Systemic Financial Risk Arising From Residential Flood Losses

Direct damage from flooding at residential properties has typically been categorized as insured, with liabilities accruing to insurers, or uninsured, with costs accruing to property owners. However, residential flooding can also expose lenders and local governments to financial risk, though the distribution of this risk is not well understood. Flood losses are not limited to direct damages, but also include indirect effects such as decreases in property values, which can be substantial, though are rarely well quantified. The combination of direct damage and property value decrease influences rates of mortgage default and property abandonment in the wake of a flood, creating financial risk. In this research, property-level data on sales, mortgages, and insurance claims are used in combination with machine learning techniques and geostatistical methods to provide estimates of flood losses that are then utilized to evaluate the risk of default and abandonment in eastern North Carolina following Hurricane Florence (2018). Within the study area, Hurricane Florence generated $366M in observed insured damages and an estimated$1.77B in combined uninsured damages and property value decreases. Property owners, lenders, and local governments were exposed to an additional $562M in potential losses due to increased rates of default and abandonment. Areas with lower pre-flood property values were exposed to greater risk than areas with higher valued properties. Results suggest more highly resolved estimates of a flooding event's systemic financial risk may be useful in developing improved flood resilience strategies

Assessment of E. coli partitioning behavior via both culture-based and qPCR methods

Quantitative polymerase chain reaction (qPCR) offers a rapid, highly sensitive analytical alternative to the traditional culture-based techniques of microbial enumeration typically used in water quality monitoring. Before qPCR can be widely applied within surface water monitoring programs and stormwater assessment research, the relationships between microbial concentrations measured by qPCR and culture-based methods must be assessed across a range of water types. Previous studies investigating fecal indicator bacteria quantification using molecular and culture-based techniques have compared measures of total concentration, but have not examined particle-associated microorganisms, which may be more important from a transport perspective, particularly during the calibration of predictive water quality models for watershed management purposes. This study compared total, free-phase, and particle-associated Escherichia coli concentrations as determined by the Colilert defined substrate method and qPCR targeting the uidA gene in stream grab samples partitioned via a calibrated centrifugation technique. Free-phase concentrations detected through qPCR were significantly higher than those detected using Colilert although total concentrations were statistically equivalent, suggesting a source of analytical bias. Although a specimen processing complex was used to identify and correct for inhibition of the qPCR reaction, high particle concentrations may have resulted in underestimation of total cell counts, particularly at low concentrations. Regardless, qPCR-based techniques will likely have an important future role in stormwater assessment and management

Financial tools to induce cooperation in power asymmetrical water systems

In multi-purpose water systems, power asymmetry is often responsible of inefficient and inequitable water allocations. Climate Change and anthropogenic pressure are expected to exacerbate such disparities at the expense of already disadvantaged groups. The intervention of a third party, charged with redefining water sharing policies to give greater consideration to equity and social justice, may be desirable. Nevertheless, to be accepted by private actors, this interposition should be coupled with some form of compensation. For a public agency, compensation measures may be burdensome, especially when the allowance is triggered by natural events whose timing and magnitude are subject to uncertainty. In this context, index based insurance contracts may represent a viable alternative option and reduce the cost of achieving socially desirable outcomes. In this study we explore soft measures to achieve global change mitigation by designing a hybrid coordination mechanism composed of i) a direct normative constraint and ii) an indirect financial compensatory tool. The performance of an index-based insurance (i.e. hedging) contract to be used as a compensation tool is evaluated relative to more traditional alternatives. First, the performance of the status quo system, or baseline (BL), is contrasted to an idealized scenario in which a central planner (CP) maximizes global efficiency. Then, the CP management is analyzed in order to identify an efficient water rights redistribution to be legally imposed on the advantaged stakeholders in the BL scenario. Finally, a hedging contract is designed to compensate those stakeholders more negatively affected by the legal constraint. The approach is demonstrated on a multi-purpose water system in Italy, where different decision makers individually manage the same resource. The system is characterized by a manifest power asymmetry: the upstream users, i.e. hydropower companies, are free to release their stored water in time irrespective of the timing of downstream users, i.e. farmers, demands. This situation can lead to financial losses by the farmers, an already disadvantaged group, and, as demonstrated by previous work, lead the global system to underperform. Results suggest that financial hedging tools may offer a reliable and relatively inexpensive alternative to other forms of compensation, and thereby favor more equitable management of multi-purpose water systems characterized by power asymmetry. This finding is especially relevant in times where granting of licenses to use/withdrawal water are often being reviewed with attention to environmental protection and social justice issues

Appraising the Corporate Sustainability Reports – Text Mining and Multi-Discriminatory Analysis

The voluntary disclosure of the sustainability reports by the companies attracts wider stakeholder groups. Diversity in these reports poses challenge to the users of information and regulators. This study appraises the corporate sustainability reports as per GRI (Global Reporting Initiative) guidelines (the most widely accepted and used) across all industrial sectors. Text mining is adopted to carry out the initial analysis with a large sample size of 2650 reports. Statistical analyses were performed for further investigation. The results indicate that the disclosures made by the companies differ across the industrial sectors. Multivariate Discriminant Analysis (MDA) shows that the environmental variable is a greater significant contributing factor towards explanation of sustainability report