Prediction Model of Data Envelopment Analysis with Undesirable Outputs

Data envelopment analysis (DEA) has become a standard non parametric approach to productivity analysis, especially to relative efficiency analysis of decision making units (DMUs). Extended to the prediction field, it can solve the prediction problem with multiple inputs and outputs which can not be solved easily by the regression analysis method.But the traditional DEA models can not solve the problem with undesirable outputs,so in this paper the inherent relationship between goal programming and the DEA method based on the relationship between multiple goal programming and goal programming is explored,and a mixed DEA model which can make all factors of inputs and undesirable outputs decrease in different proportions is built.And at the same time,all the factors of desirable outputs increase in different proportions.

Discriminating Among Relatively Efficient Units in Data Envelopment Analysis: A Comparison of Alternative Methods and Some Extensions

This paper concentrates on methods for comparing activity units found relatively efficient by data envelopment analysis (DEA). The use of the basic DEA models does not provide direct information regarding the performance of such units. The paper provides a systematic framework of alternative ways for ranking DEA-efficient units. The framework contains criteria derived as by-products of the basic DEA models and also criteria derived from complementary DEA analysis that needs to be carried out. The proposed framework is applied to rank a set of relatively efficient restaurants on the basis of their market efficiency.

A New Approach for Ranking Efficient Units in Data Envelopment Analysis and Application to a Sample of Vietnamese Agricultural Bank Branches

This paper proposes a new approach for ranking efficiency units in data envelopment analysis as a modification of the super-efficiency models developed by Tone [1]. The new approach based on slacks-based measure of efficiency (SBM) for dealing with objective function used to classify all of the decision-making units allows the ranking of all inefficient DMUs and overcomes the disadvantages of infeasibility. This method also is applied to rank super-efficient scores for the sample of 145 agricultural bank branches in Viet Nam during 2007-2010. We then compare the estimated results from the new SCI model and the exsisting SBM model by using some statistical tests.

Assessment of Dry Port Efficiency in Africa Using Data Envelopment Analysis

This work involves the evaluation of dry port competitiveness through analysis of efficiencies for selected dry ports in Africa. Five dry ports were selected and analysis carried out over a period of four years. The dry ports considered were Mojo and Kality in Ethiopia, Mombasa in Kenya, Isaka in Tanzania and Casablanca in Casablanca, Morocco. Data Envelopment Analysis (DEA) was applied for this work. Container throughputs for the various ports under consideration were used as the output variable for the data analysis model, while the number of reach stackers, the number of tractors, the number of forklifts and the size of the dry port were used as the input variables. From the results, the Mombasa dry port was found to be the most efficient with an average score of approximately 1 over the period under consideration. Casablanca was the second efficient dry port with an average score of 0.762, while Isaka was the least efficient with an average score of 0.142. This research is significant since the African countries have embraced the dry port concept, as witnessed in the huge investments in this sector, and would serve to highlight areas that need improvement for the few existing dry port facilities, most of which are undergoing expansion as well as modernization.

Evaluation of Energy Efficiency and Analysis of Influencing Factors of Company CW’s Manufacturing Workshops

In this work,the Slacks-Based Measure(SBM)model within Data Envelopment Analysis was employed to establish a set of indicators for evaluating the energy efficiency of manufacturing workshops.The energy efficiency of 12 Company CW’s manufacturing workshops from 2016 to 2022 was assessed.The findings indicated that aside from a few workshops operating at the production frontier,the rest exhibit significant fluctuations in energy efficiency and generally low energy efficiency.Subsequently,a combined GRA-Tobit analysis model was introduced to identify factors influencing the energy efficiency of Company CW’s manufacturing workshops.Regression analysis revealed that technological investments,employee quality,workshop production scale,investment in clean energy,and the level of pollution control all significantly impact the energy efficiency of Company CW’s manufacturing workshops.By evaluating the energy efficiency of Company CW’s manufacturing workshops and studying their influencing factors,this research aids company managers in understanding the energy efficiency of the manufacturing process.It optimizes the combination of various production elements,thereby offering effective guidance for improving the energy efficiency issues of the company’s manufacturing workshops,which can contribute to enhancing the corporation’s overall energy efficiency.

The Study of the Operating Performance of Marine Industry in the Blue Economic Zone: An Empirical Analysis Based on DEA and Tobit Model

The marine industry plays an important role in promoting the development of the blue economic zone,and the study of the operating performance of marine industry is of important practical significance. Using DEA method,this article measures the operating performance of marine industry in the blue economic zone. On this basis,using Tobit model,it analyzes the factors influencing the operating performance of marine industry. The results show that the industries of high carbon emission performance are basically the industries of high energy consumption,high emission and high pollution; the level of economic development and the number of important technological achievements can both significantly increase the pure technical efficiency; the sustainable environmental carrying capacity and the number of employees are conducive to improving the technological progress index and overall efficiency; the level of social infrastructure plays a role in promoting the overall efficiency,but the role is not obvious.

