We examine the linkages between power reliability, economic growth, and income inequality in the United States. Specifically, we use the two-step System Generalized Method of Moments (GMM) estimator to assess the impact of power interruptions on state-level GDP and the Gini Index. Our findings reveal that a 1 percent increase in power interruptions, measured in terms of duration (SAIDI) and frequency (SAIFI), is associated with a 0.07 to 3.7 percent decrease in real GDP and a modest increase in income inequality of approximately 0.17 to 0.20 percent relative to the mean Gini Index. Moreover, the marginal effects of power interruptions are substantial, with frequent outages resulting in GDP losses exceeding 2 trillion in the long run. We also use machine learning models to support the predictive relevance of the power reliability metrics. Overall, the results highlight the significant role that both the frequency and duration of power interruptions play in shaping regional economic performance and the importance of improving power reliability to foster economic stability and equity.

Interruptions to electric power systems are to some degree inevitable. However, the frequency with which they occur, their duration, and the ability of providers to restore power quickly could have implications for regional growth. Using information on distribution utilities and power marketers of electricity across the U.S., we examine the relationship between grid reliability and county-level growth. Specifically, we utilize a double machine-learning technique to assess how various measures of power reliability are associated with the percent change in employment and population. Overall, the results suggest that counties benefit from improvements in power reliability. We also find linkages between shorter minor interruptions and regional growth, especially in rural areas. JEL Classification : R10, Q40, C10

The capacity to adapt to disturbances is a distinguishing feature of a resilient system. A number of recent power outages have forced households to adapt to service disruptions. Households adapt to power outages in various ways, yet most existing research focuses on a single event in a specific location. This study expands the scope by analyzing four datasets to examine household adaptations across different events and locations: general outages in Los Angeles, California (CA); the 2022 North American winter storm in North Carolina (NC) and New York (NY); the 2021 Texas (TX) winter storm; and hypothetical future events in the first three locations. Using mixed logit models that integrate revealed preference (RP) and stated preference (SP) data, the study investigates household adaptation behavior across regions and events. The analysis addresses three key questions: (1) How common are different adaptations? (2) Which household adaptations tend to occur together, and which do not? (3) How do adaptations vary with household characteristics, outage duration, and geographic location? Results show that, as outage duration increases, people are more likely to consider multiple relocation adaptations and/or use a generator. Some household characteristics affect adaptation differently depending on location. For example, more prepared individuals are more likely to go to hotels in NC but less likely in CA. This study leverages mixed logit models in a novel way to estimate adaptation behavior during power outages. The models can estimate the percentage of people implementing adaptations with sufficient accuracy for practical purposes.

Investment in renewable energy production has been subject to swings in the U.S. policy stance on climate change creating uncertainty. Determining how and to what extent the renewable energy sector responds to climate policy uncertainty is relevant to understanding the energy transition from fossil fuels to renewables. This study examines the relationship between the growth in renewable energy production and its sub-components and climate policy uncertainty while accounting for oil price uncertainty and the growth in oil prices, industrial production, and carbon emissions, respectively. Utilizing generalized impulse response analysis within a vector autoregressive model framework, we find that total renewable energy production responds negatively to shocks to climate policy uncertainty but exhibits only a small positive response to oil price uncertainty. Further examination of renewable energy production by its sub-components (i.e., hydropower, biomass, geothermal, wind, and solar) shows that the time path responses to uncertainty shocks differ by sub-component. The findings suggest that policies to facilitate an energy transition by treating renewables similarly may not have the desired effects and thus should be tailored to individual sub-components to achieve targeted goals for renewable energy production.

This paper advances understanding of the implementation of household adaptations in response to electric power outages—who undertakes which ones and under what circumstances. Specifically, using household survey data from New York state and North Carolina, USA following a 2022 winter storm, we apply the Household Adaptations to Service Interruption (HASI) typology for the first time. We also use revealed and stated preference data to fit mixed logit models that predict the probability a household implements an adaptation as a function of the HASI categories and adaptation attributes. For the first time, the models include generalized versions that can be applied to any adaptation type. Results suggest the hierarchical categories and adaptation attributes (e.g., expensive) in the HASI typology distinguish among adaptations in a way that relates to how frequently they are implemented, as was the typology’s intention. In particular, adaptations that require relocation (e.g., going to a hotel or public shelter) are the least likely to be implemented. Those that work by reducing or delaying consumption, or by providing alternative ways to accomplish specific uses (e.g., candles for light) generally are more likely to be implemented the more favorable attributes they have (e.g., does not require time/effort, meets needs).

