IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i7p1469-d1200685.html
   My bibliography  Save this article

Spatiotemporal Evolution and Correlation Analysis of Carbon Emissions in the Nine Provinces along the Yellow River since the 21st Century Using Nighttime Light Data

Author

Listed:
  • Yaohui Liu

    (School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China)

  • Wenyi Liu

    (School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China)

  • Peiyuan Qiu

    (School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China)

  • Jie Zhou

    (Institute of Geology, China Earthquake Administration, Beijing 100029, China
    Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China)

  • Linke Pang

    (School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China)

Abstract

Monitoring carbon emissions is crucial for assessing and addressing economic development and climate change, particularly in regions like the nine provinces along the Yellow River in China, which experiences significant urbanization and development. However, to the best of our knowledge, existing studies mainly focus on national and provincial scales, with fewer studies on municipal and county scales. To address this issue, we established a carbon emission assessment model based on the “NPP-VIIRS-like” nighttime light data, aiming to analyze the spatiotemporal variation of carbon emissions in three different levels of nine provinces along the Yellow River since the 21st century. Further, the spatial correlation of carbon emissions at the county level was explored using the Moran’s I spatial analysis method. Results show that, from 2000 to 2021, carbon emissions in this region continued to rise, but the growth rate declined, showing an overall convergence trend. Per capita carbon emission intensity showed an overall upward trend, while carbon emission intensity per unit of GDP showed an overall downward trend. Its spatial distribution generally showed high carbon emissions in the eastern region and low carbon emissions in the western region. The carbon emissions of each city mainly showed a trend of “several”; that is, the urban area around the Yellow River has higher carbon emissions. Meanwhile, there is a trend of higher carbon emissions in provincial capitals. Moran’s I showed a trend of decreasing first and then increasing and gradually tended to a stable state in the later stage, and the pattern of spatial agglomeration was relatively fixed. “High–High” and “Low–Low” were the main types of local spatial autocorrelation, and the number of counties with “High–High” agglomeration increased significantly, while the number of counties with “Low–Low” agglomeration gradually decreased. The findings of this study provide valuable insights into the carbon emission trends of the study area, as well as the references that help to achieve carbon peaking and carbon neutrality goals proposed by China.

