scholarly journals The Impact of the COVID-19 Pandemic on Electricity Consumption and Economic Growth in Romania

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2394
Author(s):  
Georgeta Soava ◽  
Anca Mehedintu ◽  
Mihaela Sterpu ◽  
Eugenia Grecu
Keyword(s):  
Economic Growth ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Negative Impact ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Electric Energy Consumption ◽  
The One ◽  
The Impact

This paper analyzes the impact of the COVID-19 pandemic on economic growth and electricity consumption and investigates the hypothesis of the influence of this consumption on the gross domestic product (GDP) for Romania. Using time series on monthly electricity consumption and quarterly GDP and a multi-linear regression model, we performed an analysis of the evolution of these indicators for 2007–2020, a comparison between their behavior during the financial crisis vs. COVID-19 crisis, and empirically explore the relationships between GDP and electricity consumption or some of its components. The results of the analysis confirm that the shock of declining activity due to the COVID-19 pandemic had a severe negative impact on electric energy consumption and GDP in the first half of 2020, followed by a slight recovery. By using a linear regression model, long-term relationships between GDP and domestic and non-household electricity consumptions were found. The empirically estimated elasticity coefficients confirm the more important impact of non-household electricity consumption on GDP compared to the one of domestic electricity consumption. In the context of the COVID-19 pandemic, the results of the study could be useful for optimizing energy and economic growth policies at the national and European levels.

scholarly journals Impact of Private Investment, Economic Growth and Financial Development on Environmental Degradation: Evidence from Pakistan

2021 ◽  
Vol 7 (1) ◽  
pp. 13-24
Author(s):  
Shabana Parveen ◽  
Bibi Aisha Sadiqa ◽  
Sher Ali ◽  
Farrah Yasmin
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Linear Regression ◽  
Regression Model ◽  
Co2 Emissions ◽  
Financial Development ◽  
Environmental Degradation ◽  
Linear Regression Model ◽  
Private Investment ◽  
The Impact

Private investment plays an important role in the process of economic growth and also impact natural environment of a country. The main purpose of the present study is to empirically analyze the impact of private investment and other macro economic variables on environmental degradation of Pakistan. For the purpose, time series data is collected for the years  1975 to 2017. The study used Linear regression model for analyzing the impact of private investment, energy consumption, financial development and economic growth on environmental degradation. Augmented Dickey Fuller (ADF) test and Phillips Perron (PP) test is used for identifying the unit root of the variables; first with an intercept then, with an intercept and a linear deterministic trend. Akaike Information Criterion (AIC) is used for selection of optimum lag whereas Johansen cointegration test is adopted for analyzing  long run association in the variables. The results of linear regression model show that energy consumption and economic growth have a positive and statistically significant impact on CO2 emissions whereas the impact of private investment on CO2 emissions is negative. It means that in Pakistan, private investment is environment friendly. Based on study results, it is recommended that  when formulating policies for economic growth and development,  motivation should be given to private inverters in order to increase private investment.

scholarly journals Analysis of the correlation between the Gross Domestic Product and the final consumption of electricity

2020 ◽  
Vol 14 (1) ◽  
pp. 59-73
Author(s):  
Liviu Valentin Vlăducu
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Linear Regression ◽  
Gross Domestic Product ◽  
Regression Model ◽  
Linear Regression Model ◽  
Electricity Consumption ◽  
Domestic Product ◽  
Efficiency Measures ◽  
The Moment

