Factor Analysis on the Industrial Environmental Efficiency and Energy Utilization Efficiency of China by DEA Method

2012 ◽  
Vol 209-211 ◽  
pp. 1620-1626
Author(s):  
Hong Jun Lei ◽  
Qiao Li Zhang ◽  
Chang Jia Li ◽  
Bei Dou Xi ◽  
Xun Feng Xia
Keyword(s):  
Technical Efficiency ◽  
Returns To Scale ◽  
High Energy ◽  
Environmental Efficiency ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Scale Model ◽  
Scale Efficiency ◽  
Production And Distribution ◽  
Dea Method

Faced with two big stresses of energy shortage and environmental pollutants, China should improve its energy utilization efficiency. Based on the data of China Statistical Yearbook and China Environmental Statistics Yearbook, the pollutants discharge and energy utilization efficiency, including technical efficiency (TE), pure technical efficiency (PTE), scale efficiency (SE) and returns to scale (RTS) of China’s industry and its sub-sectors were analyzed by constant returns to scale model (CRS) and variable returns to scale model (VRS) of non-parametric data envelopment analysis (DEA) method. Results showed that: (1) The RTS of China's total industrial environmental efficiency and energy utilization efficiency were all in "irs" state, indicating that it was beneficial to expand the entire industrial scale. (2) The TE of total industrial energy utilization efficiency was about 0.80, the minimum TE was 0.018 of production and distribution of gas sector. (3) The total industrial environmental efficiency was about 0.77, the two sectors with high pollutants discharges were mining of other ores and manufacture of paper and paper products, and TE were 0.065 and 0.067, respectively. Mostly industrial sub-sectors should improve their technologies and adjust its scales except for extraction of petroleum and natural gas, manufacture of tobacco, printing, reproduction of recording media and so on. (4) Mining of other ores, manufacture of tobacco, manufacture of communication equipment, computers and other electronic equipment, manufacture of measuring, instruments and machinery for cultural activity and office work and production and distribution of water were in high energy utilization efficiency while in low environmental efficiency and steady RTS. So these sectors should improve the technologies to achieve DEA effective. (5) Scale expanding, technology advancement, energy use pattern improvement and industry structure adjustment were suggested for energy-saving industry according to the TE, PTE, SE and RTS.

scholarly journals Measuring Technical Efficiency of Hospitals affiliated with Shahrekord University of Medical Sciences, Using a Combination Method Data Envelopment Analysis (DEA) – Principle Component Analysis (PCA)

2021 ◽  
Author(s):  
Sara Emamgholipour ◽  
Mohammad Arab ◽  
Abbas Rahimi-Foroushani ◽  
Sayede Somaye Forghani Dehnavi ◽  
Shahide Allahverdi ◽  
...  
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Principal Components ◽  
Returns To Scale ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Medical Sciences ◽  
Combined Method ◽  
Management Efficiency ◽  
Dea Method

Background: Measuring the efficiency of hospitals due to the high proportion of budget allocated to them on the one hand, and the need to ensure the best practices regarding the use of scarce resources on the other hand, is of particular importance. The purpose of this study is to evaluate the technical efficiency of the affiliated hospitals of Shahrekord University of Medical Sciences by using a combination of Principal Component Analysis and (PCA) & Data Envelopment Analysis (DEA). Methods: This was an analytical and cross-sectional study measuring the technical efficiency of all 8 hospitals affiliated to Shahrekord University of Medical Sciences. The required information was collected from the medical records unit of each hospital. For better differentiation between efficient and inefficient units, and the increase of research accuracy and further differentiation between hospitals in terms of efficiency, at first, 17 indicators were selected to assess and adjust these parameters to 3 components proportional to the number of the hospitals by using PCA and SPSS 16 software. After doing the PCA, 7 studied input variables became 7 principal components among which the first input component reflecting the 83 % of scattering data was selected as principal input component, and for being more influenced by human resource variables, it was named as a human resource index. Furthermore, among the output variables, the first 2 output components, which represented 76% of the variance of the data, were selected as the 2 principal components of the output for the study, which were mostly affected by these variables, respectively, the number of admissions and length of stay. Then, the modified input and output components were entered into the software Windeap 2.1 and the technical efficiency of hospitals and their rank were calculated by assuming constant and variable efficiency with respect to the scale. In order to evaluate the effect of using the combined method instead of the conventional method of efficiency measurement, the results of the PCA - DEA method were compared with the results of the conventional DEA method. Results: The result of DEA on the selected components showed the capacity to upgrade the Technical Efficiency (TE) of hospitals is 15 % (TE: 0.852). Moreover, out of 8 hospitals, 1 hospital was increasing return to scale, 3 decreasing returns to scale and 4 constant returns to scale. The technical efficiency of 3 hospitals was 1 (TE = 1), 2 hospitals had the technical efficiency between 0.80 to 1 (1 > TE > 0.80) and that in 3 hospitals was less than 0.80 (TE < 0.80).  The scale efficiency for 50 % of hospitals and the management efficiency for 62/5 % of them were equal 1. Conclusion: The average of total technical efficiency, management efficiency and scale efficiency were calculated to be 0.999, 1 and 0.999, respectively based on the usual comprehensive analysis method; while using the combined method, the average total technical efficiency, management efficiency and scale efficiency were 0.852, 0.947 and 0.902 respectively. The results confirm that the use of PCA method, due to its important role in reducing alignments, increases research accuracy and better differentiates between hospitals in terms of efficiency.

