IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i12p2160-d1299164.html
   My bibliography  Save this article

Urban Land Carbon Emission and Carbon Emission Intensity Prediction Based on Patch-Generating Land Use Simulation Model and Grid with Multiple Scenarios in Tianjin

Author

Listed:
  • Xiang Li

    (College of Earth Sciences, Jilin University, Changchun 130061, China)

  • Zhaoshun Liu

    (College of Earth Sciences, Jilin University, Changchun 130061, China)

  • Shujie Li

    (College of Earth Sciences, Jilin University, Changchun 130061, China)

  • Yingxue Li

    (College of Earth Sciences, Jilin University, Changchun 130061, China)

  • Weiyu Wang

    (College of Earth Sciences, Jilin University, Changchun 130061, China)

Abstract

With regard to the aims of achieving the “Dual Carbon” goal and addressing the significant greenhouse gas emissions caused by urban expansion, there has been a growing emphasis on spatial research and the prediction of urban carbon emissions. This article examines land use data from 2000 to 2020 and combines Grid and the PLUS model to predict carbon emissions in 2030 through a multi-scenario simulation. The research findings indicate the following: (1) Between 2000 and 2020, construction land increased by 95.83%, with carbon emissions also increasing. (2) By 2030, for the NDS (natural development scenario), carbon emissions are expected to peak at 6012.87 × 10 4 t. Regarding the ratio obtained through the EDS (economic development scenario), construction land is projected to grow to 3990.72 km 2 , with expected emissions of 6863.29 × 10 4 t. For the LCS (low-carbon scenario), the “carbon peak” is expected to be reached before 2030. (3) The intensity of carbon emissions decreases as the city size increases. (4) The shift of the center of carbon emission intensity and the center of construction land all indicate movement towards the southeast. Studying the trends of regional land use change and the patterns of land use carbon emissions is beneficial for optimizing the land use structure, thereby enabling us to achieve low-carbon emission reductions and sustainable urban development.

Suggested Citation

  • Xiang Li & Zhaoshun Liu & Shujie Li & Yingxue Li & Weiyu Wang, 2023. "Urban Land Carbon Emission and Carbon Emission Intensity Prediction Based on Patch-Generating Land Use Simulation Model and Grid with Multiple Scenarios in Tianjin," Land, MDPI, vol. 12(12), pages 1-22, December.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:12:p:2160-:d:1299164
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/12/2160/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/12/2160/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhaosu Meng & Huan Wang & Baona Wang, 2018. "Empirical Analysis of Carbon Emission Accounting and Influencing Factors of Energy Consumption in China," IJERPH, MDPI, vol. 15(11), pages 1-15, November.
    2. Luo, Haizhi & Li, Yingyue & Gao, Xinyu & Meng, Xiangzhao & Yang, Xiaohu & Yan, Jinyue, 2023. "Carbon emission prediction model of prefecture-level administrative region: A land-use-based case study of Xi'an city, China," Applied Energy, Elsevier, vol. 348(C).
    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. Qifan Guan, 2023. "Decomposing and Decoupling the Energy-Related Carbon Emissions in the Beijing–Tianjin–Hebei Region Using the Extended LMDI and Tapio Index Model," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    2. Zhou, Di & Huang, Qing & Chong, Zhaohui, 2022. "Analysis on the effect and mechanism of land misallocation on carbon emissions efficiency: Evidence from China," Land Use Policy, Elsevier, vol. 121(C).
    3. Yuanying Chi & Situo Xu & Xiaolei Yang & Jialin Li & Xufeng Zhang & Yahui Chen, 2023. "Research on Beijing Manufacturing Green-Oriented Transition Path under “Double Carbon” Goal-Based on the GML-SD Model," Sustainability, MDPI, vol. 15(9), pages 1-17, May.
    4. Debin Fang & Peng Hao & Zhengxin Wang & Jian Hao, 2019. "Analysis of the Influence Mechanism of CO 2 Emissions and Verification of the Environmental Kuznets Curve in China," IJERPH, MDPI, vol. 16(6), pages 1-17, March.
    5. Wang, Rongji & Laila, Ume & Nazir, Rabia & Hao, Xibin, 2023. "Unleashing the influence of industrialization and trade openness on renewable energy intensity using path model analysis: A roadmap towards sustainable development," Renewable Energy, Elsevier, vol. 202(C), pages 280-288.
    6. Linlin Ye & Xiaodong Wu & Dandan Huang, 2020. "Industrial Energy-Related CO 2 Emissions and Their Driving Factors in the Yangtze River Economic Zone (China): An Extended LMDI Analysis from 2008 to 2016," IJERPH, MDPI, vol. 17(16), pages 1-13, August.
    7. Yabo Zhao & Shifa Ma & Jianhong Fan & Yunnan Cai, 2021. "Examining the Effects of Land Use on Carbon Emissions: Evidence from Pearl River Delta," IJERPH, MDPI, vol. 18(7), pages 1-19, March.
    8. Xiaodie Liu & Xiangqian Wang & Xiangrui Meng, 2023. "Carbon Emission Scenario Prediction and Peak Path Selection in China," Energies, MDPI, vol. 16(5), pages 1-17, February.
    9. Olukorede Tijani Adenuga & Khumbulani Mpofu & Ragosebo Kgaugelo Modise, 2022. "Energy–Carbon Emissions Nexus Causal Model towards Low-Carbon Products in Future Transport-Manufacturing Industries," Energies, MDPI, vol. 15(17), pages 1-13, August.
    10. Xinhua Tong & Shurui Guo & Haiyan Duan & Zhiyuan Duan & Chang Gao & Wu Chen, 2022. "Carbon-Emission Characteristics and Influencing Factors in Growing and Shrinking Cities: Evidence from 280 Chinese Cities," IJERPH, MDPI, vol. 19(4), pages 1-17, February.
    11. Gen Li & Shihong Zeng & Tengfei Li & Qiao Peng & Muhammad Irfan, 2023. "Analysing the Effect of Energy Intensity on Carbon Emission Reduction in Beijing," IJERPH, MDPI, vol. 20(2), pages 1-19, January.

    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:jlands:v:12:y:2023:i:12:p:2160-:d:1299164. 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.