AbstractWhile the economy has shown clear signs of recovery, in quantitative terms, after the moment of the global crisis, energy production has returned to the level before the crisis, only since 2011. In this context, this paperwork aims to carry out an analysis on the existence of a correlation between the Gross Domestic Product registered in Romania and the final annual consumption of electricity. The databases used involve the data recorded for the period 2000-2018. Over time, in the specialty literature, there have been two approaches regarding the link between the economic growth and the energy consumption, respectively an approach starts from the idea that in order for economic growth to occur, energy consumption must increase, and another promotes the idea that economic growth can reduce energy consumption, by applying energy efficiency measures. To perform the analysis, a simple linear regression model was initially used in which we considered the Gross Domestic Product as a dependent variable and the Electricity Consumption as an explanatory factor (independent variable). Subsequently, analysing the results, a quadratic linear regression model was used to test the hypothesis of a more complex link between the two indicators. Following the tests performed on the two chosen variables, the Gross Domestic Product of Romania and the Final Electricity Consumption, can be argued that the energy intensity of the economy increases as economic growth reaches a certain threshold. After that threshold, economic growth is associated with the relative decrease in energy consumption.

Cognition predicts days-alive-out-of-hospital after LVAD implantation

2021 ◽  
pp. 039139882110184
Author(s):  
Marykay A Pavol ◽  
Amelia K Boehme ◽  
Melana Yuzefpolskaya ◽  
Mathew S Maurer ◽  
Jesus Casida ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Left Ventricular ◽  
Motor Speed ◽  
Ventricular Assist ◽  
Hospitalization Rates ◽  
Left Ventricular Assist ◽  
Patient Groups ◽  
The Impact

Objective: Cognition influences hospitalization rates for a variety of patient groups but this association has not been examined in heart failure (HF) patients undergoing left ventricular assist device (LVAD) implantation. We used cognition to predict days-alive-out-of-hospital (DAOH) in patients after LVAD surgery. Methods: We retrospectively identified 59 HF patients with cognitive assessment prior to LVAD. Cognitive tests of attention, memory, language, and visual motor speed were averaged into one score. DAOH was converted to a percentage based on total days from LVAD surgery to either heart transplant or 900 days post-LVAD. Variables significantly associated with DAOH in univariate analyses were included in a linear regression model to predict DAOH. Results: A linear regression model including LVAD type (continuous or pulsatile flow) and cognition significantly predicted DAOH (F(2,54) = 6.44, p = 0.003, R2 = .19). Inspection of each variable revealed that cognition was a significant predictor in the model (β = .11, SE = .04, p = 0.007) but LVAD type was not ( p = 0.08). Conclusions: Cognitive performance assessed prior to LVAD implantation predicted how much time patients spent out of the hospital following surgery. Further studies are warranted to identify the impact of pre-LVAD cognition on post-LVAD hospitalization.

scholarly journals The Impact of Public Debt on the Economic Growth of Jordan: An Empirical Study (2000- 2015)

2017 ◽  
Vol 6 (2) ◽  
pp. 114 ◽  
Author(s):  
Tawfiq Ahmad Mousa ◽  
Abudallah. M. LShawareh
Keyword(s):  
Economic Growth ◽  
Empirical Study ◽  
Regression Model ◽  
Least Squares ◽  
Public Debt ◽  
Least Squares Method ◽  
Negative Impact ◽  
External Debt ◽  
Using Data ◽  
The Impact

In the last two decades, Jordan’s economy has been relied on public debt in order to enhance the economic growth. As such, an understanding  of the dynamics between public debt and economic growth is very important in addressing the obstacles to economic growth. The study investigates the impact of public debt on economic growth using data from 2000 to 2015. The study employs least squares method and regression model to capture the impact of public debt on economic growth. The results of the analysis indicate that there is a negative impact of total public debt, especially the external debt on economic growth. 

scholarly journals A Linear Regression Model for THD Sensitivity in PV Based Microgrid with Varying Insolation Levels

2022 ◽  
pp. 179-186
Author(s):  
Yuvraj Praveen Soni ◽  
Eugene Fernandez
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Solar Irradiance ◽  
Harmonic Distortion ◽  
System Control ◽  
Solar Pv ◽  
Pv System ◽  
Simulink Model ◽  
The Impact

