Pilot Plant Keeps Aurora Water Ahead of the Curve

No sooner had Aurora (Colo.) Water established its new pilot plant than the COVID-19 pandemic disrupted its introduction to testing the utility's water treatment processes. Once put into action, Mini-Binney, as the pilot plant is called, became an invaluable tool for numerous projects to advance and improve the treatment plant's operations. The pilot plant allows for research of innovative treatment methods without affecting full-scale treatment or putting public health at risk. © 2023 American Water Works Association.

The effect of increasing health disaster risk and public spending on economy conditions: a DSGE perspective

PurposeThe main motivation of the present study is to understand the severity of the effect of health shock on Iran's oil economy and analyze the role of government under these conditions.Design/methodology/approachDynamic stochastic general equilibrium (DSGE) models can show the precise interactions between market decision-makers in the context of general equilibrium. Since the duration of the virus outbreak and its effect on the economy is not known, it is more appropriate to use these models.FindingsThe results of the survey of hands-on policies scenarios compared to the state of hands-off policy indicate that the effect of government expending shocks on the economy under pandemic disease conditions has much less feedback on macroeconomic variables.Originality/valueAs a proposed policy, it is recommended that the government play a stabilizing role under pandemic disease conditions.Key messages There is no study regarding health shock and its economic effects in Iran using DSGE models. Also, in foreign studies, the health shock in an oil economy has not been modeled.The general idea in the present study is how the prevalence of a pandemic infectious disease affects the dynamics of macroeconomic variables.In three different scenarios, according to the persistence of health disaster risk and the deterioration rate of health capital due to this shock, the model is simulated.In modeling pandemic diseases, quarantine hours are considered as part of the total time of individuals.According to the research findings, it is recommended that the government, as a policy-maker, play a stabilizing role under pandemic crises conditions.

Analysis of the Determining Factors of Informal Micro Entrepreneurs, 2016-2020

The objective of this research is to offer a trajectory of the factors that determine that Mexican heads of families decide to participate in the most visible informal microenterprise sector;before and during COVID-19. The data from the National Household Income and Expenditure Survey (ENIGH) and prepared by the National Institute of Statistics, Geography and Informatics (INEGI) of Mexico have been used and a Heckit model has been applied for its treatment and analysis. The findings show that income is the main cause of the informal economy of the heads of family, thus confirming the choice as a solution to economic difficulties. In addition, it is shown that there is a labor supply that excludes the demand with a higher educational level. In addition, informal enterprises are identified by higher remuneration for time spent, flexibility and reduction in working time, participation of more household members, and lower remuneration for women.

A Comprehensive Analysis of Clustering Public Utility Bus Passenger's Behavior during the COVID-19 Pandemic: Utilization of Machine Learning with Metaheuristic Algorithm

Public utility bus (PUB) systems and passenger behaviors drastically changed during the COVID-19 pandemic. This study assessed the clustered behavior of 505 PUB passengers using feature selection, K-means clustering, and particle swarm optimization (PSO). The wrapper method was seen to be the best among the six feature selection techniques through recursive feature selection with a 90% training set and a 10% testing set. It was revealed that this technique produced 26 optimal feature subsets. These features were then fed into K-means clustering and PSO to find PUB passengers' clusters. The algorithm was tested using 12 different parameter settings to find the best outcome. As a result, the optimal parameter combination produced 23 clusters. Utilizing the Pareto analysis, the study only considered the vital clusters. Specifically, five vital clusters were found to have comprehensive similarities in demographics and feature responses. The PUB stakeholders could use the cluster findings as a benchmark to improve the current system.

ENHANCING INVESTMENT ATTRACTIVENESS THROUGH ESG RATINGS: A SUSTAINABLE DEVELOPMENT PERSPECTIVE IN THE BANKING, ELECTRIC UTILITIES AND IPP INDUSTRIES

Objective: Main goal of the research is toto assess the level of influence of the ESG rating on the investment attractiveness of companies. The object of the study is the companies for which the ESG rating is calculated (the oil and gas, metallurgical, electric power and banking industries are observed). The hypothesis is that the management dealing with issues related to ESG should take into account the significance of the impact of the ESG rating on the investment attractiveness of companies, if the significance is proven. Method: The methodological part of this research is formed by an econometric estimation of regressions based on panel data models. Results: There were performed econometric assessment of the impact of the ESG rating on the investment attractiveness of companies. The results of econometric modeling are presented in the list of recommendations for ESG managers. In addition, results of the research proves the significance of COVID-19 pandemic impact on the investment attractiveness of the oil and gas companies. Conclusion: The novelty of the results is in the individual econometric estimation of companies' ESG-rating impact on the investment attractiveness based on the unique set of companies, which present four different industries. Based on the sample of companies from eleven countries, for which ESG scores for the period from 2016 to 2020 were calculated, the statistical significance of ESG-factors, concerning the analysis of its impact on the indicators of investment attractiveness (ROI, EPS), was identified. © 2023 The authors.