Analysis on Technical Efficiency of Rice Farms and Its Influencing Factors in South-western of Niger

The present study focused on analyzing the technical efficiency office farms in southwest of Niger. The data from January to March 2015 survey of 148 ms in three districts of south-western of Niger were analyzed by using DEA-Tobit two-step method. In the f＇ust step, data envelopment analysis （DEA） was applied to estimate technical, pure technical and scale efficiency. In the second step, Tobit regression was used to identify factors affecting technical efficiency. The results showed that rice producers in southwest of Niger could reduce their inputs by 52% and still produce the same level of rice output. The Tobit regression showed that factors, such as farm size, experience in rice farming, membership of cooperative, main occupation and land ownership had a direct impact on technical efficiency.

A New Stochastic Model for Classifying Flexible Measures in Data Envelopment Analysis

The way to deal with flexible data from their stochastic presence point of view as output or input in the evaluation of efficiency of the decision-making units(DMUs)motivates new perspectives in modeling and solving data envelopment analysis(DEA)in the presence of flexible variables.Because the orientation of flexible data is not pre-determined,and because the number of DMUs is fixed and all the DMUs are independent,flexible data can be treated as random variable in terms of both input and output selection.As a result,the selection of flexible variable as input or output for n DMUs can be regarded as binary random variable.Assuming the randomness of choosing flexible data as input or output,we deal with DEA models in the presence of flexible data whose input or output orientation determines a binomial distribution function.This study provides a new insight to classify flexible variable and investigates the input or output status of a variable using a stochastic model.The proposed model obviates the problems caused by the use of the large M number and using its different values in previous models.In addition,it can obtain the most appropriate efficiency value for decision-making units by assigning the chance of choosing the orientation of flexible variable to the model itself.The proposed method is compared with other available methods by employing numerical and empirical examples.

Efficiency of Moroccan Seaports: Application of DEA Using Window Analysis

The efficiency of 8 seaports in the Kingdom of Morocco region was evaluated. Two associated studies were conducted based on data collected for 4 years (2014-2017), Standard Data Envelopment Analysis (DEA) method was used in the first analysis through CCR and BCC models, and thus a scale of efficiency analysis was used in a second analysis. By using both methods, enhanced insight into the efficiency situation is collected, proving subsequently that a majority of the container ports and terminals in the Kingdom of Morocco are found to be technically inefficient. The research concludes through returns to scale method how port resources should be utilized in the aim to impulse inefficient ports perform better.

Application of Integrated Multiple Criteria Data Envelopment Analysis to Humanitarian Logistics Network Design

This paper considers a humanitarian logistics network(HTLN)design problem,including the emergency relief facilities(ERFs)location-allocation decision for the efficient distribution of emergency supplies from the ERFs to the affected areas.A goal programming(GP)approach is applied to consider the multiple objectives simultaneously.Solving the GP model with a given weight assigned to each goal yields a single HTLN scheme,so there will be various schemes available by solving the GP with multiple values of the weights.For evaluating these schemes and identifying the most efficient one,we apply the data envelopment analysis(DEA)methods considering each scheme as a decision-making unit(DMU).Since the classical DEA(C-DEA)intrinsically aims to identify efficient DMUs and the efficient frontier,the use of C-DEA may not lead to a full ranking in many situations.There are several independent evaluation approaches to increasing discriminating power.Among them,this study integrates the multiple criteria DEA(MC-DEA)with the following three DEA methods,(i)stratification DEA(S^DEA),(ii)cross-efficiency DEA(CE-DEA),and(iii)super-efficiency DEA(SE-DEA),to make the most use of each method's strengths.Through a case study of designing the HTLN system for South Carolina,the procedure of implementing the integrated multiple criteria DEA(IMC-DEA)method is demonstrated.It is observed that the IMCDEA method performs well in terms of designing the HTLN system and would help the decision-makers consider more efficient options and make a final decision.

