Gorontalo city's strategy as a plastic waste-free coastal city 2030: Qualitative SWOT analysis and blue economy
DOI:
https://doi.org/10.12928/optimum.v16i1.14789Keywords:
Blue Economy, Logistic-Coastal, SWOT Analysis, Plastic WasteAbstract
The phenomenon of the distribution of coastal plastic waste is dynamic—triggered by currents, winds, waves, and extreme events—so static policies fail to maintain the cleanliness of destinations. Gorontalo's main problems include drainage-estuary leaks, cross-OPD data standards that are not yet uniform, and the performance of WWTP that has not included microplastic fractions. This study aims to map the governance SWOT, estimate the economic benefits of the ecosystem for the competitiveness of blue tourism, and analyze economic-environmental vulnerabilities to formulate a roadmap for 2026–2030. Using qualitative research case studies, the data were collected through in-depth interviews, FGDs, destination-estuary observations, and document review (policies, visits, cleaning operations, WWTP). Framework/Thematic analysis is mapped to a SWOT matrix and downgraded to TOWS. Results: identified strengths (OPD–community commitments, basic facilities, GIS capacity), weaknesses (data, transport logistics, microplastics), opportunities (EPR, green procurement, mixed finance), and threats (storms/floods, re-beaching, greenwashing). The finding shows that data standardization, forecast-based rapid capture SOPs, and microplastic testing in WWTP raise hygiene scores, accelerate recovery, and increase length of stay. Discussion: findings confirm the linkage between water quality–logistics operations–destination reputation. GIS integration, performance-based service contracts, and "blue destination" labels lock in market compliance. This study is important because it integrates coastal science, water governance, and destination economics into an executable policy framework. Implication of the study to use measurable roadmap (indicators, targets, person in charge, public audit). Further studies are suggested on policy trials and strengthening of cross-season sensor datasets.
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