scholarly journals Assessing the renewable energy efficiency levels of BRICS countries and Turkey using stochastic frontier analysis and information complexity criteria

2021 ◽  
Author(s):  
Haydar KOÇ
Keyword(s):  
Energy Efficiency ◽  
Renewable Energy ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Information Complexity ◽  
Frontier Analysis ◽  
Brics Countries

scholarly journals Determining the efficiency of residential electricity consumption

2020 ◽  
Author(s):  
Mark A. Andor ◽  
David H. Bernstein ◽  
Stephan Sommer
Keyword(s):  
Energy Efficiency ◽  
Stochastic Frontier Analysis ◽  
Energy Savings ◽  
Stochastic Frontier ◽  
Electricity Consumption ◽  
Frontier Analysis ◽  
Residential Electricity ◽  
Main Determinants ◽  
Residential Electricity Consumption ◽  
Reduction Of Energy Consumption

AbstractIncreasing energy efficiency is a key global policy goal for climate protection. An important step toward an optimal reduction of energy consumption is the identification of energy saving potentials in different sectors and the best strategies for increasing efficiency. This paper analyzes these potentials in the household sector by estimating the degree of inefficiency in the use of electricity and its determinants. Using stochastic frontier analysis and disaggregated household data, we estimate an input requirement function and inefficiency on a sample of 2000 German households. Our results suggest that the mean inefficiency amounts to around 20%, indicating a notable potential for energy savings. Moreover, we find that household size and income are among the main determinants of individual inefficiency. This information can be used to increase the cost-efficiency of programs aimed to enhance energy efficiency.

scholarly journals Energy Efficiency of the Baltic Sea Countries: An Application of Stochastic Frontier Analysis

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 104 ◽  
Author(s):  
Wen-Ling Hsiao ◽  
Jin-Li Hu ◽  
Chan Hsiao ◽  
Ming-Chung Chang
Keyword(s):  
Energy Efficiency ◽  
Baltic Sea ◽  
Urban Population ◽  
Energy Use ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
The Baltic Sea ◽  
Renewable Energy Consumption ◽  
Frontier Analysis ◽  
The Baltic

Using the stochastic frontier analysis (SFA) model, this research measures total-factor energy efficiency (TFEE) and disaggregate input efficiency for 10 countries across the Baltic Sea from 2004 to 2014. Real capital, labor, energy use, and carbon dioxide (CO2) are input variables, real gross domestic product (GDP) is the output variable, and renewable energy consumption and urban population are the environmental variables. The results provide not only the TFEE scores, in which statistical noise is considered, but also the determinants of inefficiency, which show the following. (i) Norway, Sweden, Finland, and Latvia perform better with respect to energy efficiency than other countries in the Baltic Sea Region. (ii) Interestingly, the average energy use efficiency scores from 2004 to 2014 in the 10 Baltic countries exhibit a gradual upward trend except for 2009. (iii) For the inefficiency estimates, higher renewable energy consumption and urban population correspond to higher TFEE scores.

scholarly journals Technical and Energy Efficiency of Urban Logistics in China: Empirical Analysis of 216 Prefecture-Level Cities

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Dongfang Wang ◽  
Jinfeng Li ◽  
Arthur Tarasov
Keyword(s):  
Energy Efficiency ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Coastal Region ◽  
Eastern Region ◽  
Road Density ◽  
Logistics Industry ◽  
Frontier Analysis ◽  
Long Term Effect ◽  
The Difference

With the rapid economic development in China, substantial capital and resources are invested in urban logistics industry leading to quick expansion of the urban logistics. In this paper, the efficiency and energy efficiency of the urban logistics industry in China is measured through a stochastic frontier analysis based on a translog production function for a period of 2009–2017 using a sample of 216 prefecture-level cities. The results lead to several conclusions. (1) Average urban logistics efficiency and energy efficiency scores are at low levels and unbalanced between sample cities over the research period. Cities located in the eastern coastal region have the largest average efficiency scores, the central region has lower scores, and the western region has the lowest. (2) The difference in logistics efficiency between sample cities shows a downward trend for the entire country and eastern region. (3) Technical change plays an important role in promoting urban logistics efficiency. Technical inefficiency is the main cause of the nonefficient frontier of urban logistics. (4) Using the regression analysis, we found that digitalization and road density are positively correlated with the efficiency of urban logistics. Education has a long-term effect on the improvement of the urban logistics efficiency. In contrast, government intervention and environmental regulations are negatively correlated with the efficiency of urban logistics. (5) The effect of most factors on urban logistics efficiency across the sample stratified between eastern, central, and western regions is in line with the estimation results for the whole sample.

scholarly journals Assessing Eco-Efficiency in Asian and African Countries Using Stochastic Frontier Analysis

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1168
Author(s):  
Victor Moutinho ◽  
Mara Madaleno
Keyword(s):  
Renewable Energy ◽  
Fossil Fuels ◽  
Energy Use ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Policy Implications ◽  
Estimation Methods ◽  
African Countries ◽  
Renewable Energy Consumption ◽  
Frontier Analysis

This study aims to evaluate the economic and environmental efficiency of Asian and African economies. In the model proposed, Gross Domestic Product (GDP) is considered as the desired output and Greenhouse Gases (GHG), like carbon dioxide (CO2) emissions, as the undesirable output. Capital, labor, fossil fuels, and renewable energy consumption are regarded as inputs, and the GDP/CO2 ratio is the output, by using a log-linear Translog production function and using data from 2005 until 2018, including 22 Asian and 22 African countries. Results evidence cross-countries heterogeneity among production inputs, namely labor, capital, and type of energy use and its efficiency. The models complement each other and are based on different distributional assumptions and estimation methods while providing a picture of Eco-efficiency in Asian and African economies. Labor and renewable energy share increase technical Eco-efficiency, while fixed capital decreases it under time-variant models. Technical improvements in Eco-efficiency are verified through time considering the time variable into the model estimations, replacing fossil fuels with renewable sources. An inverted U-shaped Eco-efficiency function is found concerning the share of fossil fuel consumption. Important policy implications are drawn from the results regarding the empirical results.

scholarly journals Total Factor Energy Efficiency of China’s Industrial Sector: A Stochastic Frontier Analysis

2017 ◽  
Author(s):  
Xiaobo Shen ◽  
Boqiang Lin
Keyword(s):  
Energy Efficiency ◽  
Technical Efficiency ◽  
Technical Change ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Industrial Sector ◽  
Scale Efficiency ◽  
Efficiency Change ◽  
Output Data ◽  
Frontier Analysis

Based on stochastic frontier analysis and translog input distance function, this paper examines the total factor energy efficiency of China’s industry using input-output data of 30 sub-industries from 2002 to 2014, and decomposes the changes in estimated total factor energy efficiency into the effects of technical change, technical efficiency change, scale efficiency change and input-mix effect. The results show that during this period the total factor energy efficiency in China’s industry grows annually at a rate of 3.63%, technical change, technical efficiency change and input-mix effect contribute positively to the change in total factor energy efficiency, while scale efficiency change contributes negatively to it.

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