Multiscale exploration of spatiotemporal dynamics and decoupling effects of carbon emissions in China.

Studying carbon emissions (CE) at different administrative scales will help facilitate crafting tailored emission reduction policies for China's regions, which is vital for achieving the dual carbon goals. However, previous studies focused on a single administrative scale, lacking multiscale research. This paper combined energy consumption data with nighttime light data and adopted a spatial autocorrelation, variation coefficient (VC), and decoupling model to study the spatiotemporal dynamics and decoupling effect of CE at the three administrative scales of provinces, prefecture-level cities, and counties in China from 2000 to 2020. The results were as follows: (1) The VC of CE showed different trends at different scales, with its coefficient size successively ranked at the county, prefecture, and province levels. (2) CE at different scales showed positive spatial autocorrelation and the significance was strongest at the county level. (3) The decoupling trend between CE and economic growth has generally shown a positive development across different spatial scales. The average elasticity decoupling index at the provincial, prefectural, and county levels has decreased overall, from 0.88, 1.82, and 3.74 to 0.19, 1.36, and 3.05, respectively. However, the characteristics of these changes differ. The CV of the elasticity decoupling index increased at the provincial and prefectural levels, rising from 1.161 to 1.563 to 1.419 and 2.669, respectively, while it decreased slightly at the county level, from 3.862 to 3.765. (4) The dominant type of decoupling at the provincial level had changed from expansive negative decoupling (ED) to strong decoupling (SD). Meanwhile, at the prefectural and county levels, the ED was still dominant, but the number of SD had increased significantly, rising from 75 to 138 at the prefectural level and from 748 to 1160 at the county level. This study demonstrates China's carbon emissions sensitivity to scale, emphasizing the importance of adapting emission reduction measures to local conditions.