数据包络分析在临床科室绩效管理中的应用

目的 通过总结数据包络分析在临床科室效率测算中的应用,为医院绩效管理提供一种量化管理技术。方法 应用数据包络分析测算临床科室的相对效率、生产率变化率。通过对测算结果的描述分析,得到临床科室的效率评价结果。将数据测算及分析结果与职能科室管理者对临床科室效率的定性评价进行比较。结果 测算数据的描述分析结果与临床科室绩效的管理者评价具有一致性。结论 数据包络分析在医院临床科室绩效管理中具有实际应用价值、是绩效管理者的有效管理技术。     

东北地区生态资本效率时空差异与影响因素

生态资本是生态文明建设的重要支点,测度生态资本效率的时空分异和影响因素,有助于了解生态资本效率现状进而提升生态文明水平实现绿色发展.选取东北地区作为研究区域,运用超效率数据包络分析法(DEA)模型和Malmquist指数测度东北地区生态资本效率的时空变化并对影响因素进行分析.结果表明: 东北地区生态资本效率总体良好,呈现先升后降的趋势,黑龙江生态资本效率较高,呈“U”型发展,吉林、辽宁生态资本效率相对较低.Malmquist指数分析显示,技术进步是生态资本效率提升的主要推动力量.Tobit回归分析结果表明,环境的规模效应、人口结构效应变量对生态资本效率具有显著的正向影响,科技投资效率低导致环境技术效应变量与生态资本效率呈负相关.提高东北地区生态资本效率,一方面要加快产业结构调整,发展生态经济;另一方面要提升科技资金的投入效率,依托技术进步实现东北地区经济的绿色发展.     

效率视角下的省域生态文明建设评价研究

在界定生态文明概念的基础上,从省域层面入手,选取25个具有可操作性的评价指标,构建以国土空间优化、资源节约集约利用和生态环境保护为一级指标的生态文明建设评价指标体系,综合运用网络层次分析和集对分析等研究方法,定量研究我国省域生态文明建设绩效;在此基础上,将国土空间优化绩效、资源节约集约利用绩效和生态环境保护绩效作为产出指标,将就业人数、科学技术财政支出、节能环保财政支出作为投入指标,运用数据包络分析法测度各省(市)生态文明建设效率,分析指标投入冗余和产出不足情况,并修正评价绩效。研究表明:(1)规模效率低于0.6的省份共24个,占80%,规模效率不高是制约生态文明建设水平提升的重要障碍;(2)在生态文明建设投入冗余和产出不足方面,数值较高的省(市)在地理分布上具有明显的区域聚集性;(3)领先地区资源节约集约利用绩效平均分高达3.92,落后地区平均分仅为0.84,生态文明建设绩效的差异性在资源节约集约利用方面表现的尤为明显;(4)人口规模偏大、绿色生态空间建设滞后于经济发展、水资源利用效率不高是多数生态强省(市)的薄弱环节。     

基于MFA和DEA的区域经济环境效率评价-以辽宁省为例

对经济与环境效率的科学评价是实现区域可持续发展的前提。运用物质流分析将辽宁省经济系统中数据进行物质化处理,再利用改进的数据包络分析模型对环境和经济效率进行综合评价。结果表明:辽宁省物资消费不主要依赖于进口,向其它地区物质输出量大;环境效率评价的综合效率主要受规模因素影响而显著低于纯技术效率,而整体经济的综合效率却主要受纯技术效率影响而下降。第二产业比重依然偏大的产业结构特征是导致上述结果的主因。进一步改造提升传统产业,发展战略性新兴产业,提高第三产业发展水平,扩大环保规模,促进居民生活质量水平全面提升,将是辽宁省以及与之相似的资源依赖型区域可持续发展的方向。     

