• Environmental and Resource Economics Review
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• v.14 no.3
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• pp.595-626
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• 2005

The purpose of this paper is to estimate technical efficiency, scale efficiency, and environmental efficiency by income level of Korean farms, and analyze the factors to decide three efficiencies. Depending on the non-parametric methods, we estimate technical using inputs and outputs of total farms without assuming of goods or behavior of optimization. The average technical efficiency of total firms under constant return to scale and strong disposability is 0.437. The technical inefficiency was caused by 47.7% in pure technical inefficiency, 11.3% in scale failure, and 3.2% in environmental inefficiency. The number of firms under increasing return to scale occupied almost 70% and 27% of total firms respectively. Higher are income class, middle debt & long debt per asset, and N effluents per cultural land, higher technical efficiency. The increases of BOD discharges per cultural land and machines per cultural land deteriorate environmental efficiency.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.2
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• pp.161-167
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• 2017

This paper measured and evaluated the technical efficiency, pure technical efficiency and scale efficiency of port labor via three inputs and three outputs using Data Envelopment Analysis (DEA). First, the average overall technical efficiency measured about 94.18 %, with 100 % pure technical efficiency being larger than 94.18% scale efficiency. As a result, 7 ports were identified as efficient, and 4 ports were identified as inefficient. Among the 4 ineffiecient ports, Donghae had the highest Increasing Return to Scale (IRS). However, the inefficient ports were all found to be less efficient in terms of resource operation than production scale. On the other hand, the efficient ports established priorities using the super efficiency method. As a result, port efficiency was highest for Incheon, Mokpo, Pyeongtaek-Dangjin, Masan, Yeosu Gwangyang, Jeju, and Gunsan-Daesan Ports, in order. In particular, Busan Port, found to be inefficient, would benefit from using Mokpo Port and Incheon Port as benchmarks to increase trade volume and modernize equipment for full-scale commercialization to improve labor efficiency.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.3
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• pp.139-148
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• 2016

Thermodynamic analysis is conducted on the first-order approximation model for turbines and compressors. It is shown that the adiabatic efficiency could be greater than unity, depending on the entropic mean temperature, entropy generation, thermal reservoir temperature, and heat transfer. Therefore, adiabatic efficiency applied to a diabatic control volume results in an error overestimating its performance. To resolve this overestimation, it is suggested that a reversible diabatic process be referred to as an ideal process to evaluate diabatic efficiency. The diabatic efficiency suggested in this work is proven to always be less than unity and it is smaller than the exergy efficiency in most cases. The diabatic efficiency could be used as a more general definition of efficiency, which would include adiabatic efficiency.

• Journal of Information Management
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• v.40 no.3
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• pp.41-60
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• 2009

This study utilizes DEA/Window analysis in order to investigate the relative efficiency levels of korea national university libraries during the period of transition 2004-2008. The empirical results show the following findings. Firstly, a technical efficiency was not efficient which was a scale efficiency rather than a pure technical efficiency in 2008. Secondly, a technical and pure technical efficiency has increased steadily from 2004 to 2008(but a technical, pure technical efficiency has decreased since in 2008). As a result, a technical efficiency increase can be explained by a pure technical efficiency increase.

• Journal of Service Research and Studies
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• v.11 no.4
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• pp.43-55
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• 2021

This study analyzed the efficiency of medical services in OECD member countries by dividing it into operational efficiency and quality efficiency. For this purpose, data from 2017-2019 OECD Health Statistics were used. As the analysis method, super efficiency was measured by applying an output-oriented Variable Returns to Scale (VRS) model. As a result of the analysis, Switzerland, Korea, and Italy were included in the high group of operational efficiency, Canada, Greece, Denmark, etc. in the medium group, and Belgium, Germany, and Spain in the low group. Based on quality efficiency, Norway, Switzerland, and Spain are in the high group, and Greece, Denmark, Mexico, etc. are in the medium group, and the Netherlands, Germany, Belgium, etc. were included in the low group. As a result of comparative analysis of efficiency by OECD member countries as of 2018, it was found that Korea's operational efficiency was the most efficient and quality efficiency was inefficient. Korea (0.998) should improve life expectancy by 0.2 (0.2%) and subjective health perception by 44.2 (138.1%) by benchmarking Greece (0.422), Switzerland (0.207), and Spain (0.371) to improve quality efficiency. Unlike most previous studies that focused on operational efficiency, this study measured quality efficiency together and analyzed the efficiency of the medical service industry in each OECD member country. Through this, this study has implications in that it confirmed the international competitiveness of the domestic medical service industry and suggested ways to improve efficiency.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.339-344
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• 2022

