IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v26y2024i1d10.1007_s10668-022-02750-4.html
   My bibliography  Save this article

Spatiotemporal dynamics of CO2 emissions using nighttime light data: a comparative analysis between the Yellow and Yangtze River Basins in China

Author

Listed:
  • Wei Wei

    (Northwest Normal University)

  • Haibo Du

    (Nanjing Normal University)

  • Libang Ma

    (Northwest Normal University
    Northwest Normal University)

  • Chunfang Liu

    (Northwest Normal University
    Engineering Research Center of Land Utilization and Comprehension Consolidation)

  • Junju Zhou

    (Northwest Normal University)

Abstract

Scientific estimation and dynamic monitoring of CO2 emission trends are an important basis for formulating regional differentiated carbon reduction strategies. Using the integrated nighttime light data, this study estimated CO2 emissions in the Yellow River Basin (YRB) and Yangtze River Basin (YZRB) and discussed the similarities and differences of the spatial distribution of CO2 emissions for the two river basins. The results showed that: (1) The CO2 emissions in the two basins continued to rise, but the growth rate decreased from 2000 to 2018, showing an overall convergence trend, but have not yet reached carbon peak. (2) The high emission and high agglomeration areas were located in Shandong Province in the downstream of the YRB, Shanxi, Shaanxi and Inner Mongolia in the midstream and upstream, and the Yangtze River Delta (YRD). (3) Compared with the YRB, the growth rate of CO2 emissions in the YZRB is slower, and the growth rate declines greatly. In the YRB, it had higher CO2 emissions amount, wider area of high carbon emissions and more obvious spatial agglomeration than that in the YZRB. (4) According to CO2 emissions and economic development level, 220 cities of the two river basins were classified three types: low CO2–low development, high CO2–low development and high CO2–high development.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:1:d:10.1007_s10668-022-02750-4
    DOI: 10.1007/s10668-022-02750-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-022-02750-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-022-02750-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhao, Jincai & Ji, Guangxing & Yue, YanLin & Lai, Zhizhu & Chen, Yulong & Yang, Dongyang & Yang, Xu & Wang, Zheng, 2019. "Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets," Applied Energy, Elsevier, vol. 235(C), pages 612-624.
    2. Zhu Liu & Dabo Guan & Douglas Crawford-Brown & Qiang Zhang & Kebin He & Jianguo Liu, 2013. "A low-carbon road map for China," Nature, Nature, vol. 500(7461), pages 143-145, August.
    3. Fang, Guochang & Gao, Zhengye & Tian, Lixin & Fu, Min, 2022. "What drives urban carbon emission efficiency? – Spatial analysis based on nighttime light data," Applied Energy, Elsevier, vol. 312(C).
    4. Wang, Shaojian & Shi, Chenyi & Fang, Chuanglin & Feng, Kuishuang, 2019. "Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model," Applied Energy, Elsevier, vol. 235(C), pages 95-105.
    5. 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.
    6. Zhu Liu & Dabo Guan & Wei Wei & Steven J. Davis & Philippe Ciais & Jin Bai & Shushi Peng & Qiang Zhang & Klaus Hubacek & Gregg Marland & Robert J. Andres & Douglas Crawford-Brown & Jintai Lin & Hongya, 2015. "Reduced carbon emission estimates from fossil fuel combustion and cement production in China," Nature, Nature, vol. 524(7565), pages 335-338, August.
    7. Lin, Boqiang & Zhang, Zihan, 2016. "Carbon emissions in China׳s cement industry: A sector and policy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1387-1394.
    8. Zhu Liu & Philippe Ciais & Zhu Deng & Ruixue Lei & Steven J. Davis & Sha Feng & Bo Zheng & Duo Cui & Xinyu Dou & Biqing Zhu & Rui Guo & Piyu Ke & Taochun Sun & Chenxi Lu & Pan He & Yuan Wang & Xu Yue , 2020. "Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    9. Yan-Qing Kang & Tao Zhao & Peng Wu, 2016. "Impacts of energy-related CO2 emissions in China: a spatial panel data technique," 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. 81(1), pages 405-421, March.
    10. Shan, Yuli & Liu, Jianghua & Liu, Zhu & Xu, Xinwanghao & Shao, Shuai & Wang, Peng & Guan, Dabo, 2016. "New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors," Applied Energy, Elsevier, vol. 184(C), pages 742-750.
    11. 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.
    12. Zhu Liu & Philippe Ciais & Zhu Deng & Ruixue Lei & Steven J. Davis & Sha Feng & Bo Zheng & Duo Cui & Xinyu Dou & Biqing Zhu & Rui Guo & Piyu Ke & Taochun Sun & Chenxi Lu & Pan He & Yuan Wang & Xu Yue , 2020. "Author Correction: Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic," Nature Communications, Nature, vol. 11(1), pages 1-1, December.