Solar PV systems can be used for powering small microgrids in rural area of developing countries. Generally, a solar power microgrid consists of a PV array, an MPPT, a dc-dc converter and an inverter, particularly as the general loads are A.C in nature. In a PV system, reactive current, unbalancing in currents, and harmonics are generated due to the power electronics-based converters as well as nonlinear loads (computers induction motors etc). Thus, estimation of the harmonics levels measured by the Total Harmonic Distortion (THD) is an essential aspect of performance assessment of a solar powered microgrid. A major issue that needs to be examined is the impact of PV system control parameters on the THD. In this paper, we take up this assessment for a small PV based rural microgrid with varying levels of solar irradiance. A Simulink model has been developed for the study from which the THD at equilibrium conditions is estimated. This data is in turn used to design a generalized Linear Regression Model, which can be used to observe the sensitivity of three control variables on the magnitude of the THD. These variables are: Solar Irradiance levels, Power Factor (PF) of connected load magnitude of the connected load (in kVA) The results obtained show that the greatest sensitivity is obtained for load kVA variation.

scholarly journals Assessment of the influence of FDI on the economic growth of the host country: evidence from Kazakhstan

2020 ◽  
Vol 159 ◽  
pp. 06007
Author(s):  
Dinara Rakhmatullayeva ◽  
Iliyas Kuliyev ◽  
Zhaksylyk Beisenbaiyev ◽  
Talgat Tabeyev
Keyword(s):  
Economic Growth ◽  
Regression Model ◽  
Host Country ◽  
National Economy ◽  
Positive Relationship ◽  
Negative Impact ◽  
Multiple Regression Model ◽  
Fdi Inflows ◽  
Simulation Results ◽  
The Impact

The article examines the impact of FDI inflows on the economic growth of the host country, using the Kazakhstan economy as an example. The authors attempted to assess the impact of FDI using a multiple regression model. As a measure of economic growth, Kazakhstan’s GDP data for the period 2000-2017 was used. The simulation results didn’t reveal the negative impact of FDI on economic growth, but the analysis revealed that the presence of a positive relationship is not essential for assessing the growth of the national economy.

scholarly journals A Multi Linear Regression Model to Derive Dust PM10 in the Sahel Using AERONET Aerosol Optical Depth and CALIOP Aerosol Layer Products

2020 ◽  
Vol 12 (18) ◽  
pp. 3099
Author(s):  
Jean-François Léon ◽  
Nadège Martiny ◽  
Sébastien Merlet
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Linear Regression Model ◽  
Population Exposure ◽  
Aerosol Layer ◽  
Multi Linear Regression ◽  
Sun Photometer ◽  
The Impact

Due to a limited number of monitoring stations in Western Africa, the impact of mineral dust on PM10 surface concentrations is still poorly known. We propose a new method to retrieve PM10 dust surface concentrations from sun photometer aerosol optical depth (AOD) and CALIPSO/CALIOP Level 2 aerosol layer products. The method is based on a multi linear regression model that is trained using co-located PM10, AERONET and CALIOP observations at 3 different locations in the Sahel. In addition to the sun photometer AOD, the regression model uses the CALIOP-derived base and top altitude of the lowermost dust layer, its AOD, the columnar total and columnar dust AOD. Due to the low revisit period of the CALIPSO satellite, the monthly mean annual cycles of the parameters are used as predictor variables rather than instantaneous observations. The regression model improves the correlation coefficient between monthly mean PM10 and AOD from 0.15 (AERONET AOD only) to 0.75 (AERONET AOD and CALIOP parameters). The respective high and low PM10 concentration during the winter dry season and summer season are well produced. Days with surface PM10 above 100 μg/m3 are better identified when using the CALIOP parameters in the multi linear regression model. The number of true positives (actual and predicted concentrations above the threshold) is increased and leads to an improvement in the classification sensitivity (recall) by a factor 1.8. Our methodology can be extrapolated to the whole Sahel area provided that satellite derived AOD maps are used in order to create a new dataset on population exposure to dust events in this area.

