Bulk Water Pricing Policies and Strategies for Sustainable Water Management
Bibliographic record
Abstract
With projected impacts of climate change, population, and economic growth, episodes of water scarcity are expected to rise across the globe. The objective of sustainable water management is to ensure that all social, economic, and environmental water demands are fulfilled while sustaining the quality and quantity of water resources. Even though measures for sustainable water management using regulatory and economic instruments are gaining momentum in many countries, there is a need for reform in water management policies in the province of Ontario, Canada. Over the years, water policies in Ontario have come under public and academic scrutiny for being insufficient in addressing competing water demands of different sectors, incentivizing sustainable use as well as proactively signaling water risks at the sub-watershed scale. Bulk water pricing is an effective economic instrument to manage demand and incentivize use-efficiency and conservation by signaling the economic value of water as a resource to users. While the efficacy of volumetric extraction charges has been theoretically established for sustainable water management by existing studies, a comprehensive pricing framework based on sound theoretical principles and best practices has not been methodologically designed. To overcome this deficiency, this chapter investigates and synthesizes key global and provincial best practices to arrive at a conceptual bulk water pricing framework regionally tailored for the case of Ontario. It provides a tangible framework and recommendations to arrive at water extraction charges that can foster sustainable water management and trigger the transition of Ontario into a more water-efficient economy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".