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.

Promoting Balance in Output Efficiencies for Cross-Efficiency Evaluation in Data Envelopment Analysis

Cross-efficiency evaluation is recognized as an effective way of efficiency assessment for a set of decision making units (DMUs) in the framework of data envelopment analysis (DEA). It has been generally suggested that secondary goals be introduced for cross-efficiency evaluation owing to the non-uniqueness of optimal solutions in self-evaluation. This paper develops a variety of secondary goals in the spirit of promoting balance in the output efficiencies of the DMU under evaluation. The proposed models attempt to make each output contribute as equally as possible to the self-evaluated efficiency. In this way, the weight flexibility can for one thing be reduced by the introduced secondary goals with selections from alternate optimal solutions, in addition to counting on the dilution of flexibility in the subsequent peer-evaluation. The proposed approach might be applicable to evaluation problems in which multiple outputs are considered important and balance is encouraged to put all dimensions into sufficient use. The effectiveness of the proposed approach and its comparisons with some relevant secondary goals are illustrated empirically using numerical examples.

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.

Integrating dual-role variables in data envelopment analysis

Traditional data envelopment analysis（DEA） theory assumes that decision variables are regarded as inputs or outputs,and no variable can play the roles of both an input and an output at the same time.In fact,there exist some variables that work as inputs and outputs simultaneously and are called dual-role variables.Traditional DEA models cannot be used to appraise the performance of decision making units containing dual-role variables.The paper analyzes the structure and properties of the production systems comprising dual-role variables,and proposes a DEA model integrating dual-role variables.Finally the proposed model is illustrated to evaluate the efficiency of university departments.

Stock Efficiency Evaluation Based on Multiple Risk Measures:A DEA-Like Envelopment Approach

This paper proposes a new approach for stock efficiency evaluation based on multiple risk measures. A derived programming model with quadratic constraints is developed based on the envelopment form of data envelopment analysis(DEA). The derived model serves as an input-oriented DEA model by minimizing inputs such as multiple risk measures. In addition, the Russell input measure is introduced and the corresponding efficiency results are evaluated. The findings show that stock efficiency evaluation under the new framework is also effective. The efficiency values indicate that the portfolio frontier under the new framework is more externally enveloped than the DEA efficient surface under the standard DEA framework.

Data envelopment analysis for scale elasticity measurement in the stochastic case:with an application to Indian banking

In the nonparametric data envelopment analysis literature,scale elasticity is evaluated in two alternative ways:using either the technical efficiency model or the cost efficiency model.This evaluation becomes problematic in several situations,for example(a)when input proportions change in the long run,(b)when inputs are heterogeneous,and(c)when firms face ex-ante price uncertainty in making their production decisions.To address these situations,a scale elasticity evaluation was performed using a value-based cost efficiency model.However,this alternative value-based scale elasticity evaluation is sensitive to the uncertainty and variability underlying input and output data.Therefore,in this study,we introduce a stochastic cost-efficiency model based on chance-constrained programming to develop a value-based measure of the scale elasticity of firms facing data uncertainty.An illustrative empirical application to the Indian banking industry comprising 71 banks for eight years(1998–2005)was made to compare inferences about their efficiency and scale properties.The key findings are as follows:First,both the deterministic model and our proposed stochastic model yield distinctly different results concerning the efficiency and scale elasticity scores at various tolerance levels of chance constraints.However,both models yield the same results at a tolerance level of 0.5,implying that the deterministic model is a special case of the stochastic model in that it reveals the same efficiency and returns to scale characterizations of banks.Second,the stochastic model generates higher efficiency scores for inefficient banks than its deterministic counterpart.Third,public banks exhibit higher efficiency than private and foreign banks.Finally,public and old private banks mostly exhibit either decreasing or constant returns to scale,whereas foreign and new private banks experience either increasing or decreasing returns to scale.Although the application of our proposed stochastic model is illustrative,it can be potentially applied to all firms in the information and distribution-intensive industry with high fixed costs,which have ample potential for reaping scale and scope benefits.

