Education is the driving force of social development, while regional differences have restricted the balance of educational development. This paper provides a political and geospatial analysis of regional inequality across the province from 1990–2015 under a comparative spatiotemporal conceptual framework. Great differences in terms of the distribution and dynamic changes in primary school were found between southern and the northern Ningxia. The southern region has a large number of primary schools with a sharp reduction in these numbers, especially in recent years, while the north has a small but stable number of schools. Spatial accessibility analyses revealed that due to the imbalance between the south and the north, the spatial accessibility of the southern region is much worse than that of the northern region. Our study found the imbalance in the number and spatial distribution of primary schools in the north and south of Ningxia was formed because of multiple factors of policy, geography, and social-economy. The government has pursued a policy of promoting education fairness. However, neglecting differences between north and south, on the contrary, has brought negative effects to the south, worsened the educational environment, and increased education inequities. Our case study in the Ningxia district demonstrates that this research can provide a practical basis for the formulation of policies to guide the rational planning of primary education in Ningxia and/or other similar areas.

Urbanization significantly reshapes urban form, affecting the spatial and quantitative dynamics of urban land use under carbon constraints. However, the role of macro-scale urban form in guiding low-carbon urban expansion remains underexplored. Our study introduces an integrated Cellular Automata (CA) model to simulate urban land use patterns with regard to the low-carbon goal, focusing on urban form optimization. The model employs a top-down strategy to adjust future urban land demand by balancing urban development needs with carbon emission (CE) reduction targets. The adjusted demand is then used to optimize urban form parameters (i.e., the inverse S-shaped function) to predict future urban land patterns and allocate land increments within concentric rings. Subsequently, a bottom-up strategy incorporating carbon sequestration (CS) conservation is applied to refine urban land conversion. The CA model integrates a maximum probability transformation rule to allocate urban land efficiently. We used the model to simulate urban land use patterns under four scenarios (i.e., Low-carbon Urban Development Scenario (L-UDS), Top-up Urban Development Scenario (T-UDS), Bottom-up Urban Development Scenario (B-UDS), and inverse S-shaped constraint Urban Development Scenario (S-UDS)) for the Changsha–Zhuzhou–Xiangtan (CZX) urban agglomeration in 2035. Results show that the proposed model effectively reconciles the conflict between rapid urbanization and urban carbon management strategies, as evidenced by a 31.25% reduction in carbon emissions in the L-UDS and T-UDS relative to the S-UDS and B-UDS. Furthermore, urban form constraints promote the development of compact and dense urban structures, advancing sustainable urban development goals. This study not only proposes a simulation model capable of effectively promoting compact urban development at the theoretical level, but its findings also offer actionable policy insights for China to address urban sprawl and actively advance low-carbon urban development.