It is about how much fine dust can be efficiently removed with respect to the electric dust collector used in factories and large facilities. The results of evaluating the fine dust removal efficiency are as follows. At 10,000 V, Efficiency method I showed 68.1 %, and Efficiency method II was 58.6 %, which was lower than that of Efficiency method I. At 5,000 V, Efficiency method I showed 57.6%, and Efficiency method II showed 51.6%, which was lower than Efficiency method I. At 2,500 V, Efficiency method I showed 50.3%, and Efficiency method II showed 24.4%, which was lower than Efficiency method I. In case of Efficiency method I, it is the efficiency calculated by using the dust concentration before and after passing through the filter. Efficiency method II calculated Efficiency method II to measure the efficiency a little more accurately in the environment where the air after the filter and the polluted air are mixed. Efficiency method II showed lower efficiency than Efficiency method I. As a result of the electrostatic precipitator test, the dust collecting performance was highest at 10,000 V, followed by 5,000 V and 2,500 V in that order.

• Journal of Environmental Policy
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• v.11 no.1
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• pp.75-97
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• 2012

The purpose of the paper is to estimate the functions of the energy input efficiency and the energy intensity efficiency, and measure their energy efficiencies for the reduction of greenhouse gases focusing on OECD countries. The efficiency of the traditional energy intensity was rarely connected with the energy efficiencies of the stochastic frontier function. It seems that the energy efficiency by the function of energy input efficiency sensitively responds to the order of GDP, capital stock, labor, and energy input quantity as explanatory variables. In the future, we need to reduce energy quantities by the optimal mix of inputs, and pursuit low-carbon economic growth through the production of the goods consuming small energy.

• Environmental and Resource Economics Review
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• v.30 no.4
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• pp.581-605
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• 2021

The purpose of this study is to provide basic data for improving energy efficiency by estimating the regional energy efficiency in Korea using the stochastic frontier approach beyond the energy intensity that has been traditionally used as an indicator of energy efficiency. In this paper, energy efficiency and energy intensity efficiency were estimated as a stochastic distance function from 1998 to 2018 for 16 cities and provinces in Korea. In addition, the robustness of energy efficiency according to the capital stock estimation methods which had been mixed in previous studies was reviewed. As a result of the analysis, there is a significant change in regional rankings according to the three energy efficiency indicators, so they should be used complementary to each other. Second, while the energy efficiency improved little by little over time, the energy intensity efficiency decreased slightly though. Lastly, energy efficiency by region according to the capital stock estimation method was not robust. Care must be taken in estimating capital stock, which is important in economic analysis.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.11a
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• pp.191-196
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• 2004

This paper is to measure and ealuates the technical efficiency, pure technical efficiency and scale efficiency with three inputs and two outputs with the use of DEA(data envelopment analysis) in Korean RCC(Rescue Co-ordination Center/RSC(Rescue Sub-Center). Several conclusion emerge. first the average efficiency of overall technical efficiency measure about $91.03\%$ and pure technical efficiency $96.80\%$ is much large then scale efficiency $93.83\%$. It means that inefficiency has much more to do whit the inefficient utilization of resources rather then the scale of production. second, DRS(decreasing return to scale) is Tongyeong and IRS(increasing return to scale) is Incheon, Taean, Gunsan, Yeosu, Ulsan, Donghae in RCC/RSC. finally, inefficiency RCC/RSC. have to benchmarking with reference sets.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.475-487
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• 2015

This study measures the managerial efficiency of Korea's 14 public enterprises using bootstrap DEA in 2013. In addition, it examines the factors that affect on the bootstrap bias-corrected efficiency using truncated regression analysis. The results and implications of this study are as follows. First, using bootstrap DEA model analysis, the results showed that the mean technical efficiency was 0.3182, the mean pure technical efficiency was 0.4994 and the mean scale efficiency was 0.6585. The main cause of technical inefficiency was due to pure technical inefficiency. Second, rank test between technical efficiency of general DEA model and bootstrap DEA model was no significant difference under CRS and VRS assumption. Third, the main cause of the inefficiency in 11 DMUs among 14 DMUs were mainly due to the pure technology and three DMUs were because of the scale efficiency. Finally, in the truncated regression analysis, cost of labor, profit, sales, return of equity, and the number of employees appeared as factors affecting the scale efficiency at the 10% significance level.