Spatiotemporal analysis of carbon emissions in the Yangtze River Delta Urban Agglomeration: Insights from nighttime light data (1992–2019)

Continuous evaluation and monitoring of long-term energy usage and carbon emissions are essential for developing, implementing, and assessing regional carbon reduction efforts. This study presents a spatiotemporal analysis of carbon emission trends in the Yangtze River Delta Urban Agglomeration (YRDUA) from 1992 to 2019. Researchers used nighttime light data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) and the National Polar-orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) to assess the evolution of carbon emission patterns. Advanced spatial analysis methods, including geographic autocorrelation, geographical panel modeling, and spatial Markov chains, were applied to explore the spatial impacts, processes, and regional context of these trends. The results show: (1) Carbon emissions in the YRDUA increased by 262.56 %, with high-emission spheres and axial expansion. High-high emission clusters emerged in metropolitan areas, while low-low clusters formed in peripheral mountain regions. (2) Carbon emission types were stable (66.5 %), but 17.6 % showed higher emissions transitioning to lower, particularly in northeast Jiangsu. (3) The broader regional background had a stronger influence on the spatial impacts of carbon emissions than nearest neighbor effects, enhancing both outlier convergence and “club convergence” among similar regions. (4) Spatiotemporal patterns were shaped by the lock-in effect in low-carbon areas and spillover effects in high-carbon areas, with economic scale and industrial structure as key drivers. This study provides crucial insights for regional carbon reduction strategies in the YRDUA. •Analyzed carbon emission trends in the Yangtze River Delta using satellite data.•Carbon emissions in YRDUA increased by 262.56 %, with notable spatial expansion.•High-high emission clusters emerged in urban areas; low-low clusters in mountains.•Regional context more influential than nearest neighbor effects on carbon emissions.•Economic scale and industrial structure were key drivers of spatiotemporal patterns.