IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i2p215-d1336415.html
   My bibliography  Save this article

County-Level Land Use Carbon Budget in the Yangtze River Economic Belt, China: Spatiotemporal Differentiation and Coordination Zoning

Author

Listed:
  • Chong Liu

    (School of Public Administration, China University of Geosciences, Wuhan 430074, China)

  • Xiaoman Wang

    (School of Government, Sun Yat-Sen University, Guangzhou 510080, China)

  • Haiyang Li

    (School of Public Administration, China University of Geosciences, Wuhan 430074, China)

Abstract

The local land use carbon budget (LUCB) balance is an important factor in achieving regional carbon neutrality. As the basic unit of China’s economic development and social governance, the county level is an important part of the realization of the “double carbon” goal. This paper focuses on 1069 county units within the Yangtze River Economic Belt (YREB). It utilizes data on land use, nighttime light, energy consumption, and social and economic factors to construct carbon emission models. The spatiotemporal characteristics of LUCB in these county units are analyzed using standard deviational ellipse (SDE) and spatial autocorrelation methods. Additionally, a zoning study is conducted by examining the economic contribution coefficient (ECC) of carbon emissions, the ecological support coefficient (ESC), and their coupling relationship. The results show that (1) the total land use carbon emissions (LUCE) increased significantly during the research period, and the total carbon sink was relatively stable. (2) The LUCB is spatially high in the east and low in the west, with the center of gravity moving to the southwest as a whole. (3) The LUCB shows positive spatial autocorrelation and has significant spatial agglomeration characteristics, which are mainly high–high and low–low regional agglomeration types. (4) The ECC is high in the east and low in the west, the ESC is high in the west and low in the east, and the coordination and coupling degrees of the two are low. (5) According to the ECC and ESC, the county unit is divided into a low-carbon conservation area, an economic development area, a carbon sink development area, and a comprehensive optimization area. This study is helpful in promoting the sustainable development of carbon neutrality and low carbon in the YREB.