The systemic risk of US oil and natural gas companies

We analyse the evolution of the systemic risk impact of oil and natural gas companies since 2000. This period is characterised by several events that affected energy source markets: the real effect of the global financial crisis, the explosion of shale production and the diffusion of the Covid-19 pandemic. The price of oil and natural gas showed extreme swings, impacting companies' financial situations, which, accompanied by technological developments in shale production, had an impact on the debt issuance and on the overall risk level of the oil and natural gas sector. By studying the systemic impact of oil and natural gas companies on risk in the financial market, measured by the ΔCoVaR, we observe that in the most recent decade, their role is sensibly increasing compared to 2000–2010, even accounting for the possible effect associated with the increase in companies' sizes. In addition, our results show evidence of a decreasing relevance of traditional drivers of systemic risk, suggesting that additional factors might be present. Finally, when focusing on the impact of Covid-19, we document its relevant role in fuelling the increase in the oil and natural gas companies' systemic impact. © 2023 The Authors

Adapting Digital Environment to Sustain Operations Remotely

Oil and gas remote operations (RO) enabled by automation and digital solutions are reducing the number of people required to work in the wellsite;many subject matter experts can now complete their daily tasks from the safety of the office in town. We have been transitioning to these new ways of working for some time, and the progress has been greatly accelerated to help ensure business continuity for customers during COVID-19 restrictions, allowing high numbers of wellsite operatives the freedom to work from home. For the Oil & Gas companies that have experimented with more technology, the results have been incredible. Digital transformation has finally hit the industry and it's taking off to meet sustainable goal of upstream companies, this transformation is one such measure by which these goals can be approached. Despite the global availability of technology to handle analytical task from a safe distance, substantial drilling activities have been carried out traditionally across globe. Such traditional drilling operations were carried out in Thailand where client and SLB work together in fast paced factory drilling environment where an oil well can drill and complete within 7 days for 2 strings (2-sections only) 2400-2600 m in onshore operation which requires experienced people to monitor and execute tasks. To support such operation from town i.e., remotely with systematic monitoring by skilled people, one requires to adapt digitization. This paper demonstrates the ability of SLB to adapt the digital environment by introducing "Remote Operation Center" setup enabling to help client achieve their sustainable goals within budget and provided an alternate solution to sustain operations in COVID-19 pandemic. Remote Operations is the ability to operate a system or a machine at a distance;one can handle multiple operations from a safe environment of office in town using technology. It unfolds analytical task & physical task;the former is handed over to Remote Operation Center and physical task is left at rig crew. The Remote Operation Center execute both Directional Drilling (DD) and Measurement & Logging While Drilling (MLWD) services at the well site, from town. Executing Directional Drilling Remote Operation was more challenging. RO moves industry towards future and pushes all other traditional players to work on sustainable goals while adapting to digital environment. On site presence of crew was reduced by 50% while maintaining same pace of operations with better data analysis. Copyright 2023, Society of Petroleum Engineers.

Wastewater Surveillance Program Management and Execution: Lessons Learned from the COVID-19 Pandemic

Wastewater surveillance is a disease-tracking tool that supports early response to infectious diseases, such as public health decision-making and vaccination efforts. Public health agencies have partnered with local officials, wastewater utilities, research institutions, engineers, and physicians to implement practical wastewater surveillance programs. Program development involves careful planning of sampling sites via geographic information system (GIS) analysis, safe and efficient sampling support, analytical methodology development, data analysis and management, and collaboration between wastewater utilities and public health officials. We are focused on building these partnerships to facilitate wastewater testing programs around the globe for the COVID-19 pandemic and preparing for future disease outbreaks. The primary goal of this presentation is to share lessons learned from real-world wastewater surveillance programs to support widespread adoption. We aim to present case studies developed at facility, neighborhood, city, and statewide scales to discuss benefits and challenges of the approach. Copyright © 2022 Water Environment Federation.

