A Conceptual Model for Analyzing the Impact of Natural Gas on Electricity Generation Failure during the 2021 Texas Power Outage

2021 ◽  
Author(s):  
Andrew Schaper ◽  
Le Xie
Keyword(s):  
Natural Gas ◽  
Conceptual Model ◽  
Electricity Generation ◽  
Power Outage ◽  
The Impact

scholarly journals Empirical Cost of Electricity Outage on Labour and Capital Productivity in Nigeria

2022 ◽  
Vol 3 (1) ◽  
Author(s):  
Osinachi Iroh ◽  
Ijeoma Kalu ◽  
Alwell Nteegah
Keyword(s):  
Total Factor Productivity ◽  
Electricity Generation ◽  
Labour Productivity ◽  
Negative Relationship ◽  
Series Data ◽  
Electricity Pricing ◽  
Factor Productivity ◽  
Power Outages ◽  
Power Outage ◽  
The Impact

This study empirically examined the impact of electricity power outages on Nigeria’s capital and labour productivity.  The emphasis is on how frequent electricity outage reduces labour and capital effectiveness and other factors of production.  To achieve the above objective, annual time series data on Total Factor Productivity - a proxy for Nigeria’s factors productivity, Power Outage (electric power transmission and distribution losses as % of output), and other controlled variables were used to estimate the relationship and all data were from World Bank Development Indicators (WDI). The Fully Modified Ordinary Least Square (FOLS) technique was adopted for analysis.  The empirical results showed a negative relationship between power outages and factor productivity.  The result also reveals that electricity pricing has a significant negative impact on the factor productivity while both electricity generation and population have a significant positive impact on Nigeria’s total factor productivity.  The implication is that the substitution effect between labour and capital is positive, meaning that Nigeria exhibits a labour-intensive production function. In conclusion, the study is of the opinion that power outage and electricity pricing negatively impact factors productivity while electricity generation and population have a positive relationship with factors productivity in Nigeria.

The Impact of PHEV Adoption on Natural Gas Demand in Electricity Generation

2010 ◽  
pp. 257-265
Author(s):  
Shisheng Huang ◽  
Bri-Mathias S. Hodge ◽  
Joseph F. Pekny ◽  
Gintaras V. Reklaitis
Keyword(s):  
Natural Gas ◽  
Electricity Generation ◽  
Natural Gas Demand ◽  
The Impact

The Economic Price of Natural Gas in Pakistan (Notes & Comments)

1974 ◽  
Vol 13 (4) ◽  
pp. 481-484
Author(s):  
J. Faaland ◽  
J. R. Parkinson
Keyword(s):  
Natural Gas ◽  
World Bank ◽  
Electricity Generation ◽  
Irrigate Land ◽  
Hydro Power ◽  
Power Resources ◽  
The World Bank ◽  
The World ◽  
Economic Price ◽  
Tarbela Dam

The World Bank Study," Water and Power Resources of West Pakistan" [1], is one of the most thorough-going and sophisticated of its type. In re¬reading it we have been struck by a curious argument related to the real benefits to be expected from the construction of the Tarbela dam. It was designed to produce electricity as well as to irrigate land and it was necessary to estimate the benefits that the electricity would confer. One way of doing this was to estimate the saving that would be made by using hydro-power instead of natural gas or imported fuel, for electricity generation. This meant that an appropriate set of prices had to be estimated for Pakistan's supply of natural gas. The way in which this was done was, to say the least, unusual. The relevant passage justi¬fying the approach adopted is as follows:

scholarly journals Patient preferences for use of virtual consultations in an orthopaedic rehabilitation setting: Results from a discrete choice experiment

2021 ◽  
pp. 135581962110354
Author(s):  
Anthony W Gilbert ◽  
Emmanouil Mentzakis ◽  
Carl R May ◽  
Maria Stokes ◽  
Jeremy Jones
Keyword(s):  
Conceptual Model ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Patient Preferences ◽  
Choice Experiment ◽  
Best Practice ◽  
Time Of Day ◽  
Problem Area ◽  
Fractional Factorial ◽  
The Impact

