<p>Understanding the social-ecological network relationship within urban agglomeration to reduce the synergistic emission-reduction is imperative to propose a sustainable solution for landscape change. In this study, spatial mapping, land use dynamic attitude, comprehensive index of land use degree, modified gravity model, social network analysis, and QAP are used to analyze the temporal changes and spatial characteristics of land use types in the urban agglomeration in the middle reaches of the Yangtze River (MRYRUA). Moreover, the study emphasizes the structure and characteristics of the spatial association network of carbon emissions and the influence of land use change and other socio-economic factors on the spatial association network. Results found that Wuhan, Changsha, and Nanchang dominated the MRYRUA as the apex and the expansion of “three corridors” in the middle triangle. Meanwhile, the land use types show the characteristics of “interwoven and staggered distribution”, and the composite index of land use degree shows a “decreasing from south to north”. The spatial correlation network structure of carbon emission shows a distribution pattern, and the core–edge structure of the network is obvious with the core position of the main node cities gradually weakening due to the slowdown of their expansion. Similarly, the position of the secondary node cities gradually rising due to the intensification of their construction land expansion. The network relevance, density, and efficiency showed fluctuating growth, while the network hierarchy showed a fluctuating decline. Moreover, the four major sectors are dominated by intra-sector exchanges and supplemented by inter-sector spillover effects. Finally, it is easier to build a network of spatial correlations for carbon emissions when the differences in land use indices between cities are lower. The more similar the energy consumption intensity is, the more advantageous the building of carbon emission networks among cities. Similarly, disparities in labor productivity, economic agglomeration, per capita income, technical advancement, and tertiary industry were also found. The study provides a basis for constructing an emission reduction mechanism for urban clusters and sustainable solution for landscape change.</p>