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- W4313585098 abstract "The rapid and disorderly expansion of urban construction land has exacerbated the contradiction between land use and low-carbon development. In this paper, we use the spatial autocorrelation model and coupling model to analyze the spatial characteristics of the coupled coordination degree of land transfer and carbon emissions in 291 cities in China. The multi-scale geographically weighted regression (MGWR) model is used to explore the spatial heterogeneity of the influence of socioeconomic factors on their coupled coordination degree. The results show that: from 2005 to 2015, the scale of land transfer and carbon emissions has been increasing quantitatively and spatially showing a shift from the southeast coast to the central and western regions. In 2005, 2010, and 2015, the global Moran’s I of the coupled coordination degree are 0.3045, 0.3725, and 0.3388, respectively, indicating that the coupled coordination degree between land transfer and carbon emissions has a significant positive spatial autocorrelation. The MGWR model indicates that the influence of socioeconomic factors on the coupling coordination degree has significant spatial heterogeneity at different time nodes. In 2005 and 2015, the coefficients of the NGR on the coupling coordination of land transfer and carbon emissions have obvious stratification characteristics, with the coefficients decreasing from northeast to southwest. In 2010, the high coefficient (0.924∼0.989) of GPC is mainly distributed in the central region. The coefficient of the PD ranges from 0.464 to 0.918, but the difference of influence degree between the southeast coast and the northwest is obvious. This study may provide new clues for sustainable urban development and carbon reduction." @default.
- W4313585098 created "2023-01-06" @default.
- W4313585098 creator A5022049957 @default.
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- W4313585098 date "2023-01-04" @default.
- W4313585098 modified "2023-10-11" @default.
- W4313585098 title "Analysis of the coupling characteristics of land transfer and carbon emissions and its influencing factors: A case study of China" @default.
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- W4313585098 doi "https://doi.org/10.3389/fenvs.2022.1105552" @default.
- W4313585098 hasPublicationYear "2023" @default.
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