Recognizing Value from Virtual Reality During Design – Case-Study Example of a Wastewater Utility's Approach to and Use of this Technology

Hampton Roads Sanitation District (HRSD) provides wastewater conveyance and treatment services for 1.7 million people in southeast Virginia. Since 2017, HRSD has used Virtual Reality (VR) design reviews on more than 20 projects because of how accessible VR makes designs to every level within an organization, including the operations and maintenance staff responsible for maintaining the completed project. However, VR is not necessarily appropriate for all projects. This paper uses a recent HRSD project to show how HRSD approaches the use of VR, to what extent it is used, and how HRSD focuses on the operation and maintenance aspects of the designs during reviews. The paper also highlights features of the VR software, design-review best practices, limitations of two-dimensional design, how standard details can be incorporated into the model, and the added value from use of the internet-based, real-time reviews during the COVID-19 pandemic, when in-person meetings were impossible. Copyright © 2022 Water Environment Federation.

Data-Driven Resilient Load Forecasting Model for Smart Metered Distribution System

The change in the electricity demand pattern globally due to sudden extreme weather conditions or situations like COVID 19 pandemic has brought unanticipated challenges for the electric utilities and operators around the world. This work primarily deals with the issue of load forecasting during such type of high impact low frequency (HILF) events. In this paper, we propose a novel resilient short-term load forecasting model capable of producing good forecasting performance for normal as well as critical situations during the COVID 19 pandemic and will also be useful for load forecasting for other HILF situations like natural calamity effect on load demand of the power system. The proposed method uses a feed-forward neural network (FFNN) with an added training feature named resiliency factor to forecast load in both regular and special scenarios. The resiliency factor for any type of node in the distribution system is decided by the power utility using the historical data and declared in advance. The proposed model is tested using the smart metered data available from a real-life distribution grid of an academic cum residential campus. The model is giving satisfactory results for both normal as well as COVID scenario for the said network. © 2023 Taylor & Francis Group, LLC.

Risk management during the Covid-19 crisis: business responses in the Polish water utilities

Water utilities are an essential service that helps protect public health during crises. The Covid-19 pandemic revealed that crisis preparedness is a crucial capability that water utilities must possess. The purpose of this paper is to identify managerial actions and responses that were undertaken by water utility managers in order to reduce the risk related to the first economic lockdown caused by the unexpected Covid-19 crisis. As water utilities should learn from Covid-19 so as to strengthen their future risk preparedness, the paper offers some theoretical underpinnings on risk management. As a result of literature analysis, we focus on the risk management framework that distinguishes five types of risk. The survey was carried out among 116 waterworks in Poland in April 2020. The results indicate the importance of minimising liquidity risk and supply chain risk, which is relevant to the adopted theoretical framework. The findings also highlight the importance of a category that was not originally included in the research model – that is human resource risk, an area that requires managerial attention in the water utility sector. The results could also provide useful pointers for other water utilities, especially those operating in the same or similar legislative regime.

The impact of COVID-19 on urban water use: a review

The COVID-19 pandemic had significant impact on water utilities, which had to continue providing clean water under safe-distancing measures. Water use patterns were affected, shifting peak demand and changing volumes, though changes varied from place to place. This study analyses the effects of the safe-distancing measures on water use patterns in different countries and cities with the aim of drawing general conclusions on causes and impacts of changes in water use patterns, as well as providing some insights on the impacts on finances of utilities and potential long-term implications. The analysis is based on information collected by the members of the IWA Specialist Group on Statistics and Economics for Belgium, Cyprus, Germany, Japan, Switzerland, Portugal, Romania, the Netherlands and Singapore. Temporal, spatial/sectoral and volume changes can be distinguished. The main temporal change in domestic water use was a delay in the morning peak, while commercial water use patterns changed significantly. In general, the volume of domestic water use increased between about 3% and 8%, while non-domestic water use decreased between about 2% and 11% over 2020. Indirect evidence suggests shifts have taken place between sectors and spatially. The impact on finances of utilities has likely been only short-term.

Water safety management during the initial phase of the Covid-19 pandemic: challenges, responses and guidance

Water safety plans address both routine operations and incident responses to support risk management in drinking water utilities. Their use and relevance in facing the challenges of the Covid-19 crisis were investigated via a survey distributed to water utilities and health or environmental agencies across the globe. Responses from 86 respondents from 38 countries were analysed to identify the water safety challenges faced and responses. Water safety plans appear to provide some preparedness and organizational advantages to utilities in facing the Covid-19 crisis, including stronger communication links between utilities and governing agencies. Guidance for future water safety planning is provided.

