Rural green development is essential for coordinating ecological protection, rural revitalisation and spatial governance in ecological spaces within urban agglomerations. Taking the Chang-Zhu-Tan Ecological Green Heart region in China as a case study, this study evaluates rural green development in 10 counties, county-level cities and districts from 2013 to 2022. A combined weighting–TOPSIS model is used to measure development levels, while Global Moran’s I, the Theil index and XGBoost–SHAP are applied to examine spatial patterns, regional disparities and key associated factors. The results show that rural green development improved overall, with the regional average index increasing from 0.315 in 2013 to 0.378 in 2022. High-value areas gradually expanded, and the Changsha group formed the main high-value core, but no statistically significant global spatial clustering was identified. Regional disparities narrowed during the study period, although intra-group disparities, especially within the Zhuzhou group, remained the main source of imbalance. Human capital, medical resources, income level, urbanisation level, PM_2.5 concentration and pesticide use intensity were closely associated with rural green development, with socioeconomic factors generally contributing positively and environmental pressures acting as constraints. These findings suggest that rural green development in ecological green heart regions is a spatially differentiated and multi-factor process, providing empirical evidence for differentiated county-level governance, coordinated ecological protection and rural green transformation in urban agglomerations.

High-quality growth of the construction industry and an improved human settlements environment are essential to sustainable urbanization. Existing studies have paid limited systematic attention to the spatial and temporal dynamics of the coordinated development between the construction industry and human settlements, as well as the underlying factors driving regional disparities. This gap restricts the formulation of precise, differentiated sustainable policies tailored to regions at different development stages and with varying resource endowments. Southern China, characterized by pronounced spatial heterogeneity and unique development trends, offers a natural laboratory for examining the spatio-temporal interaction between these two dimensions. Using panel data for 15 southern provinces (2013–2022), we applied the entropy method, coupling coordination model, Dagum Gini coefficient, spatial trend surface analysis, gravity model, and grey forecasting to evaluate current conditions and predict future trends. The main findings are as follows. (1) The coupling coordination degree rose steadily, forming a stepped spatial pattern from the southwest through the center to the southeast. (2) The coupling coordination degree appears obvious polarization effect, presenting a spatial linkage pattern with Jiangsu-Shanghai-Zhejiang, Hubei-Hunan-Jiangxi, and Sichuan-Chongqing as the core of the three major clusters. (3) The overall Dagum Gini coefficient declined, but intra-regional disparities persisted: values were highest in the southeast, moderate in the center, and lowest in the southwest; inter-regional differences dominated the total inequality. (4) Forecasts for 2023–2027 suggest further improvement in the coupling coordination degree, yet spatial divergence will widen, creating a configuration of “eastern leadership, central catch-up acceleration, and differentiated southwestern development.” This study provides an evidence base for policies that foster high-quality construction sector growth and enhance the living environment. The findings of this study indicate that policymaking should prioritize promoting synergistic regional development, enhancing the radiating and driving role of core regions, and establishing a multi-level coordinated governance mechanism to bridge regional disparities and foster more balanced and sustainable development.