The effects of connectedness to nature and perceptions of water scarcity on behavioral tendency towards sustainable water consumption
Bibliographic record
Abstract
This study introduces a roadmap for developing social indices of water resource sustainability, focusing on water scarcity perception and connectedness to nature, and examines their influence on farmers’ water consumption tendencies. Using a mixed-methods design, we identified 41 indicators of water scarcity perception and 10 indicators of connectedness to nature. Exploratory factor analysis grouped these into three dimensions of water scarcity perception— perceiving driving factors of water shortage (PDFWS), perceiving consequences of water shortage (PCWS), and knowledge of strategies for adaptation to water shortage (KSDWS)—and three dimensions of connectedness to nature—cognitive, emotional, and behavioral. Structural equation modeling demonstrated that PDFWS (Beta = 0.45; Sig = 0.001; T = 4.90), PCWS (Beta = 0.13; Sig = 0.008; T = 2.75), KSDWS (Beta = 0.21; Sig = 0.001; T = 3.28), cognitive connectedness (Beta = 0.16; Sig = 0.005; T = 2.83), affective connectedness (Beta = 0.26; Sig = 0.003; T = 2.95), and behavioral connectedness (Beta = 0.26; Sig = 0.001; T = 4.05) had significant positive effects on farmers’ sustainable water consumption tendencies. Among them, PDFWS exerted the strongest influence, indicating that farmers more aware of the d driving factors of water shortage were most inclined toward sustainable practices. These results highlight the novelty of developing validated social indices for understanding farmers’ sustainability orientations and demonstrate their practical role in shaping pro-environmental behavior. The findings provide evidence-based guidance for policymakers and practitioners to design targeted communication and training strategies that strengthen water conservation behavior in agricultural communities. Additionally, the study offers theoretical insights into the psychological and social drivers of water conservation and informs policy development for sustainable agricultural water management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".