Overnight Hours Research Articles

Comparing Distributions of Overshooting Convection in HRRR Forecasts to Observations

AbstractOvershooting convection can significantly impact the chemical and radiative properties of the upper troposphere and lower stratosphere through the transport of various chemical species. These impacts include enhancements of water vapor and ozone‐depleting halocarbons, which both have important consequences for climate change. Therefore, accurate prediction of the Earth's climate system requires convective overshooting to be included. To better understand how convective transport is represented in current state‐of‐the‐art models, approximately 75,000 individual updrafts in the central and eastern United States are analyzed from High‐Resolution Rapid Refresh (HRRR) simulations and NEXRAD radar observations during May and July 2021. Distributions of echo top potential temperatures and heights, as well as diurnal cycles of overshooting frequency, are compared to observations. These distributions show mean, median, and maximum echo tops 2–3 km lower than observations, both in absolute and tropopause‐relative space, with evidence of updrafts losing momentum too rapidly above the tropopause. Diurnal cycles show accurate times of maximum and minimum overshooting, but significant errors at model initialization and evidence that some simulated overshoots continue too late into the overnight hours. Despite these deficiencies, distributions of simulated levels of maximum detrainment show decent agreement with observations. All results, including the severe underprediction of echo top heights, persist at shorter forecast lead times. This indicates a need to improve representation of overshooting storms in weather and climate models, even those that are convection‐permitting, or introduce a transport parameterization.

Medical ChatBot Assistance for Primary Clinical Guidance using Machine Learning Techniques

Hospitals serve as critical hubs for global healthcare, offering essential medical services. However, a pressing issue is the lack of 24/7 medical facilities in rural areas, particularly during the overnight hours. To bridge this gap, we propose the Robotic Medical Support ChatBot (RMSCB) system. This innovative online platform leverages machine learning to autonomously predict medical diagnoses and provide temporary solutions, accessible at any time. The RMSCB system comprises three key stages: pre-processing, training, and classification. In the pre-processing stage, relevant keywords are extracted using the Pre-Fixed Stopping Words Model (PFSWM). The training stage categorizes pre-processed questions into classes based on the Pre-Fixed Class Label Model (PFCLM), associating medical solutions via the Pre-Fixed Answer and Question Model (PFAQM). In the classification stage, the RMSCB processes clinical questions from patients via mobile or personal systems, mapping and identifying solutions based on probability. Experimental results demonstrate the efficacy of the RMSCB system, offering timely first aid medication information and alleviating the need for immediate physician consultation. This technological solution addresses critical healthcare gaps, especially in regions with limited access to continuous medical services.

Determination of Behavioral Changes Associated with Bovine Respiratory Disease in Australian Feedlots

Accurately identifying bovine respiratory disease is challenging in feedlots, and previous studies suggest behavioral monitoring is important. The study objective was to describe individual differences in physical activity (distance traveled), feeding/watering patterns (proximity to feed and water), and social behavior (average cattle within 3 m) when associated with health status in commercially raised beef cattle during the first 28 days on feed. Data from a previous Australian feedlot study monitoring cattle behavior and associated health outcomes were analyzed. Health status categories were generated for all cattle, and each animal was categorized as known healthy (HLTH), known diseased (SICK), or intermediate/uncertain (INTR). The INTR animals were excluded from the final analysis. Key findings included: differentiation in activity between SICK (n = 138) and HLTH (n = 1508) cattle dependent on time of day, SICK cattle spending more time in water and feeding zones early in the feeding phase (<6 days on feed), SICK cattle spending more time in the water and feeding zone during the overnight hours, and SICK cattle spending more time in groups early in the feeding phase but more time in isolation after the first week on feed. Results illustrate behavioral data were associated with important health outcomes.

