Evolving environmental awareness and shifts in management priorities: a socioeconomic lens on the min river basin, China
Bibliographic record
Abstract
Watersheds have experienced economic and demographic development for decades. In China, this development has been associated with environmental degradation, including water quality deterioration, abnormal stream flow, and biotic resource depletion. Effective watershed management incorporates sustainability and public involvement, enabling the long-term security of the human and natural world. Management strategies however need to take into account local conditions, as every watershed is unique. This paper adopts the analytic hierarchy process (AHP) combined with the random forest model to investigate the shift in participants’ environmental awareness across different socioeconomic groups over the past 15 years. Additionally, it scrutinizes the changing public perceptions on the management priorities and areas requiring enhancement. The AHP index highlighted the importance of environmental behavioral intentions (EBI) as a component of environmental awareness (EA). Between 2006 and 2021, significant changes occurred in public environmental awareness (perception, knowledge, behavioral intention) and perceived management priorities, stressing the need for timely adjustment of management policies. Notably, environmental concern (EC) appears to have decreased over time, reflecting effective management and increased governmental attention. Emphasis on the recreational ecosystem services offered by watershed forests has increased. Males, individuals aged over 40-years-old, and individuals located in the upper reaches possessed higher risk perceptions than other groups. These findings may help policymakers to adjust management priorities based on geographic region and may assist them in promoting more effective measures to communicate watershed sustainable management goals and strategies to the public.
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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 | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".