Data Envelopment Analysis Window Research Articles

Evaluating the performance of Chinese fossil fuel power plants by data environment analysis: An application of three intermediate approaches in a time horizon

To our knowledge, the previous studies did not answer the following questions: how much does window analysis differ from the original Data Envelopment Analysis (DEA) approaches and how to develop DEA window analysis? To address these questions, we propose three intermediate approaches (cross-sectional approach, window efficiency analysis and window index approach) and statistically compare their performances. The underlying concern is that we need to examine whether a metrological bias exists in our study. The concern is important in guiding policy issues such as energy policy and climate change. Empirically, the proposed approaches are applied to measure the unified efficiency of fossil fuel power plants at a provincial level in China from 2007 to 2017, since these plants are important energy consumers and CO2 emitters. We obtain two important implications. First, considerable differences were confirmed among the three approaches, thus implying an existence of the methodological bias. Second, Beijing (the capital of China) outperformed the other provinces in terms of their measures, since the government has paid serious attention to air pollution emitted from fossil fuel power plants. The governmental regulation in Beijing is a good example of pollution prevention in China. It is expected that the success on Beijing will be disseminated to the other provinces.

Evolution of the Efficiency of Nationwide Commercial Banks in China Based on an SBM-Undesirable Model and DEA Window Analysis

In recent years, Chinese economic development has slowed down and competition in the financial industry has become increasingly fierce. The purpose of this paper is to study the efficiency characteristics of China’s banking industry in the new environment and provide suggestions for banks to improve efficiency. This paper uses a data envelopment analysis (DEA) SBM-undesirable model and window analysis to measure the technical efficiency of 13 nationwide commercial banks in China during the period from 2008 to 2017. Furthermore, the convergence characteristics of bank technical efficiency are examined. The empirical results show that state-owned banks were more efficient than joint stock banks before 2012. After 2012, state-owned banks were less efficient than joint stock banks. Finally, this paper explores the influential factors of technical efficiency. Noninterest income ratio, net interest margin, growth rate of total investment in fixed assets, and consumer price index have a significant positive impact on bank efficiency. The cost-to-income ratio has a significant negative impact on bank efficiency. Further research using the threshold model shows that noninterest income ratio has a threshold effect on bank efficiency.

Efficiency evaluation and dynamic evolution of China’s regional green economy: A method based on the Super-PEBM model and DEA window analysis

Given the current circumstances of increasingly serious resource consumption and environmental pollution, the development of China’s green economy will profoundly impact the nation’s future economic prosperity and even global economic development. Based on panel data from 2008 to 2017, this paper measures the green economic efficiency (GEE) of Chinese regions and analyzes its dynamic evolution using time series, based on a novel data envelopment analysis (DEA) model. Using environmental DEA techniques, this paper introduces the Super-PEBM (EBM based on pearson correlation coefficient) model, which is developed from the Epsilon based measure (EBM) model, and combines Super-PEBM with the window analysis method. Firstly, environmental DEA technology is used to analyze GEE at the regional level in China. Secondly, based on panel data, the window analysis method is used to analyze the regional differences of China’s regional GEE. Finally, the development trend of China’s regional GEE in different time windows is obtained by combining the Super-PEBM model and window analysis. The empirical analysis results using panel data for regions in China from 2008 to 2017 show that: (1) the overall GEE of China is slowly increasing, and the regional differences are still significant, and (2) the improvement of GEE can help to reduce regional differences.

Back to the Future: Does the use of information and communication technology enhance the performance of public historical archives?

The diffusion of digital information and communication technologies (ICT) in public services is generally assumed to improve their performance. However, the impact of ICT on the efficiency of public services is rarely verified. This study focuses on public historical archives (PHAs), which are a widely diffuse—yet little explored—public service. It provides an assessment of the impact of a specific ICT on PHAs’ efficiency, namely the introduction of websites. The analysis adopts a two-stage approach involving the estimation of the frontier using Data Envelopment Analysis (DEA) and Window DEA. Results show that the diffusion of ICT is efficiency improving: Italian PHAs having a website are generally more efficient than others. These empirical findings are robust to the use of alternative estimators and empirical strategies in both first and second stage assessment. Finally, we suggest some directions for further research in the field.

