资本占用系数与直接消耗系数变动

生产能力利用程度的变化会引起资本占用系数的变化。本文从边际投入的角度,研究了资本占用系数变化对投入产出表中直接消耗系数的总体影响,并对我国1987—1990年投入产出表进行计算,得出在这两年直接消耗系数的总体变动主要是由资本占用系数的变动所致。     

基于投入产出特征法的我国产业结构分析

技术经济结构是社会经济活动的基础结构,对我国投入产出表应用特征分析法,定量分析我国农业、能源化工业、工业建筑业、服务业四个产业部门的产业结构变化及产业协调程度,研究各产业部门的产业结构协调发展方向.     

RAS方法可靠程度的实证分析

RAS法是进行投入产出消耗系数及时修正的重要工具 ,本文提出了评价系数修正可靠程度的标准并利用中国 1 987和 1 992年可比价投入产出表 ,对 RAS法的可靠程度进行了深入分析 ,比较了不同类型、不同部门分类消耗系数修正结果可靠程度的差别 ,从而揭示了 RAS修正结果中所蕴涵的相对可靠的信息 .     

投资内生化的房地产投入占用产出局部闭模型及其应用

房地产在国民经济中占据特殊地位,为全面反映房地产与国民经济间的相互联系和促进关系,设计和构造了房地产投入占用产出表.分析表中数据可知,房地产发展对国民经济的主要影响与其他生产部门不同,主要通过增加企业毛盈余和政府收入进而拉动投资增长,最终实现经济增长.鉴于传统投入产出模型不能对此进行有效刻画,提出新的将投资内生化的投入产出局部闭模型.应用模型测算房地产对经济的拉动效应,得出的测算结果与传统模型相比明显偏大.     

基于非对称供给使用表的部门异质性分析——以中国生产能耗异质性为例

投入产出表的间断性和时滞性使得投入产出分析的应用受到一定的限制,供给使用表作为投入产出体系的基础数据,与投入产出表相比具有数据易获得、时滞期较短的优点;且供给使用表同时包含了产业和产品两个维度的信息,基于供给使用表的模型可以反映出产业部门和产品部门的生产异质性,即同一产业部门生产不同产品和同一产品被不同产业生产时遵循不...     

基于投入产出线性规划模型的燃料乙醇影子价格研究

影子价格能够反映资源的稀缺性及对国民经济的贡献程度.应用投入产出与线性规划相结合的分析方法,构建了投入产出线性规划模型,并基于笔者编制的2005年燃料乙醇投入产出表,针对燃料乙醇制造业部门的不同发展速度,运用投入产出线性规划模型对燃料乙醇的影子价格进行了具体测算.     

国家能源战略下的新疆煤制油煤制气产业投入产出分析

我国"富煤、贫油、少气"的资源禀赋决定了我国石油天然气的高对外依存度和燃煤在一次能源使用中的高占比,这在影响国家能源安全的同时,也造成了较大的碳减排压力.在主要煤炭基地发展煤制油和煤制气产业对提升我国的能源安全程度和煤炭资源的利用效率具有重要意义.通过对新疆煤制油和煤制气已投产项目的资料进行收集分析的基础上,结合新疆2012年投入产出表,按照价值生成和产品分配两个维度新增了新疆煤制油和煤制气产业的投入产出数据,并据此分析了煤制油和煤制气产业对新疆区域经济发展的影响.研究结果表明:煤制油和煤制气产业除对煤炭采选业、交通运输行业、仓储行业、电力热力生产和供应行业以及供水行业有明显的拉动作用外,还和化学工业和金融业有着密切的联系,这对相关产业政策的制定具有参考价值.     

一般工艺假定及其投入产出模型的优化

根据供给表和使用表推算国家(地区)对称型投入产出表有两种方法,即:产品工艺假定方法和部门工艺假定方法.但是上述两种方法都属极端假设的方法,为此提出了一般工艺假定的概念和相应的优化模型,给出了国家(地区)对称型投入产出表编制的优化方法.     

