Methodology and Applications of Flood Footprint Accounting For Determining Flood Induced Economic Costs Cascading throughout Production Supply Chains

Thanks to rapid urbanization and climate change, most regions, particularly cities, are facing the risk of natural disasters and extreme weather events. Flooding, the most common type of natural disaster, has accounted for nearly 47% of all weather-related natural disasters since 1995, has killed 157,000 people, and has affected more than 2.3 billion people. Despite physical damage, floods also interrupt economic activities and result in huge and unacceptable economic costs that people cannot see directly. Thus, comprehensive analysis of the economic impact by flood disaster on the industrial and economic system has become an urgent and essential part of urban recovery and sustainable development. However, there is a lack of studies which focus on assessing the indirect economic impacts resulting from floods and thereafter providing a common quantitative approach within their assessment. This PhD thesis presents a full methodology for a flood footprint accounting framework, so-called ‘Flood Footprint Model’ that can be applied to indirect economic impact assessment for both single and multiple flood disasters. The concept of ‘flood footprint’ is employed here to measure exclusively the total economic impact to the affected region and the wider economic systems that have been directly or indirectly caused by a flood event. Within the framework of input-output analysis, the ‘Flood Footprint Model’ is built upon previous contributions, with improvements regarding the optimization of available production imbalances and the requirements for recovering damaged capital. Certain factors are considered more rationally and accurately through mathematical and logical approaches, and the main novelties of the proposed methodology are: 1) a recovery scheme for industrial and household capital loss, set by endogenous factors and by considering industrial linkages; 2) a proposal for estimating degraded productive capacity constraints regarding labour and capital; 3) an optimized rationing scheme including basic demand and reconstruction requirements; 4) various extensive sensitivity analyses (as this research proposes a more clear post-flooding recovery process based on this model scenario rather than the ‘black-box’ recovery in other studies). Three practical cases are applied in order to demonstrate this method. In particular, two hypothetical example cases are used to verify the mathematical equations of the model within single and multiple flood events. Chapter 4 describes the total and indirect flood footprint assessment of a hypothetical single-flood case, in which a hypothetical flood occurs in an economy with 3 sectors; while Chapter 6 shows a flood footprint estimation of a hypothetical two-flood event that occurred in a region with 5 sectors. In addition, the ‘Flood Footprint Model’ is successfully applied to a real single-flood case ‘2012 Beijing 721 urban flooding’ which affected 1.9 million people and caused a 11.64 billion Chinese Yuan (CNY) direct economic loss (Chapter 5). The total flood footprint is calculated as 21.19 billion CNY with a recovery period of 42 weeks (almost 1.18% of the total GDP in the Beijing area in the year 2012). In particular, the direct flood footprint was 11.64 billion CNY while the indirect footprint was 9.55 billion CNY; the tertiary industry accounted for 52%, the secondary industry accounted for 40% and the other 8% occurred in the primary industry. Regarding the 42 sectors, Construction, Water Conservation and Transportation were responsible for the largest flood footprint, and accounted for over 12%, 10% and 9% of the total area flood footprint, respectively. Such results seem to correspond closely with the industrial output composition of Beijing in 2012. Aside from the modelling process being shown in three cases, a series of sensitivity analyses of the ‘Flood Footprint Model’ are applied to a single- and two-flood events, as actual economic data for examining the post-flood economic recovery is unavailable. Several conclusions are reached: 1) regarding the results of the indirect flood footprint of a specific flood - the higher direct flood footprint does not mean that the higher indirect flood footprint is determined by inter-linkages among industries; similarly, in a multi-flood, larger direct damage cost from each disaster will result in a larger direct flood footprint of the multi-flood, but does not mean a higher indirect flood footprint; 2) flood footprints of a given single and multiple floods are sensitive to the model-related parameters, such as labour and capital recovery paths, import and basic demand; 3) in a single disaster, delayed recovery scenarios resulting from incomplete governance show results that illustrate that delayed recovery will produce an accumulated effect that can increase the flood footprint and extend the recovery period of the whole economy; 4) in a two-flood case, the total flood footprint of a multi-flood within a given region is larger than the sum of individual flood footprints and this is the same for the indirect flood footprint, as the flood footprint is highly constrained by factors like occurrence time, and physical damage caused by the ensuing flood; 5) this model enables us to find the regional or industrial threshold for damaged capital caused by multi-flooding by calculating the maximum acceptable damage level for the first and second flood in the affected region. Overall, the methodology improved by this thesis is more externally oriented and therefore is a better fit with reality: the final aim of the flood footprint assessment is not confined to an estimation of the economic cost of an urban flooding event at industrial and regional levels per week, month or year, but also provides more options and scenarios for post-disaster recovery management by considering the distribution of any remaining production and the allocation of financial assistance within the economic system after flooding.