Eco-Efficiency Evaluation of Poyang Lake Region Based on DEA Method

2011 ◽  
Vol 181-182 ◽  
pp. 118-123
Author(s):  
Hai Tao Su ◽  
Hai Qing Guo ◽  
Jin Feng Hu ◽  
Hui Zeng
Keyword(s):  
Technical Efficiency ◽  
Poyang Lake ◽  
Scale Efficiency ◽  
Maximum Output ◽  
Efficiency Evaluation ◽  
Economic Region ◽  
Poyang Lake Region ◽  
Lake Region ◽  
Decision Making Unit ◽  
Dea Method

The eco-efficiency and sustainable development have become the focus of world and the issues to be resolved urgently. In this paper, the recent research status of eco-economic region of Poyang Lake in China is analyzed, and the multi-level evaluation index system of eco-efficiency of Poyang Lake is constructed. The minimum input and maximum output method based on DEA(Data Envelopment Analysis) is proposed, the mathematical model of validity evaluation of eco-economic region of Poyang Lake is set up and programmed by MATLAB. Efficiency evaluation of a complex system with the cases from nine districts of Poyang Lake region in China is realized, which is more than one homogeneous decision-making unit of multi-input and multi-output. The MDEA (Modified DEA) method resolves the problems of ranking DEA efficient units of Poyang Lake, The DEAP2.1 software differentiates the technical efficiency and scale efficiency of eco-economic region of Poyang Lake, and adjusts the DEA inefficient units to become technical efficiency. The model can be used to analyze efficiency and diagnose different units at the same time or same unit at different time. It can be more accurate and convenient for the management process of eco-economic region of Poyang Lake and the similar eco-economic region.

scholarly journals Does Farm Size and Specialization Matter for Productive Efficiency? Results from Kansas

2011 ◽  
Vol 43 (4) ◽  
pp. 515-528 ◽  
Author(s):  
Amin W. Mugera ◽  
Michael R. Langemeier
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Production Technology ◽  
Returns To Scale ◽  
Productive Efficiency ◽  
Farm Size ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Sample Period ◽  
Analysis Techniques

In this article, we used bootstrap data envelopment analysis techniques to examine technical and scale efficiency scores for a balanced panel of 564 farms in Kansas for the period 1993–2007. The production technology is estimated under three different assumptions of returns to scale and the results are compared. Technical and scale efficiency is disaggregated by farm size and specialization. Our results suggest that farms are both scale and technically inefficient. On average, technical efficiency has deteriorated over the sample period. Technical efficiency varies directly by farm size and the differences are significant. Differences across farm specializations are not significant.

Analysis of China’s Provincial Energy Efficiency Based on the Generalized DEA Method

2014 ◽  
Vol 953-954 ◽  
pp. 890-895
Author(s):  
Hui Min Li ◽  
Cun Bin Li ◽  
Zhan Xin Ma
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Total Energy Consumption ◽  
Rapid Economic Growth ◽  
Technology Changes ◽  
Dea Method ◽  
Save Energy ◽  
The Impact

In recent years, with the rapid economic growth, the demand on the amount of energy in China is increasing. So the problem of how to improve the energy utilization efficiency and save energy consumption has to be tackled. The traditional CCR model and BCC model used in the study of provincial energy efficiency do not take the impact of technological progress into consideration. Therefore, the paper uses the generalized DEA method to research the energy utilization efficiency of China’s 29 provinces, that is, to evaluate and analyze the energy utilization efficiency by selecting the capital stock, employment and total energy consumption of China’s provinces as input factors and GDP, per capital GDP as output factors, and then draw tables showing each province’s change of average annual overall efficiency and the pure technology changes, and finally analyze the regularities underlying these changes.

