Narrow Results

The coastal zone in Bangladesh is the most powerfully lethal due to cyclones and storm hazard where 29% of the total population reside. Thus, collective disaster mitigation measures are urgent, and it is important to understand people’s pro-social attitude toward such countermeasures. However, few studies on this issue have been conducted in the context of developing countries, such as Bangladesh, and we therefore address this issue. We made a questionnaire survey of 1,000 respondents and elicited (i) a willingness to donate their labor (WDL) and (ii) a willingness to pay (WTP) to collective countermeasures for avoiding the damages from cyclones and associated disasters. With this data, we examine WDL and WTP in relation to respondents’ occupation, education and income. The novelty lies in offering respondents an option of choosing WDL and/or WTP in the questionnaire. The study finds that the poor and less educated people are likely to choose WDL and willing to donate more labor, while rich and educated people are likely to choose WTP and willing to donate more money. However, we also find that voluntary labor donation from poor and less educated people is significant in that donation from poor and less educated people exceeds that from rich and educated people on per-household basis. Poor and less educated people may be more pro-social and WDL is an important source of contribution to be utilized in natural disaster mitigation of developing countries. This finding can be considered a useful guidance for future policies in more general cases, since it is consistent with observed labor donations for the recovery in the 2011 earthquake off the Pacific coast of Tohoku, Japan.

Having achieved an export-led exponential economic growth, Singapore remains vulnerable to both natural disasters and economic crises. However, the economic repercussions and policy responses to extreme events for an island nation like Singapore are not as widely known or studied. This paper illustrates that impacts of a health disaster [Severe Acute Respiratory Syndrome (SARS)] and an economic crisis [Global Financial Crisis (GFC)] on the Singapore economy based on selected indicators of the financial market, macroeconomy and property sector. Crises of different nature entail different policy responses of different scales and this is highlighted in the policy responses to both SARS and GFC toward economic recovery. In the case of SARS, there were preventive measures toward diseases but no reactive measures as the SARS virus was a new strain. For GFC, the policy measures were simply reactive as preventive measures failed to regulate the financial markets effectively. Our paper makes the case that the impacts of such extreme events are systemic as they affect all aspects of Singaporean society and that, moreover, the island nation is more vulnerable to these shocks than is currently acknowledged.

This study investigates the determinants of fertility using a panel data set for 43 countries from 1900 to 2010 at five-year intervals. The regression results show that fertility increases with infant mortality and national disasters and decreases with total years of educational attainment and political development. Fertility rates fall initially and then rise with an increase in income. Average years of schooling of females has a significantly negative effect on fertility rates, whereas that of males are statistically insignificant. A woman’s educational attainment at the primary and secondary levels has a pronounced negative effect on fertility rates. On the contrary, an increase in a woman’s tertiary educational attainment, with the level of a man’s remaining constant, tends to raise fertility rates, particularly in advanced countries, indicating that highly educated women can have a better environment for childrearing in a society with greater gender equality.

Frequent natural disasters have had an important impact on social development and human behavior. Based on quasi-natural experiments in the flooding area of Yellow River, this study investigates the impact of the historical Yellow River flooding on the risk-taking of modern enterprises by using Regression Discontinuity design. Our study finds that those enterprises located in the flooding area of Yellow River have significantly lower risk-taking capability than those not located in the flooding area of Yellow River. Our claim remains unchanged when we use a series of robustness checks and rule out some competing explanations. This negative influence can be attributed to three mechanisms, namely trust, religious belief and uncertainty avoidance. And the negative effects are subject to heterogeneity stemming from variations in an enterprise’s ownership structure and scale. More importantly, the negative impact of the Yellow River flooding on the risk-taking of enterprises can be alleviated in areas with a higher level of formal institution.

Natural disasters have had great impact on human beings. Emergency relief is becoming more important with the increase of the frequency and scale of humanitarian emergencies resulting from more natural disasters because of climate change. In this paper, an interesting hybrid knowledge representation method during the development of a KBS for design of emergency relief structures is presented. It encapsulates ill-structured, semi-structured and structured knowledge that is gathered from literature, human expert and even knowledge gleaned during the system development. All routine as well as cumbrous activities in the emergency relief cycle are covered. The system can provide the user with advice on preliminary plan evaluation, plan optimization, plan evaluation, plan summary and miscellaneous. It would be beneficial to the field of disaster emergency relief decision by focusing on the acquisition and organization of expert knowledge through the development of knowledge-based system.