Suggested Citation

  • Chong Liu & Xiaoman Wang & Haiyang Li, 2024. "County-Level Land Use Carbon Budget in the Yangtze River Economic Belt, China: Spatiotemporal Differentiation and Coordination Zoning," Land, MDPI, vol. 13(2), pages 1-21, February.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:2:p:215-:d:1336415
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/2/215/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/13/2/215/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yue, Ting & Long, Ruyin & Chen, Hong & Zhao, Xin, 2013. "The optimal CO2 emissions reduction path in Jiangsu province: An expanded IPAT approach," Applied Energy, Elsevier, vol. 112(C), pages 1510-1517.
    2. Wang, Ping & Wu, Wanshui & Zhu, Bangzhu & Wei, Yiming, 2013. "Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China," Applied Energy, Elsevier, vol. 106(C), pages 65-71.
    3. J. Keith Ord & Arthur Getis, 2001. "Testing for Local Spatial Autocorrelation in the Presence of Global Autocorrelation," Journal of Regional Science, Wiley Blackwell, vol. 41(3), pages 411-432, August.
    4. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Chen, Zuoqi & Liu, Rui & Li, Linyi & Wu, Jianping, 2016. "Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis," Applied Energy, Elsevier, vol. 168(C), pages 523-533.
    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. Shuai, Chenyang & Shen, Liyin & Jiao, Liudan & Wu, Ya & Tan, Yongtao, 2017. "Identifying key impact factors on carbon emission: Evidences from panel and time-series data of 125 countries from 1990 to 2011," Applied Energy, Elsevier, vol. 187(C), pages 310-325.
    2. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    3. Gang Xu & Tianyi Zeng & Hong Jin & Cong Xu & Ziqi Zhang, 2023. "Spatio-Temporal Variations and Influencing Factors of Country-Level Carbon Emissions for Northeast China Based on VIIRS Nighttime Lighting Data," IJERPH, MDPI, vol. 20(1), pages 1-17, January.
    4. Liu, Liwei & Zong, Haijing & Zhao, Erdong & Chen, Chuxiang & Wang, Jianzhou, 2014. "Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development," Applied Energy, Elsevier, vol. 124(C), pages 199-212.
    5. Ying Wang & Peipei Shang & Lichun He & Yingchun Zhang & Dandan Liu, 2018. "Can China Achieve the 2020 and 2030 Carbon Intensity Targets through Energy Structure Adjustment?," Energies, MDPI, vol. 11(10), pages 1-32, October.
    6. Shi, Kaifang & Yu, Bailang & Zhou, Yuyu & Chen, Yun & Yang, Chengshu & Chen, Zuoqi & Wu, Jianping, 2019. "Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels," Applied Energy, Elsevier, vol. 233, pages 170-181.
    7. Bo Li & Xuejing Liu & Zhenhong Li, 2015. "Using the STIRPAT model to explore the factors driving regional CO 2 emissions: a case of Tianjin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(3), pages 1667-1685, April.
    8. Yang, Di & Luan, Weixin & Qiao, Lu & Pratama, Mahardhika, 2020. "Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery," Applied Energy, Elsevier, vol. 268(C).
    9. Tan, Feifei & Lu, Zhaohua, 2015. "Current status and future choices of regional sectors-energy-related CO2 emissions: The third economic growth pole of China," Applied Energy, Elsevier, vol. 159(C), pages 237-251.
    10. Huang, Junbing & Li, Xinghao & Wang, Yajun & Lei, Hongyan, 2021. "The effect of energy patents on China's carbon emissions: Evidence from the STIRPAT model," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    11. Dong, Kangyin & Hochman, Gal & Zhang, Yaqing & Sun, Renjin & Li, Hui & Liao, Hua, 2018. "CO2 emissions, economic and population growth, and renewable energy: Empirical evidence across regions," Energy Economics, Elsevier, vol. 75(C), pages 180-192.
    12. Zhengyun Jiang & Yun Feng & Jinping Song & Chengzhen Song & Xiaodi Zhao & Chi Zhang, 2023. "Study on the Spatial–Temporal Pattern Evolution and Carbon Emission Reduction Effect of Industry–City Integration in the Yellow River Basin," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    13. Ling Xiong & Shaozhou Qi, 2018. "Financial Development And Carbon Emissions In Chinese Provinces: A Spatial Panel Data Analysis," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 63(02), pages 447-464, March.
    14. Jinzhao Song & Qing Feng & Xiaoping Wang & Hanliang Fu & Wei Jiang & Baiyu Chen, 2018. "Spatial Association and Effect Evaluation of CO 2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis," Sustainability, MDPI, vol. 11(1), pages 1-19, December.
    15. Roula Inglesi-Lotz & Luis Diez del Corral Morales, 2017. "The Effect of Education on a Country’s Energy Consumption: Evidence from Developed and Developing Countries," Working Papers 201733, University of Pretoria, Department of Economics.
    16. Wei Wei & Haibo Du & Libang Ma & Chunfang Liu & Junju Zhou, 2024. "Spatiotemporal dynamics of CO2 emissions using nighttime light data: a comparative analysis between the Yellow and Yangtze River Basins in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(1), pages 1081-1102, January.
    17. Guo, Shuocheng & Kontou, Eleftheria, 2021. "Disparities and equity issues in electric vehicles rebate allocation," Energy Policy, Elsevier, vol. 154(C).
    18. Xinxuan Cheng & Longfei Fan & Jiachen Wang, 2018. "Can Energy Structure Optimization, Industrial Structure Changes, Technological Improvements, and Central and Local Governance Effectively Reduce Atmospheric Pollution in the Beijing–Tianjin–Hebei Area," Sustainability, MDPI, vol. 10(3), pages 1-16, February.
    19. Dong Jichang & He Jing & Li Xiuting & Mou Xindi & Dong Zhi, 2020. "The Effect of Industrial Structure Change on Carbon Dioxide Emissions: A Cross-Country Panel Analysis," Journal of Systems Science and Information, De Gruyter, vol. 8(1), pages 1-16, February.
    20. Huaide Wen & Jun Dai, 2021. "The Change of Sources of Growth and Sustainable Development in China: Based on the Extended EKC Explanation," Sustainability, MDPI, vol. 13(5), pages 1-19, 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:jlands:v:13:y:2024:i:2:p:215-:d:1336415. 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.