IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i13p5447-d1423010.html
   My bibliography  Save this article

Land Green Utilization Efficiency and Its Driving Mechanisms in the Zhengzhou Metropolitan Area

Author

Listed:
  • Linger Yu

    (School of Resources and Environment, Shandong Agricultural University, Taian 271000, China
    These authors contributed equally to this work.)

  • Keyi Liu

    (School of Resources and Environment, Shandong Agricultural University, Taian 271000, China
    These authors contributed equally to this work.)

Abstract

Improving land green use efficiency is of great significance for promoting high-quality economic development and promoting the modernization of harmonious coexistence between humans and nature. In this study, the super-efficiency SBM model with non-expected output was used to measure the level of land green use efficiency at county scale in the Zhengzhou metropolitan area from 2005 to 2020. Based on this, the spatio-temporal evolution and spatial agglomeration characteristics were analyzed. Finally, the driving mechanisms were revealed by using the geographical detector model. The results were as follows: (1) From 2005 to 2020, the land green use efficiency of the Zhengzhou metropolitan area fluctuated from 0.5329 to 0.5164, with an average annual decline rate of 0.21%, exhibiting three stages: decline, rise, then another slight decline. At the city level, Luohe City had the highest land green use efficiency, while Zhengzhou City had the lowest. (2) The land green use efficiency of the Zhengzhou metropolitan area showed a significant spatial positive correlation, Moran’s I index increased from 0.1472 to 0.2991, and the spatial agglomeration effect was continuously enhanced. On the local scale, high-high (H-H) aggregation and low-low (L-L) aggregation were dominant, high-high (H-H) aggregation areas were mainly distributed in the southwest and southeast of the Zhengzhou metropolitan area, and low-low (L-L) aggregation areas were mainly distributed in the central and western parts of the Zhengzhou metropolitan area. (3) There is heterogeneity in the degree of influence of different driving factors on land green use efficiency in the Zhengzhou metropolitan area, which is ranked as topographic relief (X7) > forest coverage rate (X8) > social consumption (X6) > industrial structure (X3) > urbanization rate (X2) > economic development (X1) > industrial added value scale (X5) > financial expenditure (X4). q values were 0.1856, 0.1119, 0.1082, 0.0741, 0.0673, 0.0589, 0.0492 and 0.0430, respectively. The interaction of two factors can enhance the explanatory power of land green use efficiency in the Zhengzhou metropolitan area. Except for the interaction of topographic relief and forest coverage rate, the other factors all show double factor enhancement. The explanatory power of the interaction between topographic relief and urbanization rate is the strongest, at 0.3513. In the future, policy regulation should be carried out from the perspectives of the interaction of social and economic conditions such as improving forest coverage rate, improving consumption power, optimizing industrial structure and improving land green use mechanisms to promote the improvement of land green use efficiency.

