IDEAS home Printed from https://ideas.repec.org/a/taf/tjorxx/v70y2019i7p1079-1090.html
   My bibliography  Save this article

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

Author

Listed:
  • Ali Emrouznejad
  • Guo-liang Yang
  • Gholam R. Amin

Abstract

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.

Suggested Citation

  • Ali Emrouznejad & Guo-liang Yang & Gholam R. Amin, 2019. "A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(7), pages 1079-1090, July.
    • Handle: RePEc:taf:tjorxx:v:70:y:2019:i:7:p:1079-1090
    DOI: 10.1080/01605682.2018.1489344
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01605682.2018.1489344
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01605682.2018.1489344?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Moghaddas, Zohreh & Tosarkani, Babak Mohamadpour & Yousefi, Samuel, 2022. "Resource reallocation for improving sustainable supply chain performance: An inverse data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 252(C).
    2. Gholam R. Amin & Mustapha Ibn Boamah, 2021. "A two‐stage inverse data envelopment analysis approach for estimating potential merger gains in the US banking sector," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(6), pages 1454-1465, September.
    3. Levent Kutlu & Ran Wang, 2021. "Greenhouse Gas Emission Inefficiency Spillover Effects in European Countries," IJERPH, MDPI, vol. 18(9), pages 1-14, April.
    4. Mushtaq Taleb & Ruzelan Khalid & Ali Emrouznejad & Razamin Ramli, 2023. "Environmental efficiency under weak disposability: an improved super efficiency data envelopment analysis model with application for assessment of port operations considering NetZero," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6627-6656, July.
    5. Xiaoyin Hu & Jianshu Li & Xiaoya Li & Jinchuan Cui, 2020. "A Revised Inverse Data Envelopment Analysis Model Based on Radial Models," Mathematics, MDPI, vol. 8(5), pages 1-17, May.
    6. Levent Kutlu, 2020. "Greenhouse Gas Emission Efficiencies of World Countries," IJERPH, MDPI, vol. 17(23), pages 1-11, November.
    7. Ghiyasi, Mojtaba & Soltanifar, Mehdi & Sharafi, Hamid, 2022. "A novel inverse DEA-R model with application in hospital efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    8. Sheng Dai & Natalia Kuosmanen & Timo Kuosmanen & Juuso Liesio, 2023. "Optimal resource allocation: Convex quantile regression approach," Papers 2311.06590, arXiv.org.
    9. Yang Lin & Longzhong Yan & Ying-Ming Wang, 2019. "Performance Evaluation and Investment Analysis for Container Port Sustainable Development in China: An Inverse DEA Approach," Sustainability, MDPI, vol. 11(17), pages 1-13, August.
    10. Wen-Chi Yang & Wen-Min Lu, 2023. "Achieving Net Zero—An Illustration of Carbon Emissions Reduction with A New Meta-Inverse DEA Approach," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
    11. Amin, Gholam R. & Ibn Boamah, Mustapha, 2023. "Modeling business partnerships: A data envelopment analysis approach," European Journal of Operational Research, Elsevier, vol. 305(1), pages 329-337.
    12. Gholam R. Amin & Osama El‐Temtamy & Samy Garas, 2022. "Audit Risk Evaluation Using Data Envelopment Analysis with Ordinal Data," Abacus, Accounting Foundation, University of Sydney, vol. 58(3), pages 589-602, September.
    13. Gholam R. Amin & Mustapha Ibn Boamah, 2020. "A new inverse DEA cost efficiency model for estimating potential merger gains: a case of Canadian banks," Annals of Operations Research, Springer, vol. 295(1), pages 21-36, December.
    14. Lin, Sheng-Wei & Lu, Wen-Min, 2024. "Using inverse DEA and machine learning algorithms to evaluate and predict suppliers’ performance in the apple supply chain," International Journal of Production Economics, Elsevier, vol. 271(C).
    15. Xie, Qiwei & Xu, Qifan & Zhu, Da & Rao, Kaifeng & Dai, Qianzhi, 2020. "Fair allocation of wastewater discharge permits based on satisfaction criteria using data envelopment analysis," Utilities Policy, Elsevier, vol. 66(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tjorxx:v:70:y:2019:i:7:p:1079-1090. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.