基于Malmquist模型的北京市中医医院运行效率分析

目的 分析北京市中医医院运行效率现状并对三级、二级中医医院进行对比分析,探索其影响因素并为医院决策提供建议。方法 采用描述性统计分析方法描述各项指标基本情况,采用数据包络分析Malmquist指数模型分析运行效率及变化趋势。结果 2012—2013年9家三级医院全要素生产率上升,10家二级医院全要素生产率上升;2013—2014年6家三级医院全要素生产率上升,8家二级医院全要素生产率上升。结论 近三年二级中医医院运行效率高于三级医院运行效率;三级医院运行效率差异明显,二级医院技术与效率同步提高;技术进步为医院运行效率提高有利因素;效率降低为医院运行效率降低主要原因。     

北京市三级中医医院运行效率的Malmquist指数研究

目的 分析北京市13家三级中医医院总体运行效率及中医服务提供运行效率现状,探索其影响因素。方法 采用描述性统计分析方法描述各项指标基本情况,采用数据包络分析Malmquist指数模型分析运行效率及变化趋势。结果 2012—2014年,三级中医医院总体及中医服务技术均呈现进步或不变状态,各医院效率变动差异显著。结论 中医医院中医服务效率低于总体运行效率;多数医院技术提高缓慢,技术进步为全要素生产率上升有利因素;效率降低为全要素生产率降低的主要原因。     

基于DEA和障碍度模型的江西省农业生产有效性评价

基于江西省2016年11个市的统计数据, 以农用柴油使用量、农业从业人口、农药使用量、地膜使用量、化肥使用量、机械总动力为投入值, 农业总产值为输出值, 运用数据包络分析(DEA)法中的DEA-C²R模型对江西省农业生产效率进行分析研究, 并运用发展障碍度模型对影响江西省农业生态效率的主要障碍因素进行了分析诊断。结果表明: 江西省大多数市农业生产效率较好, 除九江和鹰潭两市外, 农业生产综合效率均大于0.9, 部分城市上升空间仍然很大; 影响江西农业生态效率的主要障碍因素有化肥施用量和农业机械总动力, 它们的障碍度分别为0.49和0.43。     

太湖春季水体固有光学特性及其对遥感反射率变化的影响

吸收特性和后向散射特性是水体重要的光学特性,同时也是建立生物光学模型的基本参数。利用2009年4月太湖春季实测数据,结合生物光学模型推导了太湖春季水体颗粒物后向散射系数,在此基础上分析了太湖春季水体的吸收特性和后向散射特性,并利用经验正交分解方法对遥感反射率变化的影响因子进行了分析。结果表明：（1）非色素颗粒物是影响太湖春季水体吸收特性的主导因子,色素颗粒物和CDOM对总吸收（不包含纯水）的贡献相对较小,且色素颗粒物在梅梁湾湖区的包裹效应明显大于其他湖区。（2）颗粒物后向散射系数与总悬浮物和无机悬浮物具有很强的相关性（相关系数均在0.88以上）,与有机悬浮物的相关性相对较弱（相关系数均在0.73以下）,且水体中多次散射对水面总辐亮度有较大的贡献,平均贡献率高达93.46%。（3）利用经验正交分解方法将遥感反射率变化光谱分解成3个正交因子,3个正交因子总共解释了约99%的遥感反射率变化信息,其中,第一正交因子解释了93%的变化信息,第二和第三正交因子分别解释了5%和1%的变化信息。通过对各正交因子与水体不同组分的吸收和后向散射系数进行相关性分析得出,颗粒物的后向散射对水面反射光谱的形成具有非常重要的影响,太湖春季水体遥感反射率的变化主要取决于无机颗粒物的吸收和后向散射,有机颗粒物对遥感反射率的变化影响较小。     

