Revealing global hot spots of technological disasters: 1900–2013

Abstract

Technological disasters can happen in any country in the world and cause human fatalities, injuries, and economic damages, among other physical and social consequences. As the world adopts more technologies, becomes further industrialized, continues faster urbanization, and has larger and more concentrated population, the occurrences and impacts of technological disasters are likely to be more frequent and severe and call for more scholarly research. However, there is a lack of good models for reliable technological risk analysis, which is the foundation for effective preparation for, sound mitigation of, and quick recovery from technological disasters. This research develops an expected risk analysis model, including a base sub-model and a location quotient sub-model, for nearly 200 countries of the world, using the technological disasters recorded in the EM-DAT database for the period 1900-2013. The sub-models are based on country-level risk impacts in terms of expected fatalities, injuries, people affected, and economic losses, their standard deviations, ranges, and corresponding country percentages and ranks. The sub-models are validated using correlations and scatter plots for the observed and expected risk impacts. The results show that the sub-models perform well by yielding consistent expected risks and related measures, indicating that the expected risk analysis model is a reasonably good alternative to existing risk analysis models.

Spatial–Temporal snapshots of global natural disaster impacts Revealed from EM-DAT for 1900-2015

“…Global natural disaster impact studies are relatively few, but can be found in Dilley et al (2005) and Cvetkovi c and Dragicevi c (2014) on global natural disaster risk assessments, Peduzzi et al (2009) for global risk indexes, Giuliani and Peduzzi (2011) on natural hazard geoportals, Peduzzi et al (2005), Kron et al (2012), Wirtz et al (2014), Karsperson 2015, andShi et al (2016) for mapping and management of various global hazard datasets, and Shen and Hwang (2016) for country-level expected risks of technological disasters using the well-known EM-DAT data set. In addition, studies at the global level conducted in the most recent years are found in Shen and Hwang (2018) who proposed a risk analysis model of global technological disasters; created a risk analysis model of world natural disasters; Lesk et al (2016) who explored how extreme weather events influence global crop production; Winsemius et al (2016) who analysed how global future river flood risk can be calculated into climate change impacts; Carrivick and Tweed (2016) who examined the effects of the human systems (such as population pressure and land use) on the glacier outburst flood; Kirschbaum et al (2015) who evaluated a global landslide catalogue created based on a variety of sources including media report and online database to reveal spatial and temporal trends in landslide on a global level; Dottori et al (2018) who investigated the impacts of anthropogenic warming on human and economic losses from river flooding at the regional levels such as the Asian continent; and Bolin and Kurtz (2018) who studied how human factors such as race, class, and ethnicity are related to disaster vulnerability.…”

“…Records of natural-disaster impacts are usually affected by data availability, accuracy, and other reasons, especially for earlier years of 1900-2015 used in this research. Therefore, data collection, classification, interpretation, and recording used in EM-DAT could induce biases (Berz, 2000;WHO, 2002;Shen and Hwang, 2016). In addition, Gall et al (2009) delineate six major biases that alone or in combination slant the interpretation of disaster loss information and eventually lead to a number of common misperceptions or fallacies about hazard events.…”

Mapping terrorism in the academic landscape of business field of study: a bibliometric study of the past 30 years

“…We then removed articles related to natural or human-made disasters (Momani, 2010;Yasuyuki et al, 2019), where terrorism was not the main research focus. Human-made disasters that were removed fell under the categories of war (Sudduth, 2021;Yi et al, 2020), political unrest (Al Khattab et al, 2007Saint Akadiri et al, 2020), economic (Alvarez-Galvez et al, 2019Bartz and Winkler, 2016;Huhtala et al, 2014), industrial (Bakota et al, 2020;Pek et al, 2018), technological (Choong et al, 2018;Shen and Hwang, 2018) and biological disasters (Ghuman and Olmstead, 2015;Harris and Powell, 2009). After undertaking these exclusion steps, 1,582 articles were excluded and 1,865 articles remained in the database for further consideration and analysis.…”

A Global Expected Risk Analysis of Fatalities, Injuries, and Damages by Natural Disasters

“…However, the reliability and validity of EM-DATA are critical for all these efforts. Therefore, cross-database comparative studies, especially for databases with similar spatial and temporal coverages as used in Giuliani and Peduzzi [20], and Gregorowski et al [40], are also imperative.…”

“…Various descriptive or quantitative risk models of natural hazards exist in the literature, including risk/hazards approach [15]; political ecology approach [16]; pressure and release approach [17]; hazard-of-place approach [18]; and vulnerability/sustainability approach [19]. Furthermore, a spatial risk model was developed by Shen and Hwang [20]; a exposure model of global natural hazards by Bono and Mora [21]; a macro framework for measuring vulnerability by Joseph [22]; a natural hazard geoportal by Giuliani and Peduzzi [23]; a global risk index model by Peduzzi et al [24]; a global natural disaster risk model by Dilley et al [25]' a mapping of global hazard datasets by Peduzzi and Herold [26]; and a global natural disaster assessment model by Berke [27].…”