Assessing the relative efficiency of commercial banks in the Republic of North Macedonia: DEA window analysis

The aim of this paper is to assess the relative efficiency of commercial banks in one developing country, i.e. the Republic of North Macedonia by using the data envelopment analysis (DEA) technique-window analysis. The selection of inputs and outputs plays a key role when applying DEA for assessing the efficiency of decision-making units (DMUs). In the conducted research two inputs and two outputs have been selected. The sample consists of 14 commercial banks and the period that is being observed is an eleven year span from 2007 to 2017. According to the average efficiency score for the whole observed period, the most efficient bank belongs to the group of large banks, which simultaneously shows the highest efficiency. The banking sector in the Republic of North Macedonia, as a whole, showed the highest efficiency in 2007, and the lowest efficiency in 2011.

A new model for efficiency evaluation of a bus fleet by window analysis in DEA and data mining

ABSTRACTBus fleet performance and efficiency evaluation is one of the issues that have attracted the attention of policy makers and urban managers, who seek to improve the service level for their citizens. In this paper, a hybrid framework is proposed using the Data Envelopment Analysis (DEA) method and data mining techniques to conduct a window analysis for performance evaluation, using the bus fleet in Tehran as a case study. First, the DEA model for the efficiency evaluation of the bus fleet is implemented. To this end, a window analysis is carried out to compare bus fleet performance with the performance of other bus fleets and its own performance for various time periods. The results from the DEA window analysis are then used as the input to the data mining classification method to forecast the efficiency of the bus fleet. Several classification techniques are employed and various methods are used to identify the best algorithm. In this regard, the C5.0 algorithm outperforms the others, and finally the rules hidden in the data set are extracted to forecast the bus fleet efficiency.

An Analysis by Window DEA on the Influence of International Sanction to the Efficiency of Iranian Container Ports

The sanctions imposed on the Iranian port's operators and shipping lines during the period of year 2012 to 2018 were huge challenges for Iranian seaports and maritime trades. Its impact on container business was instantaneous and port throughput shrank by 38 percent. In order to evaluate the Iranian port's performance and their relative efficiency during the sanction period, 10-year data had been collected for five container ports of Iran. This set of data was then analyzed by using Data Envelopment Analysis (DEA). All ports had shown decreased port average efficiency after imposed sanction. Then four ports namely Khorramshahr (from 0.73 to 0.51), Bushehr (from 0.78 to 0.83), Bandar Imam Khomeni (from 0.27 to 0.33) and Chabahar (slight increase) had shown increased port average efficiency only one port namely Shahid Rajaei (from0.89 to .083).

Eco-Efficiency Assessment of Intensive Rice Production in Japan: Joint Application of Life Cycle Assessment and Data Envelopment Analysis

To promote sustainable agriculture worldwide, it is important to understand what constitutes eco-efficiency for rice, a staple food in many countries. This study examined whether expanding the scale of rice farming contributed to the improved eco-efficiency of intensive rice production in Japan. Both life cycle assessment (LCA) and data envelopment analysis (DEA) were used to measure comprehensively the eco-efficiency of rice production. A DEA window analysis technique with two DEA inputs (global warming and eutrophication from the LCA results) and one DEA output (weight-based rice yield) was applied to the statistical data for 2005–2011 categorized by the size of rice farms. The results indicate that expanding the size of rice farms is an effective way of improving the eco-efficiency of intensive rice production in Japan. The important factors for improving eco-efficiency are the implementation of economies of scale, reduced outsourcing of farm work, and savings in chemical fertilizers and pesticides. Expansion of the size of rice farms through the recent abolition of the rice production adjustment program will also contribute to improving the eco-efficiency of Japanese rice production.