Smart short-term electric load forecasting considering the Covid-19 epidemic impact based on deep learning

Short-term load forecasting is essential for the power company's operation and grid operators because it is necessary to ensure adequate capacity and proper power generation arrangement;this will affect operating efficiency and short-term decisions. Meanwhile, the Covid-19 epidemic as a nonlinear factor will be effective in short-term load forecasting and based on previous solutions, electrical load forecasting may not be accurate. A nonlinear and complex relationship between the factors affecting the load forecasting problem explains the need to use intelligent methods such as machine learning. This paper analyses the effect of Covid-19 epidemic countermeasures on short-term electric load forecasting in Iran. To forecast the short term electrical load, a deep neural network with a hybrid architecture and peak power consumption data, average temperature, and Covid-19 epidemic countermeasure data over 15 months during the Covid-19 epidemic was used. The results indicate an increase in forecasting accuracy considering the countermeasure's data. Also, the proposed model validation with data related to the fourth wave of the Covid-19 epidemic and the data of countermeasures modeling in Iran show the effectiveness and reasonable accuracy of the proposed model during the Covid19 epidemic. © 2022 IEEE.

Smart Short-Term Load Forecasting through Coordination of LSTM-Based Models and Feature Engineering Methods during the COVID-19 Pandemic

Short-term load forecasting is essential for power companies because it is necessary to ensure sufficient capacity. This article proposes a smart load forecasting scheme to forecast the short-term load for an actual sample network in the presence of uncertainties such as weather and the COVID-19 epidemic. The studied electric load data with hourly resolution from the beginning of 2020 to the first seven days of 2021 for the New York Independent Operator is the basis for the modeling. The new components used in this article include the coordination of stacked long short-term memory-based models and feature engineering methods. Also, more accurate and realistic modeling of the problem has been implemented according to the existing conditions through COVID-19 epidemic data. The influential variables for short-term load forecasting through various feature engineering methods have contributed to the problem. The achievements of this research include increasing the accuracy and speed of short-term electric load forecasting, reducing the probability of overfitting during model training, and providing an analytical comparison between different feature engineering methods. Through an analytical comparison between different feature engineering methods, the findings of this article show an increase in the accuracy and speed of short-term load forecasting. The results indicate that combining the stacked long short-term memory model and feature engineering methods based on extra-trees and principal component analysis performs well. The RMSE index for day-ahead load forecasting in the best engineering method for the proposed stacked long short-term memory model is 0.1071. © 2023 Taylor & Francis Group, LLC.

A selectivity index for public-private partnership projects in the urban water and sanitation sector in Latin America and the caribbean.

The rapid urban development, the Agenda 2030, the climate change adaptation and the COVID 19 crisis highlight the need to increase investment in public infrastructure and improve water supply and sanitation services. For this, an alternative to traditional public procurement is the participation of the private sector under the public-private partnership (PPP) model. The objective of this article is to develop a tool based on critical success factors (CSFs) that allows for evaluation during early stages of the convenience of developing a PPP project for W&S in urban areas of Latin America and the Caribbean. The index was developed based on literature review (779 variables), review of cases (20 variables) and expert opinion to assign them an estimated value of importance. The results were analysed by exploratory and confirmatory factor analysis, selecting 17 main variables grouped into 6 CSFs, the most relevant of which are Convenience, Certainty, Leadership, Attraction, Performance and Reliability. The application of this index allows an early assessment of the feasibility of a PPP project and/or the selection of the alternatives with the best chances of success. On the other hand, this study contributes to the international discussion on the most relevant elements related to the success of PPP in W&S projects.

Assessing health-related quality-of-life in Australian Multiple Sclerosis Longitudinal Study participants using six multi-attribute utility instruments: implications for healthcare decision-making