PSXIV-13 Evaluation of On-Animal Sensors for Monitoring Diurnal Physical Activity of Beef Bulls During the Breeding Season

Abstract Applications of on-animal sensors (OAS), commonly used to monitor behavioral patterns in livestock, remain largely undeveloped for extensively managed beef bulls. Thus, the objective of this study was to characterize and compare two OAS types for monitoring bull activity during the breeding season. Angus and Angus crossbred beef bulls (n = 50) aged 1 to 8 years, from six cow-calf operations in Ohio were placed into multi-sire breeding groups (n = 19) with a bull-to-female ratio of 1:25 (range 1:16 to 1:31). Bulls were placed with a female group from day 10 (range day 5 to 12) until day 60 after fixed-time artificial insemination. Before turnout, each bull was subjected to a breeding soundness examination and fitted with a collar-mounted GPS and an ear tag-mounted triaxial accelerometer (AX3). Raw GPS and AX3 activity measurements, namely speed (m/s) and motion intensity (g), were converted to standardized Z-scores and summarized to an hourly resolution by computing the median hourly activity over a 24-hour cycle at early (day 13 to 28) and late (day 34 to 49) stages of the breeding season. Linear mixed models were fitted to standardized activity measurements (expressed in log scale) to assess the dynamic effect of OAS type throughout the day at early and late stages of the breeding season. Size of the breeding group (males and females), bull age, scrotal circumference, and body condition score were evaluated as potential explanatory covariates using stepwise selection. Diurnal activity curves followed a pattern of relative inactivity overnight from hours 21 to 5 and relative activity during daylight from hours 6 to 20. For both OAS, mean activity early in the breeding season was ~47% greater (P < 0.01) than late. At both breeding season stages, mean activity of bulls differed between OAS types (P < 0.01) at hours 2, 3, 4, 5, and 6 by at least 38%, with greater activity from GPS than AX3 (P < 0.05 in all cases). Conversely, at hours 13, 14, and 16, activity derived from AX3 was greater than from GPS by at least 2.5-fold (14: P < 0.01; 13 and 16: P < 0.1). There was no evidence (P > 0.1) for differences between bull activity derived from GPS or AX3 during the remaining overnight (21-1) and daytime (7 to 12, 15, and 17 to 20) hours. In conclusion, this study provides evidence that simultaneous assessment of bull activity using independent OAS, differed between technologies throughout a 24-h day. Additionally, over the course of the breeding season, both OAS indicated a decline in activity. Bull breeding activities are expected to be less frequent as the breeding season progresses; thus, these may explain the observed differences between stages of the breeding season. Further exploration of benchmarks for bull activity may yield useful tools for the identification of health and reproductive-related behaviors.

Pulse oximetry values from 33,080 participants in the Apple Heart & Movement Study

Wearable devices that include pulse oximetry (SpO2) sensing afford the opportunity to capture oxygen saturation measurements from large cohorts under naturalistic conditions. We report here a cross-sectional analysis of 72 million SpO2 values collected from 33,080 individual participants in the Apple Heart and Movement Study, stratified by age, sex, body mass index (BMI), home altitude, and other demographic variables. Measurements aggregated by hour of day into 24-h SpO2 profiles exhibit similar circadian patterns for all demographic groups, being approximately sinusoidal with nadir near midnight local time, zenith near noon local time, and mean 0.8% lower saturation during overnight hours. Using SpO2 measurements averaged for each subject into mean nocturnal and daytime SpO2 values, we employ multivariate ordinary least squares regression to quantify population-level trends according to demographic factors. For the full cohort, regression coefficients obtained from models fit to daytime SpO2 are in close quantitative agreement with the corresponding values from published reference models for awake arterial oxygen saturation measured under controlled laboratory conditions. Regression models stratified by sex reveal significantly different age- and BMI-dependent SpO2 trends for females compared with males, although constant terms and regression coefficients for altitude do not differ between sexes. Incorporating categorical variables encoding self-reported race/ethnicity into the full-cohort regression models identifies small but statistically significant differences in daytime SpO2 (largest coefficient corresponding to 0.13% lower SpO2, for Hispanic study participants compared to White participants), but no significant differences between groups for nocturnal SpO2. Additional stratified analysis comparing regression models fit independently to subjects in each race/ethnicity group is suggestive of small differences in age- and sex-dependent trends, but indicates no significant difference in constant terms between any race/ethnicity groups for either daytime or nocturnal SpO2. The large diverse study population and study design employing automated background SpO2 measurements spanning the full 24-h circadian cycle enables the establishment of healthy population reference trends outside of clinical settings.