There is a converging consensus among the scientific community that tropical storm activity will continue to increase in severity, at least in part due to evolving climate change. Given the destructive nature of these tropical storms, it remains of utmost importance how to increase the resiliency and sustainability of communities in hurricane-prone areas. Using detailed data from a large-scale, firm-level survey post-Hurricane Harvey, we identify possible indicators/drivers of recovery and resiliency of small businesses, which are often the lifeblood of local communities. Our descriptive analysis suggests linkages between firm characteristics, financial and operational strategies, access to credit, mitigation actions, damages, and recovery trajectories. Overall, this study provides valuable insight into the role of small businesses in resilience planning and recovery improvement following severe hurricanes.

This research studies volatility dynamics of the returns to upstream (exploration and production) and midstream (pipeline and storage) energy (oil and gas) sectors over time and across sectors. We find significant structural breaks in volatility of upstream sector returns possibly triggered by major events, while no breaks are detected in the midstream sector. These results indicate the midstream sector is fairly insulated from major events relative to the upstream sector. Results based on a bivariate generalized ARCH model, which controls for breaks, indicate that the conditional variance of the upstream sector is significantly more affected by midstream volatility than vice versa. This finding could be due to the fundamental difference in functions performed by the midstream and upstream sector firms. These patterns in volatility dynamics have practical significance for financial market participants.

In February 2021, a winter storm brought snow, ice, and freezing temperatures, which caused severe interruptions in the electric power and water supply systems in Texas and surrounding areas. In this paper, we use survey data to investigate the ways that households adapted and reacted to those outages. The analysis aimed to determine (1) how common different household adaptations were, (2) how adaptations varied with outage and household characteristics, (3) how adaptations tended to occur together, (4) how unhappy households were as a result, (5) how household unhappiness varied with outage and household characteristics, and (6) what concerns influenced the unhappiness level. Results are compared with findings from a study that used an almost identical survey instrument but was based on a larger data set from Los Angeles. Findings from both studies suggest that almost everyone implemented at least one adaptation; most implemented several. They also agreed the most common adaptations were using candles, a flashlight, and/or a lantern; charging the cell phone in the car; purchasing bottled water; and delaying or reducing consumption. Both studies indicate that households experienced varied levels of unhappiness, which were similar for electric power and water interruptions. The reported levels of unhappiness were notably higher in Texas than Los Angeles, however, possibly because the outages had relatively long durations and were recent. Financial, time/effort, health, and stress concerns all were found to have a substantial influence on the extent of unhappiness, in both the Texas and Los Angeles analyses, suggesting that it is critical to consider all of them. Analysis of the Texas study introduced the new finding that repeated service outages during an event is associated with both increased adaptation implementation and greater unhappiness. Analyzing larger data sets from additional events in different locations would be helpful to further understanding of household experiences in service outages. Several practical applications emerge from this work. First, by knowing how people are likely to adapt, officials can better prepare to support their constituents during a crisis. Anticipating the distribution of unhappiness could point infrastructure operators to consider other measures for service quality besides downtime alone, as important as that is. Knowing the differential effects of outages on different populations can improve officials' understanding of social vulnerabilities in their communities, which is needed for planning, education, and outreach. Electric and water outages accompany many kinds of hazard events; the extent to which a local population can adapt, and how officials and infrastructure operators can enhance those adaptations, will be important elements of local community resilience.

Economic resilience defines a community's ability to prevent, withstand, and quickly recover from major disruptions to its economic base. Instead of having repeated damage and need for outside assistance, resilient communities proactively protect themselves against hazards, build self-sufficiency, and become more sustainable over the long term. Within these communities, small businesses are an important driver of economic growth and employment. However, small businesses are extremely vulnerable to natural disasters: about 40-60 percent of them never reopen their doors after a disaster. To gain an insight into the vulnerability and resilience of small businesses, we collected firm-level data through an online survey of primary decision-makers of small businesses located in 2017 Hurricane Harvey impacted counties. The questions in the survey covered five broad categories: general business characteristics; finance impact; operation impact; built environment impact; and mitigation actions. The analysis shows a small variation in recovery time between industry groups. However, the contrast between firms that invested in resilience and firms that did not is significant. We then modeled the small business recovery as a stochastic process and used the survey data to select probability models and then estimate model parameters. If firms chose to invest in resilience, the mean and median times could be reduced by 57% and 8.5%, respectively. Using the baseline provided, a firm could estimate the length of recovery expected and prepare a business continuity plan accordingly. In a hurricane's aftermath, their performance can be benchmarked against that of their peers. The findings would serve as basis for public policies towards incentivizing prestorm mitigation and resilience-building actions.

