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Carbon emission issues are becoming increasingly severe, and the carbon emissions in shrinking cities, primarily characterized by population loss, are often overlooked and insufficiently studied. This paper focuses on the carbon emissions from county-level administrative units in China's three northeastern provinces from 2001 to 2017. The study scientifically identified shrinking cities and measured the differences in carbon emission characteristics between growing and shrinking cities using the Theil index. Ultimately, the paper constructs a panel spatial econometric model to analyze the factors influencing them and explore their spatial effects. (1) The total carbon emissions in the Three Northeastern Provinces exhibited an inverted U-shaped trend, increasing from 734.21 million tons in 2001 to 1731.73 million tons in 2017, with the Mann–Kendall trend test showing a significant increase; spatially, this manifests as a significant positive spatial autocorrelation. (2) The region has 138 shrinking cities, accounting for over 50%; regarding carbon emission characteristics, the Theil index has consistently remained above 0.18, indicating significant differences between the carbon emissions of growing and shrinking cities. (3) The panel spatial econometric model results show that the influencing factors of carbon emissions in shrinking cities have unique directions, intensities, and spatial effects. In shrinking cities, aside from localized GDP effects and per-capita GDP acting as a suppressant, the population size has a pronounced inhibitory effect on local and surrounding carbon emissions. The analysis reveals significant differences in the carbon emission patterns and mechanisms between growing and shrinking cities; based on these results, the paper proposes differentiated carbon control strategies. [ABSTRACT FROM AUTHOR]

Shrinking cities are a global issue with regional characteristics. This paper focuses on the county-level administrative units in the Three Northeastern Provinces in China to identify and classify shrinking cities using a two-step identification method and explores their spatial-temporal evolution. The paper utilizes the panel threshold regression model for empirical testing. The results indicate the following: (1) The number of shrinking cities in the region is large and deep. Quantitatively, the shrinking cities account for about 50% of the whole; spatially, there are six major shrinking city "groups", showing the distribution trend around the "Ha-Da" urban corridor. (2) The threshold effect test reveals that GDP is a critical threshold variable influencing the formation of shrinking cities. Moreover, cities are classified into three types based on the threshold values: Type I (GDP > 2,270,731 yuan), Type II (434,832 < GDP ≤ 2,270,731), and Type III (GDP < 434,832). (3) The results of the dual-threshold and grouped regression models show significant variations in the dominant factors of shrinking cities of different scales. Variables such as impervious area, fiscal revenue, and grass area demonstrate relatively stable promoting effects. [ABSTRACT FROM AUTHOR]