Interventions Associated With Increased Nighttime Room Entries in General Medical Patients

Sleep is vital to recovery from illness, yet it is frequently interrupted in the hospital setting. Existing literature relying on survey data identifies vitals, medications, and pulse oximetry as major disruptors to sleep. This study was designed to assess the degree to which these candidate sleep disruptors are associated with objective room entries. Room entry sensors were placed on doors to 18 rooms on acute medical-surgical units at a tertiary academic center. The number of entries into rooms between 10 Pm and 6 Am were logged on patients admitted to hospital medicine services from March 2021 through February 2022. Medical records were reviewed to extract orders for vital sign frequency, medication timing, continuous pulse oximetry, and intravenous fluid use overnight. Negative binomial regression was used to evaluate associations. Room entry data were collected for 112 admissions and 192 patient-nights. There was an average of 7.8 room entries per patient-night. After adjustments for the other variables and for patients represented in multiple nights, vitals ordered every 4 hours were associated with a 1.3-fold increase in room entries (95% confidence interval 1.0-1.5; P = .013), as were medications scheduled during overnight hours (1.3; 95% confidence interval 1.0-1.5; P = .016). There was no association between room entries and continuous pulse oximetry use. After adjustment, there was also no association with administration of intravenous fluids. Vitals ordered every 4 hours and medications scheduled during sleep hours are independently associated with increased room entries and may be reasonable initial targets for quality improvement interventions designed to minimize nighttime disruptions.

Military health and performance optimization: a circadian strategy in response to governmental policies

In 2017, USS Fitzgerald and USS John S. McCain, both guided-missile destroyers, experienced underway collisions that resulted in the deaths of 17 Sailors and degradation of national defense as two warships were removed from the frontline. This incident garnered Congress’ attention leading to numerous fatigue management policies and working groups instituted at various levels across the Department of Defense. One policy of the Department of the Navy (3120.2A; Dec 11, 2020) specifically addressed risk mitigation factors for maritime operations occurring in the overnight and early morning hours around the circadian nadir or trough in alertness and vigilance. Despite these circadian challenges that come with mission demands of military service, there are many opportunities as outlined in the Department of Navy policy to reduce and/or eliminate the performance-related risks associated with circadian misalignment. In regard to actionable systems and processes aligned with these policies, the first step is to perform a risk assessment to identify circadian-related problems that could arise in response to conducting the military training exercise or operation. The second step is to integrate a means to monitor 24-hour physiology, mitigate performance risk through fatigue countermeasures, and/or re-align the circadian timing system of military personnel to enhance sleep, manage fatigue, and optimize performance. Most importantly, the approach is not a one size fits all. Each military operation will require unique adaption (re-alignment) to the environment and each military operation may require a unique countermeasure(s).

Diurnal Variation in Frequency and Severity of Croup in the Emergency Department.

