Water Pricing, Affordability and Public Choice: An Economic Assessment from a Large Indian Metropolis
Bibliographic record
Abstract
The combined use of surface and groundwater that recognizes site-specificity and communities’ preference structure can greatly determine the social and economic sustainability of communities in a growing metropolis. Utilizing both primary and secondary information pertaining to the water sector of India’s capital city, this paper collectively looks at water demand, public choice and financial sustainability of water supply augmentations in both planned urban and unplanned peri-urban areas having differing levels of planning and resource availability. Households’ preference heterogeneity for water supply scenarios differentiated by their ‘quality’ (potable or non-potable) and ‘source’ (surface or groundwater) has been examined through a carefully designed choice experiment (CE) using iterative bidding game. Household’s choice and preference behaviour for dual quality water (decentralized municipal water for drinking and local groundwater for other purposes), single potable quality water and the ‘business-as-usual’ scenarios are assessed through utility function based multinomial logit (MNL) and nested logit (NL) choice models. The values resulting from the analysis are assessed in terms of water supply augmentation options and their practical limits incorporating the choice and preferences from the heterogeneous planning environments typical of a metropolis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".