越冬和复苏时期太湖水体蓝藻群落结构的时空变化

为研究太湖蓝藻在越冬与复苏时期群落结构的时空变化规律,于2008年11月,2009年2月,2009年4月,在太湖富营养化较严重的湖区选取8个采样点 (梅梁湾、竺山湾、贡湖湾、大浦、西太湖、南太湖、湖心和湖湾交汇处),分3层采水样,过滤并提取样品DNA经PCR扩增蓝藻16S rDNA序列,采用T-RFLP(末端标记的限制性酶切片段长度多样性)技术分析蓝藻群落结构和多样性变化。共得到87个不同的T-RFs(末端限制性酶切片段),表明太湖蓝藻具有丰富的基因多样性。T-RF相对丰度和聚类分析结果表明,太湖蓝藻群落结构在垂直空间上相似性较高,相似度 > 50%;在水平空间,与Microcystis spp.对应的信号峰在8个采样点均为最强峰(相对丰度为17.7% 47.5%)。竺山湾蓝藻多样性最低,西太湖最高,但其余采样点间蓝藻群落和Shannon多样性指数没有显著差异(P>0.05)。Microcystis相对丰度与Shannon多样性指数呈显著负相关(皮尔逊相关系数为-0.958)。在时间尺度上,相似性分析(Analysis of similarity,ANOSIM)结果显示太湖蓝藻群落结构存在极显著差异( P<0.01) 。春季复苏时蓝藻多样性最高,秋季衰亡时最低。聚类分析表明样品聚成两大特征类群,秋季衰亡时样品独自聚为1支,而春季复苏期和冬季越冬期样品彼此混杂。     

基于超效率DEA模型的医院技术效率评价与影响因素分析

目的 基于2014年全国30个省份医院的横截面数据,分析其技术效率和影响因素,为提高医院运行效率提供建议。方法 采用超效率DEA模型计算各省份医院的技术效率,采用多元线性回归探讨医院技术效率的影响因素。结果 30个省份医院的超效率平均值是0.995,其中13个省份DEA有效,非DEA有效医院若达到DEA有效可少开办医院3 596所,少雇佣卫生人员429 594人,节省床位332 304张。医院数与病床使用率对技术效率的影响具有统计学意义。结论 全国30个省份医院之间技术效率差异较大,投入相对过剩,需进一步合理配置医疗卫生资源,提高医院整体运行效率。     

Closest targets in environmental efficiency evaluation based on enhanced Russell measure

As environmental problems become more and more serious all over the world, environmental efficiency evaluation has drawn increasing interests of many scholars and governments’ decision makers. Data envelopment analysis (DEA) as a non-parametric approach for evaluating the relative efficiency of a group of decision making units (DMUs) has been widely extended and applied in many areas. Among various DEA models, enhanced Russell measure (ERM) model can measure the inefficiencies from input orientation and output orientation simultaneously. In this paper, we proposed a new non-orientation DEA approach based on enhanced Russell measure for measuring the environmental efficiency of a DMU and meanwhile, provided the closest target for the evaluated DMU to efficient with less effort. At last, our approach was applied to a practical example about thermal power enterprises. The results show that our model can provide a much easier way for the inefficient enterprises to improve their efficiencies than the enhanced Russell measure method. Moreover, the provided benchmarks for enterprises through our model can be used to further rank the efficient ones.     

Eco‐efficiency Based on Social Performance and its Relationship with Financial Performance

As corporate responsibility for environmental management has gained attention, eco‐efficiency has become recognized as an important concept for improving the social performance of the business sector as well as that of the public sector. Improving eco‐efficiency is widely accepted not only as a means of increasing economic value, but also as a means of reducing environmental effects. However, managing for eco‐efficiency should take into consideration the differences among industries, because the impact of eco‐efficiency on financial and social performance varies among industries. To explore this variation, we conducted a cross‐industry analysis of eco‐efficiency based on social performance using data envelopment analysis (DEA). DEA measures relative efficiency and is a useful tool for taking into account the relative importance of industry‐specific characteristics. Using DEA, eco‐efficiency scores were derived based on the ratio of two factors of social performance: (1) value‐added inducing and production‐inducing economic spillover effects and (2) the amount of greenhouse gases emitted and energy used. Then, we identified the relationships between our eco‐efficiency score and financial performance, which is a measure of the firm's stability. The case study is based on 272 firms in 16 industries in South Korea. Results show that firms in product manufacturing and service‐intensive industries tend to have higher eco‐efficiency scores than those in raw material or chemical‐intensive industries. In addition, most of the industries reveal no relationship between traditional financial performance metrics and eco‐efficiency scores. A handful of industries had significant relationships with one or more financial performance metrics; in some cases, these relationships were negative, whereas in others they were positive. Surprisingly, almost all industries have no significant relationships between eco‐efficiency and financial performance. This result implies that government support for policies that reward firms that attempt to be eco‐efficient are needed, or that other nonfinancial metrics that influence eco‐efficiency, such as employment and brand reputation, should be considered. This article is expected to support policy makers as they formulate industry‐specific environmental strategies.     

