Evaluation of efficiency and technological bias of tourist hotels by a meta-frontier DEA model

Yu, Ming‐Miin; Chen, Li‐Hsueh

doi:10.1080/01605682.2019.1578625

Cited by 27 publications

(18 citation statements)

References 55 publications

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“…However, data envelopment analysis (DEA) is most widely used (see the following paragraphs) [10]. DEA is a nonparametric mathematical method to measure the efficiency of decision-making units (DMUs) [11,12]. DEA has been applied to evaluate the performance of hospitals, supply chains, universities, and other entities [13].…”

Section: Introductionmentioning

confidence: 99%

Measuring the Energy and Carbon Emission Efficiency of Regional Transportation Systems in China: Chance-Constrained DEA Models

Ren

Gao

Zhang

et al. 2020

Mathematical Problems in Engineering

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In China, the transportation sector contributes about 18% of the total carbon emissions. This research contributes to measuring the energy and carbon emission efficiency (ECEE) of regional transportation systems (RTS) in China considering uncertain carbon emissions. A radial chance-constrained data envelopment analysis (DEA) model is developed to estimate the overall efficiency, and a nonradial chance-constrained DEA model is presented to evaluate the pure energy efficiency (PEE) and the pure carbon emission efficiency (PCEE). We prove that the proposed chance-constrained DEA models can effectively address the uncertain carbon emissions when measuring efficiency. We find that most of China’s RTS have low ECEE and the inefficiencies are mainly due to the lower gasoline utilization efficiency and the lower kerosene utilization efficiency. In addition, east China performs better than central China, and central China performs better than west China. In China, the unbalanced regional development of the ECEE in transportation corresponds with the unbalanced regional economic development. We provide some valuable suggestions based on the evaluation of the potential cuts in each kind of energy and the potential decreases in carbon emissions.

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Section: Introductionmentioning

confidence: 99%

Measuring the Energy and Carbon Emission Efficiency of Regional Transportation Systems in China: Chance-Constrained DEA Models

Ren

Gao

Zhang

et al. 2020

Mathematical Problems in Engineering

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“…The "similar" DMUs 1 11.00 [10,12] (DMU 8, DMU9) 2 10.00 [8,12] (DMU 10, DMU 11, DMU 12) 3 11.00 [10,12] (DMU 4, DMU 6, DMU 8) 4 11.00 [10,12] (DMU 6, DMU 7) 5 7.00 [2,12] (DMU 2, DMU7, DMU 10, DMU11, DMU12) 6 9.00 [8,10] https://doi.org/10.1371/journal.pone.0234247.t005 Table 6. Multi-index comprehensive evaluation system.…”

Section: Imentioning

confidence: 99%

“…Traditional data envelopment analysis (DEA) models assume that all the inputs and outputs data are available [1,2]. If the data related to some vital variables of decision making units (DMUs) are missing, traditional DEA models cannot be applied to measure the performance of these DMUs [3,4]. However, missing data is a common problem in data analysis [5].…”

Section: Introductionmentioning

confidence: 99%

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Additive integer-valued data envelopment analysis with missing data: A multi-criteria evaluation approach

et al. 2020

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Traditional data envelopment analysis (DEA) models assume that all the inputs and outputs data are available. However, missing data is a common problem in data analysis. Although several scholars have developed techniques to conduct DEA with missing data, these techniques have some disadvantages. A multi-criteria evaluation approach is proposed to measure the efficiency of decision making units (DMUs) with missing data. In this approach, analysts first estimate the upper and lower bounds of DMUs' efficiency using the proposed I-addIDEA-U models (interval additive integer-valued DEA models with undesirable outputs) that can be applied to address integer-valued variables and undesirable outputs. Then, DMUs' "relative" efficiency is evaluated using the proposed "Halo + Hot deck" DEA method (if there is no correlation between variables) or regression DEA techniques (if there is a correlation between variables). Finally, the multi-index comprehensive evaluation method is applied to determine which scenario (the lower bound of efficiency, the "relative" efficiency, or the upper bound of efficiency) should be selected. With a case study, it is shown that the proposed multicriteria evaluation approach is more effective than traditional approaches such as the mean imputation DEA method, the deletion DEA method, and the dummy entries DEA method.

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“…Data envelopment analysis (DEA) is a widely applied nonparametric programming technique for evaluating the performance of decision making units (DMUs) [1], [2]. In 1978, the first DEA model (the CCR model) was developed by Charnes et al under the condition of Constant Returns to Scale (CRS) [3].…”

Section: Introductionmentioning

confidence: 99%

Additive Integer-Valued DEA Models With Fuzzy Undesirable Outputs: Closest Benchmarking Targets and Super-Efficiency

2020

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Many production activities generate undesirable outputs apart from desirable outputs. In addition, the data related to undesirable outputs may be fuzzy (imprecise) in some cases. Based on the fuzzy data theory, we propose a group of additive integer-valued data envelopment analysis (DEA) models with fuzzy undesirable outputs. The advantages of the proposed models are as follows: (1) they enable decision-makers to evaluate the efficiency of decision making units (DMUs) with integer-valued variables and fuzzy undesirable outputs; (2) they can be easily solved without any transformation because they are linear programming models; (3) they are better than radial DEA models as they can identify inefficiencies in all the selected inputs and outputs. Particularly, minimum distance-based additive integer-valued DEA models with fuzzy undesirable outputs are developed in this paper to find the closest benchmarking targets for inefficient DMUs, and additive super-efficiency integer-valued DEA models with fuzzy undesirable outputs are presented to differentiate and rank efficient DMUs. The validity of the proposed models is examined by an empirical application in the pallet rental industry. INDEX TERMS additive DEA, undesirable output, fuzzy variable, integer, pallet.

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Evaluation of efficiency and technological bias of tourist hotels by a meta-frontier DEA model

Cited by 27 publications

References 55 publications

Measuring the Energy and Carbon Emission Efficiency of Regional Transportation Systems in China: Chance-Constrained DEA Models

Measuring the Energy and Carbon Emission Efficiency of Regional Transportation Systems in China: Chance-Constrained DEA Models

Additive integer-valued data envelopment analysis with missing data: A multi-criteria evaluation approach

Additive Integer-Valued DEA Models With Fuzzy Undesirable Outputs: Closest Benchmarking Targets and Super-Efficiency

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