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

Chao Wang (), Jianing Wang, Le Ma, Mingming Jia, Jiaying Chen, Zhenfeng Shao and Nengcheng Chen
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Chao Wang: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, 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

Land, 2024, vol. 13, issue 9, 1-21

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.

Keywords: urban expansion; spatial prediction; LSTM; geographical detector; driving factors (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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