Emergency department (ED) presentation for croup is thought to occur more often at night, but evidence for its diurnal variation is sparse. Our objectives were to describe the diurnal patterns of ED presentation in children with croup and to determine whether time of arrival associated with severe clinical outcomes. We conducted a retrospective cohort study using the Pediatric Health Information System. We included children 3 months to 11 years of age with an ED encounter for croup from 2016 through 2020. We quantified ED encounters by arrival hour and used generalized linear mixed-effects models to determine association between arrival time and racemic epinephrine use, hospitalization, intensive care unit (ICU) admission, and revisits. There was considerable diurnal variation in ED visits for croup. A total of 126 186 children (60.1%) presented to the ED at night (between 8 pm and 8 am), and 83 763 children presented during the day (39.9%). Peak arrival time was 12:00 am (14 189 encounters) and the nadir was at 2:00 pm (5231 encounters). Children presenting during overnight hours had increased odds of the use of racemic epinephrine (odds ratio [OR] 1.33; 95% confidence interval [95% CI], 1.30-1.36), but reduced odds of hospitalization (OR 0.76; 95% CI, 0.73-0.78), ICU admission (OR 0.61; 95% CI, 0.58-0.68), and 3 day ED revisits (OR 0.86; 95% CI, 0.83-0.90). Children with croup present to the ED more commonly at night. Children presenting to the ED with croup at night have greater odds of being treated with racemic epinephrine, but reduced odds of hospitalization, ICU admission, and return visits.

A Case for Academic Teleradiology.

A Case for Academic Teleradiology.

Camera Network for Use in Weather Operations, Research and Education

Abstract Camera technology has evolved rapidly over the last decade; photo quality continues to improve while cameras are getting smaller, more rugged, and cheaper. One outcome of this technological progress is that cameras can now be deployed remotely at low-cost wherever solar power and wireless communication are available. While numerous camera networks are deployed nationwide to survey traffic conditions and monitor local security, the adoption of cameras as a weather observing tool is relatively new. The New York State Mesonet (NYSM) is a network of 126 weather stations deployed across the state of New York, collecting, archiving and disseminating a suite of atmospheric and soil variables every 5 minutes. One unique feature of the NYSM is that every station is equipped with a camera. Still images are collected every 5 minutes coincident with the standard environmental data during daylight hours, and hourly during the overnight hours. Since installation of the first station in 2015, the camera network has proven to be an essential element of information gathering, a critical data source for the forecast and emergency management communities, and a unique teaching resource of pictorial and visualized learning for kindergarten through high school (K-12) education. More specifically, the camera network supports (1) weather operations, (2) commercial applications, (3) data quality control, (4) site metadata, (5) site security, and (6) research and (7) educational opportunities. This article will review the many benefits, some challenges, and the future functional applications of cameras as part of an observation network. A strong case is made for making cameras an essential component of every weather station.

Truck and Passenger Car Connected Vehicle Penetration on Indiana Roadways

Commercially available connected vehicle (CV) probe data has been demonstrated to provide scalable and near-real-time methodologies to evaluate the performance of road networks for various applications. However, one of the major concerns of probe data for agencies is data sampling, particularly during low-volume overnight hours. This paper reports on an evaluation that looked at both connected passenger cars and connected trucks. This study analyzed 40 continuous count stations in Indiana that recorded more than 10.8 million vehicles and more than 13 million trips (3 billion records) from CV data over a 1-week period from May 9th to 15th in 2022. The average truck penetration was observed to be 3.4% during overnight hours from 1 AM to 5 AM when the connected passenger car penetration was at the lowest. When both connected trucks and connected car penetration were analyzed, the overall CV penetration was 6.32% on interstates and 5.30% on non-interstate roadways. The paper concludes by recommending that both connected car and connected truck data be used by agencies to increase penetration and reduce the hourly variation in CV penetration. This is particularly important during overnight hours.

Digestibility of Dry Matter is Better and Manure Output is Lower in Growing Cattle Limit-Fed a High-Energy Diet During the Growing Phase Compared to a Traditional Roughage-Based Diet Fed for Ad Libitum Intake

