Yes / This paper proposes a framework to cope with the lack of data at the time of a disaster by em-ploying predictive models. The framework can be used for disaster human impact assessment based on the socio-economic characteristics of the affected countries. A panel data of 4252 natural onset disasters between 1980 to 2020 is processed through concept drift phenomenon and rule-based classifiers, namely Moving Average (MA). A Predictive model for Estimating Data (PRED) is developed as a decision-making platform based on the Disaster Severity Analysis (DSA) Technique. A comparison with the real data shows that the platform can predict the human impact of a disaster (fatality, injured, homeless) up to 3% errors; thus, it is able to inform the selection of disaster relief partners for various disaster scenarios.

Disaster researchers have established the determinants of preparedness and mitigation at the household level of analysis. However, at the organizational level, there is limited research and no theory to guide research on the determinants of preparedness and mitigation. The main goal of this study is to answer the question "what are the determinants of mitigation and preparedness at the organizational level?" The data come from a survey of 227 organizations in Memphis, Tennessee. This study uses Tobit regression technique to identify the determinants. This study finds that organizational size and concern over disaster impact are strong positive determinants of mitigation and preparedness in organizations. In addition, there is a significant and non-linear relationship between organizational obstacle and mitigation and preparedness activities. The study concludes with policy implications and recommendations for future studies.

Disaster researchers have established the determinants of preparedness and mitigation at the household level of analysis. However, at the organizational level, there is limited research and no theory to guide research on the determinants of preparedness and mitigation. The main goal of this study is to answer the question "what are the determinants of mitigation and preparedness at the organizational level?" The data come from a survey of 227 organizations in Memphis, Tennessee. This study uses Tobit regression technique to identify the determinants. This study finds that organizational size and concern over disaster impact are strong positive determinants of mitigation and preparedness in organizations. In addition, there is a significant and non-linear relationship between organizational obstacle and mitigation and preparedness activities. The study concludes with policy implications and recommendations for future studies.

A warmer climate is expected to increase the risks of natural disasters globally. China is one of the hotspots of climate impacts since its infrastructures and industries are often hard hit. Yet little is known about the nature and the extent to which they are affected. This thesis builds novel system-of-systems risk assessment methodologies and data for China, representing infrastructures (energy, transport, waste, water and digital communications) as interdependent networks that support spatially distributed users of infrastructure services. A unique national-scale geo-spatial network database containing 64,834 existing infrastructure assets is assembled. For the first time, flood and drought exposure maps of China's key infrastructures are created, highlighting the locations of key urban areas to understand how its infrastructures and population could be exposed to climate impacts. To deepen the understanding of how climate change will affect the Chinese infrastructure system and hence its economy, economic impact modelling is applied. The research combines a detailed firm-level econometric analysis of 162,830 companies with a macroeconomic input-output model to estimate flood impacts on China's manufacturing sector over the period 2003 - 2010. It is estimated that flooding on average reduces firm output by 3.18% - 3.87% per year and their propagating effects on the Chinese macroeconomic system to be a 1.38% - 1.68% annual loss in total direct and indirect output, which amounts to 17,323 - 21,082 RMB billion. Several infrastructure sectors - electricity, the heat production and supply industry, gas production and supply, the water production and supply industry - are indirectly affected owing to the effects of supply chain disruptions. Taking the above analysis one step further, this thesis explores how climate disaster risks may change over the period 2016 - 2055, using flooding as a case study. A global river routing (CaMa-Flood) model at a spatial resolution of 0.25° x 0.25° is applied and downscaled for China, using the daily runoff of 11 Atmospheric and Oceanic General Circulation Models (AOGCMs). Combining the flood analysis with the infrastructure database, this research demonstrates the changing locations of exposed infrastructures and their dependent customers. We find that by 2055, the number of infrastructure assets exposed to increasing probability of flooding under RCP 4.5 are 41, 268, 115, 53, 739, 1098, 432 for airports, dams, data centres, ports, power plants, rail stations, reservoirs respectively - almost 8% of all assets for each sector. The lengths of line assets exposed to increasing flood hazards are 14,376 km, 32,740 km, 102,877 km and 25,310 km oil pipelines, rail tracks, roads and transmission lines respectively. Under RCP 8.4, the numbers increase to 51, 301, 137, 71, 812, 1066, 424 for point assets. Linear assets increase to 19,938 km, 39,859 km, 122,155 km and 30,861 km. Further, we demonstrate that indirect exposure of customers reliant on those infrastructure assets outside the floodplain could also be high. The average number of customers affected by increasing flood probabilities are 54 million, 114 million and 131 million for airports, power plants and stations respectively. However, within this aggregate increase there is large spatial variation, which has implications for spatial planning of adaptation to flood risk to infrastructure. This is a first substantial study of flood impacts to infrastructure both in terms of direct exposure and their indirect implications. Lastly, to shed some light on the potential vulnerability of China's infrastructure system to climate impacts, this thesis develops a framework that identifies the drivers of infrastructure development in China using evidence from policy documents and a unique geospatial dataset for the years 1900 - 2010. Understanding these drivers will provide a useful foundation for future research in terms of developing infrastructure models that could project the locations of future infrastructure assets and networks in China, thereby quantifying how China's infrastructure exposure and vulnerability will change over time. Overall this research provides an integrated system-of-systems perspective of understanding network and economic vulnerabilities and risks to Chinese energy, transport, water, waste and digital communication infrastructures due to climate change. This is crucial in informing the long-term planning and adaptation in China.