Fair Target Setting for Intermediate Products in Two-Stage Systems with Data Envelopment Analysis

To examine the resource utilization in different phases such as development and sales within China's real estate industry, this paper employs a two-stage Data Envelopment Analysis (DEA) model to measure the production efficiency of the real estate industry across 31 provinces, municipalities, and autonomous regions of China from 2014 to 2022. By examining both overall and phase-specific trends, the study utilizes a panel Tobit model to explore the factors affecting efficiency. Empirical results indicate that the leverage ratio of companies, per capita GDP of regions, and real estate regulatory policies significantly impact production efficiency. Further analysis of regional heterogeneity and its effect on production efficiency revealed that the per capita residential building area, which reflects the housing stock configuration in different regions, exhibits a significant single threshold effect. This not only objectively assesses the utilization of real estate resources in different areas but also delves deeper into the principal factors and their mechanisms affecting the production efficiency of the real estate industry, thus providing theoretical support and policy recommendations for effectively enhancing production efficiency.

PurposeThe power industry is the pillar industry of the Chinese economy, and also a major carbon emitter. The performances of both the production and operation of the power industry are crucial for a harmonious development of society. This study proposes an improved data envelopment analysis (DEA) model to analyze the sustainable performance of China's power supply chain (PSC).Design/methodology/approachTo analyze the sustainable performance of PSC systems in China's provincial regions, this study proposes a two-stage directional distance function (DDF) model. The proposed model not only considers the leader–follower game relationship between the power-generation system and the retail system, but also considers the factors that measure the sustainability level of the PSC.FindingsThe proposed model is applied to assess the sustainable performance of the PSCs of China's provincial regions. The findings are valuable and mainly include the following aspects: First, compared with other models, this study regards the intermediate variable of the power system as a freely disposable variable; therefore, the efficiency of the proposed model is more realistic. Second, the average efficiency of China's power retailing system is generally lower than the average efficiency of its power-generation system. Third, significant regional differences affect the power-generation efficiency, while the regional differences in power retail efficiency are not significant. The power-generation performances of PSCs in East China and Northeast China are generally higher than in other regions.Originality/valueThis study introduces the convex technique into a DEA model and thus proposes an improved two-stage DDF DEA model. In response to the game-theoretic inherent in power systems, this study also introduces the leader–follower game into the two-stage model. In addition to the theoretic novelty, all PSCs can be classified with this model. Moreover, specific recommendations for each type of PSCs are proposed based on the efficiency results, thus providing vital guidance for the practice.

PurposeThe purpose of this paper is to propose a modified model in multi-stage processes when there are intermediate measures between the stages and in this sense, the new efficiency scores are more accurate. Conventional data envelopment analysis (DEA) models disregard the internal structures of peer decision-making units (DMUs) in evaluating their relative efficiency. Such an approach would cause managers to lose important DMU information. Therefore, in multistage processes, traditional DEA models encounter problems when intermediate measures are used for efficiency evaluation.Design/methodology/approachIn this study, two-stage additive integer-valued DEA models were proposed. Three models were proposed for measuring inefficiency slacks in each stage and in the system as a whole.FindingsThree models were proposed for measuring inefficiency slacks in each stage and in the system as a whole.Originality/valueThe advantage of the proposed models for multi-stage systems is that they can accurately determine the stages with the greatest weaknesses/strengths. By introducing an applied case in the Iranian power industry, the paper demonstrated the applications and advantages of the proposed models.

Assessing integrated water use and wastewater treatment systems in China: A mixed network structure two-stage SBM DEA model

This paper uses both the stochastic and nonstochastic production function approach to measure technical efficiency in public education in Utah.The stochastic specification estimates technical efficiency, assuming half normal and exponential distributions.The nonstochastic specification uses two-stage DEA to separate the effects of fixed inputs on the measure of technical efficiency.The empirical analysis shows substantial variation in efficiency among school districts.While these measures are insensitive to the specific distributional assumptions about the one-sided component of the error term in stochastic specification, they are sensitive to the treatment of fixed socioeconomic inputs in the two-stage DEA.

The appreciative democratic voice of DEA: A case of faculty academic performance evaluation

Unoriented two-stage DEA: The case of the oscillating intermediate products

Data Envelopment Analysis (DEA) models and Free Disposal Hull (FDH) models were proposed almost simultaneously in the scientific literature. In DEA models, the constraints generate a convex set. For this reason, optimization methods and software are widely used for modeling and computations for these models. Non-convexity of production possibility set of FDH models refrained significantly the development of these models. In this paper, two methods are proposed for two-and three-dimensional frontier visualization for DEA and FDH models. Computational experiments using real-life datasets documented reliability and effectiveness of proposed methods.