A non-parametric bootstrap-data envelopment analysis approach for environmental policy planning and management of agricultural efficiency in EU countries

Globally, agriculture is a dominant form of human use of land with agro-ecosystems covering about 40% of the terrestrial surface of the Earth. In this context, the European Union considers agriculture a key sector of the economy, recognizing, however, the related environmental implications. The aim of this paper is to examine the agricultural efficiency of EU countries, through a bootstrap-Data Envelopment Analysis (DEA) approach, an effective nonparametric method for evaluating the relative efficiency of the decision-making units. European datasets, suitable for policies and focused on the integration between agricultural productivity and ecosystem services (ESs) conservation, have been used to support planners and managers. Data related to five inputs (labor, land, capital, fertilizers, and irrigation area) and to one output connected to the economic value of agricultural production were collected from 1993 to 2013. The results show that the majority of EU countries have been experiencing increasing or decreasing returns to scale, highlighting their potential to increase their production efficiency by modifying their input use. Both for the output-oriented approach and the input-oriented approach, the majority of EU countries could better rationalize their input use obtaining more outputs and achieving production efficiency. DEA, a non-parametric methodology has been applied, using the concept of a reference group of efficient decision-making units that produce a similar output (peer group). Input-oriented and output-oriented DEA results and comparison indicate that most of the oldest EU countries have a more efficient and optimized crop production process in terms of resource savings and output maximization. This is probably due to the application of the Common Agricultural Policy. Therefore, in policy planning but also in management decisions, attention should always be paid not only to the maximization of agricultural production, but also to the environmental resource overexploitation. In this sense, best agricultural practices could represent a model to follow because they can maintain ESs without depressing production by using practices like conservation tillage, crop diversification, legume intensification and biological control perform giving the same results as intensive, high-input systems.     

Have Chinese cities achieved the win–win between environmental protection and economic development? From the perspective of environmental efficiency

The development of a regional or national economy is always accompanied by a variety of environmental problems. Using meta-frontier and data envelopment analysis (DEA), this paper investigates the environmental protection mechanisms and economic development of 211 cities in China from the perspective of environmental efficiency. The major conclusions are: (1) the overall environmental efficiency of Chinese cities is low when the most advanced production technology is selected as the reference. Only 10% of cities have achieved a win–win, defined as an effective balance between environmental protection and economic growth. It is possible to increase economy and reduce pollutant emissions at the same time for most of cities. (2) The environmental efficiencies and production technologies of cities in five different groups (based on a widely accepted business index) present significant differences. First-tier cities possess more advanced technology, higher environmental efficiency and, therefore, a higher overall win–win balance between economic development and environmental protection; while the fourth-tier and fifth-tier cities have a relatively large gap between development and protection. (3) To achieve a win–win balance between environmental protection and economic development, the local government of different cities should develop appropriate policies that maximize the use of technology and management practices that enhance both growth and protection.     

