BLUE GOLD OF CANADA: THREE ESSAYS ON VALUING WATER’S ECONOMIC PROMISE
Bibliographic record
Abstract
Water resources are pivotal to sustaining Canada’s environmental integrity, economic productivity, and societal well-being. Despite the prevailing perception of abundance, escalating pressures from climate change, land-use alterations, pollution, and growing demand are compromising the availability and quality of freshwater systems. As these challenges intensify, quantifying the economic value of water is imperative for informing evidence-based policies, directing investments, and devising equitable water allocation mechanisms. This dissertation advances the understanding of water’s economic value in Canada through three essays and contributing to the academic literature of water quality valuation. The first essay employs a meta-analysis of hedonic property value studies to estimate the amenity value derived from enhancements in freshwater quality. Drawing on over 600 effect-size observations from 29 studies, the analysis derives price elasticities for water clarity and constructs a spatially explicit valuation model for freshwater amenities applicable to Canadian lake systems. This model facilitates localized benefit assessments and can be tailored for policy evaluations across varied hydrological and socioeconomic contexts. The second essay evaluates the economic value of irrigation water in the semi-arid agricultural region surrounding Lake Diefenbaker in Saskatchewan. By integrating crop simulation modeling with a realized value framework, the study quantifies the average and marginal values of irrigation water for key crops under diverse climatic conditions and water availability scenarios. The findings underscore the benefits of flexible and adaptive allocation strategies, especially amid precipitation variability, and provide insights for irrigation expansion and drought mitigation planning. The third essay explores how public preferences for water quality improvements are influenced by the specification of the status quo condition in stated preference surveys. Through a split-sample choice experiment conducted across river basins in Alberta, Saskatchewan, and Manitoba, the study reveals that variations in the depicted status quo substantially affect welfare estimates. These results emphasize the criticality of precise baseline formulation in environmental valuation to yield reliable and policy-relevant benefit measures. Collectively, this dissertation furnishes empirical evidence and methodological innovations that bolster sustainable and economically efficient water resource management in Canada.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| 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 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".