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Prediction Modeling and Driving Factor Analysis of Spatial Distribution of CO 2 Emissions from Urban Land in the Yangtze River Economic Belt, China

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  • Chao Wang

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
    Key Laboratory of Basin Water Resources and Eco-Environmental Science in Hubei Province, Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan 430010, China
    National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China)

  • Jianing Wang

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Le Ma

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Mingming Jia

    (State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China)

  • Jiaying Chen

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China)

  • Zhenfeng Shao

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Nengcheng Chen

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
    National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China)

Abstract

In recent years, China’s urbanization has accelerated, significantly impacting ecosystems and the carbon balance due to changes in urban land use. The spatial patterns of CO 2 emissions from urban land are essential for devising strategies to mitigate emissions, particularly in predicting future spatial distributions that guide urban development. Based on socioeconomic grid data, such as nighttime lights and the population, this study proposes a spatial prediction method for CO 2 emissions from urban land using a Long Short-Term Memory (LSTM) model with added fully connected layers. Additionally, the geographical detector method was applied to identify the factors driving the increase in CO 2 emissions due to urban land expansion. The results show that socioeconomic grid data can effectively predict the spatial distribution of CO 2 emissions. In the Yangtze River Economic Belt (YREB), emissions from urban land are projected to rise by 116.23% from 2020 to 2030. The analysis of driving factors indicates that economic development and population density significantly influence the increase in CO 2 emissions due to urban land expansion. In downstream cities, CO 2 emissions are influenced by both population density and economic development, whereas in midstream and upstream city clusters, they are primarily driven by economic development. Furthermore, technology investment can mitigate CO 2 emissions from upstream city clusters. In conclusion, this study provides a scientific basis for developing CO 2 mitigation strategies for urban land within the YREB.

Suggested Citation

  • Chao Wang & Jianing Wang & Le Ma & Mingming Jia & Jiaying Chen & Zhenfeng Shao & Nengcheng Chen, 2024. "Prediction Modeling and Driving Factor Analysis of Spatial Distribution of CO 2 Emissions from Urban Land in the Yangtze River Economic Belt, China," Land, MDPI, vol. 13(9), pages 1-21, September.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:9:p:1433-:d:1471443
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    References listed on IDEAS

    as
    1. Bowen Chen & Changyan Wu & Xianjin Huang & Xuefeng Yang, 2020. "Examining the Relationship between Urban Land Expansion and Economic Linkage Using Coupling Analysis: A Case Study of the Yangtze River Economic Belt, China," Sustainability, MDPI, vol. 12(3), pages 1-21, February.
    2. 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).
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    4. Ji Zhang & Pei Zhang & Xinchen Gu & Mingjiang Deng & Xiaoying Lai & Aihua Long & Xiaoya Deng, 2023. "Analysis of Spatio-Temporal Pattern Changes and Driving Forces of Xinjiang Plain Oases Based on Geodetector," Land, MDPI, vol. 12(8), pages 1-15, July.
    5. Wang, Geng & Feng, Yan, 2024. "Analysis of carbon emission drivers and peak carbon forecasts for island economies," Ecological Modelling, Elsevier, vol. 489(C).
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