Flooding in Thailand’s capital, Bangkok, impedes the development of the city and brings challenges to the well-being of its citizens. The city is situated in a flood-prone area. This study investigated perceptions of the causes among the residents of perennial floods in Bangkok, examining daily precipitation changes over the last 30 years on eight indices (R10, R20, R25, CDD, CWD, RX5day, PRCPTOT and R99p) and the characteristics of the city that promote or exacerbate flooding. The data was collected by the Thai Meteorological Department (TMD) from station ID 455201 in the period 1986 -2015. Plausible factors contributing to flooding vulnerability and to the ineffective implementation of the city’s flood corrective and preventive measures were determined. A survey of 400 residents of 20 flood-prone localities in the capital’s four districts was carried out and statistical analysis was performed. Further, levels of satisfaction with the efficiency and effectiveness of the city’s flood corrective and preventive measures were assessed. The documentary and empirical findings collectively illuminate three contributing factors or causes of persistent flooding in the capital and of the ineffectual execution of flood-related policies and measures.

This research proposes a collection of urban heat island (UHI) risk indicators under four UHI risk components: hazard, exposure, sensitivity, and adaptive capacity. There are 46 UHI risk indicators linked to three pillars of sustainability: social equity, economic viability, and environmental protection. In this study, the UHI risk indicators were first validated by experts to determine their relevancy and subsequently applied to randomly sampled dwellers of Thailand’s capital Bangkok. The UHI indicators were further validated with confirmatory factor analysis to determine the factor loadings (0–1) and reliability. Under the hazard component, the percentage of days when the daily minimum temperature is less than the 10th percentile exhibited the highest indicator-level factor loading (0.915). Vehicular traffic was the UHI exposure indicator with the highest factor loading (0.923), and the proportion of green space to build environment was the UHI sensitivity indicator with the highest factor loading (0.910). For the UHI adaptive capacity component, the highest factor loading (0.910) belonged to government policy and action. To effectively mitigate UHI impacts, greater emphasis should be placed on the indicators with highest factor loadings. Essentially, this research is the first to use statistical structural equation modeling to validate UHI indicators.

This research proposes an updated version of homestay indicators for 10 existing component-level homestay requirements. The updated indicators were aimed to replace the decade-old original indicators for Thai homestay businesses. Besides, the 31 original homestay indictors were not statistically validated. In this study, structural equation modeling (SEM) was used to determine the statistical relevancy between the updated homestay indicators and the components. The SEM analysis involved two steps: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The EFA statistically regrouped the 31 updated indictors by six new homestay components. The six components and 31 updated indicators were further validated by CFA to determine the factor loadings and statistical reliability of the components and indicators. The factor loadings indicate the levels of importance that homestay guests attach to different homestay components and indicators. Therefore, Thai homestay operators should give priority to the components and indicators with high factor loadings.