Background: Despite important discoveries/advances in treating multiple sclerosis (MS), people with MS (pwMS) can experience delays in accessing new treatments if decision-makers lack robust health economic evidence including health-related quality-of-life (HRQoL) benefits of the intervention. Health state utilities (HSU) are a HRQoL input for cost-utility analysis. Objective(s): Several multi-attribute utility instruments (MAUIs) are available from which HSUs can be derived, but the most appropriate MAUI for use in MS has not been identified. We aimed to determine the preferentially sensitive MAUI(s) that capture the full impact of MS on HRQoL. Method(s): Participants in this study came from a comprehensive HRQoL survey (mid-2020) of the Australian MS Longitudinal Study. The survey included six MAUIs (EQ-5D-5L/ EQ-5D5LPsychosocial, SF-6D versions 1 and 2, AQoL-8D and PropR), and sociodemographic, Covid19-related and subjective wellbeing data. HSUs were generated from Australian value sets. Ceiling and floor effects were investigated. Bland-Altman plots and Shannon's Indices were examined. Minimal important differences and population norms were sourced from the literature. Result(s): N=1,683 pwMS completed the survey (67% response). HSUs were derived for >97% of respondents. Mean age 58.6 years, 80% female, 19% reported severe disease and 63% had relapsing-remitting MS. Mean (SD) HSUs ranged from 0.45+/-0.29 (SF-6Dv1) to 0.63+/-0.22 (AQoL-8D). EQ-5D-5L revealed the highest ceiling (HSU=1.0;n=157,10%) and floor (HSU<=0;n=113,7%) effects. PwMS with EQ-5D-5L HSU<=0/HSU=1 reported mean HSUs of 0.37/0.91, 0.35/0.90, and 0.08/0.85 for the AQoL-8D, EQ-5D5LPsychosocial and SF-6D, respectively. Conclusion(s): While the EQ-5D is the most commonly cited MAUI (in 85% of health technology assessment guidelines), our preliminary comparison results suggest the EQ-5D-5L is not preferentially sensitive in assessing the complex HRQoL domains for pwMS.

Privacy-Preserving Publishing of Individual-Level Pandemic Data Based on a Game Theoretic Model

Sharing individual-level pandemic data is essential for accelerating the understanding of a disease. For example, COVID-19 data have been widely collected to support public health surveillance and research. In the United States, these data need to be de-identified before being released to the public due to privacy concerns. However, current data publishing approaches for individual-level pandemic data, such as those adopted by the U.S. Centers for Disease Control and Prevention (CDC), have not flexed over time to account for the dynamic nature of infection rates. Thus, the policies generated by these strategies may either raise privacy risks or impair the data utility (or usability). To optimize the tradeoff between privacy risk and data utility, we introduce a game theoretic model that adaptively generates policies to publish individual-level COVID-19 data according to infection dynamics. We model the data publishing process as a two-player Stackelberg game between a data publisher and a data recipient and then search for the best strategy for the publisher. In this game, we consider 1) the average accuracy of predicting future case counts for all demographic groups, and 2) the mutual information between the original data and the released data. We use COVID-19 case data from Vanderbilt University Medical Center from March 2020 to December 2021 to demonstrate our model and evaluate its effectiveness. The experimental results show that our game theoretic model outperforms all baseline approaches, including those adopted by CDC, while maintaining low privacy risk. © 2022 IEEE.

The future of the shale industry in light of the fluctuations in global oil prices

During the twentieth and twenty-first centuries, the oil industry has been pivotal in influencing all countries' geopolitical, economic, and human development strategies. Until recently, the debate was about peak oil and what would happen after oil finished. However, due to technological advances and hydraulic fracturing, shale oil formations have become economically viable due to the United States' desire to achieve energy security to make a qualitative shift in the oil industry and the geopolitics of oil. Therefore, this paper deals with an economic model that illustrates the impact of oil price fluctuations to the shale oil and gas companies by analyzing the main determinants of continuity of shale oil and gas companies in production if global oil prices decline or rise. In addition, the study will investigate the effects of OPEC+ policy and Covid-19 on the future of shale oil industry. The study will discuss some future scenarios for global energy trends and predict what the shale industry will look like in the future. The study concluded the shale industry faces an internal destructive process (within the industry itself) and external (Renewable energy, OPEC and Covid-19). The stability of oil prices is a critical factor that promotes the shale industry's recovery. However, shale industry is expected to continue with low productivity growth rates and continuing government support for it. © The Author(s) 2022.

Pragmatic Municipalism: Privatization and Remunicipalisation in the US

At the local government level in the US, the process of privatisation has been a dynamic one of experimentation with market delivery and return to public delivery when privatisation fails to deliver. National survey data show what drives this experimentation are pragmatic concerns with service cost and quality. Service and market characteristics, local government capacity and regulatory framework matter. In contrast to current international debates about the potential of remunicipalization to be a political reassertion of the public sector, for US local governments it is primarily a process of pragmatic municipalism. While some shifts in private finance and state regulatory environment favour private actors at the expense of local government, federal investments since COVID-19 provide funding and policy preference for maintaining a public role.