Analysis of the environmental efficiency in China based on the DEA cross‐efficiency approach under different policy objectives

AbstractThe existing studies on environmental efficiency evaluation generally have the problem of efficiency overestimation. To solve this problem, a new data envelopment analysis (DEA) cross‐efficiency approach with undesirable outputs is developed to evaluate environmental efficiency from the perspectives of both self‐evaluation and peer evaluation. Then, three new evaluation strategies, namely, economic development strategy, environmental protection strategy, and win–win strategy, are proposed to reflect the needs of decision makers under different policy objectives. The proposed cross‐efficiency approach with different evaluation strategies not only realizes the cross evaluation of environmental efficiency, but also guarantees the relative uniqueness of the optimal solution on the basis of the preferences of decision makers. Combining the metafrontier DEA approach and DEA window analysis, a new cross‐efficiency analytical framework is constructed to gradually analyse the influences of policy objectives, technology heterogeneity, and dynamic correlation on the environmental efficiency. Subsequently, the environmental efficiency of China's economic development during 2006–2015 is in‐depth analysed on the basis of the proposed analytical framework, and some interesting conclusions, and some useful suggestions are obtained.

The Determination Method of Optimal Window Length in DEA Window Analysis

본 연구에서는 시계열데이터에 대한 효율성 분석방법으로 이용되는 DEA(Data Envelopment Analysis) Window 분석에서 최적 윈도우 길이를 결정할 수 있는 방법을 제시하였다. 최적 윈도우의 길이를 결정하기 위해서 효율성 값들에 대한 변별력 평가기준으로 Shannon의 엔트로피 기반중요도 지수를 이용하였으며, 실증분석을 통해 제안된 방법의 유용성을 입증하였다. 본 연구에서 제안된 최적 윈도우 길이 결정방법의 실증분석을 위하여 3개 예제가 적용되었다. 적용결과에 따르면 제안된 방법이 기존 방법보다 적용 가능성이 높은 것으로 분석되었다. Kruskal-Wallis 순위합 검정에 따르면 윈도우 길이에 따른 DMU들 간의 효율성 순위에는 차이가 없는 것으로 분석되었다. 따라서 최적 윈도우 길이 결정 알고리즘을 DEA Window분석에 이용할수 있다. 기존 연구에서는 명확한 변별력 평가기준 없이 비교대상 DMU의 수가 가장 많은 경우가 효율성값들에 대한 변별력이 높을 것이라는 가정을 하고 있다. 본 연구에서는 이런 문제를 해결하기 위해 엔트로피 기반 변별력 평가기준을 적용한 최적 윈도우의 길이를 결정할 수 있는 방법을 제안하였다. 본 연구에서 제안한 최적 윈도우 길이 결정방법을 적용한 결과에 따르면 전체 비교횟수가 가장 많은 경우보다 1회 비교횟수가 가장 많은 경우의 윈도우 길이가 최적 윈도우 길이라는 것이 입증되었다.This paper presents a method for determining the optimal window length in DEA window analysis which is used as an efficiency analysis method of time series data. To determine the optimal window length, an algorithm was presented to determine the optimal window length using Shannon’s Entropy-based importance weights as a criterion for discriminating against efficiency values, and empirical analysis proved the usefulness of the proposed method. In this paper, three examples are applied for the empirical analysis of the optimal window length determination method proposed in this study. According to the application results, the proposed method is more applicable than the existing methods. As a result of Kruskal-Wallis rank sum test, it is analyzed that there is no difference in efficiency ranking between DMU(Decision Making Units) according to window length, and the optimal window length selection algorithm can be used for DEA window analysis. In the previous study, it is assumed that the largest number of DMU in the comparison will have a high discriminative power on the efficiency values without a clear discriminant evaluation criterion. In this paper, we propose a method to determine optimal window length using Entropy based discriminant evaluation criterion to solve this problem. According to the result of applying the optimal window length determination method proposed in this study, it is proved that the window length in the case of the largest number of comparison times at one time is the optimal window length.

A spatio-temporal cluster analysis of technical efficiency in the production of outpatient maternal health services and its structural correlates in México.