    13. Miao, Zhuang & Baležentis, Tomas & Shao, Shuai & Chang, Dongfeng, 2019. "Energy use, industrial soot and vehicle exhaust pollution—China's regional air pollution recognition, performance decomposition and governance," Energy Economics, Elsevier, vol. 83(C), pages 501-514.
    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. Yongxing Li & Wei Guo & Peixian Li & Xuesheng Zhao & Jinke Liu, 2023. "Exploring the Spatiotemporal Dynamics of CO 2 Emissions through a Combination of Nighttime Light and MODIS NDVI Data," Sustainability, MDPI, vol. 15(17), pages 1-17, August.
    2. Xiao, Huijuan & Duan, Zhiyuan & Zhou, Ya & Zhang, Ning & Shan, Yuli & Lin, Xiyan & Liu, Guosheng, 2019. "CO2 emission patterns in shrinking and growing cities: A case study of Northeast China and the Yangtze River Delta," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Zhao, Bingbing & Deng, Min & Lo, Siuming & Liu, Baoju, 2024. "Estimating built-up area carbon emissions through addressing regional development disparities with population and nighttime light data," Applied Energy, Elsevier, vol. 369(C).
    4. Hu, Ting & Huang, Xin, 2019. "A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 240(C), pages 778-792.
    5. Chen, Qianli & Cai, Bofeng & Dhakal, Shobhakar & Pei, Sha & Liu, Chunlan & Shi, Xiaoping & Hu, Fangfang, 2017. "CO2 emission data for Chinese cities," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 198-208.
    6. Hui Wang & Guifen Liu & Kaifang Shi, 2019. "What Are the Driving Forces of Urban CO 2 Emissions in China? A Refined Scale Analysis between National and Urban Agglomeration Levels," IJERPH, MDPI, vol. 16(19), pages 1-19, September.
    7. Xiao, Hongwei & Ma, Zhongyu & Mi, Zhifu & Kelsey, John & Zheng, Jiali & Yin, Weihua & Yan, Min, 2018. "Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data," Applied Energy, Elsevier, vol. 231(C), pages 1070-1078.
    8. Liu, Xiaoyu & Duan, Zhiyuan & Shan, Yuli & Duan, Haiyan & Wang, Shuo & Song, Junnian & Wang, Xian'en, 2019. "Low-carbon developments in Northeast China: Evidence from cities," Applied Energy, Elsevier, vol. 236(C), pages 1019-1033.
    9. Zhang, Bo & Yang, T.R. & Chen, B. & Sun, X.D., 2016. "China’s regional CH4 emissions: Characteristics, interregional transfer and mitigation policies," Applied Energy, Elsevier, vol. 184(C), pages 1184-1195.
    10. Xiao, Huijuan & Wang, Daoping & Qi, Yu & Shao, Shuai & Zhou, Ya & Shan, Yuli, 2021. "The governance-production nexus of eco-efficiency in Chinese resource-based cities: A two-stage network DEA approach," Energy Economics, Elsevier, vol. 101(C).
    11. Tong, Zheming & Chen, Yujiao & Malkawi, Ali & Liu, Zhu & Freeman, Richard B., 2016. "Energy saving potential of natural ventilation in China: The impact of ambient air pollution," Applied Energy, Elsevier, vol. 179(C), pages 660-668.
    12. Li, Yanmei & Cui, Yifei & Cai, Bofeng & Guo, Jingpeng & Cheng, Tianhai & Zheng, Fengjie, 2020. "Spatial characteristics of CO2 emissions and PM2.5 concentrations in China based on gridded data," Applied Energy, Elsevier, vol. 266(C).
    13. Dapeng Liang & Jianjun Liu & Mengting Liu & Jiayin Sun, 2024. "Does information infrastructure and technological infrastructure reduce carbon dioxide emissions in the context of sustainable development? Examining spatial spillover effect," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(3), pages 1599-1615, June.
    14. Wang, Hanjie & Yu, Xiaohua, 2023. "Carbon dioxide emission typology and policy implications: Evidence from machine learning," China Economic Review, Elsevier, vol. 78(C).
    15. Hui Wen & Yi Li & Zirong Li & Xiaoxue Cai & Fengxia Wang, 2022. "Spatial Differentiation of Carbon Budgets and Carbon Balance Zoning in China Based on the Land Use Perspective," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    16. Wang, Changjian & Miao, Zhuang & Chen, Xiaodong & Cheng, Yu, 2021. "Factors affecting changes of greenhouse gas emissions in Belt and Road countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    17. Siting Hong & Ting Fu & Ming Dai, 2025. "Machine Learning-Based Carbon Emission Predictions and Customized Reduction Strategies for 30 Chinese Provinces," Sustainability, MDPI, vol. 17(5), pages 1-29, February.
    18. Juchao Zhao & Shaohua Zhang & Kun Yang & Yanhui Zhu & Yuling Ma, 2020. "Spatio-Temporal Variations of CO 2 Emission from Energy Consumption in the Yangtze River Delta Region of China and Its Relationship with Nighttime Land Surface Temperature," Sustainability, MDPI, vol. 12(20), pages 1-17, October.

    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:spr:endesu:v:26:y:2024:i:1:d:10.1007_s10668-022-02750-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.