Objective: Evaluate the impact on intake and digestion of a high-energy diet limit-fed at 85% of the ad libitum daily consumption of a roughage-based diet on a dry matter (DM) basis compared to a traditional roughage-based growing diet in growing cattle. Study Description: Eight ruminally cannulated crossbred Angus heifers (body weight = 450 ± 24 lb) were used in a cross-over design with two consecutive 15-day periods at the Kansas State University Beef Stocker Unit. Two dietary treatments were fed: (1) 45 Mcal of net energy for gain (NEg) per 100 lb of DM fed for ad libitum DM intake (45AL), or (2) 60 Mcal NEg per 100 lb of DM limit-fed at 85% of 45AL diet intake on a DM basis (60LF85%). Both diets contained 40% of DM as Sweet Bran (Cargill Animal Nutrition, Blair, NE). Heifers were fed once daily at 10:00 a.m. Each 15-day period included 10 days for diet adaption, 4 days for fecal sampling, and 1 day for ruminal sampling. Daily nutrient intake was measured and apparent total-tract diet digestibility using chromic oxide (Cr2O3) marker was determined. Results: Ruminal pH was greater (P < 0.01) from 10:00 p.m. to 10:00 a.m. just before feeding for heifers fed 60LF85% than 45AL heifers. Diet digestibility was improved by 5.2% (P < 0.01) and fecal DM output 35% lower (P < 0.01) in heifers fed 60LF85% than heifers fed 45AL. The Bottom Line: Heifers limit-fed a high-energy diet based on corn and Sweet Bran had greater ruminal pH during the overnight hours, better diet digestibility, and reduced output of manure than heifers full-fed a traditional roughage-based diet.

Machine learning imputation of missing Mesonet temperature observations

Uninterrupted and reliable weather data is a necessary foundation for agricultural decision making, required for models based on accumulated growing degree days (GDD), chill units, and evapotranspiration. When a weather station experiences a mechanical or communications failure, a replacement (imputed) value should be substituted for any missing data. This study introduces a machine learning, network-based approach to imputing missing 15-minute and daily maximum/minimum air temperature observations from 8.5 years of air temperature, relative humidity, wind, and solar radiation observations at 134 AgWeatherNet (AWN) stations in Washington State. A random forest imputation model trained on temperature and humidity observations from the full network predicted 15-minute, daily maximum, and daily minimum temperature values with mean absolute errors of 0.43 °C, 0.53 °C, and 0.70 °C, respectively. Sensitivity experiments determined that imputation skill was related a number of external factors including volume and type of training data, proximity of surrounding stations, and regional topography. In particular, nocturnal cold air flows in the upper Yakima Valley of south-central Washington caused temperature to be less correlated with surrounding stations in the overnight hours. In a separate experiment, the imputation model was used to predict base- 10 °C GDD on 2020 July 1 trained entirely on 15-minute station data from previous years. Even with the entire season of observations missing, the model predicted the GDD value within an average error 1.4% with 125 of 134 stations within 5% of observations. Since missing data can typically be resolved within a timeframe of a few days, the network-based imputation model is a sufficient substitute for short periods of missing observational weather data. Other potential applications of an imputation model are briefly discussed.

Early-life heat stress exposure impacts dairy calf feeding and thermoregulatory behavior

Heat stress has well-known influences on dairy calf physiology, but less is understood about calf behavioral responses to heat stress. Herein, we evaluated milk replacer intake, standing activity, and lying behaviors of calves exposed to prenatal or postnatal heat stress or both. Holstein calves were born to dams experiencing heat stress (HT; shade of a freestall barn) or cooling (CL; shade, fans, and soakers) during late gestation [~44 d before calving, prenatal; mean daily temperature-humidity index (THI) = 78]. They were then subsequently exposed to postnatal heat stress (shade and natural ventilation of an open-sided barn) or cooling (shade of the barn and forced ventilation by fans) from birth to weaning (56 d; mean daily THI = 77; n = 12 per prenatal × postnatal treatment). Heat stress was confirmed by elevated respiration rate and rectal temperature of the prenatal dam and the postnatal calf. Calves were group-housed with automatic milk feeders, from which milk replacer (MR) intake was assessed. Calf behavior was monitored using loggers and video. Postnatal-HT calves tended to consume less MR per hour in the late morning and drank less MR per visit relative to postnatal-CL calves. Postnatal-HT calves spent more time lying laterally and less time lying sternally in a tucked position during overnight hours. Prenatal-HT calves stood longer across the day, particularly overnight, compared with prenatal-CL calves. This study characterized behavioral responses of preweaning dairy calves exposed to chronic heat stress or active cooling during early-life developmental windows.

