IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i10p3456-d358706.html
   My bibliography  Save this article

Measuring Regional Eco-Efficiency in China (2003–2016): A “Full World” Perspective and Network Data Envelopment Analysis

Author

Listed:
  • Weizhen Ren

    (Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China)

  • Zilong Zhang

    (Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
    Institute of Green Development for the Yellow River Drainage Basin, Lanzhou University, Lanzhou 730000, China
    Institute for Circular Economy in Western China, Lanzhou University, Lanzhou 730000, China)

  • Yueju Wang

    (Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China)

  • Bing Xue

    (Key Lab of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 10016, China
    Institute for Advanced Sustainability Studies (IASS), 14467 Potsdam, Germany)

  • Xingpeng Chen

    (Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
    Institute for Circular Economy in Western China, Lanzhou University, Lanzhou 730000, China)

Abstract

Eco-efficiency enhancement is an inherent requirement of green development and an important indicator of high-quality development in general. It aims to achieve the coordinated development of nature, the economy, and society. Therefore, eco-efficiency measurements should focus on not only total factor input, but also process analysis. Based on the “full world” model in ecological economic theory, this study constructed a theoretical framework for a composite economic-environmental-social system that reflects human welfare and sustainability. To this end, using network data envelopment analysis (DEA), this study established a staged eco-efficiency evaluation model that uses economic, environmental, and social factors to measure the overall and staged eco-efficiency of China’s provinces from 2003 to 2016 and analyze its spatiotemporal characteristics. A geographically weighted regression (GWR) model was also used to analyze the influencing factors of eco-efficiency changes and the spatial differentiation in their effect intensity. The findings were as follows: (1) China’s overall eco-efficiency is still at a low level. It varies significantly from region to region, and only three regions are at the frontier of production. The eastern region has the highest eco-efficiency, followed by the central region, and the gap between the central and western regions has gradually narrowed. In terms of staged efficiency, the level of eco-efficiency in the production stage is less than in the environmental governance stage, which is less than that in the social input stage. (2) In terms of the efficiency of each stage, the efficiency level of the production stage showed a downward trend throughout the entire process, and the decline in the central and western regions was more obvious. The social input stage and the environmental governance stage both showed upward trends. The social input stage showed a higher level, and the increase was relatively flat during the period of study. Efficiency continued to rise during the environmental governance stage from 2003 to 2010 and rose overall, but with some fluctuations from 2011 to 2016. (3) Geographically weighted regression showed that the effects of the influencing factors on eco-efficiency had obvious spatial heterogeneity. The factors affecting overall, production stage, and social input eco-efficiency were, in order of effect intensity from high to low, economic growth level, marketization level, and social input level. In terms of environmental governance, social input level had the greatest impact, followed by economic growth; marketization level did not show a significant impact.

Suggested Citation

  • Weizhen Ren & Zilong Zhang & Yueju Wang & Bing Xue & Xingpeng Chen, 2020. "Measuring Regional Eco-Efficiency in China (2003–2016): A “Full World” Perspective and Network Data Envelopment Analysis," IJERPH, MDPI, vol. 17(10), pages 1-15, May.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:10:p:3456-:d:358706
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/10/3456/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/10/3456/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kiani Mavi, Reza & Saen, Reza Farzipoor & Goh, Mark, 2019. "Joint analysis of eco-efficiency and eco-innovation with common weights in two-stage network DEA: A big data approach," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 553-562.
    2. Shujuan Li & Hongyan Ren & Wensheng Hu & Liang Lu & Xinliang Xu & Dafang Zhuang & Qiyong Liu, 2014. "Spatiotemporal Heterogeneity Analysis of Hemorrhagic Fever with Renal Syndrome in China Using Geographically Weighted Regression Models," IJERPH, MDPI, vol. 11(12), pages 1-19, November.
    3. 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.
    4. Haojie Liu & Jinyue Liu & Weixin Yang & Jianing Chen & Mingyang Zhu, 2020. "Analysis and Prediction of Land Use in Beijing-Tianjin-Hebei Region: A Study Based on the Improved Convolutional Neural Network Model," Sustainability, MDPI, vol. 12(7), pages 1-25, April.
    5. Fukuyama, Hirofumi & Weber, William L., 2010. "A slacks-based inefficiency measure for a two-stage system with bad outputs," Omega, Elsevier, vol. 38(5), pages 398-409, October.
    6. Toshiyuki Sueyoshi & Zemin Du & Derek Wang, 2020. "Regional Sustainable Development with Environmental Performance: Measuring Growth Indexes on Chinese Provinces," Energies, MDPI, vol. 13(8), pages 1-21, April.
    7. Xionghe Qin & Yanming Sun, 2019. "Cross-Regional Comparative Study on Environmental–Economic Efficiency and Driving Forces behind Efficiency Improvement in China: A Multistage Perspective," IJERPH, MDPI, vol. 16(7), pages 1-21, March.
    8. Li Yue & Dan Xue & Muhammad Umar Draz & Fayyaz Ahmad & Jiaojiao Li & Farrukh Shahzad & Shahid Ali, 2020. "The Double-Edged Sword of Urbanization and Its Nexus with Eco-Efficiency in China," IJERPH, MDPI, vol. 17(2), pages 1-20, January.
    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. Bing Xia & Suocheng Dong & Yu Li & Zehong Li & Dongqi Sun & Wenbiao Zhang & Wenlong Li, 2021. "Evolution Characters and Influencing Factors of Regional Eco-Efficiency in a Developing Country: Evidence from Mongolia," IJERPH, MDPI, vol. 18(20), pages 1-20, October.
    2. Dongmei Shi & Lili Ren & Hongyu Li & Haizhen Zhang & Rufei Zhang, 2023. "Analysis of the Spatial Differentiation and Promotion Potential for Agricultural Eco-Efficiency—Evidence of Pollution’s Strong Disposability," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    3. Yumei Wu & Rong Wang & Fayuan Wang, 2023. "Exploring the Role of Foreign Direct Investment and Environmental Regulation in Regional Ecological Efficiency in the Context of Sustainable Development," Sustainability, MDPI, vol. 15(11), pages 1-19, June.

