A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries


This paper aims to address the problem of allocating the CO2 emissions quota set by government goal in Chinese manufacturing industries to different Chinese regions. The CO2 emission reduction is conducted in a three-stage phases. The first stage is to obtain the total amount CO2 emission reduction from the Chinese government goal as our total CO2 emission quota to reduce. The second stage is to allocate the reduction quota to different two-digit level manufacturing industries in China. The third stage is to further allocate the reduction quota for each industry into different provinces. A new inverse data envelopment analysis (InvDEA) model is developed to achieve our goal to allocate CO2 emission quota under several assumptions. At last we obtain the empirical results based on the real data from Chinese manufacturing industries.

Publication DOI: https://doi.org/10.1080/01605682.2018.1489344
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: © 2018 Palgrave Macmillan Publishers Limited, part of Springer Nature. This is a post-peer-review, pre-copyedited version of an article published in Journal of the Operational Research Society. Funding: National Natural Science Foundation of China and Newton Fund from Royal Academy of Engineering.
Uncontrolled Keywords: Data envelopment analysis ,Inverse DEA ,CO2 emissions ,manufacturing industries
Publication ISSN: 1476-9360
Last Modified: 17 Apr 2024 07:15
Date Deposited: 13 Jun 2018 10:10
Full Text Link:
Related URLs: https://www.tan ... 82.2018.1489344 (Publisher URL)
PURE Output Type: Article
Published Date: 2019
Published Online Date: 2018-10-18
Accepted Date: 2018-06-13
Authors: Emrouznejad, Ali (ORCID Profile 0000-0001-8094-4244)
Yang, Guoliang
Amin, Gholam R.



Version: Accepted Version

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