IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i10p5397-d557200.html
   My bibliography  Save this article

Mining Eco-Efficiency Measurement and Driving Factors Identification Based on Meta-US-SBM in Guangxi Province, China

Author

Listed:
  • Yonglin Li

    (School of Economics and Management, China University of Geosciences, Wuhan 430074, China
    Research Center of Resource and Environment Economics, Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China)

  • Zhili Zuo

    (School of Economics and Management, China University of Geosciences, Wuhan 430074, China
    Research Center of Resource and Environment Economics, Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China)

  • Deyi Xu

    (School of Economics and Management, China University of Geosciences, Wuhan 430074, China
    Research Center of Resource and Environment Economics, Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China)

  • Yi Wei

    (School of Economics and Management, China University of Geosciences, Wuhan 430074, China)

Abstract

The mining industry is one of the pillar industries of Guangxi’s economic and social development. The output value of mining and related industries accounts for 27% of the whole district’s total industrial output value. Therefore, the mining eco-efficiency measurement in Guangxi can be of great significance for the sustainable development of Guangxi’s mining industry. This study adopted Meta-US-SBM to measure the mining eco-efficiency in Guangxi from 2008 to 2018, including economic efficiency, resource efficiency, and environmental efficiency. It used the standard deviation ellipse model to simulate the migration trend of four efficiencies in Guangxi and used GeoDetector and Tobit models to explore the internal and external factors that affect the mining eco-efficiency. The four efficiencies in Guangxi show large temporal and spatial heterogeneity, and the internal and external factors that affect the mining eco-efficiency are different. The following conclusions can be drawn. (1) Environmental efficiency and mining eco-efficiency are improving, while economic efficiency and resource efficiency are deteriorating. Cities bordering other provinces have a significantly better mining eco-efficiency than non-bordering cities. (2) The development center in Guangxi has migrated to the Beibu Gulf Economic Zone. (3) Natural resources index and mining economic scale have a great impact on the mining eco-efficiency, and with the increase of the mining economic scale, the mining eco-efficiency showed a typical “U-shaped” curve. Finally, this study put forward corresponding policy recommendations to improve the mining eco-efficiency in Guangxi from four aspects: opening-up, technological progress, regional coordination, and government control.

Suggested Citation

  • Yonglin Li & Zhili Zuo & Deyi Xu & Yi Wei, 2021. "Mining Eco-Efficiency Measurement and Driving Factors Identification Based on Meta-US-SBM in Guangxi Province, China," IJERPH, MDPI, vol. 18(10), pages 1-22, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5397-:d:557200
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/10/5397/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/10/5397/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Yansui & Zou, Lilin & Wang, Yongsheng, 2020. "Spatial-temporal characteristics and influencing factors of agricultural eco-efficiency in China in recent 40 years," Land Use Policy, Elsevier, vol. 97(C).
    2. J. Robert Baum & Stefan Wally, 2003. "Strategic decision speed and firm performance," Strategic Management Journal, Wiley Blackwell, vol. 24(11), pages 1107-1129, November.
    3. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    4. Yu, Yantuan & Peng, Chong & Li, Yushuang, 2019. "Do neighboring prefectures matter in promoting eco-efficiency? Empirical evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 456-465.
    5. Richard Mulwa & Ali Emrouznejad & Lutta Muhammad, 2009. "Economic Efficiency of smallholder maize producers in Western Kenya: a DEA meta-frontier analysis," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 4(3), pages 250-267.
    6. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    7. Timo Kuosmanen & Mika Kortelainen, 2005. "Measuring Eco‐efficiency of Production with Data Envelopment Analysis," Journal of Industrial Ecology, Yale University, vol. 9(4), pages 59-72, October.
    8. Yantuan Yu & Jianhuan Huang & Nengsheng Luo, 2018. "Can More Environmental Information Disclosure Lead to Higher Eco-Efficiency? Evidence from China," Sustainability, MDPI, vol. 10(2), pages 1-20, February.
    9. Li, Lan-Bing & Hu, Jin-Li, 2012. "Ecological total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 46(C), pages 216-224.
    10. Tao, Feng & Zhang, Huiqin & Hu, Jun & Xia, X.H., 2017. "Dynamics of green productivity growth for major Chinese urban agglomerations," Applied Energy, Elsevier, vol. 196(C), pages 170-179.
    11. Jian Gao & Gary H. Jefferson, 2007. "Science and Technology Take-off in China?: Sources of Rising R&D Intensity," Asia Pacific Business Review, Taylor & Francis Journals, vol. 13(3), pages 357-371, July.
    12. Honma, Satoshi & Hu, Jin-Li, 2008. "Total-factor energy efficiency of regions in Japan," Energy Policy, Elsevier, vol. 36(2), pages 821-833, February.
    13. Fang, Chuandi & Cheng, Jinhua & Zhu, Yongguang & Chen, Jiahao & Peng, Xinjie, 2021. "Green total factor productivity of extractive industries in China: An explanation from technology heterogeneity," Resources Policy, Elsevier, vol. 70(C).
    14. He, Jie, 2010. "What is the role of openness for China's aggregate industrial SO2 emission?: A structural analysis based on the Divisia decomposition method," Ecological Economics, Elsevier, vol. 69(4), pages 868-886, February.
    15. Du, Limin & Hanley, Aoife & Zhang, Ning, 2016. "Environmental technical efficiency, technology gap and shadow price of coal-fuelled power plants in China: A parametric meta-frontier analysis," Resource and Energy Economics, Elsevier, vol. 43(C), pages 14-32.
    16. Zhang, Bing & Bi, Jun & Fan, Ziying & Yuan, Zengwei & Ge, Junjie, 2008. "Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach," Ecological Economics, Elsevier, vol. 68(1-2), pages 306-316, December.
    17. Xiancun Hu & Chunlu Liu, 2017. "Slacks-based data envelopment analysis for eco-efficiency assessment in the Australian construction industry," Construction Management and Economics, Taylor & Francis Journals, vol. 35(11-12), pages 693-706, December.
    18. Shuhong Wang & Xiaoli Sun & Malin Song, 2021. "Environmental Regulation, Resource Misallocation, and Ecological Efficiency," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(3), pages 410-429, February.
    19. Chen, Nengcheng & Xu, Lei & Chen, Zeqiang, 2017. "Environmental efficiency analysis of the Yangtze River Economic Zone using super efficiency data envelopment analysis (SEDEA) and tobit models," Energy, Elsevier, vol. 134(C), pages 659-671.
    20. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    Full references (including those not matched with items on IDEAS)

