Integrating Environmental Education into Collaborative Governance for Municipal Solid Waste Management in Peri-Urban Communities
Bibliographic record
Abstract
In this study we explore the pressing issue of solid waste management in peri-urban communities, where urban expansion and rural traditions intersect. Focusing on Wang Taku Subdistrict in Nakhon Pathom Province, we employed a mixed-methods approach, combining quantitative surveys (n = 380) with qualitative interviews of local leaders and community workshops that represented 12 occupational groups. The findings reveal a consistently high level of waste-related issues, particularly in disposal inefficiency (= 4.22). Lack of waste separation ( = 4.10) resulted in an overall problem severity rated at = 3.69, SD = 0.49. While community interest in participating was notable (= 3.52), actual involvement in planning, monitoring and decision-making remained low, contributing to an overall moderate participation level ( = 2.52, SD = 0.25). In response, we introduced a multilateral waste management model grounded in joint decision-making approaches and reinforced by education focused on environmental awareness. The model emphasised source separation, stakeholder coordination and behavioural change through localised training and practical demonstrations. Post-intervention assessments showed statistically significant improvements in waste management knowledge (t = 18.71, p < 0.01), environmental awareness (t = 19.09, p < 0.01) and self-reported practices (t = 18.15, p < 0.01). These results highlight the model’s ability to promote sustainable behavioural change and community ownership, aligning effectively with Sustainable Development Goal 12 on responsible consumption and production. The model presented here offers a realistic, replicable framework for addressing solid waste challenges in similarly situated communities, especially where institutional support is limited but local networks are strong.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".