Application of a mixed DEA model to evaluate relative efficiency validity

Data envelopment analysis(DEA) model is widely used to evaluate the relative efficiency of producers. It is a kind of objective decision method with multiple indexes. However, the two basic models frequently used at present, the C2R model and the C2GS2 model have limitations when used alone,resulting in evaluations that are often unsatisfactory. In order to solve this problem, a mixed DEA model is built and is used to evaluate the validity of the business efficiency of listed companies. An explanation of how to use this mixed DEA model is offered and its feasibility is verified.

Coordinated development efficiency between cultivated land spatial morphology and agricultural economy in underdeveloped areas in China:Evidence from western Hubei province

Suitable spatial morphology of cultivated land is a basic requirement for sustaining agricultural economic development in mountainous areas.Coordinated development efficiency of cultivated land spatial morphology and agricultural economy(CECA)is of great practical significance to measure the efficiency of cultivated land use,and thereby promote regional rural revitalization.However,few studies to date have focused on coordinated development efficiency between cultivated land use and agricultural economy in mountainous areas from the perspective of cultivated land spatial morphology.Thus,the present study explores CECA with this focus using the data envelopment analysis method,and analyzes the key influencing factors via a geographical detector model in 16 counties in western Hubei province.The results show the following:(1)CECA exhibits significant spatial heterogeneity that is high in the south of the study area and low in the north;(2)scale efficiency is the primary limiting factor for CECA;(3)the insufficient output of cultivated land use mainly restricts CECA in the south of the study area,while individual county in the north suffered from input redundancy and insufficient output;and(4)population density in the southern region has the most significant effect on CECA,and gross domestic product has the greatest impact in the northern region.The results contribute to the derivation of specific measures by which to promote cultivated land use efficiency and sustainable development of the social economy.

An Alternative Approach to Rank Efficient DMUs in DEA via Cross-Efficiency Evaluation,Gini Coefficient,and Bonferroni Mean

In this paper,we focus on a critical problem in data envelopment analysis(DEA)and propose a simple resolution for it.The major problem of the DEA is the existence of several efficient decision-making units(DMUs).To deal with this issue,we introduce a method that involves cross-efficiency evaluation,Gini coefficient,and Bonferroni mean.First,a cross-efficiency matrix is developed.Then,mixing the Gini coefficient and Bonferroni mean,a Gini–Bonferroni(GB)index is proposed for ranking efficient DMUs,where the DMUs with bigger GB are ranked higher.The proposed method broke the tie between efficient DMUs.Finally,a numerical example and real application of this method are presented in the ranking of research and development(R&D)investment companies in the pharmaceutical and biotechnology industries.

基于波士顿矩阵分析法的区域医院科技创新效率研究

目的通过客观评价了解区域医院科技创新效率,推动医院医学学科建设,为医院争创国家区域医疗中心的建设目标提供助力。方法基于医院科技量值指标体系,选取深圳市67家三级医院作为研究对象,分析医院科技创新发展情况,利用波士顿矩阵分析法反映医院科技创新效率。结果深圳市医院间科技创新效率有较大差距:6家医院以相对低的科技投入产生了较高的科技产出,科技创新效率最好;13家医院以较高的科技投入产生了较高的科技产出,具有较好的科技创新效率,科技产出仍可再提升;1家医院科技投入高而科技产出较低,具有较低的科技创新效率,在科技产出方面仍有较大提升空间;47家医院科技投入较低且科技产出也较低,在科技投入及科技产出两个维度仍需发力。结论应依据城市经济高质量发展水平,加强医院科技创新投入水平,引导产出高质量医学科技成果,提升医院科技创新效率。

煤炭企业安全生产费用效率评价

为评估我国的煤炭企业安全生产费用效率,选用数据包络分析(DEA)方法,选取2012—2020年煤矿安全投入产出数据构建DEA模型,对煤炭企业每年的安全生产费用效率进行静态分析和评价;随后,利用Malmquist指数对煤炭企业安全生产费用效率作进一步动态分析研究。研究结果表明:从静态分析来看,纯技术效率呈负增长趋势是阻碍综合效率增长的主要原因,煤炭企业安全生产费用应占营业收入的4.23%;从动态分析来看,技术进步和技术效率的提高都会相应提高Malmquist指数,提高我国煤炭企业全要素生产率指数的关键是提高技术进步指数。研究结果可为煤炭企业规范使用安全生产费用提供参考。