Suggested Citation

  • Linger Yu & Keyi Liu, 2024. "Land Green Utilization Efficiency and Its Driving Mechanisms in the Zhengzhou Metropolitan Area," Sustainability, MDPI, vol. 16(13), pages 1-17, June.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:13:p:5447-:d:1423010
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/13/5447/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/13/5447/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kaoru Tone & Miki Tsutsui, 2010. "An epsilon-based measure of efficiency in DEA revisited -A third pole of technical efficiency," GRIPS Discussion Papers 09-21, National Graduate Institute for Policy Studies.
    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. A. M. Aldanondo & V. L. Casasnovas, 2015. "Input aggregation bias in technical efficiency with multiple criteria analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 22(6), pages 430-435, April.
    2. Zebin Zheng & Wenjun Xiao & Ziye Cheng, 2023. "China’s Green Total Factor Energy Efficiency Assessment Based on Coordinated Reduction in Pollution and Carbon Emission: From the 11th to the 13th Five-Year Plan," Sustainability, MDPI, vol. 15(9), pages 1-20, April.
    3. Gómez-Calvet, Roberto & Conesa, David & Gómez-Calvet, Ana Rosa & Tortosa-Ausina, Emili, 2014. "Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?," Applied Energy, Elsevier, vol. 132(C), pages 137-154.
    4. Xiangqian Wang & Shudong Wang & Yongqiu Xia, 2022. "Evaluation and Dynamic Evolution of the Total Factor Environmental Efficiency in China’s Mining Industry," Energies, MDPI, vol. 15(3), pages 1-19, February.
    5. 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.
    6. Mehmet Pinar & Thanasis Stengos & Nikolas Topaloglou, 2022. "Stochastic dominance spanning and augmenting the human development index with institutional quality," Annals of Operations Research, Springer, vol. 315(1), pages 341-369, August.
    7. Zhou, Anhua & Li, Jun, 2021. "Investigate the impact of market reforms on the improvement of manufacturing energy efficiency under China’s provincial-level data," Energy, Elsevier, vol. 228(C).
    8. Jin, Peizhen & Peng, Chong & Song, Malin, 2019. "Macroeconomic uncertainty, high-level innovation, and urban green development performance in China," China Economic Review, Elsevier, vol. 55(C), pages 1-18.
    9. Cui, Qiang & Li, Ye, 2020. "A cross efficiency distinguishing method to explore the cooperation degree in dynamic airline environmental efficiency," Transport Policy, Elsevier, vol. 99(C), pages 31-43.
    10. Zhen Deng & Fan Xiao & Jing Huang & Yizhen Zhang & Fang Zhang, 2024. "Spillover Effects of Urban Expansion on Land Green Use Efficiency: An Empirical Study Based on Multi-Source Remote Sensing Data in China," Land, MDPI, vol. 13(7), pages 1-21, July.
    11. Wang, Yi & Wang, Huiping, 2023. "Spatial spillover effect of urban sprawl on total factor energy ecological efficiency: Evidence from 272 cities in China," Energy, Elsevier, vol. 273(C).
    12. Jun Gao & Ning Xu & Ju Zhou, 2023. "Innovative City Construction and Urban Environmental Performance: Empirical Evidence from China," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    13. Zhou Zhou & Jianqiang Duan & Shaoqing Geng & Ran Li, 2023. "Spatial Network and Driving Factors of Agricultural Green Total Factor Productivity in China," Energies, MDPI, vol. 16(14), pages 1-26, July.
    14. Yashuo Liu & Huanan Liu, 2023. "Temporal and Spatial Evolution Characteristics and Influencing Factors Analysis of Green Production in China’s Dairy Industry: Based on the Perspective of Green Total Factor Productivity," Sustainability, MDPI, vol. 15(23), pages 1-21, November.
    15. Yongwang Zhang & Minjuan Zhao, 2024. "Environmental regulations or expected revenue: What plays a more important role in China's green transition of agriculture?," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 70(9), pages 425-435.
    16. Pengfei Zhang & Hu Yu & Mingzhe Shen & Wei Guo, 2022. "Evaluation of Tourism Development Efficiency and Spatial Spillover Effect Based on EBM Model: The Case of Hainan Island, China," IJERPH, MDPI, vol. 19(7), pages 1-21, March.
    17. Xin Fang & Yun Cao, 2023. "Spatial Association Network Evolution and Variance Decomposition of Economic Sustainability Development Efficiency in China," IJERPH, MDPI, vol. 20(4), pages 1-22, February.
    18. Cui, Qiang & Jia, Zi-ke, 2023. "Measuring the dynamic airline energy efficiency with non-homogeneous structures," Energy, Elsevier, vol. 266(C).
    19. Xuefen Liu & Chang Gan & Mihai Voda, 2024. "Analysis of the Effect of Environmental Regulation on Eco-Efficiency of Service Sector," Sustainability, MDPI, vol. 16(13), pages 1-20, July.
    20. Yiyang Sun & Guolin Hou, 2021. "Analysis on the Spatial-Temporal Evolution Characteristics and Spatial Network Structure of Tourism Eco-Efficiency in the Yangtze River Delta Urban Agglomeration," IJERPH, MDPI, vol. 18(5), pages 1-29, March.

    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:jsusta:v:16:y:2024:i:13:p:5447-:d:1423010. 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.