Analyzing carbon emissions and influencing factors in Chengdu-Chongqing urban agglomeration counties

被引:3
|
作者
Zhang, Ji [1 ,2 ]
Lu, Heng [1 ,2 ]
Peng, Wenfu [1 ,2 ]
Zhang, Lindan [1 ,2 ]
机构
[1] Sichuan Normal Univ, Key Lab Evaluat & Monitoring Southwest Land Resour, Minist Educ, Chengdu 610068, Peoples R China
[2] Sichuan Normal Univ, Inst Geog & Resources Sci, Chengdu 610068, Peoples R China
基金
中国国家自然科学基金;
关键词
Carbon emissions; Chengdu-Chongqing urban; agglomeration; Spatial autocorrelation; Geographically and temporally; weighted regression;
D O I
10.1016/j.jes.2024.04.019
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Majority of carbon emissions originate from fossil energy consumption, thus necessitating calculation and monitoring of carbon emissions from energy consumption. In this study, we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions. We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu -Chongqing urban agglomeration. Furthermore, we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation, and we used the Geographically and Temporally Weighted Regression (GTWR) model to analyze the influencing factors of carbon emissions at this scale. The results of this study are as follows: (1) from 2000 to 2019, the overall carbon emissions in the Chengdu -Chongqing urban agglomeration showed an increasing trend followed by a decrease, with an average annual growth rate of 4.24%. However, in recent years, it has stabilized, and 2012 was the peak year for carbon emissions in the ChengduChongqing urban agglomeration; (2) carbon emissions exhibited significant spatial clustering, with high -high clustering observed in the core urban areas of Chengdu and Chongqing and low -low clustering in the southern counties of the Chengdu -Chongqing urban agglomeration; (3) factors such as GDP, population (Pop), urbanization rate (Ur), and industrialization structure (Ic) all showed a significant influence on carbon emissions; (4) the spatial heterogeneity of each influencing factor was evident. (c) 2024 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.
引用
收藏
页码:640 / 651
页数:12
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