Allocating water efficiently taking into account consumptive and non-consumptive values
Bibliographic record
Abstract
The objective of this thesis was to contribute to the understanding of how water resources can be managed to address both consumptive values and non-consumptive values of water. Theoretical analysis looked to answer the question: Can water permit markets within stream systems achieve efficient allocations when endogenously accounting for non-consumptive values? Results showed that third party effects of trade complicated allocations and a Nash Equilibrium achieving the optimal outcome was not possible within a laissez-faire system. The laissez-faire model was unable to address the free rider problem and achieve efficient prices and allocations. A Groves-Ledyard-like mechanism was proposed. Any third-parties affected by any trade could contribute 'blocking' or 'contributing' bids. The solution frees the social planner from determining prices, avoids the public good problem, allows users to endogenously set instream flow constraints and reach the optimal solution. In the empirical study, the objective was to assess current and alternative policies in the allocation of Ontario water among competing consumptive and non-consumptive uses, with a focus on the agri-food sector. Big Creek Watershed was used as a case study. A mathematical programming model was constructed incorporating the estimated values of agricultural, domestic and environmental/recreational water. The current Ontario Low Water Response program was analyzed. Results showed that a broad 20% reduction from all consumptive users cost the region $3.2-$3.7 million and improved stream flow by 21%-24%. A targeted 20% reduction reduced the loss to $1.5-1.9 million and improved stream flow by 26%-32%. Alternative policy models were also simulated. A maximum permitted volume charge was found to provide no incentive to increase water use efficiency. A volumetric charge was found to add some incentive in the long-run but little in the short-run. A permit trading system however, was found to provide strong incentives to achieve efficiency of use and allocation. Water charges were improvements to current policy but overall, a blunt tool. It is unlikely a governing body would understand constantly changing supply and demand conditions to determine the optimal fee. The permit trading model was found to be the better policy to maximize the benefits of water use.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".