The enhanced Russell-based directional distance measure with undesirable outputs: Numerical example considering CO2 emissions
The enhanced Russell-based directional distance measure with undesirable outputs: Numerical example considering CO2 emissions
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DOI：
10.1016/j.omega.2014.12.001
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发表时间：
2015-06-01
影响因子：
6.9
通讯作者：
Managi, Shunsuke
中科院分区：
文献类型：
--
作者：
Chen, Po-Chi;Yu, Ming-Miin;Managi, Shunsuke
基于改进的Russell图测度的思想，提出了一种改进的基于Russell图的方向距离测度（ERBDDM）模型，用于处理数据包络分析（DEA）中的期望输出和非期望输出，并允许部分输入和输出为零。所提出的方法类似于面向输出的基于松弛的测量（OSBM）和定向输出距离函数方法，因为它允许扩展期望的输出和收缩不期望的输出。与OSBM模型和传统方法相比，ERBDDM具有上级优势，因为它不仅能够识别出OSBM模型中的所有无效松弛，而且避免了OSBM模型中无法识别商品和商品的零联合生产的误解和错误描述。本文还对ERBDDM模型施加了一个强互补松弛条件，以处理多重投影的出现。此外，我们使用Penn Table数据来帮助我们在111个国家的环境政策评估和绩效改进指导的背景下探索我们的新方法。(C)2014爱思唯尔有限公司版权所有。
Following the spirit of the enhanced Russell graph measure, this paper proposes an enhanced Russell-based directional distance measure (ERBDDM) model for dealing with desirable and undesirable outputs in data envelopment analysis (DEA) and allowing some inputs and outputs to be zero. The proposed method is analogous to the output oriented slacks-based measure (OSBM) and directional output distance function approach because it allows the expansion of desirable outputs and the contraction of undesirable outputs. The ERBDDM is superior to the OSBM model and traditional approach since it is not only able to identify all the inefficiency slacks just as the latter, but also avoids the misperception and misspecification of the former, which fails to identify null-jointness production of goods and bads. The paper also imposes a strong complementary slackness condition on the ERBDDM model to deal with the occurrence of multiple projections. Furthermore, we use the Penn Table data to help us explore our new approach in the context of environmental policy evaluations and guidance for performance improvements in 111 countries. (C) 2014 Elsevier Ltd. All rights reserved.