This paper constructs a county-level carbon emission inversion model in Northeast China. We first fit the nighttime light data of the Visible Infrared Imaging Radiometer Suite (VIIRS) with local energy consumption statistics and carbon emissions data. We analyze the temporal and spatial characteristics of county-level energy-related carbon emissions in Northeast China from 2012 to 2020. At the same time, we use the geographic detector method to analyze the impact of various socio-economic factors on county carbon emissions under the single effect and interaction. The main results are as follows: (1) The county-level carbon emission model in Northeast China is relatively more accurate. The regression coefficient is 0.1217 and the determination coefficient R² of the regression equation is 0.7722. More than 80% of the provinces have an error of less than 25%, meeting the estimation accuracy requirements. (2) From 2012 to 2020, the carbon emissions of county-level towns in Northeast China showed a trend of increasing first and then decreasing from 461.1159 million tons in 2012 to 405.752 million tons in 2020. It reached a peak of 486.325 million tons in 2014. (3) The regions with higher carbon emission growth rates are concentrated in the northern and coastal areas of Northeast China. The areas with low carbon emission growth rates are mainly distributed in some underdeveloped areas in the south and north in Northeast China. (4) Under the effect of the single factor urbanization rate, the added values of the secondary industry and public finance income have higher explanatory power to regional emissions. These factors promote the increase of county carbon emissions. When fiscal revenue and expenditure and the added value of the secondary industry and per capita GDP interact with the urbanization rate, respectively, the explanatory power of these factors on regional carbon emissions will be enhanced and the promotion of carbon emissions will be strengthened. The research results are helpful for exploring the changing rules and influencing factors of county carbon emissions in Northeast China and for providing data support for low-carbon development and decision making in Northeast China.

As the largest ecological background system and basic economic unit in China, counties are of great significance to China’s carbon emission reduction targets. This article conducts theoretical model construction and empirical test research from a contraction perspective, using population and built-up area change as variables and combining indicators of county scale structure in an attempt to find key scale structure elements and representative indicators that affect the carbon emission intensity of counties. By using data from 140 counties in Northeast China during the period of 2015–2020, an empirical study was conducted on population shrinkage clustering, county size structure, and carbon emission intensity. The results show that: (1) population shrinkage significantly increases the carbon intensity of counties, but the contribution of population shrinkage to carbon intensity is scale-heterogeneous, the contribution effect decreases with population size, and the effect on large counties is minimal; (2) population size and industrial structure are the main factors influencing carbon intensity in counties, both have a negative linear elasticity relationship, and GDP per capita is not included in the overall model and is only significant in large counties; (3) the relationship between total construction land and carbon intensity is an inverted U-shaped Kuznets curve, with a critical value of 30 km², and the total construction land in most counties is below or close to the critical value.