基于模糊DEMATEL-超效率DEA的可再生能源发电技术综合效益评估方法

“双碳”目标下,各类可再生能源发电技术发展迅速,综合权衡不同可再生能源发电方案的综合效益对可再生能源的优化设计具有重要意义。综合考虑经济效益、环境效益、能源效益和社会效益4个层面,提出了一种基于模糊决策试验和评价实验(decision making trial and evaluation laboratory,DEMATEL)与超效率数据包络分析(data envelopment analysis,DEA)模型的可再生能源发电技术综合效益评估方法。该方法分为投入-产出指标体系构建和综合评估2个阶段。首先,利用三角直觉模糊数处理模糊评价信息,将其与DEMATEL相结合量化各指标之间相互影响关系,基于指标间逻辑分析结果建立投入-产出评估指标体系。然后,基于超效率DEA模型对各可再生能源发电方案进行评估排序,结合投入冗余和产出不足分析结果给出各方案的针对性改善建议,以期为进一步选择和确定可再生能源产业发展战略提供参考。最后以某省10类可再生能源发电单元为研究对象,基于所提研究方法进行综合评估和分析,并与多准则妥协解排序法和熵权法进行对比分析,验证了所提方法的有效性。

突发公共卫生事件下各省医疗物资供给与医疗服务综合效率评价--基于DEA-RSR模型

文章对2020年新冠疫情下我国31个省份医院服务效率进行综合评价,并进行防疫前后综合效率对比,以帮助各省医疗系统提高弹性,在后疫情时代有效应对突发公共卫生事件。从提高不良输出DMU识别能力的角度,将“数据包络分析法(DEA)”和“秩和比综合比较法(RSR)”相结合,利用DEA和反向DEA模型分别从乐观和悲观的角度计算综合效率,在此基础上采用RSR法进行排序、分档。各省医院综合效率划分为四个等级,其中山东、四川、广东3个省份位于第四等级,青海和西藏位于第一等级;青海和西藏的医院在防疫期间的综合实力还有待加强,部分地区医疗资源出现挤兑,各省应调整医疗资源分配、扩大医疗服务功能。

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。

基于DEA-Malmquist指数模型的我国中医医院服务效率评价研究

目的评价2017—2023年全国30个省(区、市)中医医院的服务效率,为促进中医医院高质量发展提供参考。方法运用数据包络分析法中的BCC模型和Malmquist指数模型测算全国中医医院服务效率的静态和动态变化,利用时间序列预测法预测2022—2023年的指标变化并进行动态评价。结果2021年全国中医医院的综合效率均值为0.852,50%的省(区、市)处于DEA无效状态,2017—2021年全国中医医院的总体服务效率呈现下降态势,全要素生产率指数均值为0.958,平均下降4.2%,东、中、西部地区的指数均值分别为0.978、0.946和0.951,3大地区均出现不同程度的下降,经预测分析后的2017—2023年整体效率变化趋势不大。结论全国中医医院的总体服务效率处于较低水平,且区域发展差异明显,其中技术进步指数是一项重要影响因素。各省(区、市)应合理控制中医医院的发展规模,不断推动技术进步,提高资源利用率,逐步缩小地区差异,进而提升中医医院的服务效率。

基于数据包络分析法的纺织服装业效率研究进展

为评估纺织服装业内外部效率,进而辅助产业可持续发展或合理配置资源,通过对国内外基于数据包络分析法(DEA)的纺织服装业效率研究进展进行分析,梳理基于DEA法的纺织服装业效率分类及研究内容,总结纺织服装业效率评价DEA法是以传统、动态、多阶段、非径向模型为主流。指出基于DEA法的纺织服装业效率评价过程是由6个模块单元组成,并展望相关研究未来的发展方向,包括创建新模型、拓展效率研究视角、细化效率影响因素、突破内部网络结构等,为纺织服装领域效率评价研究提供参考。