Sensitivity of cell-based biosensors to environmental variables

Electrically active living cells cultured on extracellular electrode arrays are utilized to detect biologically active agents. Because cells are highly sensitive to environmental conditions, environmental fluctuations can elicit cellular responses that contribute to the noise in a cell-based biosensor system. Therefore, the characterization and control of environmental factors such as temperature, pH, and osmolarity is critical in such a system. The cell-based biosensor platform described here utilizes the measurement of action potentials from cardiac cells cultured on electrode arrays. A recirculating fluid flow system is presented for use in dose-response experiments that regulates temperature within +/-0.2 degrees C, pH to within +/-0.05 units, and allows no significant change in osmolarity. Using this system, the relationship between the sensor output parameters and environmental variation was quantified. Under typical experimental conditions, beat rate varied approximately 10% per degree change in temperature or per 0.1 unit change in pH. Similar relationships were measured for action potential amplitude, duration, and conduction velocity. For the specific flow system used in this work, the measured environmental sensitivity resulted in an overall beat rate variation of +/-4.7% and an overall amplitude variation of +/-3.3%. The magnitude of the noise due to environmental sensitivity has a large impact on the detection capability of the cell-based system. The significant responses to temperature, pH, and osmolarity have important implications for the use of living cells in detection systems and should be considered in the design and evaluation of such systems.     

Data envelopment analysis of cities – Investigation of the ecological and economic efficiency of cities using a benchmarking concept from production management

To better understand the economic performance of cities and the accompanying social and environmental implications, one focus of research has been on ways to quantify performance advantages of growth and size while considering the impact of economies of scale. An important aspect of the current discussion is the introduction of the merely environmental driven concept of resource efficiency, defined as minimizing resource consumption while enhancing the quality of life. However, as yet there is no commonly agreed method on how best to measure efficiency. In order to contribute to this debate, an approach is described here of applying Data Envelopment Analysis (DEA) to study the resource efficiency of cities. Originating in the field of economics, DEA is a non-parametric, deterministic method to measure the efficiency of economic production, specifically the relative efficiency of Decision Making Units (DMUs).Here we test the usefulness of DEA to analyze urban efficiency by applying it to an investigation of 116 cities throughout Germany. This entailed the development of two separate economic and ecological models in order to allow more precise identification of the relevance of individual parameters during the evaluation process. The results allow a ranking of cities as well as an estimation of the ratios of economic and ecological efficiencies of the investigated cities, realized with the aid of a nine-field matrix (portfolio).DEA is at the same time a promising heuristic tool to help draw the basic outlines of a resource efficient city and to shed light on the underlying factors that boost or reduce efficiency. We recommend a three-step approach. First, two separate models should be defined (ecological, economic) and used to feed the DEA computation. Second, the results are spread in a portfolio to give an overview of the ecological and economic efficiency scores. This provides a basic overview of the DEA results for the selected cities following a basic and abstract model without determination of causal relationships between these values. Third, the field-dependent commonalities between the cities are considered. Additional indicators that also characterize the selected cities (but which were not selected as inputs to the algorithm) can now be examined. In this way, it is possible to understand the common factors that determine the level of efficiency as well as to learn about the qualitative difference and specific features of cities in the particular matrix quadrants.     

An under-appreciated difficulty: sampling of plant populations for analysis using molecular markers

Results of studies using molecular markers for determining demographic and genetical population parameters especially in plants or sessile animals under field conditions are strongly dependent on the sampling strategy adopted. There are two critical decisions to make when determining this strategy: (i) what is the unit to be sampled?, (ii) how should units to be sampled in the field be selected? For the first decision, there are two conceptually different approaches: sampling ramets of clonal plants as units (to get information about within-genet parameters, such as genet sizes or numbers) and sampling genets of clonal or non-clonal plants as units (to get information of the genetic structure of the population). For the second decision, it is critically important to make the goal of the study explicit. We argue that in this case fully random sampling is needed only when an estimate of the true value of the population parameter is needed; if a comparison between populations is the goal, however, other sampling schemes may be adopted. The efficiency of different types of sampling strategies to recover relative values in a spatially extended population is studied by means of a spatially explicit simulation model. The results show that a regular pattern of sampling is best for obtaining information on genet sizes or inbreeding coefficients; in contrast, random or hierarchical sampling strategies are better for obtaining information on parameters that are based on comparison of pairs of individuals, such as distribution of genet sizes or autocorrelation in genetic structure. A set of recommendations is provided for designing a good sampling strategy.     

