Carbon efficiency evaluation:an analytical framework using fuzzy DEA


Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency of alternatives based on their inputs and outputs. The alternatives can be in the form of countries who attempt to enhance their productivity and environmental efficiencies concurrently. However, when desirable outputs such as productivity increases, undesirable outputs increase as well (e.g. carbon emissions), thus making the performance evaluation questionable. In addition, traditional environmental efficiency has been typically measured by crisp input and output (desirable and undesirable). However, the input and output data, such as CO2 emissions, in real-world evaluation problems are often imprecise or ambiguous. This paper proposes a DEA-based framework where the input and output data are characterized by symmetrical and asymmetrical fuzzy numbers. The proposed method allows the environmental evaluation to be assessed at different levels of certainty. The validity of the proposed model has been tested and its usefulness is illustrated using two numerical examples. An application of energy efficiency among 23 European Union (EU) member countries is further presented to show the applicability and efficacy of the proposed approach under asymmetric fuzzy numbers.

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Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Uncontrolled Keywords: data envelopment analysis,energy efficiency,fuzzy expected interval,fuzzy expected value,fuzzy ranking approach,Management Science and Operations Research,Modelling and Simulation,Information Systems and Management
Publication ISSN: 1872-6860
Last Modified: 25 Mar 2024 08:17
Date Deposited: 23 Feb 2016 11:25
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 0340?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2016-09-01
Published Online Date: 2016-02-18
Accepted Date: 2016-02-10
Submitted Date: 2014-12-21
Authors: Ignatius, Joshua
Ghasemi, Mohammadreza
Zhang, Feng
Emrouznejad, Ali (ORCID Profile 0000-0001-8094-4244)
Hatami-Marbini, Adel

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