TECHNICAL AND SCALE EFFICIENCY OF FOOD PRODUCERS IN POLAND IN 2006-2016. DOES THE SIZE AND LEGAL STATUS OF ENTERPRISES MATTER ?

2018 ◽  
Vol XX (3) ◽  
pp. 90-95
Author(s):  
Sylwester Kozak
Keyword(s):  
Technical Efficiency ◽  
Legal Status ◽  
Scale Efficiency ◽  
Financial Reports ◽  
Macroeconomic Conditions ◽  
Economy Of Scale ◽  
Dea Method ◽  
Non Parametric

The article empirically examines how the size and legal status of an enterprise influence the technical and scale efficiency of Polish food producers. Technical and scale efficiency indices are measured using the non-parametric DEA method. The study is based on the annual financial reports of 52 sugar and confectionery producers operating in 2006-2016. The analysis covers all enterprises in the sector, as well as groups of larger and smaller enterprises (distinguished by the median of assets), as well as groups of capital companies and cooperatives. Research has shown that enterprises were characterized by relatively high technical efficiency and scale efficiency in the range, respectively, between 82 and 93% and between 93 and 98%. All enterprises operated more efficiently in more favorable macroeconomic conditions. The level of enterprises’ technical and scale efficiency depends on the value of their assets and the legal status. Larger enterprises were less technically effective than smaller ones, but they made greater use of the effect of scale. Capital companies were more effective than cooperatives, but to a lesser extent used economy of scale.

The Empirical Research of Energy Utilization Efficiency in Industrial Sectors Based on DEA-Malmquist

2012 ◽  
Vol 463-464 ◽  
pp. 1025-1029
Author(s):  
Ming Xiang Lu ◽  
Guang Wen Ma ◽  
Yan Long Hu ◽  
Jing Wang
Keyword(s):  
Energy Consumption ◽  
Technical Efficiency ◽  
Energy Production ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Regional Research ◽  
Industrial Sectors ◽  
Production And Consumption ◽  
Consumption Energy ◽  
Reducing Energy

This study selected Sichuan Province of China's energy production and consumption province as the regional research object. It studied energy utilization efficiency in industrial sectors from output of energy consumption, energy utilization technical efficiency, pure technical efficiency, and change index of energy utilization efficiency by the data of energy consumption and GDP. The output of energy consumption and energy utilization efficiency were big difference in industrial sectors. It provided the basis of taking different measures according to the different characteristic in industrial sectors while reducing energy consumption and energy conservation

scholarly journals Efficiency Assessment of Universities with DEA Method Based on Public Data

2017 ◽  
Vol 2 (1) ◽  
pp. 106-114 ◽  
Author(s):  
Gabriela Vica Olariu ◽  
Stelian Brad
Keyword(s):  
Higher Education ◽  
Returns To Scale ◽  
Scale Efficiency ◽  
Public Authorities ◽  
Public Funds ◽  
National Education ◽  
Public Data ◽  
Dea Method ◽  
Funds Allocation ◽  
Constant Returns To Scale

Abstract Assessment of efficiency in spending public funds for higher education is an important task of the Ministry of National Education. This paper illustrates the application of some models of Data Envelopment Analysis (DEA) to evaluate the relative efficiency of public universities from Romania using data collected from the official reports of the universities’ Rectors throughout the years 2012- 2015. We use the constant returns to scale (CRS) model and the variable returns to scale (VRS) model to determine the output. Afterwards, we calculate the value of scale efficiency. Based on these results, universities can be grouped into several layers of efficiency. We conclude that public authorities would consider the application of DEA method to generate additional performance indicators in assessing higher education institutions for improving accuracy in public funds allocation and distribution.

scholarly journals BANKING SECTOR EFFICIENCY: A DEA AND TIME SERIES APPROACH

Ekonomika ◽  
2010 ◽  
Vol 89 (2) ◽  
pp. 111-119 ◽  
Author(s):  
Fadzlan Suflan
Keyword(s):  
Technical Efficiency ◽  
Banking Sector ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Bank Efficiency ◽  
New Approach ◽  
Time Series Approach ◽  
The Mean ◽  
Dea Method