The response to a natural disaster ultimately depends on credible and real-time information regarding impacted people and areas. Nowadays, social media platforms such as Twitter have emerged as the primary and fastest means of disseminating information. Due to the massive, imprecise, and redundant information on Twitter, efficient automatic sentiment analysis (SA) plays a crucial role in enhancing disaster response. This paper proposes a novel methodology to efficiently perform SA of Twitter data during a natural disaster. The tweets during a natural calamity are biased toward the negative polarity, producing imbalanced data. The proposed methodology has reduced the misclassification of minority class samples through the adaptive synthetic sampling technique. A binary modified equilibrium optimizer has been used to remove irrelevant and redundant features. The k-nearest neighbor has been used for sentiment classification with the optimized value of k. The nine datasets on natural disasters have been used for evaluation. The performance of the proposed methodology has been validated using the Friedman mean rank test against nine state-of-the-art techniques, including two optimized, one transfer learning, one deep learning, two ensemble learning, and three baseline classifiers. The results show the significance of the proposed methodology through the average improvement of 6.9%, 13.3%, 20.2%, and 18% for accuracy, precision, recall, and F1-score, respectively, as compared to nine state-of-the-art techniques.

Recently, social media has become a key platform that allowed people to interact and share information. The use of social media is expanding significantly and can serve a variety of purposes. Over the last few years, users of social media have played an increasing role in the dissemination of emergency and disaster information. In this paper, we conduct a case study exploring how Thai people used social media such as Twitter in response to one of the country's worst disasters in recent history: the 2011 Thai Flood. We combine multiple analysis methods in this study, including content analysis of Twitter messages, trend analysis of different message categories, and influential Twitter users analysis. This study helps us understand the role of social media in time of natural disaster.

Microblog activity logs are useful to determine user’s interest and sentiment towards specific and broader category of events such as natural disaster and national election. In this paper, we present a corpus model to show how personal attitudes can be predicted from social media or microblog activities for a specific domain of events such as natural disasters. More specifically, given a user’s tweet and an event, the model is used to predict whether the user will be willing to help or show a positive attitude towards that event or similar events in the future. We present a new dataset related to a specific natural disaster event, i.e. Hurricane Harvey, that distinguishes user’s tweets into positive and non-positive attitudes. We build Term Embeddings for Tweet (TEmT) to generate features to model personal attitudes for arbitrary user’s tweets. In addition, we present sentiment analysis on the same disaster event dataset using enhanced feature learning on TEmT generated features by applying Convolutional Neural Network (CNN). Finally, we evaluate the effectiveness of our method by employing multiple classification techniques and comparative methods on the newly created dataset.

This paper analyzes the dynamics of assets held by low-income households facing various types of income shocks in pre-independence and post-independence Pakistan. Focusing on the province of Khyber Pakhtunkhwa (formerly known as the North–West Frontier Province or NWFP), the paper first investigates long-run data at the district level beginning 1902. Results show that the population of livestock, the major asset of rural households, experienced a persistent decline after crop shocks due to droughts, but did not respond much to the Great Depression. In the post-independence period, crop agriculture continued to be vulnerable to natural disasters, although less substantially so, while the response of livestock to such shocks was indiscernible from district-level data. To examine microeconomic mechanisms underlying such asset dynamics, I analyze a panel dataset collected from approximately 300 households in three villages in the NWFP during the late 1990s. Results show that the dynamics of household landholding and livestock are associated with a single long-run equilibrium. When human capital is included, the dynamics curve changes its shape but this is not sufficiently nonlinear to produce statistically significant multiple equilibriums. The size of livestock holding was reduced in all villages hit by macroeconomic stagnation, while land depletion was reported only in a village with inferior access to markets. The patterns of asset dynamics established from historical and contemporary analyses are consistent with limited but improving access to consumption smoothing measures in the study region over the century.

The catastrophic earthquake that erupted in Sichuan Province in May 2008 left nearly 70,000 dead, over 370,000 injured, and 18,000 missing. Zhang Ziyi, one of China’s most acclaimed actresses, was in Cannes, France, for the International Film Festival when the earthquake struck; she responded hastily by making a personal pledge of RMB 1 million yuan, as well as raising USD 500,000 (RMB 3.3 million yuan) in donations for earthquake relief. Despite these efforts, a discussion started on whether celebrity philanthropy was nothing more than a scam and Ms. Zhang came under attack. The case discusses the questions on whether philanthropy always has to be altruistic or can also have other objectives.

This paper introduces the theory of social marketing to the areas of disaster management that deal with natural disasters such as earthquakes, flooding, hurricanes, etc. The theory of social marketing as a sub-theory of marketing is introduced and its implications in the area of disaster management are discussed. The paper further illustrates how social marketing can benefit various levels of government as well as organizations and communities in the disaster affected regions. A model of social marketing related to disaster preparedness will advance the use of social marketing concepts such as product, promotion, place and price to help governments, organizations, and local communities prepare for possible natural disasters with minimum negative impact.