To analyze the formation of spatial clusters of technical efficiency (TE) in the production of outpatient maternal health services in México for the period 2008 to 2015. We performed a longitudinal analysis of administrative and structural data related to the 243 Mexican health jurisdictions. We use window data envelopment analysis and spatial and econometric techniques. Structural correlates of each TE cluster obtained were identified estimating a pooled multinomial logit model. We observed an increase in the overall TE, accompanied by a reduction in its standard deviation. Furthermore, we identified positive TE spatial dependence both globally and locally. Multiple regression analysis showed that the maximum-performance TE cluster was composed of health jurisdictions located in the North of México and characterized by social marginalization, a reduced indigenous population, and a low demand for maternal health services. The identification of TE clusters can provide elements to induce decision makers to innovative in ways of allocating resources and manage their utilization. In México as other low- and middle-income countries, it is key to develop targeting strategies to implement specific health services innovations putting to the population to be served at the front of the strategy. This implies testing new modalities to strengthen primary health services, the empowerment of community participation, the training and allocation of health personnel that could respond adequately to population's demand, and the active involvement of competent state and local authorities in the assessment of the results of these innovations.

Performance Evaluation of Major Asian Airline Companies Using DEA Window Model and Grey Theory

Being an essential part of the global transport system, airline transportation is capable of delivering fast service for passengers and commodities. In the past decade, the Asia aviation industry has experienced a high growth rate of transport due to higher economic development in this area, and this trend is expected to continue in the next two decades. However, competition in the Asia aviation industry will increase dramatically. To survive in the Asia aviation industry, the Asia airline companies should understand their current and future performance. A methodology for this purpose is required. This paper proposes a hybrid approach, combining Grey model GM(1,1) and Data Envelopment Analysis (DEA) Window model, to satisfy this need. The hybrid approach proposed in this research has been used to evaluate the current and future performance of 16 major Asia airline companies. The DEA Window analysis showed that airline companies Emirates, Cebu Pacific, and Sri Lankan were leading companies in the time period of 2012 to 2016, while Singapore Airlines, Japan Airlines, and All Nippon Airways followed behind. We found that Chinese airline corporations are rising in the Asia aviation industry. All 16 Asia airline companies studied in the research were found to improve their productivity in the time period of 2017 to 2021 as their Malmquist productivity indexes (MPIs) are greater than 1.

Long-term Financial Analysis of the Slovenian Wood Industry Using DEA

Long-term financial analysis is an important tool for assessing the financial position of a company and/or sector. The aim of the research was to analyze selected financial indicators of the Slovenian wood industry in sub-sectors C16 (wood processing – except furniture) and C31 (manufacture of furniture) for the last 10 years (from 2007-2016). The efficiency evaluation was performed by DEA (Data Envelopment Analysis) window analysis. Other comparisons between two sub-sectors were performed by analysis of time series of financial indicators and by t-test. We have analyzed 40 financial indicators, 15 of which in more detail. Some models, analyzed by DEA method, show relative deterioration in efficiency in the years 2007-2010, but in most cases efficiency has increased in recent years. With this, we have proven that the efficiency of both sub-sectors is improving. For all models, the performance of the sub-sector C16 is better than that of C31. It has been proven by t-test that the difference between these two sub-sectors is statistically significant, as differences of more than half of the financial indicators between the two sub-sectors are statistically significant. Therefore, improvements in profitability ratios at the company’s level should be done and appropriate measures within the sectoral economic policies should be taken to achieve the conditions needed for greater efficiency and success.

Benchmarking Toronto wastewater treatment plants using DEA window and Tobit regression analysis with a dynamic efficiency perspective.

The environmental-economic focus of wastewater treatment and management attracts growing attentions in recent years. The static efficiencies and their dynamic changes are helpful to systematically assess the environmental performance of the water agencies and wastewater treatment plants (WWTPs). Additionally, identifying key factors of efficiencies is critical to improve the operation of WWTPs. In this study, the window method of data envelopment analysis (DEA) was applied to estimate the annual efficiency for four Canadian WWTPs and to explore the variations of annual efficiency under different window lengths. Meanwhile, the Tobit regression analysis was developed to determine the driving forces for WWTPs' efficiency. The empirical results showed that: (i) the selected DEA window length remarkably affected both the average efficiency and the variations; however, it had no impact on the ranking of plants' efficiency; (ii) lower efficiencies were observed in plants with larger capacities due to higher infrastructure and operation investments involved; (iii) both the influent total phosphorus concentrations and influent flow rates had significant effects on the WWTPs' performance. Moreover, the staff and utility expenditures should be reduced to generate greater potential cost savings and efficiency improvement given the treatment technologies employed.

A longitudinal assessment of technical efficiency in the outpatient production of maternal health services in México.

