Performance evaluation of organizations considering economic incentives for emission reduction:A carbon emission permit trading approach


The emissions trading system allows organizations to transact emission permits to fit their production practice. This paper develops a new nonparametric methodology for performance evaluation of organizations (or decision-making units, DMUs) considering carbon emission permit trading. Explicit production axioms are discussed, and a new production technology considering carbon emission permit trading is proposed. Models based on the new production technology are established for evaluating the carbon emission reduction potential and performance of the DMUs. Comparing the proposed models with previous ones, the adoption of carbon emission permit trading increases the potentials of DMUs to reduce carbon dioxide emission and improve inputs and outputs. In addition, a proper increase of the carbon emission permit trading price can increase the potential of DMUs to reduce carbon dioxide emissions. The proposed approach contributes to the literature by explicitly explaining how adopting carbon emission permit trading affects production technology. A numeral example illustrates the proposed approach while the usefulness and practicality of the models are explained by applying them to China's thermal power industry.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Uncontrolled Keywords: Data envelopment analysis,Carbon emission permit trading,Production technology,Efficiency evaluation,Abatement potential
Publication ISSN: 1873-6181
Last Modified: 17 Jul 2024 07:10
Date Deposited: 25 Jun 2021 09:47
Full Text Link:
Related URLs: https://linking ... 140988321002978 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-09-01
Published Online Date: 2021-06-24
Accepted Date: 2021-06-19
Authors: Chu, Junfei
Shao, Caifeng
Emrouznejad, Ali (ORCID Profile 0000-0001-8094-4244)
Wu, Jie
Yuan, Zhe

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