我国沿海地区卫生资源配置效率评价及预测分析

目的:对2012~2021年我国沿海地区卫生资源配置效率进行评价,并预测2022~2030年我国沿海地区卫生资源的变化趋势,为我国沿海地区卫生资源优化配置提供依据。方法:运用DEA-BCC和DEA-Malmquist指数模型以静态和动态视角对我国沿海地区卫生资源配置效率进行评价,并利用灰色预测模型GM(1,1)预测分析2022~2030年卫生资源情况。结果:2021年我国沿海地区卫生资源配置综合技术效率为0.971,DEA有效地区3个;2012~2021年我国沿海地区卫生资源配置效率的全要素生产率在1上下波动,均值为0.984,技术进步对于全要素生产率的影响更大;预测2022~2030年我国沿海地区卫生资源投入产出量将逐年递增,其中医疗卫生机构总支出年均增长最快。结论:我国沿海地区需要充分考虑卫生资源配置差异产生的原因,改革资源分配方式,并持续提高管理服务和技术开发能力;发挥“互联网+医疗健康”优势,结合独特区位制定合理的资源规划目标;建立医疗资源跨区域合作模式,实现沿海中心城市的辐射带动作用,推动地区卫生健康事业高质量、可持续发展。

基于数据包络分析的中国污染地块治理绩效评价研究

中国高速的城市化进程产生了大量工业企业搬迁,造成众多污染地块需要治理。中国为治理污染地块耗资巨大,但缺乏相关的绩效评价。筛选了40个典型污染地块治理工程(涉及全国33个区县)进行绩效评价并探究了污染地块治理绩效的影响因素。结果表明:(1)山东省、河北省及湖南省中南部地区的污染地块治理绩效较高,重庆市、湖南省等华中地区及东南沿海地区零星有一些污染地块治理绩效相对较低。(2)地块尺度,资金投入不足、时间投入过短、人员投入过少是影响污染地块治理绩效的重要因素,期望产出方面也仍有很大提升空间,非期望产出方面需要进一步控制污染排放。(3)区县尺度,污染地块治理绩效主要与工业增加值占国内生产总值比例和年末总人口呈显著(p<0.05)正相关,说明经济和人口是主要影响因素。

公路隧道入口区域驾驶人吸睛效应综合评价

为全面和系统评价公路隧道入口区域视觉吸引对驾驶人吸睛效应视觉表现的影响,本文招募30名被试开展自然驾驶实验,采集驾驶人在公路隧道入口区域不同视觉吸引条件下的眼动数据,基于因子分析法选取驾驶人吸睛效应敏感评价指标,并分别构建线性混合效应模型和数据包络分析模型,识别和探究公路隧道入口区域视觉吸引对驾驶人吸睛效应视觉表现的影响特征和作用机理。结果表明:因子分析结果显示,驾驶人吸睛效应敏感视觉指标为注视持续时间、瞳孔直径、扫视持续时间和扫视幅度;公路隧道入口区域不同视觉吸引条件对驾驶人吸睛效应的视觉表现和综合效率有显著影响,且受驾驶人年龄和驾驶经验的个体特质因素影响显著,而性别因素对其没有显著性影响;视觉吸引的存在均会不同程度地损害驾驶人的正常视觉绩效,降低其合理有效性;提示标语视觉吸引条件下,驾驶人的视觉注意水平最差,视觉认知负荷程度最高,吸睛效应的负面影响最大,广告牌条件次之。

基于超效率DEA模型的陆军部队指挥所指挥效率评价

指挥所指挥效率评估是复杂的系统性问题,对提高部队指挥、作战、保障能力具有重要意义。主要运用数据包络的方法,不探讨指挥所的内部结构问题,通过对陆军典型部队指挥所输入输出数据进行分析,运用CCR-DEA和超效率DEA模型,对指挥效率问题进行研究排序,并针对输入数据提出改进建议,评估方法简单、可操作性强,对于陆军部队指挥所构建和指挥能力提升具有重要意义。

中国水资源利用效率及影响因素研究

本文研究中国省际用水效率及影响因素,为节水型社会建设提供参考。采用改进的数据包络分析模型测算2015—2020年各地区用水效率;对比普通最小二乘回归结果,进一步利用分位数回归探究影响因素对不同等级用水效率的影响。结果显示:全国平均用水效率在0.45左右波动,呈现倒“U”型趋势。区域用水效率由高至低依次为东部、中部和西部。自然因素、经济发展水平、可持续利用水平、科技进步、人文素养和企业成本对用水效率具有显著影响。其中,水资源禀赋对低用水效率地区的影响显著,而对中、高用水效率地区的影响并不显著,这与“资源诅咒”假说不同。因此,中国用水效率还有较大提升空间,特别是低用水效率地区的改善空间和可选方法最为丰富。

