Adaptive Mechanisms for Addressing Coastal Erosion through Environmental Education: A Case Study of Samut Sakhon Province, Thailand
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| Title: | Adaptive Mechanisms for Addressing Coastal Erosion through Environmental Education: A Case Study of Samut Sakhon Province, Thailand |
|---|---|
| Language: | English |
| Authors: | Pinyaphat Aksarapornpithak, Porntida Visaetsilapanonta, Patrarabool Pichayapaiboon |
| Source: | Journal of Education and Learning. 2026 15(2):291-303. |
| Availability: | Canadian Center of Science and Education. 1595 Sixteenth Ave Suite 301, Richmond Hill, Ontario, L4B 3N9 Canada. Tel: 416-642-2606; Fax: 416-642-2608; e-mail: jel@ccsenet.org; Web site: http://www.ccsenet.org/journal/index.php/jel |
| Peer Reviewed: | Y |
| Page Count: | 13 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Foreign Countries, Environmental Education, Public Agencies, Community Leaders, Community Attitudes, Public Officials, Private Sector, Industry, Attitudes, Local Issues, Conservation (Environment) |
| Geographic Terms: | Thailand |
| ISSN: | 1927-5250 1927-5269 |
| Abstract: | Coastal erosion poses a severe and growing threat to shoreline communities in Thailand, particularly in Samut Sakhon Province, where socioeconomic vulnerability and environmental degradation intersect. In this study we employ a mixed-methods approach to examine the mechanisms of community-based adaptation through the lens of the environmental education process (EEP). The approach integrates qualitative interviews (n = 85), quantitative surveys (n = 364), and spatial vulnerability mapping. The findings reveal significant economic insecurity, low levels of community participation in environmental organizations, and limited knowledge and preventive behavior regarding coastal erosion. Statistical analysis indicates a strong correlation between adaptive behavior and various factors, including knowledge, attitudes, community participation, access to information, and land use. Knowledge emerges as the strongest predictor of adaptive behavior (β = 0.389, p < 0.001). These insights form the basis of a participatory adaptation model that connects local knowledge systems with nature-based solutions and environmental learning frameworks. The study emphasizes the significance of integrating education, participatory governance, and ecosystem restoration to enhance coastal resilience. The proposed model serves as a scalable foundation for policy innovation and sustainable shoreline management in vulnerable coastal regions. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1507232 |
| Database: | ERIC |
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