Empirical causal analysis of flood risk factors on U.S. flood insurance payouts:Implications for solvency and risk reduction.

This paper presents a regression model that quantifies the causal relationship between flood risk factors and the flood insurance payout in the U.S. The flood risk factors that have been considered in this research are flood exposure, infrastructure vulnerability, social vulnerability, and the number of mobile homes. Historical data for the annual flood insurance payout, flood risk factors, and other control variables were collected for six years between 2016 and 2021 and used in a Mixed Effects Regression model to derive the empirical relationships. The regression model expressed the natural logarithm of the annual flood insurance payout in a county based on the flood risk factors and control variables. The paper presents the regression coefficients that quantify the causal influence. It has been found that all four flood risk factors have statistically significant positive influence on the flood insurance payout in a county. However, the extent of the influence is different for different flood risk factors. Among them, flood exposure has the highest influence on the flood insurance payout, which is followed by the number of mobile homes, infrastructure vulnerability, and social vulnerability. Since the federal flood insurance program in the U.S. has a large debt to the U.S. treasury, the government should plan for effective risk reduction that can reduce the flood insurance payout in future to keep the program solvent. The outcomes of this research are expected to facilitate that decision-making process by providing the empirical relationship between flood risk factors and flood insurance payout.

A comparison study on environmental policies for expressway construction projects between China and the US: A tiered analysis approach.

The development of expressway construction projects (ECPs) poses overwhelming challenges to the physical environment around the world. The challenges are supposed to be addressed with the enforcement of environmental policies (EPs). In this regard, developed countries have gained rich experience in EP formulation while developing countries are making efforts to improve policy decision-making on environmental sustainability. This study compares ECP-related EPs (EREPs) between China and the US by conducting a historical analysis with materials from 1960 to 2018 and text mining-based evaluation with materials from 2009 to 2019. The comparison results indicate that (1) an EREP framework is composed of two systems, namely outer factors and inner EPs; (2) the upper-level EPs exhibit a periodic and plan-dominating trend in China and an explanatory tendency in the US; (3) Chinese EPs are focused on pollution mitigation, whereas US EPs highlight the impacts on human health; (4) Both attach less importance to environmental protection measures at the project-level EPs. This paper provides a longitudinal comparison and analysis of EREPs in two huge countries, implying that EREPs are a snapshot of national rules and backgrounds. The findings lay a foundation for future research to examine the innovation of environmental policies, especially for those countries with massive expressway construction projects and the related environmental issues.

A data-driven approach to quantify disparities in power outages.

This research proposes a data-driven approach to identify possible disparities in a utility's outage management practices. The approach has been illustrated for an Investor-Owned Utility located in the Midwest region in the U.S. Power outage data for approximately 5 years between March 2017 and January 2022 was collected for 36 ZIP/postal codes located within the utility's service territory. The collected data was used to calculate the total number of outages, customers affected, and the duration of outages during those 5 years for each ZIP code. Next, each variable was normalized with respect to the population density of the ZIP code. After normalizing, a K-means clustering algorithm was implemented that created five clusters out of those 36 ZIP codes. The difference in the outage parameters was found to be statistically significant. This indicated differential experience with power outages in different ZIP codes. Next, three Generalized Linear Models were developed to test if the presence of critical facilities such as hospitals, 911 centers, and fire stations, as socioeconomic and demographic characteristics of the ZIP codes, can explain their differential experience with the power outage. It was found that the annual duration of outages is lower in the ZIP codes where critical facilities are located. On the other hand, ZIP codes with lower median household income have experienced more power outages, i.e., higher outage counts in those 5 years. Lastly, the ZIP codes with a higher percentage of the White population have experienced more severe outages that have affected more customers.

Data on major power outage events in the continental U.S.

This paper presents the data that is used in the article entitled "A Multi-Hazard Approach to Assess Severe Weather-Induced Major Power Outage Risks in the U.S." (Mukherjee et al., 2018) [1]. The data described in this article pertains to the major outages witnessed by different states in the continental U.S. during January 2000-July 2016. As defined by the Department of Energy, the major outages refer to those that impacted atleast 50,000 customers or caused an unplanned firm load loss of atleast 300 MW. Besides major outage data, this article also presents data on geographical location of the outages, date and time of the outages, regional climatic information, land-use characteristics, electricity consumption patterns and economic characteristics of the states affected by the outages. This dataset can be used to identify and analyze the historical trends and patterns of the major outages and identify and assess the risk predictors associated with sustained power outages in the continental U.S. as described in Mukherjee et al. [1].