatchewan, and Manitoba) individually, and as a whole over the period 1990-2020. Historical data used in this study are at annual temporal and provincial spatial resolution. A correlation-and-causality analysis has been conducted for different pairs of WEF sectors to measure the degree of relationships and to explore the cause-and-effects between each pair of sectors. The Multispatial Convergent Cross Mapping method, as a causal inference tool, has been used for identifying and assessing the causal relations. Determining the causal relationships among WEF sectors helps researchers identify critical components, of a large and complex system, for further investigation and modelling. It can also guide policy-makers for better allocation of resources. Results showed that water has a stronger influence on food and energy than the other way around in the upstream province of Alberta. It was also found that food had more influence on energy than the other way around in the three prairie provinces. This study is a step forward toward a better understanding of the WEF nexus by using causal inference methods for tracking the strength of interactions to identify dominant sectors at both the provincial and regional scales. This can help build more parsimonious and efficient WEF nexus models for further simulation and scenario analysis.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| 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".