Objective Virtual Consultations may reduce the need for face-to-face outpatient appointments, thereby potentially reducing the cost and time involved in delivering health care. This study reports a discrete choice experiment (DCE) that identifies factors that influence patient preferences for virtual consultations in an orthopaedic rehabilitation setting. Methods Previous research from the CONNECT (Care in Orthopaedics, burdeN of treatmeNt and the Effect of Communication Technology) Project and best practice guidance informed the development of our DCE. An efficient fractional factorial design with 16 choice scenarios was created that identified all main effects and partial two-way interactions. The design was divided into two blocks of eight scenarios each, to reduce the impact of cognitive fatigue. Data analysis were conducted using binary logit regression models. Results Sixty-one paired response sets (122 subjects) were available for analysis. DCE factors (whether the therapist is known to the patient, duration of appointment, time of day) and demographic factors (patient qualifications, access to equipment, difficulty with activities, multiple health issues, travel costs) were significant predictors of preference. We estimate that a patient is less than 1% likely to prefer a virtual consultation if the patient has a degree, is without access to the equipment and software to undertake a virtual consultation, does not have difficulties with day-to-day activities, is undergoing rehabilitation for one problem area, has to pay less than £5 to travel, is having a consultation with a therapist not known to them, in 1 weeks’ time, lasting 60 minutes, at 2 pm. We have developed a simple conceptual model to explain how these factors interact to inform preference, including patients’ access to resources, context for the consultation and the requirements of the consultation. Conclusions This conceptual model provides the framework to focus attention towards factors that might influence patient preference for virtual consultations. Our model can inform the development of future technologies, trials, and qualitative work to further explore the mechanisms that influence preference.

scholarly journals The Feasibility of Renewable Natural Gas in New Jersey

2021 ◽  
Vol 13 (4) ◽  
pp. 1618
Author(s):  
Anneliese Dyer ◽  
Amelia Christine Miller ◽  
Brianna Chandra ◽  
Juan Galindo Maza ◽  
Carley Tran ◽  
...  
Keyword(s):  
New Jersey ◽  
Natural Gas ◽  
Conceptual Model ◽  
Greenhouse Gas Emission ◽  
Wastewater Treatment Plants ◽  
The United States ◽  
Outreach Program ◽  
Financial Barriers ◽  
The Us ◽  
Renewable Natural Gas

With traditional natural gas being one of the top options for heating in the United States and the present threat of climate change, there is a demand for an alternative clean fuel source. A Renewable Natural Gas Implementation Decision-Making Conceptual Model was created to provide a framework for considering the feasibility of renewable natural gas (RNG) projects and applied to New Jersey, specifically investigating landfills and wastewater treatment plants (WWTPs). Data from the US EPA’s Landfill Methane Outreach Program and New Jersey’s Department of Environmental Protection Sewage Sludge databases were used to identify seven landfills and 22 WWTPs as possible locations for RNG projects. Landfills were found to have a higher potential for producing RNG, on average potentially producing enough RNG to heat 12,792 homes per year versus 1227 for the average WWTP. Additionally, landfills, while having higher capital expenses, have lower projected payback periods, averaging 5.19 years compared to WWTP’s 11.78 years. WWTPs, however, generally are located closer to existing natural gas pipelines than landfills and when they produce more than 362 million standard cubic feet per year (MMSCFY) of biogas are financially feasible. RNG projects at Monmouth County Reclamation Center, Ocean County Landfill, and Passaic Valley Sewerage Commission WWTP show the greatest potential. Greenhouse gas emission reductions from RNG projects at these facilities utilizing all available biogas would be 1.628 million metric tons CO2 equivalents per year, synonymous to removing over 351,000 passenger vehicles from the road each year. In addition, expanding federal and state incentives to encompass RNG as a heating fuel is necessary to reduce financial barriers to RNG projects throughout the US. Overall, this paper supports the hypothesized conceptual model in examining the feasibility of RNG projects through examples from New Jersey and confirms the potential for RNG production utilizing existing waste streams.

scholarly journals Assessing the Energy Resilience of Office Buildings: Development and Testing of a Simplified Metric for Real Estate Stakeholders

Buildings ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 96
Author(s):  
Paul Mathew ◽  
Lino Sanchez ◽  
Sang Hoon Lee ◽  
Travis Walter
Keyword(s):  
Regression Models ◽  
Health And Safety ◽  
Office Buildings ◽  
Extreme Weather Events ◽  
Economic Losses ◽  
Power Outage ◽  
Continuity Of Operations ◽  
Quasi Monte Carlo ◽  
Building Characteristics ◽  
The Impact

Increasing concern over higher frequency extreme weather events is driving a push towards a more resilient built environment. In recent years there has been growing interest in understanding how to evaluate, measure, and improve building energy resilience, i.e., the ability of a building to provide energy-related services in the event of a local or regional power outage. In addition to human health and safety, many stakeholders are keenly interested in the ability of a building to allow continuity of operations and minimize business disruption. Office buildings are subject to significant economic losses when building operations are disrupted due to a power outage. We propose “occupant hours lost” (OHL) as a means to measure the business productivity lost as the result of a power outage in office buildings. OHL is determined based on indoor conditions in each space for each hour during a power outage, and then aggregated spatially and temporally to determine the whole building OHL. We used quasi-Monte Carlo parametric energy simulations to demonstrate how the OHL metric varies due to different building characteristics across different climate zones and seasons. The simulation dataset was then used to develop simple regression models for assessing the impact of ten key building characteristics on OHL. The most impactful were window-to-wall ratio and window characteristics. The regression models show promise as a simple means to assess and screen for resilience using basic building characteristics, especially for non-critical facilities where it may not be viable to conduct detailed engineering analysis.

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