Carbon emissions (CEs) are a primary driver of global climate change, particularly pronounced in China’s Yangtze River Delta (YRD) region, where rapid economic development and urbanization have led to a substantial increase in CEs. At fine spatial scales (e.g., county level) or in regions with limited statistical data, traditional methods for CE accounting are constrained by data gaps and inconsistencies, which hinders the accurate characterization of regional disparities. Therefore, this study proposes a CE spatial downscaling method based on nighttime light (NTL) data. By integrating remote sensing data with the IPCC emission inventory model, energy consumption-related carbon emissions (ECCEs) across the YRD region from 2000 to 2020 were quantified. Through global spatial autocorrelation analysis and standard deviation ellipse (SDE) analysis, the spatial distribution characteristics and temporal variation trends of ECCEs were revealed. Results indicate that total CEs increased significantly over the study period. CE hotspots were concentrated in the Hangzhou Bay area and the Shanghai–Nanjing corridor, while coldspots were identified in southwestern Anhui and Zhejiang. From 2010, the CE centroid shifted toward the southwest or northwest, and the regional CE distribution evolved from a point pattern to a band-shaped pattern. These findings offer a novel approach for CE monitoring and can provide scientific support for low-carbon development policies and precise emission reduction strategies in data-scarce regions of developing countries.
Ye et al. (Sat,) studied this question.