Prospects for Planet Earth

2020 ◽  
pp. 273-293
Author(s):  
John Evans
Keyword(s):  
Power Generation ◽  
Growth Rate ◽  
Energy Consumption ◽  
Solar Power ◽  
Good Evidence ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Sharp Drop ◽  
Solar Power Generation ◽  
Global Population

Here the elemental spheres are considered against the context of plant Earth in 2019. There is good evidence that the global population might reach 11.2 billion by 2100 and stabilise later. The greenhouse emissions per person may have stabilised but the overall direction for anthropogenic emissions is upward. The protections to achieve a limit of 1.5 oC by 2100 envisaged a sharp drop in emissions in the 2020s, and that is not yet projected. Polar ice loss is increasing and annual sea level rise is ca. 3.4 mm. Energy consumption in 2018 was dominated by fossil fuel sources. However, the annual growth rate in renewable energy provision was ca. 15% in that year; the annual growth in solar power generation was ca. 30%. Wind and solar power are evidently the technologies available to effect reductions in emissions in the coming decade.

The Impact of COVID-19 on Energy Consumption in the United States: An Overview

2021 ◽  
Author(s):  
Lindsey Kahn ◽  
Hamidreza Najafi
Keyword(s):  
Growth Rate ◽  
Energy Consumption ◽  
Energy Use ◽  
The United States ◽  
Average Annual Growth Rate ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
End User ◽  
End Use ◽  
The Impact

Abstract Lockdown measures and mobility restrictions implemented to combat the spread of the novel COVID-19 virus have impacted energy consumption patterns, particularly in the United States. A review of available data and literature on the impact of the pandemic on energy consumption is performed to understand the current knowledge on this topic. The overall decline of energy use during lockdown restrictions can best be identified through the analysis of energy consumption by source and end-user breakdown. Using monthly energy consumption data, the total 9-months use between January and September for the years 2015–2020 are calculated for each end-use. The cumulative consumption within these 9 months of the petroleum, natural gas, biomass, and electricity energy by the various end-use sectors are compared to identify a shift in use throughout time with the calculation of the percent change from 2019 to 2020. The analysis shows that the transportation sector experienced the most dramatic decline, having a subsequent impact on the primary energy it uses. A steep decline in the use of petroleum and natural gas by the transportation sector has had an inevitable impact on the emission of carbon dioxide and other air pollutants during the pandemic. Additionally, the most current data for the consumption of electricity by each state and each end-user in the times before and during the pandemic highlights the impact of specific lockdown procedures on energy use. The average total consumption for each state was found for the years 2015–2019. This result is used calculation of yearly growth rate and average annual growth rate in 2020 for each state and end-user. The total average annual growth rate for 2020 was used to find a correlation coefficient between COVID-19 case and death rates as well as population density and lockdown duration. To further examine the relationship a correlation coefficient was calculated between the 2020 average annual growth rate for all sectors and average annual growth rate for each individual end-user.

scholarly journals Forecast of Energy Consumption Based on FGM(1, 1) Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haijun Chen ◽  
Yanzeng Tong ◽  
Lifeng Wu
Keyword(s):  
Growth Rate ◽  
Energy Consumption ◽  
Fuel Oil ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Oil Consumption ◽  
Coal Consumption ◽  
Gasoline Consumption ◽  
Gas Consumption ◽  
Natural Gas Consumption

