scholarly journals Measuring plant level energy efficiency and technical change in the U.S. metal-based durable manufacturing sector using stochastic frontier analysis

2019 ◽  
Vol 81 ◽  
pp. 159-174 ◽  
Author(s):  
Gale A. Boyd ◽  
Jonathan M. Lee
Keyword(s):  
Energy Efficiency ◽  
Technical Change ◽  
Stochastic Frontier Analysis ◽  
Manufacturing Sector ◽  
Stochastic Frontier ◽  
Level Energy ◽  
Frontier Analysis ◽  
Plant Level ◽  
The U.S

Stochastic Frontier Analysis and Measurement of Productivity and Technical Efficiency of Indian Manufacturing Sector

2017 ◽  
Vol 5 (1) ◽  
pp. 52-69
Author(s):  
Manoj Kumar
Keyword(s):  
Technical Efficiency ◽  
Stochastic Frontier Analysis ◽  
Manufacturing Sector ◽  
Stochastic Frontier ◽  
Value Added ◽  
Frontier Analysis ◽  
Use Of Resources ◽  
Plant Level ◽  
Indian Manufacturing Sector ◽  
Factor Inputs

It is generally believed the structural reforms that usher in competition and force companies to become more efficient were introduced later in India following the macroeconomic crisis in 1991. However, whether or not the post-1991 growth is an outcome of more efficient use of resources or greater use of factor inputs, especially capital, remains an open empirical question. In this article the author uses plant-level data from 1990 and 2015 to address this question. The results indicate that while there was an increase in the productivity of factor inputs during the 1990s, most of the growth in value added is explained by growth in the use of factor inputs. The author also finds that median technical efficiency declined in all but one of the industries between the two years, and change in technical efficiency explains a very small proportion in the change in gross value added.

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.

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 Productivity of Nations: A Stochastic Frontier Approach to TFP Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Jorge Oliveira Pires ◽  
Fernando Garcia
Keyword(s):  
Technical Change ◽  
Stochastic Frontier Analysis ◽  
Scale Effects ◽  
Stochastic Frontier ◽  
Allocative Efficiency ◽  
Developing Nations ◽  
Production Frontier ◽  
Stochastic Frontier Approach ◽  
Frontier Analysis ◽  
Long Period

This paper tackles the problem of aggregate TFP measurement using stochastic frontier analysis. We estimate a world production frontier for a sample of 75 countries over a long period. The “Bauer-Kumbhakar” decomposition of TFP is applied to a smaller sample in order to evaluate the effects of changes in efficiency (technical and allocative), scale effects, and technical change. Estimated technical efficiency scores are compared to productivity indexes offered by nonfrontier studies. We conclude that differences in productivity are responsible for virtually all the differences of growth performance between developed and developing nations and that a large part of this is due to allocative efficiency.

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