Suggested Citation

  • Yaohui Liu & Wenyi Liu & Peiyuan Qiu & Jie Zhou & Linke Pang, 2023. "Spatiotemporal Evolution and Correlation Analysis of Carbon Emissions in the Nine Provinces along the Yellow River since the 21st Century Using Nighttime Light Data," Land, MDPI, vol. 12(7), pages 1-19, July.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:7:p:1469-:d:1200685
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/7/1469/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/7/1469/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Li, Huanan & Mu, Hailin & Zhang, Ming & Gui, Shusen, 2012. "Analysis of regional difference on impact factors of China’s energy – Related CO2 emissions," Energy, Elsevier, vol. 39(1), pages 319-326.
    3. Du, Mengbing & Zhang, Xiaoling & Xia, Lang & Cao, Libin & Zhang, Zhe & Zhang, Li & Zheng, Heran & Cai, Bofeng, 2022. "The China Carbon Watch (CCW) system: A rapid accounting of household carbon emissions in China at the provincial level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    4. Huafang Huang & Xiaomao Wu & Xianfu Cheng, 2021. "The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning," Land, MDPI, vol. 10(12), pages 1-23, December.
    5. Jinjie Zhao & Lei Kou & Haitao Wang & Xiaoyu He & Zhihui Xiong & Chaoqiang Liu & Hao Cui, 2022. "Carbon Emission Prediction Model and Analysis in the Yellow River Basin Based on a Machine Learning Method," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    6. Shaoqi Sun & Yuanli Xie & Yunmei Li & Kansheng Yuan & Lifa Hu, 2022. "Analysis of Dynamic Evolution and Spatial-Temporal Heterogeneity of Carbon Emissions at County Level along “The Belt and Road”—A Case Study of Northwest China," IJERPH, MDPI, vol. 19(20), pages 1-20, October.
    7. Muhammad Kamran Khan & Hai Hong Trinh & Ikram Ullah Khan & Subhan Ullah, 2022. "Sustainable economic activities, climate change, and carbon risk: an international evidence," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(7), pages 9642-9664, July.
    8. Tianjiao Yang & Jing Liu & Haibo Mi & Zhicheng Cao & Yiting Wang & Huichao Han & Jiahui Luan & Zhaoxuan Wang, 2022. "An Estimating Method for Carbon Emissions of China Based on Nighttime Lights Remote Sensing Satellite Images," Sustainability, MDPI, vol. 14(4), pages 1-23, February.
    9. 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.
    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. 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).
    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. 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.
    4. Liu, Xingjian & Wang, Mingshu & Qiang, Wei & Wu, Kang & Wang, Xiaomi, 2020. "Urban form, shrinking cities, and residential carbon emissions: Evidence from Chinese city-regions," Applied Energy, Elsevier, vol. 261(C).
    5. Du, Xiaoyun & Meng, Conghui & Guo, Zhenhua & Yan, Hang, 2023. "An improved approach for measuring the efficiency of low carbon city practice in China," Energy, Elsevier, vol. 268(C).
    6. Yanbin Li & Zhen Li & Min Wu & Feng Zhang & Gejirifu De, 2018. "Regional-Level Allocation of CO 2 Emission Permits in China: Evidence from the Boltzmann Distribution Method," Sustainability, MDPI, vol. 10(8), pages 1-16, July.
    7. Kangjuan Lv & Yu Cheng & Yousen Wang, 2021. "Does regional innovation system efficiency facilitate energy-related carbon dioxide intensity reduction in China?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(1), pages 789-813, January.
    8. Feng, Zhiying & Tang, Wenhu & Niu, Zhewen & Wu, Qinghua, 2018. "Bi-level allocation of carbon emission permits based on clustering analysis and weighted voting: A case study in China," Applied Energy, Elsevier, vol. 228(C), pages 1122-1135.
    9. Bian, Yiwen & He, Ping & Xu, Hao, 2013. "Estimation of potential energy saving and carbon dioxide emission reduction in China based on an extended non-radial DEA approach," Energy Policy, Elsevier, vol. 63(C), pages 962-971.
    10. Yan-Qing Kang & Tao Zhao & Peng Wu, 2016. "Impacts of energy-related CO 2 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.
    11. Xiaoqing Zhu & Tiancheng Zhang & Weijun Gao & Danying Mei, 2020. "Analysis on Spatial Pattern and Driving Factors of Carbon Emission in Urban–Rural Fringe Mixed-Use Communities: Cases Study in East Asia," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    12. Wei Sun & Ming Meng & Yujun He & Hong Chang, 2016. "CO 2 Emissions from China’s Power Industry: Scenarios and Policies for 13th Five-Year Plan," Energies, MDPI, vol. 9(10), pages 1-16, October.
    13. Cheng Zhang & Xiong Zou & Chuan Lin, 2023. "Carbon Footprint Prediction of Thermal Power Industry under the Dual-Carbon Target: A Case Study of Zhejiang Province, China," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    14. Yuhong Zhao & Ruirui Liu & Zhansheng Liu & Liang Liu & Jingjing Wang & Wenxiang Liu, 2023. "A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    15. Jiang Zhu & Xiang Li & Huiming Huang & Xiangdong Yin & Jiangchun Yao & Tao Liu & Jiexuan Wu & Zhangcheng Chen, 2023. "Spatiotemporal Evolution of Carbon Emissions According to Major Function-Oriented Zones: A Case Study of Guangdong Province, China," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    16. Ming Zhang & Yan Song, 2015. "Exploring influence factors governing the changes in China’s final energy consumption under a new framework," 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. 78(1), pages 653-668, August.
    17. Yanan Wang & Wei Chen & Minjuan Zhao & Bowen Wang, 2019. "Analysis of the influencing factors on CO2 emissions at different urbanization levels: regional difference in China based on panel estimation," 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. 96(2), pages 627-645, March.
    18. Xiuqin Zhang & Xudong Shi & Yasir Khan & Majid Khan & Saba Naz & Taimoor Hassan & Chenchen Wu & Tahir Rahman, 2023. "The Impact of Energy Intensity, Energy Productivity and Natural Resource Rents on Carbon Emissions in Morocco," Sustainability, MDPI, vol. 15(8), pages 1-22, April.
    19. 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.
    20. Ngo, Thanh & Trinh, Hai Hong & Haouas, Ilham & Ullah, Subhan, 2022. "Examining the bidirectional nexus between financial development and green growth: International evidence through the roles of human capital and education expenditure," Resources Policy, Elsevier, vol. 79(C).

    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:7:p:1469-:d:1200685. 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.