[Estimating Carbon Emissions and Their Spatiotemporal Dynamics in Urban Agglomerations Using Intercalibrated...

Accounting for approximately 85% of carbon emissions, cities in China will play a crucial role in carbon reduction. Taking the Beijing-Tianjin-Hebei, Central Plains, and Chengdu-Chongqing urban agglomerations as examples, energy-related carbon emissions were estimated using intercalibrated nighttime lights. Slope index, standard deviation ellipse, and exploratory spatial-temporal data analysis were employed to explore the dynamics of carbon emission patterns in these urban agglomerations. The results indicated that: ① From 2000 to 2022, carbon emission growth in the three urban agglomerations showed a trend of "rapid growth, declining growth rate, and stabilization." Handan, Zhengzhou, and Chongqing were characterized by rapid growth in carbon emissions, while Tianjin, Xingtai, Xinxiang, Heze, Luoyang, Nanyang, and Chengdu were characterized by relatively rapid growth in carbon emissions. ② The carbon emission distribution in the Beijing-Tianjin-Hebei and Chengdu-Chongqing urban agglomerations followed "northeast-southwest" and "northwest-southeast" orientations, respectively, while no distinct directional pattern was observed in the Central Plains urban agglomeration. The migration trajectory of the carbon emissions gravity center was influenced by industrial transfer and core cities. ③ At the county level, the three urban agglomerations exhibited spatial autocorrelation characteristics, mainly high-high and low-low agglomeration. The autocorrelation of the Beijing-Tianjin-Hebei and Chengdu-Chongqing urban agglomerations was stronger than that of the Central Plains urban agglomeration due to its relatively balanced internal development. ④ The local spatial structures of carbon emissions in the three urban agglomerations were generally stable, but the rapidly developing areas and surrounding areas of core cities exhibited strong dynamism. The findings emphasize the importance of integrating multi-source data to improve the accuracy of carbon emissions estimation, while also focusing on efficiency and equity to promote collaborative carbon reduction in urban agglomerations.