Bulk water extraction charge calculator: a tool for sustainable water management in Ontario
Bibliographic record
Abstract
Given growing anthropogenic pressures on water resources and uncertain climatic conditions, sustainable water management using effective demand management strategies will be crucial to tread the path towards sustainable development. This paper makes a contribution to the interdisciplinary realm of water management by designing a flexible tool that overcomes the weaknesses of current extraction charges and reliance on general taxes to finance water management initiatives. These dynamic water extraction charges are envisioned to be important policy instruments to increase water security in Ontario. To ensure sustainable use of provincial water resources, the province of Ontario not only extended the moratorium imposed in 2016 on new groundwater extraction permits for water bottling until 1 January 2020, but is also reviewing its water taking policies. While the moratorium and the extraction charge hike of $500/Million liters is focused on water bottlers, the need for more long-term comprehensive policy approaches to sustainable management is apparent. Economic theory suggests that pricing a resource by reflecting its economic value and scarcity can incentivize optimal consumption. While economic instruments like extraction charges exist in Ontario, a methodological calculation linking the level of these charges to the actual costs and hydrological water risks is largely lacking. Addressing this gap, this paper designs a calculator that operationalizes the conceptual pricing framework for Ontario to transparently arrive at water extraction charges. The public cost data for water resource management initiatives are considered along with extreme water contamination events as contingency environmental costs. Using data from Statistics Canada for annual volume of water withdrawn, the base provincial water extraction charge for all permit liable sectors is calculated to be $23.08/Million liters. Moreover, price multipliers reflecting the sub-watershed scale water quantity risks (identified in sub-watershed source protection assessment reports) along with sector and source specific risks were embedded in the calculator.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".