Downstream Willingness to Pay for Upstream Nature‐Based Solutions to Improve Water Security in a Thirsty Brazilian Megacity
Bibliographic record
Abstract
Abstract This is the first study to assess public attitudes and willingness to pay (WTP) for water security in one of the global mega‐cities facing Day Zero by taking Nature‐based Solutions (NbS) in the upstream basin feeding the city. Data were collected using in‐person interviews in the São Paulo Metropolitan Region (SPMR) in Brazil. The survey included a state‐of‐the‐art discrete choice experiment (DCE) to estimate WTP for a wide range of possible water security improvement scenarios. Public recollection of past water supply disruptions gradually fades over time as does its impact on choice behavior in the DCE until people remember the extreme restrictions and rationing experienced during the close to Day Zero episode in the SPMR between 2014 and 2015. The latter sparks a significantly higher renewed interest in and WTP for improved water security. Public WTP for a reduction of 10% in frequency and 12 hr in duration of future water shortages is US$8 per month to implement agroforestry in the Jaguari basin. The increase in respondents' water bill corresponds to almost 1% of their disposable household income for the most consequential improvement in water security, indicating that water security is a high priority. The estimated WTP values show that there is sufficient support to provide the critically needed additional financial resources to continue and expand the existing Payments for Ecosystem Services scheme “Conservador das Águas” in the upstream basin feeding the SPMR.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".