Researchers are investigating a broad spectrum of factors affecting positively and/or negatively the evacuation decision-making process occurring after people at risk receive cyclone warnings and advisories. Previous studies suggest that early warnings themselves do not propagate evacuation processes to be investigated but, rather, that human risk perceptions do so. This in turn encourages the sociopsychological dimensions of risk perception to be evaluated, which must be done within a country’s own cultural context. In applying content analysis here, we review the literature on evacuation decision-making processes during rapidonset hazards, i.e., tropical cyclones, in coastal Bangladesh. We focus on three broad overlapping themes – early warning, risk perception, and evacuation decision-making. Major content-analysis findings suggest that two things – a lack of credibility in early warning messages and an inefficient dissemination process – tend to affect the risk perception of people at risk and are likely to eventually determine the success of evacuation decision-making. Findings also show that different socioeconomic and socio-cultural issues related to risk perception appear to be more influential than formal warning messages in propagating decisions to evacuate during a cyclone. Based on these results, we suggest specific policy recommendations for improving local evacuation efficiency.
Microgrids comprising renewable energy technologies are often modelled and optimised from a theoretical point of view. Verification of theoretical systems with data of actually implemented systems in the field rarely occurs in an open manner, especially on the intermediate scale of research buildings. To enable modelling of the actual microgrid performance of a research environment, we present a multiyear dataset of a microgrid with solar arrays and a battery. The main energy datasets comprise data per second supplemented by hourly solar irradiation data. These may be combined with data concerning the hourly electricity prices from the main grid and the low-electricity-price periods of national holidays. The level of detail of the data per second in combination with the hourly data in these datasets allows for a comparison to the efficiency and weather-parameter correlation of other renewable energy technologies, as well as forecasting future energy generation and consumption.
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