Land use change is one of the key elements leading to carbon emission changes, and is of great significance to the process of achieving the goals of carbon peaking and carbon neutrality. In this study, we calculated the land-use carbon emissions (LCE) in the Guanzhong area (GZA) of Shaanxi province from 2000 to 2019 by using an improved LCE measurement model. Meanwhile, the spatial and temporal changes of LCE were analyzed and the driving forces were investigated based on the correlation analysis and multi-scale geographical weighting regression (MGWR). The results showed that the total amount of LCE showed a significant increasing trend from 2000 to 2019. Regions where the LCE significantly increased occupied 71.20% of the total area; these regions were distributed in the central and eastern parts of the study area. The LCE showed a significant positive spatial correlation and had a remarkable aggregation state. The H-H agglomeration area of LCE was distributed in the central urban agglomeration. The L-L agglomeration areas were always distributed in the southwest part of the GZA with low carbon emissions. The average correlation coefficients between LCE and nighttime light (NTL), population density (PD), and gross primary productivity (GPP) were 0.13, 0.21, and −0.05, respectively. The NLT and PD had obvious positive effects on LCE, while GPP has obvious negative effects on carbon emissions, which can be ascribed to the carbon sink effect of forests and grasslands. The results of this study have important reference value regarding the formulation of carbon emission reduction policies and the development of a low-carbon social economy.

Urbanization in African countries entails substantial growth in the urban population and economic development. The interdependent progress of the population and economy significantly impacts the sustainable development of these nations. By constructing an evaluation framework, this paper assesses the urban population growth and economic development systems in African countries. Building upon the coupling coordination model, it quantitatively investigates the relationship between the two and utilizes a geographical detector model to analyze the driving factors of the coordination of evolution. The findings reveal a continuous improvement in the quality of urban population growth and economic development between 2001 and 2020. Nevertheless, their overall quality remains relatively low, exhibiting considerable variation across different countries. Many African countries struggle with a low level of development coordination, with economic progress often trailing behind the pace of urban population growth. The average coupling coordination degree increased from 0.464 to 0.526 over 20 years, with 48.08% of countries still in uncoordinated development by 2020. Factors such as industrialization, foreign trade dependence, government spending, international aid, and political stability are all influential factors affecting the degree of coordination. The absence of industrialization in conjunction with urbanization poses a major impediment to effectively harnessing urban population growth for economic development. Ultimately, this study provides a targeted framework for integrating urban population growth and economic development to address low coupling coordination.