Abstract 10847: Circadian Variation of In-Hospital and Out-of-Hospital Sudden Cardiac Arrest

Introduction: Several studies have reported circadian periodicity of sudden cardiac arrest (SCA), most commonly a nadir in event frequency during overnight hours. It remains unclear to what extent this circadian pattern is influenced by variation in patients’ physical activities. One way to elucidate this is to compare patients with out-of-hospital (OHCA) versus in-hospital (IHCA) cardiac arrests, which has not been previously done. We hypothesize that the circadian pattern of SCA will be preserved in a mixed contemporary cohort of OHCA and IHCA survivors. Methods: A total of 1,433 consecutive survivors of SCA in the Pittsburgh area from 2002 to 2012 were included. Patient demographics including clinical histories and details of SCA were collected using records from emergency medical services and rapid response teams. Unwitnessed SCA and those with potential non-cardiac confounders were excluded. The distribution of SCA throughout the day and associated patient characteristics were tested for differences using chi-square test and student’s t-test. Results: Of the 1,224 patients analyzed, 706 had IHCA and 518 OHCA. We observed a nadir of SCA in the nighttime hours between 0000 - 0600 in both IHCA and OHCA groups (p&lt;0.001). Patients who arrested in this nighttime window had more co-morbidities (p=0.01) and lower percent of angiographically confirmed acute myocardial infarction (p=0.025). A similar circadian pattern was noted for patients with higher or lower comorbidity burden (p&lt;0.001), although more blunted in sicker patients, as well as for patients whose arrest was due to a shockable rhythm (p&lt;0.001). Correspondingly, the IHCA group had higher co-morbidity burden (p&lt;0.001) and a blunted nighttime nadir compared to the OHCA group (p&lt;0.001). We did not observe a temporal variation by day of week but did see a seasonal pattern with a peak in SCA in the Pittsburgh cold months (p&lt;0.001). Conclusion: The typical pattern of nighttime nadir in SCA is seen in both OHCA and IHCA but is more blunted in sicker patients and in the hospital. This suggests a common mechanism that transcends differences between the two settings but may be influenced by non-cardiac comorbidities or environmental factors such as activity level.

Abstract 11136: Obstructive Sleep Apnea in Aortic Dissection

Background: Obstructive sleep apnea (OSA) imposes an afterload burden on the left ventricle and increases the pressure gradient across the intrathoracic aorta. This might increase the likelihood of aortic dissection (AD). We hypothesized that patients with acute AD would have a high prevalence of previously undiagnosed OSA and a largely nocturnal presentation. Methods: 40 consecutive, consenting subjects with acute AD who survived to hospital discharge were collected from 4 institutions. Mean age was 57 (range 28-75), 24 were male. 25 had type A dissection and 15 type B. 37 completed the modified Berlin Questionnaire and 30 had attended overnight polysomnography. Presenting CT scans had aortic diameter measured at a) the sinotubular junction b) ascending aorta c) origin of innominate d) mid-arch e) origin of left subclavian f) mid-descending aorta g) level of diaphragm; CT scans at 3 months were available in 24 subjects. Results: AHI ranged from 0 - 89; prevalence of OSA (defined by apnea-hypopnea index [AHI] &gt; 5) was 63%. There were no differences in aortic diameter at any level comparing OSA vs non-OSA, regardless of AHI cutoff (&gt;5, &gt;15, &gt;30; figure). Over time aortic diameter decreased at the level of the sinotubular junction and ascending aorta (expected - due to surgery). Whether dissection occurred during overnight hours (10 PM - 7 AM) did not differ by presence/absence of OSA (regardless of AHI cutoff). In those with high-risk Berlin questionnaires (&gt;=2 categories positive) 10/21 (48%) dissections occurred during sleeping hours vs 2/13 (15%) in low risk Berlin group (p = 0.08, Fisher’s exact test). Conclusions: Among patients with acute AD the prevalence of OSA was high. Aortic diameters, however, were not different according to level of AHI. Presence of OSA defined by polysomnography was not associated with a nocturnal presentation in this sample.