The paper provides a new approach to modeling bank efficiency. Unlike previous bank efficiency studies, the present paper employs the data envelopment analysis (DEA) method on quarterly data to construct the efficiency frontiers. The Malaysian banking sector is used for a case study. The results show that the Malaysian banking sector has exhibited the mean technical efficiency of 97.3%, suggesting the minimal input waste of 2.7%. The empirical findings suggest that the pure technical efficiency outweighs the scale efficiency in determining the Malaysian banking sector’s technical efficiency. The results imply that, although the Malaysian banking sector has been efficient in managerial terms, it has been operating at a non-optimal scale of operations.p>

scholarly journals A comparative study of efficiency and its determinants in Islamic, conventional, and socially responsible banks

2016 ◽  
Vol 13 (4) ◽  
pp. 470-482 ◽  
Author(s):  
Majed Alharthi
Keyword(s):  
Financial Crisis ◽  
Technical Efficiency ◽  
Returns To Scale ◽  
Scale Efficiency ◽  
Islamic Banks ◽  
Socially Responsible ◽  
Market Capitalization ◽  
Efficiency Measures ◽  
Conventional Banks ◽  
Constant Returns To Scale

This study empirically estimates efficiency and its determinants in 190 Islamic (IBs), conventional (CBs), and socially responsible banks (SRBs) in 22 countries during the period 2005-2012. The study first uses non-parametric approaches to estimate the efficiency measures (scale efficiency (SE), technical efficiency-constant returns to scale (CRS), and technical efficiency-variable returns to scale (VRS)) and second employs ordinary least squares, fixed effects, random effects, and TOBIT models to get the efficiency determinants. The findings indicate that the average efficiency is 0.966, 0.952, and 0.983 for the SE, CRS, and VRS, respectively. However, efficiency measures show that the SRBs are most efficient banks whereas, the least efficiency scores archived by Islamic banks. Islamic bank efficiency is positively correlated with size, loan intensity, ROA, inflation rates, market capitalization and financial crisis. However, conventional banks’ TE and CRS efficiency are positively and significantly correlated with size, ROA, and market capitalization, while their VRS efficiency is negatively and significantly related to capital ratio, age and GDP. In addition, SRBs’ efficiency is increased by size, capital ratio, loan intensity, ROA, foreign ownership, domestic ownership, inflation and financial crisis. Furthermore, the financial crisis affects the SE and CRS efficiency measures in Islamic banks while socially responsible banks SE efficiency measure is positively affected by the financial crisis, which means that socially responsible banks were stabled and resisted during the crisis period. Finally, there is no significant correlation between financial crisis and efficiency indictors in conventional banks during the period

scholarly journals Efficiency of Micro and Small Enterprises in Ethiopia: Evidence from Data Envelope Analysis Model

2021 ◽  
Author(s):  
Abebe Birhanu Ayele
Keyword(s):  
Technical Efficiency ◽  
Service Sector ◽  
Returns To Scale ◽  
Least Square ◽  
Scale Efficiency ◽  
Small Enterprises ◽  
Analysis Model ◽  
Micro And Small Enterprises ◽  
The Government ◽  
Constant Returns To Scale

This study measures the technical and scale efficiency of Micro and Small Enterprises (MSEs) and input slacks using Data Envelop Analysis (DEA) model and identifies the determinants of efficiencies of MSEs by employing ordinary least square (OLS) econometrics model. A sample of 375 randomly selected MESs are included in the study. The study found that the average technical and scale efficiency of MSEs are relatively low; technical efficiency averaged at 30 percent and 38.4 percent under constant returns to scale (CRS) and variable returns to scale (VRS) assumptions, respectively. Besides, the overall average scale efficiency score of MSEs was estimated at 77.8 percent. The highest mean technical and scale efficiencies were registered in the construction (71.8 percent) and manufacturing (85.7 percent) sectors, respectively. Whereas, the lowest technical and scale efficiency goes to urban agriculture sector and service sector, with 38.9 percent and 67.2 percent, respectively. The level of inputs, enterprise age and sector, human capital, labor productivity variables significantly affect relative technical efficiency level of MSEs with different directions while variables such as start-up capital, gender of the enterprise manager and availability of support from the government identified statistically not significant in determining the MSEs&rsquo; technical efficiency.

Sign in / Sign up

Export Citation Format

Share Document