就业训练中心的职业技能培训效率研究——基于DEA-Malmquist评价模型的分析

基于2012—2021年份《中国劳动统计年鉴》中全国23个省、自治区、直辖市就业训练中心的面板数据,构建DEA-Malmquist评价模型,对各省就业训练中心职业技能培训的投入产出效率进行分析。结果显示,当前各地就业训练中心投入产出效率整体虽处于规模递增状态,但粗放型特征比较明显。建议各地提高对职业技能培训重要性的认识;协调区域间优质资源共享、构建清晰完整的职业技能发展路径;化解潜在的技术退步风险、改进资源投入方式;抓住“互联网+”机遇,积极探索新的效率增长点;实现培训效率和就业水平的整体提升。

北京市养老机构医养结合服务效率评价研究

背景我国老龄化程度不断加深,居民医疗与养老服务的需求与日俱增。探讨医养结合服务如何高效率、高质量发展,对积极应对人口老龄化具有重要的现实意义。目的评价北京市养老机构的医养结合服务效率,为改善养老机构医养结合服务效率、优化资源配置提供参考依据。方法2018年5月对北京市养老机构现场调查。采用数据包络分析(DEA)的产出导向的BCC模型,对北京市62家养老机构医养结合服务的综合效率、纯技术效率、规模效率分别进行评价。投入指标包括:机构床位数、医务人员数、养老护理员(护工)人员数。产出指标包括:完全失能/重度障碍老人数量、部分失能/中轻度障碍老人数量、自理老人数量。结果北京市62家养老机构医养结合服务综合效率、纯技术效率、规模效率中,DEA有效单元比例分别占12.903%、35.484%、12.903%,综合效率、纯技术效率、规模效率平均值分别为0.599、0.788、0.760。其中,规模报酬不变的占12.903%,规模效益递减的占70.968%,规模效益递增的占16.129%。结论所调查的北京市养老机构医养结合服务综合效率偏低,存在资源浪费、供需不匹配的现象。建议养老机构科学定位目标群体,提供精准服务,合理计算入住容量,推进医疗机构与养老机构的服务合作,而政府要继续贯彻相关扶持政策的进一步落地。

基于DEA模型部分传统工业城市转型效率评价研究

在建国初期,国家在部分城市建立了重要的能源战略安全基地,有力保障了新中国工业经济实现大跨越大发展。但随着我国经济发展方式逐渐向高效集约的转变,这些工业型城市也面临着可采能源逐渐枯竭、转型发展迫在眉睫的情况。现从国家分三批确定的69个典型资源枯竭型城市选取8个样本,运用相关性分析法和数据包络分析法(DEA)[1],从技术效益、规模效益、产出不足率等方面对2022年部分工业城市转型效率进行研究,根据模型运算结果剖析各城市投入产出情况,并以数据和现实需求有针对性地给出建议。

政府开放数据资源利用与创新应用效能评价研究

[目的/意义]针对不同省市政府开放数据建设情况的偏差,以城市创新应用为例对政府开放数据资源利用效能情况进行评价,旨在为政府开放数据建设与数据资源创新应用提供启发式建议。[方法/过程]以上海、浙江、广州等18个省级、副省级政府开放数据平台作为研究样本,选择数据集数量、容量、可机读性、更新情况、开放情况、浏览及下载情况、发明专利申请量和高新技术企业数量作为评价指标,使用数据包络分析法进行评价。[结果/结论]在政府数据利用效能方面,政府应通过提供社会、使用者需要的热点数据资源与核心数据资源来缓解投入冗余,在保证平台曝光度的同时加强引导,促进数据资源的利用。在创新应用方面,应以政府部门为主导、开放数据平台为核心、数据资源用户为主体进行数据创新应用,在各方参与者的相互作用下实现城市创新水平的提高。