scholarly journals Influence of the key account manager in the provisioning management: Evidence from staple companies during the events of COVID-19

Accounting ◽  
2022 ◽  
Vol 8 (2) ◽  
pp. 161-170 ◽  
Author(s):  
Luis-Ricardo Flores-Vilcapoma ◽  
Cynthia-Paola A lbengrin-Mendoza ◽  
Gabriela-Briggite Gomez-Rojas ◽  
Yuri Sánchez-Solis ◽  
Wagner Vicente-Ramos
Keyword(s):  
Experimental Design ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Cross Sectional ◽  
Key Account ◽  
The Impact

The purpose of this research was to evaluate the degree of influence exercised by the Key Account Manager in the provisioning management in the main companies called Staple in Peru, during the events of COVID-19. The research was of type quantitative, cross-sectional and temporal, with a non-experimental design, using a multiple linear regression model and correlation analysis to determine the impact that exists between the variables. The data belongs to the Industrias San Miguel company, distributed in a weekly period from June 2019 to March 2021, which gives 88 observations. The results allow us to conclude that the Key Account Manager is an important manager of the supply of goods during the crisis caused by COVID-19 in staple companies.

scholarly journals Analysis of the beef supply in Veracruz, Jalisco and Chiapas states, Mexico, 2000-2019

2021 ◽  
Vol 14 (3) ◽  
Author(s):  
Eulogio Rebollar Rebollar ◽  
Samuel Rebollar Rebollar ◽  
Eugenio Guzmán Soria ◽  
Juvencio Hernández Martínez ◽  
Felipe de Jesús González Razo
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Annual Rainfall ◽  
Beef Production ◽  
Economic Significance ◽  
A Value ◽  
The One

Objective: to determine the effect of the variables that impact the supply of beef in Veracruz, Jalisco and Chiapas states, Mexico, from 2000 to 2019.Methodological design/approach: a multiple linear regression model was used; where the supply was the dependent variable and the price of beef, corn price and annual rainfall were the explanatory variables.Results: the dynamics of the beef production in Veracruz, Jalisco and Chiapas were directly and inelastically explainedby its price with a value of 0.89, 0.13 and 0.49; inversely and inelastically by the price of corn (-0.05, 0.005 and -0.05)and directly and inelastically by the state annual precipitation (0.16, 0.01 and 0.21).Study limitations/implications: it is suggested to test the statistical and economic significance with the Cobb-Douglas supply models to contrast their elasticities.Findings/conclusions: the variable that explained the dynamics of bovine production in these Mexican states was the price of the product, while the price of corn was the one with the least impact

scholarly journals Extreme return, extreme volatility and investor sentiment

Filomat ◽  
2016 ◽  
Vol 30 (15) ◽  
pp. 3949-3961 ◽  
Author(s):  
Xu Gong ◽  
Fenghua Wen ◽  
Zhifang He ◽  
Jia Yang ◽  
Xiaoguang Yang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Investor Sentiment ◽  
Generalized Least Squares ◽  
Regression Methods ◽  
Leading Role ◽  
The Impact

The extreme return and extreme volatility have great influences on the investor sentiment in stock market. However, few researchers have taken the phenomenon into consideration. In this paper, we first distinguish the extreme situations from non-extreme situations. Then we use the ordinary generalized least squares and quantile regression methods to estimate a linear regression model by applying the standardized AAII, the return and volatility of SP 500. The results indicate that, except for extremely negative return, other return sequences can cause great changes in investor sentiment, and non-extreme return plays a leading role in affecting the overall American investor sentiment. Extremely positive (negative) return can rapidly improve (further reduce) the level of investor sentiment when investors encounter extremely pessimistic situations. The impact gradually decreases with improvement of the sentiment until the situation turns optimistic. In addition, we find that extreme and non-extreme volatility cannot a_ect the overall investor sentiment.

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