发展贫困视角下经济效率的空间差异及驱动因素研究——基于云南省40个生态脆弱县的数据

基于云南省40个生态脆弱县的全样本数据,运用DEA-BCC模型、Moran’s I指数、地理加权回归模型探究云南省生态脆弱县经济效率的空间属性和影响因素,为提升区域抵御内外部风险能力,稳定脱贫成效,加快乡村振兴步伐,步入高质量发展之列服务。研究结果表明:(1)云南省40个生态脆弱县的平均经济效率处于中上水平,其中乌蒙山区和滇西边境山区的经济效率较低。(2)云南省生态脆弱县经济效率的空间集聚性弱,近似随机分布,且生态脆弱县主要分布在云南省边缘地带。(3)县域经济效率主要受到务工人口数占比、村集体收入、人均耕地面积等的影响,尽管不同区域的主导因素有所区别,但经济发展总体处于“U”型关系的前端,县域规模效益还未彰显。由此建议通过进一步提高帮扶投入精准度,加强普通教育、职业教育和技能培训,壮大集体经济,推进县域经济效率的提升。

山东省农业用水效率评价及促进对策研究

为了科学评价山东省农业用水效率,采用DEA-BCC-I模型测算山东省农业用水效率,采用Malmquist指数对山东省2011—2021年16个地级市用水效率的动态演变机理进行了研究。研究结果表明:2021年山东省农业用水综合效率值为0.822,总体水平相对较高;农业用水效率由高到低的区域排序为:鲁中>鲁东>鲁西南>鲁西北;2011—2021年间山东省农业用水全要素生产率呈上升趋势,但用水综合效率呈下降趋势;技术进步是山东省用水全要素生产率提高的主要推动力;建议采取技术创新,促进各地区均衡发展,提高投入要素的匹配度,合理控制投入规模,并对水资源价格进行调控,以提高山东省农业用水效率。

长三角两业融合对服务业全要素生产率的影响

随着数字技术的发展,先进制造业与现代服务业之间的边界逐渐模糊,两业融合发展成为提升服务业生产率进而促进经济增长的重要方式。利用耦合协调模型和DEA(数据包络分析)-Malmquist指数法分别对长三角地区两业融合水平和服务业全要素生产率进行测度,实证两业融合对服务业全要素生产率的正向作用,且主要通过两业融合对技术进步的促进作用来实现。

科技创新、效率测度与政策检验:基于30个省(区、市)“双创”政策的评估实践

【目的】科学评估中国30个省(区、市)的创新创业政策执行效果,解析当前创新创业发展现状并提出优化建议,对推动创新创业高质量发展具有重要意义。【方法】本研究以时间视域选择我国30个省(区、市)2016—2020年的创新创业政策数据,构建基于投入和产出的创新创业政策执行效果评价指标体系,用三阶段DEA模型和Tobit模型分别分析各省级政府创新创业政策的执行效果及其影响因素。【结果】实证结果表明:5年内创新创业政策执行达到综合有效的省份呈起伏状态,多数省份未达到技术有效和规模有效,整体上创新创业政策落实效果较差,政策执行仍有较大的提升空间;剔除环境变量和随机干扰项后,发现政策宣传和外商投资会对创新创业政策执行产生投入冗余;各省份创新创业政策执行效果较差的主要原因是规模效率水平较低和区域差异较大,通过分析各影响因素作用方向与程度差异,发现地区经济发展水平、政策宣传力度和科技创新支撑能力显著影响创新创业政策的执行效果。【结论】根据研究结果,从完善创新政策体系,搭建创业政策平台;调整地区产业规模,提高创新规模效益;平衡区域创新发展,东中西部地区协调并进;夯实创新制度基础,完善政策激励机制等方面为提升我国创新创业政策执行效果提供建议。

基于Malmquist-DEA模型的区域物流低碳效率评价

在低碳经济背景下,构建物流低碳效率评价指标体系,从多维度来衡量物流低碳经营方式,将物流业引向低碳发展的道路,对我国的经济低碳化发展有重要意义。以2016-2021年我国30个省级地区(含省、自治区、直辖市)为研究对象,构建了物流低碳绩效评价指标体系。在考虑二氧化碳排放总量为固定和的情况下,通过构建均衡有效前沿面对2016-2021年我国30个省份的物流低碳效率进行了静态分析,使用Malmquist指数模型测算了30个省的MPI指数,结果表明:从全国整体角度来看,六年间我国整体的物流低碳效率较高且始终保持在一个相对稳定的状态,但省份间的碳排放效率有明显的差距,全要素生产率呈现出一种稳步上升的趋势。从地区角度来看,中部地区效率最高,中东部地区效率明显高于西部和东北部地区,西部地区效率最低,双方差距较大。基于物流业碳排放效率和全要素生产率的评价结果和分析,提出了相应的发展建议。