Natural disasters such as hurricanes, earthquakes, and floods cause massive damage and losses around the world. Investigating their impact on affected communities and delineating them from nondisaster forces have major scientific and policy implications. To that end, a series of econometric tests were applied to the Hurricane Resiliency Index (HRI) for six selected study areas to identify and date-stamp periods of mildly explosive behavior (MEB). Evidence of MEB signals a structural break or nonlinearity in an otherwise stationary time series. The starting and ending points of multiple MEB episodes indicate the extent to which a community recovers toward normalcy. It is found that the recovery time associated with hurricanes is much shorter than that with economic recessions. The overall severity of MBE, when considering both duration and amplitude, is most pronounced when a major hurricane strikes dense populations. The findings also highlight that the compounding effect of economic recession and hurricane poses the most serious threat to a local economy. Finally, the Hurricane Resiliency Index is shown to outperform the Federal Reserve Bank of Dallas's Metro Business-Cycle Index in capturing such behaviors.

This paper presents a new conceptual framework of the disaster risk of critical infrastructure systems in terms of societal impacts. Much research on infrastructure reliability focuses on specific issues related to the technical system or human coping. Focusing on the end goal of infrastructure services - societal functioning - this framework offers a new way to understand how those more focused research areas connect and the current thinking in each. Following an overview of the framework, each component is discussed in turn, including the initial buildout of physical systems; event occurrence; service interruptions; service provider response; user adaptations to preserve or create needed services; and the ending deficit in societal function. Possible uses of the framework include catalysing and guiding a systematic research agenda that could ultimately lead to a computational framework and stimulating discussion on resilience within utility and emergency management organisations and the larger community.

Critical infrastructure systems derive their importance from the societal needs they help meet. Yet the relationship between infrastructure system functioning and societal functioning is not well-understood, nor are the impacts of infrastructure system disruptions on consumers. We develop two empirical measures of societal impacts-willingness to pay (WTP) to avoid service interruptions and a constructed scale of unhappiness, compare them to each other and others from the literature, and use them to examine household impacts of service interruptions. Focusing on household-level societal impacts of electric power and water service interruptions, we use survey-based data from Los Angeles County, USA, to fit a random effects within-between model of WTP and an ordinal logit with mixed effects to predict unhappiness, both as a function of infrastructure type, outage duration, and household attributes. Results suggest household impact increases nonlinearly with outage duration, and the impact of electric power disruptions is greater than water supply disruptions. Unhappiness is better able to distinguish the effects of shorter-duration outages than WTP is. Some people experience at least some duration of outage without negative impact. Increased household impact was also associated with using electricity for medical devices or water for work or business, perceived likelihood of an emergency, worry about an emergency, past negative experiences with emergencies, lower level of preparation, less connection to the neighborhood, higher income, being married, being younger, having pets, and having someone with a medical condition in the house. Financial, time/effort, health, and stress concerns all substantially influence the stated level of unhappiness.

A recent trend has been a move toward greater reliance on renewable or “green” energy sources, especially in the residential sector. Using a choice experiment, this paper examines how providing information regarding the efficiency, cost, and environmental impacts of different power-generating sources impact consumers’ stated preferences for selecting voluntary green-power plans. Based on 21,000 plan choices from two different samples totaling over 1,800 respondents, our results indicate that information nudges significantly impact respondents’ choice of plan. Promoting the advantages of the green plan or the disadvantages of the “gray” plan increase green plan selection. The magnitudes of these estimated effects are economically significant being roughly equivalent to a change in the monthly green price premium of $4/month. We also find that promoting the advantages of the green plan is more effective when the green plan premium is relatively small, while highlighting the drawbacks of the gray plan is more effective when the green plan premium is relatively large. Our results suggest that information nudges have the potential to be a plausible, economical, and effective mechanism to increase adoption of voluntary green-power plans.

Recent attention has turned to drilled but uncompleted wells (DUCs) that exploration and production (E&P) companies may manage in order to address market uncertainties. DUCs act as potential supply that bridges the gap when supply and demand are unequal and can play a key role in the operations strategies of producers. This study provides evidence of mildly explosive behavior in the time series of DUCs by applying the Right-Tailed Augmented Dickey Fuller test of Phillips, Shi, and Yu (2015) (PSY). The PSY method tests for mildly explosive behavior of a time series defined by when the volume of DUCs move beyond what would be expected given the fundamental market conditions. The sample spans from December 2013 until May 2020 and includes the major oil producing regions of the United States. The analysis date stamps periods of mild explosivity and matches surges in DUCs with major contemporaneous events including the June 2014 precipitous drop in oil price, midstream bottlenecks and resource constraints, as well as the COVID-related fall in demand for oil. The findings are discussed in the context of exploration and production firms and the use of DUCs as a tool for understanding value and optimization of production over time.

