The current efficiency of residential land utilization is witnessing a decline, attributable to accelerated urbanization and inefficient resource allocation, thereby presenting unprecedented threats and challenges to the quality of urban living and the pursuit of high-quality urban development. To enhance residents’ satisfaction and well-being, and to effectively activate existing land resources, it is imperative to accurately identify inefficient residential landuse and its driving factors. While the literature on identifying inefficient urban landuse is expanding, research specifically focusing on residential land, which is closely linked to residents’ lives, remains limited. Furthermore, the factors contributing to inefficient land use are relatively inadequate. Therefore, this study employs a “two-step identification method” to comprehensively identify inefficient residential landuse and utilizes standard deviation ellipses and kernel density assessment methods to analyze the spatial distribution characteristics of such land. Subsequently, the study employs the Random Forest (RF) model to quantitatively analyze factors such as building quality, economic, social, and ecological factors, aiming to provide a scientific basis for subsequent redevelopment initiatives. The findings reveal that inefficient residential landuse is primarily concentrated in city centers, particularly in districts such as Nangang and Xiangfang. In relative inefficient residential areas, aside from Nangang District and Xiangfang District, Songbei District also holds a significant proportion. The intensity of these associations with inefficient residential landuse formation varies depending on urban development history and regional development intensity. In areas other than Songbei District, factors such as aging residential neighborhoods and inadequate green spaces are major contributors to inefficient land use efficiency, whereas in Songbei District, insufficient medical and educational facilities are the primary factors. The RF algorithm, distinguished by its flexibility and accuracy, offers novel perspectives and methods for analyzing issues related to inefficient residential landuse. Moreover, it effectively manages nonlinear relationships between the data, avoiding overfitting and generating precise regression and classification results. Thus, the RF algorithm demonstrates significant promise for widespread application in urban land studies.

The reconstruction of land spatial planning and the increasing severity of carbon emissions pose significant challenges to carbon peak and carbon neutrality strategies. To establish low-carbon and sustainable agricultural spatial planning while achieving dual carbon strategy goals, it is essential to accurately analyze the mechanisms of agricultural spatial transfer and their carbon emission effects, as well as the key factors influencing carbon emissions from agricultural spatial transfer. Therefore, this study, based on land use remote sensing data from 2000 to 2020, proposes a carbon emission accounting system for agricultural space transfer. The carbon emission total from agricultural space transfer in the Harbin-Changchun urban agglomeration over the 20-year period is calculated using the carbon emission coefficient method. Additionally, the spatiotemporal patterns and influencing factors are analyzed using the standard deviation ellipse method and the geographical detector model. The results indicate that: (1) The agricultural space in the Harbin-Changchun urban agglomeration has increased, with a reduction in living space and an expansion of production space. Among land type conversions, the conversion between cultivated land and forest land has been the most intense. (2) The conversion of agricultural space to grassland and built-up land has been the primary source of net carbon emissions. The carbon emission center has shown a migration path characterized by “eastward movement and southward progression,” with a high-north to low-south distribution pattern. Significant carbon emission differences were observed at different spatial scales. (3) Natural environmental factors dominate the carbon emissions from agricultural space transfer, while socioeconomic and policy factors act as driving forces. Elevation is the primary factor influencing carbon emissions from agricultural space transfer. Interactions between factors generally exhibit nonlinear enhancement, with the interaction between elevation, annual precipitation, and industrial structure showing a strong explanatory power. Notably, the interactions between elevation, average annual precipitation, and industrial structure demonstrate significant explanatory power. These findings highlight the necessity for government action to balance agricultural spatial use with ecological protection and economic development, thereby providing scientific references for optimizing future land spatial structures and formulating regional carbon balance policies.