Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources (Ministry of Education), Sichuan Normal University, Chengdu 610068, China; The Institute of Geography and Resources Science, Sichuan Normal University, Chengdu 610068, China.
Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources (Ministry of Education), Sichuan Normal University, Chengdu 610068, China; The Institute of Geography and Resources Science, Sichuan Normal University, Chengdu 610068, China.
J Environ Sci (China). 2025 May;151:640-651. doi: 10.1016/j.jes.2024.04.019. Epub 2024 Apr 21.
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 Chengdu-Chongqing 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.
大部分碳排放源于化石能源消耗，因此需要计算和监测能源消耗的碳排放。在本研究中，我们利用四川省和重庆市 2000 年至 2019 年的能源消费数据来估算其统计碳排放量。然后，我们利用夜间灯光数据对成都-重庆城市群内县级碳排放量的空间分布进行降尺度和推断。此外，我们使用变异系数和空间自相关分析县级碳排放量的空间格局，并使用地理和时间加权回归（GTWR）模型分析该尺度下的碳排放影响因素。研究结果如下：（1）2000 年至 2019 年，成都-重庆城市群的整体碳排放量呈增长趋势，随后下降，年均增长率为 4.24%。然而，近年来，碳排放量已趋于稳定，2012 年是成都-重庆城市群碳排放量的峰值年；（2）碳排放量存在显著的空间集聚，成都和重庆核心城区呈现高-高集聚，成都-重庆城市群南部各县呈现低-低集聚；（3）GDP、人口（Pop）、城市化率（Ur）和工业化结构（Ic）等因素均对碳排放量有显著影响；（4）各影响因素的空间异质性明显。