Building a robust cyberthreat profile for institutions of higher education: An empirical analysis of external cyberattacks against a large University's computer network

This study investigated twelve categories of exploits targeting a large southern university to build a build a more robust cyberthreat profile for institutions of higher education. In one week, the university experienced 12.4 million external cyberattacks from 110 countries. Malware constituted 58% of all malicious activities. Attacks were statistically more likely to occur during the overnight hours of weekdays. Moreover, the majority of cyberattack activity was generated by a limited number of IP addresses originating from a handful of countries. Dutch-originating IP addresses were responsible for 71% of all cyberattacks in this study, and spyware attacks almost exclusively originated from a single IP address in the U.S.

Multi-scale temporal variation in bird-window collisions in the central United States

Expansion of urbanization and infrastructure associated with human activities has numerous impacts on wildlife including causing wildlife-structure collisions. Collisions with building windows represent a top bird mortality source, but a lack of research into timing of these collisions hampers efforts to predict them and mitigate effects on avian populations. In Stillwater, Oklahoma, USA, we investigated patterns of bird-window collisions at multiple temporal scales, from within-day to monthly and seasonal variation. We found that collisions peaked during overnight and early morning hours, a pattern that was consistent across seasons. Further, temporal variation in fatal collisions was explained by an interaction between season and avian residency status. This interaction illustrated the expected pattern that more migrant individuals than residents collided in fall, but we also documented unexpected patterns. For example, the highest monthly total of collisions occurred in spring migration during May. We also found similarly high numbers of resident and migrant collisions in spring, and a roughly similar amount of migrant mortality in spring and fall migration. These findings, which provide unprecedented quantitative information regarding temporal variation in bird-window collisions, have important implications for understanding mechanisms by which birds collide and improving timing of measures to reduce this major bird mortality source.

Updating allowance allocations in cap-and-trade: Evidence from the NOx Budget Program

The level and distribution of the costs of tradable allowance schemes are important determinants of whether the regulation is enacted. Theoretical and simulation models have shown that updating allowance allocations based on firm emissions or output can improve the efficiency of the scheme by acting as a production subsidy. Using the U.S. NOx Budget Program as a case study, this analysis tests whether electricity generators in states which chose an updating allocation increase their electricity production relative to plants in states that chose a fixed allocation. Econometric results provide evidence that updating allocations lead to an increase in generation for natural gas combined cycle generators, but not for coal generators. This effect is concentrated in low-demand overnight hours and varies both by the local mixture of coal and NGCC generators and by percentile. These findings imply that an updating allocation confers a modest but meaningful subsidy to production relative to a fixed allocation and that firm responses are heterogeneous based on production technology and market conditions.

Sequential forecasting of downside extreme risk during overnight and daytime: Evidence from the Chinese Stock Market☆

This paper proposes a sequential methodology to forecast the stock markets downside extreme risk during overnight and daytime periods. We jointly characterize Value at Risk (VaR) and Expected Shortfall (ES) dynamics during overnight and daytime using a sequential hybrid GAS model conditional on lagged information. We implement the Chernozhukov-Hong MCMC methods for parameter estimation and model evaluation. The results show that the sequential models in forecasting VaR and ES outperform the non-sequential ones, and accumulated information during non-trading overnight hours can help improve the forecasting of daytime downside extreme risk. Moreover, overnight and daytime extreme risks evidently increase during the economic downturn.