河北省高耗能行业创新绩效评价研究

创新绩效评价是对创新投入产出过程和结果的监督和管理,通过创新绩效评价,发现创新中存在的问题和改进的方向,使后续的创新工程更能满足社会经济需要.运用突变级数法,从投入产出的角度对河北省高耗能行业创新绩效进行了研究.结果表明,除黑色金属冶炼和压延加工业属于高投入高产出、创新绩效一般外,其他高耗能行业创新绩效低.最后针对此种情况,提出了提升高耗能行业创新绩效的对策建议.     

关于湖北省节能的投入产出分析

运用投入产出法,对湖北省达成"十一五"节能目标的万元GDP综合能耗进行了分能源品种的分解,对湖北省未来五年的能源经济指标进行了预测和分析.首先,本文把64部门的2002年湖北省投入产出表整理成6部门的投入产出表,推测出2005年湖北省能源生产和消费指标,分析了湖北省能源供需的基本形势.其次,本文以实现"十一五"期间万元GDP综合能耗降低20%为目标,对不同GDP增长率下的能源需求情景进行了预测,计算了湖北省2010年分品种的万元GDP综合能耗、能源消费弹性系数等指标,分析了不同能源品种对单位GDP综合能耗下降的贡献程度.最后本文提出了政策建议,并指出模型的发展方向.     

Output-specific input-assurance regions in DEA

The use of assurance region (AR) constraints to restrict multipliers in data envelopment analysis (DEA) is well-established, and has been discussed at length in the literature. The conventional assumption in imposing such restrictions is that they apply universally. Specifically, AR constraints on input multipliers are intended to control the relative importance of the individual inputs in terms of how they impact the entire bundle of outputs. In many settings the relative importance of inputs is different for some of the outputs than for others. A typical example of this in the financial services sector is where the importance of sales staff versus service staff is different in regard to sales outputs than is true for service outputs. In this paper we develop a general DEA framework that incorporates multiple input-AR structures that cater to multiple output classes. We examine the cases of both divisible and indivisible inputs, and as well as mutually exclusive and overlapping output sets. The concepts are applied to a financial services situation.     

An extension to data envelopment analysis with preference structure for estimating overall inefficiency

This paper extends data envelopment analysis (DEA) with preference structure by fully considering the substitution effects among different inputs or outputs. When the unit cost and price information on inputs and outputs are available, the generalized weighted CCR (GWCCR) models proposed in this paper can provide some scalar values for measuring the overall inefficiency. It is found that the GWCCR models focus on the relative aspects of overall inefficiency instead of the absolute aspects focused on by the weighted additive DEA model.     

Modeling undesirable factors in data envelopment analysis

Data envelopment analysis is a mathematical programming technique for identifying efficient frontiers for peer decision making units with multiple inputs and multiple outputs. These performance factors (inputs and outputs) are classified into two groups: desirable and undesirable. Obviously, undesirable factors in production process should be reduced to improve the performance. In the current paper, we present a data envelopment analysis (DEA) model in which can be used to improve the relative performance via increasing undesirable inputs and decreasing undesirable outputs.     

Unobservable or omitted production variables in data envelopment analysis through unit-specific production trade-offs

The paper focuses upon situations in which decision-making units carry out their production activities with some inputs or outputs unobservable (or possibly omitted), and when there are a priori known constraints on the relative significance of otherwise observable (or explicitly considered) inputs and outputs. For such settings, the paper proposes a modification that alters traditional construction of the production possibility set and isolates the role of the unobservable (or omitted) variables in production by means of restrictions on virtual inputs and outputs being converted into production trade-offs. In effect, the proposed procedure induces unit-specific production possibility sets that derive from production trade-offs framed for units assessed separately to reflect their specific observed production conditions. The modification is implemented within a weighted slacks-based measure with restricted direction of slacks in order to make technical efficiency measurement more informative and consistent with the operating conditions under which production activities are accomplished. These ideas are illustrated and models implemented in a case study of bank branch performance measurement.     