An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies

Eco-efficiency is a useful guide to dairy farm sustainability analysis aimed at increasing output (physical or value added) and minimizing environmental impacts (EIs). Widely used partial eco-efficiency ratios (EIs per some functional unit, e.g. kg milk) can be problematic because (i) substitution possibilities between EIs are ignored, (ii) multiple ratios can complicate decision making and (iii) EIs are not usually associated with just the functional unit in the ratio’s denominator. The objective of this study was to demonstrate a ‘global’ eco-efficiency modelling framework dealing with issues (i) to (iii) by combining Life Cycle Analysis (LCA) data and the multiple-input, multiple-output production efficiency method Data Envelopment Analysis (DEA). With DEA each dairy farm’s outputs and LCA-derived EIs are aggregated into a single, relative, bounded, dimensionless eco-efficiency score, thus overcoming issues (i) to (iii). A novelty of this study is that a model providing a number of additional desirable properties was employed, known as the Range Adjusted Measure (RAM) of inefficiency. These properties altogether make RAM advantageous over other DEA models and are as follows. First, RAM is able to simultaneously minimize EIs and maximize outputs. Second, it indicates which EIs and/or outputs contribute the most to a farm’s eco-inefficiency. Third it can be used to rank farms in terms of eco-efficiency scores. Thus, non-parametric rank tests can be employed to test for significant differences in terms of eco-efficiency score ranks between different farm groups. An additional DEA methodology was employed to ‘correct’ the farms’ eco-efficiency scores for inefficiencies attributed to managerial factors. By removing managerial inefficiencies it was possible to detect differences in eco-efficiency between farms solely attributed to uncontrollable factors such as region. Such analysis is lacking in previous dairy studies combining LCA with DEA. RAM and the ‘corrective’ methodology were demonstrated with LCA data from French specialized dairy farms grouped by region (West France, Continental France) and feeding strategy (regardless of region). Mean eco-efficiency score ranks were significantly higher for farms with <10% and 10% to 30% maize than farms with >30% maize in the total forage area before correcting for managerial inefficiencies. Mean eco-efficiency score ranks were higher for West than Continental farms, but significantly higher only after correcting for managerial inefficiencies. These results helped identify the eco-efficiency potential of each region and feeding strategy and could therefore aid advisors and policy makers at farm or region/sector level. The proposed framework helped better measure and understand (dairy) farm eco-efficiency, both within and between different farm groups.     

The environmental efficiency of Wanjiang demonstration area: A Bayesian estimation approach

With the implementation of the strategic policy and the construction of Wanjiang demonstration area recently, the government cares more about the natural environment. To protect the environment of this demonstration area, a quantitative analysis of environmental efficiency and its influencing factors is needed. In this paper, we measure the environmental efficiency of the demonstration area, and then analyze the total factor productivity of the area by Malmquist productivity index through data envelopment analysis (DEA) approach. Through the index and its decomposition, we are able to reveal the changing trend of the environmental efficiency. Moreover, we apply Bayesian estimation approach to analyze the influencing factors of the efficiency and discuss the relationship between these factors and the environmental efficiency. Results show that the efficiency of Wanjiang demonstration area bares a significant difference among constituent cities. Each area should devise proper environmental policy according to its particular circumstance.     

应用数据包络分析方法评价医院临床科室相对效率的探讨

目的 利用数据包络分析方法评价厦门市某三级综合性医院临床科室的配置效率，为医院管理者提供有益的决策信息。方法 在Excel的规划求解模块上进行数据包络分析计算，得出各临床科室的相对效率得分。结果 数据包络分析有效单元为15个，其余单元不同程度存在产出不足的现象。结论 大型综合性医院在自身的生存和发展中，应注重以资源调整和优化配置为主的内涵式发展。