多元能源效率分析的Copula方法

Merve Sözen, M. Cengiz
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引用次数: 2

摘要

数据包络分析(DEA)是一种使用大量输入和输出变量来发现现有系统有效性的方法。在本研究中,我们使用DEA获得了经合组织国家2011年建筑、工业、电力和交通部门的能源效率。在不同的部门实现效率是可能的。然而,我们的目标是找到所有部门的联合能源效率得分。文献中提出的获得联合效率的方法之一是网络数据包络分析(network DEA)。网络DEA将扇区视为子过程,通过最优权值获得系统效率和过程效率。另外,我们使用了一种新颖的基于copula的方法来获得共同的效率分数。在这种方法中,可以证明用DEA获得的相似质量的效率分数之间的依赖结构。利用联合概率分布得到了新的效率分数。然后,利用这些效率分数,通过copula法得到联合效率分数。最后,通过网络DEA得到了相同部门的联合效率得分。因此,我们将网络DEA与copula方法进行了比较,并解释了每个能源部门的效率和联合效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Copula Approach to Multivariate Energy Efficiency Analysis
Data envelopment analysis (DEA) is a method that finds the effectiveness of an existing system using a number of input and output variables. In this study, we obtained energy efficiencies of construction, industrial, power, and transportation sectors in OECD countries for 2011 using DEA. It is possible to achieve the efficiencies in different sectors. However, we aim to find joint energy efficiency scores for all sectors. One of the methods proposed in the literature to obtain joint efficiency is network data envelopment analysis (network DEA). Network DEA treats sectors as sub-processes and obtains system and process efficiencies through optimal weights. Alternatively, we used a novel copula-based approach to achieve common efficiency scores. In this approach, it is possible to demonstrate the dependency structure between the efficiency scores of similar qualities obtained with DEA by copula families. New efficiency scores are obtained with the help of joint probability distribution. Then, we obtained joint efficiency scores through the copula approach using these efficiency scores. Finally, we obtained the joint efficiency scores of the same sectors through network DEA. As a result, we compared network DEA with the copula approach and interpreted the efficiencies of each energy sector and joint efficiencies.
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