We assess technical efficiency (TE) level for Mexican Ministry of Health (MoH) primary care units. Assessment was focused on the production of adequate maternal health services defined as the coverage level of women who received timely and frequent antenatal care, and institutional and medical care during childbirth. We conducted a longitudinal analysis of administrative and socio-demographic information concerning 233 health jurisdictions for the period 2008-15. Crude TE was calculated using window data envelopment analysis (Windows-DEA). Empirical analysis included the description of several factors affecting the production of maternal health services, including the heterogeneity and trends assessment of TE among health jurisdictions. We estimated a pooled regression model with robust standard errors to identify correlates of TE and estimated adjusted performance scores. Results indicate that while the production of adequate maternal-health services and TE in health jurisdictions proved insufficient, they rose by 22% (from 40.9% to 49.8%) and 14% (from 54.3% to 62%), respectively, over time. Furthermore, variance in efficiency among production units diminished and persistent regularities were observed. Performance was highest in the Northern as opposed to the Southern and Southeastern health jurisdictions, but lowest in the most marginalized zones of the country marked by economic inequality and the presence of indigenous populations. The Mexican Health System has reached a paradoxical situation: the steady escalation of financial resources in the public health subsystem over the past 15 years has yielded sub-optimal results as regards coverage for essential maternal health interventions among the poorest. Mexican government must put in place a set of measures to guarantee efficiency in the system's performance without affecting equity gains. This necessarily involves reconsidering, and where necessary replacing, the criteria behind the allocation and distribution of resources, as well as the mechanisms for controlling how resources are used and accountability is fulfilled.

DEA Window Analysis with slack-based measure of Efficiency in Indian Cement Industry

In data envelopment analysis (DEA), the concept of efficiency is examined either in radial measure or in non-radial measure. Radial measure gives the proportion or a percentage by which all the inputs (outputs) to be reduced(increased) simultaneously, while as the non-radial measure deals directly with the input output slacks which must be minimized, hence inputs and outputs need not to optimized by same proportion. Radial measures often provide a weak efficiency score while as there is no such case for Non-radial models. The aim of this paper is to employ the non-radial measure(Slack-based model) in DEA window analysis to measure the Technical Efficiency change over years of various Indian cement companies. The research is based on unbalanced panel data of Indian cement companies during the period 2005-15. DEA window analysis is used to determine the efficiency of cement companies and to observe the possibility of changes in the technical efficiency over time. A study is conducted to evaluate the efficiency of cement companies in India in order to identify the sources of inefficiencies and formulate proposals for improving the productivity of those companies and their operations through a three-year window analysis.

The effect of urbanization on carbon dioxide emissions efficiency in the Yangtze River Delta, China.

Cities have been one of the most important areas of CO2 emissions. It is increasingly important to research the effect of urbanization on CO2 emissions, especially in large emerging and developing economies, due to the indispensable need for understanding the effect of urbanization on CO2 emissions, evaluating carbon reduction tasks and providing the scientific basis for low-carbon urbanization. Utilizing a balanced panel dataset in the Yangtze River Delta (YRD), China, during the period of 2000–2010, this paper employed data envelopment analysis (DEA) window analysis and a spatial lag panel Tobit model to investigate the effect of urbanization on CO2 emissions efficiency (the ratio of the target CO2 emissions to the actual CO2 emissions). The results show that the average CO2 emissions efficiency was 0.959 in 2010, and CO2 emissions efficiency ranged from 0.816 to 1 and exhibited spatial clustering in the region. The larger potential of CO2 emissions reduction appeared in Zhenjiang and Yangzhou, indicating that more CO2 emissions reduction tasks should be allocated to these two cities. Urbanization has negative effects on improving CO2 emissions efficiency, and there is a U-curve relation between CO2 emissions efficiency and urbanization, indicating that CO2 emissions efficiency decreases at the early stage of urbanization, then increases when urbanization reach a high level. There is spatial spillover effect among the prefecture-level cities, suggesting that different prefecture-level governments should coordinate with each other to improve CO2 emissions efficiency in the whole area. Gross domestic product (GDP) per capita also plays a markedly positive role in improving CO2 emissions efficiency. This research highlights the effect of urbanization on CO2 emissions efficiency and the importance of improving CO2 emissions efficiency in developing countries.

