County-level energy-related carbon emissions and sustainable low-carbon transition in the Central-Southern Liaoning Urban Agglomeration: spatiotemporal evolution and spatial spillover effects

For old industrial urban agglomerations, low-carbon planning requires emission information at a finer spatial scale, but county-level energy statistics are often incomplete. This study focuses on the Central-Southern Liaoning Urban Agglomeration, a typical heavy-industrial region in Northeast China. County-level energy-related carbon emissions for 73 units from 2005 to 2024 are reconstructed by combining socioeconomic panel data with harmonized DMSP-OLS-like nighttime light data. On this basis, global and local spatial autocorrelation, Moran scatterplots, Markov and spatial Markov transition matrices, and a spatial STIRPAT-based Spatial Durbin Model are used to examine the spatial pattern, transition process, and driving factors of emissions. The results show that emissions continued to increase during the study period, although the growth rate became slower and no clear regional peak was observed. Moran’s I rose from 0.627 in 2005 to 0.675 in 2024, which means that county-level emissions became more spatially clustered. The traditional Markov matrix shows strong state persistence, with diagonal probabilities ranging from 0.8793 to 0.9852. The spatial Markov results further suggest that counties surrounded by high-emission neighbors face greater pressure to move upward. In the SDM results, the spatial autoregressive coefficient is significant at the 1% level, with rho = 0.537. GDPPC and POP show negative direct effects, SEC increases local emissions but has a negative indirect effect, and PE is positively related to local emissions. Spatially, high-emission counties are mainly distributed around Shenyang, Anshan, Liaoyang, Dalian, and other industrial cores, while eastern ecological counties remain at relatively low emission levels. These findings provide county-scale evidence for differentiated low-carbon governance in old industrial regions.