Evaluation of willingness to pay and challenges to community empowerment in urban drinkable water
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Environmental degradation, especially that related to water, has the potential to result in an unhealthy life. Humans drinkable water for basic needs, but poor water quality can cause disease. One of the solutions of households to obtain drinkable water is to subscribe to water companies. This study presents the notion of community engagement related to urban drinkable water supply, specifically by examining the willingness of community members to pay for such services in response to environmental pressures.METHODS: This study used purposive sampling methods to determine the value of willingness to pay, identified challenges in the community through a questionnaire on drinkable water in Jakarta, Indonesia, and conducted estimation using ordinary least squares. This research used a sample of 503 households in Jakarta.FINDINGS: The coefficient values of the control variables, namely, daily income (0.448), education level (4.344), and age (628.1), exhibited a positive correlation and statistically significant impact. Results indicate a positive and statistically significant association between the coefficient values of the variables of interest, namely, water quality (8.663) and water source (21.248), in willingness to pay for drinkable water. A one-unit increase in the coefficient score impacts the willingness to pay value, measured in Indonesian rupiahs. Findings indicate that the majority of the respondents expressed readiness to pay for drinkable water valued below 100,000 Indonesian rupiah per month, which is equivalent to under 6.30 United States Dollars. The suggested strategies for addressing the diverse issues encompass the necessity of implementing structural reforms involving the engagement of local leaders to enhance empowerment. This approach holds promise for effectively resolving the drinkable water crisis. Technical effort in shaping the behavior of urban communities in using and appreciating water is also essential to sustain the environment.CONCLUSION: Environmental contamination issues have become a reason for households to subscribe to water companies. Customers are willing to pay to obtain clean and potable water. This study is essential as a basis for formulating policies that can be used by drinkable water companies regarding community members’ ability to pay for water, preferences, and participation in protecting the environment.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".