Characterizing and predicting carbon emissions from an emerging land use perspective: A comprehensive review

Luo, Haizhi; Liu, Zhengguang; Li, Yingyue; Meng, Xiangzhao; Yang, Xiaohu

doi:10.1016/j.uclim.2024.102141

Urban Climate

2024

DOI: 10.1016/j.uclim.2024.102141

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Characterizing and predicting carbon emissions from an emerging land use perspective: A comprehensive review

Haizhi Luo,

Zhengguang Liu,

Yingyue Li

et al.

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2024

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Cited by 2 publications

References 184 publications

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Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China

Luo,

Zhang,

Gao

et al. 2024

Advances in Applied Energy

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Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China

Luo,

Zhang,

Gao

et al. 2024

Advances in Applied Energy

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Characteristics of Spatial Correlation Network Structure and Carbon Balance Zoning of Land Use Carbon Emission in the Tarim River Basin

Gao,

Ye,

Zhu

et al. 2024

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An accurate understanding of the structure of spatial correlation networks of land use carbon emissions (LUCEs) and carbon balance zoning plays a guiding role in promoting regional emission reductions and achieving high-quality coordinated development. In this study, 42 counties in the Tarim River Basin from 2002 to 2022 were chosen as samples (Corps cities were excluded due to missing statistics). The LUCE spatial correlation network characteristics and carbon balance zoning were analyzed by using the Ecological Support Coefficient (ESC), Social Network Analysis (SNA), and Spatial Clustering Data Analysis (SCDA), and a targeted optimization strategy was proposed for each zone. The results of the study indicate the following: (1) The LUCEs showed an overall upward trend, but the increase in LUCEs gradually slowed down, presenting a spatial characteristic of “high in the mid-north and low at the edges”. In addition, the ESC showed an overall decreasing trend, with a spatial characteristic opposite to that of the LUCEs. (2) With an increasingly close spatial LUCE correlation network in the Tarim River Basin, the network structure presented better accessibility and stability, but the individual network characteristics differed significantly. Aksu City, Korla City, Bachu County, Shache County, Hotan City, and Kuqa City, which were at the center of the network, displayed a remarkable ability to control and master the network correlation. (3) Based on the carbon balance analysis, the counties were subdivided into six carbon balance functional zones and targeted synergistic emission reduction strategies were proposed for each zone to promote fair and efficient low-carbon transformational development among the regions.

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Characterizing and predicting carbon emissions from an emerging land use perspective: A comprehensive review

Cited by 2 publications

References 184 publications

Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China

Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China

Characteristics of Spatial Correlation Network Structure and Carbon Balance Zoning of Land Use Carbon Emission in the Tarim River Basin

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