The normal supply of energy is related to the stable development of the economy and society. Forecasting energy consumption helps prepare for the normal supply of energy. In the study of energy consumption forecasting, different scholars have used different forecasting models. This paper uses five-year energy consumption data in the Beijing-Tianjin-Hebei region and uses the grey fractional FGM(1, 1) model to analyze the next six years. Then, the energy consumption of three places is predicted. The advantage of the grey score FGM(1, 1) model is that it can get more accurate prediction results based on a small amount of information. In this study, relatively outdated information affects the accuracy of prediction results. However, other prediction models have great limitations on data. Choosing the grey number fractional model for prediction research can get a more reasonable prediction result. We use the FGM(1, 1) model to make predictions and get the prediction results. In Beijing, the growth rate of natural gas consumption has slowed down and will be basically stable by 2023. The average annual deceleration of coal consumption is 32%. The average annual deceleration of coke consumption is 10%. Crude oil consumption decreased by 6.3% annually. Gasoline consumption is slowly increasing. The consumption of kerosene increased about 8% annually. Diesel consumption is slowly decreasing. Fuel oil consumption is reduced by 17% annually. The average annual growth rate of power consumption exceeds 6%. In Tianjin, the annual growth rate of natural gas consumption is about 5%. Coal consumption is reduced by about 8% every year. The average annual deceleration of coke consumption is 7%. Crude oil consumption decreased by 2.4% annually. Gasoline consumption is slowly decreasing. The consumption of kerosene has increased by about 20% annually. Diesel consumption is slowly decreasing. Fuel oil consumption is reduced by 20% annually. Electricity consumption is slowly increasing. In Hebei Province, the annual growth rate of natural gas consumption is about 15%. Annual coal consumption is reduced by about 3%. Coke consumption remained stable. Crude oil consumption is reduced by 3% annually. Gasoline consumption is slowly increasing, and kerosene consumption has increased by about 31% annually. Diesel consumption is reduced by about 3% annually. Fuel oil consumption remained stable. Electricity consumption is slowly increasing.

Ageing Saudi Population in the Facebook Era

2018 ◽  
Author(s):  
Asharaf Abdul Salam
Keyword(s):  
Growth Rate ◽  
Saudi Arabia ◽  
Population Ageing ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Saudi Population ◽  
Aged Population ◽  
Facebook Usage

<p>Data pertaining to 1974, 1992, 2004 and 2010 Censuses in Saudi Arabia was collected. Some reviews and literature on population ageing in Saudi Arabia as well as Facebook usage obtained. Statistics pertaining to Saudi population was utilized.</p> <p>Aged population in 2010 estimated by assuming the annual growth rate of 1974-2004.</p>

scholarly journals Association between triglyceride-glucose index and risk of arterial stiffness: a cohort study

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Shouling Wu ◽  
Luli Xu ◽  
Mingyang Wu ◽  
Shuohua Chen ◽  
Youjie Wang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Linear Regression ◽  
Arterial Stiffness ◽  
Multivariate Linear Regression ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Cross Sectional ◽  
Tyg Index ◽  
Longitudinal Association ◽  
The Cross

Abstract Background Triglyceride–glucose (TyG) index, a simple surrogate marker of insulin resistance, has been reported to be associated with arterial stiffness. However, previous studies were limited by the cross-sectional design. The purpose of this study was to explore the longitudinal association between TyG index and progression of arterial stiffness. Methods A total of 6028 participants were derived from the Kailuan study. TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Arterial stiffness was measured using brachial-ankle pulse wave velocity (baPWV). Arterial stiffness progression was assessed by the annual growth rate of repeatedly measured baPWV. Multivariate linear regression models were used to estimate the cross-sectional association of TyG index with baPWV, and Cox proportional hazard models were used to investigate the longitudinal association between TyG index and the risk of arterial stiffness. Results Multivariate linear regression analyses showed that each one unit increase in the TyG index was associated with a 39 cm/s increment (95%CI, 29–48 cm/s, P < 0.001) in baseline baPWV and a 0.29 percent/year increment (95%CI, 0.17–0.42 percent/year, P < 0.001) in the annual growth rate of baPWV. During 26,839 person-years of follow-up, there were 883 incident cases with arterial stiffness. Participants in the highest quartile of TyG index had a 58% higher risk of arterial stiffness (HR, 1.58; 95%CI, 1.25–2.01, P < 0.001), as compared with those in the lowest quartile of TyG index. Additionally, restricted cubic spline analysis showed a significant dose–response relationship between TyG index and the risk of arterial stiffness (P non-linearity = 0.005). Conclusion Participants with a higher TyG index were more likely to have a higher risk of arterial stiffness. Subjects with a higher TyG index should be aware of the following risk of arterial stiffness progression, so as to establish lifestyle changes at an early stage.

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