Public perception of droughts and water shortages and metropolitan willingness to pay for water saving measures to improve water security
Bibliographic record
Abstract
The increase in water demand and droughts have exacerbated water inequalities and weakened the economy worldwide. Nonetheless, droughts alone do not justify the severity of water scarcity events, leading to public water rationing and restrictions. It is of paramount importance to obtain a better understanding of how public perceptions of and attitudes towards drought adaptation strategies influence the severity and extent of impacts on water consumption. Furthermore, studies on the general public’s willingness to pay for mitigation and adaptation measures are key for the design and implementation of efficient and effective water management policies. This study aims to inform policy and decision-making by investigating people’s experiences, perceptions, and assessments of past water scarcity events to understand how these past events may have changed their attitudes and behaviour towards water availability and saving. Data were collected by surveying a sample of 800 residents in the Metropolitan Area of São Paulo (MASP) in South-eastern Brazil, and in Campo Grande in Midwestern Brazil. The MASP faced a severe drought from 2014 until and including 2015 due to a decrease in rainfall and human factors, including water resource mismanagement. This vulnerability frame is also observed in Campo Grande, where residents faced serious water rationing in 2016 and 2019. Our results show that more than 80% of the respondents think the frequency of drought periods increased over the last 10 years and will continue to increase in the next 10 years, and 95% of the respondents believe climate change is real. These results also corroborate the fact that 80% of the sample have faced water restrictions or rationing in the past, of which most lasted more than one day. The study provides policy and decision-makers with important information about the future design of payments for watershed services (PWS) to improve water security. However, one in every fifth respondent does not believe that a possible payment for water saving measures in the surrounding watersheds supplying the cities with drinking water would be invested by the responsible authorities to reduce future water restrictions and rationing and, therefore, improve water security. This reveals a considerable mistrust in the local, regional, and state governments responsible for water supply. Our findings provide important insights into relevant feedbacks between hydrological events such as droughts and societal vulnerability and response inserted into the present-day Brazilian cultural, socioeconomic, and political context, and the effectiveness of economic policy instruments like PWS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".