Data envelopment analysis (DEA) is a non-parametric technique for measuring the efficiency of decision making units (DMU) with exact input and output terms. So far, a number of DEA models with interval data have been developed. The radial DEA model such as CCR or BCC model with interval data are well known as basic DEA models with interval data. However, these radial DEA models have some defects. In this study, we develop a new approach based upon the slacks-based measurement (SBM) of super-efficiency model for dealing with interval data in DEA. The SBM super-efficiency DEA model can not only discriminate the performance of efficient DMUs from inefficient ones, but can distinguish between the efficient DMUs. In addition, this model can overcome the drawback that the super-efficiency model can be infeasible under variable returns to scale (VRS) assumption. An illustrative example is provided to verify the idea of this paper and show its potential application and validity.

Relevance. Public health effectiveness is crucial for population health, especially in the face of global challenges like the COVID-19 pandemic. The study applies the Data Envelopment Analysis (DEA) model to measure the efficiency of operating costs in the health care system across various regions of Kazakhstan from 2017 to 2021. Existing methods for assessing healthcare effectiveness often overlook the system's complexity, which turns DEA into a valuable tool to identify inequalities in healthcare availability and quality. Research objective. This study aims to employ the DEA model to measure the efficiency of operating costs in the health care system across regions of Kazakhstan in 2017-2021. Data and methods. The DEA model was chosen for its ability to analyze the efficiency of operating costs. Data were collected from the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan. Results. Our findings indicate the need for increased healthcare financing in specific regions, emphasizing the importance of transparent spending. The study concludes that the DEA model can regularly assess health financing, ensuring resources are directed where most needed. The novelty lies in establishing a link between financing and health outcomes. Conclusions. The study's results and methodology can be used by public health authorities in assessing operating costs' effectiveness, allocating resources judiciously, and making informed decisions to enhance the healthcare system.

Scale-up and energy estimations of single- and two-stage vinasse anaerobic digestion systems for hydrogen and methane production

This paper investigates the extent, dynamics, and factors influencing technical efficiency (TE) and capacity utilisation (CU) in small-scale fisheries (SSF) using a two-stage data envelopment analysis (DEA) approach covering the period 2010–2012. A considerable extent of boat-level technical inefficiency, capacity underutilisation and scale inefficiency were evident. On average, TE and CU levels under the constant returns to scale (CRS) and variable returns to scale (VRS) models declined over time. The TE and CU scores of 2010 remained unaltered with the addition of ‘fishing time’ as an input to the model. The proportion of boats with unitary scale efficiency (SE) decreased from 26 % in 2010 to 12 % in 2012. The underutilisation rates of the inputs ‘crew’ and ‘fishing time’ ranged from 15.5 % to 31.6 % and 15.8 % to 28.6 %, respectively. Among the species category, the extent of excess capacity was 70 % to 156 % and 47 % to 119 % under the CRS and VRS models, respectively. The second-stage DEA results indicated that the explanatory variables ‘fishing location’, ‘catch per unit of effort’ (CPUE), ‘fuel costs’ and ‘crew share’ significantly influenced CU under the CRS model. In contrast, the significant influence of subsidies and other operating costs were noted under the VRS model. For the TE case, ‘age’, ‘education’, ‘subsidy’ and ‘CPUE’ were found to be significant under the CRS and VRS models. Other significant variables were found in the study under CRS and VRS models. Finally, the results from the descriptive and empirical analysis under the two-stage DEA model are discussed together with policy implications.

A two-stage data envelopment analysis model for measuring performance in three-level supply chains

Urban health efficiency is influenced by both urban planning decisions and the functioning of the public health system. However, there is currently a lack of effective methods to simultaneously examine urban planning and public health when assessing urban health efficiency. This study proposes a two-stage network data envelopment analysis (DEA) model for evaluating urban health efficiency. The urban planning and public health sectors act as interconnected stages where the public facilities and living quality are mediating variables. The urban health efficiencies of 303 Japanese cities are examined using the proposed model, showing that most Japanese cities still have room for urban planning improvement with pure technical efficiency ranging between 0.7 and 0.9. At the same time, most public health sectors of Japanese cities reach pure technical efficiencies larger than 0.95 but are limited by the scale efficiency. Further analysis points out that public health sectors are in decreasing-return-to-scale, meaning that excessive urban scale adversely affects urban health efficiency. Furthermore, cities that joined the Alliance for Healthy Cities (AFHC) performed better on average, suggesting that information sharing and national and international networking can boost urban health. The study shows that the proposed two-stage network DEA model can be applied to analyze urban health efficiency and identify the impact of technology and urban scale in different stages.

Data Envelopment Analysis (DEA) aims at assessing the relative efficiency of a number of comparable operating units. Network DEA is used when the units consist of interrelated processes. The simplest network DEA structure is a two-stage serial system. In this paper a well-known Fuzzy DEA approach is extended to this network DEA context. The proposed approach is simpler than the existing approach with which it is compared using a dataset from the literature.