Energy efficiency measurement of Chinese Yangtze River Delta’s cities transportation: a DEA window analysis approach

In this paper, the transportation energy efficiency of Yangtze River Delta’s 15 cities is studied in the period from 2009 to 2013. To measure transportation’s dynamic performance, the window analysis of data envelopment analysis (DEA) is used to estimate the efficiency in cross-sectional and time-varying data. Capital inputs and labor inputs are selected as the two non-energy inputs, energy consumption as the energy input, passenger volume and freight volume are selected as the two desirable outputs, and carbon dioxide is chosen as the undesirable output. The empirical study shows that Shanghai had the highest transportation energy efficiency, followed by Zhejiang province, and the average efficiency of Jiangsu province was the worst. A regression analysis on the efficiency values and their three influencing factors was carried out. The results show that per capita gross domestic product and the per capita area of paved roads in a city negatively influenced the efficiency values, while the number of public transportation vehicles per 10,000 persons positively influenced the transportation efficiency values. Lastly, some policy recommendations, conclusions, and suggestions for further research are given.

Healthcare efficiency assessment using DEA analysis in the Slovak Republic

A regional disparity is becoming increasingly important growth constraint. Policy makers need quantitative knowledge to design effective and targeted policies. In this paper, the regional efficiency of healthcare facilities in Slovakia is measured (2008–2015) using data envelopment analysis (DEA). The DEA is the dominant approach to assessing the efficiency of the healthcare system but also other economic areas. In this study, the window approach is introduced as an extension to the basic DEA models to evaluate healthcare technical efficiency in individual regions and quantify the basic regional disparities and discrepancies. The window DEA method was chosen since it leads to increased discrimination on results especially when applied to small samples and it enables year-by-year comparisons of the results. Two stable inputs (number of beds, number of medical staff), three variable inputs (number of all medical equipment, number of magnetic resonance (MR) devices, number of computed tomography (CT) devices) and two stable outputs (use of beds, average nursing time) were chosen as production variable in an output-oriented 4-year window DEA model for the assessment of technical efficiency in 8 regions. The database was made available from the National Health Information Center and the Slovak Statistical Office, as well as from the online databases Slovstat and DataCube. The aim of the paper is to quantify the impact of the non-standard Data Envelopment Analysis (DEA) variables as the use of medical technologies (MR, CT) on the results of the assessment of the efficiency of the healthcare facilities and their adequacy in the evaluation of the monitored processes. The results of the analysis have shown that there is an indirect dependence between the values of the variables over time and the results of the estimated efficiency in all regions. The regions that had low values of the variables over time achieved a high degree of efficiency and vice versa. Interesting knowledge was that the gradual addition of variables number of MR, number of CT and number of medical devices together, to the input side did not have a significant impact on the overall estimated efficiency of healthcare facilities.

Social Sustainability Assessment across Provinces in China: An Analysis of Combining Intermediate Approach with Data Envelopment Analysis (DEA) Window Analysis

There are two categories (i.e., radial and non-radial category) in conventional DEA (Data Envelopment Analysis). Recently, intermediate approach was put forward as a new third category. Intermediate approach is a newly proposed approach and there are quite limited related studies. This study contributes to the DEA studies by putting forward an analytical framework of combining intermediate approach and DEA window analysis along with the concepts of natural and managerial disposability. Such combination is quite meaningful and this new approach has three important features. To the best of our knowledge, such type of research has never been investigated by the existing studies. As an application, this approach is used to evaluate the performance of provinces in China from 2007 to 2014. Furthermore, this study develops a series of performance indices from different perspectives. This study identifies the three important findings. Firstly, eco-technology advancements can achieve economic prosperity and environmental protection simultaneously, and thus should become a new direction of climate policies. Secondly, considerable differences exist in a series of indices that evaluates the performance of various provinces and pollutants from different respective. Then, sufficient attention should be given to the provinces and the pollutants with poor performance. Finally, the Chinese government should promote efficiency improvement by “catching up” for provinces with poor performance in the short term. In addition, the central government should reduce regional disparity in order to promote the social sustainability in the long term.