    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. Yang, Guo-liang & Fukuyama, Hirofumi & Chen, Kun, 2019. "Investigating the regional sustainable performance of the Chinese real estate industry: A slack-based DEA approach," Omega, Elsevier, vol. 84(C), pages 141-159.
    2. AGRELL, Per & HATAMI-MARBINI, Adel, 2011. "Frontier-based performance analysis models for supply chain management; state of the art and research directions," LIDAM Discussion Papers CORE 2011069, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Li, Yongjun & Chen, Yao & Liang, Liang & Xie, Jianhui, 2012. "DEA models for extended two-stage network structures," Omega, Elsevier, vol. 40(5), pages 611-618.
    4. Shujuan Li & Lingli Zhu & Lidan Zhang & Guoyan Zhang & Hongyan Ren & Liang Lu, 2023. "Urbanization-Related Environmental Factors and Hemorrhagic Fever with Renal Syndrome: A Review Based on Studies Taken in China," IJERPH, MDPI, vol. 20(4), pages 1-20, February.
    5. Zhang, Linyan & Chen, Kun, 2019. "Hierarchical network systems: An application to high-technology industry in China," Omega, Elsevier, vol. 82(C), pages 118-131.
    6. Khoveyni, Mohammad & Fukuyama, Hirofumi & Eslami, Robabeh & Yang, Guo-liang, 2019. "Variations effect of intermediate products on the second stage in two-stage processes," Omega, Elsevier, vol. 85(C), pages 35-48.
    7. Belasri, Sanaa & Gomes, Mathieu & Pijourlet, Guillaume, 2020. "Corporate social responsibility and bank efficiency," Journal of Multinational Financial Management, Elsevier, vol. 54(C).
    8. S. Lozano & G. Villa, 2019. "Data envelopment analysis of systems with multiple modes of functioning," Annals of Operations Research, Springer, vol. 278(1), pages 17-41, July.
    9. Chen, Ya & Li, Yongjun & Liang, Liang & Salo, Ahti & Wu, Huaqing, 2016. "Frontier projection and efficiency decomposition in two-stage processes with slacks-based measures," European Journal of Operational Research, Elsevier, vol. 250(2), pages 543-554.
    10. Sánchez-González, Carlos & Sarto, José Luis & Vicente, Luis, 2017. "The efficiency of mutual fund companies: Evidence from an innovative network SBM approach," Omega, Elsevier, vol. 71(C), pages 114-128.
    11. Zhao, Haoran & Guo, Sen & Zhao, Huiru, 2019. "Provincial energy efficiency of China quantified by three-stage data envelopment analysis," Energy, Elsevier, vol. 166(C), pages 96-107.
    12. Victor John M. Cantor & Kim Leng Poh, 2020. "Efficiency measurement for general network systems: a slacks-based measure model," Journal of Productivity Analysis, Springer, vol. 54(1), pages 43-57, August.
    13. Huaqing Wu & Jingyu Yang & Wensheng Wu & Ya Chen, 2023. "Interest rate liberalization and bank efficiency: A DEA analysis of Chinese commercial banks," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(2), pages 467-498, June.
    14. Guo, Chuanyin & Abbasi Shureshjani, Roohollah & Foroughi, Ali Asghar & Zhu, Joe, 2017. "Decomposition weights and overall efficiency in two-stage additive network DEA," European Journal of Operational Research, Elsevier, vol. 257(3), pages 896-906.
    15. Sebastian Lozano & Belarmino Adenso-Diaz, 2018. "Network DEA-based biobjective optimization of product flows in a supply chain," Annals of Operations Research, Springer, vol. 264(1), pages 307-323, May.
    16. Cook, Wade D. & Liang, Liang & Zhu, Joe, 2010. "Measuring performance of two-stage network structures by DEA: A review and future perspective," Omega, Elsevier, vol. 38(6), pages 423-430, December.
    17. Li Jia & Wang Ping & Tao Xiangyang & Wen Yao, 2021. "Productivity Analysis for Banks’ Merger and Acquisition Using Two-Stage DEA: Evidence from China," Journal of Systems Science and Information, De Gruyter, vol. 9(6), pages 627-659, December.
    18. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    19. 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.
    20. Manuel Mocholi-Arce & Ramon Sala-Garrido & Maria Molinos-Senante & Alexandros Maziotis, 2022. "Measuring the eco-efficiency of the provision of drinking water by two-stage network data envelopment analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(11), pages 12883-12899, November.

    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:gam:jijerp:v:17:y:2020:i:10:p:3456-:d:358706. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.