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Objective: The objective of the research is to estimate the cost of ecosystem service value (ESV) due to the Rohingya refugee influx in Ukhiya and Teknaf upazilas of Bangladesh. Methods: Artificial neural network (ANN) supervised classification technique was used to estimate land use/land cover (LULC) dynamics between 2017 (ie, before the Rohingya refugee influx) and 2021. The ESV changes between 2017 and 2021 were assessed using the benefit transfer approach. Results: According to the findings, the forest lost 54.88 km2 (9.58%) because of the refugee influx during the study. Around 47.26 km2 (8.25%) of settlement was increased due to the need to provide shelter for Rohingya refugees in camp areas. Due to the increase in Rohingya refugee settlements, the total ESV increased from US $310.13 million in 2017 to US $332.94 million in 2021. Because of the disappearance of forest areas, the ESV for raw materials and biodiversity fell by 13.58% and 14.57%, respectively. Conclusion: Natural resource conservation for long-term development will benefit from the findings of this study.

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Objective: The objective of this study is to map vulnerability of Asian countries to the COVID-19 pandemic. Method: According to the Intergovernmental Panel on Climate Change (IPCC) 2007 framework for natural hazards, vulnerability is a function of exposure, sensitivity, and adaptive capacity. From an extensive literature review, we identified 16 socioeconomic, meteorological, environmental, and health factors that influence coronavirus disease 2019 (COVID-19) cases and deaths. The underlying factors of vulnerability were identified using principal component analysis. Results: Our findings indicate that the percentage of the urban population, obesity rate, air connectivity, and the population aged 65 and over, diabetes prevalence, and PM2.5 levels all contributed significantly to COVID-19 sensitivity. Subsequently, governance effectiveness, human development index (HDI), vaccination rate, and life expectancy at birth, and gross domestic product (GDP) all had a positive effect on adaptive capacity. The estimated vulnerability was corroborated by a Pearson correlation of 0.615 between death per million population and vulnerability. Conclusion: This study demonstrates the application of universal indicators for assessing pandemic vulnerability for informed policy interventions such as the COVAX vaccine roll-out priority. Despite data limitations and a lack of spatiotemporal analysis, this study’s methodological framework allows for ample data incorporation and replication.

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