Equivalence in two-stage DEA approaches

Data envelopment analysis (DEA) is a linear programming problem approach for evaluating the relative efficiency of peer decision making units (DMUs) that have multiple inputs and outputs. DMUs can have a two-stage structure where all the outputs from the first stage are the only inputs to the second stage, in addition to the inputs to the first stage and the outputs from the second stage. The outputs from the first stage to the second stage are called intermediate measures. This paper examines relations and equivalence between two existing DEA approaches that address measuring the performance of two-stage processes.     

Finding strong defining hyperplanes of Production Possibility Set

Production Possibility Set (PPS) is defined as the set of all inputs and outputs of a system in which inputs can produce outputs. Data Envelopment Analysis models implicitly use PPS to evaluate relative efficiency of Decision Making Units (DMUs). Although DEA models can determine the efficiency of a DMU, they cannot present efficient frontiers of PPS. In this paper, we propose a method for finding all Strong Defining Hyperplanes of PPS (SDHP). They are equations that form efficient surfaces. These equations are useful in Sensitivity and Stability Analysis, the status of Returns to Scale of a DMU, incorporating performance information into the efficient frontier analysis and so on.     

A compromise solution approach for finding common weights in DEA: an improvement to Kao and Hung's approach

Data envelopment analysis (DEA) is the leading technique for measuring the relative efficiency of decision-making units (DMUs) on the basis of multiple inputs and multiple outputs. In this technique, the weights for inputs and outputs are estimated in the best advantage for each unit so as to maximize its relative efficiency. But, this flexibility in selecting the weights deters the comparison among DMUs on a common base. For dealing with this difficulty, Kao and Hung (2005) proposed a compromise solution approach for generating common weights under the DEA framework. The proposed multiple criteria decision-making (MCDM) model was derived from the original non-linear DEA model. This paper presents an improvement to Kao and Hung's approach by means of introducing an MCDM model which is derived from a new linear DEA model.     

Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis,DEA and Neural Networks

Data Envelopment Analysis (DEA) is a powerful data analytic tool that is widely used by researchers and practitioners alike to assess relative performance of Decision Making Units (DMU). Commonly, the difference in the scores of relative performance of DMUs in the sample is considered to reflect their differences in the efficiency of conversion of inputs into outputs. In the presence of scale heterogeneity, however, the source of the difference in scores becomes less clear, for it is also possible that the difference in scores is caused by heterogeneity of the levels of inputs and outputs of DMUs in the sample. By augmenting DEA with Cluster Analysis (CA) and Neural Networks (NN), we propose a five-step methodology allowing an investigator to determine whether the difference in the scores of scale heterogeneous DMUs is due to the heterogeneity of the levels of inputs and outputs, or whether it is caused by their efficiency of conversion of inputs into outputs. An illustrative example demonstrates the application of the proposed methodology in action.     

Ranking DMUs by l1-norm with fuzzy data in DEA

The relative efficiency of a DMU is the result of comparing the inputs and outputs of the DMU and those of other DMUs in the PPS (production possibility set). If the inputs and outputs are fuzzy, the DMUs cannot be easily evaluated and ranked using the obtained efficiency scores. In this paper, presenting a new idea for ranking of DMUs with fuzzy data. And finally, we introduce a numerical example.     

Cross-country comparison of the efficiency of the European forest sector and second stage DEA approach

In this paper the relative efficiency of the forest sector of 28 EU/EFTA countries during the period 2010–2015 is assessed using Data Envelopment Analysis (DEA). Three non-discretionary inputs (persons employed, forest available for wood supply and initial growing stock) are considered. The outputs are roundwood production, gross value added and final growing stock. The proposed DEA model not only computes efficiency scores but also improvement targets. The countries with the lowest efficiency scores during the period under study are Greece, Bulgaria and Italy. In the second stage, a fractional regression model is fitted and the factors that have an influence on the estimated efficiency are identified. The factors that have an influence are GDP and belonging to the NORTH Europe and CENTRAL-WEST Europe regions. Quantitative estimates of the partial effects of these factors are provided. The results can contribute in providing guidance towards the best practice in roundwood production.