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As the world’s second-largest economy, China has experienced rapid industrialization and urbanization, resulting in high energy consumption and significant carbon emissions. This development has intensified conflicts between human-land relations and environmental conservation, contributing to global warming and urban air pollution, both of which pose serious health risks. This study uses nighttime light (NTL) data from 2005 to 2019, along with scaling techniques and statistical analysis, to estimate city-scale energy carbon emissions over a 15-year period. Focusing on Northeast China, a traditional industrial region comprising 36 cities across three provinces, we examine spatial patterns of energy carbon emissions and assess spatiotemporal evolution through spatial autocorrelation and dynamic changes. These changes are further evaluated using standard deviation ellipse (SDE) parameters and SLOPE values. Additionally, the Tapio decoupling index is applied to explore the relationship between city-scale emissions and economic growth. Our findings for the 36 cities over 15 years are: (1) Heilongjiang shows low, declining emissions; Jilin improves; Liaoning has high, steadily increasing emissions. (2) The global spatial autocorrelation of energy carbon emissions is significant, with a positive Moran’s I, while significant local Moran’s I clusters are concentrated in Heilongjiang and Liaoning. (3) The greatest emission changes occurred in 2015, followed by 2019, 2005, and 2010. (4) Emission growth is fastest in Heilongjiang, followed by Liaoning and Jilin. (5) Tapio analysis shows positive decoupling in Heilongjiang, declining decoupling in Jilin, and no change in Liaoning. This study provides a quantitative basis for dual carbon goals and offers emission reduction strategies for government, industry, and residents, supporting energy transition and sustainable urban planning.
Bin et al. (Wed,) studied this question.