When infrastructure system services are interrupted, households implement adaptations to address needs that were previously met by the infrastructure system. In this paper, we introduce the Household Adaptations to Service Interruption (HASI) typology, which offers a new way to systematically enumerate, organize, and describe adaptations. Development of the typology was guided by a desire to better understand the implementation and effects of adaptations-who undertakes them, when, under what circumstances, and with what consequences. These, in turn, depend on tradeoffs between the replacement service provided by an adaptation and the re-quirements to implement it. The HASI typology, thus, includes two parts: (1) the main groupings, which have a hierarchical structure, and (2) additional adaptation characteristics that are related to the replacement service and requirements to implement. The features used to group adaptations include modifying supply vs. demand; substituting the infrastructure system service (electricity) vs. specific uses it typically provides (e. g., lighting or cooking); applicable infrastructure systems; uses served; levels of substitution provided for each use; financial cost; health and safety cost; time and effort cost; effort frequency; special resources required; and side effects. The typology advances understanding of adaptations by providing a consistent vocabulary to discuss them; helping to identify new forms of adapta-tions; facilitating understanding of important adaptation features; and suggesting future research questions.

Alaska has long been important to U.S. oil production. With the sixth largest amount of proven reserves of any state and >95% of oil production occurring on the North Slope, Alaska will continue to be an important factor in U.S. energy production. Given the importance of the North Slope crude oil to West Coast refineries, it is surprising that very few studies have investigated the prevailing price of crude oil from the North Slope: the Alaska North Slope West Coast price (ANS). This study provides evidence of mildly explosive behavior in the time series of ANS price relative to the consumer price level, Alaska oil production, and a measure of oil stocks by utilizing the recursive generalized supremum Augmented Dickey-Fuller of Phillips, Shi, and Yu (2015). The sample period spans from May 1987 until September 2020 and we find evidence of mild explosive behavior prior to and during the 2007–2009 recession. Our findings are consistent with others who found mildly explosive behavior in other oil prices during the same period.

Los Angeles is a community that is susceptible to earthquakes, wildfires and other disasters that may cause water utility disruption. This study estimates water production in Los Angeles using a vector autoregressive error correction model (VECM). The model captures the short- and long-run dynamics among water production and elements of the economic system related to the labor market, built environment, energy and transportation networks in the Los Angeles area. We find evidence of a single cointegrating relationship between water production as measured by total monthly potable in gallons, employment, the S&P/Case-Shiller CA-Los Angeles Home Price index, and retail unleaded gasoline prices. VECM results suggest that after a shock that disrupts the equilibrium, such as an earthquake, system moves about 24% toward eliminating the disequilibrium in the first month, with a return to equilibrium in about 4–5 months. The results have implications both domestically and internationally for understanding a community’s resilience and recovery to shocks and, thus, may shed light on how natural disasters affect a local economy.

This study examines the role of information systems (IS) on environmental sustainability by gaining an understanding of how benefits may be realized from using IS in a green context (a particular IS, regional mesonet (RM) equipped with information- and communication-based technologies and a comprehensive information system) through the use of duel approaches: a survey (218 respondents) and a case study (six interviews of stakeholders of a RM). Our results provide evidence how IS use contributes to different goals at different levels of sustainability and advance knowledge of utilizing IS for providing actual as well as anticipated benefits to sustainability. In addition, our findings provide suggestions on how successful IS might be used to further induce actions and advance goals of environmental sustainability that can contribute to energy policy-making.

With active drilling rigs essential for replenishing oil resources depleted through production, this study examines the potential asymmetries between drilling rig trajectory (vertical, directional, and horizontal), oil prices and oil production in the U.S. within a nonlinear autoregressive distributed lag framework. Based on weekly data, the results reveal long-run symmetry with respect to oil prices irrespective of drilling rig trajectory. However, there is long-run asymmetry for oil production consistent with the capital-intensive nature of drilling and the fixed costs associated with new wells. The results also show short-run asymmetry with respect to both oil prices and oil production consistent with companies taking advantage of upturns quickly and refraining from costly shut-in, plug and abandon, or increased expenditures on improved oil recovery during downturns.

We review the history of oil spot and futures prices. Our review and analysis confirms the major findings related to the time series properties of real oil prices and we link some of the observed experiences to real-world events. Moreover, in light of recent oil market activity, we provide new insight regarding the relationship between real oil spot and futures prices and the exploration and development activity in the United States.