IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0272633.html

Eco-efficiency evaluation of Chinese provincial industrial system: A dynamic hybrid two-stage DEA approach

Author

Listed:
  • Kai He
  • Nan Zhu

Abstract

In China, industrial pollution has become an urgent problem for policy makers and enterprise managers. To better support industrial development, we need to determine the effectiveness of policies through efficiency evaluation. China’s provincial industrial system consists of two stages: production and emission reduction. The emission reduction stage is composed of three parallel sub stages: solid waste treatment, waste gas treatment and wastewater treatment. In this process, the treatment capacity of industrial wastewater treatment facilities can be used as carry forward variable, which is not only the desirable output of the previous emission reduction stage, but also the input of the current emission reduction stage. Therefore, this paper proposes a dynamic hybrid two-stage data envelopment analysis (DEA) model for eco-efficiency evaluation of industrial systems, and applies it to a case study of Chinese regional industry. Applying the data collected from 2011 to 2015 to the model, the following conclusions can be drawn: (1) During the whole survey period, the average eco-efficiency was 0.9027. The overall eco-inefficiency of China’s provincial industrial system during the study period is mainly due to low efficiency of solid waste treatment and waste gas treatment. (2) The average eco-efficiency of provincial industrial system increased steadily from 2011 (0.6448) to 2014 (0.6777), but decreased slightly in 2015 (0.5908). (3) The carry forward treatment capacity of industrial wastewater treatment facilities has a remarkable impact on provincial industrial system efficiency scores, especially at the wastewater treatment stage (0.6002 vs 0.3691). (4) Provincial industrial system exists distinct geographical characteristics of low efficiency. This study has important guiding significance for policy makers and enterprise managers who are concerned about industrial pollution control.

Suggested Citation

  • Kai He & Nan Zhu, 2022. "Eco-efficiency evaluation of Chinese provincial industrial system: A dynamic hybrid two-stage DEA approach," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-26, August.
  • Handle: RePEc:plo:pone00:0272633
    DOI: 10.1371/journal.pone.0272633
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0272633
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0272633&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0272633?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
    ---><---

