Determinants of Public Participation in Watershed Management in Southeast China: An Application of the Institutional Analysis and Development Framework
Bibliographic record
Abstract
Increasingly, adaptive processes and decentralization are vital aspects of watershed governance. Equitable and sustainable water governance requires an understanding that different societal members have unique relationships with the environment and varying levels of interaction with policymakers. However, the factors facilitating public involvement under centralized governance remain less understood. This study combined the Institutional Analysis and Development framework with ordered probit regression to empirically investigate the determinants of willingness to participate (WTP) and actual participation of the public in integrated watershed management (IWM). Data from 933 valid questionnaires collected across 36 counties in Fujian, China, were used to define stakeholders’ perceptions of IWM. Results show that stakeholders are predominantly willing to participate in watershed conservation, management, or planning (85.9%), while only 32.8% frequently attend related events. Pro-environmental intentions were mainly shaped by interactional capacity—information exposure, interpersonal exchanges, and cross-reach support recognition—while actual participation was influenced by perceived biophysical conditions, rules-in-use, socioeconomic factors, and interactional capacity. Frequent observations of poor forest management practices were correlated with higher behavioral intentions, and socioeconomic dynamics significantly affected self-reported actual participation. Information sharing had the most substantial positive impact on both WTP and actual participation. These findings reinforce the necessity for an integrated and holistic approach to regional watershed resource management that fosters inclusivity and sustainability. This study provides workable insights into the social and institutional factors that shape public participation in watershed governance as it evolves toward decentralization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".