    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. Zhou, Haibo & Yang, Yi & Chen, Yao & Zhu, Joe, 2018. "Data envelopment analysis application in sustainability: The origins, development and future directions," European Journal of Operational Research, Elsevier, vol. 264(1), pages 1-16.
    2. Shih-Heng Yu & Yu Gao & Yih-Chearng Shiue, 2017. "A Comprehensive Evaluation of Sustainable Development Ability and Pathway for Major Cities in China," Sustainability, MDPI, vol. 9(8), pages 1-15, August.
    3. Shujing Yue & Yang Yang & Jun Shao & Yuting Zhu, 2016. "International Comparison of Total Factor Ecology Efficiency: Focused on G20 from 1999–2013," Sustainability, MDPI, vol. 8(11), pages 1-13, November.
    4. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Jusoh, Ahmad & Khoshnoudi, Masoumeh, 2017. "A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1298-1322.
    5. Shuangjie Li & Li Li & Liming Wang, 2020. "2030 Target for Energy Efficiency and Emission Reduction in the EU Paper Industry," Energies, MDPI, vol. 14(1), pages 1-17, December.
    6. Lucio Cecchini & Francesco Romagnoli & Massimo Chiorri & Biancamaria Torquati, 2023. "Eco-Efficiency and Its Determinants: The Case of the Italian Beef Cattle Sector," Agriculture, MDPI, vol. 13(5), pages 1-18, May.
    7. Yongyi Cheng & Tianyuan Shao & Huilin Lai & Manhong Shen & Yi Li, 2019. "Total-Factor Eco-Efficiency and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration, China," IJERPH, MDPI, vol. 16(20), pages 1-14, October.
    8. Du, Huibin & Matisoff, Daniel C. & Wang, Yangyang & Liu, Xi, 2016. "Understanding drivers of energy efficiency changes in China," Applied Energy, Elsevier, vol. 184(C), pages 1196-1206.
    9. Hu, Jin-Li & Chang, Ming-Chung & Tsay, Hui-Wen, 2017. "The congestion total-factor energy efficiency of regions in Taiwan," Energy Policy, Elsevier, vol. 110(C), pages 710-718.
    10. Yang, Wen-Chi & Lee, Yuh-Ming & Hu, Jin-Li, 2016. "Urban sustainability assessment of Taiwan based on data envelopment analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 341-353.
    11. Qin, Quande & Li, Xin & Li, Li & Zhen, Wei & Wei, Yi-Ming, 2017. "Air emissions perspective on energy efficiency: An empirical analysis of China’s coastal areas," Applied Energy, Elsevier, vol. 185(P1), pages 604-614.
    12. Junlong Li & Chuangneng Cai & Feng Zhang, 2020. "Assessment of Ecological Efficiency and Environmental Sustainability of the Minjiang-Source in China," Sustainability, MDPI, vol. 12(11), pages 1-15, June.
    13. Weixin Yang & Lingguang Li, 2017. "Energy Efficiency, Ownership Structure, and Sustainable Development: Evidence from China," Sustainability, MDPI, vol. 9(6), pages 1-26, June.
    14. Alizadeh, Reza & Gharizadeh Beiragh, Ramin & Soltanisehat, Leili & Soltanzadeh, Elham & Lund, Peter D., 2020. "Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach," Energy Economics, Elsevier, vol. 91(C).
    15. Du, Minzhe & Wang, Bing & Zhang, Ning, 2018. "National research funding and energy efficiency: Evidence from the National Science Foundation of China," Energy Policy, Elsevier, vol. 120(C), pages 335-346.
    16. Xiangxiang Sun & Lawrence Loh, 2019. "Sustainability Governance in China: An Analysis of Regional Ecological Efficiency," Sustainability, MDPI, vol. 11(7), pages 1-16, April.
    17. Shuangjie Li & Hongyu Diao & Liming Wang & Chunqi Li, 2021. "Energy Efficiency Measurement: A VO TFEE Approach and Its Application," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    18. Zhang, Bin & Lu, Danting & He, Yan & Chiu, Yung-ho, 2018. "The efficiencies of resource-saving and environment: A case study based on Chinese cities," Energy, Elsevier, vol. 150(C), pages 493-507.
    19. Ze Tian & Fang-Rong Ren & Qin-Wen Xiao & Yung-Ho Chiu & Tai-Yu Lin, 2019. "Cross-Regional Comparative Study on Carbon Emission Efficiency of China’s Yangtze River Economic Belt Based on the Meta-Frontier," IJERPH, MDPI, vol. 16(4), pages 1-19, February.
    20. Chang, Ming-Chung, 2013. "A comment on the calculation of the total-factor energy efficiency (TFEE) index," Energy Policy, Elsevier, vol. 53(C), pages 500-504.

    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:18:y:2021:i:10:p:5397-:d:557200. 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.