    References listed on IDEAS

    as
    1. Yingnan Liu & Ke Wang, 2015. "Energy efficiency of China's industry sector: An adjusted network DEA-based decomposition analysis," CEEP-BIT Working Papers 83, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    2. Zhou, D.Q. & Wang, Qunwei & Su, B. & Zhou, P. & Yao, L.X., 2016. "Industrial energy conservation and emission reduction performance in China: A city-level nonparametric analysis," Applied Energy, Elsevier, vol. 166(C), pages 201-209.
    3. Emrouznejad, Ali & Yang, Guo-liang, 2018. "A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 4-8.
    4. Chien-Ming Chen & Magali A. Delmas, 2012. "Measuring Eco-Inefficiency: A New Frontier Approach," Operations Research, INFORMS, vol. 60(5), pages 1064-1079, October.
    5. Chiang Kao, 2014. "Efficiency Decomposition in Network Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 55-77, Springer.
    6. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    7. Liu, Yingnan & Wang, Ke, 2015. "Energy efficiency of China's industry sector: An adjusted network DEA (data envelopment analysis)-based decomposition analysis," Energy, Elsevier, vol. 93(P2), pages 1328-1337.
    8. Zhang, Lin & Zhao, Linlin & Zha, Yong, 2021. "Efficiency evaluation of Chinese regional industrial systems using a dynamic two-stage DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    9. Fare, Rolf & Grosskopf, Shawna & Noh, Dong-Woon & Weber, William, 2005. "Characteristics of a polluting technology: theory and practice," Journal of Econometrics, Elsevier, vol. 126(2), pages 469-492, June.
    10. Huppes, Gjalt & Ishikawa, Masanobu, 2009. "Eco-efficiency guiding micro-level actions towards sustainability: Ten basic steps for analysis," Ecological Economics, Elsevier, vol. 68(6), pages 1687-1700, April.
    11. Dyckhoff, H. & Allen, K., 2001. "Measuring ecological efficiency with data envelopment analysis (DEA)," European Journal of Operational Research, Elsevier, vol. 132(2), pages 312-325, July.
    12. Chiang Kao, 2017. "Network Data Envelopment Analysis," International Series in Operations Research and Management Science, Springer, number 978-3-319-31718-2, September.
    13. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    14. A. Charnes & W. W. Cooper, 1962. "Programming with linear fractional functionals," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 9(3‐4), pages 181-186, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Tao Xie & Ying Li & Yung-Ho Chiu & Shiyou Ao, 2025. "Research on the Operation, Market and ESG Efficiency of China's Local Commercial Banks in the Context of COVID-19," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 715-755, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peykani, Pejman & Seyed Esmaeili, Fatemeh Sadat & Pishvaee, Mir Saman & Rostamy-Malkhalifeh, Mohsen & Hosseinzadeh Lotfi, Farhad, 2024. "Matrix-based network data envelopment analysis: A common set of weights approach," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    2. Kao, Chiang, 2022. "A maximum slacks-based measure of efficiency for closed series production systems," Omega, Elsevier, vol. 106(C).
    3. Kai He & Nan Zhu & Wu Jiang & Chuanjin Zhu, 2022. "Efficiency Evaluation of Chinese Provincial Industrial System Based on Network DEA Method," Sustainability, MDPI, vol. 14(9), pages 1-24, April.
    4. Fatemeh Sadat Seyed Esmaeili & Emran Mohammadi, 2024. "Z-number network data envelopment analysis approach: A case study on the Iranian insurance industry," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-26, July.
    5. Xiao, Huijuan & Wang, Daoping & Qi, Yu & Shao, Shuai & Zhou, Ya & Shan, Yuli, 2021. "The governance-production nexus of eco-efficiency in Chinese resource-based cities: A two-stage network DEA approach," Energy Economics, Elsevier, vol. 101(C).
    6. Koronakos, Gregory & Sotiros, Dimitris & Despotis, Dimitris K. & Kritikos, Manolis N., 2022. "Fair efficiency decomposition in network DEA: A compromise programming approach," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    7. Kao, Chiang, 2020. "Decomposition of slacks-based efficiency measures in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 283(2), pages 588-600.
    8. Tatiana Bencova & Andrea Bohacikova, 2022. "DEA in Performance Measurement of Two-Stage Processes: Comparative Overview of the Literature," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 111-129.
    9. Amirteimoori, Alireza & Kou, Gang, 2024. "Technical efficiency and managerial ability in two-stage production processes with undesirable products: A case on Asian banks," Socio-Economic Planning Sciences, Elsevier, vol. 96(C).
    10. Chen, Lei & Wang, Ying-Ming, 2025. "Efficiency decomposition and frontier projection of two-stage network DEA under variable returns to scale," European Journal of Operational Research, Elsevier, vol. 322(1), pages 157-170.
    11. Kao, Chiang & Liu, Shiang-Tai, 2022. "Group decision making in data envelopment analysis: A robot selection application," European Journal of Operational Research, Elsevier, vol. 297(2), pages 592-599.
    12. Lívia Torres & Francisco S. Ramos, 2024. "Allocating Benefits Due to Shared Resources Using Shapley Value and Nucleolus in Dynamic Network Data Envelopment Analysis," Mathematics, MDPI, vol. 12(5), pages 1-23, February.
    13. Harald Dyckhoff, 2025. "General network production theory for good and bad inputs and outputs," Journal of Productivity Analysis, Springer, vol. 64(1), pages 43-60, August.
    14. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    15. Liu, Wenbin & Zhou, Zhongbao & Ma, Chaoqun & Liu, Debin & Shen, Wanfang, 2015. "Two-stage DEA models with undesirable input-intermediate-outputs," Omega, Elsevier, vol. 56(C), pages 74-87.
    16. Zhang, Lin & Zhao, Linlin & Zha, Yong, 2021. "Efficiency evaluation of Chinese regional industrial systems using a dynamic two-stage DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    17. Khezrimotlagh, Dariush & Kaffash, Sepideh & Zhu, Joe, 2022. "U.S. airline mergers’ performance and productivity change," Journal of Air Transport Management, Elsevier, vol. 102(C).
    18. Ming-Fu Hsu & Ying-Shao Hsin & Fu-Jiing Shiue, 2022. "Business analytics for corporate risk management and performance improvement," Annals of Operations Research, Springer, vol. 315(2), pages 629-669, August.
    19. Kremantzis, Marios Dominikos & Beullens, Patrick & Kyrgiakos, Leonidas Sotirios & Klein, Jonathan, 2022. "Measurement and evaluation of multi-function parallel network hierarchical DEA systems," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    20. Kao, Chiang, 2019. "Inefficiency identification for closed series production systems," European Journal of Operational Research, Elsevier, vol. 275(2), pages 599-607.

    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:plo:pone00:0272633. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.