An accurate understanding of the structure of spatial correlation networks of land use carbon emissions (LUCEs) and carbon balance zoning plays a guiding role in promoting regional emission reductions and achieving high-quality coordinated development. In this study, 42 counties in the Tarim River Basin from 2002 to 2022 were chosen as samples (Corps cities were excluded due to missing statistics). The LUCE spatial correlation network characteristics and carbon balance zoning were analyzed by using the Ecological Support Coefficient (ESC), Social Network Analysis (SNA), and Spatial Clustering Data Analysis (SCDA), and a targeted optimization strategy was proposed for each zone. The results of the study indicate the following: (1) The LUCEs showed an overall upward trend, but the increase in LUCEs gradually slowed down, presenting a spatial characteristic of “high in the mid-north and low at the edges”. In addition, the ESC showed an overall decreasing trend, with a spatial characteristic opposite to that of the LUCEs. (2) With an increasingly close spatial LUCE correlation network in the Tarim River Basin, the network structure presented better accessibility and stability, but the individual network characteristics differed significantly. Aksu City, Korla City, Bachu County, Shache County, Hotan City, and Kuqa City, which were at the center of the network, displayed a remarkable ability to control and master the network correlation. (3) Based on the carbon balance analysis, the counties were subdivided into six carbon balance functional zones and targeted synergistic emission reduction strategies were proposed for each zone to promote fair and efficient low-carbon transformational development among the regions.

Revealing the relationship between intensive land use (ILU) and land ecological security (LES) is crucial for achieving high-quality land resource development and ensuring the sustainability of land ecosystems, especially in arid regions with fragile human–land relationships. In this study, we evaluated ILU and LES in the urban agglomeration on the northern slope of the Tianshan Mountains (UANSTM) and analyzed the evolution of ILU and LES using Pearson correlation analysis, four-quadrant model, and coupling coordination degree model. The results indicated that from 2012 to 2022, ILU and its three subsystems generally showed an upward trend in the UANSTM, with an average annual growth rate of 2.96%, 2.50%, 3.32%, and 0.289%, respectively, and with ILU levels significantly higher in the eastern and central counties compared to other areas. LES and its three subsystems also exhibited a general increase, with average annual growth rates of 1.63%, 0.86%, 3.10%, and 1.51%, respectively, although high-level areas displayed distinct spatial variations. The Pearson correlation analysis showed that the land input level had a significant negative effect on the pressure, but the positive effects of state and response enhanced the LES. The number of double-high cities increased significantly from 1 to 6, and the number of double-low cities decreased from 6 to 0. Compared with other cities and counties, the LES-lagging counties and cities faced the challenge of coordinating urban development and ecological conservation. While coupling coordination levels across all counties improved to varying degrees, none reached the optimal coordination stage. The findings and methodologies of this study provide helpful insights into human–land relationships in arid regions, supporting the goals of high-quality urban development and sustainability.

Enhancing intensive land use is of great significance in promoting sustainable urban economicsocial- ecological development. We evaluated the integrated intensive land use level (ILU) of the Urban Agglomeration on the North Slope of Tianshan Mountain (UANSTM) and analyzed the difference between groups, barrier factors, and interaction between subsystems by using the Theil index, the obstacle degree model, and the structural equation model. The results showed that: the overall ILU in the UANSTM gradually increased from 2010 to 2022. In the four subsystems of the ILU, land use degree, land output effect, and land sustainable benefit displayed an increase, except for the land input level; the higher ILU was in the cities with higher socio-economic development, such as Shihezi, Urumqi, Karamay, and Kuitun, while Manasi, Wusu, and Shawan were on the low side. The ILU level decreased around the high value of Urumqi, Shihezi, and Karamay. The main obstacle factors were concentrated in the construction land and arable land indicators. The four subsystems of the ILU had an interplay with each other, and the land input level had an important role in promoting the other three subsystems, which was the fundamental driving force of the whole system. This paper provided a useful reference for the intensive land use of cities in arid areas and contributed to the high-quality development of cities.